test: Add comprehensive unit tests for AsyncExecutor functionality

.claude/settings.local.json

@@ -1,28 +0,0 @@
-{
-  "permissions": {
-    "allow": [
-      "Bash(cd:*)",
-      "Bash(python3:*)",
-      "Bash(python:*)",
-      "Bash(grep:*)",
-      "Bash(mkdir:*)",
-      "Bash(cp:*)",
-      "Bash(rm:*)",
-      "Bash(true)",
-      "Bash(./package-extension.sh:*)",
-      "Bash(find:*)",
-      "Bash(chmod:*)",
-      "Bash(rg:*)",
-      "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 5 -B 5 \"Script Builder\" docs/md_v2/apps/crawl4ai-assistant/)",
-      "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg -A 30 \"generateCode\\(events, format\\)\" docs/md_v2/apps/crawl4ai-assistant/content/content.js)",
-      "Bash(/Users/unclecode/.npm-global/lib/node_modules/@anthropic-ai/claude-code/vendor/ripgrep/arm64-darwin/rg \"<style>\" docs/md_v2/apps/crawl4ai-assistant/index.html -A 5)",
-      "Bash(git checkout:*)",
-      "Bash(docker logs:*)",
-      "Bash(curl:*)",
-      "Bash(docker compose:*)",
-      "Bash(./test-final-integration.sh:*)",
-      "Bash(mv:*)"
-    ]
-  },
-  "enableAllProjectMcpServers": false
-}

.gitattributes

@@ -1,12 +0,0 @@
-# Documentation
-*.html linguist-documentation
-docs/* linguist-documentation
-docs/examples/* linguist-documentation
-docs/md_v2/* linguist-documentation
-
-# Explicitly mark Python as the main language
-*.py linguist-detectable=true
-*.py linguist-language=Python
-
-# Exclude HTML from language statistics
-*.html linguist-detectable=false

.github/DISCUSSION_TEMPLATE/feature-requests.yml

@@ -1,59 +0,0 @@
-title: "[Feature Request]: "
-labels: ["⚙️ New"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thank you for your interest in suggesting a new feature! Before you submit, please take a moment to check if already exists in
-        this discussions category to avoid duplicates. 😊
-
-  - type: textarea
-    id: needs_to_be_done
-    attributes:
-      label: What needs to be done?
-      description: Please describe the feature or functionality you'd like to see.
-      placeholder: "e.g., Return alt text along with images scraped from a webpages in Result"
-    validations:
-      required: true
-
-  - type: textarea
-    id: problem_to_solve
-    attributes:
-      label: What problem does this solve?
-      description: Explain the pain point or issue this feature will help address.
-      placeholder: "e.g., Bypass Captchas added by cloudflare"
-    validations:
-      required: true
-
-  - type: textarea
-    id: target_users
-    attributes:
-      label: Target users/beneficiaries
-      description: Who would benefit from this feature? (e.g., specific teams, developers, users, etc.)
-      placeholder: "e.g., Marketing teams, developers"
-    validations:
-      required: false
-
-  - type: textarea
-    id: current_workarounds
-    attributes:
-      label: Current alternatives/workarounds
-      description: Are there any existing solutions or workarounds? How does this feature improve upon them?
-      placeholder: "e.g., Users manually select the css classes mapped to data fields to extract them"
-    validations:
-      required: false
-
-  - type: markdown
-    attributes:
-      value: |
-        ### 💡 Implementation Ideas
-
-  - type: textarea
-    id: proposed_approach
-    attributes:
-      label: Proposed approach
-      description: Share any ideas you have for how this feature could be implemented. Point out any challenges your foresee
-       and the success metrics for this feature
-      placeholder: "e.g., Implement a breadth first traversal algorithm for scraper"
-    validations:
-      required: false

.github/FUNDING.yml

@@ -1,7 +0,0 @@
-# These are supported funding model platforms
-
-# GitHub Sponsors
-github: unclecode
-
-# Custom links for enterprise inquiries (uncomment when ready)
-# custom: ["https://crawl4ai.com/enterprise"]

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -1,127 +0,0 @@
-name: Bug Report
-description: Report a bug with the Crawl4AI.
-title: "[Bug]: "
-labels: ["🐞 Bug","🩺 Needs Triage"]
-body:
-  - type: input
-    id: crawl4ai_version
-    attributes:
-      label: crawl4ai version
-      description: Specify the version of crawl4ai you are using.
-      placeholder: "e.g., 2.0.0"
-    validations:
-      required: true
-
-  - type: textarea
-    id: expected_behavior
-    attributes:
-      label: Expected Behavior
-      description: Describe what you expected to happen.
-      placeholder: "Provide a detailed explanation of the expected outcome."
-    validations:
-      required: true
-
-  - type: textarea
-    id: current_behavior
-    attributes:
-      label: Current Behavior
-      description: Describe what is happening instead of the expected behavior.
-      placeholder: "Describe the actual result or issue you encountered."
-    validations:
-      required: true
-
-  - type: dropdown
-    id: reproducible
-    attributes:
-      label: Is this reproducible?
-      description: Indicate whether this bug can be reproduced consistently.
-      options:
-        - "Yes"
-        - "No"
-    validations:
-      required: true
-
-  - type: textarea
-    id: inputs
-    attributes:
-      label: Inputs Causing the Bug
-      description: Provide details about the inputs causing the issue.
-      placeholder: |
-        - URL(s): 
-        - Settings used: 
-        - Input data (if applicable): 
-      render: bash
-  
-  - type: textarea
-    id: steps_to_reproduce
-    attributes:
-      label: Steps to Reproduce
-      description: Provide step-by-step instructions to reproduce the issue.
-      placeholder: |
-        1. Go to...
-        2. Click on...
-        3. Observe the issue...
-      render: bash
-  
-  - type: textarea
-    id: code_snippets
-    attributes:
-      label: Code snippets
-      description: Provide code snippets(if any). Add comments as necessary
-      placeholder: print("Hello world")
-      render: python
-
-  # Header Section with Title
-  - type: markdown
-    attributes:
-      value: |
-          ## Supporting Information
-          Please provide the following details to help us understand and resolve your issue. This will assist us in reproducing and diagnosing the problem
-
-  - type: input
-    id: os
-    attributes:
-      label: OS
-      description: Please provide the operating system & distro where the issue occurs.
-      placeholder: "e.g., Windows, macOS, Linux"
-    validations:
-      required: true
-
-  - type: input
-    id: python_version
-    attributes:
-      label: Python version
-      description: Specify the Python version being used.
-      placeholder: "e.g., 3.8.5"
-    validations:
-      required: true
-
-  # Browser Field
-  - type: input
-    id: browser
-    attributes:
-      label: Browser
-      description: Provide the name of the browser you are using.
-      placeholder: "e.g., Chrome, Firefox, Safari"
-    validations:
-      required: false
-
-  # Browser Version Field
-  - type: input
-    id: browser_version
-    attributes:
-      label: Browser version
-      description: Provide the version of the browser you are using.
-      placeholder: "e.g., 91.0.4472.124"
-    validations:
-      required: false
-
-  # Error Logs Field (Text Area)
-  - type: textarea
-    id: error_logs
-    attributes:
-      label: Error logs & Screenshots (if applicable)
-      description: If you encountered any errors, please provide the error logs. Attach any relevant screenshots to help us understand the issue.
-      placeholder: "Paste error logs here and attach your screenshots"
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/config.yml

@@ -1,8 +0,0 @@
-blank_issues_enabled: false
-contact_links:
-  - name: Feature Requests
-    url: https://github.com/unclecode/crawl4ai/discussions/categories/feature-requests
-    about: "Suggest new features or enhancements for Crawl4AI"
-  - name: Forums - Q&A
-    url: https://github.com/unclecode/crawl4ai/discussions/categories/forums-q-a
-    about: "Ask questions or engage in general discussions about Crawl4AI"

.github/pull_request_template.md

@@ -1,19 +0,0 @@
-## Summary
-Please include a summary of the change and/or which issues are fixed.
-
-eg: `Fixes #123` (Tag GitHub issue numbers in this format, so it automatically links the issues with your PR)
-
-## List of files changed and why
-eg: quickstart.py - To update the example as per new changes
-
-## How Has This Been Tested?
-Please describe the tests that you ran to verify your changes.
-
-## Checklist:
-
-- [ ] My code follows the style guidelines of this project
-- [ ] I have performed a self-review of my own code
-- [ ] I have commented my code, particularly in hard-to-understand areas
-- [ ] I have made corresponding changes to the documentation
-- [ ] I have added/updated unit tests that prove my fix is effective or that my feature works
-- [ ] New and existing unit tests pass locally with my changes

.github/workflows/main.yml

@@ -1,46 +0,0 @@
-name: Discord GitHub Notifications
-
-on:
-  issues:
-    types: [opened]
-  issue_comment:
-    types: [created]
-  pull_request:
-    types: [opened]
-  discussion:
-    types: [created]
-  watch:
-    types: [started]
-
-jobs:
-  notify-discord:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Send to Google Apps Script (Stars only)
-        if: github.event_name == 'watch'
-        run: |
-          curl -fSs -X POST "${{ secrets.GOOGLE_SCRIPT_ENDPOINT }}" \
-            -H 'Content-Type: application/json' \
-            -d '{"url":"${{ github.event.sender.html_url }}"}'
-      - name: Set webhook based on event type
-        id: set-webhook
-        run: |
-          if [ "${{ github.event_name }}" == "discussion" ]; then
-            echo "webhook=${{ secrets.DISCORD_DISCUSSIONS_WEBHOOK }}" >> $GITHUB_OUTPUT
-          elif [ "${{ github.event_name }}" == "watch" ]; then
-            echo "webhook=${{ secrets.DISCORD_STAR_GAZERS }}" >> $GITHUB_OUTPUT
-          else
-            echo "webhook=${{ secrets.DISCORD_WEBHOOK }}" >> $GITHUB_OUTPUT
-          fi
-
-      - name: Discord Notification
-        uses: Ilshidur/action-discord@master
-        env:
-          DISCORD_WEBHOOK: ${{ steps.set-webhook.outputs.webhook }}
-        with:
-          args: |
-            ${{ github.event_name == 'issues' && format('📣 New issue created: **{0}** by {1} - {2}', github.event.issue.title, github.event.issue.user.login, github.event.issue.html_url) || 
-            github.event_name == 'issue_comment' && format('💬 New comment on issue **{0}** by {1} - {2}', github.event.issue.title, github.event.comment.user.login, github.event.comment.html_url) ||
-            github.event_name == 'pull_request' && format('🔄 New PR opened: **{0}** by {1} - {2}', github.event.pull_request.title, github.event.pull_request.user.login, github.event.pull_request.html_url) ||
-            github.event_name == 'watch' && format('⭐ {0} starred Crawl4AI 🥳! Check out their profile: {1}', github.event.sender.login, github.event.sender.html_url) ||
-            format('💬 New discussion started: **{0}** by {1} - {2}', github.event.discussion.title, github.event.discussion.user.login, github.event.discussion.html_url) }}

.github/workflows/release.yml

@@ -1,142 +0,0 @@
-name: Release Pipeline
-on:
-  push:
-    tags:
-      - 'v*'
-      - '!test-v*'  # Exclude test tags
-
-jobs:
-  release:
-    runs-on: ubuntu-latest
-    permissions:
-      contents: write  # Required for creating releases
-    
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Extract version from tag
-        id: get_version
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/v}
-          echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
-          echo "Releasing version: $TAG_VERSION"
-      
-      - name: Install package dependencies
-        run: |
-          pip install -e .
-      
-      - name: Check version consistency
-        run: |
-          TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
-          PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
-          
-          echo "Tag version: $TAG_VERSION"
-          echo "Package version: $PACKAGE_VERSION"
-          
-          if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
-            echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
-            echo "Please update crawl4ai/__version__.py to match the tag version"
-            exit 1
-          fi
-          echo "✅ Version check passed: $TAG_VERSION"
-      
-      - name: Install build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install build twine
-      
-      - name: Build package
-        run: python -m build
-      
-      - name: Check package
-        run: twine check dist/*
-      
-      - name: Upload to PyPI
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
-        run: |
-          echo "📦 Uploading to PyPI..."
-          twine upload dist/*
-          echo "✅ Package uploaded to https://pypi.org/project/crawl4ai/"
-      
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-      
-      - name: Extract major and minor versions
-        id: versions
-        run: |
-          VERSION=${{ steps.get_version.outputs.VERSION }}
-          MAJOR=$(echo $VERSION | cut -d. -f1)
-          MINOR=$(echo $VERSION | cut -d. -f1-2)
-          echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
-          echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
-      
-      - name: Build and push Docker images
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          tags: |
-            unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
-            unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
-            unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
-            unclecode/crawl4ai:latest
-          platforms: linux/amd64,linux/arm64
-      
-      - name: Create GitHub Release
-        uses: softprops/action-gh-release@v2
-        with:
-          tag_name: v${{ steps.get_version.outputs.VERSION }}
-          name: Release v${{ steps.get_version.outputs.VERSION }}
-          body: |
-            ## 🎉 Crawl4AI v${{ steps.get_version.outputs.VERSION }} Released!
-            
-            ### 📦 Installation
-            
-            **PyPI:**
-            ```bash
-            pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}
-            ```
-            
-            **Docker:**
-            ```bash
-            docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
-            docker pull unclecode/crawl4ai:latest
-            ```
-            
-            ### 📝 What's Changed
-            See [CHANGELOG.md](https://github.com/${{ github.repository }}/blob/main/CHANGELOG.md) for details.
-          draft: false
-          prerelease: false
-          token: ${{ secrets.GITHUB_TOKEN }}
-      
-      - name: Summary
-        run: |
-          echo "## 🚀 Release Complete!" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 📦 PyPI Package" >> $GITHUB_STEP_SUMMARY
-          echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
-          echo "- URL: https://pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
-          echo "- Install: \`pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 📋 GitHub Release" >> $GITHUB_STEP_SUMMARY
-          echo "https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY

.github/workflows/test-release.yml.disabled

@@ -1,116 +0,0 @@
-name: Test Release Pipeline
-on:
-  push:
-    tags:
-      - 'test-v*'
-
-jobs:
-  test-release:
-    runs-on: ubuntu-latest
-    
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-      
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-      
-      - name: Extract version from tag
-        id: get_version
-        run: |
-          TAG_VERSION=${GITHUB_REF#refs/tags/test-v}
-          echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
-          echo "Testing with version: $TAG_VERSION"
-      
-      - name: Install package dependencies
-        run: |
-          pip install -e .
-      
-      - name: Check version consistency
-        run: |
-          TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
-          PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
-          
-          echo "Tag version: $TAG_VERSION"
-          echo "Package version: $PACKAGE_VERSION"
-          
-          if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
-            echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
-            echo "Please update crawl4ai/__version__.py to match the tag version"
-            exit 1
-          fi
-          echo "✅ Version check passed: $TAG_VERSION"
-      
-      - name: Install build dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install build twine
-      
-      - name: Build package
-        run: python -m build
-      
-      - name: Check package
-        run: twine check dist/*
-      
-      - name: Upload to Test PyPI
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.TEST_PYPI_TOKEN }}
-        run: |
-          echo "📦 Uploading to Test PyPI..."
-          twine upload --repository testpypi dist/* || {
-            if [ $? -eq 1 ]; then
-              echo "⚠️ Upload failed - likely version already exists on Test PyPI"
-              echo "Continuing anyway for test purposes..."
-            else
-              exit 1
-            fi
-          }
-          echo "✅ Test PyPI step complete"
-      
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-      
-      - name: Log in to Docker Hub
-        uses: docker/login-action@v3
-        with:
-          username: ${{ secrets.DOCKER_USERNAME }}
-          password: ${{ secrets.DOCKER_TOKEN }}
-      
-      - name: Build and push Docker test images
-        uses: docker/build-push-action@v5
-        with:
-          context: .
-          push: true
-          tags: |
-            unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}
-            unclecode/crawl4ai:test-latest
-          platforms: linux/amd64,linux/arm64
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
-      
-      - name: Summary
-        run: |
-          echo "## 🎉 Test Release Complete!" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 📦 Test PyPI Package" >> $GITHUB_STEP_SUMMARY
-          echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
-          echo "- URL: https://test.pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
-          echo "- Install: \`pip install -i https://test.pypi.org/simple/ crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 🐳 Docker Test Images" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
-          echo "- \`unclecode/crawl4ai:test-latest\`" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "### 🧹 Cleanup Commands" >> $GITHUB_STEP_SUMMARY
-          echo "\`\`\`bash" >> $GITHUB_STEP_SUMMARY
-          echo "# Remove test tag" >> $GITHUB_STEP_SUMMARY
-          echo "git tag -d test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
-          echo "git push origin :test-v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
-          echo "" >> $GITHUB_STEP_SUMMARY
-          echo "# Remove Docker test images" >> $GITHUB_STEP_SUMMARY
-          echo "docker rmi unclecode/crawl4ai:test-${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
-          echo "docker rmi unclecode/crawl4ai:test-latest" >> $GITHUB_STEP_SUMMARY
-          echo "\`\`\`" >> $GITHUB_STEP_SUMMARY

.gitignore

@@ -1,13 +1,3 @@
-# Scripts folder (private tools)
-.scripts/
-
-# Database files
-*.db
-
-# Environment files
-.env
-.env.local
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -216,80 +206,7 @@ pypi_build.sh
 git_issues.py
 git_issues.md
 
-.next/
 .tests/
-# .issues/
-.docs/
 .issues/
-.gitboss/
-todo_executor.md
-protect-all-except-feature.sh
-manage-collab.sh
-publish.sh
-combine.sh
-combined_output.txt
-.local
-.scripts
-tree.md
-tree.md
-.scripts
-.local
-.do
-/plans
-plans/
-
-# Codeium
-.codeiumignore
-todo/
-
-# Continue development files
-.continue/
-.continuerc.json
-continue.lock
-continue_core.log
-contextProviders/
-continue_workspace/
-.continue-cache/
-continue_config.json
-
-# Continue temporary files
-.continue-temp/
-.continue-logs/
-.continue-downloads/
-
-# Continue VS Code specific
-.vscode-continue/
-.vscode-continue-cache/
-
-.prompts/
-
-.llm.env
-.private/
-
-CLAUDE_MONITOR.md
-CLAUDE.md
-
-.claude/
-
-tests/**/test_site
-tests/**/reports
-tests/**/benchmark_reports
-test_scripts/
-docs/**/data
-.codecat/
-
-docs/apps/linkdin/debug*/
-docs/apps/linkdin/samples/insights/*
-
-scripts/
-
-
-# Databse files
-*.sqlite3
-*.sqlite3-journal
-*.db-journal
-*.db-wal
-*.db-shm
-*.db
-*.rdb
-*.ldb
+.docs/
+.issues/

CODE_OF_CONDUCT.md

@@ -1,131 +0,0 @@
-# Crawl4AI Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official email address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-unclecode@crawl4ai.com. All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

CONTRIBUTORS.md

@@ -6,44 +6,21 @@ We would like to thank the following people for their contributions to Crawl4AI:
 
 - [Unclecode](https://github.com/unclecode) - Project Creator and Main Developer
 - [Nasrin](https://github.com/ntohidi) - Project Manager and Developer
-- [Aravind Karnam](https://github.com/aravindkarnam) - Head of Community and Product 
+- [Aravind Karnam](https://github.com/aravindkarnam) - Developer
 
 ## Community Contributors
 
-- [aadityakanjolia4](https://github.com/aadityakanjolia4) - Fix for `CustomHTML2Text` is not defined.
 - [FractalMind](https://github.com/FractalMind) - Created the first official Docker Hub image and fixed Dockerfile errors
 - [ketonkss4](https://github.com/ketonkss4) - Identified Selenium's new capabilities, helping reduce dependencies
 - [jonymusky](https://github.com/jonymusky) - Javascript execution documentation, and wait_for
 - [datehoer](https://github.com/datehoer) - Add browser prxy support
 
-## Pull Requests
-
-- [dvschuyl](https://github.com/dvschuyl) - AsyncPlaywrightCrawlerStrategy page-evaluate context destroyed by navigation [#304](https://github.com/unclecode/crawl4ai/pull/304)
-- [nelzomal](https://github.com/nelzomal) - Enhance development installation instructions [#286](https://github.com/unclecode/crawl4ai/pull/286)
-- [HamzaFarhan](https://github.com/HamzaFarhan) - Handled the cases where markdown_with_citations, references_markdown, and filtered_html might not be defined [#293](https://github.com/unclecode/crawl4ai/pull/293)
-- [NanmiCoder](https://github.com/NanmiCoder) - fix: crawler strategy exception handling and fixes [#271](https://github.com/unclecode/crawl4ai/pull/271)
-- [paulokuong](https://github.com/paulokuong) - fix: RAWL4_AI_BASE_DIRECTORY should be Path object instead of string [#298](https://github.com/unclecode/crawl4ai/pull/298)
-
-#### Feb-Alpha-1
-- [sufianuddin](https://github.com/sufianuddin) - fix: [Documentation for JsonCssExtractionStrategy](https://github.com/unclecode/crawl4ai/issues/651)
-- [tautikAg](https://github.com/tautikAg) - fix: [Markdown output has incorect spacing](https://github.com/unclecode/crawl4ai/issues/599)
-- [cardit1](https://github.com/cardit1) - fix: ['AsyncPlaywrightCrawlerStrategy' object has no attribute 'downloads_path'](https://github.com/unclecode/crawl4ai/issues/585)
-- [dmurat](https://github.com/dmurat) - fix: [ Incorrect rendering of inline code inside of links ](https://github.com/unclecode/crawl4ai/issues/583)
-- [Sparshsing](https://github.com/Sparshsing) - fix: [Relative Urls in the webpage not extracted properly ](https://github.com/unclecode/crawl4ai/issues/570)
-
-
-
 ## Other Contributors
 
 - [Gokhan](https://github.com/gkhngyk) 
 - [Shiv Kumar](https://github.com/shivkumar0757)
 - [QIN2DIM](https://github.com/QIN2DIM)
 
-#### Typo fixes
-- [ssoydan](https://github.com/ssoydan)
-- [Darshan](https://github.com/Darshan2104)
-- [tuhinmallick](https://github.com/tuhinmallick)
-
 ## Acknowledgements
 
 We also want to thank all the users who have reported bugs, suggested features, or helped in any other way to make Crawl4AI better.

Dockerfile

@@ -1,36 +1,29 @@
-FROM python:3.12-slim-bookworm AS build
+# syntax=docker/dockerfile:1.4
 
-# C4ai version
-ARG C4AI_VER=0.7.0-r1
-ENV C4AI_VERSION=$C4AI_VER
-LABEL c4ai.version=$C4AI_VER
+# Build arguments
+ARG PYTHON_VERSION=3.10
 
-# Set build arguments
-ARG APP_HOME=/app
-ARG GITHUB_REPO=https://github.com/unclecode/crawl4ai.git
-ARG GITHUB_BRANCH=main
-ARG USE_LOCAL=true
+# Base stage with system dependencies
+FROM python:${PYTHON_VERSION}-slim as base
 
-ENV PYTHONFAULTHANDLER=1 \
-    PYTHONHASHSEED=random \
-    PYTHONUNBUFFERED=1 \
-    PIP_NO_CACHE_DIR=1 \
-    PYTHONDONTWRITEBYTECODE=1 \
-    PIP_DISABLE_PIP_VERSION_CHECK=1 \
-    PIP_DEFAULT_TIMEOUT=100 \
-    DEBIAN_FRONTEND=noninteractive \
-    REDIS_HOST=localhost \
-    REDIS_PORT=6379
-
-ARG PYTHON_VERSION=3.12
-ARG INSTALL_TYPE=default
+# Declare ARG variables again within the build stage
+ARG INSTALL_TYPE=all
 ARG ENABLE_GPU=false
-ARG TARGETARCH
 
+# Platform-specific labels
 LABEL maintainer="unclecode"
-LABEL description="🔥🕷️ Crawl4AI: Open-source LLM Friendly Web Crawler & scraper"
+LABEL description="Crawl4AI - Advanced Web Crawler with AI capabilities"
 LABEL version="1.0"
 
+# Environment setup
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PIP_DEFAULT_TIMEOUT=100 \
+    DEBIAN_FRONTEND=noninteractive
+
+# Install system dependencies
 RUN apt-get update && apt-get install -y --no-install-recommends \
     build-essential \
     curl \
@@ -41,11 +34,10 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     pkg-config \
     python3-dev \
     libjpeg-dev \
-    redis-server \
-    supervisor \
-    && apt-get clean \ 
+    libpng-dev \
     && rm -rf /var/lib/apt/lists/*
 
+# Playwright system dependencies for Linux
 RUN apt-get update && apt-get install -y --no-install-recommends \
     libglib2.0-0 \
     libnss3 \
@@ -68,66 +60,28 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     libcairo2 \
     libasound2 \
     libatspi2.0-0 \
-    && apt-get clean \ 
     && rm -rf /var/lib/apt/lists/*
 
-RUN apt-get update && apt-get dist-upgrade -y \
-    && rm -rf /var/lib/apt/lists/*
-
-RUN if [ "$ENABLE_GPU" = "true" ] && [ "$TARGETARCH" = "amd64" ] ; then \
+# GPU support if enabled
+RUN if [ "$ENABLE_GPU" = "true" ] ; then \
     apt-get update && apt-get install -y --no-install-recommends \
     nvidia-cuda-toolkit \
-    && apt-get clean \ 
     && rm -rf /var/lib/apt/lists/* ; \
-else \
-    echo "Skipping NVIDIA CUDA Toolkit installation (unsupported platform or GPU disabled)"; \
-fi
+    fi
 
-RUN if [ "$TARGETARCH" = "arm64" ]; then \
-    echo "🦾 Installing ARM-specific optimizations"; \
-    apt-get update && apt-get install -y --no-install-recommends \
-    libopenblas-dev \
-    && apt-get clean \ 
-    && rm -rf /var/lib/apt/lists/*; \
-elif [ "$TARGETARCH" = "amd64" ]; then \
-    echo "🖥️ Installing AMD64-specific optimizations"; \
-    apt-get update && apt-get install -y --no-install-recommends \
-    libomp-dev \
-    && apt-get clean \ 
-    && rm -rf /var/lib/apt/lists/*; \
-else \
-    echo "Skipping platform-specific optimizations (unsupported platform)"; \
-fi
+# Create and set working directory
+WORKDIR /app
 
-# Create a non-root user and group
-RUN groupadd -r appuser && useradd --no-log-init -r -g appuser appuser
+# Copy the entire project
+COPY . .
 
-# Create and set permissions for appuser home directory
-RUN mkdir -p /home/appuser && chown -R appuser:appuser /home/appuser
-
-WORKDIR ${APP_HOME}
-
-RUN echo '#!/bin/bash\n\
-if [ "$USE_LOCAL" = "true" ]; then\n\
-    echo "📦 Installing from local source..."\n\
-    pip install --no-cache-dir /tmp/project/\n\
-else\n\
-    echo "🌐 Installing from GitHub..."\n\
-    for i in {1..3}; do \n\
-        git clone --branch ${GITHUB_BRANCH} ${GITHUB_REPO} /tmp/crawl4ai && break || \n\
-        { echo "Attempt $i/3 failed! Taking a short break... ☕"; sleep 5; }; \n\
-    done\n\
-    pip install --no-cache-dir /tmp/crawl4ai\n\
-fi' > /tmp/install.sh && chmod +x /tmp/install.sh
-
-COPY . /tmp/project/
-
-# Copy supervisor config first (might need root later, but okay for now)
-COPY deploy/docker/supervisord.conf .
-
-COPY deploy/docker/requirements.txt .
+# Install base requirements
 RUN pip install --no-cache-dir -r requirements.txt
 
+# Install required library for FastAPI
+RUN pip install fastapi uvicorn psutil
+
+# Install ML dependencies first for better layer caching
 RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
         pip install --no-cache-dir \
             torch \
@@ -140,61 +94,33 @@ RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
         python -m nltk.downloader punkt stopwords ; \
     fi
 
+# Install the package
 RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
-        pip install "/tmp/project/[all]" && \
+        pip install -e ".[all]" && \
         python -m crawl4ai.model_loader ; \
     elif [ "$INSTALL_TYPE" = "torch" ] ; then \
-        pip install "/tmp/project/[torch]" ; \
+        pip install -e ".[torch]" ; \
     elif [ "$INSTALL_TYPE" = "transformer" ] ; then \
-        pip install "/tmp/project/[transformer]" && \
+        pip install -e ".[transformer]" && \
         python -m crawl4ai.model_loader ; \
     else \
-        pip install "/tmp/project" ; \
+        pip install -e "." ; \
     fi
 
-RUN pip install --no-cache-dir --upgrade pip && \
-    /tmp/install.sh && \
-    python -c "import crawl4ai; print('✅ crawl4ai is ready to rock!')" && \
-    python -c "from playwright.sync_api import sync_playwright; print('✅ Playwright is feeling dramatic!')"
+# Install Playwright and browsers
+RUN playwright install
 
-RUN crawl4ai-setup
+# Health check
+HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
+    CMD curl -f http://localhost:8000/health || exit 1
 
-RUN playwright install --with-deps
+# Expose port
+EXPOSE 8000 11235 9222 8080
 
-RUN mkdir -p /home/appuser/.cache/ms-playwright \
-    && cp -r /root/.cache/ms-playwright/chromium-* /home/appuser/.cache/ms-playwright/ \
-    && chown -R appuser:appuser /home/appuser/.cache/ms-playwright
+# Optional: Increase shared memory size to prevent browser crashes
+# when loading heavy pages
+RUN mkdir /dev/shm
+VOLUME /dev/shm
 
-RUN crawl4ai-doctor
-
-# Copy application code
-COPY deploy/docker/* ${APP_HOME}/
-
-# copy the playground + any future static assets
-COPY deploy/docker/static ${APP_HOME}/static
-
-# Change ownership of the application directory to the non-root user
-RUN chown -R appuser:appuser ${APP_HOME}
-
-# give permissions to redis persistence dirs if used
-RUN mkdir -p /var/lib/redis /var/log/redis && chown -R appuser:appuser /var/lib/redis /var/log/redis
-
-HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
-    CMD bash -c '\
-    MEM=$(free -m | awk "/^Mem:/{print \$2}"); \
-    if [ $MEM -lt 2048 ]; then \
-        echo "⚠️ Warning: Less than 2GB RAM available! Your container might need a memory boost! 🚀"; \
-        exit 1; \
-    fi && \
-    redis-cli ping > /dev/null && \
-    curl -f http://localhost:11235/health || exit 1'
-
-EXPOSE 6379
-# Switch to the non-root user before starting the application
-USER appuser
-
-# Set environment variables to ptoduction
-ENV PYTHON_ENV=production 
-
-# Start the application using supervisord
-CMD ["supervisord", "-c", "supervisord.conf"]
+# Start the FastAPI server
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "11235"]

JOURNAL.md

@@ -1,339 +0,0 @@
-# Development Journal
-
-This journal tracks significant feature additions, bug fixes, and architectural decisions in the crawl4ai project. It serves as both documentation and a historical record of the project's evolution.
-
-## [2025-04-17] Added Content Source Selection for Markdown Generation
-
-**Feature:** Configurable content source for markdown generation
-
-**Changes Made:**
-1. Added `content_source: str = "cleaned_html"` parameter to `MarkdownGenerationStrategy` class
-2. Updated `DefaultMarkdownGenerator` to accept and pass the content source parameter
-3. Renamed the `cleaned_html` parameter to `input_html` in the `generate_markdown` method
-4. Modified `AsyncWebCrawler.aprocess_html` to select the appropriate HTML source based on the generator's config
-5. Added `preprocess_html_for_schema` import in `async_webcrawler.py`
-
-**Implementation Details:**
-- Added a new `content_source` parameter to specify which HTML input to use for markdown generation
-- Options include: "cleaned_html" (default), "raw_html", and "fit_html"
-- Used a dictionary dispatch pattern in `aprocess_html` to select the appropriate HTML source
-- Added proper error handling with fallback to cleaned_html if content source selection fails
-- Ensured backward compatibility by defaulting to "cleaned_html" option
-
-**Files Modified:**
-- `crawl4ai/markdown_generation_strategy.py`: Added content_source parameter and updated the method signature
-- `crawl4ai/async_webcrawler.py`: Added HTML source selection logic and updated imports
-
-**Examples:**
-- Created `docs/examples/content_source_example.py` demonstrating how to use the new parameter
-
-**Challenges:**
-- Maintaining backward compatibility while reorganizing the parameter flow
-- Ensuring proper error handling for all content source options
-- Making the change with minimal code modifications
-
-**Why This Feature:**
-The content source selection feature allows users to choose which HTML content to use as input for markdown generation:
-1. "cleaned_html" - Uses the post-processed HTML after scraping strategy (original behavior)
-2. "raw_html" - Uses the original raw HTML directly from the web page
-3. "fit_html" - Uses the preprocessed HTML optimized for schema extraction
-
-This feature provides greater flexibility in how users generate markdown, enabling them to:
-- Capture more detailed content from the original HTML when needed
-- Use schema-optimized HTML when working with structured data
-- Choose the approach that best suits their specific use case
-## [2025-04-17] Implemented High Volume Stress Testing Solution for SDK
-
-**Feature:** Comprehensive stress testing framework using `arun_many` and the dispatcher system to evaluate performance, concurrency handling, and identify potential issues under high-volume crawling scenarios.
-
-**Changes Made:**
-1.  Created a dedicated stress testing framework in the `benchmarking/` (or similar) directory.
-2.  Implemented local test site generation (`SiteGenerator`) with configurable heavy HTML pages.
-3.  Added basic memory usage tracking (`SimpleMemoryTracker`) using platform-specific commands (avoiding `psutil` dependency for this specific test).
-4.  Utilized `CrawlerMonitor` from `crawl4ai` for rich terminal UI and real-time monitoring of test progress and dispatcher activity.
-5.  Implemented detailed result summary saving (JSON) and memory sample logging (CSV).
-6.  Developed `run_benchmark.py` to orchestrate tests with predefined configurations.
-7.  Created `run_all.sh` as a simple wrapper for `run_benchmark.py`.
-
-**Implementation Details:**
--   Generates a local test site with configurable pages containing heavy text and image content.
--   Uses Python's built-in `http.server` for local serving, minimizing network variance.
--   Leverages `crawl4ai`'s `arun_many` method for processing URLs.
--   Utilizes `MemoryAdaptiveDispatcher` to manage concurrency via the `max_sessions` parameter (note: memory adaptation features require `psutil`, not used by `SimpleMemoryTracker`).
--   Tracks memory usage via `SimpleMemoryTracker`, recording samples throughout test execution to a CSV file.
--   Uses `CrawlerMonitor` (which uses the `rich` library) for clear terminal visualization and progress reporting directly from the dispatcher.
--   Stores detailed final metrics in a JSON summary file.
-
-**Files Created/Updated:**
--   `stress_test_sdk.py`: Main stress testing implementation using `arun_many`.
--   `benchmark_report.py`: (Assumed) Report generator for comparing test results.
--   `run_benchmark.py`: Test runner script with predefined configurations.
--   `run_all.sh`: Simple bash script wrapper for `run_benchmark.py`.
--   `USAGE.md`: Comprehensive documentation on usage and interpretation (updated).
-
-**Testing Approach:**
--   Creates a controlled, reproducible test environment with a local HTTP server.
--   Processes URLs using `arun_many`, allowing the dispatcher to manage concurrency up to `max_sessions`.
--   Optionally logs per-batch summaries (when not in streaming mode) after processing chunks.
--   Supports different test sizes via `run_benchmark.py` configurations.
--   Records memory samples via platform commands for basic trend analysis.
--   Includes cleanup functionality for the test environment.
-
-**Challenges:**
--   Ensuring proper cleanup of HTTP server processes.
--   Getting reliable memory tracking across platforms without adding heavy dependencies (`psutil`) to this specific test script.
--   Designing `run_benchmark.py` to correctly pass arguments to `stress_test_sdk.py`.
-
-**Why This Feature:**
-The high volume stress testing solution addresses critical needs for ensuring Crawl4AI's `arun_many` reliability:
-1.  Provides a reproducible way to evaluate performance under concurrent load.
-2.  Allows testing the dispatcher's concurrency control (`max_session_permit`) and queue management.
-3.  Enables performance tuning by observing throughput (`URLs/sec`) under different `max_sessions` settings.
-4.  Creates a controlled environment for testing `arun_many` behavior.
-5.  Supports continuous integration by providing deterministic test conditions for `arun_many`.
-
-**Design Decisions:**
--   Chose local site generation for reproducibility and isolation from network issues.
--   Utilized the built-in `CrawlerMonitor` for real-time feedback, leveraging its `rich` integration.
--   Implemented optional per-batch logging in `stress_test_sdk.py` (when not streaming) to provide chunk-level summaries alongside the continuous monitor.
--   Adopted `arun_many` with a `MemoryAdaptiveDispatcher` as the core mechanism for parallel execution, reflecting the intended SDK usage.
--   Created `run_benchmark.py` to simplify running standard test configurations.
--   Used `SimpleMemoryTracker` to provide basic memory insights without requiring `psutil` for this particular test runner.
-
-**Future Enhancements to Consider:**
--   Create a separate test variant that *does* use `psutil` to specifically stress the memory-adaptive features of the dispatcher.
--   Add support for generated JavaScript content.
--   Add support for Docker-based testing with explicit memory limits.
--   Enhance `benchmark_report.py` to provide more sophisticated analysis of performance and memory trends from the generated JSON/CSV files.
-
----
-
-## [2025-04-17] Refined Stress Testing System Parameters and Execution
-
-**Changes Made:**
-1.  Corrected `run_benchmark.py` and `stress_test_sdk.py` to use `--max-sessions` instead of the incorrect `--workers` parameter, accurately reflecting dispatcher configuration.
-2.  Updated `run_benchmark.py` argument handling to correctly pass all relevant custom parameters (including `--stream`, `--monitor-mode`, etc.) to `stress_test_sdk.py`.
-3.  (Assuming changes in `benchmark_report.py`) Applied dark theme to benchmark reports for better readability.
-4.  (Assuming changes in `benchmark_report.py`) Improved visualization code to eliminate matplotlib warnings.
-5.  Updated `run_benchmark.py` to provide clickable `file://` links to generated reports in the terminal output.
-6.  Updated `USAGE.md` with comprehensive parameter descriptions reflecting the final script arguments.
-7.  Updated `run_all.sh` wrapper to correctly invoke `run_benchmark.py` with flexible arguments.
-
-**Details of Changes:**
-
-1.  **Parameter Correction (`--max-sessions`)**:
-    *   Identified the fundamental misunderstanding where `--workers` was used incorrectly.
-    *   Refactored `stress_test_sdk.py` to accept `--max-sessions` and configure the `MemoryAdaptiveDispatcher`'s `max_session_permit` accordingly.
-    *   Updated `run_benchmark.py` argument parsing and command construction to use `--max-sessions`.
-    *   Updated `TEST_CONFIGS` in `run_benchmark.py` to use `max_sessions`.
-
-2.  **Argument Handling (`run_benchmark.py`)**:
-    *   Improved logic to collect all command-line arguments provided to `run_benchmark.py`.
-    *   Ensured all relevant arguments (like `--stream`, `--monitor-mode`, `--port`, `--use-rate-limiter`, etc.) are correctly forwarded when calling `stress_test_sdk.py` as a subprocess.
-
-3.  **Dark Theme & Visualization Fixes (Assumed in `benchmark_report.py`)**:
-    *   (Describes changes assumed to be made in the separate reporting script).
-
-4.  **Clickable Links (`run_benchmark.py`)**:
-    *   Added logic to find the latest HTML report and PNG chart in the `benchmark_reports` directory after `benchmark_report.py` runs.
-    *   Used `pathlib` to generate correct `file://` URLs for terminal output.
-
-5.  **Documentation Improvements (`USAGE.md`)**:
-    *   Rewrote sections to explain `arun_many`, dispatchers, and `--max-sessions`.
-    *   Updated parameter tables for all scripts (`stress_test_sdk.py`, `run_benchmark.py`).
-    *   Clarified the difference between batch and streaming modes and their effect on logging.
-    *   Updated examples to use correct arguments.
-
-**Files Modified:**
--   `stress_test_sdk.py`: Changed `--workers` to `--max-sessions`, added new arguments, used `arun_many`.
--   `run_benchmark.py`: Changed argument handling, updated configs, calls `stress_test_sdk.py`.
--   `run_all.sh`: Updated to call `run_benchmark.py` correctly.
--   `USAGE.md`: Updated documentation extensively.
--   `benchmark_report.py`: (Assumed modifications for dark theme and viz fixes).
-
-**Testing:**
--   Verified that `--max-sessions` correctly limits concurrency via the `CrawlerMonitor` output.
--   Confirmed that custom arguments passed to `run_benchmark.py` are forwarded to `stress_test_sdk.py`.
--   Validated clickable links work in supporting terminals.
--   Ensured documentation matches the final script parameters and behavior.
-
-**Why These Changes:**
-These refinements correct the fundamental approach of the stress test to align with `crawl4ai`'s actual architecture and intended usage:
-1.  Ensures the test evaluates the correct components (`arun_many`, `MemoryAdaptiveDispatcher`).
-2.  Makes test configurations more accurate and flexible.
-3.  Improves the usability of the testing framework through better argument handling and documentation.
-
-
-**Future Enhancements to Consider:**
-- Add support for generated JavaScript content to test JS rendering performance
-- Implement more sophisticated memory analysis like generational garbage collection tracking
-- Add support for Docker-based testing with memory limits to force OOM conditions
-- Create visualization tools for analyzing memory usage patterns across test runs
-- Add benchmark comparisons between different crawler versions or configurations
-
-## [2025-04-17] Fixed Issues in Stress Testing System
-
-**Changes Made:**
-1. Fixed custom parameter handling in run_benchmark.py
-2. Applied dark theme to benchmark reports for better readability
-3. Improved visualization code to eliminate matplotlib warnings
-4. Added clickable links to generated reports in terminal output
-5. Enhanced documentation with comprehensive parameter descriptions
-
-**Details of Changes:**
-
-1. **Custom Parameter Handling Fix**
-   - Identified bug where custom URL count was being ignored in run_benchmark.py
-   - Rewrote argument handling to use a custom args dictionary
-   - Properly passed parameters to the test_simple_stress.py command
-   - Added better UI indication of custom parameters in use
-
-2. **Dark Theme Implementation**
-   - Added complete dark theme to HTML benchmark reports
-   - Applied dark styling to all visualization components
-   - Used Nord-inspired color palette for charts and graphs
-   - Improved contrast and readability for data visualization
-   - Updated text colors and backgrounds for better eye comfort
-
-3. **Matplotlib Warning Fixes**
-   - Resolved warnings related to improper use of set_xticklabels()
-   - Implemented correct x-axis positioning for bar charts
-   - Ensured proper alignment of bar labels and data points
-   - Updated plotting code to use modern matplotlib practices
-
-4. **Documentation Improvements**
-   - Created comprehensive USAGE.md with detailed instructions
-   - Added parameter documentation for all scripts
-   - Included examples for all common use cases
-   - Provided detailed explanations for interpreting results
-   - Added troubleshooting guide for common issues
-
-**Files Modified:**
-- `tests/memory/run_benchmark.py`: Fixed custom parameter handling
-- `tests/memory/benchmark_report.py`: Added dark theme and fixed visualization warnings
-- `tests/memory/run_all.sh`: Added clickable links to reports
-- `tests/memory/USAGE.md`: Created comprehensive documentation
-
-**Testing:**
-- Verified that custom URL counts are now correctly used
-- Confirmed dark theme is properly applied to all report elements
-- Checked that matplotlib warnings are no longer appearing
-- Validated clickable links to reports work in terminals that support them
-
-**Why These Changes:**
-These improvements address several usability issues with the stress testing system:
-1. Better parameter handling ensures test configurations work as expected
-2. Dark theme reduces eye strain during extended test review sessions
-3. Fixing visualization warnings improves code quality and output clarity
-4. Enhanced documentation makes the system more accessible for future use
-
-**Future Enhancements:**
-- Add additional visualization options for different types of analysis
-- Implement theme toggle to support both light and dark preferences
-- Add export options for embedding reports in other documentation
-- Create dedicated CI/CD integration templates for automated testing
-
-## [2025-04-09] Added MHTML Capture Feature
-
-**Feature:** MHTML snapshot capture of crawled pages
-
-**Changes Made:**
-1. Added `capture_mhtml: bool = False` parameter to `CrawlerRunConfig` class
-2. Added `mhtml: Optional[str] = None` field to `CrawlResult` model
-3. Added `mhtml_data: Optional[str] = None` field to `AsyncCrawlResponse` class
-4. Implemented `capture_mhtml()` method in `AsyncPlaywrightCrawlerStrategy` class to capture MHTML via CDP
-5. Modified the crawler to capture MHTML when enabled and pass it to the result
-
-**Implementation Details:**
-- MHTML capture uses Chrome DevTools Protocol (CDP) via Playwright's CDP session API
-- The implementation waits for page to fully load before capturing MHTML content
-- Enhanced waiting for JavaScript content with requestAnimationFrame for better JS content capture
-- We ensure all browser resources are properly cleaned up after capture
-
-**Files Modified:**
-- `crawl4ai/models.py`: Added the mhtml field to CrawlResult
-- `crawl4ai/async_configs.py`: Added capture_mhtml parameter to CrawlerRunConfig
-- `crawl4ai/async_crawler_strategy.py`: Implemented MHTML capture logic
-- `crawl4ai/async_webcrawler.py`: Added mapping from AsyncCrawlResponse.mhtml_data to CrawlResult.mhtml
-
-**Testing:**
-- Created comprehensive tests in `tests/20241401/test_mhtml.py` covering:
-  - Capturing MHTML when enabled
-  - Ensuring mhtml is None when disabled explicitly
-  - Ensuring mhtml is None by default
-  - Capturing MHTML on JavaScript-enabled pages
-
-**Challenges:**
-- Had to improve page loading detection to ensure JavaScript content was fully rendered
-- Tests needed to be run independently due to Playwright browser instance management
-- Modified test expected content to match actual MHTML output
-
-**Why This Feature:**
-The MHTML capture feature allows users to capture complete web pages including all resources (CSS, images, etc.) in a single file. This is valuable for:
-1. Offline viewing of captured pages
-2. Creating permanent snapshots of web content for archival
-3. Ensuring consistent content for later analysis, even if the original site changes
-
-**Future Enhancements to Consider:**
-- Add option to save MHTML to file
-- Support for filtering what resources get included in MHTML
-- Add support for specifying MHTML capture options
-
-## [2025-04-10] Added Network Request and Console Message Capturing
-
-**Feature:** Comprehensive capturing of network requests/responses and browser console messages during crawling
-
-**Changes Made:**
-1. Added `capture_network_requests: bool = False` and `capture_console_messages: bool = False` parameters to `CrawlerRunConfig` class
-2. Added `network_requests: Optional[List[Dict[str, Any]]] = None` and `console_messages: Optional[List[Dict[str, Any]]] = None` fields to both `AsyncCrawlResponse` and `CrawlResult` models
-3. Implemented event listeners in `AsyncPlaywrightCrawlerStrategy._crawl_web()` to capture browser network events and console messages
-4. Added proper event listener cleanup in the finally block to prevent resource leaks
-5. Modified the crawler flow to pass captured data from AsyncCrawlResponse to CrawlResult
-
-**Implementation Details:**
-- Network capture uses Playwright event listeners (`request`, `response`, and `requestfailed`) to record all network activity
-- Console capture uses Playwright event listeners (`console` and `pageerror`) to record console messages and errors
-- Each network event includes metadata like URL, headers, status, and timing information
-- Each console message includes type, text content, and source location when available
-- All captured events include timestamps for chronological analysis
-- Error handling ensures even failed capture attempts won't crash the main crawling process
-
-**Files Modified:**
-- `crawl4ai/models.py`: Added new fields to AsyncCrawlResponse and CrawlResult
-- `crawl4ai/async_configs.py`: Added new configuration parameters to CrawlerRunConfig
-- `crawl4ai/async_crawler_strategy.py`: Implemented capture logic using event listeners
-- `crawl4ai/async_webcrawler.py`: Added data transfer from AsyncCrawlResponse to CrawlResult
-
-**Documentation:**
-- Created detailed documentation in `docs/md_v2/advanced/network-console-capture.md`
-- Added feature to site navigation in `mkdocs.yml`
-- Updated CrawlResult documentation in `docs/md_v2/api/crawl-result.md`
-- Created comprehensive example in `docs/examples/network_console_capture_example.py`
-
-**Testing:**
-- Created `tests/general/test_network_console_capture.py` with tests for:
-  - Verifying capture is disabled by default
-  - Testing network request capturing
-  - Testing console message capturing
-  - Ensuring both capture types can be enabled simultaneously
-  - Checking correct content is captured in expected formats
-
-**Challenges:**
-- Initial implementation had synchronous/asynchronous mismatches in event handlers
-- Needed to fix type of property access vs. method calls in handlers
-- Required careful cleanup of event listeners to prevent memory leaks
-
-**Why This Feature:**
-The network and console capture feature provides deep visibility into web page activity, enabling:
-1. Debugging complex web applications by seeing all network requests and errors
-2. Security analysis to detect unexpected third-party requests and data flows
-3. Performance profiling to identify slow-loading resources
-4. API discovery in single-page applications
-5. Comprehensive analysis of web application behavior
-
-**Future Enhancements to Consider:**
-- Option to filter captured events by type, domain, or content
-- Support for capturing response bodies (with size limits)
-- Aggregate statistics calculation for performance metrics
-- Integration with visualization tools for network waterfall analysis
-- Exporting captures in HAR format for use with external tools

LICENSE

@@ -48,22 +48,4 @@ You may add Your own copyright statement to Your modifications and may provide a
 
 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
 
-END OF TERMS AND CONDITIONS
-
----
-Attribution Requirement
-
-All distributions, publications, or public uses of this software, or derivative works based on this software, must include the following attribution:
-
-"This product includes software developed by UncleCode (https://x.com/unclecode) as part of the Crawl4AI project (https://github.com/unclecode/crawl4ai)."
-
-This attribution must be displayed in a prominent and easily accessible location, such as:
-
--   For software distributions: In a NOTICE file, README file, or equivalent documentation.
--   For publications (research papers, articles, blog posts): In the acknowledgments section or a footnote.
--   For websites/web applications: In an "About" or "Credits" section.
--   For command-line tools: In the help/usage output.
-
-This requirement ensures proper credit is given for the use of Crawl4AI and helps promote the project.
-
----
+END OF TERMS AND CONDITIONS

MANIFEST.in

@@ -1,2 +1 @@
-include requirements.txt
-recursive-include crawl4ai/js_snippet *.js
+include requirements.txt

PROGRESSIVE_CRAWLING.md

@@ -1,320 +0,0 @@
-# Progressive Web Crawling with Adaptive Information Foraging
-
-## Abstract
-
-This paper presents a novel approach to web crawling that adaptively determines when sufficient information has been gathered to answer a given query. Unlike traditional exhaustive crawling methods, our Progressive Information Sufficiency (PIS) framework uses statistical measures to balance information completeness against crawling efficiency. We introduce a multi-strategy architecture supporting pure statistical, embedding-enhanced, and LLM-assisted approaches, with theoretical guarantees on convergence and practical evaluation methods using synthetic datasets.
-
-## 1. Introduction
-
-Traditional web crawling approaches follow predetermined patterns (breadth-first, depth-first) without consideration for information sufficiency. This work addresses the fundamental question: *"When do we have enough information to answer a query and similar queries in its domain?"*
-
-We formalize this as an optimal stopping problem in information foraging, introducing metrics for coverage, consistency, and saturation that enable crawlers to make intelligent decisions about when to stop crawling and which links to follow.
-
-## 2. Problem Formulation
-
-### 2.1 Definitions
-
-Let:
-- **K** = {d₁, d₂, ..., dₙ} be the current knowledge base (crawled documents)
-- **Q** be the user query
-- **L** = {l₁, l₂, ..., lₘ} be available links with preview metadata
-- **θ** be the confidence threshold for information sufficiency
-
-### 2.2 Objectives
-
-1. **Minimize** |K| (number of crawled pages)
-2. **Maximize** P(answers(Q) | K) (probability of answering Q given K)
-3. **Ensure** coverage of Q's domain (similar queries)
-
-## 3. Mathematical Framework
-
-### 3.1 Information Sufficiency Metric
-
-We define Information Sufficiency as:
-
-```
-IS(K, Q) = min(Coverage(K, Q), Consistency(K, Q), 1 - Redundancy(K)) × DomainCoverage(K, Q)
-```
-
-### 3.2 Coverage Score
-
-Coverage measures how well current knowledge covers query terms and related concepts:
-
-```
-Coverage(K, Q) = Σ(t ∈ Q) log(df(t, K) + 1) × idf(t) / |Q|
-```
-
-Where:
-- df(t, K) = document frequency of term t in knowledge base K
-- idf(t) = inverse document frequency weight
-
-### 3.3 Consistency Score
-
-Consistency measures information coherence across documents:
-
-```
-Consistency(K, Q) = 1 - Var(answers from random subsets of K)
-```
-
-This captures the principle that sufficient knowledge should provide stable answers regardless of document subset.
-
-### 3.4 Saturation Score
-
-Saturation detects diminishing returns:
-
-```
-Saturation(K) = 1 - (ΔInfo(Kₙ) / ΔInfo(K₁))
-```
-
-Where ΔInfo represents marginal information gain from the nth crawl.
-
-### 3.5 Link Value Prediction
-
-Expected information gain from uncrawled links:
-
-```
-ExpectedGain(l) = Relevance(l, Q) × Novelty(l, K) × Authority(l)
-```
-
-Components:
-- **Relevance**: BM25(preview_text, Q)
-- **Novelty**: 1 - max_similarity(preview, K)
-- **Authority**: f(url_structure, domain_metrics)
-
-## 4. Algorithmic Approach
-
-### 4.1 Progressive Crawling Algorithm
-
-```
-Algorithm: ProgressiveCrawl(start_url, query, θ)
-  K ← ∅
-  crawled ← {start_url}
-  pending ← extract_links(crawl(start_url))
-  
-  while IS(K, Q) < θ and |crawled| < max_pages:
-    candidates ← rank_by_expected_gain(pending, Q, K)
-    if max(ExpectedGain(candidates)) < min_gain:
-      break  // Diminishing returns
-    
-    to_crawl ← top_k(candidates)
-    new_docs ← parallel_crawl(to_crawl)
-    K ← K ∪ new_docs
-    crawled ← crawled ∪ to_crawl
-    pending ← extract_new_links(new_docs) - crawled
-  
-  return K
-```
-
-### 4.2 Stopping Criteria
-
-Crawling terminates when:
-1. IS(K, Q) ≥ θ (sufficient information)
-2. d(IS)/d(crawls) < ε (plateau reached)
-3. |crawled| ≥ max_pages (resource limit)
-4. max(ExpectedGain) < min_gain (no promising links)
-
-## 5. Multi-Strategy Architecture
-
-### 5.1 Strategy Pattern Design
-
-```
-AbstractStrategy
-  ├── StatisticalStrategy (no LLM, no embeddings)
-  ├── EmbeddingStrategy (with semantic similarity)
-  └── LLMStrategy (with language model assistance)
-```
-
-### 5.2 Statistical Strategy
-
-Pure statistical approach using:
-- BM25 for relevance scoring
-- Term frequency analysis for coverage
-- Graph structure for authority
-- No external models required
-
-**Advantages**: Fast, no API costs, works offline
-**Best for**: Technical documentation, specific terminology
-
-### 5.3 Embedding Strategy (Implemented)
-
-Semantic understanding through embeddings:
-- Query expansion into semantic variations
-- Coverage mapping in embedding space
-- Gap-driven link selection
-- Validation-based stopping criteria
-
-**Mathematical Framework**:
-```
-Coverage(K, Q) = mean(max_similarity(q, K) for q in Q_expanded)
-Gap(q) = 1 - max_similarity(q, K)
-LinkScore(l) = Σ(Gap(q) × relevance(l, q)) × (1 - redundancy(l, K))
-```
-
-**Key Parameters**:
-- `embedding_k_exp`: Exponential decay factor for distance-to-score mapping
-- `embedding_coverage_radius`: Distance threshold for query coverage
-- `embedding_min_confidence_threshold`: Minimum relevance threshold
-
-**Advantages**: Semantic understanding, handles ambiguity, detects irrelevance
-**Best for**: Research queries, conceptual topics, diverse content
-
-### 5.4 Progressive Enhancement Path
-
-1. **Level 0**: Statistical only (implemented)
-2. **Level 1**: + Embeddings for semantic similarity (implemented)
-3. **Level 2**: + LLM for query understanding (future)
-
-## 6. Evaluation Methodology
-
-### 6.1 Synthetic Dataset Generation
-
-Using LLM to create evaluation data:
-
-```python
-def generate_synthetic_dataset(domain_url):
-    # 1. Fully crawl domain
-    full_knowledge = exhaustive_crawl(domain_url)
-    
-    # 2. Generate answerable queries
-    queries = llm_generate_queries(full_knowledge)
-    
-    # 3. Create query variations
-    for q in queries:
-        variations = generate_variations(q)  # synonyms, sub/super queries
-    
-    return queries, variations, full_knowledge
-```
-
-### 6.2 Evaluation Metrics
-
-1. **Efficiency**: Information gained / Pages crawled
-2. **Completeness**: Answerable queries / Total queries
-3. **Redundancy**: 1 - (Unique information / Total information)
-4. **Convergence Rate**: Pages to 95% completeness
-
-### 6.3 Ablation Studies
-
-- Impact of each score component (coverage, consistency, saturation)
-- Sensitivity to threshold parameters
-- Performance across different domain types
-
-## 7. Theoretical Properties
-
-### 7.1 Convergence Guarantee
-
-**Theorem**: For finite websites, ProgressiveCrawl converges to IS(K, Q) ≥ θ or exhausts all reachable pages.
-
-**Proof sketch**: IS(K, Q) is monotonically non-decreasing with each crawl, bounded above by 1.
-
-### 7.2 Optimality
-
-Under certain assumptions about link preview accuracy:
-- Expected crawls ≤ 2 × optimal_crawls
-- Approximation ratio improves with preview quality
-
-## 8. Implementation Design
-
-### 8.1 Core Components
-
-1. **CrawlState**: Maintains crawl history and metrics
-2. **AdaptiveConfig**: Configuration parameters
-3. **CrawlStrategy**: Pluggable strategy interface
-4. **AdaptiveCrawler**: Main orchestrator
-
-### 8.2 Integration with Crawl4AI
-
-- Wraps existing AsyncWebCrawler
-- Leverages link preview functionality
-- Maintains backward compatibility
-
-### 8.3 Persistence
-
-Knowledge base serialization for:
-- Resumable crawls
-- Knowledge sharing
-- Offline analysis
-
-## 9. Future Directions
-
-### 9.1 Advanced Scoring
-
-- Temporal information value
-- Multi-query optimization
-- Active learning from user feedback
-
-### 9.2 Distributed Crawling
-
-- Collaborative knowledge building
-- Federated information sufficiency
-
-### 9.3 Domain Adaptation
-
-- Transfer learning across domains
-- Meta-learning for threshold selection
-
-## 10. Conclusion
-
-Progressive crawling with adaptive information foraging provides a principled approach to efficient web information extraction. By combining coverage, consistency, and saturation metrics, we can determine information sufficiency without ground truth labels. The multi-strategy architecture allows graceful enhancement from pure statistical to LLM-assisted approaches based on requirements and resources.
-
-## References
-
-1. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
-
-2. Robertson, S., & Zaragoza, H. (2009). The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval.
-
-3. Pirolli, P., & Card, S. (1999). Information Foraging. Psychological Review, 106(4), 643-675.
-
-4. Dasgupta, S. (2005). Analysis of a greedy active learning strategy. Advances in Neural Information Processing Systems.
-
-## Appendix A: Implementation Pseudocode
-
-```python
-class StatisticalStrategy:
-    def calculate_confidence(self, state):
-        coverage = self.calculate_coverage(state)
-        consistency = self.calculate_consistency(state)
-        saturation = self.calculate_saturation(state)
-        return min(coverage, consistency, saturation)
-    
-    def calculate_coverage(self, state):
-        # BM25-based term coverage
-        term_scores = []
-        for term in state.query.split():
-            df = state.document_frequencies.get(term, 0)
-            idf = self.idf_cache.get(term, 1.0)
-            term_scores.append(log(df + 1) * idf)
-        return mean(term_scores) / max_possible_score
-    
-    def rank_links(self, state):
-        scored_links = []
-        for link in state.pending_links:
-            relevance = self.bm25_score(link.preview_text, state.query)
-            novelty = self.calculate_novelty(link, state.knowledge_base)
-            authority = self.url_authority(link.href)
-            score = relevance * novelty * authority
-            scored_links.append((link, score))
-        return sorted(scored_links, key=lambda x: x[1], reverse=True)
-```
-
-## Appendix B: Evaluation Protocol
-
-1. **Dataset Creation**:
-   - Select diverse domains (documentation, blogs, e-commerce)
-   - Generate 100 queries per domain using LLM
-   - Create query variations (5-10 per query)
-
-2. **Baseline Comparisons**:
-   - BFS crawler (depth-limited)
-   - DFS crawler (depth-limited)
-   - Random crawler
-   - Oracle (knows relevant pages)
-
-3. **Metrics Collection**:
-   - Pages crawled vs query answerability
-   - Time to sufficient confidence
-   - False positive/negative rates
-
-4. **Statistical Analysis**:
-   - ANOVA for strategy comparison
-   - Regression for parameter sensitivity
-   - Bootstrap for confidence intervals

README-first.md

@@ -1,809 +0,0 @@
-# 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.
-
-<div align="center">
-
-<a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
-
-[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
-[![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
-
-[![PyPI version](https://badge.fury.io/py/crawl4ai.svg)](https://badge.fury.io/py/crawl4ai)
-[![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/)
-[![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai)
-[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
-
-<p align="center">
-    <a href="https://x.com/crawl4ai">
-      <img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" />
-    </a>
-    <a href="https://www.linkedin.com/company/crawl4ai">
-      <img src="https://img.shields.io/badge/Follow%20on%20LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white" alt="Follow on LinkedIn" />
-    </a>
-    <a href="https://discord.gg/jP8KfhDhyN">
-      <img src="https://img.shields.io/badge/Join%20our%20Discord-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join our Discord" />
-    </a>
-  </p>
-</div>
-
-Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.  
-
-[✨ Check out latest update v0.7.0](#-recent-updates)
-
-🎉 **Version 0.7.0 is now available!** The Adaptive Intelligence Update introduces groundbreaking features: Adaptive Crawling that learns website patterns, Virtual Scroll support for infinite pages, intelligent Link Preview with 3-layer scoring, Async URL Seeder for massive discovery, and significant performance improvements. [Read the release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.0.md)
-
-<details>
-<summary>🤓 <strong>My Personal Story</strong></summary>
-
-My journey with computers started in childhood when my dad, a computer scientist, introduced me to an Amstrad computer. Those early days sparked a fascination with technology, leading me to pursue computer science and specialize in NLP during my postgraduate studies. It was during this time that I first delved into web crawling, building tools to help researchers organize papers and extract information from publications a challenging yet rewarding experience that honed my skills in data extraction.
-
-Fast forward to 2023, I was working on a tool for a project and needed a crawler to convert a webpage into markdown. While exploring solutions, I found one that claimed to be open-source but required creating an account and generating an API token. Worse, it turned out to be a SaaS model charging $16, and its quality didn’t meet my standards. Frustrated, I realized this was a deeper problem. That frustration turned into turbo anger mode, and I decided to build my own solution. In just a few days, I created Crawl4AI. To my surprise, it went viral, earning thousands of GitHub stars and resonating with a global community.
-
-I made Crawl4AI open-source for two reasons. First, it’s my way of giving back to the open-source community that has supported me throughout my career. Second, I believe data should be accessible to everyone, not locked behind paywalls or monopolized by a few. Open access to data lays the foundation for the democratization of AI, a vision where individuals can train their own models and take ownership of their information. This library is the first step in a larger journey to create the best open-source data extraction and generation tool the world has ever seen, built collaboratively by a passionate community.
-
-Thank you to everyone who has supported this project, used it, and shared feedback. Your encouragement motivates me to dream even bigger. Join us, file issues, submit PRs, or spread the word. Together, we can build a tool that truly empowers people to access their own data and reshape the future of AI.
-</details>
-
-## 🧐 Why Crawl4AI?
-
-1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.  
-2. **Lightning Fast**: Delivers results faster with real-time, cost-efficient performance.  
-3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.  
-4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.  
-5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.  
-6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
-
-## 🚀 Quick Start 
-
-1. Install Crawl4AI:
-```bash
-# Install the package
-pip install -U crawl4ai
-
-# For pre release versions
-pip install crawl4ai --pre
-
-# Run post-installation setup
-crawl4ai-setup
-
-# Verify your installation
-crawl4ai-doctor
-```
-
-If you encounter any browser-related issues, you can install them manually:
-```bash
-python -m playwright install --with-deps chromium
-```
-
-2. Run a simple web crawl with Python:
-```python
-import asyncio
-from crawl4ai import *
-
-async def main():
-    async with AsyncWebCrawler() as crawler:
-        result = await crawler.arun(
-            url="https://www.nbcnews.com/business",
-        )
-        print(result.markdown)
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-3. Or use the new command-line interface:
-```bash
-# Basic crawl with markdown output
-crwl https://www.nbcnews.com/business -o markdown
-
-# Deep crawl with BFS strategy, max 10 pages
-crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10
-
-# Use LLM extraction with a specific question
-crwl https://www.example.com/products -q "Extract all product prices"
-```
-
-## ✨ Features 
-
-<details>
-<summary>📝 <strong>Markdown Generation</strong></summary>
-
-- 🧹 **Clean Markdown**: Generates clean, structured Markdown with accurate formatting.
-- 🎯 **Fit Markdown**: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing.
-- 🔗 **Citations and References**: Converts page links into a numbered reference list with clean citations.
-- 🛠️ **Custom Strategies**: Users can create their own Markdown generation strategies tailored to specific needs.
-- 📚 **BM25 Algorithm**: Employs BM25-based filtering for extracting core information and removing irrelevant content. 
-</details>
-
-<details>
-<summary>📊 <strong>Structured Data Extraction</strong></summary>
-
-- 🤖 **LLM-Driven Extraction**: Supports all LLMs (open-source and proprietary) for structured data extraction.
-- 🧱 **Chunking Strategies**: Implements chunking (topic-based, regex, sentence-level) for targeted content processing.
-- 🌌 **Cosine Similarity**: Find relevant content chunks based on user queries for semantic extraction.
-- 🔎 **CSS-Based Extraction**: Fast schema-based data extraction using XPath and CSS selectors.
-- 🔧 **Schema Definition**: Define custom schemas for extracting structured JSON from repetitive patterns.
-
-</details>
-
-<details>
-<summary>🌐 <strong>Browser Integration</strong></summary>
-
-- 🖥️ **Managed Browser**: Use user-owned browsers with full control, avoiding bot detection.
-- 🔄 **Remote Browser Control**: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction.
-- 👤 **Browser Profiler**: Create and manage persistent profiles with saved authentication states, cookies, and settings.
-- 🔒 **Session Management**: Preserve browser states and reuse them for multi-step crawling.
-- 🧩 **Proxy Support**: Seamlessly connect to proxies with authentication for secure access.
-- ⚙️ **Full Browser Control**: Modify headers, cookies, user agents, and more for tailored crawling setups.
-- 🌍 **Multi-Browser Support**: Compatible with Chromium, Firefox, and WebKit.
-- 📐 **Dynamic Viewport Adjustment**: Automatically adjusts the browser viewport to match page content, ensuring complete rendering and capturing of all elements.
-
-</details>
-
-<details>
-<summary>🔎 <strong>Crawling & Scraping</strong></summary>
-
-- 🖼️ **Media Support**: Extract images, audio, videos, and responsive image formats like `srcset` and `picture`.
-- 🚀 **Dynamic Crawling**: Execute JS and wait for async or sync for dynamic content extraction.
-- 📸 **Screenshots**: Capture page screenshots during crawling for debugging or analysis.
-- 📂 **Raw Data Crawling**: Directly process raw HTML (`raw:`) or local files (`file://`).
-- 🔗 **Comprehensive Link Extraction**: Extracts internal, external links, and embedded iframe content.
-- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior.
-- 💾 **Caching**: Cache data for improved speed and to avoid redundant fetches.
-- 📄 **Metadata Extraction**: Retrieve structured metadata from web pages.
-- 📡 **IFrame Content Extraction**: Seamless extraction from embedded iframe content.
-- 🕵️ **Lazy Load Handling**: Waits for images to fully load, ensuring no content is missed due to lazy loading.
-- 🔄 **Full-Page Scanning**: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.
-
-</details>
-
-<details>
-<summary>🚀 <strong>Deployment</strong></summary>
-
-- 🐳 **Dockerized Setup**: Optimized Docker image with FastAPI server for easy deployment.
-- 🔑 **Secure Authentication**: Built-in JWT token authentication for API security.
-- 🔄 **API Gateway**: One-click deployment with secure token authentication for API-based workflows.
-- 🌐 **Scalable Architecture**: Designed for mass-scale production and optimized server performance.
-- ☁️ **Cloud Deployment**: Ready-to-deploy configurations for major cloud platforms.
-
-</details>
-
-<details>
-<summary>🎯 <strong>Additional Features</strong></summary>
-
-- 🕶️ **Stealth Mode**: Avoid bot detection by mimicking real users.
-- 🏷️ **Tag-Based Content Extraction**: Refine crawling based on custom tags, headers, or metadata.
-- 🔗 **Link Analysis**: Extract and analyze all links for detailed data exploration.
-- 🛡️ **Error Handling**: Robust error management for seamless execution.
-- 🔐 **CORS & Static Serving**: Supports filesystem-based caching and cross-origin requests.
-- 📖 **Clear Documentation**: Simplified and updated guides for onboarding and advanced usage.
-- 🙌 **Community Recognition**: Acknowledges contributors and pull requests for transparency.
-
-</details>
-
-## Try it Now!
-
-✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SgRPrByQLzjRfwoRNq1wSGE9nYY_EE8C?usp=sharing)
-
-✨ Visit our [Documentation Website](https://docs.crawl4ai.com/)
-
-## Installation 🛠️
-
-Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
-
-<details>
-<summary>🐍 <strong>Using pip</strong></summary>
-
-Choose the installation option that best fits your needs:
-
-### Basic Installation
-
-For basic web crawling and scraping tasks:
-
-```bash
-pip install crawl4ai
-crawl4ai-setup # Setup the browser
-```
-
-By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
-
-👉 **Note**: When you install Crawl4AI, the `crawl4ai-setup` should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:
-
-1. Through the command line:
-
-   ```bash
-   playwright install
-   ```
-
-2. If the above doesn't work, try this more specific command:
-
-   ```bash
-   python -m playwright install chromium
-   ```
-
-This second method has proven to be more reliable in some cases.
-
----
-
-### Installation with Synchronous Version
-
-The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
-
-```bash
-pip install crawl4ai[sync]
-```
-
----
-
-### Development Installation
-
-For contributors who plan to modify the source code:
-
-```bash
-git clone https://github.com/unclecode/crawl4ai.git
-cd crawl4ai
-pip install -e .                    # Basic installation in editable mode
-```
-
-Install optional features:
-
-```bash
-pip install -e ".[torch]"           # With PyTorch features
-pip install -e ".[transformer]"     # With Transformer features
-pip install -e ".[cosine]"          # With cosine similarity features
-pip install -e ".[sync]"            # With synchronous crawling (Selenium)
-pip install -e ".[all]"             # Install all optional features
-```
-
-</details>
-
-<details>
-<summary>🐳 <strong>Docker Deployment</strong></summary>
-
-> 🚀 **Now Available!** Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.
-
-### New Docker Features
-
-The new Docker implementation includes:
-- **Browser pooling** with page pre-warming for faster response times
-- **Interactive playground** to test and generate request code
-- **MCP integration** for direct connection to AI tools like Claude Code
-- **Comprehensive API endpoints** including HTML extraction, screenshots, PDF generation, and JavaScript execution
-- **Multi-architecture support** with automatic detection (AMD64/ARM64)
-- **Optimized resources** with improved memory management
-
-### Getting Started
-
-```bash
-# Pull and run the latest release candidate
-docker pull unclecode/crawl4ai:0.7.0
-docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:0.7.0
-
-# Visit the playground at http://localhost:11235/playground
-```
-
-For complete documentation, see our [Docker Deployment Guide](https://docs.crawl4ai.com/core/docker-deployment/).
-
-</details>
-
----
-
-### Quick Test
-
-Run a quick test (works for both Docker options):
-
-```python
-import requests
-
-# Submit a crawl job
-response = requests.post(
-    "http://localhost:11235/crawl",
-    json={"urls": ["https://example.com"], "priority": 10}
-)
-if response.status_code == 200:
-    print("Crawl job submitted successfully.")
-    
-if "results" in response.json():
-    results = response.json()["results"]
-    print("Crawl job completed. Results:")
-    for result in results:
-        print(result)
-else:
-    task_id = response.json()["task_id"]
-    print(f"Crawl job submitted. Task ID:: {task_id}")
-    result = requests.get(f"http://localhost:11235/task/{task_id}")
-```
-
-For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://docs.crawl4ai.com/basic/docker-deployment/).
-
-</details>
-
-
-## 🔬 Advanced Usage Examples 🔬
-
-You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
-
-<details>
-<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
-from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
-
-async def main():
-    browser_config = BrowserConfig(
-        headless=True,  
-        verbose=True,
-    )
-    run_config = CrawlerRunConfig(
-        cache_mode=CacheMode.ENABLED,
-        markdown_generator=DefaultMarkdownGenerator(
-            content_filter=PruningContentFilter(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
-        ),
-        # markdown_generator=DefaultMarkdownGenerator(
-        #     content_filter=BM25ContentFilter(user_query="WHEN_WE_FOCUS_BASED_ON_A_USER_QUERY", bm25_threshold=1.0)
-        # ),
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url="https://docs.micronaut.io/4.7.6/guide/",
-            config=run_config
-        )
-        print(len(result.markdown.raw_markdown))
-        print(len(result.markdown.fit_markdown))
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-</details>
-
-<details>
-<summary>🖥️ <strong>Executing JavaScript & Extract Structured Data without LLMs</strong></summary>
-
-```python
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
-from crawl4ai import JsonCssExtractionStrategy
-import json
-
-async def main():
-    schema = {
-    "name": "KidoCode Courses",
-    "baseSelector": "section.charge-methodology .w-tab-content > div",
-    "fields": [
-        {
-            "name": "section_title",
-            "selector": "h3.heading-50",
-            "type": "text",
-        },
-        {
-            "name": "section_description",
-            "selector": ".charge-content",
-            "type": "text",
-        },
-        {
-            "name": "course_name",
-            "selector": ".text-block-93",
-            "type": "text",
-        },
-        {
-            "name": "course_description",
-            "selector": ".course-content-text",
-            "type": "text",
-        },
-        {
-            "name": "course_icon",
-            "selector": ".image-92",
-            "type": "attribute",
-            "attribute": "src"
-        }
-    }
-}
-
-    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
-
-    browser_config = BrowserConfig(
-        headless=False,
-        verbose=True
-    )
-    run_config = CrawlerRunConfig(
-        extraction_strategy=extraction_strategy,
-        js_code=["""(async () => {const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");for(let tab of tabs) {tab.scrollIntoView();tab.click();await new Promise(r => setTimeout(r, 500));}})();"""],
-        cache_mode=CacheMode.BYPASS
-    )
-        
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        
-        result = await crawler.arun(
-            url="https://www.kidocode.com/degrees/technology",
-            config=run_config
-        )
-
-        companies = json.loads(result.extracted_content)
-        print(f"Successfully extracted {len(companies)} companies")
-        print(json.dumps(companies[0], indent=2))
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-</details>
-
-<details>
-<summary>📚 <strong>Extracting Structured Data with LLMs</strong></summary>
-
-```python
-import os
-import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
-from crawl4ai import LLMExtractionStrategy
-from pydantic import BaseModel, Field
-
-class OpenAIModelFee(BaseModel):
-    model_name: str = Field(..., description="Name of the OpenAI model.")
-    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
-    output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
-
-async def main():
-    browser_config = BrowserConfig(verbose=True)
-    run_config = CrawlerRunConfig(
-        word_count_threshold=1,
-        extraction_strategy=LLMExtractionStrategy(
-            # Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
-            # provider="ollama/qwen2", api_token="no-token", 
-            llm_config = LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')), 
-            schema=OpenAIModelFee.schema(),
-            extraction_type="schema",
-            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
-            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
-            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
-        ),            
-        cache_mode=CacheMode.BYPASS,
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        result = await crawler.arun(
-            url='https://openai.com/api/pricing/',
-            config=run_config
-        )
-        print(result.extracted_content)
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-</details>
-
-<details>
-<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
-
-```python
-import os, sys
-from pathlib import Path
-import asyncio, time
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
-
-async def test_news_crawl():
-    # Create a persistent user data directory
-    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
-    os.makedirs(user_data_dir, exist_ok=True)
-
-    browser_config = BrowserConfig(
-        verbose=True,
-        headless=True,
-        user_data_dir=user_data_dir,
-        use_persistent_context=True,
-    )
-    run_config = CrawlerRunConfig(
-        cache_mode=CacheMode.BYPASS
-    )
-    
-    async with AsyncWebCrawler(config=browser_config) as crawler:
-        url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
-        
-        result = await crawler.arun(
-            url,
-            config=run_config,
-            magic=True,
-        )
-        
-        print(f"Successfully crawled {url}")
-        print(f"Content length: {len(result.markdown)}")
-```
-
-</details>
-
-## ✨ Recent Updates
-
-### Version 0.7.0 Release Highlights - The Adaptive Intelligence Update
-
-- **🧠 Adaptive Crawling**: Your crawler now learns and adapts to website patterns automatically:
-  ```python
-  config = AdaptiveConfig(
-      confidence_threshold=0.7, # Min confidence to stop crawling
-      max_depth=5, # Maximum crawl depth
-      max_pages=20, # Maximum number of pages to crawl
-      strategy="statistical"
-  )
-  
-  async with AsyncWebCrawler() as crawler:
-      adaptive_crawler = AdaptiveCrawler(crawler, config)
-      state = await adaptive_crawler.digest(
-          start_url="https://news.example.com",
-          query="latest news content"
-      )
-  # Crawler learns patterns and improves extraction over time
-  ```
-
-- **🌊 Virtual Scroll Support**: Complete content extraction from infinite scroll pages:
-  ```python
-  scroll_config = VirtualScrollConfig(
-      container_selector="[data-testid='feed']",
-      scroll_count=20,
-      scroll_by="container_height",
-      wait_after_scroll=1.0
-  )
-  
-  result = await crawler.arun(url, config=CrawlerRunConfig(
-      virtual_scroll_config=scroll_config
-  ))
-  ```
-
-- **🔗 Intelligent Link Analysis**: 3-layer scoring system for smart link prioritization:
-  ```python
-  link_config = LinkPreviewConfig(
-      query="machine learning tutorials",
-      score_threshold=0.3,
-      concurrent_requests=10
-  )
-  
-  result = await crawler.arun(url, config=CrawlerRunConfig(
-      link_preview_config=link_config,
-      score_links=True
-  ))
-  # Links ranked by relevance and quality
-  ```
-
-- **🎣 Async URL Seeder**: Discover thousands of URLs in seconds:
-  ```python
-  seeder = AsyncUrlSeeder(SeedingConfig(
-      source="sitemap+cc",
-      pattern="*/blog/*",
-      query="python tutorials",
-      score_threshold=0.4
-  ))
-  
-  urls = await seeder.discover("https://example.com")
-  ```
-
-- **⚡ Performance Boost**: Up to 3x faster with optimized resource handling and memory efficiency
-
-Read the full details in our [0.7.0 Release Notes](https://docs.crawl4ai.com/blog/release-v0.7.0) or check the [CHANGELOG](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md).
-
-## Version Numbering in Crawl4AI
-
-Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.
-
-### Version Numbers Explained
-
-Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
-
-#### Pre-release Versions
-We use different suffixes to indicate development stages:
-
-- `dev` (0.4.3dev1): Development versions, unstable
-- `a` (0.4.3a1): Alpha releases, experimental features
-- `b` (0.4.3b1): Beta releases, feature complete but needs testing
-- `rc` (0.4.3): Release candidates, potential final version
-
-#### Installation
-- Regular installation (stable version):
-  ```bash
-  pip install -U crawl4ai
-  ```
-
-- Install pre-release versions:
-  ```bash
-  pip install crawl4ai --pre
-  ```
-
-- Install specific version:
-  ```bash
-  pip install crawl4ai==0.4.3b1
-  ```
-
-#### Why Pre-releases?
-We use pre-releases to:
-- Test new features in real-world scenarios
-- Gather feedback before final releases
-- Ensure stability for production users
-- Allow early adopters to try new features
-
-For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
-
-## 📖 Documentation & Roadmap 
-
-> 🚨 **Documentation Update Alert**: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!
-
-For current documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://docs.crawl4ai.com/).
-
-To check our development plans and upcoming features, visit our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
-
-<details>
-<summary>📈 <strong>Development TODOs</strong></summary>
-
-- [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
-- [ ] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
-- [ ] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
-- [ ] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
-- [ ] 4. Automated Schema Generator: Convert natural language to extraction schemas
-- [ ] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
-- [ ] 6. Web Embedding Index: Semantic search infrastructure for crawled content
-- [ ] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
-- [ ] 8. Performance Monitor: Real-time insights into crawler operations
-- [ ] 9. Cloud Integration: One-click deployment solutions across cloud providers
-- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
-- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
-
-</details>
-
-## 🤝 Contributing 
-
-We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTORS.md) for more information.
-
-I'll help modify the license section with badges. For the halftone effect, here's a version with it:
-
-Here's the updated license section:
-
-## 📄 License & Attribution
-
-This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE) file for details.
-
-### Attribution Requirements
-When using Crawl4AI, you must include one of the following attribution methods:
-
-#### 1. Badge Attribution (Recommended)
-Add one of these badges to your README, documentation, or website:
-
-| Theme | Badge |
-|-------|-------|
-| **Disco Theme (Animated)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/></a> |
-| **Night Theme (Dark with Neon)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/></a> |
-| **Dark Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/></a> |
-| **Light Theme (Classic)** | <a href="https://github.com/unclecode/crawl4ai"><img src="./docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/></a> |
- 
-
-HTML code for adding the badges:
-```html
-<!-- Disco Theme (Animated) -->
-<a href="https://github.com/unclecode/crawl4ai">
-  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
-</a>
-
-<!-- Night Theme (Dark with Neon) -->
-<a href="https://github.com/unclecode/crawl4ai">
-  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
-</a>
-
-<!-- Dark Theme (Classic) -->
-<a href="https://github.com/unclecode/crawl4ai">
-  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
-</a>
-
-<!-- Light Theme (Classic) -->
-<a href="https://github.com/unclecode/crawl4ai">
-  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
-</a>
-
-<!-- Simple Shield Badge -->
-<a href="https://github.com/unclecode/crawl4ai">
-  <img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
-</a>
-```
-
-#### 2. Text Attribution
-Add this line to your documentation:
-```
-This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
-```
-
-## 📚 Citation
-
-If you use Crawl4AI in your research or project, please cite:
-
-```bibtex
-@software{crawl4ai2024,
-  author = {UncleCode},
-  title = {Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper},
-  year = {2024},
-  publisher = {GitHub},
-  journal = {GitHub Repository},
-  howpublished = {\url{https://github.com/unclecode/crawl4ai}},
-  commit = {Please use the commit hash you're working with}
-}
-```
-
-Text citation format:
-```
-UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software]. 
-GitHub. https://github.com/unclecode/crawl4ai
-```
-
-## 📧 Contact 
-
-For questions, suggestions, or feedback, feel free to reach out:
-
-- GitHub: [unclecode](https://github.com/unclecode)
-- Twitter: [@unclecode](https://twitter.com/unclecode)
-- Website: [crawl4ai.com](https://crawl4ai.com)
-
-Happy Crawling! 🕸️🚀
-
-## 💖 Support Crawl4AI
-
-> 🎉 **Sponsorship Program Just Launched!** Be among the first 50 **Founding Sponsors** and get permanent recognition in our Hall of Fame!
-
-Crawl4AI is the #1 trending open-source web crawler with 51K+ stars. Your support ensures we stay independent, innovative, and free forever.
-
-<div align="center">
-
-[![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode)
-[![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)
-
-</div>
-
-### 🤝 Sponsorship Tiers
-
-- **🌱 Believer ($5/mo)**: Join the movement for data democratization
-- **🚀 Builder ($50/mo)**: Get priority support and early feature access  
-- **💼 Growing Team ($500/mo)**: Bi-weekly syncs and optimization help
-- **🏢 Data Infrastructure Partner ($2000/mo)**: Full partnership with dedicated support
-
-**Why sponsor?** Every tier includes real benefits. No more rate-limited APIs. Own your data pipeline. Build data sovereignty together.
-
-[View All Tiers & Benefits →](https://github.com/sponsors/unclecode)
-
-### 🏆 Our Sponsors
-
-#### 👑 Founding Sponsors (First 50)
-*Be part of history - [Become a Founding Sponsor](https://github.com/sponsors/unclecode)*
-
-<!-- Founding sponsors will be permanently recognized here -->
-
-#### Current Sponsors
-Thank you to all our sponsors who make this project possible!
-
-<!-- Sponsors will be automatically added here -->
-
-## 🗾 Mission
-
-Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.  
-
-We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.
-
-<details>
-<summary>🔑 <strong>Key Opportunities</strong></summary>
- 
-- **Data Capitalization**: Transform digital footprints into measurable, valuable assets.  
-- **Authentic AI Data**: Provide AI systems with real human insights.  
-- **Shared Economy**: Create a fair data marketplace that benefits data creators.  
-
-</details>
-
-<details>
-<summary>🚀 <strong>Development Pathway</strong></summary>
-
-1. **Open-Source Tools**: Community-driven platforms for transparent data extraction.  
-2. **Digital Asset Structuring**: Tools to organize and value digital knowledge.  
-3. **Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.  
-
-For more details, see our [full mission statement](./MISSION.md).
-</details>
-
-## Star History
-
-[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

README.sync.md

@@ -0,0 +1,244 @@
+# Crawl4AI v0.2.77 🕷️🤖
+
+[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers)
+[![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)
+[![GitHub Issues](https://img.shields.io/github/issues/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/issues)
+[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/pulls)
+[![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
+
+Crawl4AI simplifies web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
+
+#### [v0.2.77] - 2024-08-02
+
+Major improvements in functionality, performance, and cross-platform compatibility! 🚀
+
+- 🐳 **Docker enhancements**:
+  - Significantly improved Dockerfile for easy installation on Linux, Mac, and Windows.
+- 🌐 **Official Docker Hub image**:
+  - Launched our first official image on Docker Hub for streamlined deployment (unclecode/crawl4ai).
+- 🔧 **Selenium upgrade**:
+  - Removed dependency on ChromeDriver, now using Selenium's built-in capabilities for better compatibility.
+- 🖼️ **Image description**:
+  - Implemented ability to generate textual descriptions for extracted images from web pages.
+- ⚡ **Performance boost**:
+  - Various improvements to enhance overall speed and performance.
+  
+## Try it Now!
+
+✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1sJPAmeLj5PMrg2VgOwMJ2ubGIcK0cJeX?usp=sharing)
+
+✨ visit our [Documentation Website](https://crawl4ai.com/mkdocs/)
+
+✨ Check [Demo](https://crawl4ai.com/mkdocs/demo)
+
+## Features ✨
+
+- 🆓 Completely free and open-source
+- 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown)
+- 🌍 Supports crawling multiple URLs simultaneously
+- 🎨 Extracts and returns all media tags (Images, Audio, and Video)
+- 🔗 Extracts all external and internal links
+- 📚 Extracts metadata from the page
+- 🔄 Custom hooks for authentication, headers, and page modifications before crawling
+- 🕵️ User-agent customization
+- 🖼️ Takes screenshots of the page
+- 📜 Executes multiple custom JavaScripts before crawling
+- 📚 Various chunking strategies: topic-based, regex, sentence, and more
+- 🧠 Advanced extraction strategies: cosine clustering, LLM, and more
+- 🎯 CSS selector support
+- 📝 Passes instructions/keywords to refine extraction
+
+# Crawl4AI
+
+## 🌟 Shoutout to Contributors of v0.2.77!
+
+A big thank you to the amazing contributors who've made this release possible:
+
+- [@aravindkarnam](https://github.com/aravindkarnam) for the new image description feature
+- [@FractalMind](https://github.com/FractalMind) for our official Docker Hub image
+- [@ketonkss4](https://github.com/ketonkss4) for helping streamline our Selenium setup
+
+Your contributions are driving Crawl4AI forward! 🚀
+
+## Cool Examples 🚀
+
+### Quick Start
+
+```python
+from crawl4ai import WebCrawler
+
+# Create an instance of WebCrawler
+crawler = WebCrawler()
+
+# Warm up the crawler (load necessary models)
+crawler.warmup()
+
+# Run the crawler on a URL
+result = crawler.run(url="https://www.nbcnews.com/business")
+
+# Print the extracted content
+print(result.markdown)
+```
+
+## How to install 🛠 
+
+### Using pip 🐍
+```bash
+virtualenv venv
+source venv/bin/activate
+pip install "crawl4ai @ git+https://github.com/unclecode/crawl4ai.git"
+```
+
+### Using Docker 🐳
+
+```bash
+# For Mac users (M1/M2)
+# docker build --platform linux/amd64 -t crawl4ai .
+docker build -t crawl4ai .
+docker run -d -p 8000:80 crawl4ai
+```
+
+### Using Docker Hub 🐳
+
+```bash
+docker pull unclecode/crawl4ai:latest
+docker run -d -p 8000:80 unclecode/crawl4ai:latest
+```
+
+
+## Speed-First Design 🚀
+
+Perhaps the most important design principle for this library is speed. We need to ensure it can handle many links and resources in parallel as quickly as possible. By combining this speed with fast LLMs like Groq, the results will be truly amazing.
+
+```python
+import time
+from crawl4ai.web_crawler import WebCrawler
+crawler = WebCrawler()
+crawler.warmup()
+
+start = time.time()
+url = r"https://www.nbcnews.com/business"
+result = crawler.run( url, word_count_threshold=10, bypass_cache=True)
+end = time.time()
+print(f"Time taken: {end - start}")
+```
+
+Let's take a look the calculated time for the above code snippet:
+
+```bash
+[LOG] 🚀 Crawling done, success: True, time taken: 1.3623387813568115 seconds
+[LOG] 🚀 Content extracted, success: True, time taken: 0.05715131759643555 seconds
+[LOG] 🚀 Extraction, time taken: 0.05750393867492676 seconds.
+Time taken: 1.439958095550537
+```
+Fetching the content from the page took 1.3623 seconds, and extracting the content took 0.0575 seconds. 🚀
+
+### Extract Structured Data from Web Pages 📊
+
+Crawl all OpenAI models and their fees from the official page.
+
+```python
+import os
+from crawl4ai import WebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from pydantic import BaseModel, Field
+
+class OpenAIModelFee(BaseModel):
+    model_name: str = Field(..., description="Name of the OpenAI model.")
+    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
+    output_fee: str = Field(..., description="Fee for output token ßfor the OpenAI model.")
+
+url = 'https://openai.com/api/pricing/'
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+        url=url,
+        word_count_threshold=1,
+        extraction_strategy= LLMExtractionStrategy(
+            provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
+            schema=OpenAIModelFee.schema(),
+            extraction_type="schema",
+            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
+            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
+            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
+        ),            
+        bypass_cache=True,
+    )
+
+print(result.extracted_content)
+```
+
+### Execute JS, Filter Data with CSS Selector, and Clustering
+
+```python
+from crawl4ai import WebCrawler
+from crawl4ai.chunking_strategy import CosineStrategy
+
+js_code = ["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"]
+
+crawler = WebCrawler()
+crawler.warmup()
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    js=js_code,
+    css_selector="p",
+    extraction_strategy=CosineStrategy(semantic_filter="technology")
+)
+
+print(result.extracted_content)
+```
+
+### Extract Structured Data from Web Pages With Proxy and BaseUrl
+
+```python
+from crawl4ai import WebCrawler
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
+
+def create_crawler():
+    crawler = WebCrawler(verbose=True, proxy="http://127.0.0.1:7890")
+    crawler.warmup()
+    return crawler
+
+crawler = create_crawler()
+
+crawler.warmup()
+
+result = crawler.run(
+    url="https://www.nbcnews.com/business",
+    extraction_strategy=LLMExtractionStrategy(
+        provider="openai/gpt-4o",
+        api_token="sk-",
+        base_url="https://api.openai.com/v1"
+    )
+)
+
+print(result.markdown)
+```
+
+## Documentation 📚
+
+For detailed documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://crawl4ai.com/mkdocs/).
+
+## Contributing 🤝
+
+We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md) for more information.
+
+## License 📄
+
+Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
+
+## Contact 📧
+
+For questions, suggestions, or feedback, feel free to reach out:
+
+- GitHub: [unclecode](https://github.com/unclecode)
+- Twitter: [@unclecode](https://twitter.com/unclecode)
+- Website: [crawl4ai.com](https://crawl4ai.com)
+
+Happy Crawling! 🕸️🚀
+
+## Star History
+
+[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

SPONSORS.md

@@ -1,65 +0,0 @@
-# 💖 Sponsors & Supporters
-
-Thank you to everyone supporting Crawl4AI! Your sponsorship helps keep this project open-source and actively maintained.
-
-## 👑 Founding Sponsors
-*The first 50 sponsors who believed in our vision - permanently recognized*
-
-<!-- Founding sponsors will be listed here with special recognition -->
-🎉 **Become a Founding Sponsor!** Only [X/50] spots remaining! [Join now →](https://github.com/sponsors/unclecode)
-
----
-
-## 🏢 Data Infrastructure Partners ($2000/month)
-*These organizations are building their data sovereignty with Crawl4AI at the core*
-
-<!-- Data Infrastructure Partners will be listed here -->
-*Be the first Data Infrastructure Partner! [Join us →](https://github.com/sponsors/unclecode)*
-
----
-
-## 💼 Growing Teams ($500/month)
-*Teams scaling their data extraction with Crawl4AI*
-
-<!-- Growing Teams will be listed here -->
-*Your team could be here! [Become a sponsor →](https://github.com/sponsors/unclecode)*
-
----
-
-## 🚀 Builders ($50/month)
-*Developers and entrepreneurs building with Crawl4AI*
-
-<!-- Builders will be listed here -->
-*Join the builders! [Start sponsoring →](https://github.com/sponsors/unclecode)*
-
----
-
-## 🌱 Believers ($5/month)
-*The community supporting data democratization*
-
-<!-- Believers will be listed here -->
-*Thank you to all our community believers!*
-
----
-
-## 🤝 Want to Sponsor?
-
-Crawl4AI is the #1 trending open-source web crawler. We're building the future of data extraction - where organizations own their data pipelines instead of relying on rate-limited APIs.
-
-### Available Sponsorship Tiers:
-- **🌱 Believer** ($5/mo) - Support the movement
-- **🚀 Builder** ($50/mo) - Priority support & early access
-- **💼 Growing Team** ($500/mo) - Bi-weekly syncs & optimization
-- **🏢 Data Infrastructure Partner** ($2000/mo) - Full partnership & dedicated support
-
-[View all tiers and benefits →](https://github.com/sponsors/unclecode)
-
-### Enterprise & Custom Partnerships
-
-Building data extraction at scale? Need dedicated support or infrastructure? Let's talk about a custom partnership.
-
-📧 Contact: [hello@crawl4ai.com](mailto:hello@crawl4ai.com) | 📅 [Schedule a call](https://calendar.app.google/rEpvi2UBgUQjWHfJ9)
-
----
-
-*This list is updated regularly. Sponsors at $50+ tiers can submit their logos via [hello@crawl4ai.com](mailto:hello@crawl4ai.com)*

cliff.toml

@@ -1,24 +0,0 @@
-[changelog]
-# Template format
-header = """
-# Changelog\n
-All notable changes to this project will be documented in this file.\n
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).\n
-"""
-
-# Organize commits by type
-[git]
-conventional_commits = true
-filter_unconventional = true
-commit_parsers = [
-    { message = "^feat", group = "Added"},
-    { message = "^fix", group = "Fixed"},
-    { message = "^doc", group = "Documentation"},
-    { message = "^perf", group = "Performance"},
-    { message = "^refactor", group = "Changed"},
-    { message = "^style", group = "Changed"},
-    { message = "^test", group = "Testing"},
-    { message = "^chore\\(release\\): prepare for", skip = true},
-    { message = "^chore", group = "Miscellaneous Tasks"},
-]

crawl4ai/__init__.py

@@ -1,226 +1,30 @@
 # __init__.py
-import warnings
 
-from .async_webcrawler import AsyncWebCrawler, CacheMode
-# MODIFIED: Add SeedingConfig and VirtualScrollConfig here
-from .async_configs import BrowserConfig, CrawlerRunConfig, HTTPCrawlerConfig, LLMConfig, ProxyConfig, GeolocationConfig, SeedingConfig, VirtualScrollConfig, LinkPreviewConfig, MatchMode
-
-from .content_scraping_strategy import (
-    ContentScrapingStrategy,
-    LXMLWebScrapingStrategy,
-    WebScrapingStrategy,  # Backward compatibility alias
-)
-from .async_logger import (
-    AsyncLoggerBase,
-    AsyncLogger,
-)
-from .proxy_strategy import (
-    ProxyRotationStrategy,
-    RoundRobinProxyStrategy,
-)
-from .extraction_strategy import (
-    ExtractionStrategy,
-    LLMExtractionStrategy,
-    CosineStrategy,
-    JsonCssExtractionStrategy,
-    JsonXPathExtractionStrategy,
-    JsonLxmlExtractionStrategy,
-    RegexExtractionStrategy
-)
-from .chunking_strategy import ChunkingStrategy, RegexChunking
-from .markdown_generation_strategy import DefaultMarkdownGenerator
-from .table_extraction import (
-    TableExtractionStrategy,
-    DefaultTableExtraction,
-    NoTableExtraction,
-    LLMTableExtraction,
-)
-from .content_filter_strategy import (
-    PruningContentFilter,
-    BM25ContentFilter,
-    LLMContentFilter,
-    RelevantContentFilter,
-)
-from .models import CrawlResult, MarkdownGenerationResult, DisplayMode
-from .components.crawler_monitor import CrawlerMonitor
-from .link_preview import LinkPreview
-from .async_dispatcher import (
-    MemoryAdaptiveDispatcher,
-    SemaphoreDispatcher,
-    RateLimiter,
-    BaseDispatcher,
-)
-from .docker_client import Crawl4aiDockerClient
-from .hub import CrawlerHub
-from .browser_profiler import BrowserProfiler
-from .deep_crawling import (
-    DeepCrawlStrategy,
-    BFSDeepCrawlStrategy,
-    FilterChain,
-    URLPatternFilter,
-    DomainFilter,
-    ContentTypeFilter,
-    URLFilter,
-    FilterStats,
-    SEOFilter,
-    KeywordRelevanceScorer,
-    URLScorer,
-    CompositeScorer,
-    DomainAuthorityScorer,
-    FreshnessScorer,
-    PathDepthScorer,
-    BestFirstCrawlingStrategy,
-    DFSDeepCrawlStrategy,
-    DeepCrawlDecorator,
-)
-# NEW: Import AsyncUrlSeeder
-from .async_url_seeder import AsyncUrlSeeder
-# Adaptive Crawler
-from .adaptive_crawler import (
-    AdaptiveCrawler,
-    AdaptiveConfig,
-    CrawlState,
-    CrawlStrategy,
-    StatisticalStrategy
-)
-
-# C4A Script Language Support
-from .script import (
-    compile as c4a_compile,
-    validate as c4a_validate,
-    compile_file as c4a_compile_file,
-    CompilationResult,
-    ValidationResult,
-    ErrorDetail
-)
-
-# Browser Adapters
-from .browser_adapter import (
-    BrowserAdapter,
-    PlaywrightAdapter,
-    UndetectedAdapter
-)
-
-from .utils import (
-    start_colab_display_server,
-    setup_colab_environment
-)
+from .async_webcrawler import AsyncWebCrawler
+from .models import CrawlResult
+from ._version import __version__
+# __version__ = "0.3.73"
 
 __all__ = [
-    "AsyncLoggerBase",
-    "AsyncLogger",
     "AsyncWebCrawler",
-    "BrowserProfiler",
-    "LLMConfig",
-    "GeolocationConfig",
-    # NEW: Add SeedingConfig and VirtualScrollConfig
-    "SeedingConfig",
-    "VirtualScrollConfig",
-    # NEW: Add AsyncUrlSeeder
-    "AsyncUrlSeeder",
-    # Adaptive Crawler
-    "AdaptiveCrawler",
-    "AdaptiveConfig", 
-    "CrawlState",
-    "CrawlStrategy",
-    "StatisticalStrategy",
-    "DeepCrawlStrategy",
-    "BFSDeepCrawlStrategy",
-    "BestFirstCrawlingStrategy",
-    "DFSDeepCrawlStrategy",
-    "FilterChain",
-    "URLPatternFilter",
-    "ContentTypeFilter",
-    "DomainFilter",
-    "FilterStats",
-    "URLFilter",
-    "SEOFilter",
-    "KeywordRelevanceScorer",
-    "URLScorer",
-    "CompositeScorer",
-    "DomainAuthorityScorer",
-    "FreshnessScorer",
-    "PathDepthScorer",
-    "DeepCrawlDecorator",
     "CrawlResult",
-    "CrawlerHub",
-    "CacheMode",
-    "MatchMode",
-    "ContentScrapingStrategy",
-    "WebScrapingStrategy",
-    "LXMLWebScrapingStrategy",
-    "BrowserConfig",
-    "CrawlerRunConfig",
-    "HTTPCrawlerConfig",
-    "ExtractionStrategy",
-    "LLMExtractionStrategy",
-    "CosineStrategy",
-    "JsonCssExtractionStrategy",
-    "JsonXPathExtractionStrategy",
-    "JsonLxmlExtractionStrategy",
-    "RegexExtractionStrategy",
-    "ChunkingStrategy",
-    "RegexChunking",
-    "DefaultMarkdownGenerator",
-    "TableExtractionStrategy",
-    "DefaultTableExtraction",
-    "NoTableExtraction",
-    "RelevantContentFilter",
-    "PruningContentFilter",
-    "BM25ContentFilter",
-    "LLMContentFilter",
-    "BaseDispatcher",
-    "MemoryAdaptiveDispatcher",
-    "SemaphoreDispatcher",
-    "RateLimiter",
-    "CrawlerMonitor",
-    "LinkPreview",
-    "DisplayMode",
-    "MarkdownGenerationResult",
-    "Crawl4aiDockerClient",
-    "ProxyRotationStrategy",
-    "RoundRobinProxyStrategy",
-    "ProxyConfig",
-    "start_colab_display_server",
-    "setup_colab_environment",
-    # C4A Script additions
-    "c4a_compile",
-    "c4a_validate", 
-    "c4a_compile_file",
-    "CompilationResult",
-    "ValidationResult",
-    "ErrorDetail",
-    # Browser Adapters
-    "BrowserAdapter",
-    "PlaywrightAdapter", 
-    "UndetectedAdapter",
-    "LinkPreviewConfig"
 ]
 
+def is_sync_version_installed():
+    try:
+        import selenium
+        return True
+    except ImportError:
+        return False
 
-# def is_sync_version_installed():
-#     try:
-#         import selenium # noqa
-
-#         return True
-#     except ImportError:
-#         return False
-
-
-# if is_sync_version_installed():
-#     try:
-#         from .web_crawler import WebCrawler
-
-#         __all__.append("WebCrawler")
-#     except ImportError:
-#         print(
-#             "Warning: Failed to import WebCrawler even though selenium is installed. This might be due to other missing dependencies."
-#         )
-# else:
-#     WebCrawler = None
-#     # import warnings
-#     # print("Warning: Synchronous WebCrawler is not available. Install crawl4ai[sync] for synchronous support. However, please note that the synchronous version will be deprecated soon.")
-
-# Disable all Pydantic warnings
-warnings.filterwarnings("ignore", module="pydantic")
-# pydantic_warnings.filter_warnings()
+if is_sync_version_installed():
+    try:
+        from .web_crawler import WebCrawler
+        __all__.append("WebCrawler")
+    except ImportError:
+        import warnings
+        print("Warning: Failed to import WebCrawler even though selenium is installed. This might be due to other missing dependencies.")
+else:
+    WebCrawler = None
+    # import warnings
+    # print("Warning: Synchronous WebCrawler is not available. Install crawl4ai[sync] for synchronous support. However, please note that the synchronous version will be deprecated soon.")

crawl4ai/__version__.py

@@ -1,8 +0,0 @@
-# crawl4ai/__version__.py
-
-# This is the version that will be used for stable releases
-__version__ = "0.7.4"
-
-# For nightly builds, this gets set during build process
-__nightly_version__ = None
-

crawl4ai/_version.py

@@ -0,0 +1,2 @@
+# crawl4ai/_version.py
+__version__ = "0.3.731"

crawl4ai/async_crawler_strategy_0.3.73.py

@@ -0,0 +1,965 @@
+import asyncio
+import base64
+import time
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Any, List, Optional, Awaitable
+import os, sys, shutil
+import tempfile, subprocess
+from playwright.async_api import async_playwright, Page, Browser, Error
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+from pathlib import Path
+from playwright.async_api import ProxySettings
+from pydantic import BaseModel
+import hashlib
+import json
+import uuid
+
+from playwright_stealth import StealthConfig, stealth_async
+
+stealth_config = StealthConfig(
+    webdriver=True,
+    chrome_app=True,
+    chrome_csi=True,
+    chrome_load_times=True,
+    chrome_runtime=True,
+    navigator_languages=True,
+    navigator_plugins=True,
+    navigator_permissions=True,
+    webgl_vendor=True,
+    outerdimensions=True,
+    navigator_hardware_concurrency=True,
+    media_codecs=True,
+)
+
+
+class ManagedBrowser:
+    def __init__(self, browser_type: str = "chromium", user_data_dir: Optional[str] = None, headless: bool = False):
+        self.browser_type = browser_type
+        self.user_data_dir = user_data_dir
+        self.headless = headless
+        self.browser_process = None
+        self.temp_dir = None
+        self.debugging_port = 9222
+
+    async def start(self) -> str:
+        """
+        Starts the browser process and returns the CDP endpoint URL.
+        If user_data_dir is not provided, creates a temporary directory.
+        """
+        
+        # Create temp dir if needed
+        if not self.user_data_dir:
+            self.temp_dir = tempfile.mkdtemp(prefix="browser-profile-")
+            self.user_data_dir = self.temp_dir
+
+        # Get browser path and args based on OS and browser type
+        browser_path = self._get_browser_path()
+        args = self._get_browser_args()
+
+        # Start browser process
+        try:
+            self.browser_process = subprocess.Popen(
+                args,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.PIPE
+            )
+            await asyncio.sleep(2)  # Give browser time to start
+            return f"http://localhost:{self.debugging_port}"
+        except Exception as e:
+            await self.cleanup()
+            raise Exception(f"Failed to start browser: {e}")
+
+    def _get_browser_path(self) -> str:
+        """Returns the browser executable path based on OS and browser type"""
+        if sys.platform == "darwin":  # macOS
+            paths = {
+                "chromium": "/Applications/Google Chrome.app/Contents/MacOS/Google Chrome",
+                "firefox": "/Applications/Firefox.app/Contents/MacOS/firefox",
+                "webkit": "/Applications/Safari.app/Contents/MacOS/Safari"
+            }
+        elif sys.platform == "win32":  # Windows
+            paths = {
+                "chromium": "C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
+                "firefox": "C:\\Program Files\\Mozilla Firefox\\firefox.exe",
+                "webkit": None  # WebKit not supported on Windows
+            }
+        else:  # Linux
+            paths = {
+                "chromium": "google-chrome",
+                "firefox": "firefox",
+                "webkit": None  # WebKit not supported on Linux
+            }
+        
+        return paths.get(self.browser_type)
+
+    def _get_browser_args(self) -> List[str]:
+        """Returns browser-specific command line arguments"""
+        base_args = [self._get_browser_path()]
+        
+        if self.browser_type == "chromium":
+            args = [
+                f"--remote-debugging-port={self.debugging_port}",
+                f"--user-data-dir={self.user_data_dir}",
+            ]
+            if self.headless:
+                args.append("--headless=new")
+        elif self.browser_type == "firefox":
+            args = [
+                "--remote-debugging-port", str(self.debugging_port),
+                "--profile", self.user_data_dir,
+            ]
+            if self.headless:
+                args.append("--headless")
+        else:
+            raise NotImplementedError(f"Browser type {self.browser_type} not supported")
+            
+        return base_args + args
+
+    async def cleanup(self):
+        """Cleanup browser process and temporary directory"""
+        if self.browser_process:
+            try:
+                self.browser_process.terminate()
+                await asyncio.sleep(1)
+                if self.browser_process.poll() is None:
+                    self.browser_process.kill()
+            except Exception as e:
+                print(f"Error terminating browser: {e}")
+
+        if self.temp_dir and os.path.exists(self.temp_dir):
+            try:
+                shutil.rmtree(self.temp_dir)
+            except Exception as e:
+                print(f"Error removing temporary directory: {e}")
+
+class AsyncCrawlResponse(BaseModel):
+    html: str
+    response_headers: Dict[str, str]
+    status_code: int
+    screenshot: Optional[str] = None
+    get_delayed_content: Optional[Callable[[Optional[float]], Awaitable[str]]] = None
+
+    class Config:
+        arbitrary_types_allowed = True
+
+class AsyncCrawlerStrategy(ABC):
+    @abstractmethod
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        pass
+    
+    @abstractmethod
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        pass
+    
+    @abstractmethod
+    async def take_screenshot(self, **kwargs) -> str:
+        pass
+    
+    @abstractmethod
+    def update_user_agent(self, user_agent: str):
+        pass
+    
+    @abstractmethod
+    def set_hook(self, hook_type: str, hook: Callable):
+        pass
+
+class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
+    def __init__(self, use_cached_html=False, js_code=None, **kwargs):
+        self.use_cached_html = use_cached_html
+        self.user_agent = kwargs.get(
+            "user_agent",
+            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
+            "(KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
+        )
+        self.proxy = kwargs.get("proxy")
+        self.proxy_config = kwargs.get("proxy_config")
+        self.headless = kwargs.get("headless", True)
+        self.browser_type = kwargs.get("browser_type", "chromium")
+        self.headers = kwargs.get("headers", {})
+        self.sessions = {}
+        self.session_ttl = 1800 
+        self.js_code = js_code
+        self.verbose = kwargs.get("verbose", False)
+        self.playwright = None
+        self.browser = None
+        self.sleep_on_close = kwargs.get("sleep_on_close", False)
+        self.use_managed_browser = kwargs.get("use_managed_browser", False)
+        self.user_data_dir = kwargs.get("user_data_dir", None)
+        self.use_persistent_context = kwargs.get("use_persistent_context", False)
+        self.chrome_channel = kwargs.get("chrome_channel", "chrome")
+        self.managed_browser = None
+        self.default_context = None
+        self.hooks = {
+            'on_browser_created': None,
+            'on_user_agent_updated': None,
+            'on_execution_started': None,
+            'before_goto': None,
+            'after_goto': None,
+            'before_return_html': None,
+            'before_retrieve_html': None
+        }
+        self.extra_args = kwargs.get("extra_args", [])
+
+    async def __aenter__(self):
+        await self.start()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        await self.close()
+
+    async def start(self):
+        if self.playwright is None:
+            self.playwright = await async_playwright().start()
+        if self.browser is None:
+            if self.use_managed_browser:
+                # Use managed browser approach
+                self.managed_browser = ManagedBrowser(
+                    browser_type=self.browser_type,
+                    user_data_dir=self.user_data_dir,
+                    headless=self.headless
+                )
+                cdp_url = await self.managed_browser.start()
+                self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
+                
+                # Get the default context that maintains the user profile
+                contexts = self.browser.contexts
+                if contexts:
+                    self.default_context = contexts[0]
+                else:
+                    # If no default context exists, create one
+                    self.default_context = await self.browser.new_context(
+                        viewport={"width": 1920, "height": 1080}
+                    )
+                
+                # Set up the default context
+                if self.default_context:
+                    await self.default_context.set_extra_http_headers(self.headers)
+                    
+                    if self.user_agent:
+                        await self.default_context.set_extra_http_headers({
+                            "User-Agent": self.user_agent
+                        })
+            else:
+                browser_args = {
+                    "headless": self.headless,
+                    "args": [
+                        "--disable-gpu",
+                        "--no-sandbox",
+                        "--disable-dev-shm-usage",
+                        "--disable-blink-features=AutomationControlled",
+                        "--disable-infobars",
+                        "--window-position=0,0",
+                        "--ignore-certificate-errors",
+                        "--ignore-certificate-errors-spki-list",
+                        # "--disable-http2",
+                        # "--headless=new",  # Use the new headless mode
+                    ]
+                }
+                
+                # Add extra args if provided
+                if self.extra_args:
+                    browser_args["args"].extend(self.extra_args)
+                
+                # Add proxy settings if a proxy is specified
+                if self.proxy:
+                    proxy_settings = ProxySettings(server=self.proxy)
+                    browser_args["proxy"] = proxy_settings
+                elif self.proxy_config:
+                    proxy_settings = ProxySettings(server=self.proxy_config.get("server"), username=self.proxy_config.get("username"), password=self.proxy_config.get("password"))
+                    browser_args["proxy"] = proxy_settings
+                    
+                # Select the appropriate browser based on the browser_type
+                if self.browser_type == "firefox":
+                    self.browser = await self.playwright.firefox.launch(**browser_args)
+                elif self.browser_type == "webkit":
+                    self.browser = await self.playwright.webkit.launch(**browser_args)
+                else:
+                    self.browser = await self.playwright.chromium.launch(**browser_args)
+                    
+                # Update the headless configuration
+                if self.headless:
+                    # Use the new headless mode explicitly
+                    browser_args["args"].append("--headless=new")
+
+            await self.execute_hook('on_browser_created', self.browser)
+
+    async def close(self):
+        if self.sleep_on_close:
+            await asyncio.sleep(0.5)
+            
+        # Close all active sessions
+        session_ids = list(self.sessions.keys())
+        for session_id in session_ids:
+            await self.kill_session(session_id)
+            
+        if self.browser:
+            await self.browser.close()
+            self.browser = None
+            
+        if self.managed_browser:
+            await self.managed_browser.cleanup()
+            self.managed_browser = None
+            
+        if self.playwright:
+            await self.playwright.stop()
+            self.playwright = None
+
+    def __del__(self):
+        if self.browser or self.playwright:
+            asyncio.get_event_loop().run_until_complete(self.close())
+
+    def set_hook(self, hook_type: str, hook: Callable):
+        if hook_type in self.hooks:
+            self.hooks[hook_type] = hook
+        else:
+            raise ValueError(f"Invalid hook type: {hook_type}")
+
+    async def execute_hook(self, hook_type: str, *args):
+        hook = self.hooks.get(hook_type)
+        if hook:
+            if asyncio.iscoroutinefunction(hook):
+                return await hook(*args)
+            else:
+                return hook(*args)
+        return args[0] if args else None
+
+    def update_user_agent(self, user_agent: str):
+        self.user_agent = user_agent
+
+    def set_custom_headers(self, headers: Dict[str, str]):
+        self.headers = headers
+
+    async def kill_session(self, session_id: str):
+        if session_id in self.sessions:
+            context, page, _ = self.sessions[session_id]
+            await page.close()
+            if not self.use_managed_browser:
+                await context.close()
+            del self.sessions[session_id]
+
+    def _cleanup_expired_sessions(self):
+        current_time = time.time()
+        expired_sessions = [
+            sid for sid, (_, _, last_used) in self.sessions.items() 
+            if current_time - last_used > self.session_ttl
+        ]
+        for sid in expired_sessions:
+            asyncio.create_task(self.kill_session(sid))
+            
+    async def smart_wait(self, page: Page, wait_for: str, timeout: float = 30000):
+        wait_for = wait_for.strip()
+        
+        if wait_for.startswith('js:'):
+            # Explicitly specified JavaScript
+            js_code = wait_for[3:].strip()
+            return await self.csp_compliant_wait(page, js_code, timeout)
+        elif wait_for.startswith('css:'):
+            # Explicitly specified CSS selector
+            css_selector = wait_for[4:].strip()
+            try:
+                await page.wait_for_selector(css_selector, timeout=timeout)
+            except Error as e:
+                if 'Timeout' in str(e):
+                    raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{css_selector}'")
+                else:
+                    raise ValueError(f"Invalid CSS selector: '{css_selector}'")
+        else:
+            # Auto-detect based on content
+            if wait_for.startswith('()') or wait_for.startswith('function'):
+                # It's likely a JavaScript function
+                return await self.csp_compliant_wait(page, wait_for, timeout)
+            else:
+                # Assume it's a CSS selector first
+                try:
+                    await page.wait_for_selector(wait_for, timeout=timeout)
+                except Error as e:
+                    if 'Timeout' in str(e):
+                        raise TimeoutError(f"Timeout after {timeout}ms waiting for selector '{wait_for}'")
+                    else:
+                        # If it's not a timeout error, it might be an invalid selector
+                        # Let's try to evaluate it as a JavaScript function as a fallback
+                        try:
+                            return await self.csp_compliant_wait(page, f"() => {{{wait_for}}}", timeout)
+                        except Error:
+                            raise ValueError(f"Invalid wait_for parameter: '{wait_for}'. "
+                                             "It should be either a valid CSS selector, a JavaScript function, "
+                                             "or explicitly prefixed with 'js:' or 'css:'.")
+    
+    async def csp_compliant_wait(self, page: Page, user_wait_function: str, timeout: float = 30000):
+        wrapper_js = f"""
+        async () => {{
+            const userFunction = {user_wait_function};
+            const startTime = Date.now();
+            while (true) {{
+                if (await userFunction()) {{
+                    return true;
+                }}
+                if (Date.now() - startTime > {timeout}) {{
+                    throw new Error('Timeout waiting for condition');
+                }}
+                await new Promise(resolve => setTimeout(resolve, 100));
+            }}
+        }}
+        """
+        
+        try:
+            await page.evaluate(wrapper_js)
+        except TimeoutError:
+            raise TimeoutError(f"Timeout after {timeout}ms waiting for condition")
+        except Exception as e:
+            raise RuntimeError(f"Error in wait condition: {str(e)}")
+
+    async def process_iframes(self, page):
+        # Find all iframes
+        iframes = await page.query_selector_all('iframe')
+        
+        for i, iframe in enumerate(iframes):
+            try:
+                # Add a unique identifier to the iframe
+                await iframe.evaluate(f'(element) => element.id = "iframe-{i}"')
+                
+                # Get the frame associated with this iframe
+                frame = await iframe.content_frame()
+                
+                if frame:
+                    # Wait for the frame to load
+                    await frame.wait_for_load_state('load', timeout=30000)  # 30 seconds timeout
+                    
+                    # Extract the content of the iframe's body
+                    iframe_content = await frame.evaluate('() => document.body.innerHTML')
+                    
+                    # Generate a unique class name for this iframe
+                    class_name = f'extracted-iframe-content-{i}'
+                    
+                    # Replace the iframe with a div containing the extracted content
+                    _iframe = iframe_content.replace('`', '\\`')
+                    await page.evaluate(f"""
+                        () => {{
+                            const iframe = document.getElementById('iframe-{i}');
+                            const div = document.createElement('div');
+                            div.innerHTML = `{_iframe}`;
+                            div.className = '{class_name}';
+                            iframe.replaceWith(div);
+                        }}
+                    """)
+                else:
+                    print(f"Warning: Could not access content frame for iframe {i}")
+            except Exception as e:
+                print(f"Error processing iframe {i}: {str(e)}")
+
+        # Return the page object
+        return page  
+    
+    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
+        response_headers = {}
+        status_code = None
+        
+        self._cleanup_expired_sessions()
+        session_id = kwargs.get("session_id")
+        
+        # Handle page creation differently for managed browser
+        if self.use_managed_browser:
+            if session_id:
+                # Reuse existing session if available
+                context, page, _ = self.sessions.get(session_id, (None, None, None))
+                if not page:
+                    # Create new page in default context if session doesn't exist
+                    page = await self.default_context.new_page()
+                    self.sessions[session_id] = (self.default_context, page, time.time())
+            else:
+                # Create new page in default context for non-session requests
+                page = await self.default_context.new_page()
+        else:
+            if session_id:
+                context, page, _ = self.sessions.get(session_id, (None, None, None))
+                if not context:
+                    context = await self.browser.new_context(
+                        user_agent=self.user_agent,
+                        viewport={"width": 1920, "height": 1080},
+                        proxy={"server": self.proxy} if self.proxy else None,
+                        accept_downloads=True,
+                        java_script_enabled=True
+                    )
+                    await context.add_cookies([{"name": "cookiesEnabled", "value": "true", "url": url}])
+                    await context.set_extra_http_headers(self.headers)
+                    page = await context.new_page()
+                    self.sessions[session_id] = (context, page, time.time())
+            else:
+                context = await self.browser.new_context(
+                    user_agent=self.user_agent,
+                    viewport={"width": 1920, "height": 1080},
+                    proxy={"server": self.proxy} if self.proxy else None
+                )
+                await context.set_extra_http_headers(self.headers)
+                
+                if kwargs.get("override_navigator", False) or kwargs.get("simulate_user", False) or kwargs.get("magic", False):
+                    # Inject scripts to override navigator properties
+                    await context.add_init_script("""
+                        // Pass the Permissions Test.
+                        const originalQuery = window.navigator.permissions.query;
+                        window.navigator.permissions.query = (parameters) => (
+                            parameters.name === 'notifications' ?
+                                Promise.resolve({ state: Notification.permission }) :
+                                originalQuery(parameters)
+                        );
+                        Object.defineProperty(navigator, 'webdriver', {
+                            get: () => undefined
+                        });
+                        window.navigator.chrome = {
+                            runtime: {},
+                            // Add other properties if necessary
+                        };
+                        Object.defineProperty(navigator, 'plugins', {
+                            get: () => [1, 2, 3, 4, 5],
+                        });
+                        Object.defineProperty(navigator, 'languages', {
+                            get: () => ['en-US', 'en'],
+                        });
+                        Object.defineProperty(document, 'hidden', {
+                            get: () => false
+                        });
+                        Object.defineProperty(document, 'visibilityState', {
+                            get: () => 'visible'
+                        });
+                    """)
+                
+                page = await context.new_page()
+                if kwargs.get("magic", False):
+                    await stealth_async(page, stealth_config)
+
+        # Add console message and error logging
+        if kwargs.get("log_console", False):
+            page.on("console", lambda msg: print(f"Console: {msg.text}"))
+            page.on("pageerror", lambda exc: print(f"Page Error: {exc}"))
+        
+        try:
+            if self.verbose:
+                print(f"[LOG] 🕸️ Crawling {url} using AsyncPlaywrightCrawlerStrategy...")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                if os.path.exists(cache_file_path):
+                    html = ""
+                    with open(cache_file_path, "r") as f:
+                        html = f.read()
+                    # retrieve response headers and status code from cache
+                    with open(cache_file_path + ".meta", "r") as f:
+                        meta = json.load(f)
+                        response_headers = meta.get("response_headers", {})
+                        status_code = meta.get("status_code")
+                    response = AsyncCrawlResponse(
+                        html=html, response_headers=response_headers, status_code=status_code
+                    )
+                    return response
+
+            if not kwargs.get("js_only", False):
+                await self.execute_hook('before_goto', page)
+                
+                # response = await page.goto(
+                #     url, wait_until="domcontentloaded", timeout=kwargs.get("page_timeout", 60000)
+                # )
+                
+                # Add retry logic for HTTP2 errors
+                max_retries = kwargs.get("max_retries", 3)
+                current_try = 0
+                
+                while current_try < max_retries:
+                    try:
+                        response = await page.goto(
+                            url,
+                            # wait_until=kwargs.get("wait_until", ["domcontentloaded", "networkidle"]),
+                            wait_until=kwargs.get("wait_until", "networkidle"),
+                            timeout=kwargs.get("page_timeout", 60000)
+                        )
+                        break
+                    except Exception as e:
+                        current_try += 1
+                        if "ERR_HTTP2_PROTOCOL_ERROR" in str(e):
+                            if current_try < max_retries:
+                                # Add exponential backoff
+                                await asyncio.sleep(2 ** current_try)
+                                # Try with different protocol
+                                if 'args' not in kwargs:
+                                    kwargs['args'] = []
+                                kwargs['args'].extend(['--disable-http2'])
+                                continue
+                        if current_try == max_retries:
+                            raise
+                
+                # response = await page.goto("about:blank")
+                # await page.evaluate(f"window.location.href = '{url}'")
+                
+                await self.execute_hook('after_goto', page)
+                
+                # Get status code and headers
+                status_code = response.status
+                response_headers = response.headers
+            else:
+                status_code = 200
+                response_headers = {}
+
+            # Replace the current wait_for_selector line with this more robust check:
+            try:
+                # First wait for body to exist, regardless of visibility
+                await page.wait_for_selector('body', state='attached', timeout=30000)
+                
+                # Then wait for it to become visible by checking CSS
+                await page.wait_for_function("""
+                    () => {
+                        const body = document.body;
+                        const style = window.getComputedStyle(body);
+                        return style.display !== 'none' && 
+                            style.visibility !== 'hidden' && 
+                            style.opacity !== '0';
+                    }
+                """, timeout=30000)
+                
+            except Error as e:
+                # If waiting fails, let's try to diagnose the issue
+                visibility_info = await page.evaluate("""
+                    () => {
+                        const body = document.body;
+                        const style = window.getComputedStyle(body);
+                        return {
+                            display: style.display,
+                            visibility: style.visibility,
+                            opacity: style.opacity,
+                            hasContent: body.innerHTML.length,
+                            classList: Array.from(body.classList)
+                        }
+                    }
+                """)
+                
+                if self.verbose:
+                    print(f"Body visibility debug info: {visibility_info}")
+                
+                # Even if body is hidden, we might still want to proceed
+                if kwargs.get('ignore_body_visibility', True):
+                    if self.verbose:
+                        print("Proceeding despite hidden body...")
+                    pass
+                else:
+                    raise Error(f"Body element is hidden: {visibility_info}")
+            
+            await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
+
+            js_code = kwargs.get("js_code", kwargs.get("js", self.js_code))
+            if js_code:
+                if isinstance(js_code, str):
+                    await page.evaluate(js_code)
+                elif isinstance(js_code, list):
+                    for js in js_code:
+                        await page.evaluate(js)
+                
+                await page.wait_for_load_state('networkidle')
+                # Check for on execution event
+                await self.execute_hook('on_execution_started', page)
+                
+            if kwargs.get("simulate_user", False) or kwargs.get("magic", False):
+                # Simulate user interactions
+                await page.mouse.move(100, 100)
+                await page.mouse.down()
+                await page.mouse.up()
+                await page.keyboard.press('ArrowDown')
+
+            # Handle the wait_for parameter
+            wait_for = kwargs.get("wait_for")
+            if wait_for:
+                try:
+                    await self.smart_wait(page, wait_for, timeout=kwargs.get("page_timeout", 60000))
+                except Exception as e:
+                    raise RuntimeError(f"Wait condition failed: {str(e)}")
+
+            # Update image dimensions
+            update_image_dimensions_js = """
+            () => {
+                return new Promise((resolve) => {
+                    const filterImage = (img) => {
+                        // Filter out images that are too small
+                        if (img.width < 100 && img.height < 100) return false;
+                        
+                        // Filter out images that are not visible
+                        const rect = img.getBoundingClientRect();
+                        if (rect.width === 0 || rect.height === 0) return false;
+                        
+                        // Filter out images with certain class names (e.g., icons, thumbnails)
+                        if (img.classList.contains('icon') || img.classList.contains('thumbnail')) return false;
+                        
+                        // Filter out images with certain patterns in their src (e.g., placeholder images)
+                        if (img.src.includes('placeholder') || img.src.includes('icon')) return false;
+                        
+                        return true;
+                    };
+
+                    const images = Array.from(document.querySelectorAll('img')).filter(filterImage);
+                    let imagesLeft = images.length;
+                    
+                    if (imagesLeft === 0) {
+                        resolve();
+                        return;
+                    }
+
+                    const checkImage = (img) => {
+                        if (img.complete && img.naturalWidth !== 0) {
+                            img.setAttribute('width', img.naturalWidth);
+                            img.setAttribute('height', img.naturalHeight);
+                            imagesLeft--;
+                            if (imagesLeft === 0) resolve();
+                        }
+                    };
+
+                    images.forEach(img => {
+                        checkImage(img);
+                        if (!img.complete) {
+                            img.onload = () => {
+                                checkImage(img);
+                            };
+                            img.onerror = () => {
+                                imagesLeft--;
+                                if (imagesLeft === 0) resolve();
+                            };
+                        }
+                    });
+
+                    // Fallback timeout of 5 seconds
+                    // setTimeout(() => resolve(), 5000);
+                    resolve();
+                });
+            }
+            """
+            await page.evaluate(update_image_dimensions_js)
+
+            # Wait a bit for any onload events to complete
+            await page.wait_for_timeout(100)
+
+            # Process iframes
+            if kwargs.get("process_iframes", False):
+                page = await self.process_iframes(page)
+            
+            await self.execute_hook('before_retrieve_html', page)
+            # Check if delay_before_return_html is set then wait for that time
+            delay_before_return_html = kwargs.get("delay_before_return_html")
+            if delay_before_return_html:
+                await asyncio.sleep(delay_before_return_html)
+                
+            # Check for remove_overlay_elements parameter
+            if kwargs.get("remove_overlay_elements", False):
+                await self.remove_overlay_elements(page)
+            
+            html = await page.content()
+            await self.execute_hook('before_return_html', page, html)
+            
+            # Check if kwargs has screenshot=True then take screenshot
+            screenshot_data = None
+            if kwargs.get("screenshot"):
+                # Check we have screenshot_wait_for parameter, if we have simply wait for that time
+                screenshot_wait_for = kwargs.get("screenshot_wait_for")
+                if screenshot_wait_for:
+                    await asyncio.sleep(screenshot_wait_for)
+                screenshot_data = await self.take_screenshot(page)          
+
+            if self.verbose:
+                print(f"[LOG] ✅ Crawled {url} successfully!")
+
+            if self.use_cached_html:
+                cache_file_path = os.path.join(
+                    Path.home(), ".crawl4ai", "cache", hashlib.md5(url.encode()).hexdigest()
+                )
+                with open(cache_file_path, "w", encoding="utf-8") as f:
+                    f.write(html)
+                # store response headers and status code in cache
+                with open(cache_file_path + ".meta", "w", encoding="utf-8") as f:
+                    json.dump({
+                        "response_headers": response_headers,
+                        "status_code": status_code
+                    }, f)
+
+            async def get_delayed_content(delay: float = 5.0) -> str:
+                if self.verbose:
+                    print(f"[LOG] Waiting for {delay} seconds before retrieving content for {url}")
+                await asyncio.sleep(delay)
+                return await page.content()
+                
+            response = AsyncCrawlResponse(
+                html=html, 
+                response_headers=response_headers, 
+                status_code=status_code,
+                screenshot=screenshot_data,
+                get_delayed_content=get_delayed_content
+            )
+            return response
+        except Error as e:
+            raise Error(f"[ERROR] 🚫 crawl(): Failed to crawl {url}: {str(e)}")
+        # finally:
+        #     if not session_id:
+        #         await page.close()
+        #         await context.close()
+
+    async def crawl_many(self, urls: List[str], **kwargs) -> List[AsyncCrawlResponse]:
+        semaphore_count = kwargs.get('semaphore_count', 5)  # Adjust as needed
+        semaphore = asyncio.Semaphore(semaphore_count)
+
+        async def crawl_with_semaphore(url):
+            async with semaphore:
+                return await self.crawl(url, **kwargs)
+
+        tasks = [crawl_with_semaphore(url) for url in urls]
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        return [result if not isinstance(result, Exception) else str(result) for result in results]
+
+    async def remove_overlay_elements(self, page: Page) -> None:
+        """
+        Removes popup overlays, modals, cookie notices, and other intrusive elements from the page.
+        
+        Args:
+            page (Page): The Playwright page instance
+        """
+        remove_overlays_js = """
+        async () => {
+            // Function to check if element is visible
+            const isVisible = (elem) => {
+                const style = window.getComputedStyle(elem);
+                return style.display !== 'none' && 
+                       style.visibility !== 'hidden' && 
+                       style.opacity !== '0';
+            };
+
+            // Common selectors for popups and overlays
+            const commonSelectors = [
+                // Close buttons first
+                'button[class*="close" i]', 'button[class*="dismiss" i]', 
+                'button[aria-label*="close" i]', 'button[title*="close" i]',
+                'a[class*="close" i]', 'span[class*="close" i]',
+                
+                // Cookie notices
+                '[class*="cookie-banner" i]', '[id*="cookie-banner" i]',
+                '[class*="cookie-consent" i]', '[id*="cookie-consent" i]',
+                
+                // Newsletter/subscription dialogs
+                '[class*="newsletter" i]', '[class*="subscribe" i]',
+                
+                // Generic popups/modals
+                '[class*="popup" i]', '[class*="modal" i]', 
+                '[class*="overlay" i]', '[class*="dialog" i]',
+                '[role="dialog"]', '[role="alertdialog"]'
+            ];
+
+            // Try to click close buttons first
+            for (const selector of commonSelectors.slice(0, 6)) {
+                const closeButtons = document.querySelectorAll(selector);
+                for (const button of closeButtons) {
+                    if (isVisible(button)) {
+                        try {
+                            button.click();
+                            await new Promise(resolve => setTimeout(resolve, 100));
+                        } catch (e) {
+                            console.log('Error clicking button:', e);
+                        }
+                    }
+                }
+            }
+
+            // Remove remaining overlay elements
+            const removeOverlays = () => {
+                // Find elements with high z-index
+                const allElements = document.querySelectorAll('*');
+                for (const elem of allElements) {
+                    const style = window.getComputedStyle(elem);
+                    const zIndex = parseInt(style.zIndex);
+                    const position = style.position;
+                    
+                    if (
+                        isVisible(elem) && 
+                        (zIndex > 999 || position === 'fixed' || position === 'absolute') &&
+                        (
+                            elem.offsetWidth > window.innerWidth * 0.5 ||
+                            elem.offsetHeight > window.innerHeight * 0.5 ||
+                            style.backgroundColor.includes('rgba') ||
+                            parseFloat(style.opacity) < 1
+                        )
+                    ) {
+                        elem.remove();
+                    }
+                }
+
+                // Remove elements matching common selectors
+                for (const selector of commonSelectors) {
+                    const elements = document.querySelectorAll(selector);
+                    elements.forEach(elem => {
+                        if (isVisible(elem)) {
+                            elem.remove();
+                        }
+                    });
+                }
+            };
+
+            // Remove overlay elements
+            removeOverlays();
+
+            // Remove any fixed/sticky position elements at the top/bottom
+            const removeFixedElements = () => {
+                const elements = document.querySelectorAll('*');
+                elements.forEach(elem => {
+                    const style = window.getComputedStyle(elem);
+                    if (
+                        (style.position === 'fixed' || style.position === 'sticky') &&
+                        isVisible(elem)
+                    ) {
+                        elem.remove();
+                    }
+                });
+            };
+
+            removeFixedElements();
+            
+            // Remove empty block elements as: div, p, span, etc.
+            const removeEmptyBlockElements = () => {
+                const blockElements = document.querySelectorAll('div, p, span, section, article, header, footer, aside, nav, main, ul, ol, li, dl, dt, dd, h1, h2, h3, h4, h5, h6');
+                blockElements.forEach(elem => {
+                    if (elem.innerText.trim() === '') {
+                        elem.remove();
+                    }
+                });
+            };
+
+            // Remove margin-right and padding-right from body (often added by modal scripts)
+            document.body.style.marginRight = '0px';
+            document.body.style.paddingRight = '0px';
+            document.body.style.overflow = 'auto';
+
+            // Wait a bit for any animations to complete
+            await new Promise(resolve => setTimeout(resolve, 100));
+        }
+        """
+        
+        try:
+            await page.evaluate(remove_overlays_js)
+            await page.wait_for_timeout(500)  # Wait for any animations to complete
+        except Exception as e:
+            if self.verbose:
+                print(f"Warning: Failed to remove overlay elements: {str(e)}")
+
+    async def take_screenshot(self, page: Page) -> str:
+        try:
+            # The page is already loaded, just take the screenshot
+            screenshot = await page.screenshot(full_page=True)
+            return base64.b64encode(screenshot).decode('utf-8')
+        except Exception as e:
+            error_message = f"Failed to take screenshot: {str(e)}"
+            print(error_message)
+
+            # Generate an error image
+            img = Image.new('RGB', (800, 600), color='black')
+            draw = ImageDraw.Draw(img)
+            font = ImageFont.load_default()
+            draw.text((10, 10), error_message, fill=(255, 255, 255), font=font)
+            
+            buffered = BytesIO()
+            img.save(buffered, format="JPEG")
+            return base64.b64encode(buffered.getvalue()).decode('utf-8')
+        finally:
+            await page.close()
+

crawl4ai/async_database.py

@@ -2,94 +2,32 @@ import os
 from pathlib import Path
 import aiosqlite
 import asyncio
-from typing import Optional, Dict
+from typing import Optional, Tuple, Dict
 from contextlib import asynccontextmanager
-import json  
-from .models import CrawlResult, MarkdownGenerationResult, StringCompatibleMarkdown
-import aiofiles
-from .async_logger import AsyncLogger
+import logging
+import json  # Added for serialization/deserialization
 
-from .utils import ensure_content_dirs, generate_content_hash
-from .utils import VersionManager
-from .utils import get_error_context, create_box_message
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
-base_directory = DB_PATH = os.path.join(
-    os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
-)
+DB_PATH = os.path.join(Path.home(), ".crawl4ai")
 os.makedirs(DB_PATH, exist_ok=True)
-DB_PATH = os.path.join(base_directory, "crawl4ai.db")
-
+DB_PATH = os.path.join(DB_PATH, "crawl4ai.db")
 
 class AsyncDatabaseManager:
     def __init__(self, pool_size: int = 10, max_retries: int = 3):
         self.db_path = DB_PATH
-        self.content_paths = ensure_content_dirs(os.path.dirname(DB_PATH))
         self.pool_size = pool_size
         self.max_retries = max_retries
         self.connection_pool: Dict[int, aiosqlite.Connection] = {}
         self.pool_lock = asyncio.Lock()
-        self.init_lock = asyncio.Lock()
         self.connection_semaphore = asyncio.Semaphore(pool_size)
-        self._initialized = False
-        self.version_manager = VersionManager()
-        self.logger = AsyncLogger(
-            log_file=os.path.join(base_directory, ".crawl4ai", "crawler_db.log"),
-            verbose=False,
-            tag_width=10,
-        )
-
+        
     async def initialize(self):
         """Initialize the database and connection pool"""
-        try:
-            self.logger.info("Initializing database", tag="INIT")
-            # Ensure the database file exists
-            os.makedirs(os.path.dirname(self.db_path), exist_ok=True)
-
-            # Check if version update is needed
-            needs_update = self.version_manager.needs_update()
-
-            # Always ensure base table exists
-            await self.ainit_db()
-
-            # Verify the table exists
-            async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
-                async with db.execute(
-                    "SELECT name FROM sqlite_master WHERE type='table' AND name='crawled_data'"
-                ) as cursor:
-                    result = await cursor.fetchone()
-                    if not result:
-                        raise Exception("crawled_data table was not created")
-
-            # If version changed or fresh install, run updates
-            if needs_update:
-                self.logger.info("New version detected, running updates", tag="INIT")
-                await self.update_db_schema()
-                from .migrations import (
-                    run_migration,
-                )  # Import here to avoid circular imports
-
-                await run_migration()
-                self.version_manager.update_version()  # Update stored version after successful migration
-                self.logger.success(
-                    "Version update completed successfully", tag="COMPLETE"
-                )
-            else:
-                self.logger.success(
-                    "Database initialization completed successfully", tag="COMPLETE"
-                )
-
-        except Exception as e:
-            self.logger.error(
-                message="Database initialization error: {error}",
-                tag="ERROR",
-                params={"error": str(e)},
-            )
-            self.logger.info(
-                message="Database will be initialized on first use", tag="INIT"
-            )
-
-            raise
-
+        await self.ainit_db()
+        
     async def cleanup(self):
         """Cleanup connections when shutting down"""
         async with self.pool_lock:
@@ -99,111 +37,30 @@ class AsyncDatabaseManager:
 
     @asynccontextmanager
     async def get_connection(self):
-        """Connection pool manager with enhanced error handling"""
-        if not self._initialized:
-            async with self.init_lock:
-                if not self._initialized:
-                    try:
-                        await self.initialize()
-                        self._initialized = True
-                    except Exception as e:
-                        import sys
-
-                        error_context = get_error_context(sys.exc_info())
-                        self.logger.error(
-                            message="Database initialization failed:\n{error}\n\nContext:\n{context}\n\nTraceback:\n{traceback}",
-                            tag="ERROR",
-                            force_verbose=True,
-                            params={
-                                "error": str(e),
-                                "context": error_context["code_context"],
-                                "traceback": error_context["full_traceback"],
-                            },
+        """Connection pool manager"""
+        async with self.connection_semaphore:
+            task_id = id(asyncio.current_task())
+            try:
+                async with self.pool_lock:
+                    if task_id not in self.connection_pool:
+                        conn = await aiosqlite.connect(
+                            self.db_path,
+                            timeout=30.0
                         )
-                        raise
-
-        await self.connection_semaphore.acquire()
-        task_id = id(asyncio.current_task())
-
-        try:
-            async with self.pool_lock:
-                if task_id not in self.connection_pool:
-                    try:
-                        conn = await aiosqlite.connect(self.db_path, timeout=30.0)
-                        await conn.execute("PRAGMA journal_mode = WAL")
-                        await conn.execute("PRAGMA busy_timeout = 5000")
-
-                        # Verify database structure
-                        async with conn.execute(
-                            "PRAGMA table_info(crawled_data)"
-                        ) as cursor:
-                            columns = await cursor.fetchall()
-                            column_names = [col[1] for col in columns]
-                            expected_columns = {
-                                "url",
-                                "html",
-                                "cleaned_html",
-                                "markdown",
-                                "extracted_content",
-                                "success",
-                                "media",
-                                "links",
-                                "metadata",
-                                "screenshot",
-                                "response_headers",
-                                "downloaded_files",
-                            }
-                            missing_columns = expected_columns - set(column_names)
-                            if missing_columns:
-                                raise ValueError(
-                                    f"Database missing columns: {missing_columns}"
-                                )
-
+                        await conn.execute('PRAGMA journal_mode = WAL')
+                        await conn.execute('PRAGMA busy_timeout = 5000')
                         self.connection_pool[task_id] = conn
-                    except Exception as e:
-                        import sys
-
-                        error_context = get_error_context(sys.exc_info())
-                        error_message = (
-                            f"Unexpected error in db get_connection at line {error_context['line_no']} "
-                            f"in {error_context['function']} ({error_context['filename']}):\n"
-                            f"Error: {str(e)}\n\n"
-                            f"Code context:\n{error_context['code_context']}"
-                        )
-                        self.logger.error(
-                            message="{error}",
-                            tag="ERROR",
-                            params={"error": str(error_message)},
-                            boxes=["error"],
-                        )
-
-                        raise
-
-            yield self.connection_pool[task_id]
-
-        except Exception as e:
-            import sys
-
-            error_context = get_error_context(sys.exc_info())
-            error_message = (
-                f"Unexpected error in db get_connection at line {error_context['line_no']} "
-                f"in {error_context['function']} ({error_context['filename']}):\n"
-                f"Error: {str(e)}\n\n"
-                f"Code context:\n{error_context['code_context']}"
-            )
-            self.logger.error(
-                message="{error}",
-                tag="ERROR",
-                params={"error": str(error_message)},
-                boxes=["error"],
-            )
-            raise
-        finally:
-            async with self.pool_lock:
-                if task_id in self.connection_pool:
-                    await self.connection_pool[task_id].close()
-                    del self.connection_pool[task_id]
-            self.connection_semaphore.release()
+                    
+                yield self.connection_pool[task_id]
+                
+            except Exception as e:
+                logger.error(f"Connection error: {e}")
+                raise
+            finally:
+                async with self.pool_lock:
+                    if task_id in self.connection_pool:
+                        await self.connection_pool[task_id].close()
+                        del self.connection_pool[task_id]
 
     async def execute_with_retry(self, operation, *args):
         """Execute database operations with retry logic"""
@@ -215,20 +72,14 @@ class AsyncDatabaseManager:
                     return result
             except Exception as e:
                 if attempt == self.max_retries - 1:
-                    self.logger.error(
-                        message="Operation failed after {retries} attempts: {error}",
-                        tag="ERROR",
-                        force_verbose=True,
-                        params={"retries": self.max_retries, "error": str(e)},
-                    )
+                    logger.error(f"Operation failed after {self.max_retries} attempts: {e}")
                     raise
                 await asyncio.sleep(1 * (attempt + 1))  # Exponential backoff
 
     async def ainit_db(self):
         """Initialize database schema"""
-        async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
-            await db.execute(
-                """
+        async def _init(db):
+            await db.execute('''
                 CREATE TABLE IF NOT EXISTS crawled_data (
                     url TEXT PRIMARY KEY,
                     html TEXT,
@@ -240,325 +91,145 @@ class AsyncDatabaseManager:
                     links TEXT DEFAULT "{}",
                     metadata TEXT DEFAULT "{}",
                     screenshot TEXT DEFAULT "",
-                    response_headers TEXT DEFAULT "{}",
-                    downloaded_files TEXT DEFAULT "{}"  -- New column added
+                    response_headers TEXT DEFAULT "{}"  -- New column added
                 )
-            """
-            )
-            await db.commit()
+            ''')
+        
+        await self.execute_with_retry(_init)
+        await self.update_db_schema()
 
     async def update_db_schema(self):
         """Update database schema if needed"""
-        async with aiosqlite.connect(self.db_path, timeout=30.0) as db:
+        async def _check_columns(db):
             cursor = await db.execute("PRAGMA table_info(crawled_data)")
             columns = await cursor.fetchall()
-            column_names = [column[1] for column in columns]
+            return [column[1] for column in columns]
 
-            # List of new columns to add
-            new_columns = [
-                "media",
-                "links",
-                "metadata",
-                "screenshot",
-                "response_headers",
-                "downloaded_files",
-            ]
+        column_names = await self.execute_with_retry(_check_columns)
+        
+        # List of new columns to add
+        new_columns = ['media', 'links', 'metadata', 'screenshot', 'response_headers']
+        
+        for column in new_columns:
+            if column not in column_names:
+                await self.aalter_db_add_column(column)
 
-            for column in new_columns:
-                if column not in column_names:
-                    await self.aalter_db_add_column(column, db)
-            await db.commit()
-
-    async def aalter_db_add_column(self, new_column: str, db):
+    async def aalter_db_add_column(self, new_column: str):
         """Add new column to the database"""
-        if new_column == "response_headers":
-            await db.execute(
-                f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT "{{}}"'
-            )
-        else:
-            await db.execute(
-                f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""'
-            )
-        self.logger.info(
-            message="Added column '{column}' to the database",
-            tag="INIT",
-            params={"column": new_column},
-        )
+        async def _alter(db):
+            if new_column == 'response_headers':
+                await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT "{{}}"')
+            else:
+                await db.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
+            logger.info(f"Added column '{new_column}' to the database.")
 
-    async def aget_cached_url(self, url: str) -> Optional[CrawlResult]:
-        """Retrieve cached URL data as CrawlResult"""
+        await self.execute_with_retry(_alter)
 
+    async def aget_cached_url(self, url: str) -> Optional[Tuple[str, str, str, str, str, bool, str, str, str, str]]:
+        """Retrieve cached URL data"""
         async def _get(db):
             async with db.execute(
-                "SELECT * FROM crawled_data WHERE url = ?", (url,)
+                '''
+                SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot, response_headers
+                FROM crawled_data WHERE url = ?
+                ''',
+                (url,)
             ) as cursor:
                 row = await cursor.fetchone()
-                if not row:
-                    return None
-
-                # Get column names
-                columns = [description[0] for description in cursor.description]
-                # Create dict from row data
-                row_dict = dict(zip(columns, row))
-
-                # Load content from files using stored hashes
-                content_fields = {
-                    "html": row_dict["html"],
-                    "cleaned_html": row_dict["cleaned_html"],
-                    "markdown": row_dict["markdown"],
-                    "extracted_content": row_dict["extracted_content"],
-                    "screenshot": row_dict["screenshot"],
-                    "screenshots": row_dict["screenshot"],
-                }
-
-                for field, hash_value in content_fields.items():
-                    if hash_value:
-                        content = await self._load_content(
-                            hash_value,
-                            field.split("_")[0],  # Get content type from field name
-                        )
-                        row_dict[field] = content or ""
-                    else:
-                        row_dict[field] = ""
-
-                # Parse JSON fields
-                json_fields = [
-                    "media",
-                    "links",
-                    "metadata",
-                    "response_headers",
-                    "markdown",
-                ]
-                for field in json_fields:
-                    try:
-                        row_dict[field] = (
-                            json.loads(row_dict[field]) if row_dict[field] else {}
-                        )
-                    except json.JSONDecodeError:
-                        # Very UGLY, never mention it to me please
-                        if field == "markdown" and isinstance(row_dict[field], str):
-                            row_dict[field] = MarkdownGenerationResult(
-                                raw_markdown=row_dict[field] or "",
-                                markdown_with_citations="",
-                                references_markdown="",
-                                fit_markdown="",
-                                fit_html="",
-                            )
-                        else:
-                            row_dict[field] = {}
-
-                if isinstance(row_dict["markdown"], Dict):
-                    if row_dict["markdown"].get("raw_markdown"):
-                        row_dict["markdown"] = row_dict["markdown"]["raw_markdown"]
-
-                # Parse downloaded_files
-                try:
-                    row_dict["downloaded_files"] = (
-                        json.loads(row_dict["downloaded_files"])
-                        if row_dict["downloaded_files"]
-                        else []
+                if row:
+                    # Deserialize JSON fields
+                    return (
+                        row[0],  # url
+                        row[1],  # html
+                        row[2],  # cleaned_html
+                        row[3],  # markdown
+                        row[4],  # extracted_content
+                        row[5],  # success
+                        json.loads(row[6] or '{}'),  # media
+                        json.loads(row[7] or '{}'),  # links
+                        json.loads(row[8] or '{}'),  # metadata
+                        row[9],  # screenshot
+                        json.loads(row[10] or '{}')  # response_headers
                     )
-                except json.JSONDecodeError:
-                    row_dict["downloaded_files"] = []
-
-                # Remove any fields not in CrawlResult model
-                valid_fields = CrawlResult.__annotations__.keys()
-                filtered_dict = {k: v for k, v in row_dict.items() if k in valid_fields}
-                filtered_dict["markdown"] = row_dict["markdown"]
-                return CrawlResult(**filtered_dict)
+                return None
 
         try:
             return await self.execute_with_retry(_get)
         except Exception as e:
-            self.logger.error(
-                message="Error retrieving cached URL: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)},
-            )
+            logger.error(f"Error retrieving cached URL: {e}")
             return None
 
-    async def acache_url(self, result: CrawlResult):
-        """Cache CrawlResult data"""
-        # Store content files and get hashes
-        content_map = {
-            "html": (result.html, "html"),
-            "cleaned_html": (result.cleaned_html or "", "cleaned"),
-            "markdown": None,
-            "extracted_content": (result.extracted_content or "", "extracted"),
-            "screenshot": (result.screenshot or "", "screenshots"),
-        }
-
-        try:
-            if isinstance(result.markdown, StringCompatibleMarkdown):
-                content_map["markdown"] = (
-                    result.markdown,
-                    "markdown",
-                )
-            elif isinstance(result.markdown, MarkdownGenerationResult):
-                content_map["markdown"] = (
-                    result.markdown.model_dump_json(),
-                    "markdown",
-                )
-            elif isinstance(result.markdown, str):
-                markdown_result = MarkdownGenerationResult(raw_markdown=result.markdown)
-                content_map["markdown"] = (
-                    markdown_result.model_dump_json(),
-                    "markdown",
-                )
-            else:
-                content_map["markdown"] = (
-                    MarkdownGenerationResult().model_dump_json(),
-                    "markdown",
-                )
-        except Exception as e:
-            self.logger.warning(
-                message=f"Error processing markdown content: {str(e)}", tag="WARNING"
-            )
-            # Fallback to empty markdown result
-            content_map["markdown"] = (
-                MarkdownGenerationResult().model_dump_json(),
-                "markdown",
-            )
-
-        content_hashes = {}
-        for field, (content, content_type) in content_map.items():
-            content_hashes[field] = await self._store_content(content, content_type)
-
+    async def acache_url(
+        self,
+        url: str,
+        html: str,
+        cleaned_html: str,
+        markdown: str,
+        extracted_content: str,
+        success: bool,
+        media: str = "{}",
+        links: str = "{}",
+        metadata: str = "{}",
+        screenshot: str = "",
+        response_headers: str = "{}"  # New parameter added
+    ):
+        """Cache URL data with retry logic"""
         async def _cache(db):
-            await db.execute(
-                """
+            await db.execute('''
                 INSERT INTO crawled_data (
-                    url, html, cleaned_html, markdown,
-                    extracted_content, success, media, links, metadata,
-                    screenshot, response_headers, downloaded_files
+                    url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot, response_headers
                 )
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                 ON CONFLICT(url) DO UPDATE SET
                     html = excluded.html,
                     cleaned_html = excluded.cleaned_html,
                     markdown = excluded.markdown,
                     extracted_content = excluded.extracted_content,
                     success = excluded.success,
-                    media = excluded.media,
-                    links = excluded.links,
-                    metadata = excluded.metadata,
+                    media = excluded.media,      
+                    links = excluded.links,    
+                    metadata = excluded.metadata,      
                     screenshot = excluded.screenshot,
-                    response_headers = excluded.response_headers,
-                    downloaded_files = excluded.downloaded_files
-            """,
-                (
-                    result.url,
-                    content_hashes["html"],
-                    content_hashes["cleaned_html"],
-                    content_hashes["markdown"],
-                    content_hashes["extracted_content"],
-                    result.success,
-                    json.dumps(result.media),
-                    json.dumps(result.links),
-                    json.dumps(result.metadata or {}),
-                    content_hashes["screenshot"],
-                    json.dumps(result.response_headers or {}),
-                    json.dumps(result.downloaded_files or []),
-                ),
-            )
+                    response_headers = excluded.response_headers  -- Update response_headers
+            ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot, response_headers))
 
         try:
             await self.execute_with_retry(_cache)
         except Exception as e:
-            self.logger.error(
-                message="Error caching URL: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)},
-            )
+            logger.error(f"Error caching URL: {e}")
 
     async def aget_total_count(self) -> int:
         """Get total number of cached URLs"""
-
         async def _count(db):
-            async with db.execute("SELECT COUNT(*) FROM crawled_data") as cursor:
+            async with db.execute('SELECT COUNT(*) FROM crawled_data') as cursor:
                 result = await cursor.fetchone()
                 return result[0] if result else 0
 
         try:
             return await self.execute_with_retry(_count)
         except Exception as e:
-            self.logger.error(
-                message="Error getting total count: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)},
-            )
+            logger.error(f"Error getting total count: {e}")
             return 0
 
     async def aclear_db(self):
         """Clear all data from the database"""
-
         async def _clear(db):
-            await db.execute("DELETE FROM crawled_data")
+            await db.execute('DELETE FROM crawled_data')
 
         try:
             await self.execute_with_retry(_clear)
         except Exception as e:
-            self.logger.error(
-                message="Error clearing database: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)},
-            )
+            logger.error(f"Error clearing database: {e}")
 
     async def aflush_db(self):
         """Drop the entire table"""
-
         async def _flush(db):
-            await db.execute("DROP TABLE IF EXISTS crawled_data")
+            await db.execute('DROP TABLE IF EXISTS crawled_data')
 
         try:
             await self.execute_with_retry(_flush)
         except Exception as e:
-            self.logger.error(
-                message="Error flushing database: {error}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"error": str(e)},
-            )
-
-    async def _store_content(self, content: str, content_type: str) -> str:
-        """Store content in filesystem and return hash"""
-        if not content:
-            return ""
-
-        content_hash = generate_content_hash(content)
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-
-        # Only write if file doesn't exist
-        if not os.path.exists(file_path):
-            async with aiofiles.open(file_path, "w", encoding="utf-8") as f:
-                await f.write(content)
-
-        return content_hash
-
-    async def _load_content(
-        self, content_hash: str, content_type: str
-    ) -> Optional[str]:
-        """Load content from filesystem by hash"""
-        if not content_hash:
-            return None
-
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-        try:
-            async with aiofiles.open(file_path, "r", encoding="utf-8") as f:
-                return await f.read()
-        except:
-            self.logger.error(
-                message="Failed to load content: {file_path}",
-                tag="ERROR",
-                force_verbose=True,
-                params={"file_path": file_path},
-            )
-            return None
-
+            logger.error(f"Error flushing database: {e}")
 
 # Create a singleton instance
 async_db_manager = AsyncDatabaseManager()

crawl4ai/async_dispatcher.py

@@ -1,771 +0,0 @@
-from typing import Dict, Optional, List, Tuple, Union
-from .async_configs import CrawlerRunConfig
-from .models import (
-    CrawlResult,
-    CrawlerTaskResult,
-    CrawlStatus,
-    DomainState,
-)
-
-from .components.crawler_monitor import CrawlerMonitor
-
-from .types import AsyncWebCrawler
-
-from collections.abc import AsyncGenerator
-
-import time
-import psutil
-import asyncio
-import uuid
-
-from urllib.parse import urlparse
-import random
-from abc import ABC, abstractmethod
-
-from .utils import get_true_memory_usage_percent
-
-
-class RateLimiter:
-    def __init__(
-        self,
-        base_delay: Tuple[float, float] = (1.0, 3.0),
-        max_delay: float = 60.0,
-        max_retries: int = 3,
-        rate_limit_codes: List[int] = None,
-    ):
-        self.base_delay = base_delay
-        self.max_delay = max_delay
-        self.max_retries = max_retries
-        self.rate_limit_codes = rate_limit_codes or [429, 503]
-        self.domains: Dict[str, DomainState] = {}
-
-    def get_domain(self, url: str) -> str:
-        return urlparse(url).netloc
-
-    async def wait_if_needed(self, url: str) -> None:
-        domain = self.get_domain(url)
-        state = self.domains.get(domain)
-
-        if not state:
-            self.domains[domain] = DomainState()
-            state = self.domains[domain]
-
-        now = time.time()
-        if state.last_request_time:
-            wait_time = max(0, state.current_delay - (now - state.last_request_time))
-            if wait_time > 0:
-                await asyncio.sleep(wait_time)
-
-        # Random delay within base range if no current delay
-        if state.current_delay == 0:
-            state.current_delay = random.uniform(*self.base_delay)
-
-        state.last_request_time = time.time()
-
-    def update_delay(self, url: str, status_code: int) -> bool:
-        domain = self.get_domain(url)
-        state = self.domains[domain]
-
-        if status_code in self.rate_limit_codes:
-            state.fail_count += 1
-            if state.fail_count > self.max_retries:
-                return False
-
-            # Exponential backoff with random jitter
-            state.current_delay = min(
-                state.current_delay * 2 * random.uniform(0.75, 1.25), self.max_delay
-            )
-        else:
-            # Gradually reduce delay on success
-            state.current_delay = max(
-                random.uniform(*self.base_delay), state.current_delay * 0.75
-            )
-            state.fail_count = 0
-
-        return True
-
-
-
-class BaseDispatcher(ABC):
-    def __init__(
-        self,
-        rate_limiter: Optional[RateLimiter] = None,
-        monitor: Optional[CrawlerMonitor] = None,
-    ):
-        self.crawler = None
-        self._domain_last_hit: Dict[str, float] = {}
-        self.concurrent_sessions = 0
-        self.rate_limiter = rate_limiter
-        self.monitor = monitor
-
-    def select_config(self, url: str, configs: Union[CrawlerRunConfig, List[CrawlerRunConfig]]) -> Optional[CrawlerRunConfig]:
-        """Select the appropriate config for a given URL.
-        
-        Args:
-            url: The URL to match against
-            configs: Single config or list of configs to choose from
-            
-        Returns:
-            The matching config, or None if no match found
-        """
-        # Single config - return as is
-        if isinstance(configs, CrawlerRunConfig):
-            return configs
-        
-        # Empty list - return None
-        if not configs:
-            return None
-        
-        # Find first matching config
-        for config in configs:
-            if config.is_match(url):
-                return config
-        
-        # No match found - return None to indicate URL should be skipped
-        return None
-
-    @abstractmethod
-    async def crawl_url(
-        self,
-        url: str,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-        task_id: str,
-        monitor: Optional[CrawlerMonitor] = None,
-    ) -> CrawlerTaskResult:
-        pass
-
-    @abstractmethod
-    async def run_urls(
-        self,
-        urls: List[str],
-        crawler: AsyncWebCrawler,  # noqa: F821
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-        monitor: Optional[CrawlerMonitor] = None,
-    ) -> List[CrawlerTaskResult]:
-        pass
-
-
-class MemoryAdaptiveDispatcher(BaseDispatcher):
-    def __init__(
-        self,
-        memory_threshold_percent: float = 90.0,
-        critical_threshold_percent: float = 95.0,  # New critical threshold
-        recovery_threshold_percent: float = 85.0,  # New recovery threshold
-        check_interval: float = 1.0,
-        max_session_permit: int = 20,
-        fairness_timeout: float = 600.0,  # 10 minutes before prioritizing long-waiting URLs
-        memory_wait_timeout: Optional[float] = 600.0,
-        rate_limiter: Optional[RateLimiter] = None,
-        monitor: Optional[CrawlerMonitor] = None,
-    ):
-        super().__init__(rate_limiter, monitor)
-        self.memory_threshold_percent = memory_threshold_percent
-        self.critical_threshold_percent = critical_threshold_percent
-        self.recovery_threshold_percent = recovery_threshold_percent
-        self.check_interval = check_interval
-        self.max_session_permit = max_session_permit
-        self.fairness_timeout = fairness_timeout
-        self.memory_wait_timeout = memory_wait_timeout
-        self.result_queue = asyncio.Queue()
-        self.task_queue = asyncio.PriorityQueue()  # Priority queue for better management
-        self.memory_pressure_mode = False  # Flag to indicate when we're in memory pressure mode
-        self.current_memory_percent = 0.0  # Track current memory usage
-        self._high_memory_start_time: Optional[float] = None
-        
-    async def _memory_monitor_task(self):
-        """Background task to continuously monitor memory usage and update state"""
-        while True:
-            self.current_memory_percent = get_true_memory_usage_percent()
-
-            # Enter memory pressure mode if we cross the threshold
-            if self.current_memory_percent >= self.memory_threshold_percent:
-                if not self.memory_pressure_mode:
-                    self.memory_pressure_mode = True
-                    self._high_memory_start_time = time.time()
-                    if self.monitor:
-                        self.monitor.update_memory_status("PRESSURE")
-                else:
-                    if self._high_memory_start_time is None:
-                        self._high_memory_start_time = time.time()
-                    if (
-                        self.memory_wait_timeout is not None
-                        and self._high_memory_start_time is not None
-                        and time.time() - self._high_memory_start_time >= self.memory_wait_timeout
-                    ):
-                        raise MemoryError(
-                            "Memory usage exceeded threshold for"
-                            f" {self.memory_wait_timeout} seconds"
-                        )
-
-            # Exit memory pressure mode if we go below recovery threshold
-            elif self.memory_pressure_mode and self.current_memory_percent <= self.recovery_threshold_percent:
-                self.memory_pressure_mode = False
-                self._high_memory_start_time = None
-                if self.monitor:
-                    self.monitor.update_memory_status("NORMAL")
-            elif self.current_memory_percent < self.memory_threshold_percent:
-                self._high_memory_start_time = None
-            
-            # In critical mode, we might need to take more drastic action
-            if self.current_memory_percent >= self.critical_threshold_percent:
-                if self.monitor:
-                    self.monitor.update_memory_status("CRITICAL")
-                # We could implement additional memory-saving measures here
-                
-            await asyncio.sleep(self.check_interval)
-    
-    def _get_priority_score(self, wait_time: float, retry_count: int) -> float:
-        """Calculate priority score (lower is higher priority)
-        - URLs waiting longer than fairness_timeout get higher priority
-        - More retry attempts decreases priority
-        """
-        if wait_time > self.fairness_timeout:
-            # High priority for long-waiting URLs
-            return -wait_time
-        # Standard priority based on retries
-        return retry_count
-    
-    async def crawl_url(
-        self,
-        url: str,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-        task_id: str,
-        retry_count: int = 0,
-    ) -> CrawlerTaskResult:
-        start_time = time.time()
-        error_message = ""
-        memory_usage = peak_memory = 0.0
-        
-        # Select appropriate config for this URL
-        selected_config = self.select_config(url, config)
-        
-        # If no config matches, return failed result
-        if selected_config is None:
-            error_message = f"No matching configuration found for URL: {url}"
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id, 
-                    status=CrawlStatus.FAILED,
-                    error_message=error_message
-                )
-            
-            return CrawlerTaskResult(
-                task_id=task_id,
-                url=url,
-                result=CrawlResult(
-                    url=url, 
-                    html="", 
-                    metadata={"status": "no_config_match"}, 
-                    success=False, 
-                    error_message=error_message
-                ),
-                memory_usage=0,
-                peak_memory=0,
-                start_time=start_time,
-                end_time=time.time(),
-                error_message=error_message,
-                retry_count=retry_count
-            )
-        
-        # Get starting memory for accurate measurement
-        process = psutil.Process()
-        start_memory = process.memory_info().rss / (1024 * 1024)
-        
-        try:
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id, 
-                    status=CrawlStatus.IN_PROGRESS, 
-                    start_time=start_time,
-                    retry_count=retry_count
-                )
-                
-            self.concurrent_sessions += 1
-            
-            if self.rate_limiter:
-                await self.rate_limiter.wait_if_needed(url)
-                
-            # Check if we're in critical memory state
-            if self.current_memory_percent >= self.critical_threshold_percent:
-                # Requeue this task with increased priority and retry count
-                enqueue_time = time.time()
-                priority = self._get_priority_score(enqueue_time - start_time, retry_count + 1)
-                await self.task_queue.put((priority, (url, task_id, retry_count + 1, enqueue_time)))
-                
-                # Update monitoring
-                if self.monitor:
-                    self.monitor.update_task(
-                        task_id,
-                        status=CrawlStatus.QUEUED,
-                        error_message="Requeued due to critical memory pressure"
-                    )
-                
-                # Return placeholder result with requeued status
-                return CrawlerTaskResult(
-                    task_id=task_id,
-                    url=url,
-                    result=CrawlResult(
-                        url=url, html="", metadata={"status": "requeued"}, 
-                        success=False, error_message="Requeued due to critical memory pressure"
-                    ),
-                    memory_usage=0,
-                    peak_memory=0,
-                    start_time=start_time,
-                    end_time=time.time(),
-                    error_message="Requeued due to critical memory pressure",
-                    retry_count=retry_count + 1
-                )
-            
-            # Execute the crawl with selected config
-            result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
-            
-            # Measure memory usage
-            end_memory = process.memory_info().rss / (1024 * 1024)
-            memory_usage = peak_memory = end_memory - start_memory
-            
-            # Handle rate limiting
-            if self.rate_limiter and result.status_code:
-                if not self.rate_limiter.update_delay(url, result.status_code):
-                    error_message = f"Rate limit retry count exceeded for domain {urlparse(url).netloc}"
-                    if self.monitor:
-                        self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-                        
-            # Update status based on result
-            if not result.success:
-                error_message = result.error_message
-                if self.monitor:
-                    self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-            elif self.monitor:
-                self.monitor.update_task(task_id, status=CrawlStatus.COMPLETED)
-                
-        except Exception as e:
-            error_message = str(e)
-            if self.monitor:
-                self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-            result = CrawlResult(
-                url=url, html="", metadata={}, success=False, error_message=str(e)
-            )
-            
-        finally:
-            end_time = time.time()
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id,
-                    end_time=end_time,
-                    memory_usage=memory_usage,
-                    peak_memory=peak_memory,
-                    error_message=error_message,
-                    retry_count=retry_count
-                )
-            self.concurrent_sessions -= 1
-            
-        return CrawlerTaskResult(
-            task_id=task_id,
-            url=url,
-            result=result,
-            memory_usage=memory_usage,
-            peak_memory=peak_memory,
-            start_time=start_time,
-            end_time=end_time,
-            error_message=error_message,
-            retry_count=retry_count
-        )
-        
-    async def run_urls(
-        self,
-        urls: List[str],
-        crawler: AsyncWebCrawler,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-    ) -> List[CrawlerTaskResult]:
-        self.crawler = crawler
-        
-        # Start the memory monitor task
-        memory_monitor = asyncio.create_task(self._memory_monitor_task())
-        
-        if self.monitor:
-            self.monitor.start()
-            
-        results = []
-
-        try:
-            # Initialize task queue
-            for url in urls:
-                task_id = str(uuid.uuid4())
-                if self.monitor:
-                    self.monitor.add_task(task_id, url)
-                # Add to queue with initial priority 0, retry count 0, and current time
-                await self.task_queue.put((0, (url, task_id, 0, time.time())))
-
-            active_tasks = []
-
-            # Process until both queues are empty
-            while not self.task_queue.empty() or active_tasks:
-                if memory_monitor.done():
-                    exc = memory_monitor.exception()
-                    if exc:
-                        for t in active_tasks:
-                            t.cancel()
-                        raise exc
-
-                # If memory pressure is low, greedily fill all available slots
-                if not self.memory_pressure_mode:
-                    slots = self.max_session_permit - len(active_tasks)
-                    while slots > 0:
-                        try:
-                            # Use get_nowait() to immediately get tasks without blocking
-                            priority, (url, task_id, retry_count, enqueue_time) = self.task_queue.get_nowait()
-                            
-                            # Create and start the task
-                            task = asyncio.create_task(
-                                self.crawl_url(url, config, task_id, retry_count)
-                            )
-                            active_tasks.append(task)
-                            
-                            # Update waiting time in monitor
-                            if self.monitor:
-                                wait_time = time.time() - enqueue_time
-                                self.monitor.update_task(
-                                    task_id, 
-                                    wait_time=wait_time,
-                                    status=CrawlStatus.IN_PROGRESS
-                                )
-                            
-                            slots -= 1
-                            
-                        except asyncio.QueueEmpty:
-                            # No more tasks in queue, exit the loop
-                            break
-                        
-                # Wait for completion even if queue is starved
-                if active_tasks:
-                    done, pending = await asyncio.wait(
-                        active_tasks, timeout=0.1, return_when=asyncio.FIRST_COMPLETED
-                    )
-                    
-                    # Process completed tasks
-                    for completed_task in done:
-                        result = await completed_task
-                        results.append(result)
-                        
-                    # Update active tasks list
-                    active_tasks = list(pending)
-                else:
-                    # If no active tasks but still waiting, sleep briefly
-                    await asyncio.sleep(self.check_interval / 2)
-                    
-                # Update priorities for waiting tasks if needed
-                await self._update_queue_priorities()
-
-        except Exception as e:
-            if self.monitor:
-                self.monitor.update_memory_status(f"QUEUE_ERROR: {str(e)}")                
-        
-        finally:
-            # Clean up
-            memory_monitor.cancel()
-            if self.monitor:
-                self.monitor.stop()
-            return results
-                
-    async def _update_queue_priorities(self):
-        """Periodically update priorities of items in the queue to prevent starvation"""
-        # Skip if queue is empty
-        if self.task_queue.empty():
-            return
-            
-        # Use a drain-and-refill approach to update all priorities
-        temp_items = []
-        
-        # Drain the queue (with a safety timeout to prevent blocking)
-        try:
-            drain_start = time.time()
-            while not self.task_queue.empty() and time.time() - drain_start < 5.0:  # 5 second safety timeout
-                try:
-                    # Get item from queue with timeout
-                    priority, (url, task_id, retry_count, enqueue_time) = await asyncio.wait_for(
-                        self.task_queue.get(), timeout=0.1
-                    )
-                    
-                    # Calculate new priority based on current wait time
-                    current_time = time.time()
-                    wait_time = current_time - enqueue_time
-                    new_priority = self._get_priority_score(wait_time, retry_count)
-                    
-                    # Store with updated priority
-                    temp_items.append((new_priority, (url, task_id, retry_count, enqueue_time)))
-                    
-                    # Update monitoring stats for this task
-                    if self.monitor and task_id in self.monitor.stats:
-                        self.monitor.update_task(task_id, wait_time=wait_time)
-                        
-                except asyncio.TimeoutError:
-                    # Queue might be empty or very slow
-                    break
-        except Exception as e:
-            # If anything goes wrong, make sure we refill the queue with what we've got
-            self.monitor.update_memory_status(f"QUEUE_ERROR: {str(e)}")
-        
-        # Calculate queue statistics
-        if temp_items and self.monitor:
-            total_queued = len(temp_items)
-            wait_times = [item[1][3] for item in temp_items]
-            highest_wait_time = time.time() - min(wait_times) if wait_times else 0
-            avg_wait_time = sum(time.time() - t for t in wait_times) / len(wait_times) if wait_times else 0
-            
-            # Update queue statistics in monitor
-            self.monitor.update_queue_statistics(
-                total_queued=total_queued,
-                highest_wait_time=highest_wait_time,
-                avg_wait_time=avg_wait_time
-            )
-        
-        # Sort by priority (lowest number = highest priority)
-        temp_items.sort(key=lambda x: x[0])
-        
-        # Refill the queue with updated priorities
-        for item in temp_items:
-            await self.task_queue.put(item)
-                
-    async def run_urls_stream(
-        self,
-        urls: List[str],
-        crawler: AsyncWebCrawler,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-    ) -> AsyncGenerator[CrawlerTaskResult, None]:
-        self.crawler = crawler
-        
-        # Start the memory monitor task
-        memory_monitor = asyncio.create_task(self._memory_monitor_task())
-        
-        if self.monitor:
-            self.monitor.start()
-            
-        try:
-            # Initialize task queue
-            for url in urls:
-                task_id = str(uuid.uuid4())
-                if self.monitor:
-                    self.monitor.add_task(task_id, url)
-                # Add to queue with initial priority 0, retry count 0, and current time
-                await self.task_queue.put((0, (url, task_id, 0, time.time())))
-                
-            active_tasks = []
-            completed_count = 0
-            total_urls = len(urls)
-
-            while completed_count < total_urls:
-                if memory_monitor.done():
-                    exc = memory_monitor.exception()
-                    if exc:
-                        for t in active_tasks:
-                            t.cancel()
-                        raise exc
-                # If memory pressure is low, greedily fill all available slots
-                if not self.memory_pressure_mode:
-                    slots = self.max_session_permit - len(active_tasks)
-                    while slots > 0:
-                        try:
-                            # Use get_nowait() to immediately get tasks without blocking
-                            priority, (url, task_id, retry_count, enqueue_time) = self.task_queue.get_nowait()
-                            
-                            # Create and start the task
-                            task = asyncio.create_task(
-                                self.crawl_url(url, config, task_id, retry_count)
-                            )
-                            active_tasks.append(task)
-                            
-                            # Update waiting time in monitor
-                            if self.monitor:
-                                wait_time = time.time() - enqueue_time
-                                self.monitor.update_task(
-                                    task_id, 
-                                    wait_time=wait_time,
-                                    status=CrawlStatus.IN_PROGRESS
-                                )
-                            
-                            slots -= 1
-                            
-                        except asyncio.QueueEmpty:
-                            # No more tasks in queue, exit the loop
-                            break
-                        
-                # Process completed tasks and yield results
-                if active_tasks:
-                    done, pending = await asyncio.wait(
-                        active_tasks, timeout=0.1, return_when=asyncio.FIRST_COMPLETED
-                    )
-                    
-                    for completed_task in done:
-                        result = await completed_task
-                        
-                        # Only count as completed if it wasn't requeued
-                        if "requeued" not in result.error_message:
-                            completed_count += 1
-                            yield result
-                        
-                    # Update active tasks list
-                    active_tasks = list(pending)
-                else:
-                    # If no active tasks but still waiting, sleep briefly
-                    await asyncio.sleep(self.check_interval / 2)
-                
-                # Update priorities for waiting tasks if needed
-                await self._update_queue_priorities()
-                
-        finally:
-            # Clean up
-            memory_monitor.cancel()
-            if self.monitor:
-                self.monitor.stop()
-                
-
-class SemaphoreDispatcher(BaseDispatcher):
-    def __init__(
-        self,
-        semaphore_count: int = 5,
-        max_session_permit: int = 20,
-        rate_limiter: Optional[RateLimiter] = None,
-        monitor: Optional[CrawlerMonitor] = None,
-    ):
-        super().__init__(rate_limiter, monitor)
-        self.semaphore_count = semaphore_count
-        self.max_session_permit = max_session_permit
-
-    async def crawl_url(
-        self,
-        url: str,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-        task_id: str,
-        semaphore: asyncio.Semaphore = None,
-    ) -> CrawlerTaskResult:
-        start_time = time.time()
-        error_message = ""
-        memory_usage = peak_memory = 0.0
-
-        # Select appropriate config for this URL
-        selected_config = self.select_config(url, config)
-        
-        # If no config matches, return failed result
-        if selected_config is None:
-            error_message = f"No matching configuration found for URL: {url}"
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id, 
-                    status=CrawlStatus.FAILED,
-                    error_message=error_message
-                )
-            
-            return CrawlerTaskResult(
-                task_id=task_id,
-                url=url,
-                result=CrawlResult(
-                    url=url, 
-                    html="", 
-                    metadata={"status": "no_config_match"}, 
-                    success=False, 
-                    error_message=error_message
-                ),
-                memory_usage=0,
-                peak_memory=0,
-                start_time=start_time,
-                end_time=time.time(),
-                error_message=error_message
-            )
-
-        try:
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id, status=CrawlStatus.IN_PROGRESS, start_time=start_time
-                )
-
-            if self.rate_limiter:
-                await self.rate_limiter.wait_if_needed(url)
-
-            async with semaphore:
-                process = psutil.Process()
-                start_memory = process.memory_info().rss / (1024 * 1024)
-                result = await self.crawler.arun(url, config=selected_config, session_id=task_id)
-                end_memory = process.memory_info().rss / (1024 * 1024)
-
-                memory_usage = peak_memory = end_memory - start_memory
-
-                if self.rate_limiter and result.status_code:
-                    if not self.rate_limiter.update_delay(url, result.status_code):
-                        error_message = f"Rate limit retry count exceeded for domain {urlparse(url).netloc}"
-                        if self.monitor:
-                            self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-                        return CrawlerTaskResult(
-                            task_id=task_id,
-                            url=url,
-                            result=result,
-                            memory_usage=memory_usage,
-                            peak_memory=peak_memory,
-                            start_time=start_time,
-                            end_time=time.time(),
-                            error_message=error_message,
-                        )
-
-                if not result.success:
-                    error_message = result.error_message
-                    if self.monitor:
-                        self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-                elif self.monitor:
-                    self.monitor.update_task(task_id, status=CrawlStatus.COMPLETED)
-
-        except Exception as e:
-            error_message = str(e)
-            if self.monitor:
-                self.monitor.update_task(task_id, status=CrawlStatus.FAILED)
-            result = CrawlResult(
-                url=url, html="", metadata={}, success=False, error_message=str(e)
-            )
-
-        finally:
-            end_time = time.time()
-            if self.monitor:
-                self.monitor.update_task(
-                    task_id,
-                    end_time=end_time,
-                    memory_usage=memory_usage,
-                    peak_memory=peak_memory,
-                    error_message=error_message,
-                )
-
-        return CrawlerTaskResult(
-            task_id=task_id,
-            url=url,
-            result=result,
-            memory_usage=memory_usage,
-            peak_memory=peak_memory,
-            start_time=start_time,
-            end_time=end_time,
-            error_message=error_message,
-        )
-
-    async def run_urls(
-        self,
-        crawler: AsyncWebCrawler,  # noqa: F821
-        urls: List[str],
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
-    ) -> List[CrawlerTaskResult]:
-        self.crawler = crawler
-        if self.monitor:
-            self.monitor.start()
-
-        try:
-            semaphore = asyncio.Semaphore(self.semaphore_count)
-            tasks = []
-
-            for url in urls:
-                task_id = str(uuid.uuid4())
-                if self.monitor:
-                    self.monitor.add_task(task_id, url)
-                task = asyncio.create_task(
-                    self.crawl_url(url, config, task_id, semaphore)
-                )
-                tasks.append(task)
-
-            return await asyncio.gather(*tasks, return_exceptions=True)
-        finally:
-            if self.monitor:
-                self.monitor.stop()

crawl4ai/async_executor.py

@@ -0,0 +1,741 @@
+from __future__ import annotations
+import asyncio
+import psutil
+import logging
+import time
+import sqlite3
+import aiosqlite
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Dict, List, Optional, Callable, Any, Set, Type
+from typing import Awaitable
+from pathlib import Path
+import json
+from datetime import datetime
+from typing import ClassVar, Type, Union
+import inspect
+
+# Imports from your crawler package
+from .async_crawler_strategy import AsyncCrawlerStrategy, AsyncPlaywrightCrawlerStrategy
+from .chunking_strategy import ChunkingStrategy, RegexChunking
+from .extraction_strategy import ExtractionStrategy
+from .models import CrawlResult
+from .config import MIN_WORD_THRESHOLD
+from .async_webcrawler import AsyncWebCrawler
+from .config import MAX_METRICS_HISTORY
+
+import logging
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s [%(levelname)s] %(name)s - %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+# self.logger.error(f"Executor {self.__class__.__name__}: Error message", exc_info=True)
+# self.logger.info(f"Executor {self.__class__.__name__}: Info message")
+# self.logger.warning(f"Executor {self.__class__.__name__}: Warning message")
+
+
+# Enums and Constants
+class ExecutionMode(Enum):
+    """Execution mode for the crawler executor."""
+    SPEED = "speed"
+    RESOURCE = "resource"
+
+class TaskState(Enum):
+    """Possible states for a crawling task."""
+    PENDING = "pending"
+    SCHEDULED = "scheduled"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    RETRYING = "retrying"
+
+# Types of callbacks we should support
+class CallbackType(Enum):
+    PRE_EXECUTION = "pre_execution"      # Before processing a URL
+    POST_EXECUTION = "post_execution"    # After successful processing
+    ON_ERROR = "on_error"               # When an error occurs
+    ON_RETRY = "on_retry"               # Before retrying a failed URL
+    ON_BATCH_START = "on_batch_start"   # Before starting a batch
+    ON_BATCH_END = "on_batch_end"       # After completing a batch
+    ON_COMPLETE = "on_complete"         # After all URLs are processed
+
+ 
+@dataclass
+class SystemMetrics:
+    """System resource metrics."""
+    cpu_percent: float
+    memory_percent: float
+    available_memory: int
+    timestamp: float
+
+    @classmethod
+    def capture(cls) -> 'SystemMetrics':
+        """Capture current system metrics."""
+        return cls(
+            cpu_percent=psutil.cpu_percent(),
+            memory_percent=psutil.virtual_memory().percent,
+            available_memory=psutil.virtual_memory().available,
+            timestamp=time.time()
+        )
+
+@dataclass
+class TaskMetadata:
+    """Metadata for a crawling task."""
+    url: str
+    state: TaskState
+    attempts: int = 0
+    last_attempt: Optional[float] = None
+    error: Optional[str] = None
+    result: Optional[Any] = None
+
+@dataclass
+class ExecutorMetrics:
+    """Performance and resource metrics for the executor."""
+    # Performance metrics
+    total_urls: int = 0
+    completed_urls: int = 0
+    failed_urls: int = 0
+    start_time: Optional[float] = None
+    total_retries: int = 0
+    response_times: List[float] = field(default_factory=list)
+    
+    # Resource metrics
+    system_metrics: List[SystemMetrics] = field(default_factory=list)
+    active_connections: int = 0
+    
+    def capture_system_metrics(self):
+        """Capture system metrics and enforce history size limit."""
+        metrics = SystemMetrics.capture()
+        self.system_metrics.append(metrics)
+        if len(self.system_metrics) > MAX_METRICS_HISTORY:
+            self.system_metrics.pop(0)  # Remove the oldest metric
+    
+    @property
+    def urls_per_second(self) -> float:
+        """Calculate URLs processed per second."""
+        if not self.start_time or not self.completed_urls:
+            return 0.0
+        duration = time.time() - self.start_time
+        return self.completed_urls / duration if duration > 0 else 0
+    
+    @property
+    def success_rate(self) -> float:
+        """Calculate success rate as percentage."""
+        if not self.total_urls:
+            return 0.0
+        return (self.completed_urls / self.total_urls) * 100
+    
+    @property
+    def retry_rate(self) -> float:
+        """Calculate retry rate as percentage."""
+        if not self.total_urls:
+            return 0.0
+        return (self.total_retries / self.total_urls) * 100
+    
+    @property
+    def average_response_time(self) -> float:
+        """Calculate average response time in seconds."""
+        if not self.response_times:
+            return 0.0
+        return sum(self.response_times) / len(self.response_times)
+    
+    def to_dict(self) -> Dict[str, Any]:
+        """Convert metrics to dictionary format."""
+        return {
+            "performance": {
+                "urls_per_second": self.urls_per_second,
+                "success_rate": self.success_rate,
+                "retry_rate": self.retry_rate,
+                "average_response_time": self.average_response_time,
+                "total_urls": self.total_urls,
+                "completed_urls": self.completed_urls,
+                "failed_urls": self.failed_urls
+            },
+            "resources": {
+                "cpu_utilization": self.system_metrics[-1].cpu_percent if self.system_metrics else 0,
+                "memory_usage": self.system_metrics[-1].memory_percent if self.system_metrics else 0,
+                "active_connections": self.active_connections
+            }
+        }
+
+class ResourceMonitor:
+    """Monitors and manages system resources."""
+
+    def __init__(self, mode: ExecutionMode):
+        self.mode = mode
+        self.metrics_history: List[SystemMetrics] = []
+        self._setup_thresholds()
+
+    def _setup_thresholds(self):
+        """Set up resource thresholds based on execution mode."""
+        if self.mode == ExecutionMode.SPEED:
+            self.memory_threshold = 80  # 80% memory usage limit
+            self.cpu_threshold = 90     # 90% CPU usage limit
+        else:
+            self.memory_threshold = 40  # 40% memory usage limit
+            self.cpu_threshold = 30     # 30% CPU usage limit
+
+    async def check_resources(self) -> bool:
+        """Check if system resources are within acceptable limits."""
+        metrics = SystemMetrics.capture()
+        self.metrics_history.append(metrics)
+
+        # Keep only last hour of metrics
+        cutoff_time = time.time() - 3600
+        self.metrics_history = [m for m in self.metrics_history if m.timestamp > cutoff_time]
+
+        return (metrics.cpu_percent < self.cpu_threshold and
+                metrics.memory_percent < self.memory_threshold)
+
+    def get_optimal_batch_size(self, total_urls: int) -> int:
+        metrics = SystemMetrics.capture()
+        if self.mode == ExecutionMode.SPEED:
+            base_size = min(1000, total_urls)
+
+            # Adjust based on resource usage
+            cpu_factor = max(0.0, (self.cpu_threshold - metrics.cpu_percent) / self.cpu_threshold)
+            mem_factor = max(0.0, (self.memory_threshold - metrics.memory_percent) / self.memory_threshold)
+
+            min_factor = min(cpu_factor, mem_factor)
+            adjusted_size = max(1, int(base_size * min_factor))
+            return min(total_urls, adjusted_size)
+        else:
+            # For resource optimization, use a conservative batch size based on resource usage
+            cpu_factor = max(0.1, (self.cpu_threshold - metrics.cpu_percent) / self.cpu_threshold)
+            mem_factor = max(0.1, (self.memory_threshold - metrics.memory_percent) / self.memory_threshold)
+
+            min_factor = min(cpu_factor, mem_factor)
+            adjusted_size = max(1, int(50 * min_factor))
+            return min(total_urls, adjusted_size)
+
+class ExecutorControl:
+    """Control interface for the executor."""
+    
+    def __init__(self):
+        self._paused = False
+        self._cancelled = False
+        self._pause_event = asyncio.Event()
+        self._pause_event.set()  # Not paused initially
+        self._lock = asyncio.Lock()  # Lock to protect shared state
+        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
+        
+    async def pause(self):
+        """Pause the execution."""
+        async with self._lock:
+            self._paused = True
+            self._pause_event.clear()
+            
+    async def resume(self):
+        """Resume the execution."""
+        async with self._lock:
+            self._paused = False
+            self._pause_event.set()
+            
+    async def cancel(self):
+        """Cancel all pending operations."""
+        async with self._lock:
+            self._cancelled = True
+            self._pause_event.set()  # Release any paused operations
+            
+    async def is_paused(self) -> bool:
+        """Check if execution is paused."""
+        async with self._lock:
+            return self._paused
+        
+    async def is_cancelled(self) -> bool:
+        """Check if execution is cancelled."""
+        async with self._lock:
+            return self._cancelled
+        
+    async def wait_if_paused(self, timeout: Optional[float] = None):
+        """Wait if execution is paused, with an optional timeout."""
+        try:
+            await asyncio.wait_for(self._pause_event.wait(), timeout=timeout)
+        except asyncio.TimeoutError:
+            # Timeout occurred, handle as needed
+            async with self._lock:
+                self._paused = False  # Optionally reset the paused state
+                self._pause_event.set()
+            # Optionally log a warning
+            self.logger.warning(f"ExecutorControl: wait_if_paused() timed out after {timeout} seconds. Proceeding with execution.")
+                
+    async def reset(self):
+        """Reset control state."""
+        async with self._lock:
+            self._paused = False
+            self._cancelled = False
+            self._pause_event.set()
+
+
+class ExecutorStrategy(ABC):
+    """Abstract Base class for executor strategies.
+    
+    Callbacks:
+        - PRE_EXECUTION: Callable[[str, Dict[str, Any]], None]
+        - POST_EXECUTION: Callable[[str, Any, Dict[str, Any]], None]
+        - ON_ERROR: Callable[[str, Exception, Dict[str, Any]], None]
+        - ON_RETRY: Callable[[str, int, Dict[str, Any]], None]
+        - ON_BATCH_START: Callable[[List[str], Dict[str, Any]], None]
+        - ON_BATCH_END: Callable[[List[str], Dict[str, Any]], None]
+        - ON_COMPLETE: Callable[[Dict[str, Any], Dict[str, Any]], None]
+    """
+
+    def __init__(
+        self,
+        crawler: AsyncWebCrawler,
+        mode: ExecutionMode,
+        # callbacks: Optional[Dict[CallbackType, Callable]] = None,
+        callbacks: Optional[Dict[CallbackType, Callable[[Any], Union[Awaitable[None], None]]]] = None,
+        persistence_path: Optional[Path] = None,
+        **crawl_config_kwargs
+    ):
+        self.crawler = crawler
+        self.mode = mode
+        self.callbacks = callbacks or {}
+        self.resource_monitor = ResourceMonitor(mode)
+        self.tasks: Dict[str, TaskMetadata] = {}
+        self.active_tasks: Set[str] = set()
+        self.logger = logging.getLogger(f"{__name__}.{self.__class__.__name__}")
+        self.metrics = ExecutorMetrics()
+        self.control = ExecutorControl()
+        self.crawl_config_kwargs = crawl_config_kwargs  # Store parameters for arun
+
+    async def get_status(self) -> Dict[str, Any]:
+        """Get current executor status and metrics."""
+        return {
+            "status": {
+                "paused": await self.control.is_paused(),
+                "cancelled": await self.control.is_cancelled(),
+                "active_tasks": len(self.active_tasks)
+            },
+            "metrics": self.metrics.to_dict()
+        }
+
+    async def clear_state(self):
+        """Reset executor state."""
+        self.tasks.clear()
+        self.active_tasks.clear()
+        self.metrics = ExecutorMetrics()
+        await self.control.reset()
+        await self.persistence.clear()  # Implement this method
+
+    async def _execute_callback(
+        self,
+        callback_type: CallbackType,
+        *args,
+        **kwargs
+    ):
+        """Execute callback if it exists."""
+        if callback := self.callbacks.get(callback_type):
+            try:
+                if inspect.iscoroutinefunction(callback):
+                    await callback(*args, **kwargs)
+                else:
+                    callback(*args, **kwargs)
+            except Exception as e:
+                # self.logger.error(f"Callback {callback_type} failed: {e}")
+                self.logger.error(f"Executor {self.__class__.__name__}: Callback {callback_type.value} failed: {e}", exc_info=True)
+
+    async def _process_url(self, url: str) -> CrawlResult:
+        max_retries = self.crawl_config_kwargs.get('max_retries', 3)
+        backoff_factor = self.crawl_config_kwargs.get('backoff_factor', 1)
+        attempts = 0
+
+        while attempts <= max_retries:
+            # Invoke PRE_EXECUTION callback
+            await self._execute_callback(CallbackType.PRE_EXECUTION, url, self.metrics.to_dict())
+
+            """Process a single URL using the crawler."""
+            # Wait if execution is paused
+            await self.control.wait_if_paused(timeout=300)
+
+            # Check if cancelled
+            if await self.control.is_cancelled():
+                raise asyncio.CancelledError("Execution was cancelled")
+
+            start_time = time.time()
+            self.metrics.active_connections += 1
+
+            try:
+                result = await self.crawler.arun(url, **self.crawl_config_kwargs)
+                self.metrics.completed_urls += 1
+                self.metrics.response_times.append(time.time() - start_time)
+                # Invoke POST_EXECUTION callback
+                await self._execute_callback(CallbackType.POST_EXECUTION, url, result, self.metrics.to_dict())
+                
+                return result
+
+            except Exception as e:
+                attempts += 1
+                self.metrics.failed_urls += 1
+                # self.logger.error(f"Error processing URL {url}: {e}")
+                self.logger.error(f"Executor {self.__class__.__name__}: Error processing URL {url}: {e}", exc_info=True)
+                # Invoke ON_ERROR callback
+                await self._execute_callback(CallbackType.ON_ERROR, url, e, self.metrics.to_dict())
+
+                if attempts <= max_retries:
+                    # Invoke ON_RETRY callback
+                    await self._execute_callback(CallbackType.ON_RETRY, url, attempts, self.metrics.to_dict())
+                    # Wait before retrying
+                    await asyncio.sleep(backoff_factor * attempts)
+                else:
+                    raise e
+
+            finally:
+                self.metrics.active_connections -= 1
+                # Update system metrics
+                # INFO: Uncomment this line if you want to capture system metrics after each URL, but it causes a performance hit
+                # self.metrics.system_metrics.append(SystemMetrics.capture())
+                # Exit the loop if successful or retries exceeded
+                if attempts > max_retries:
+                    break                
+
+    async def execute(self, urls: List[str]) -> Dict[str, Any]:
+        """Execute crawling tasks."""
+        # Initialize metrics
+        self.metrics.total_urls = len(urls)
+        self.metrics.start_time = time.time()
+
+        # Create context with metrics (used for callbacks)
+        context = {
+            "mode": self.mode,
+            "start_time": self.metrics.start_time,
+            "total_urls": self.metrics.total_urls
+        }
+
+        # Invoke ON_BATCH_START callback
+        await self._execute_callback(CallbackType.ON_BATCH_START, urls, context)
+
+        results = {}
+        batch_errors = []
+
+        # Use the crawler within an async context manager
+        async with self.crawler:
+            # Check for cancellation before starting
+            if await self.control.is_cancelled():
+                raise asyncio.CancelledError("Execution was cancelled")
+
+            # Wait if paused
+            await self.control.wait_if_paused(timeout=300)
+
+            # Prepare list of batches
+            batches = []
+            total_urls_remaining = len(urls)
+            index = 0
+
+            while index < len(urls):
+                batch_size = self.resource_monitor.get_optimal_batch_size(total_urls_remaining)
+                batch_urls = urls[index:index + batch_size]
+                batches.append(batch_urls)
+                index += batch_size
+                total_urls_remaining -= batch_size
+
+            # Process each batch
+            for batch_urls in batches:
+                # Check for cancellation
+                if await self.control.is_cancelled():
+                    raise asyncio.CancelledError("Execution was cancelled")
+
+                # Wait if paused
+                await self.control.wait_if_paused(timeout=300)
+
+                try:
+                    # Process the batch
+                    batch_results = await self.process_batch(batch_urls)
+                    # Update results
+                    results.update(batch_results)
+                    # Capture system metrics after each batch
+                    self.metrics.capture_system_metrics()
+                    # Update system metrics after each batch
+                    # self.metrics.system_metrics.append(SystemMetrics.capture()) # Has memory leak issue
+                    # Invoke ON_BATCH_END callback
+                    await self._execute_callback(CallbackType.ON_BATCH_END, batch_urls, context)
+                except Exception as e:
+                    # Handle batch-level exceptions
+                    self.logger.error(f"Error processing batch: {e}")
+                    await self._execute_callback(CallbackType.ON_ERROR, "batch", e, context)
+                    # Collect the error
+                    batch_errors.append((batch_urls, e))
+                    # Continue to next batch instead of raising
+                    continue
+
+        # Execution complete
+        await self._execute_callback(CallbackType.ON_COMPLETE, results, context)
+
+        # Log final metrics and batch errors if any
+        final_status = await self.get_status()
+        # self.logger.info(f"Execution completed. Metrics: {final_status}")
+        self.logger.info(f"Executor {self.__class__.__name__}: Execution completed. Metrics: {final_status}")
+
+        if batch_errors:
+            # self.logger.warning(f"Execution completed with errors in {len(batch_errors)} batches.")
+            self.logger.warning(f"Executor {self.__class__.__name__}: Execution completed with errors in {len(batch_errors)} batches.")
+
+        return results
+
+
+    @abstractmethod
+    async def process_batch(self, batch_urls: List[str]) -> Dict[str, Any]:
+        """Process a batch of URLs."""
+        pass
+
+class SpeedOptimizedExecutor(ExecutorStrategy):
+    """Executor optimized for speed."""
+
+    def __init__(
+        self,
+        crawler: AsyncWebCrawler,
+        callbacks: Optional[Dict[CallbackType, Callable]] = None,
+        persistence_path: Optional[Path] = None,
+        word_count_threshold=MIN_WORD_THRESHOLD,
+        extraction_strategy: ExtractionStrategy = None,
+        chunking_strategy: ChunkingStrategy = None,
+        bypass_cache: bool = False,
+        css_selector: str = None,
+        screenshot: bool = False,
+        user_agent: str = None,
+        verbose=True,
+        connection_pool_size: int = 1000,
+        dns_cache_size: int = 10000,
+        backoff_factor: int = 1,
+        **kwargs
+    ):
+        if chunking_strategy is None:
+            chunking_strategy = RegexChunking()
+
+        super().__init__(
+            crawler=crawler,
+            mode=ExecutionMode.SPEED,
+            callbacks=callbacks,
+            persistence_path=persistence_path,
+            word_count_threshold=word_count_threshold,
+            extraction_strategy=extraction_strategy,
+            chunking_strategy=chunking_strategy,
+            bypass_cache=bypass_cache,
+            css_selector=css_selector,
+            screenshot=screenshot,
+            user_agent=user_agent,
+            verbose=verbose,
+            **kwargs
+        )
+
+        self.connection_pool_size = connection_pool_size
+        self.dns_cache_size = dns_cache_size
+        self.backoff_factor = backoff_factor
+
+        self.logger.info(
+            # "Initialized speed-optimized executor with:"
+            f"Executor {self.__class__.__name__}: Initialized with:"
+            f" connection_pool_size={self.connection_pool_size},"
+            f" dns_cache_size={self.dns_cache_size}"
+        )
+
+    async def process_batch(self, batch_urls: List[str]) -> Dict[str, Any]:
+        """Process a batch of URLs concurrently."""
+        batch_tasks = [self._process_url(url) for url in batch_urls]
+
+        # Execute batch with concurrency control
+        batch_results_list = await asyncio.gather(*batch_tasks, return_exceptions=True)
+
+        batch_results = {}
+        for url, result in zip(batch_urls, batch_results_list):
+            if isinstance(result, Exception):
+                batch_results[url] = {"success": False, "error": str(result)}
+            else:
+                batch_results[url] = {"success": True, "result": result}
+
+        return batch_results
+
+class ResourceOptimizedExecutor(ExecutorStrategy):
+    """Executor optimized for resource usage."""
+
+    def __init__(
+        self,
+        crawler: AsyncWebCrawler,
+        callbacks: Optional[Dict[CallbackType, Callable]] = None,
+        persistence_path: Optional[Path] = None,
+        word_count_threshold=MIN_WORD_THRESHOLD,
+        extraction_strategy: ExtractionStrategy = None,
+        chunking_strategy: ChunkingStrategy = None,
+        bypass_cache: bool = False,
+        css_selector: str = None,
+        screenshot: bool = False,
+        user_agent: str = None,
+        verbose=True,
+        connection_pool_size: int = 50,
+        dns_cache_size: int = 1000,
+        backoff_factor: int = 5,
+        max_concurrent_tasks: int = 5,
+        **kwargs
+    ):
+        if chunking_strategy is None:
+            chunking_strategy = RegexChunking()
+
+        super().__init__(
+            crawler=crawler,
+            mode=ExecutionMode.RESOURCE,
+            callbacks=callbacks,
+            persistence_path=persistence_path,
+            word_count_threshold=word_count_threshold,
+            extraction_strategy=extraction_strategy,
+            chunking_strategy=chunking_strategy,
+            bypass_cache=bypass_cache,
+            css_selector=css_selector,
+            screenshot=screenshot,
+            user_agent=user_agent,
+            verbose=verbose,
+            **kwargs
+        )
+
+        self.connection_pool_size = connection_pool_size
+        self.dns_cache_size = dns_cache_size
+        self.backoff_factor = backoff_factor
+        self.max_concurrent_tasks = max_concurrent_tasks
+
+        self.logger.info(
+            # "Initialized resource-optimized executor with:"
+            f"Executor {self.__class__.__name__}: Initialized with:"
+            f" connection_pool_size={self.connection_pool_size},"
+            f" dns_cache_size={self.dns_cache_size},"
+            f" max_concurrent_tasks={self.max_concurrent_tasks}"
+        )
+
+    async def process_batch(self, batch_urls: List[str]) -> Dict[str, Any]:
+        """Process a batch of URLs with resource optimization."""
+        batch_results = {}
+        semaphore = asyncio.Semaphore(self.max_concurrent_tasks)
+
+        # Wait until resources are available before processing batch
+        while not await self.resource_monitor.check_resources():
+            # self.logger.warning("Resource limits reached, waiting...")
+            self.logger.warning(f"Executor {self.__class__.__name__}: Resource limits reached, waiting...")
+            await asyncio.sleep(self.backoff_factor)
+            # Check for cancellation
+            if await self.control.is_cancelled():
+                raise asyncio.CancelledError("Execution was cancelled")
+
+        async def process_url_with_semaphore(url):
+            async with semaphore:
+                # Check for cancellation
+                if await self.control.is_cancelled():
+                    raise asyncio.CancelledError("Execution was cancelled")
+                # Wait if paused
+                await self.control.wait_if_paused(timeout=300)
+
+                try:
+                    result = await self._process_url(url)
+                    batch_results[url] = {"success": True, "result": result}
+                except Exception as e:
+                    batch_results[url] = {"success": False, "error": str(e)}
+                finally:
+                    # Update system metrics after each URL
+                    # INFO: Uncomment this line if you want to capture system metrics after each URL, but it causes a performance hit
+                    # self.metrics.system_metrics.append(SystemMetrics.capture())
+                    # Controlled delay between URLs
+                    await asyncio.sleep(0.1)  # Small delay for resource management
+
+        tasks = [process_url_with_semaphore(url) for url in batch_urls]
+        await asyncio.gather(*tasks)
+
+        return batch_results
+    
+
+
+
+
+async def main():
+    # Sample callback functions
+    async def pre_execution_callback(url: str, context: Dict[str, Any]):
+        print(f"Pre-execution callback: About to process URL {url}")
+
+    async def post_execution_callback(url: str, result: Any, context: Dict[str, Any]):
+        print(f"Post-execution callback: Successfully processed URL {url}")
+
+    async def on_error_callback(url: str, error: Exception, context: Dict[str, Any]):
+        print(f"Error callback: Error processing URL {url}: {error}")
+
+    async def on_retry_callback(url: str, attempt: int, context: Dict[str, Any]):
+        print(f"Retry callback: Retrying URL {url}, attempt {attempt}")
+
+    async def on_batch_start_callback(urls: List[str], context: Dict[str, Any]):
+        print(f"Batch start callback: Starting batch with {len(urls)} URLs")
+
+    async def on_batch_end_callback(urls: List[str], context: Dict[str, Any]):
+        print(f"Batch end callback: Completed batch with {len(urls)} URLs")
+
+    async def on_complete_callback(results: Dict[str, Any], context: Dict[str, Any]):
+        print(f"Complete callback: Execution completed with {len(results)} results")
+
+    # Sample URLs to crawl
+    urls = [
+        "https://www.example.com",
+        "https://www.python.org",
+        "https://www.asyncio.org",
+        # Add more URLs as needed
+    ]
+
+    # Instantiate the crawler
+    crawler = AsyncWebCrawler()
+
+    # Set up callbacks
+    callbacks = {
+        CallbackType.PRE_EXECUTION: pre_execution_callback,
+        CallbackType.POST_EXECUTION: post_execution_callback,
+        CallbackType.ON_ERROR: on_error_callback,
+        CallbackType.ON_RETRY: on_retry_callback,
+        CallbackType.ON_BATCH_START: on_batch_start_callback,
+        CallbackType.ON_BATCH_END: on_batch_end_callback,
+        CallbackType.ON_COMPLETE: on_complete_callback,
+    }
+
+    # Instantiate the executors
+    speed_executor = SpeedOptimizedExecutor(
+        crawler=crawler,
+        callbacks=callbacks,
+        max_retries=2,  # Example additional config
+    )
+
+    resource_executor = ResourceOptimizedExecutor(
+        crawler=crawler,
+        callbacks=callbacks,
+        max_concurrent_tasks=3,  # Limit concurrency
+        max_retries=2,           # Example additional config
+    )
+
+    # Choose which executor to use
+    executor = speed_executor  # Or resource_executor
+
+    # Start the execution in a background task
+    execution_task = asyncio.create_task(executor.execute(urls))
+
+    # Simulate control operations
+    await asyncio.sleep(2)  # Let it run for a bit
+    print("Pausing execution...")
+    await executor.control.pause()
+    await asyncio.sleep(2)  # Wait while paused
+    print("Resuming execution...")
+    await executor.control.resume()
+
+    # Wait for execution to complete
+    results = await execution_task
+
+    # Print the results
+    print("Execution results:")
+    for url, result in results.items():
+        print(f"{url}: {result}")
+
+    # Get and print final metrics
+    final_status = await executor.get_status()
+    print("Final executor status and metrics:")
+    print(final_status)
+
+# Run the main function
+if __name__ == "__main__":
+    asyncio.run(main())
+
+if __name__ == "__main__":
+    asyncio.run(main())

crawl4ai/async_logger.py

@@ -1,374 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Optional, Dict, Any, List
-import os
-from datetime import datetime
-from urllib.parse import unquote
-from rich.console import Console
-from rich.text import Text
-from .utils import create_box_message
-
-
-class LogLevel(Enum):
-    DEFAULT = 0
-    DEBUG = 1
-    INFO = 2
-    SUCCESS = 3
-    WARNING = 4
-    ERROR = 5
-    CRITICAL = 6
-    ALERT = 7
-    NOTICE = 8
-    EXCEPTION = 9
-    FATAL = 10
-    
-
-    def __str__(self):
-        return self.name.lower()
-
-class LogColor(str, Enum):
-    """Enum for log colors."""
-
-    DEBUG = "bright_black"
-    INFO = "cyan"
-    SUCCESS = "green"
-    WARNING = "yellow"
-    ERROR = "red"
-    CYAN = "cyan"
-    GREEN = "green"
-    YELLOW = "yellow"
-    MAGENTA = "magenta"
-    DIM_MAGENTA = "dim magenta"
-    RED = "red"
-
-    def __str__(self):
-        """Automatically convert rich color to string."""
-        return self.value
-
-
-class AsyncLoggerBase(ABC):
-    @abstractmethod
-    def debug(self, message: str, tag: str = "DEBUG", **kwargs):
-        pass
-
-    @abstractmethod
-    def info(self, message: str, tag: str = "INFO", **kwargs):
-        pass
-
-    @abstractmethod
-    def success(self, message: str, tag: str = "SUCCESS", **kwargs):
-        pass
-
-    @abstractmethod
-    def warning(self, message: str, tag: str = "WARNING", **kwargs):
-        pass
-
-    @abstractmethod
-    def error(self, message: str, tag: str = "ERROR", **kwargs):
-        pass
-
-    @abstractmethod
-    def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 100):
-        pass
-
-    @abstractmethod
-    def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 100):
-        pass
-
-
-class AsyncLogger(AsyncLoggerBase):
-    """
-    Asynchronous logger with support for colored console output and file logging.
-    Supports templated messages with colored components.
-    """
-
-    DEFAULT_ICONS = {
-        "INIT": "→",
-        "READY": "✓",
-        "FETCH": "↓",
-        "SCRAPE": "◆",
-        "EXTRACT": "■",
-        "COMPLETE": "●",
-        "ERROR": "×",
-        "DEBUG": "⋯",
-        "INFO": "ℹ",
-        "WARNING": "⚠",
-        "SUCCESS": "✔",
-        "CRITICAL": "‼",
-        "ALERT": "⚡",
-        "NOTICE": "ℹ",
-        "EXCEPTION": "❗",
-        "FATAL": "☠",
-        "DEFAULT": "•",
-    }
-
-    DEFAULT_COLORS = {
-        LogLevel.DEBUG: LogColor.DEBUG,
-        LogLevel.INFO: LogColor.INFO,
-        LogLevel.SUCCESS: LogColor.SUCCESS,
-        LogLevel.WARNING: LogColor.WARNING,
-        LogLevel.ERROR: LogColor.ERROR,
-    }
-
-    def __init__(
-        self,
-        log_file: Optional[str] = None,
-        log_level: LogLevel = LogLevel.DEBUG,
-        tag_width: int = 10,
-        icons: Optional[Dict[str, str]] = None,
-        colors: Optional[Dict[LogLevel, LogColor]] = None,
-        verbose: bool = True,
-    ):
-        """
-        Initialize the logger.
-
-        Args:
-            log_file: Optional file path for logging
-            log_level: Minimum log level to display
-            tag_width: Width for tag formatting
-            icons: Custom icons for different tags
-            colors: Custom colors for different log levels
-            verbose: Whether to output to console
-        """
-        self.log_file = log_file
-        self.log_level = log_level
-        self.tag_width = tag_width
-        self.icons = icons or self.DEFAULT_ICONS
-        self.colors = colors or self.DEFAULT_COLORS
-        self.verbose = verbose
-        self.console = Console()
-
-        # Create log file directory if needed
-        if log_file:
-            os.makedirs(os.path.dirname(os.path.abspath(log_file)), exist_ok=True)
-
-    def _format_tag(self, tag: str) -> str:
-        """Format a tag with consistent width."""
-        return f"[{tag}]".ljust(self.tag_width, ".")
-
-    def _get_icon(self, tag: str) -> str:
-        """Get the icon for a tag, defaulting to info icon if not found."""
-        return self.icons.get(tag, self.icons["INFO"])
-    
-    def _shorten(self, text, length, placeholder="..."):
-        """Truncate text in the middle if longer than length, or pad if shorter."""
-        if len(text) <= length:
-            return text.ljust(length)  # Pad with spaces to reach desired length
-        half = (length - len(placeholder)) // 2
-        shortened = text[:half] + placeholder + text[-half:]
-        return shortened.ljust(length)  # Also pad shortened text to consistent length
-
-    def _write_to_file(self, message: str):
-        """Write a message to the log file if configured."""
-        if self.log_file:
-            text = Text.from_markup(message)
-            plain_text = text.plain
-            timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
-            with open(self.log_file, "a", encoding="utf-8") as f:
-                f.write(f"[{timestamp}] {plain_text}\n")
-
-    def _log(
-        self,
-        level: LogLevel,
-        message: str,
-        tag: str,
-        params: Optional[Dict[str, Any]] = None,
-        colors: Optional[Dict[str, LogColor]] = None,
-        boxes: Optional[List[str]] = None,
-        base_color: Optional[LogColor] = None,
-        **kwargs,
-    ):
-        """
-        Core logging method that handles message formatting and output.
-
-        Args:
-            level: Log level for this message
-            message: Message template string
-            tag: Tag for the message
-            params: Parameters to format into the message
-            colors: Color overrides for specific parameters
-            boxes: Box overrides for specific parameters
-            base_color: Base color for the entire message
-        """
-        if level.value < self.log_level.value:
-            return
-
-        # avoid conflict with rich formatting
-        parsed_message = message.replace("[", "[[").replace("]", "]]")
-        if params:
-            # FIXME: If there are formatting strings in floating point format, 
-            # this may result in colors and boxes not being applied properly.
-            # such as {value:.2f}, the value is 0.23333 format it to 0.23,
-            # but we replace("0.23333", "[color]0.23333[/color]")
-            formatted_message = parsed_message.format(**params)
-            for key, value in params.items():
-                # value_str may discard `[` and `]`, so we need to replace it. 
-                value_str = str(value).replace("[", "[[").replace("]", "]]")
-                # check is need apply color
-                if colors and key in colors:
-                    color_str = f"[{colors[key]}]{value_str}[/{colors[key]}]"
-                    formatted_message = formatted_message.replace(value_str, color_str)
-                    value_str = color_str
-
-                # check is need apply box
-                if boxes and key in boxes:
-                    formatted_message = formatted_message.replace(value_str,
-                        create_box_message(value_str, type=str(level)))
-
-        else:
-            formatted_message = parsed_message
-
-        # Construct the full log line
-        color: LogColor = base_color or self.colors[level]
-        log_line = f"[{color}]{self._format_tag(tag)} {self._get_icon(tag)} {formatted_message} [/{color}]"
-
-        # Output to console if verbose
-        if self.verbose or kwargs.get("force_verbose", False):
-            self.console.print(log_line)
-
-        # Write to file if configured
-        self._write_to_file(log_line)
-
-    def debug(self, message: str, tag: str = "DEBUG", **kwargs):
-        """Log a debug message."""
-        self._log(LogLevel.DEBUG, message, tag, **kwargs)
-
-    def info(self, message: str, tag: str = "INFO", **kwargs):
-        """Log an info message."""
-        self._log(LogLevel.INFO, message, tag, **kwargs)
-
-    def success(self, message: str, tag: str = "SUCCESS", **kwargs):
-        """Log a success message."""
-        self._log(LogLevel.SUCCESS, message, tag, **kwargs)
-
-    def warning(self, message: str, tag: str = "WARNING", **kwargs):
-        """Log a warning message."""
-        self._log(LogLevel.WARNING, message, tag, **kwargs)
-        
-    def critical(self, message: str, tag: str = "CRITICAL", **kwargs):
-        """Log a critical message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-    def exception(self, message: str, tag: str = "EXCEPTION", **kwargs):
-        """Log an exception message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-    def fatal(self, message: str, tag: str = "FATAL", **kwargs):
-        """Log a fatal message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-    def alert(self, message: str, tag: str = "ALERT", **kwargs):
-        """Log an alert message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-    def notice(self, message: str, tag: str = "NOTICE", **kwargs):
-        """Log a notice message."""
-        self._log(LogLevel.INFO, message, tag, **kwargs)
-
-    def error(self, message: str, tag: str = "ERROR", **kwargs):
-        """Log an error message."""
-        self._log(LogLevel.ERROR, message, tag, **kwargs)
-
-    def url_status(
-        self,
-        url: str,
-        success: bool,
-        timing: float,
-        tag: str = "FETCH",
-        url_length: int = 100,
-    ):
-        """
-        Convenience method for logging URL fetch status.
-
-        Args:
-            url: The URL being processed
-            success: Whether the operation was successful
-            timing: Time taken for the operation
-            tag: Tag for the message
-            url_length: Maximum length for URL in log
-        """
-        decoded_url = unquote(url)
-        readable_url = self._shorten(decoded_url, url_length)
-        self._log(
-            level=LogLevel.SUCCESS if success else LogLevel.ERROR,
-            message="{url} | {status} | ⏱: {timing:.2f}s",
-            tag=tag,
-            params={
-                "url": readable_url,
-                "status": "✓" if success else "✗",
-                "timing": timing,
-            },
-            colors={
-                "status": LogColor.SUCCESS if success else LogColor.ERROR,
-                "timing": LogColor.WARNING,
-            },
-        )
-
-    def error_status(
-        self, url: str, error: str, tag: str = "ERROR", url_length: int = 50
-    ):
-        """
-        Convenience method for logging error status.
-
-        Args:
-            url: The URL being processed
-            error: Error message
-            tag: Tag for the message
-            url_length: Maximum length for URL in log
-        """
-        decoded_url = unquote(url)
-        readable_url = self._shorten(decoded_url, url_length)
-        self._log(
-            level=LogLevel.ERROR,
-            message="{url} | Error: {error}",
-            tag=tag,
-            params={"url": readable_url, "error": error},
-        )
-
-class AsyncFileLogger(AsyncLoggerBase):
-    """
-    File-only asynchronous logger that writes logs to a specified file.
-    """
-
-    def __init__(self, log_file: str):
-        """
-        Initialize the file logger.
-
-        Args:
-            log_file: File path for logging
-        """
-        self.log_file = log_file
-        os.makedirs(os.path.dirname(os.path.abspath(log_file)), exist_ok=True)
-
-    def _write_to_file(self, level: str, message: str, tag: str):
-        """Write a message to the log file."""
-        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
-        with open(self.log_file, "a", encoding="utf-8") as f:
-            f.write(f"[{timestamp}] [{level}] [{tag}] {message}\n")
-
-    def debug(self, message: str, tag: str = "DEBUG", **kwargs):
-        """Log a debug message to file."""
-        self._write_to_file("DEBUG", message, tag)
-
-    def info(self, message: str, tag: str = "INFO", **kwargs):
-        """Log an info message to file."""
-        self._write_to_file("INFO", message, tag)
-
-    def success(self, message: str, tag: str = "SUCCESS", **kwargs):
-        """Log a success message to file."""
-        self._write_to_file("SUCCESS", message, tag)
-
-    def warning(self, message: str, tag: str = "WARNING", **kwargs):
-        """Log a warning message to file."""
-        self._write_to_file("WARNING", message, tag)
-
-    def error(self, message: str, tag: str = "ERROR", **kwargs):
-        """Log an error message to file."""
-        self._write_to_file("ERROR", message, tag)
-
-    def url_status(self, url: str, success: bool, timing: float, tag: str = "FETCH", url_length: int = 100):
-        """Log URL fetch status to file."""
-        status = "SUCCESS" if success else "FAILED"
-        message = f"{url[:url_length]}... | Status: {status} | Time: {timing:.2f}s"
-        self._write_to_file("URL_STATUS", message, tag)
-
-    def error_status(self, url: str, error: str, tag: str = "ERROR", url_length: int = 100):
-        """Log error status to file."""
-        message = f"{url[:url_length]}... | Error: {error}"
-        self._write_to_file("ERROR", message, tag)

crawl4ai/browser_adapter.py

@@ -1,421 +0,0 @@
-# browser_adapter.py
-"""
-Browser adapter for Crawl4AI to support both Playwright and undetected browsers
-with minimal changes to existing codebase.
-"""
-
-from abc import ABC, abstractmethod
-from typing import List, Dict, Any, Optional, Callable
-import time
-import json
-
-# Import both, but use conditionally
-try:
-    from playwright.async_api import Page
-except ImportError:
-    Page = Any
-
-try:
-    from patchright.async_api import Page as UndetectedPage
-except ImportError:
-    UndetectedPage = Any
-
-
-class BrowserAdapter(ABC):
-    """Abstract adapter for browser-specific operations"""
-    
-    @abstractmethod
-    async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
-        """Execute JavaScript in the page"""
-        pass
-    
-    @abstractmethod
-    async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup console message capturing, returns handler function if needed"""
-        pass
-    
-    @abstractmethod
-    async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup error capturing, returns handler function if needed"""
-        pass
-    
-    @abstractmethod
-    async def retrieve_console_messages(self, page: Page) -> List[Dict]:
-        """Retrieve captured console messages (for undetected browsers)"""
-        pass
-    
-    @abstractmethod
-    async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
-        """Clean up console event listeners"""
-        pass
-    
-    @abstractmethod
-    def get_imports(self) -> tuple:
-        """Get the appropriate imports for this adapter"""
-        pass
-
-
-class PlaywrightAdapter(BrowserAdapter):
-    """Adapter for standard Playwright"""
-    
-    async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
-        """Standard Playwright evaluate"""
-        if arg is not None:
-            return await page.evaluate(expression, arg)
-        return await page.evaluate(expression)
-    
-    async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup console capture using Playwright's event system"""
-        def handle_console_capture(msg):
-            try:
-                message_type = "unknown"
-                try:
-                    message_type = msg.type
-                except:
-                    pass
-                    
-                message_text = "unknown"
-                try:
-                    message_text = msg.text
-                except:
-                    pass
-                    
-                entry = {
-                    "type": message_type,
-                    "text": message_text,
-                    "timestamp": time.time()
-                }
-                
-                captured_console.append(entry)
-                
-            except Exception as e:
-                captured_console.append({
-                    "type": "console_capture_error", 
-                    "error": str(e), 
-                    "timestamp": time.time()
-                })
-        
-        page.on("console", handle_console_capture)
-        return handle_console_capture
-    
-    async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup error capture using Playwright's event system"""
-        def handle_pageerror_capture(err):
-            try:
-                error_message = "Unknown error"
-                try:
-                    error_message = err.message
-                except:
-                    pass
-                    
-                error_stack = ""
-                try:
-                    error_stack = err.stack
-                except:
-                    pass
-                    
-                captured_console.append({
-                    "type": "error",
-                    "text": error_message,
-                    "stack": error_stack,
-                    "timestamp": time.time()
-                })
-            except Exception as e:
-                captured_console.append({
-                    "type": "pageerror_capture_error", 
-                    "error": str(e), 
-                    "timestamp": time.time()
-                })
-        
-        page.on("pageerror", handle_pageerror_capture)
-        return handle_pageerror_capture
-    
-    async def retrieve_console_messages(self, page: Page) -> List[Dict]:
-        """Not needed for Playwright - messages are captured via events"""
-        return []
-    
-    async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
-        """Remove event listeners"""
-        if handle_console:
-            page.remove_listener("console", handle_console)
-        if handle_error:
-            page.remove_listener("pageerror", handle_error)
-    
-    def get_imports(self) -> tuple:
-        """Return Playwright imports"""
-        from playwright.async_api import Page, Error
-        from playwright.async_api import TimeoutError as PlaywrightTimeoutError
-        return Page, Error, PlaywrightTimeoutError
-
-
-class StealthAdapter(BrowserAdapter):
-    """Adapter for Playwright with stealth features using playwright_stealth"""
-
-    def __init__(self):
-        self._console_script_injected = {}
-        self._stealth_available = self._check_stealth_availability()
-
-    def _check_stealth_availability(self) -> bool:
-        """Check if playwright_stealth is available and get the correct function"""
-        try:
-            from playwright_stealth import stealth_async
-            self._stealth_function = stealth_async
-            return True
-        except ImportError:
-            try:
-                from playwright_stealth import stealth_sync
-                self._stealth_function = stealth_sync
-                return True
-            except ImportError:
-                self._stealth_function = None
-                return False
-
-    async def apply_stealth(self, page: Page):
-        """Apply stealth to a page if available"""
-        if self._stealth_available and self._stealth_function:
-            try:
-                if hasattr(self._stealth_function, '__call__'):
-                    if 'async' in getattr(self._stealth_function, '__name__', ''):
-                        await self._stealth_function(page)
-                    else:
-                        self._stealth_function(page)
-            except Exception as e:
-                # Fail silently or log error depending on requirements
-                pass
-
-    async def evaluate(self, page: Page, expression: str, arg: Any = None) -> Any:
-        """Standard Playwright evaluate with stealth applied"""
-        if arg is not None:
-            return await page.evaluate(expression, arg)
-        return await page.evaluate(expression)
-
-    async def setup_console_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup console capture using Playwright's event system with stealth"""
-        # Apply stealth to the page first
-        await self.apply_stealth(page)
-
-        def handle_console_capture(msg):
-            try:
-                message_type = "unknown"
-                try:
-                    message_type = msg.type
-                except:
-                    pass
-
-                message_text = "unknown"
-                try:
-                    message_text = msg.text
-                except:
-                    pass
-
-                entry = {
-                    "type": message_type,
-                    "text": message_text,
-                    "timestamp": time.time()
-                }
-
-                captured_console.append(entry)
-
-            except Exception as e:
-                captured_console.append({
-                    "type": "console_capture_error",
-                    "error": str(e),
-                    "timestamp": time.time()
-                })
-
-        page.on("console", handle_console_capture)
-        return handle_console_capture
-
-    async def setup_error_capture(self, page: Page, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup error capture using Playwright's event system"""
-        def handle_pageerror_capture(err):
-            try:
-                error_message = "Unknown error"
-                try:
-                    error_message = err.message
-                except:
-                    pass
-
-                error_stack = ""
-                try:
-                    error_stack = err.stack
-                except:
-                    pass
-
-                captured_console.append({
-                    "type": "error",
-                    "text": error_message,
-                    "stack": error_stack,
-                    "timestamp": time.time()
-                })
-            except Exception as e:
-                captured_console.append({
-                    "type": "pageerror_capture_error",
-                    "error": str(e),
-                    "timestamp": time.time()
-                })
-
-        page.on("pageerror", handle_pageerror_capture)
-        return handle_pageerror_capture
-
-    async def retrieve_console_messages(self, page: Page) -> List[Dict]:
-        """Not needed for Playwright - messages are captured via events"""
-        return []
-
-    async def cleanup_console_capture(self, page: Page, handle_console: Optional[Callable], handle_error: Optional[Callable]):
-        """Remove event listeners"""
-        if handle_console:
-            page.remove_listener("console", handle_console)
-        if handle_error:
-            page.remove_listener("pageerror", handle_error)
-
-    def get_imports(self) -> tuple:
-        """Return Playwright imports"""
-        from playwright.async_api import Page, Error
-        from playwright.async_api import TimeoutError as PlaywrightTimeoutError
-        return Page, Error, PlaywrightTimeoutError
-
-
-class UndetectedAdapter(BrowserAdapter):
-    """Adapter for undetected browser automation with stealth features"""
-    
-    def __init__(self):
-        self._console_script_injected = {}
-    
-    async def evaluate(self, page: UndetectedPage, expression: str, arg: Any = None) -> Any:
-        """Undetected browser evaluate with isolated context"""
-        # For most evaluations, use isolated context for stealth
-        # Only use non-isolated when we need to access our injected console capture
-        isolated = not (
-            "__console" in expression or 
-            "__captured" in expression or
-            "__error" in expression or
-            "window.__" in expression
-        )
-        
-        if arg is not None:
-            return await page.evaluate(expression, arg, isolated_context=isolated)
-        return await page.evaluate(expression, isolated_context=isolated)
-    
-    async def setup_console_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup console capture using JavaScript injection for undetected browsers"""
-        if not self._console_script_injected.get(page, False):
-            await page.add_init_script("""
-                // Initialize console capture
-                window.__capturedConsole = [];
-                window.__capturedErrors = [];
-                
-                // Store original console methods
-                const originalConsole = {};
-                ['log', 'info', 'warn', 'error', 'debug'].forEach(method => {
-                    originalConsole[method] = console[method];
-                    console[method] = function(...args) {
-                        try {
-                            window.__capturedConsole.push({
-                                type: method,
-                                text: args.map(arg => {
-                                    try {
-                                        if (typeof arg === 'object') {
-                                            return JSON.stringify(arg);
-                                        }
-                                        return String(arg);
-                                    } catch (e) {
-                                        return '[Object]';
-                                    }
-                                }).join(' '),
-                                timestamp: Date.now()
-                            });
-                        } catch (e) {
-                            // Fail silently to avoid detection
-                        }
-                        
-                        // Call original method
-                        originalConsole[method].apply(console, args);
-                    };
-                });
-            """)
-            self._console_script_injected[page] = True
-        
-        return None  # No handler function needed for undetected browser
-    
-    async def setup_error_capture(self, page: UndetectedPage, captured_console: List[Dict]) -> Optional[Callable]:
-        """Setup error capture using JavaScript injection for undetected browsers"""
-        if not self._console_script_injected.get(page, False):
-            await page.add_init_script("""
-                // Capture errors
-                window.addEventListener('error', (event) => {
-                    try {
-                        window.__capturedErrors.push({
-                            type: 'error',
-                            text: event.message,
-                            stack: event.error ? event.error.stack : '',
-                            filename: event.filename,
-                            lineno: event.lineno,
-                            colno: event.colno,
-                            timestamp: Date.now()
-                        });
-                    } catch (e) {
-                        // Fail silently
-                    }
-                });
-                
-                // Capture unhandled promise rejections
-                window.addEventListener('unhandledrejection', (event) => {
-                    try {
-                        window.__capturedErrors.push({
-                            type: 'unhandledrejection',
-                            text: event.reason ? String(event.reason) : 'Unhandled Promise Rejection',
-                            stack: event.reason && event.reason.stack ? event.reason.stack : '',
-                            timestamp: Date.now()
-                        });
-                    } catch (e) {
-                        // Fail silently
-                    }
-                });
-            """)
-            self._console_script_injected[page] = True
-        
-        return None  # No handler function needed for undetected browser
-    
-    async def retrieve_console_messages(self, page: UndetectedPage) -> List[Dict]:
-        """Retrieve captured console messages and errors from the page"""
-        messages = []
-        
-        try:
-            # Get console messages
-            console_messages = await page.evaluate(
-                "() => { const msgs = window.__capturedConsole || []; window.__capturedConsole = []; return msgs; }",
-                isolated_context=False
-            )
-            messages.extend(console_messages)
-            
-            # Get errors
-            errors = await page.evaluate(
-                "() => { const errs = window.__capturedErrors || []; window.__capturedErrors = []; return errs; }",
-                isolated_context=False
-            )
-            messages.extend(errors)
-            
-            # Convert timestamps from JS to Python format
-            for msg in messages:
-                if 'timestamp' in msg and isinstance(msg['timestamp'], (int, float)):
-                    msg['timestamp'] = msg['timestamp'] / 1000.0  # Convert from ms to seconds
-                    
-        except Exception:
-            # If retrieval fails, return empty list
-            pass
-        
-        return messages
-    
-    async def cleanup_console_capture(self, page: UndetectedPage, handle_console: Optional[Callable], handle_error: Optional[Callable]):
-        """Clean up for undetected browser - retrieve final messages"""
-        # For undetected browser, we don't have event listeners to remove
-        # but we should retrieve any final messages
-        final_messages = await self.retrieve_console_messages(page)
-        return final_messages
-    
-    def get_imports(self) -> tuple:
-        """Return undetected browser imports"""
-        from patchright.async_api import Page, Error
-        from patchright.async_api import TimeoutError as PlaywrightTimeoutError
-        return Page, Error, PlaywrightTimeoutError

crawl4ai/cache_context.py

@@ -1,117 +0,0 @@
-from enum import Enum
-
-
-class CacheMode(Enum):
-    """
-    Defines the caching behavior for web crawling operations.
-
-    Modes:
-    - ENABLED: Normal caching behavior (read and write)
-    - DISABLED: No caching at all
-    - READ_ONLY: Only read from cache, don't write
-    - WRITE_ONLY: Only write to cache, don't read
-    - BYPASS: Bypass cache for this operation
-    """
-
-    ENABLED = "enabled"
-    DISABLED = "disabled"
-    READ_ONLY = "read_only"
-    WRITE_ONLY = "write_only"
-    BYPASS = "bypass"
-
-
-class CacheContext:
-    """
-    Encapsulates cache-related decisions and URL handling.
-
-    This class centralizes all cache-related logic and URL type checking,
-    making the caching behavior more predictable and maintainable.
-
-    Attributes:
-        url (str): The URL being processed.
-        cache_mode (CacheMode): The cache mode for the current operation.
-        always_bypass (bool): If True, bypasses caching for this operation.
-        is_cacheable (bool): True if the URL is cacheable, False otherwise.
-        is_web_url (bool): True if the URL is a web URL, False otherwise.
-        is_local_file (bool): True if the URL is a local file, False otherwise.
-        is_raw_html (bool): True if the URL is raw HTML, False otherwise.
-        _url_display (str): The display name for the URL (web, local file, or raw HTML).
-    """
-
-    def __init__(self, url: str, cache_mode: CacheMode, always_bypass: bool = False):
-        """
-        Initializes the CacheContext with the provided URL and cache mode.
-
-        Args:
-            url (str): The URL being processed.
-            cache_mode (CacheMode): The cache mode for the current operation.
-            always_bypass (bool): If True, bypasses caching for this operation.
-        """
-        self.url = url
-        self.cache_mode = cache_mode
-        self.always_bypass = always_bypass
-        self.is_cacheable = url.startswith(("http://", "https://", "file://"))
-        self.is_web_url = url.startswith(("http://", "https://"))
-        self.is_local_file = url.startswith("file://")
-        self.is_raw_html = url.startswith("raw:")
-        self._url_display = url if not self.is_raw_html else "Raw HTML"
-
-    def should_read(self) -> bool:
-        """
-        Determines if cache should be read based on context.
-
-        How it works:
-        1. If always_bypass is True or is_cacheable is False, return False.
-        2. If cache_mode is ENABLED or READ_ONLY, return True.
-
-        Returns:
-            bool: True if cache should be read, False otherwise.
-        """
-        if self.always_bypass or not self.is_cacheable:
-            return False
-        return self.cache_mode in [CacheMode.ENABLED, CacheMode.READ_ONLY]
-
-    def should_write(self) -> bool:
-        """
-        Determines if cache should be written based on context.
-
-        How it works:
-        1. If always_bypass is True or is_cacheable is False, return False.
-        2. If cache_mode is ENABLED or WRITE_ONLY, return True.
-
-        Returns:
-            bool: True if cache should be written, False otherwise.
-        """
-        if self.always_bypass or not self.is_cacheable:
-            return False
-        return self.cache_mode in [CacheMode.ENABLED, CacheMode.WRITE_ONLY]
-
-    @property
-    def display_url(self) -> str:
-        """Returns the URL in display format."""
-        return self._url_display
-
-
-def _legacy_to_cache_mode(
-    disable_cache: bool = False,
-    bypass_cache: bool = False,
-    no_cache_read: bool = False,
-    no_cache_write: bool = False,
-) -> CacheMode:
-    """
-    Converts legacy cache parameters to the new CacheMode enum.
-
-    This is an internal function to help transition from the old boolean flags
-    to the new CacheMode system.
-    """
-    if disable_cache:
-        return CacheMode.DISABLED
-    if bypass_cache:
-        return CacheMode.BYPASS
-    if no_cache_read and no_cache_write:
-        return CacheMode.DISABLED
-    if no_cache_read:
-        return CacheMode.WRITE_ONLY
-    if no_cache_write:
-        return CacheMode.READ_ONLY
-    return CacheMode.ENABLED

crawl4ai/chunking_strategy.py

@@ -3,52 +3,23 @@ import re
 from collections import Counter
 import string
 from .model_loader import load_nltk_punkt
+from .utils import *
 
 # Define the abstract base class for chunking strategies
 class ChunkingStrategy(ABC):
-    """
-    Abstract base class for chunking strategies.
-    """
-
+    
     @abstractmethod
     def chunk(self, text: str) -> list:
         """
         Abstract method to chunk the given text.
-
-        Args:
-            text (str): The text to chunk.
-
-        Returns:
-            list: A list of chunks.
         """
         pass
-
-
-# Create an identity chunking strategy f(x) = [x]
-class IdentityChunking(ChunkingStrategy):
-    """
-    Chunking strategy that returns the input text as a single chunk.
-    """
-
-    def chunk(self, text: str) -> list:
-        return [text]
-
-
+    
 # Regex-based chunking
 class RegexChunking(ChunkingStrategy):
-    """
-    Chunking strategy that splits text based on regular expression patterns.
-    """
-
     def __init__(self, patterns=None, **kwargs):
-        """
-        Initialize the RegexChunking object.
-
-        Args:
-            patterns (list): A list of regular expression patterns to split text.
-        """
         if patterns is None:
-            patterns = [r"\n\n"]  # Default split pattern
+            patterns = [r'\n\n']  # Default split pattern
         self.patterns = patterns
 
     def chunk(self, text: str) -> list:
@@ -59,20 +30,12 @@ class RegexChunking(ChunkingStrategy):
                 new_paragraphs.extend(re.split(pattern, paragraph))
             paragraphs = new_paragraphs
         return paragraphs
-
-
-# NLP-based sentence chunking
+    
+# NLP-based sentence chunking 
 class NlpSentenceChunking(ChunkingStrategy):
-    """
-    Chunking strategy that splits text into sentences using NLTK's sentence tokenizer.
-    """
-
     def __init__(self, **kwargs):
-        """
-        Initialize the NlpSentenceChunking object.
-        """
-        from crawl4ai.le.legacy.model_loader import load_nltk_punkt
         load_nltk_punkt()
+        pass
 
     def chunk(self, text: str) -> list:
         # Improved regex for sentence splitting
@@ -80,34 +43,18 @@ class NlpSentenceChunking(ChunkingStrategy):
         #     r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z][A-Z]\.)(?<![A-Za-z]\.)(?<=\.|\?|\!|\n)\s'
         # )
         # sentences = sentence_endings.split(text)
-        # sens =  [sent.strip() for sent in sentences if sent]
+        # sens =  [sent.strip() for sent in sentences if sent]            
         from nltk.tokenize import sent_tokenize
-
         sentences = sent_tokenize(text)
-        sens = [sent.strip() for sent in sentences]
-
+        sens =  [sent.strip() for sent in sentences]        
+        
         return list(set(sens))
-
-
+    
 # Topic-based segmentation using TextTiling
 class TopicSegmentationChunking(ChunkingStrategy):
-    """
-    Chunking strategy that segments text into topics using NLTK's TextTilingTokenizer.
-
-    How it works:
-    1. Segment the text into topics using TextTilingTokenizer
-    2. Extract keywords for each topic segment
-    """
-
+    
     def __init__(self, num_keywords=3, **kwargs):
-        """
-        Initialize the TopicSegmentationChunking object.
-
-        Args:
-            num_keywords (int): The number of keywords to extract for each topic segment.
-        """
         import nltk as nl
-
         self.tokenizer = nl.tokenize.TextTilingTokenizer()
         self.num_keywords = num_keywords
 
@@ -119,14 +66,8 @@ class TopicSegmentationChunking(ChunkingStrategy):
     def extract_keywords(self, text: str) -> list:
         # Tokenize and remove stopwords and punctuation
         import nltk as nl
-
         tokens = nl.toknize.word_tokenize(text)
-        tokens = [
-            token.lower()
-            for token in tokens
-            if token not in nl.corpus.stopwords.words("english")
-            and token not in string.punctuation
-        ]
+        tokens = [token.lower() for token in tokens if token not in nl.corpus.stopwords.words('english') and token not in string.punctuation]
 
         # Calculate frequency distribution
         freq_dist = Counter(tokens)
@@ -137,27 +78,15 @@ class TopicSegmentationChunking(ChunkingStrategy):
         # Segment the text into topics
         segments = self.chunk(text)
         # Extract keywords for each topic segment
-        segments_with_topics = [
-            (segment, self.extract_keywords(segment)) for segment in segments
-        ]
+        segments_with_topics = [(segment, self.extract_keywords(segment)) for segment in segments]
         return segments_with_topics
-
-
+    
 # Fixed-length word chunks
 class FixedLengthWordChunking(ChunkingStrategy):
-    """
-    Chunking strategy that splits text into fixed-length word chunks.
-
-    How it works:
-    1. Split the text into words
-    2. Create chunks of fixed length
-    3. Return the list of chunks
-    """
-
     def __init__(self, chunk_size=100, **kwargs):
         """
         Initialize the fixed-length word chunking strategy with the given chunk size.
-
+        
         Args:
             chunk_size (int): The size of each chunk in words.
         """
@@ -165,28 +94,15 @@ class FixedLengthWordChunking(ChunkingStrategy):
 
     def chunk(self, text: str) -> list:
         words = text.split()
-        return [
-            " ".join(words[i : i + self.chunk_size])
-            for i in range(0, len(words), self.chunk_size)
-        ]
-
-
+        return [' '.join(words[i:i + self.chunk_size]) for i in range(0, len(words), self.chunk_size)]
+    
 # Sliding window chunking
 class SlidingWindowChunking(ChunkingStrategy):
-    """
-    Chunking strategy that splits text into overlapping word chunks.
-
-    How it works:
-    1. Split the text into words
-    2. Create chunks of fixed length
-    3. Return the list of chunks
-    """
-
     def __init__(self, window_size=100, step=50, **kwargs):
         """
         Initialize the sliding window chunking strategy with the given window size and
         step size.
-
+        
         Args:
             window_size (int): The size of the sliding window in words.
             step (int): The step size for sliding the window in words.
@@ -197,37 +113,27 @@ class SlidingWindowChunking(ChunkingStrategy):
     def chunk(self, text: str) -> list:
         words = text.split()
         chunks = []
-
+        
         if len(words) <= self.window_size:
             return [text]
-
+        
         for i in range(0, len(words) - self.window_size + 1, self.step):
-            chunk = " ".join(words[i : i + self.window_size])
+            chunk = ' '.join(words[i:i + self.window_size])
             chunks.append(chunk)
-
+        
         # Handle the last chunk if it doesn't align perfectly
         if i + self.window_size < len(words):
-            chunks.append(" ".join(words[-self.window_size :]))
-
+            chunks.append(' '.join(words[-self.window_size:]))
+        
         return chunks
-
+    
 
 class OverlappingWindowChunking(ChunkingStrategy):
-    """
-    Chunking strategy that splits text into overlapping word chunks.
-
-    How it works:
-    1. Split the text into words using whitespace
-    2. Create chunks of fixed length equal to the window size
-    3. Slide the window by the overlap size
-    4. Return the list of chunks
-    """
-
     def __init__(self, window_size=1000, overlap=100, **kwargs):
         """
         Initialize the overlapping window chunking strategy with the given window size and
         overlap size.
-
+        
         Args:
             window_size (int): The size of the window in words.
             overlap (int): The size of the overlap between consecutive chunks in words.
@@ -238,19 +144,19 @@ class OverlappingWindowChunking(ChunkingStrategy):
     def chunk(self, text: str) -> list:
         words = text.split()
         chunks = []
-
+        
         if len(words) <= self.window_size:
             return [text]
-
+        
         start = 0
         while start < len(words):
             end = start + self.window_size
-            chunk = " ".join(words[start:end])
+            chunk = ' '.join(words[start:end])
             chunks.append(chunk)
-
+            
             if end >= len(words):
                 break
-
+            
             start = end - self.overlap
-
-        return chunks
+        
+        return chunks

crawl4ai/components/crawler_monitor.py

@@ -1,837 +0,0 @@
-import time
-import uuid
-import threading
-import psutil
-from datetime import datetime, timedelta
-from typing import Dict, Optional, List
-import threading
-from rich.console import Console
-from rich.layout import Layout
-from rich.panel import Panel
-from rich.table import Table
-from rich.text import Text
-from rich.live import Live
-from rich import box
-from ..models import CrawlStatus
-
-class TerminalUI:
-    """Terminal user interface for CrawlerMonitor using rich library."""
-    
-    def __init__(self, refresh_rate: float = 1.0, max_width: int = 120):
-        """
-        Initialize the terminal UI.
-        
-        Args:
-            refresh_rate: How often to refresh the UI (in seconds)
-            max_width: Maximum width of the UI in characters
-        """
-        self.console = Console(width=max_width)
-        self.layout = Layout()
-        self.refresh_rate = refresh_rate
-        self.stop_event = threading.Event()
-        self.ui_thread = None
-        self.monitor = None  # Will be set by CrawlerMonitor
-        self.max_width = max_width
-        
-        # Setup layout - vertical layout (top to bottom)
-        self.layout.split(
-            Layout(name="header", size=3),
-            Layout(name="pipeline_status", size=10),
-            Layout(name="task_details", ratio=1),
-            Layout(name="footer", size=3)  # Increased footer size to fit all content
-        )
-        
-    def start(self, monitor):
-        """Start the UI thread."""
-        self.monitor = monitor
-        self.stop_event.clear()
-        self.ui_thread = threading.Thread(target=self._ui_loop)
-        self.ui_thread.daemon = True
-        self.ui_thread.start()
-        
-    def stop(self):
-        """Stop the UI thread."""
-        if self.ui_thread and self.ui_thread.is_alive():
-            self.stop_event.set()
-            # Only try to join if we're not in the UI thread
-            # This prevents "cannot join current thread" errors
-            if threading.current_thread() != self.ui_thread:
-                self.ui_thread.join(timeout=5.0)
-    
-    def _ui_loop(self):
-        """Main UI rendering loop."""
-        import sys
-        import select
-        import termios
-        import tty
-        
-        # Setup terminal for non-blocking input
-        old_settings = termios.tcgetattr(sys.stdin)
-        try:
-            tty.setcbreak(sys.stdin.fileno())
-            
-            # Use Live display to render the UI
-            with Live(self.layout, refresh_per_second=1/self.refresh_rate, screen=True) as live:
-                self.live = live  # Store the live display for updates
-                
-                # Main UI loop
-                while not self.stop_event.is_set():
-                    self._update_display()
-                    
-                    # Check for key press (non-blocking)
-                    if select.select([sys.stdin], [], [], 0)[0]:
-                        key = sys.stdin.read(1)
-                        # Check for 'q' to quit
-                        if key == 'q':
-                            # Signal stop but don't call monitor.stop() from UI thread
-                            # as it would cause the thread to try to join itself
-                            self.stop_event.set()
-                            self.monitor.is_running = False
-                            break
-                    
-                    time.sleep(self.refresh_rate)
-                    
-                    # Just check if the monitor was stopped
-                    if not self.monitor.is_running:
-                        break
-        finally:
-            # Restore terminal settings
-            termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)
-    
-    def _update_display(self):
-        """Update the terminal display with current statistics."""
-        if not self.monitor:
-            return
-            
-        # Update crawler status panel
-        self.layout["header"].update(self._create_status_panel())
-        
-        # Update pipeline status panel and task details panel
-        self.layout["pipeline_status"].update(self._create_pipeline_panel())
-        self.layout["task_details"].update(self._create_task_details_panel())
-        
-        # Update footer
-        self.layout["footer"].update(self._create_footer())
-    
-    def _create_status_panel(self) -> Panel:
-        """Create the crawler status panel."""
-        summary = self.monitor.get_summary()
-        
-        # Format memory status with icon
-        memory_status = self.monitor.get_memory_status()
-        memory_icon = "🟢"  # Default NORMAL
-        if memory_status == "PRESSURE":
-            memory_icon = "🟠"
-        elif memory_status == "CRITICAL":
-            memory_icon = "🔴"
-        
-        # Get current memory usage
-        current_memory = psutil.Process().memory_info().rss / (1024 * 1024)  # MB
-        memory_percent = (current_memory / psutil.virtual_memory().total) * 100
-        
-        # Format runtime
-        runtime = self.monitor._format_time(time.time() - self.monitor.start_time if self.monitor.start_time else 0)
-        
-        # Create the status text
-        status_text = Text()
-        status_text.append(f"Web Crawler Dashboard | Runtime: {runtime} | Memory: {memory_percent:.1f}% {memory_icon}\n")
-        status_text.append(f"Status: {memory_status} | URLs: {summary['urls_completed']}/{summary['urls_total']} | ")
-        status_text.append(f"Peak Mem: {summary['peak_memory_percent']:.1f}% at {self.monitor._format_time(summary['peak_memory_time'])}")
-        
-        return Panel(status_text, title="Crawler Status", border_style="blue")
-    
-    def _create_pipeline_panel(self) -> Panel:
-        """Create the pipeline status panel."""
-        summary = self.monitor.get_summary()
-        queue_stats = self.monitor.get_queue_stats()
-        
-        # Create a table for status counts
-        table = Table(show_header=True, box=None)
-        table.add_column("Status", style="cyan")
-        table.add_column("Count", justify="right")
-        table.add_column("Percentage", justify="right")
-        table.add_column("Stat", style="cyan")
-        table.add_column("Value", justify="right")
-        
-        # Calculate overall progress
-        progress = f"{summary['urls_completed']}/{summary['urls_total']}"
-        progress_percent = f"{summary['completion_percentage']:.1f}%"
-        
-        # Add rows for each status
-        table.add_row(
-            "Overall Progress", 
-            progress, 
-            progress_percent,
-            "Est. Completion", 
-            summary.get('estimated_completion_time', "N/A")
-        )
-        
-        # Add rows for each status
-        status_counts = summary['status_counts']
-        total = summary['urls_total'] or 1  # Avoid division by zero
-        
-        # Status rows
-        table.add_row(
-            "Completed", 
-            str(status_counts.get(CrawlStatus.COMPLETED.name, 0)),
-            f"{status_counts.get(CrawlStatus.COMPLETED.name, 0) / total * 100:.1f}%",
-            "Avg. Time/URL",
-            f"{summary.get('avg_task_duration', 0):.2f}s"
-        )
-        
-        table.add_row(
-            "Failed", 
-            str(status_counts.get(CrawlStatus.FAILED.name, 0)),
-            f"{status_counts.get(CrawlStatus.FAILED.name, 0) / total * 100:.1f}%",
-            "Concurrent Tasks",
-            str(status_counts.get(CrawlStatus.IN_PROGRESS.name, 0))
-        )
-        
-        table.add_row(
-            "In Progress", 
-            str(status_counts.get(CrawlStatus.IN_PROGRESS.name, 0)),
-            f"{status_counts.get(CrawlStatus.IN_PROGRESS.name, 0) / total * 100:.1f}%",
-            "Queue Size",
-            str(queue_stats['total_queued'])
-        )
-        
-        table.add_row(
-            "Queued", 
-            str(status_counts.get(CrawlStatus.QUEUED.name, 0)),
-            f"{status_counts.get(CrawlStatus.QUEUED.name, 0) / total * 100:.1f}%",
-            "Max Wait Time",
-            f"{queue_stats['highest_wait_time']:.1f}s"
-        )
-        
-        # Requeued is a special case as it's not a status
-        requeued_count = summary.get('requeued_count', 0)
-        table.add_row(
-            "Requeued", 
-            str(requeued_count),
-            f"{summary.get('requeue_rate', 0):.1f}%",
-            "Avg Wait Time",
-            f"{queue_stats['avg_wait_time']:.1f}s"
-        )
-        
-        # Add empty row for spacing
-        table.add_row(
-            "", 
-            "",
-            "",
-            "Requeue Rate",
-            f"{summary.get('requeue_rate', 0):.1f}%"
-        )
-        
-        return Panel(table, title="Pipeline Status", border_style="green")
-    
-    def _create_task_details_panel(self) -> Panel:
-        """Create the task details panel."""
-        # Create a table for task details
-        table = Table(show_header=True, expand=True)
-        table.add_column("Task ID", style="cyan", no_wrap=True, width=10)
-        table.add_column("URL", style="blue", ratio=3)
-        table.add_column("Status", style="green", width=15)
-        table.add_column("Memory", justify="right", width=8)
-        table.add_column("Peak", justify="right", width=8)
-        table.add_column("Duration", justify="right", width=10)
-        
-        # Get all task stats
-        task_stats = self.monitor.get_all_task_stats()
-        
-        # Add summary row
-        active_tasks = sum(1 for stats in task_stats.values() 
-                          if stats['status'] == CrawlStatus.IN_PROGRESS.name)
-        
-        total_memory = sum(stats['memory_usage'] for stats in task_stats.values())
-        total_peak = sum(stats['peak_memory'] for stats in task_stats.values())
-        
-        # Summary row with separators
-        table.add_row(
-            "SUMMARY", 
-            f"Total: {len(task_stats)}", 
-            f"Active: {active_tasks}",
-            f"{total_memory:.1f}",
-            f"{total_peak:.1f}",
-            "N/A"
-        )
-        
-        # Add a separator
-        table.add_row("—" * 10, "—" * 20, "—" * 10, "—" * 8, "—" * 8, "—" * 10)
-        
-        # Status icons
-        status_icons = {
-            CrawlStatus.QUEUED.name: "⏳",
-            CrawlStatus.IN_PROGRESS.name: "🔄",
-            CrawlStatus.COMPLETED.name: "✅",
-            CrawlStatus.FAILED.name: "❌"
-        }
-        
-        # Calculate how many rows we can display based on available space
-        # We can display more rows now that we have a dedicated panel
-        display_count = min(len(task_stats), 20)  # Display up to 20 tasks
-        
-        # Add rows for each task
-        for task_id, stats in sorted(
-            list(task_stats.items())[:display_count],
-            # Sort: 1. IN_PROGRESS first, 2. QUEUED, 3. COMPLETED/FAILED by recency
-            key=lambda x: (
-                0 if x[1]['status'] == CrawlStatus.IN_PROGRESS.name else 
-                1 if x[1]['status'] == CrawlStatus.QUEUED.name else 
-                2,
-                -1 * (x[1].get('end_time', 0) or 0)  # Most recent first
-            )
-        ):
-            # Truncate task_id and URL for display
-            short_id = task_id[:8]
-            url = stats['url']
-            if len(url) > 50:  # Allow longer URLs in the dedicated panel
-                url = url[:47] + "..."
-                
-            # Format status with icon
-            status = f"{status_icons.get(stats['status'], '?')} {stats['status']}"
-            
-            # Add row
-            table.add_row(
-                short_id,
-                url,
-                status,
-                f"{stats['memory_usage']:.1f}",
-                f"{stats['peak_memory']:.1f}",
-                stats['duration'] if 'duration' in stats else "0:00"
-            )
-        
-        return Panel(table, title="Task Details", border_style="yellow")
-    
-    def _create_footer(self) -> Panel:
-        """Create the footer panel."""
-        from rich.columns import Columns
-        from rich.align import Align
-        
-        memory_status = self.monitor.get_memory_status()
-        memory_icon = "🟢"  # Default NORMAL
-        if memory_status == "PRESSURE":
-            memory_icon = "🟠"
-        elif memory_status == "CRITICAL":
-            memory_icon = "🔴"
-        
-        # Left section - memory status
-        left_text = Text()
-        left_text.append("Memory Status: ", style="bold")
-        status_style = "green" if memory_status == "NORMAL" else "yellow" if memory_status == "PRESSURE" else "red bold"
-        left_text.append(f"{memory_icon} {memory_status}", style=status_style)
-        
-        # Center section - copyright
-        center_text = Text("© Crawl4AI 2025 | Made by UnclecCode", style="cyan italic")
-        
-        # Right section - quit instruction
-        right_text = Text()
-        right_text.append("Press ", style="bold")
-        right_text.append("q", style="white on blue")
-        right_text.append(" to quit", style="bold")
-        
-        # Create columns with the three sections
-        footer_content = Columns(
-            [
-                Align.left(left_text),
-                Align.center(center_text),
-                Align.right(right_text)
-            ],
-            expand=True
-        )
-        
-        # Create a more visible footer panel
-        return Panel(
-            footer_content,
-            border_style="white",
-            padding=(0, 1)  # Add padding for better visibility
-        )
-
-
-class CrawlerMonitor:
-    """
-    Comprehensive monitoring and visualization system for tracking web crawler operations in real-time.
-    Provides a terminal-based dashboard that displays task statuses, memory usage, queue statistics,
-    and performance metrics.
-    """
-    
-    def __init__(
-        self,
-        urls_total: int = 0,
-        refresh_rate: float = 1.0,
-        enable_ui: bool = True,
-        max_width: int = 120
-    ):
-        """
-        Initialize the CrawlerMonitor.
-        
-        Args:
-            urls_total: Total number of URLs to be crawled
-            refresh_rate: How often to refresh the UI (in seconds)
-            enable_ui: Whether to display the terminal UI
-            max_width: Maximum width of the UI in characters
-        """
-        # Core monitoring attributes
-        self.stats = {}  # Task ID -> stats dict
-        self.memory_status = "NORMAL"
-        self.start_time = None
-        self.end_time = None
-        self.is_running = False
-        self.queue_stats = {
-            "total_queued": 0,
-            "highest_wait_time": 0.0,
-            "avg_wait_time": 0.0
-        }
-        self.urls_total = urls_total
-        self.urls_completed = 0
-        self.peak_memory_percent = 0.0
-        self.peak_memory_time = 0.0
-        
-        # Status counts
-        self.status_counts = {
-            CrawlStatus.QUEUED.name: 0,
-            CrawlStatus.IN_PROGRESS.name: 0,
-            CrawlStatus.COMPLETED.name: 0,
-            CrawlStatus.FAILED.name: 0
-        }
-        
-        # Requeue tracking
-        self.requeued_count = 0
-        
-        # Thread-safety
-        self._lock = threading.RLock()
-        
-        # Terminal UI
-        self.enable_ui = enable_ui
-        self.terminal_ui = TerminalUI(
-            refresh_rate=refresh_rate, 
-            max_width=max_width
-        ) if enable_ui else None
-    
-    def start(self):
-        """
-        Start the monitoring session.
-        
-        - Initializes the start_time
-        - Sets is_running to True
-        - Starts the terminal UI if enabled
-        """
-        with self._lock:
-            self.start_time = time.time()
-            self.is_running = True
-            
-            # Start the terminal UI
-            if self.enable_ui and self.terminal_ui:
-                self.terminal_ui.start(self)
-    
-    def stop(self):
-        """
-        Stop the monitoring session.
-        
-        - Records end_time
-        - Sets is_running to False
-        - Stops the terminal UI
-        - Generates final summary statistics
-        """
-        with self._lock:
-            self.end_time = time.time()
-            self.is_running = False
-            
-            # Stop the terminal UI
-            if self.enable_ui and self.terminal_ui:
-                self.terminal_ui.stop()
-    
-    def add_task(self, task_id: str, url: str):
-        """
-        Register a new task with the monitor.
-        
-        Args:
-            task_id: Unique identifier for the task
-            url: URL being crawled
-            
-        The task is initialized with:
-            - status: QUEUED
-            - url: The URL to crawl
-            - enqueue_time: Current time
-            - memory_usage: 0
-            - peak_memory: 0
-            - wait_time: 0
-            - retry_count: 0
-        """
-        with self._lock:
-            self.stats[task_id] = {
-                "task_id": task_id,
-                "url": url,
-                "status": CrawlStatus.QUEUED.name,
-                "enqueue_time": time.time(),
-                "start_time": None,
-                "end_time": None,
-                "memory_usage": 0.0,
-                "peak_memory": 0.0,
-                "error_message": "",
-                "wait_time": 0.0,
-                "retry_count": 0,
-                "duration": "0:00",
-                "counted_requeue": False
-            }
-            
-            # Update status counts
-            self.status_counts[CrawlStatus.QUEUED.name] += 1
-    
-    def update_task(
-        self, 
-        task_id: str, 
-        status: Optional[CrawlStatus] = None,
-        start_time: Optional[float] = None,
-        end_time: Optional[float] = None,
-        memory_usage: Optional[float] = None,
-        peak_memory: Optional[float] = None,
-        error_message: Optional[str] = None,
-        retry_count: Optional[int] = None,
-        wait_time: Optional[float] = None
-    ):
-        """
-        Update statistics for a specific task.
-        
-        Args:
-            task_id: Unique identifier for the task
-            status: New status (QUEUED, IN_PROGRESS, COMPLETED, FAILED)
-            start_time: When task execution started
-            end_time: When task execution ended
-            memory_usage: Current memory usage in MB
-            peak_memory: Maximum memory usage in MB
-            error_message: Error description if failed
-            retry_count: Number of retry attempts
-            wait_time: Time spent in queue
-            
-        Updates task statistics and updates status counts.
-        If status changes, decrements old status count and 
-        increments new status count.
-        """
-        with self._lock:
-            # Check if task exists
-            if task_id not in self.stats:
-                return
-            
-            task_stats = self.stats[task_id]
-            
-            # Update status counts if status is changing
-            old_status = task_stats["status"]
-            if status and status.name != old_status:
-                self.status_counts[old_status] -= 1
-                self.status_counts[status.name] += 1
-                
-                # Track completion
-                if status == CrawlStatus.COMPLETED:
-                    self.urls_completed += 1
-                
-                # Track requeues
-                if old_status in [CrawlStatus.COMPLETED.name, CrawlStatus.FAILED.name] and not task_stats.get("counted_requeue", False):
-                    self.requeued_count += 1
-                    task_stats["counted_requeue"] = True
-            
-            # Update task statistics
-            if status:
-                task_stats["status"] = status.name
-            if start_time is not None:
-                task_stats["start_time"] = start_time
-            if end_time is not None:
-                task_stats["end_time"] = end_time
-            if memory_usage is not None:
-                task_stats["memory_usage"] = memory_usage
-                
-                # Update peak memory if necessary
-                current_percent = (memory_usage / psutil.virtual_memory().total) * 100
-                if current_percent > self.peak_memory_percent:
-                    self.peak_memory_percent = current_percent
-                    self.peak_memory_time = time.time()
-                
-            if peak_memory is not None:
-                task_stats["peak_memory"] = peak_memory
-            if error_message is not None:
-                task_stats["error_message"] = error_message
-            if retry_count is not None:
-                task_stats["retry_count"] = retry_count
-            if wait_time is not None:
-                task_stats["wait_time"] = wait_time
-            
-            # Calculate duration
-            if task_stats["start_time"]:
-                end = task_stats["end_time"] or time.time()
-                duration = end - task_stats["start_time"]
-                task_stats["duration"] = self._format_time(duration)
-    
-    def update_memory_status(self, status: str):
-        """
-        Update the current memory status.
-        
-        Args:
-            status: Memory status (NORMAL, PRESSURE, CRITICAL, or custom)
-            
-        Also updates the UI to reflect the new status.
-        """
-        with self._lock:
-            self.memory_status = status
-    
-    def update_queue_statistics(
-        self,
-        total_queued: int,
-        highest_wait_time: float,
-        avg_wait_time: float
-    ):
-        """
-        Update statistics related to the task queue.
-        
-        Args:
-            total_queued: Number of tasks currently in queue
-            highest_wait_time: Longest wait time of any queued task
-            avg_wait_time: Average wait time across all queued tasks
-        """
-        with self._lock:
-            self.queue_stats = {
-                "total_queued": total_queued,
-                "highest_wait_time": highest_wait_time,
-                "avg_wait_time": avg_wait_time
-            }
-    
-    def get_task_stats(self, task_id: str) -> Dict:
-        """
-        Get statistics for a specific task.
-        
-        Args:
-            task_id: Unique identifier for the task
-            
-        Returns:
-            Dictionary containing all task statistics
-        """
-        with self._lock:
-            return self.stats.get(task_id, {}).copy()
-    
-    def get_all_task_stats(self) -> Dict[str, Dict]:
-        """
-        Get statistics for all tasks.
-        
-        Returns:
-            Dictionary mapping task_ids to their statistics
-        """
-        with self._lock:
-            return self.stats.copy()
-    
-    def get_memory_status(self) -> str:
-        """
-        Get the current memory status.
-        
-        Returns:
-            Current memory status string
-        """
-        with self._lock:
-            return self.memory_status
-    
-    def get_queue_stats(self) -> Dict:
-        """
-        Get current queue statistics.
-        
-        Returns:
-            Dictionary with queue statistics including:
-            - total_queued: Number of tasks in queue
-            - highest_wait_time: Longest wait time
-            - avg_wait_time: Average wait time
-        """
-        with self._lock:
-            return self.queue_stats.copy()
-    
-    def get_summary(self) -> Dict:
-        """
-        Get a summary of all crawler statistics.
-        
-        Returns:
-            Dictionary containing:
-            - runtime: Total runtime in seconds
-            - urls_total: Total URLs to process
-            - urls_completed: Number of completed URLs
-            - completion_percentage: Percentage complete
-            - status_counts: Count of tasks in each status
-            - memory_status: Current memory status
-            - peak_memory_percent: Highest memory usage
-            - peak_memory_time: When peak memory occurred
-            - avg_task_duration: Average task processing time
-            - estimated_completion_time: Projected finish time
-            - requeue_rate: Percentage of tasks requeued
-        """
-        with self._lock:
-            # Calculate runtime
-            current_time = time.time()
-            runtime = current_time - (self.start_time or current_time)
-            
-            # Calculate completion percentage
-            completion_percentage = 0
-            if self.urls_total > 0:
-                completion_percentage = (self.urls_completed / self.urls_total) * 100
-            
-            # Calculate average task duration for completed tasks
-            completed_tasks = [
-                task for task in self.stats.values() 
-                if task["status"] == CrawlStatus.COMPLETED.name and task.get("start_time") and task.get("end_time")
-            ]
-            
-            avg_task_duration = 0
-            if completed_tasks:
-                total_duration = sum(task["end_time"] - task["start_time"] for task in completed_tasks)
-                avg_task_duration = total_duration / len(completed_tasks)
-            
-            # Calculate requeue rate
-            requeue_rate = 0
-            if len(self.stats) > 0:
-                requeue_rate = (self.requeued_count / len(self.stats)) * 100
-            
-            # Calculate estimated completion time
-            estimated_completion_time = "N/A"
-            if avg_task_duration > 0 and self.urls_total > 0 and self.urls_completed > 0:
-                remaining_tasks = self.urls_total - self.urls_completed
-                estimated_seconds = remaining_tasks * avg_task_duration
-                estimated_completion_time = self._format_time(estimated_seconds)
-            
-            return {
-                "runtime": runtime,
-                "urls_total": self.urls_total,
-                "urls_completed": self.urls_completed,
-                "completion_percentage": completion_percentage,
-                "status_counts": self.status_counts.copy(),
-                "memory_status": self.memory_status,
-                "peak_memory_percent": self.peak_memory_percent,
-                "peak_memory_time": self.peak_memory_time,
-                "avg_task_duration": avg_task_duration,
-                "estimated_completion_time": estimated_completion_time,
-                "requeue_rate": requeue_rate,
-                "requeued_count": self.requeued_count
-            }
-    
-    def render(self):
-        """
-        Render the terminal UI.
-        
-        This is the main UI rendering loop that:
-        1. Updates all statistics
-        2. Formats the display
-        3. Renders the ASCII interface
-        4. Handles keyboard input
-        
-        Note: The actual rendering is handled by the TerminalUI class
-        which uses the rich library's Live display.
-        """
-        if self.enable_ui and self.terminal_ui:
-            # Force an update of the UI
-            if hasattr(self.terminal_ui, '_update_display'):
-                self.terminal_ui._update_display()
-    
-    def _format_time(self, seconds: float) -> str:
-        """
-        Format time in hours:minutes:seconds.
-        
-        Args:
-            seconds: Time in seconds
-            
-        Returns:
-            Formatted time string (e.g., "1:23:45")
-        """
-        delta = timedelta(seconds=int(seconds))
-        hours, remainder = divmod(delta.seconds, 3600)
-        minutes, seconds = divmod(remainder, 60)
-        
-        if hours > 0:
-            return f"{hours}:{minutes:02}:{seconds:02}"
-        else:
-            return f"{minutes}:{seconds:02}"
-    
-    def _calculate_estimated_completion(self) -> str:
-        """
-        Calculate estimated completion time based on current progress.
-        
-        Returns:
-            Formatted time string
-        """
-        summary = self.get_summary()
-        return summary.get("estimated_completion_time", "N/A")
-
-
-# Example code for testing
-if __name__ == "__main__":
-    # Initialize the monitor
-    monitor = CrawlerMonitor(urls_total=100)
-    
-    # Start monitoring
-    monitor.start()
-    
-    try:
-        # Simulate some tasks
-        for i in range(20):
-            task_id = str(uuid.uuid4())
-            url = f"https://example.com/page{i}"
-            monitor.add_task(task_id, url)
-            
-            # Simulate 20% of tasks are already running
-            if i < 4:
-                monitor.update_task(
-                    task_id=task_id,
-                    status=CrawlStatus.IN_PROGRESS,
-                    start_time=time.time() - 30,  # Started 30 seconds ago
-                    memory_usage=10.5
-                )
-                
-            # Simulate 10% of tasks are completed
-            if i >= 4 and i < 6:
-                start_time = time.time() - 60
-                end_time = time.time() - 15
-                monitor.update_task(
-                    task_id=task_id,
-                    status=CrawlStatus.IN_PROGRESS,
-                    start_time=start_time,
-                    memory_usage=8.2
-                )
-                monitor.update_task(
-                    task_id=task_id,
-                    status=CrawlStatus.COMPLETED,
-                    end_time=end_time,
-                    memory_usage=0,
-                    peak_memory=15.7
-                )
-                
-            # Simulate 5% of tasks fail
-            if i >= 6 and i < 7:
-                start_time = time.time() - 45
-                end_time = time.time() - 20
-                monitor.update_task(
-                    task_id=task_id,
-                    status=CrawlStatus.IN_PROGRESS,
-                    start_time=start_time,
-                    memory_usage=12.3
-                )
-                monitor.update_task(
-                    task_id=task_id,
-                    status=CrawlStatus.FAILED,
-                    end_time=end_time,
-                    memory_usage=0,
-                    peak_memory=18.2,
-                    error_message="Connection timeout"
-                )
-        
-        # Simulate memory pressure
-        monitor.update_memory_status("PRESSURE")
-        
-        # Simulate queue statistics
-        monitor.update_queue_statistics(
-            total_queued=16,  # 20 - 4 (in progress)
-            highest_wait_time=120.5,
-            avg_wait_time=60.2
-        )
-        
-        # Keep the monitor running for a demonstration
-        print("Crawler Monitor is running. Press 'q' to exit.")
-        while monitor.is_running:
-            time.sleep(0.1)
-            
-    except KeyboardInterrupt:
-        print("\nExiting crawler monitor...")
-    finally:
-        # Stop the monitor
-        monitor.stop()
-        print("Crawler monitor exited successfully.")

crawl4ai/config.py

@@ -4,84 +4,43 @@ from dotenv import load_dotenv
 load_dotenv()  # Load environment variables from .env file
 
 # Default provider, ONLY used when the extraction strategy is LLMExtractionStrategy
-DEFAULT_PROVIDER = "openai/gpt-4o"
-DEFAULT_PROVIDER_API_KEY = "OPENAI_API_KEY"
+DEFAULT_PROVIDER = "openai/gpt-4o-mini"
 MODEL_REPO_BRANCH = "new-release-0.0.2"
 # Provider-model dictionary, ONLY used when the extraction strategy is LLMExtractionStrategy
 PROVIDER_MODELS = {
-    "ollama/llama3": "no-token-needed",  # Any model from Ollama no need for API token
+    "ollama/llama3": "no-token-needed", # Any model from Ollama no need for API token
     "groq/llama3-70b-8192": os.getenv("GROQ_API_KEY"),
     "groq/llama3-8b-8192": os.getenv("GROQ_API_KEY"),
     "openai/gpt-4o-mini": os.getenv("OPENAI_API_KEY"),
     "openai/gpt-4o": os.getenv("OPENAI_API_KEY"),
-    "openai/o1-mini": os.getenv("OPENAI_API_KEY"),
-    "openai/o1-preview": os.getenv("OPENAI_API_KEY"),
-    "openai/o3-mini": os.getenv("OPENAI_API_KEY"),
-    "openai/o3-mini-high": os.getenv("OPENAI_API_KEY"),
     "anthropic/claude-3-haiku-20240307": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-opus-20240229": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-sonnet-20240229": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-5-sonnet-20240620": os.getenv("ANTHROPIC_API_KEY"),
-    "gemini/gemini-pro": os.getenv("GEMINI_API_KEY"),
-    'gemini/gemini-1.5-pro': os.getenv("GEMINI_API_KEY"),
-    'gemini/gemini-2.0-flash': os.getenv("GEMINI_API_KEY"),
-    'gemini/gemini-2.0-flash-exp': os.getenv("GEMINI_API_KEY"),
-    'gemini/gemini-2.0-flash-lite-preview-02-05': os.getenv("GEMINI_API_KEY"),
-    "deepseek/deepseek-chat": os.getenv("DEEPSEEK_API_KEY"),
-}
-PROVIDER_MODELS_PREFIXES = {
-    "ollama": "no-token-needed",  # Any model from Ollama no need for API token
-    "groq": os.getenv("GROQ_API_KEY"),
-    "openai": os.getenv("OPENAI_API_KEY"),
-    "anthropic": os.getenv("ANTHROPIC_API_KEY"),
-    "gemini": os.getenv("GEMINI_API_KEY"),
-    "deepseek": os.getenv("DEEPSEEK_API_KEY"),
 }
 
 # Chunk token threshold
-CHUNK_TOKEN_THRESHOLD = 2**11  # 2048 tokens
+CHUNK_TOKEN_THRESHOLD = 2 ** 11 # 2048 tokens
 OVERLAP_RATE = 0.1
 WORD_TOKEN_RATE = 1.3
 
-# Threshold for the minimum number of word in a HTML tag to be considered
+# Threshold for the minimum number of word in a HTML tag to be considered 
 MIN_WORD_THRESHOLD = 1
 IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD = 1
 
-IMPORTANT_ATTRS = ["src", "href", "alt", "title", "width", "height"]
-ONLY_TEXT_ELIGIBLE_TAGS = [
-    "b",
-    "i",
-    "u",
-    "span",
-    "del",
-    "ins",
-    "sub",
-    "sup",
-    "strong",
-    "em",
-    "code",
-    "kbd",
-    "var",
-    "s",
-    "q",
-    "abbr",
-    "cite",
-    "dfn",
-    "time",
-    "small",
-    "mark",
-]
+IMPORTANT_ATTRS = ['src', 'href', 'alt', 'title', 'width', 'height'] 
+ONLY_TEXT_ELIGIBLE_TAGS = ['b', 'i', 'u', 'span', 'del', 'ins', 'sub', 'sup', 'strong', 'em', 'code', 'kbd', 'var', 's', 'q', 'abbr', 'cite', 'dfn', 'time', 'small', 'mark']
 SOCIAL_MEDIA_DOMAINS = [
-    "facebook.com",
-    "twitter.com",
-    "x.com",
-    "linkedin.com",
-    "instagram.com",
-    "pinterest.com",
-    "tiktok.com",
-    "snapchat.com",
-    "reddit.com",
-]
+                            'facebook.com',
+                            'twitter.com',
+                            'x.com',
+                            'linkedin.com',
+                            'instagram.com',
+                            'pinterest.com',
+                            'tiktok.com',
+                            'snapchat.com',
+                            'reddit.com',
+                        ]
 
 # Threshold for the Image extraction - Range is 1 to 6
 # Images are scored based on point based system, to filter based on usefulness. Points are assigned
@@ -93,54 +52,4 @@ SOCIAL_MEDIA_DOMAINS = [
 # If image is in the first half of the total images extracted from the page
 IMAGE_SCORE_THRESHOLD = 2
 
-MAX_METRICS_HISTORY = 1000
-
-NEED_MIGRATION = True
-URL_LOG_SHORTEN_LENGTH = 30
-SHOW_DEPRECATION_WARNINGS = True
-SCREENSHOT_HEIGHT_TRESHOLD = 10000
-PAGE_TIMEOUT = 60000
-DOWNLOAD_PAGE_TIMEOUT = 60000
-
-# Global user settings with descriptions and default values
-USER_SETTINGS = {
-    "DEFAULT_LLM_PROVIDER": {
-        "default": "openai/gpt-4o",
-        "description": "Default LLM provider in 'company/model' format (e.g., 'openai/gpt-4o', 'anthropic/claude-3-sonnet')",
-        "type": "string"
-    },
-    "DEFAULT_LLM_PROVIDER_TOKEN": {
-        "default": "",
-        "description": "API token for the default LLM provider",
-        "type": "string",
-        "secret": True
-    },
-    "VERBOSE": {
-        "default": False,
-        "description": "Enable verbose output for all commands",
-        "type": "boolean"
-    },
-    "BROWSER_HEADLESS": {
-        "default": True,
-        "description": "Run browser in headless mode by default",
-        "type": "boolean"
-    },
-    "BROWSER_TYPE": {
-        "default": "chromium",
-        "description": "Default browser type (chromium or firefox)",
-        "type": "string",
-        "options": ["chromium", "firefox"]
-    },
-    "CACHE_MODE": {
-        "default": "bypass",
-        "description": "Default cache mode (bypass, use, or refresh)",
-        "type": "string",
-        "options": ["bypass", "use", "refresh"]
-    },
-    "USER_AGENT_MODE": {
-        "default": "default",
-        "description": "Default user agent mode (default, random, or mobile)",
-        "type": "string",
-        "options": ["default", "random", "mobile"]
-    }
-}
+MAX_METRICS_HISTORY = 1000

crawl4ai/content_cleaning_strategy.py

@@ -0,0 +1,198 @@
+from bs4 import BeautifulSoup, Tag
+import re
+from typing import Optional
+
+class ContentCleaningStrategy:
+    def __init__(self):
+        # Precompile regex patterns for performance
+        self.negative_patterns = re.compile(r'nav|footer|header|sidebar|ads|comment', re.I)
+        self.positive_patterns = re.compile(r'content|article|main|post', re.I)
+        self.priority_tags = {'article', 'main', 'section', 'div'}
+        self.non_content_tags = {'nav', 'footer', 'header', 'aside'}
+        # Thresholds
+        self.text_density_threshold = 9.0
+        self.min_word_count = 50
+        self.link_density_threshold = 0.2
+        self.max_dom_depth = 10  # To prevent excessive DOM traversal
+
+    def clean(self, clean_html: str, soup = None) -> str:
+        """
+        Main function that takes cleaned HTML and returns super cleaned HTML.
+
+        Args:
+            clean_html (str): The cleaned HTML content.
+
+        Returns:
+            str: The super cleaned HTML containing only the main content.
+        """
+        try:
+            if not clean_html or not isinstance(clean_html, str):
+                return ''
+            if not soup:
+                # soup = BeautifulSoup(clean_html, 'html.parser')
+                soup = BeautifulSoup(clean_html, 'lxml')
+            main_content = self.extract_main_content(soup)
+            if main_content:
+                super_clean_element = self.clean_element(main_content)
+                return super_clean_element.encode_contents().decode('utf-8')
+            else:
+                return ''
+        except Exception:
+            # Handle exceptions silently or log them as needed
+            return ''
+
+    def extract_main_content(self, soup) -> Optional[Tag]:
+        """
+        Identifies and extracts the main content element from the HTML.
+
+        Args:
+            soup (BeautifulSoup): The parsed HTML soup.
+
+        Returns:
+            Optional[Tag]: The Tag object containing the main content, or None if not found.
+        """
+        candidates = []
+        for element in soup.find_all(self.priority_tags):
+            if self.is_non_content_tag(element):
+                continue
+            if self.has_negative_class_id(element):
+                continue
+            score = self.calculate_content_score(element)
+            candidates.append((score, element))
+        
+        if not candidates:
+            return None
+
+        # Sort candidates by score in descending order
+        candidates.sort(key=lambda x: x[0], reverse=True)
+        # Select the element with the highest score
+        best_element = candidates[0][1]
+        return best_element
+
+    def calculate_content_score(self, element: Tag) -> float:
+        """
+        Calculates a score for an element based on various heuristics.
+
+        Args:
+            element (Tag): The HTML element to score.
+
+        Returns:
+            float: The content score of the element.
+        """
+        score = 0.0
+
+        if self.is_priority_tag(element):
+            score += 5.0
+        if self.has_positive_class_id(element):
+            score += 3.0
+        if self.has_negative_class_id(element):
+            score -= 3.0
+        if self.is_high_text_density(element):
+            score += 2.0
+        if self.is_low_link_density(element):
+            score += 2.0
+        if self.has_sufficient_content(element):
+            score += 2.0
+        if self.has_headings(element):
+            score += 3.0
+
+        dom_depth = self.calculate_dom_depth(element)
+        score += min(dom_depth, self.max_dom_depth) * 0.5  # Adjust weight as needed
+
+        return score
+
+    def is_priority_tag(self, element: Tag) -> bool:
+        """Checks if the element is a priority tag."""
+        return element.name in self.priority_tags
+
+    def is_non_content_tag(self, element: Tag) -> bool:
+        """Checks if the element is a non-content tag."""
+        return element.name in self.non_content_tags
+
+    def has_negative_class_id(self, element: Tag) -> bool:
+        """Checks if the element has negative indicators in its class or id."""
+        class_id = ' '.join(filter(None, [
+            self.get_attr_str(element.get('class')),
+            element.get('id', '')
+        ]))
+        return bool(self.negative_patterns.search(class_id))
+
+    def has_positive_class_id(self, element: Tag) -> bool:
+        """Checks if the element has positive indicators in its class or id."""
+        class_id = ' '.join(filter(None, [
+            self.get_attr_str(element.get('class')),
+            element.get('id', '')
+        ]))
+        return bool(self.positive_patterns.search(class_id))
+
+    @staticmethod
+    def get_attr_str(attr) -> str:
+        """Converts an attribute value to a string."""
+        if isinstance(attr, list):
+            return ' '.join(attr)
+        elif isinstance(attr, str):
+            return attr
+        else:
+            return ''
+
+    def is_high_text_density(self, element: Tag) -> bool:
+        """Determines if the element has high text density."""
+        text_density = self.calculate_text_density(element)
+        return text_density > self.text_density_threshold
+
+    def calculate_text_density(self, element: Tag) -> float:
+        """Calculates the text density of an element."""
+        text_length = len(element.get_text(strip=True))
+        tag_count = len(element.find_all())
+        tag_count = tag_count or 1  # Prevent division by zero
+        return text_length / tag_count
+
+    def is_low_link_density(self, element: Tag) -> bool:
+        """Determines if the element has low link density."""
+        link_density = self.calculate_link_density(element)
+        return link_density < self.link_density_threshold
+
+    def calculate_link_density(self, element: Tag) -> float:
+        """Calculates the link density of an element."""
+        text = element.get_text(strip=True)
+        if not text:
+            return 0.0
+        link_text = ' '.join(a.get_text(strip=True) for a in element.find_all('a'))
+        return len(link_text) / len(text) if text else 0.0
+
+    def has_sufficient_content(self, element: Tag) -> bool:
+        """Checks if the element has sufficient word count."""
+        word_count = len(element.get_text(strip=True).split())
+        return word_count >= self.min_word_count
+
+    def calculate_dom_depth(self, element: Tag) -> int:
+        """Calculates the depth of an element in the DOM tree."""
+        depth = 0
+        current_element = element
+        while current_element.parent and depth < self.max_dom_depth:
+            depth += 1
+            current_element = current_element.parent
+        return depth
+
+    def has_headings(self, element: Tag) -> bool:
+        """Checks if the element contains heading tags."""
+        return bool(element.find(['h1', 'h2', 'h3']))
+
+    def clean_element(self, element: Tag) -> Tag:
+        """
+        Cleans the selected element by removing unnecessary attributes and nested non-content elements.
+
+        Args:
+            element (Tag): The HTML element to clean.
+
+        Returns:
+            Tag: The cleaned HTML element.
+        """
+        for tag in element.find_all(['script', 'style', 'aside']):
+            tag.decompose()
+        for tag in element.find_all():
+            attrs = dict(tag.attrs)
+            for attr in attrs:
+                if attr in ['style', 'onclick', 'onmouseover', 'align', 'bgcolor']:
+                    del tag.attrs[attr]
+        return element

crawl4ai/content_scraping_strategy.py

@@ -1,900 +0,0 @@
-import re
-from itertools import chain
-from abc import ABC, abstractmethod
-from typing import Dict, Any, Optional
-from bs4 import BeautifulSoup
-import asyncio
-import requests
-from .config import (
-    MIN_WORD_THRESHOLD,
-    IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
-    IMAGE_SCORE_THRESHOLD,
-    ONLY_TEXT_ELIGIBLE_TAGS,
-    IMPORTANT_ATTRS,
-    SOCIAL_MEDIA_DOMAINS,
-)
-from bs4 import NavigableString, Comment
-from bs4 import PageElement, Tag
-from urllib.parse import urljoin
-from requests.exceptions import InvalidSchema
-from .utils import (
-    extract_metadata,
-    normalize_url,
-    is_external_url,
-    get_base_domain,
-    extract_metadata_using_lxml,
-    extract_page_context,
-    calculate_link_intrinsic_score,
-)
-from lxml import etree
-from lxml import html as lhtml
-from typing import List
-from .models import ScrapingResult, MediaItem, Link, Media, Links
-import copy
-
-# Pre-compile regular expressions for Open Graph and Twitter metadata
-OG_REGEX = re.compile(r"^og:")
-TWITTER_REGEX = re.compile(r"^twitter:")
-DIMENSION_REGEX = re.compile(r"(\d+)(\D*)")
-
-
-# Function to parse srcset
-def parse_srcset(s: str) -> List[Dict]:
-    if not s:
-        return []
-    variants = []
-    for part in s.split(","):
-        part = part.strip()
-        if not part:
-            continue
-        parts = part.split()
-        if len(parts) >= 1:
-            url = parts[0]
-            width = (
-                parts[1].rstrip("w").split('.')[0]
-                if len(parts) > 1 and parts[1].endswith("w")
-                else None
-            )
-            variants.append({"url": url, "width": width})
-    return variants
-
-
-# Function to parse image height/width value and units
-def parse_dimension(dimension):
-    if dimension:
-        # match = re.match(r"(\d+)(\D*)", dimension)
-        match = DIMENSION_REGEX.match(dimension)
-        if match:
-            number = int(match.group(1))
-            unit = match.group(2) or "px"  # Default unit is 'px' if not specified
-            return number, unit
-    return None, None
-
-
-# Fetch image file metadata to extract size and extension
-def fetch_image_file_size(img, base_url):
-    # If src is relative path construct full URL, if not it may be CDN URL
-    img_url = urljoin(base_url, img.get("src"))
-    try:
-        response = requests.head(img_url)
-        if response.status_code == 200:
-            return response.headers.get("Content-Length", None)
-        else:
-            print(f"Failed to retrieve file size for {img_url}")
-            return None
-    except InvalidSchema:
-        return None
-    finally:
-        return
-
-
-class ContentScrapingStrategy(ABC):
-    @abstractmethod
-    def scrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
-        pass
-
-    @abstractmethod
-    async def ascrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
-        pass
-
-
-class LXMLWebScrapingStrategy(ContentScrapingStrategy):
-    """
-    LXML-based implementation for fast web content scraping.
-    
-    This is the primary scraping strategy in Crawl4AI, providing high-performance
-    HTML parsing and content extraction using the lxml library.
-    
-    Note: WebScrapingStrategy is now an alias for this class to maintain
-    backward compatibility.
-    """
-    def __init__(self, logger=None):
-        self.logger = logger
-        self.DIMENSION_REGEX = re.compile(r"(\d+)(\D*)")
-        self.BASE64_PATTERN = re.compile(r'data:image/[^;]+;base64,([^"]+)')
-
-    def _log(self, level, message, tag="SCRAPE", **kwargs):
-        """Helper method to safely use logger."""
-        if self.logger:
-            log_method = getattr(self.logger, level)
-            log_method(message=message, tag=tag, **kwargs)
-
-    def scrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
-        """
-        Main entry point for content scraping.
-
-        Args:
-            url (str): The URL of the page to scrape.
-            html (str): The HTML content of the page.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            ScrapingResult: A structured result containing the scraped content.
-        """
-        actual_url = kwargs.get("redirected_url", url)
-        raw_result = self._scrap(actual_url, html, **kwargs)
-        if raw_result is None:
-            return ScrapingResult(
-                cleaned_html="",
-                success=False,
-                media=Media(),
-                links=Links(),
-                metadata={},
-            )
-
-        # Convert media items
-        media = Media(
-            images=[
-                MediaItem(**img)
-                for img in raw_result.get("media", {}).get("images", [])
-                if img
-            ],
-            videos=[
-                MediaItem(**vid)
-                for vid in raw_result.get("media", {}).get("videos", [])
-                if vid
-            ],
-            audios=[
-                MediaItem(**aud)
-                for aud in raw_result.get("media", {}).get("audios", [])
-                if aud
-            ],
-            tables=raw_result.get("media", {}).get("tables", [])
-        )
-
-        # Convert links
-        links = Links(
-            internal=[
-                Link(**link)
-                for link in raw_result.get("links", {}).get("internal", [])
-                if link
-            ],
-            external=[
-                Link(**link)
-                for link in raw_result.get("links", {}).get("external", [])
-                if link
-            ],
-        )
-
-        return ScrapingResult(
-            cleaned_html=raw_result.get("cleaned_html", ""),
-            success=raw_result.get("success", False),
-            media=media,
-            links=links,
-            metadata=raw_result.get("metadata", {}),
-        )
-
-    async def ascrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
-        """
-        Main entry point for asynchronous content scraping.
-
-        Args:
-            url (str): The URL of the page to scrape.
-            html (str): The HTML content of the page.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            ScrapingResult: A structured result containing the scraped content.
-        """
-        return await asyncio.to_thread(self.scrap, url, html, **kwargs)
-
-    def process_element(self, url, element: lhtml.HtmlElement, **kwargs) -> Dict[str, Any]:
-        """
-        Process an HTML element.
-
-        How it works:
-        1. Check if the element is an image, video, or audio.
-        2. Extract the element's attributes and content.
-        3. Process the element based on its type.
-        4. Return the processed element information.
-
-        Args:
-            url (str): The URL of the page containing the element.
-            element (lhtml.HtmlElement): The HTML element to process.
-            **kwargs: Additional keyword arguments.
-
-        Returns:
-            dict: A dictionary containing the processed element information.
-        """
-        media = {"images": [], "videos": [], "audios": [], "tables": []}
-        internal_links_dict = {}
-        external_links_dict = {}
-        self._process_element(
-            url, element, media, internal_links_dict, external_links_dict, **kwargs
-        )
-        return {
-            "media": media,
-            "internal_links_dict": internal_links_dict,
-            "external_links_dict": external_links_dict,
-        }
-
-    def _process_element(
-        self,
-        url: str,
-        element: lhtml.HtmlElement,
-        media: Dict[str, List],
-        internal_links_dict: Dict[str, Any],
-        external_links_dict: Dict[str, Any],
-        page_context: dict = None,
-        **kwargs,
-    ) -> bool:
-        base_domain = kwargs.get("base_domain", get_base_domain(url))
-        exclude_domains = set(kwargs.get("exclude_domains", []))
-
-        # Process links
-        try:
-            base_element = element.xpath("//head/base[@href]")
-            if base_element:
-                base_href = base_element[0].get("href", "").strip()
-                if base_href:
-                    url = base_href
-        except Exception as e:
-            self._log("error", f"Error extracting base URL: {str(e)}", "SCRAPE")
-            pass
-
-        for link in element.xpath(".//a[@href]"):
-            href = link.get("href", "").strip()
-            if not href:
-                continue
-
-            try:
-                normalized_href = normalize_url(
-                    href, url,
-                    preserve_https=kwargs.get('preserve_https_for_internal_links', False),
-                    original_scheme=kwargs.get('original_scheme')
-                )
-                link_data = {
-                    "href": normalized_href,
-                    "text": link.text_content().strip(),
-                    "title": link.get("title", "").strip(),
-                    "base_domain": base_domain,
-                }
-                
-                # Add intrinsic scoring if enabled
-                if kwargs.get("score_links", False) and page_context is not None:
-                    try:
-                        intrinsic_score = calculate_link_intrinsic_score(
-                            link_text=link_data["text"],
-                            url=normalized_href,
-                            title_attr=link_data["title"],
-                            class_attr=link.get("class", ""),
-                            rel_attr=link.get("rel", ""),
-                            page_context=page_context
-                        )
-                        link_data["intrinsic_score"] = intrinsic_score
-                    except Exception:
-                        # Fail gracefully - assign default score
-                        link_data["intrinsic_score"] = 0
-                else:
-                    # No scoring enabled - assign infinity (all links equal priority)
-                    link_data["intrinsic_score"] = 0
-
-                is_external = is_external_url(normalized_href, base_domain)
-                if is_external:
-                    link_base_domain = get_base_domain(normalized_href)
-                    link_data["base_domain"] = link_base_domain
-                    if (
-                        kwargs.get("exclude_external_links", False)
-                        or link_base_domain in exclude_domains
-                    ):
-                        link.getparent().remove(link)
-                        continue
-
-                    if normalized_href not in external_links_dict:
-                        external_links_dict[normalized_href] = link_data
-                else:
-                    if normalized_href not in internal_links_dict:
-                        internal_links_dict[normalized_href] = link_data
-
-            except Exception as e:
-                self._log("error", f"Error processing link: {str(e)}", "SCRAPE")
-                continue
-
-        # Process images
-        images = element.xpath(".//img")
-        total_images = len(images)
-
-        for idx, img in enumerate(images):
-            src = img.get("src") or ""
-            img_domain = get_base_domain(src)
-
-            # Decide if we need to exclude this image
-            # 1) If its domain is in exclude_domains, remove.
-            # 2) Or if exclude_external_images=True and it's an external domain, remove.
-            if (img_domain in exclude_domains) or (
-                kwargs.get("exclude_external_images", False)
-                and is_external_url(src, base_domain)
-            ):
-                parent = img.getparent()
-                if parent is not None:
-                    parent.remove(img)
-                continue
-
-            # Otherwise, process the image as usual.
-            try:
-                processed_images = self.process_image(
-                    img, url, idx, total_images, **kwargs
-                )
-                if processed_images:
-                    media["images"].extend(processed_images)
-            except Exception as e:
-                self._log("error", f"Error processing image: {str(e)}", "SCRAPE")
-
-        # Process videos and audios
-        for media_type in ["video", "audio"]:
-            for elem in element.xpath(f".//{media_type}"):
-                media_info = {
-                    "src": elem.get("src"),
-                    "alt": elem.get("alt"),
-                    "type": media_type,
-                    "description": self.find_closest_parent_with_useful_text(
-                        elem, **kwargs
-                    ),
-                }
-                media[f"{media_type}s"].append(media_info)
-
-                # Process source tags within media elements
-                for source in elem.xpath(".//source"):
-                    if src := source.get("src"):
-                        media[f"{media_type}s"].append({**media_info, "src": src})
-
-        # Clean up unwanted elements
-        if kwargs.get("remove_forms", False):
-            for form in element.xpath(".//form"):
-                form.getparent().remove(form)
-
-        if excluded_tags := kwargs.get("excluded_tags", []):
-            for tag in excluded_tags:
-                for elem in element.xpath(f".//{tag}"):
-                    elem.getparent().remove(elem)
-
-        if excluded_selector := kwargs.get("excluded_selector", ""):
-            try:
-                for elem in element.cssselect(excluded_selector):
-                    elem.getparent().remove(elem)
-            except Exception:
-                pass  # Invalid selector
-
-        return True
-
-    def find_closest_parent_with_useful_text(
-        self, element: lhtml.HtmlElement, **kwargs
-    ) -> Optional[str]:
-        image_description_min_word_threshold = kwargs.get(
-            "image_description_min_word_threshold", IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD
-        )
-        current = element
-        while current is not None:
-            if (
-                current.text
-                and len(current.text_content().split())
-                >= image_description_min_word_threshold
-            ):
-                return current.text_content().strip()
-            current = current.getparent()
-        return None
-
-    def flatten_nested_elements(self, element: lhtml.HtmlElement) -> lhtml.HtmlElement:
-        """Flatten nested elements of the same type in LXML tree"""
-        if len(element) == 1 and element.tag == element[0].tag:
-            return self.flatten_nested_elements(element[0])
-
-        for child in element:
-            child_idx = element.index(child)
-            flattened_child = self.flatten_nested_elements(child)
-            if flattened_child is not child:  # Only replace if actually flattened
-                element[child_idx] = flattened_child
-
-        return element
-
-    def process_image(
-        self, img: lhtml.HtmlElement, url: str, index: int, total_images: int, **kwargs
-    ) -> Optional[List[Dict]]:
-        # Quick validation checks
-        style = img.get("style", "")
-        alt = img.get("alt", "")
-        src = img.get("src", "")
-        data_src = img.get("data-src", "")
-        srcset = img.get("srcset", "")
-        data_srcset = img.get("data-srcset", "")
-
-        if "display:none" in style:
-            return None
-
-        parent = img.getparent()
-        if parent.tag in ["button", "input"]:
-            return None
-
-        parent_classes = parent.get("class", "").split()
-        if any(
-            "button" in cls or "icon" in cls or "logo" in cls for cls in parent_classes
-        ):
-            return None
-
-        # If src is in class or alt, likely an icon
-        if (src and any(c in src for c in ["button", "icon", "logo"])) or (
-            alt and any(c in alt for c in ["button", "icon", "logo"])
-        ):
-            return None
-
-        # Score calculation
-        score = 0
-        if (width := img.get("width")) and width.isdigit():
-            score += 1 if int(width) > 150 else 0
-        if (height := img.get("height")) and height.isdigit():
-            score += 1 if int(height) > 150 else 0
-        if alt:
-            score += 1
-        score += index / total_images < 0.5
-
-        # Check formats in all possible sources
-        image_formats = {"jpg", "jpeg", "png", "webp", "avif", "gif"}
-        detected_format = None
-        for url in [src, data_src, srcset, data_srcset]:
-            if url:
-                format_matches = [fmt for fmt in image_formats if fmt in url.lower()]
-                if format_matches:
-                    detected_format = format_matches[0]
-                    score += 1
-                    break
-
-        if srcset or data_srcset:
-            score += 1
-
-        if picture := img.xpath("./ancestor::picture[1]"):
-            score += 1
-
-        if score <= kwargs.get("image_score_threshold", IMAGE_SCORE_THRESHOLD):
-            return None
-
-        # Process image variants
-        unique_urls = set()
-        image_variants = []
-        base_info = {
-            "alt": alt,
-            "desc": self.find_closest_parent_with_useful_text(img, **kwargs),
-            "score": score,
-            "type": "image",
-            "group_id": index,
-            "format": detected_format,
-        }
-
-        def add_variant(src: str, width: Optional[str] = None):
-            if src and not src.startswith("data:") and src not in unique_urls:
-                unique_urls.add(src)
-                variant = {**base_info, "src": src}
-                if width:
-                    variant["width"] = width
-                image_variants.append(variant)
-
-        # Add variants from different sources
-        add_variant(src)
-        add_variant(data_src)
-
-        for srcset_attr in [srcset, data_srcset]:
-            if srcset_attr:
-                for source in parse_srcset(srcset_attr):
-                    add_variant(source["url"], source["width"])
-
-        # Handle picture element
-        if picture:
-            for source in picture[0].xpath(".//source[@srcset]"):
-                if source_srcset := source.get("srcset"):
-                    for src_data in parse_srcset(source_srcset):
-                        add_variant(src_data["url"], src_data["width"])
-
-        # Check framework-specific attributes
-        for attr, value in img.attrib.items():
-            if (
-                attr.startswith("data-")
-                and ("src" in attr or "srcset" in attr)
-                and "http" in value
-            ):
-                add_variant(value)
-
-        return image_variants if image_variants else None
-
-    def remove_empty_elements_fast(self, root, word_count_threshold=5):
-        """
-        Remove elements that fall below the desired word threshold in a single pass from the bottom up.
-        Skips non-element nodes like HtmlComment and bypasses certain tags that are allowed to have no content.
-        """
-        bypass_tags = {
-            "a",
-            "img",
-            "br",
-            "hr",
-            "input",
-            "meta",
-            "link",
-            "source",
-            "track",
-            "wbr",
-            "tr",
-            "td",
-            "th",
-        }
-
-        for el in reversed(list(root.iterdescendants())):
-            if not isinstance(el, lhtml.HtmlElement):
-                continue
-
-            if el.tag in bypass_tags:
-                continue
-
-            text_content = (el.text_content() or "").strip()
-            if (
-                len(text_content.split()) < word_count_threshold
-                and not el.getchildren()
-            ):
-                parent = el.getparent()
-                if parent is not None:
-                    parent.remove(el)
-
-        return root
-
-    def remove_unwanted_attributes_fast(
-        self, root: lhtml.HtmlElement, important_attrs=None, keep_data_attributes=False
-    ) -> lhtml.HtmlElement:
-        """
-        Removes all attributes from each element (including root) except those in `important_attrs`.
-        If `keep_data_attributes=True`, also retain any attribute starting with 'data-'.
-
-        Returns the same root element, mutated in-place, for fluent usage.
-        """
-        if important_attrs is None:
-            important_attrs = set(IMPORTANT_ATTRS)
-
-        # If you want to handle the root as well, use 'include_self=True'
-        # so you don't miss attributes on the top-level element.
-        # Manually include the root, then all its descendants
-        for el in chain((root,), root.iterdescendants()):
-            # We only remove attributes on HtmlElement nodes, skip comments or text nodes
-            if not isinstance(el, lhtml.HtmlElement):
-                continue
-
-            old_attribs = dict(el.attrib)
-            new_attribs = {}
-
-            for attr_name, attr_val in old_attribs.items():
-                # If it's an important attribute, keep it
-                if attr_name in important_attrs:
-                    new_attribs[attr_name] = attr_val
-                # Or if keep_data_attributes is True and it's a 'data-*' attribute
-                elif keep_data_attributes and attr_name.startswith("data-"):
-                    new_attribs[attr_name] = attr_val
-
-            # Clear old attributes and set the filtered set
-            el.attrib.clear()
-            el.attrib.update(new_attribs)
-
-        return root
-
-
-    def _scrap(
-        self,
-        url: str,
-        html: str,
-        word_count_threshold: int = MIN_WORD_THRESHOLD,
-        css_selector: str = None,
-        target_elements: List[str] = None,
-        **kwargs,
-    ) -> Dict[str, Any]:
-        if not html:
-            return None
-
-        success = True
-        try:
-            doc = lhtml.document_fromstring(html)
-            # Match BeautifulSoup's behavior of using body or full doc
-            # body = doc.xpath('//body')[0] if doc.xpath('//body') else doc
-            body = doc
-
-            base_domain = get_base_domain(url)
-            
-            # Extract page context for link scoring (if enabled) - do this BEFORE any removals
-            page_context = None
-            if kwargs.get("score_links", False):
-                try:
-                    # Extract title
-                    title_elements = doc.xpath('//title')
-                    page_title = title_elements[0].text_content() if title_elements else ""
-                    
-                    # Extract headlines
-                    headlines = []
-                    for tag in ['h1', 'h2', 'h3']:
-                        elements = doc.xpath(f'//{tag}')
-                        for el in elements:
-                            text = el.text_content().strip()
-                            if text:
-                                headlines.append(text)
-                    headlines_text = ' '.join(headlines)
-                    
-                    # Extract meta description
-                    meta_desc_elements = doc.xpath('//meta[@name="description"]/@content')
-                    meta_description = meta_desc_elements[0] if meta_desc_elements else ""
-                    
-                    # Create page context
-                    page_context = extract_page_context(page_title, headlines_text, meta_description, url)
-                except Exception:
-                    page_context = {}  # Fail gracefully
-            
-            # Early removal of all images if exclude_all_images is set
-            # This is more efficient in lxml as we remove elements before any processing
-            if kwargs.get("exclude_all_images", False):
-                for img in body.xpath('//img'):
-                    if img.getparent() is not None:
-                        img.getparent().remove(img)
-
-            # Add comment removal
-            if kwargs.get("remove_comments", False):
-                comments = body.xpath("//comment()")
-                for comment in comments:
-                    comment.getparent().remove(comment)
-
-            # Handle tag-based removal first
-            excluded_tags = set(kwargs.get("excluded_tags", []) or [])
-            if excluded_tags:
-                for tag in excluded_tags:
-                    for element in body.xpath(f".//{tag}"):
-                        if element.getparent() is not None:
-                            element.getparent().remove(element)
-
-            # Handle CSS selector-based exclusion
-            excluded_selector = kwargs.get("excluded_selector", "")
-            if excluded_selector:
-                try:
-                    for element in body.cssselect(excluded_selector):
-                        if element.getparent() is not None:
-                            element.getparent().remove(element)
-                except Exception as e:
-                    self._log(
-                        "error", f"Error with excluded CSS selector: {str(e)}", "SCRAPE"
-                    )
-
-            # Extract metadata before any content filtering
-            try:
-                meta = extract_metadata_using_lxml(
-                    "", doc
-                )  # Using same function as BeautifulSoup version
-            except Exception as e:
-                self._log("error", f"Error extracting metadata: {str(e)}", "SCRAPE")
-                meta = {}
-
-            content_element = None
-            if target_elements:
-                try:
-                    for_content_targeted_element = []
-                    for target_element in target_elements:
-                        for_content_targeted_element.extend(body.cssselect(target_element))
-                    content_element = lhtml.Element("div")
-                    content_element.extend(copy.deepcopy(for_content_targeted_element))
-                except Exception as e:
-                    self._log("error", f"Error with target element detection: {str(e)}", "SCRAPE")
-                    return None
-            else:
-                content_element = body
-
-            # Remove script and style tags
-            for tag in ["script", "style", "link", "meta", "noscript"]:
-                for element in body.xpath(f".//{tag}"):
-                    if element.getparent() is not None:
-                        element.getparent().remove(element)
-
-            # Handle social media and domain exclusions
-            kwargs["exclude_domains"] = set(kwargs.get("exclude_domains", []))
-            if kwargs.get("exclude_social_media_links", False):
-                kwargs["exclude_social_media_domains"] = set(
-                    kwargs.get("exclude_social_media_domains", [])
-                    + SOCIAL_MEDIA_DOMAINS
-                )
-                kwargs["exclude_domains"].update(kwargs["exclude_social_media_domains"])
-
-            # Process forms if needed
-            if kwargs.get("remove_forms", False):
-                for form in body.xpath(".//form"):
-                    if form.getparent() is not None:
-                        form.getparent().remove(form)
-
-            # Process content
-            media = {"images": [], "videos": [], "audios": [], "tables": []}
-            internal_links_dict = {}
-            external_links_dict = {}
-
-            self._process_element(
-                url,
-                body,
-                media,
-                internal_links_dict,
-                external_links_dict,
-                page_context=page_context,
-                base_domain=base_domain,
-                **kwargs,
-            )
-
-            # Extract tables using the table extraction strategy if provided
-            if 'table' not in excluded_tags:
-                table_extraction = kwargs.get('table_extraction')
-                if table_extraction:
-                    # Pass logger to the strategy if it doesn't have one
-                    if not table_extraction.logger:
-                        table_extraction.logger = self.logger
-                    # Extract tables using the strategy
-                    extracted_tables = table_extraction.extract_tables(body, **kwargs)
-                    media["tables"].extend(extracted_tables)
-
-            # Handle only_text option
-            if kwargs.get("only_text", False):
-                for tag in ONLY_TEXT_ELIGIBLE_TAGS:
-                    for element in body.xpath(f".//{tag}"):
-                        if element.text:
-                            new_text = lhtml.Element("span")
-                            new_text.text = element.text_content()
-                            if element.getparent() is not None:
-                                element.getparent().replace(element, new_text)
-
-            # Clean base64 images
-            for img in body.xpath(".//img[@src]"):
-                src = img.get("src", "")
-                if self.BASE64_PATTERN.match(src):
-                    img.set("src", self.BASE64_PATTERN.sub("", src))
-
-            # Remove empty elements
-            self.remove_empty_elements_fast(body, 1)
-
-            # Remove unneeded attributes
-            self.remove_unwanted_attributes_fast(
-                body, keep_data_attributes=kwargs.get("keep_data_attributes", False)
-            )
-
-            # Generate output HTML
-            cleaned_html = lhtml.tostring(
-                # body,   
-                content_element,
-                encoding="unicode",
-                pretty_print=True,
-                method="html",
-                with_tail=False,
-            ).strip()
-            
-            # Create links dictionary in the format expected by LinkPreview
-            links = {
-                "internal": list(internal_links_dict.values()),
-                "external": list(external_links_dict.values()),
-            }
-            
-            # Extract head content for links if configured
-            link_preview_config = kwargs.get("link_preview_config")
-            if link_preview_config is not None:
-                try:
-                    import asyncio
-                    from .link_preview import LinkPreview
-                    from .models import Links, Link
-                    
-                    verbose = link_preview_config.verbose
-                    
-                    if verbose:
-                        self._log("info", "Starting link head extraction for {internal} internal and {external} external links",
-                                  params={"internal": len(links["internal"]), "external": len(links["external"])}, tag="LINK_EXTRACT")
-                    
-                    # Convert dict links to Link objects
-                    internal_links = [Link(**link_data) for link_data in links["internal"]]
-                    external_links = [Link(**link_data) for link_data in links["external"]]
-                    links_obj = Links(internal=internal_links, external=external_links)
-                    
-                    # Create a config object for LinkPreview
-                    class TempCrawlerRunConfig:
-                        def __init__(self, link_config, score_links):
-                            self.link_preview_config = link_config
-                            self.score_links = score_links
-                    
-                    config = TempCrawlerRunConfig(link_preview_config, kwargs.get("score_links", False))
-                    
-                    # Extract head content (run async operation in sync context)
-                    async def extract_links():
-                        async with LinkPreview(self.logger) as extractor:
-                            return await extractor.extract_link_heads(links_obj, config)
-                    
-                    # Run the async operation
-                    try:
-                        # Check if we're already in an async context
-                        loop = asyncio.get_running_loop()
-                        # If we're in an async context, we need to run in a thread
-                        import concurrent.futures
-                        with concurrent.futures.ThreadPoolExecutor() as executor:
-                            future = executor.submit(asyncio.run, extract_links())
-                            updated_links = future.result()
-                    except RuntimeError:
-                        # No running loop, we can use asyncio.run directly
-                        updated_links = asyncio.run(extract_links())
-                    
-                    # Convert back to dict format
-                    links["internal"] = [link.dict() for link in updated_links.internal]
-                    links["external"] = [link.dict() for link in updated_links.external]
-                    
-                    if verbose:
-                        successful_internal = len([l for l in updated_links.internal if l.head_extraction_status == "valid"])
-                        successful_external = len([l for l in updated_links.external if l.head_extraction_status == "valid"])
-                        self._log("info", "Link head extraction completed: {internal_success}/{internal_total} internal, {external_success}/{external_total} external",
-                                  params={
-                                      "internal_success": successful_internal,
-                                      "internal_total": len(updated_links.internal),
-                                      "external_success": successful_external,
-                                      "external_total": len(updated_links.external)
-                                  }, tag="LINK_EXTRACT")
-                    else:
-                        self._log("info", "Link head extraction completed successfully", tag="LINK_EXTRACT")
-                        
-                except Exception as e:
-                    self._log("error", f"Error during link head extraction: {str(e)}", tag="LINK_EXTRACT")
-                    # Continue with original links if head extraction fails
-            
-            return {
-                "cleaned_html": cleaned_html,
-                "success": success,
-                "media": media,
-                "links": links,
-                "metadata": meta,
-            }
-
-        except Exception as e:
-            self._log("error", f"Error processing HTML: {str(e)}", "SCRAPE")
-            # Create error message in case of failure
-            error_body = lhtml.Element("div")
-            # Use etree.SubElement rather than lhtml.SubElement
-            error_div = etree.SubElement(error_body, "div", id="crawl4ai_error_message")
-            error_div.text = f"""
-            Crawl4AI Error: This page is not fully supported.
-            
-            Error Message: {str(e)}
-            
-            Possible reasons:
-            1. The page may have restrictions that prevent crawling.
-            2. The page might not be fully loaded.
-            
-            Suggestions:
-            - Try calling the crawl function with these parameters:
-            magic=True,
-            - Set headless=False to visualize what's happening on the page.
-            
-            If the issue persists, please check the page's structure and any potential anti-crawling measures.
-            """
-            cleaned_html = lhtml.tostring(
-                error_body, encoding="unicode", pretty_print=True
-            )
-            return {
-                "cleaned_html": cleaned_html,
-                "success": False,
-                "media": {
-                    "images": [],
-                    "videos": [],
-                    "audios": [],
-                    "tables": []
-                },
-                "links": {"internal": [], "external": []},
-                "metadata": {},
-            }
-
-
-# Backward compatibility alias
-WebScrapingStrategy = LXMLWebScrapingStrategy

crawl4ai/content_scrapping_strategy.py

@@ -0,0 +1,549 @@
+import re  # Point 1: Pre-Compile Regular Expressions
+from abc import ABC, abstractmethod
+from typing import Dict, Any
+from bs4 import BeautifulSoup
+from concurrent.futures import ThreadPoolExecutor
+import asyncio, requests, re, os
+from .config import *
+from bs4 import element, NavigableString, Comment
+from urllib.parse import urljoin
+from requests.exceptions import InvalidSchema
+from .content_cleaning_strategy import ContentCleaningStrategy
+
+from .utils import (
+    sanitize_input_encode,
+    sanitize_html,
+    extract_metadata,
+    InvalidCSSSelectorError,
+    # CustomHTML2Text,
+    normalize_url,
+    is_external_url
+    
+)
+
+from .html2text import HTML2Text
+class CustomHTML2Text(HTML2Text):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.inside_pre = False
+        self.inside_code = False
+        self.preserve_tags = set()  # Set of tags to preserve
+        self.current_preserved_tag = None
+        self.preserved_content = []
+        self.preserve_depth = 0
+        
+        # Configuration options
+        self.skip_internal_links = False
+        self.single_line_break = False
+        self.mark_code = False
+        self.include_sup_sub = False
+        self.body_width = 0
+        self.ignore_mailto_links = True
+        self.ignore_links = False
+        self.escape_backslash = False
+        self.escape_dot = False
+        self.escape_plus = False
+        self.escape_dash = False
+        self.escape_snob = False
+
+    def update_params(self, **kwargs):
+        """Update parameters and set preserved tags."""
+        for key, value in kwargs.items():
+            if key == 'preserve_tags':
+                self.preserve_tags = set(value)
+            else:
+                setattr(self, key, value)
+
+    def handle_tag(self, tag, attrs, start):
+        # Handle preserved tags
+        if tag in self.preserve_tags:
+            if start:
+                if self.preserve_depth == 0:
+                    self.current_preserved_tag = tag
+                    self.preserved_content = []
+                    # Format opening tag with attributes
+                    attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
+                    self.preserved_content.append(f'<{tag}{attr_str}>')
+                self.preserve_depth += 1
+                return
+            else:
+                self.preserve_depth -= 1
+                if self.preserve_depth == 0:
+                    self.preserved_content.append(f'</{tag}>')
+                    # Output the preserved HTML block with proper spacing
+                    preserved_html = ''.join(self.preserved_content)
+                    self.o('\n' + preserved_html + '\n')
+                    self.current_preserved_tag = None
+                return
+
+        # If we're inside a preserved tag, collect all content
+        if self.preserve_depth > 0:
+            if start:
+                # Format nested tags with attributes
+                attr_str = ''.join(f' {k}="{v}"' for k, v in attrs.items() if v is not None)
+                self.preserved_content.append(f'<{tag}{attr_str}>')
+            else:
+                self.preserved_content.append(f'</{tag}>')
+            return
+
+        # Handle pre tags
+        if tag == 'pre':
+            if start:
+                self.o('```\n')
+                self.inside_pre = True
+            else:
+                self.o('\n```')
+                self.inside_pre = False
+        # elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
+        #     pass
+        else:
+            super().handle_tag(tag, attrs, start)
+
+    def handle_data(self, data, entity_char=False):
+        """Override handle_data to capture content within preserved tags."""
+        if self.preserve_depth > 0:
+            self.preserved_content.append(data)
+            return
+        super().handle_data(data, entity_char)
+
+# Pre-compile regular expressions for Open Graph and Twitter metadata
+OG_REGEX = re.compile(r'^og:')
+TWITTER_REGEX = re.compile(r'^twitter:')
+DIMENSION_REGEX = re.compile(r"(\d+)(\D*)")
+
+# Function to parse image height/width value and units
+def parse_dimension(dimension):
+    if dimension:
+        # match = re.match(r"(\d+)(\D*)", dimension)
+        match = DIMENSION_REGEX.match(dimension)
+        if match:
+            number = int(match.group(1))
+            unit = match.group(2) or 'px'  # Default unit is 'px' if not specified
+            return number, unit
+    return None, None
+
+# Fetch image file metadata to extract size and extension
+def fetch_image_file_size(img, base_url):
+    #If src is relative path construct full URL, if not it may be CDN URL
+    img_url = urljoin(base_url,img.get('src'))
+    try:
+        response = requests.head(img_url)
+        if response.status_code == 200:
+            return response.headers.get('Content-Length',None)
+        else:
+            print(f"Failed to retrieve file size for {img_url}")
+            return None
+    except InvalidSchema as e:
+        return None
+    finally:
+        return
+
+class ContentScrapingStrategy(ABC):
+    @abstractmethod
+    def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        pass
+
+    @abstractmethod
+    async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        pass
+
+class WebScrapingStrategy(ContentScrapingStrategy):
+    def scrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        return self._get_content_of_website_optimized(url, html, is_async=False, **kwargs)
+
+    async def ascrap(self, url: str, html: str, **kwargs) -> Dict[str, Any]:
+        return await asyncio.to_thread(self._get_content_of_website_optimized, url, html, **kwargs)
+
+    def _get_content_of_website_optimized(self, url: str, html: str, word_count_threshold: int = MIN_WORD_THRESHOLD, css_selector: str = None, **kwargs) -> Dict[str, Any]:
+        success = True
+        if not html:
+            return None
+
+        # soup = BeautifulSoup(html, 'html.parser')
+        soup = BeautifulSoup(html, 'lxml')
+        body = soup.body
+        
+        try:
+            meta = extract_metadata("", soup)
+        except Exception as e:
+            print('Error extracting metadata:', str(e))
+            meta = {}
+        
+        
+        image_description_min_word_threshold = kwargs.get('image_description_min_word_threshold', IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD)
+
+        for tag in kwargs.get('excluded_tags', []) or []:
+            for el in body.select(tag):
+                el.decompose()
+        
+        if css_selector:
+            selected_elements = body.select(css_selector)
+            if not selected_elements:
+                return {
+                    'markdown': '',
+                    'cleaned_html': '',
+                    'success': True,
+                    'media': {'images': [], 'videos': [], 'audios': []},
+                    'links': {'internal': [], 'external': []},
+                    'metadata': {},
+                    'message': f"No elements found for CSS selector: {css_selector}"
+                }
+                # raise InvalidCSSSelectorError(f"Invalid CSS selector, No elements found for CSS selector: {css_selector}")
+            body = soup.new_tag('div')
+            for el in selected_elements:
+                body.append(el)
+
+        links = {'internal': [], 'external': []}
+        media = {'images': [], 'videos': [], 'audios': []}
+        internal_links_dict = {}
+        external_links_dict = {}
+
+        # Extract meaningful text for media files from closest parent
+        def find_closest_parent_with_useful_text(tag):
+                current_tag = tag
+                while current_tag:
+                    current_tag = current_tag.parent
+                    # Get the text content of the parent tag
+                    if current_tag:
+                        text_content = current_tag.get_text(separator=' ',strip=True)
+                        # Check if the text content has at least word_count_threshold
+                        if len(text_content.split()) >= image_description_min_word_threshold:
+                            return text_content
+                return None
+
+        def process_image(img, url, index, total_images):
+            #Check if an image has valid display and inside undesired html elements
+            def is_valid_image(img, parent, parent_classes):
+                style = img.get('style', '')
+                src = img.get('src', '')
+                classes_to_check = ['button', 'icon', 'logo']
+                tags_to_check = ['button', 'input']
+                return all([
+                    'display:none' not in style,
+                    src,
+                    not any(s in var for var in [src, img.get('alt', ''), *parent_classes] for s in classes_to_check),
+                    parent.name not in tags_to_check
+                ])
+
+            #Score an image for it's usefulness
+            def score_image_for_usefulness(img, base_url, index, images_count):
+
+
+                image_height = img.get('height')
+                height_value, height_unit = parse_dimension(image_height)
+                image_width =  img.get('width')
+                width_value, width_unit = parse_dimension(image_width)
+                image_size = 0 #int(fetch_image_file_size(img,base_url) or 0)
+                image_src = img.get('src','')
+                if "data:image/" in image_src:
+                    image_format = image_src.split(',')[0].split(';')[0].split('/')[1]
+                else:
+                    image_format = os.path.splitext(img.get('src',''))[1].lower()
+                # Remove . from format
+                image_format = image_format.strip('.').split('?')[0]
+                score = 0
+                if height_value:
+                    if height_unit == 'px' and height_value > 150:
+                        score += 1
+                    if height_unit in ['%','vh','vmin','vmax'] and height_value >30:
+                        score += 1
+                if width_value:
+                    if width_unit == 'px' and width_value > 150:
+                        score += 1
+                    if width_unit in ['%','vh','vmin','vmax'] and width_value >30:
+                        score += 1
+                if image_size > 10000:
+                    score += 1
+                if img.get('alt') != '':
+                    score+=1
+                if any(image_format==format for format in ['jpg','png','webp']):
+                    score+=1
+                if index/images_count<0.5:
+                    score+=1
+                return score
+
+            
+            
+            if not is_valid_image(img, img.parent, img.parent.get('class', [])):
+                return None
+            score = score_image_for_usefulness(img, url, index, total_images)
+            if score <= IMAGE_SCORE_THRESHOLD:
+                return None
+            return {
+                'src': img.get('src', ''),
+                'data-src': img.get('data-src', ''),
+                'alt': img.get('alt', ''),
+                'desc': find_closest_parent_with_useful_text(img),
+                'score': score,
+                'type': 'image'
+            }
+
+        def remove_unwanted_attributes(element, important_attrs, keep_data_attributes=False):
+            attrs_to_remove = []
+            for attr in element.attrs:
+                if attr not in important_attrs:
+                    if keep_data_attributes:
+                        if not attr.startswith('data-'):
+                            attrs_to_remove.append(attr)
+                    else:
+                        attrs_to_remove.append(attr)
+            
+            for attr in attrs_to_remove:
+                del element[attr]
+        
+        def process_element(element: element.PageElement) -> bool:
+            try:
+                if isinstance(element, NavigableString):
+                    if isinstance(element, Comment):
+                        element.extract()
+                    return False
+                
+                # if element.name == 'img':
+                #     process_image(element, url, 0, 1)
+                #     return True
+
+                if element.name in ['script', 'style', 'link', 'meta', 'noscript']:
+                    element.decompose()
+                    return False
+
+                keep_element = False
+                
+                exclude_social_media_domains = SOCIAL_MEDIA_DOMAINS + kwargs.get('exclude_social_media_domains', [])
+                exclude_social_media_domains = list(set(exclude_social_media_domains))
+                
+                try:
+                    if element.name == 'a' and element.get('href'):
+                        href = element.get('href', '').strip()
+                        if not href:  # Skip empty hrefs
+                            return False
+                            
+                        url_base = url.split('/')[2]
+                        
+                        # Normalize the URL
+                        try:
+                            normalized_href = normalize_url(href, url)
+                        except ValueError as e:
+                            # logging.warning(f"Invalid URL format: {href}, Error: {str(e)}")
+                            return False
+                            
+                        link_data = {
+                            'href': normalized_href,
+                            'text': element.get_text().strip(),
+                            'title': element.get('title', '').strip()
+                        }
+                        
+                        # Check for duplicates and add to appropriate dictionary
+                        is_external = is_external_url(normalized_href, url_base)
+                        if is_external:
+                            if normalized_href not in external_links_dict:
+                                external_links_dict[normalized_href] = link_data
+                        else:
+                            if normalized_href not in internal_links_dict:
+                                internal_links_dict[normalized_href] = link_data
+                                
+                        keep_element = True
+                        
+                        # Handle external link exclusions
+                        if is_external:
+                            if kwargs.get('exclude_external_links', False):
+                                element.decompose()
+                                return False
+                            elif kwargs.get('exclude_social_media_links', False):
+                                if any(domain in normalized_href.lower() for domain in exclude_social_media_domains):
+                                    element.decompose()
+                                    return False
+                            elif kwargs.get('exclude_domains', []):
+                                if any(domain in normalized_href.lower() for domain in kwargs.get('exclude_domains', [])):
+                                    element.decompose()
+                                    return False
+                                    
+                except Exception as e:
+                    raise Exception(f"Error processing links: {str(e)}")
+
+                try:
+                    if element.name == 'img':
+                        potential_sources = ['src', 'data-src', 'srcset' 'data-lazy-src', 'data-original']
+                        src = element.get('src', '')
+                        while not src and potential_sources:
+                            src = element.get(potential_sources.pop(0), '')
+                        if not src:
+                            element.decompose()
+                            return False
+                        
+                        # If it is srcset pick up the first image
+                        if 'srcset' in element.attrs:
+                            src = element.attrs['srcset'].split(',')[0].split(' ')[0]
+                            
+                        # Check flag if we should remove external images
+                        if kwargs.get('exclude_external_images', False):
+                            src_url_base = src.split('/')[2]
+                            url_base = url.split('/')[2]
+                            if url_base not in src_url_base:
+                                element.decompose()
+                                return False
+                            
+                        if not kwargs.get('exclude_external_images', False) and kwargs.get('exclude_social_media_links', False):
+                            src_url_base = src.split('/')[2]
+                            url_base = url.split('/')[2]
+                            if any(domain in src for domain in exclude_social_media_domains):
+                                element.decompose()
+                                return False
+                            
+                        # Handle exclude domains
+                        if kwargs.get('exclude_domains', []):
+                            if any(domain in src for domain in kwargs.get('exclude_domains', [])):
+                                element.decompose()
+                                return False
+                        
+                        return True  # Always keep image elements
+                except Exception as e:
+                    raise "Error processing images"
+                
+                
+                # Check if flag to remove all forms is set
+                if kwargs.get('remove_forms', False) and element.name == 'form':
+                    element.decompose()
+                    return False
+                
+                if element.name in ['video', 'audio']:
+                    media[f"{element.name}s"].append({
+                        'src': element.get('src'),
+                        'alt': element.get('alt'),
+                        'type': element.name,
+                        'description': find_closest_parent_with_useful_text(element)
+                    })
+                    source_tags = element.find_all('source')
+                    for source_tag in source_tags:
+                        media[f"{element.name}s"].append({
+                        'src': source_tag.get('src'),
+                        'alt': element.get('alt'),
+                        'type': element.name,
+                        'description': find_closest_parent_with_useful_text(element)
+                    })
+                    return True  # Always keep video and audio elements
+
+                if element.name in ONLY_TEXT_ELIGIBLE_TAGS:
+                    if kwargs.get('only_text', False):
+                        element.replace_with(element.get_text())
+
+                try:
+                    remove_unwanted_attributes(element, IMPORTANT_ATTRS, kwargs.get('keep_data_attributes', False))
+                except Exception as e:
+                    print('Error removing unwanted attributes:', str(e))
+                
+
+                # Process children
+                for child in list(element.children):
+                    if isinstance(child, NavigableString) and not isinstance(child, Comment):
+                        if len(child.strip()) > 0:
+                            keep_element = True
+                    else:
+                        if process_element(child):
+                            keep_element = True
+                    
+
+                # Check word count
+                if not keep_element:
+                    word_count = len(element.get_text(strip=True).split())
+                    keep_element = word_count >= word_count_threshold
+
+                if not keep_element:
+                    element.decompose()
+
+                return keep_element
+            except Exception as e:
+                print('Error processing element:', str(e))
+                return False
+       
+        process_element(body)
+        
+        # Update the links dictionary with unique links
+        links['internal'] = list(internal_links_dict.values())
+        links['external'] = list(external_links_dict.values())
+
+
+        # # Process images using ThreadPoolExecutor
+        imgs = body.find_all('img')
+        
+        with ThreadPoolExecutor() as executor:
+            image_results = list(executor.map(process_image, imgs, [url]*len(imgs), range(len(imgs)), [len(imgs)]*len(imgs)))
+        media['images'] = [result for result in image_results if result is not None]
+
+        def flatten_nested_elements(node):
+            if isinstance(node, NavigableString):
+                return node
+            if len(node.contents) == 1 and isinstance(node.contents[0], element.Tag) and node.contents[0].name == node.name:
+                return flatten_nested_elements(node.contents[0])
+            node.contents = [flatten_nested_elements(child) for child in node.contents]
+            return node
+
+        body = flatten_nested_elements(body)
+        base64_pattern = re.compile(r'data:image/[^;]+;base64,([^"]+)')
+        for img in imgs:
+            src = img.get('src', '')
+            if base64_pattern.match(src):
+                # Replace base64 data with empty string
+                img['src'] = base64_pattern.sub('', src)
+                
+        str_body = ""
+        try:
+            str_body = body.encode_contents().decode('utf-8')
+        except Exception as e:
+            # Reset body to the original HTML
+            success = False
+            body = BeautifulSoup(html, 'html.parser')
+            
+            # Create a new div with a special ID
+            error_div = body.new_tag('div', id='crawl4ai_error_message')
+            error_div.string = '''
+            Crawl4AI Error: This page is not fully supported.
+            
+            Possible reasons:
+            1. The page may have restrictions that prevent crawling.
+            2. The page might not be fully loaded.
+            
+            Suggestions:
+            - Try calling the crawl function with these parameters:
+            magic=True,
+            - Set headless=False to visualize what's happening on the page.
+            
+            If the issue persists, please check the page's structure and any potential anti-crawling measures.
+            '''
+            
+            # Append the error div to the body
+            body.body.append(error_div)
+            str_body = body.encode_contents().decode('utf-8')
+            
+            print(f"[LOG] 😧 Error: After processing the crawled HTML and removing irrelevant tags, nothing was left in the page. Check the markdown for further details.")
+
+
+        cleaned_html = str_body.replace('\n\n', '\n').replace('  ', ' ')
+
+        try:
+            h = CustomHTML2Text()
+            h.update_params(**kwargs.get('html2text', {}))            
+            markdown = h.handle(cleaned_html)
+        except Exception as e:
+            markdown = h.handle(sanitize_html(cleaned_html))
+        markdown = markdown.replace('    ```', '```')
+
+        
+            
+        fit_markdown = "Set flag 'fit_markdown' to True to get cleaned HTML content."
+        fit_html = "Set flag 'fit_markdown' to True to get cleaned HTML content."
+        if kwargs.get('fit_markdown', False):
+            cleaner = ContentCleaningStrategy()
+            fit_html = cleaner.clean(cleaned_html)
+            fit_markdown = h.handle(fit_html)
+
+        cleaned_html = sanitize_html(cleaned_html)
+        return {
+            'markdown': markdown,
+            'fit_markdown': fit_markdown,
+            'fit_html': fit_html,
+            'cleaned_html': cleaned_html,
+            'success': success,
+            'media': media,
+            'links': links,
+            'metadata': meta
+        }

crawl4ai/crawler_strategy.py

@@ -15,53 +15,54 @@ import logging, time
 import base64
 from PIL import Image, ImageDraw, ImageFont
 from io import BytesIO
-from typing import Callable
+from typing import List, Callable
 import requests
 import os
 from pathlib import Path
 from .utils import *
 
-logger = logging.getLogger("selenium.webdriver.remote.remote_connection")
+logger = logging.getLogger('selenium.webdriver.remote.remote_connection')
 logger.setLevel(logging.WARNING)
 
-logger_driver = logging.getLogger("selenium.webdriver.common.service")
+logger_driver = logging.getLogger('selenium.webdriver.common.service')
 logger_driver.setLevel(logging.WARNING)
 
-urllib3_logger = logging.getLogger("urllib3.connectionpool")
+urllib3_logger = logging.getLogger('urllib3.connectionpool')
 urllib3_logger.setLevel(logging.WARNING)
 
 # Disable http.client logging
-http_client_logger = logging.getLogger("http.client")
+http_client_logger = logging.getLogger('http.client')
 http_client_logger.setLevel(logging.WARNING)
 
 # Disable driver_finder and service logging
-driver_finder_logger = logging.getLogger("selenium.webdriver.common.driver_finder")
+driver_finder_logger = logging.getLogger('selenium.webdriver.common.driver_finder')
 driver_finder_logger.setLevel(logging.WARNING)
 
 
+
+
 class CrawlerStrategy(ABC):
     @abstractmethod
     def crawl(self, url: str, **kwargs) -> str:
         pass
-
+    
     @abstractmethod
     def take_screenshot(self, save_path: str):
         pass
-
+    
     @abstractmethod
     def update_user_agent(self, user_agent: str):
         pass
-
+    
     @abstractmethod
     def set_hook(self, hook_type: str, hook: Callable):
         pass
 
-
 class CloudCrawlerStrategy(CrawlerStrategy):
-    def __init__(self, use_cached_html=False):
+    def __init__(self, use_cached_html = False):
         super().__init__()
         self.use_cached_html = use_cached_html
-
+        
     def crawl(self, url: str) -> str:
         data = {
             "urls": [url],
@@ -75,7 +76,6 @@ class CloudCrawlerStrategy(CrawlerStrategy):
         html = response["results"][0]["html"]
         return sanitize_input_encode(html)
 
-
 class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
     def __init__(self, use_cached_html=False, js_code=None, **kwargs):
         super().__init__()
@@ -87,25 +87,20 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         if kwargs.get("user_agent"):
             self.options.add_argument("--user-agent=" + kwargs.get("user_agent"))
         else:
-            user_agent = kwargs.get(
-                "user_agent",
-                "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36",
-            )
+            user_agent = kwargs.get("user_agent", "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")
             self.options.add_argument(f"--user-agent={user_agent}")
-            self.options.add_argument(
-                "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
-            )
-
+            self.options.add_argument("user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36")
+                  
         self.options.headless = kwargs.get("headless", True)
         if self.options.headless:
             self.options.add_argument("--headless")
-
-        self.options.add_argument("--disable-gpu")
+        
+        self.options.add_argument("--disable-gpu")  
         self.options.add_argument("--window-size=1920,1080")
         self.options.add_argument("--no-sandbox")
         self.options.add_argument("--disable-dev-shm-usage")
-        self.options.add_argument("--disable-blink-features=AutomationControlled")
-
+        self.options.add_argument("--disable-blink-features=AutomationControlled")     
+        
         # self.options.add_argument("--disable-dev-shm-usage")
         self.options.add_argument("--disable-gpu")
         # self.options.add_argument("--disable-extensions")
@@ -125,45 +120,48 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         self.use_cached_html = use_cached_html
         self.js_code = js_code
         self.verbose = kwargs.get("verbose", False)
-
+        
         # Hooks
         self.hooks = {
-            "on_driver_created": None,
-            "on_user_agent_updated": None,
-            "before_get_url": None,
-            "after_get_url": None,
-            "before_return_html": None,
+            'on_driver_created': None,
+            'on_user_agent_updated': None,
+            'before_get_url': None,
+            'after_get_url': None,
+            'before_return_html': None
         }
 
         # chromedriver_autoinstaller.install()
         # import chromedriver_autoinstaller
-        # crawl4ai_folder = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
+        # crawl4ai_folder = os.path.join(Path.home(), ".crawl4ai")
         # driver = webdriver.Chrome(service=ChromeService(ChromeDriverManager().install()), options=self.options)
         # chromedriver_path = chromedriver_autoinstaller.install()
         # chromedriver_path = chromedriver_autoinstaller.utils.download_chromedriver()
         # self.service = Service(chromedriver_autoinstaller.install())
-
+        
+        
         # chromedriver_path = ChromeDriverManager().install()
         # self.service = Service(chromedriver_path)
         # self.service.log_path = "NUL"
         # self.driver = webdriver.Chrome(service=self.service, options=self.options)
-
+        
         # Use selenium-manager (built into Selenium 4.10.0+)
         self.service = Service()
         self.driver = webdriver.Chrome(options=self.options)
-
-        self.driver = self.execute_hook("on_driver_created", self.driver)
-
+        
+        self.driver = self.execute_hook('on_driver_created', self.driver)
+        
         if kwargs.get("cookies"):
             for cookie in kwargs.get("cookies"):
                 self.driver.add_cookie(cookie)
+            
+        
 
     def set_hook(self, hook_type: str, hook: Callable):
         if hook_type in self.hooks:
             self.hooks[hook_type] = hook
         else:
             raise ValueError(f"Invalid hook type: {hook_type}")
-
+    
     def execute_hook(self, hook_type: str, *args):
         hook = self.hooks.get(hook_type)
         if hook:
@@ -172,9 +170,7 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                 if isinstance(result, webdriver.Chrome):
                     return result
                 else:
-                    raise TypeError(
-                        f"Hook {hook_type} must return an instance of webdriver.Chrome or None."
-                    )
+                    raise TypeError(f"Hook {hook_type} must return an instance of webdriver.Chrome or None.")
         # If the hook returns None or there is no hook, return self.driver
         return self.driver
 
@@ -182,77 +178,60 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         self.options.add_argument(f"user-agent={user_agent}")
         self.driver.quit()
         self.driver = webdriver.Chrome(service=self.service, options=self.options)
-        self.driver = self.execute_hook("on_user_agent_updated", self.driver)
+        self.driver = self.execute_hook('on_user_agent_updated', self.driver)
 
     def set_custom_headers(self, headers: dict):
         # Enable Network domain for sending headers
-        self.driver.execute_cdp_cmd("Network.enable", {})
+        self.driver.execute_cdp_cmd('Network.enable', {})
         # Set extra HTTP headers
-        self.driver.execute_cdp_cmd("Network.setExtraHTTPHeaders", {"headers": headers})
+        self.driver.execute_cdp_cmd('Network.setExtraHTTPHeaders', {'headers': headers})
 
-    def _ensure_page_load(self, max_checks=6, check_interval=0.01):
+    def _ensure_page_load(self,  max_checks=6, check_interval=0.01):
         initial_length = len(self.driver.page_source)
-
+        
         for ix in range(max_checks):
             # print(f"Checking page load: {ix}")
             time.sleep(check_interval)
             current_length = len(self.driver.page_source)
-
+            
             if current_length != initial_length:
                 break
 
         return self.driver.page_source
-
+    
     def crawl(self, url: str, **kwargs) -> str:
         # Create md5 hash of the URL
         import hashlib
-
         url_hash = hashlib.md5(url.encode()).hexdigest()
-
+        
         if self.use_cached_html:
-            cache_file_path = os.path.join(
-                os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()),
-                ".crawl4ai",
-                "cache",
-                url_hash,
-            )
+            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url_hash)
             if os.path.exists(cache_file_path):
                 with open(cache_file_path, "r") as f:
                     return sanitize_input_encode(f.read())
 
         try:
-            self.driver = self.execute_hook("before_get_url", self.driver)
+            self.driver = self.execute_hook('before_get_url', self.driver)
             if self.verbose:
                 print(f"[LOG] 🕸️ Crawling {url} using LocalSeleniumCrawlerStrategy...")
-            self.driver.get(url)  # <html><head></head><body></body></html>
-
+            self.driver.get(url) #<html><head></head><body></body></html>
+            
             WebDriverWait(self.driver, 20).until(
-                lambda d: d.execute_script("return document.readyState") == "complete"
+                lambda d: d.execute_script('return document.readyState') == 'complete'
             )
             WebDriverWait(self.driver, 10).until(
                 EC.presence_of_all_elements_located((By.TAG_NAME, "body"))
             )
-
-            self.driver.execute_script(
-                "window.scrollTo(0, document.body.scrollHeight);"
-            )
-
-            self.driver = self.execute_hook("after_get_url", self.driver)
-            html = sanitize_input_encode(
-                self._ensure_page_load()
-            )  # self.driver.page_source
-            can_not_be_done_headless = (
-                False  # Look at my creativity for naming variables
-            )
-
+            
+            self.driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
+            
+            self.driver = self.execute_hook('after_get_url', self.driver)
+            html = sanitize_input_encode(self._ensure_page_load()) # self.driver.page_source                                        
+            can_not_be_done_headless = False # Look at my creativity for naming variables
+            
             # TODO: Very ugly approach, but promise to change it!
-            if (
-                kwargs.get("bypass_headless", False)
-                or html == "<html><head></head><body></body></html>"
-            ):
-                print(
-                    "[LOG] 🙌 Page could not be loaded in headless mode. Trying non-headless mode..."
-                )
+            if kwargs.get('bypass_headless', False) or html == "<html><head></head><body></body></html>":
+                print("[LOG] 🙌 Page could not be loaded in headless mode. Trying non-headless mode...")
                 can_not_be_done_headless = True
                 options = Options()
                 options.headless = False
@@ -260,31 +239,27 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                 options.add_argument("--window-size=5,5")
                 driver = webdriver.Chrome(service=self.service, options=options)
                 driver.get(url)
-                self.driver = self.execute_hook("after_get_url", driver)
+                self.driver = self.execute_hook('after_get_url', driver)
                 html = sanitize_input_encode(driver.page_source)
                 driver.quit()
-
+            
             # Execute JS code if provided
             self.js_code = kwargs.get("js_code", self.js_code)
             if self.js_code and type(self.js_code) == str:
                 self.driver.execute_script(self.js_code)
                 # Optionally, wait for some condition after executing the JS code
                 WebDriverWait(self.driver, 10).until(
-                    lambda driver: driver.execute_script("return document.readyState")
-                    == "complete"
+                    lambda driver: driver.execute_script("return document.readyState") == "complete"
                 )
             elif self.js_code and type(self.js_code) == list:
                 for js in self.js_code:
                     self.driver.execute_script(js)
                     WebDriverWait(self.driver, 10).until(
-                        lambda driver: driver.execute_script(
-                            "return document.readyState"
-                        )
-                        == "complete"
+                        lambda driver: driver.execute_script("return document.readyState") == "complete"
                     )
-
+            
             # Optionally, wait for some condition after executing the JS code : Contributed by (https://github.com/jonymusky)
-            wait_for = kwargs.get("wait_for", False)
+            wait_for = kwargs.get('wait_for', False)
             if wait_for:
                 if callable(wait_for):
                     print("[LOG] 🔄 Waiting for condition...")
@@ -293,37 +268,32 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
                     print("[LOG] 🔄 Waiting for condition...")
                     WebDriverWait(self.driver, 20).until(
                         EC.presence_of_element_located((By.CSS_SELECTOR, wait_for))
-                    )
-
+                    ) 
+            
             if not can_not_be_done_headless:
                 html = sanitize_input_encode(self.driver.page_source)
-            self.driver = self.execute_hook("before_return_html", self.driver, html)
-
+            self.driver = self.execute_hook('before_return_html', self.driver, html)
+            
             # Store in cache
-            cache_file_path = os.path.join(
-                os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()),
-                ".crawl4ai",
-                "cache",
-                url_hash,
-            )
+            cache_file_path = os.path.join(Path.home(), ".crawl4ai", "cache", url_hash)
             with open(cache_file_path, "w", encoding="utf-8") as f:
                 f.write(html)
-
+                
             if self.verbose:
                 print(f"[LOG] ✅ Crawled {url} successfully!")
-
+            
             return html
-        except InvalidArgumentException as e:
-            if not hasattr(e, "msg"):
+        except InvalidArgumentException:
+            if not hasattr(e, 'msg'):
                 e.msg = sanitize_input_encode(str(e))
             raise InvalidArgumentException(f"Failed to crawl {url}: {e.msg}")
         except WebDriverException as e:
             # If e does nlt have msg attribute create it and set it to str(e)
-            if not hasattr(e, "msg"):
+            if not hasattr(e, 'msg'):
                 e.msg = sanitize_input_encode(str(e))
-            raise WebDriverException(f"Failed to crawl {url}: {e.msg}")
+            raise WebDriverException(f"Failed to crawl {url}: {e.msg}")  
         except Exception as e:
-            if not hasattr(e, "msg"):
+            if not hasattr(e, 'msg'):
                 e.msg = sanitize_input_encode(str(e))
             raise Exception(f"Failed to crawl {url}: {e.msg}")
 
@@ -331,9 +301,7 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
         try:
             # Get the dimensions of the page
             total_width = self.driver.execute_script("return document.body.scrollWidth")
-            total_height = self.driver.execute_script(
-                "return document.body.scrollHeight"
-            )
+            total_height = self.driver.execute_script("return document.body.scrollHeight")
 
             # Set the window size to the dimensions of the page
             self.driver.set_window_size(total_width, total_height)
@@ -345,27 +313,25 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
             image = Image.open(BytesIO(screenshot))
 
             # Convert image to RGB mode (this will handle both RGB and RGBA images)
-            rgb_image = image.convert("RGB")
+            rgb_image = image.convert('RGB')
 
             # Convert to JPEG and compress
             buffered = BytesIO()
             rgb_image.save(buffered, format="JPEG", quality=85)
-            img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
 
             if self.verbose:
-                print("[LOG] 📸 Screenshot taken and converted to base64")
+                print(f"[LOG] 📸 Screenshot taken and converted to base64")
 
             return img_base64
         except Exception as e:
-            error_message = sanitize_input_encode(
-                f"Failed to take screenshot: {str(e)}"
-            )
+            error_message = sanitize_input_encode(f"Failed to take screenshot: {str(e)}")
             print(error_message)
 
             # Generate an image with black background
-            img = Image.new("RGB", (800, 600), color="black")
+            img = Image.new('RGB', (800, 600), color='black')
             draw = ImageDraw.Draw(img)
-
+            
             # Load a font
             try:
                 font = ImageFont.truetype("arial.ttf", 40)
@@ -379,16 +345,16 @@ class LocalSeleniumCrawlerStrategy(CrawlerStrategy):
 
             # Calculate text position
             text_position = (10, 10)
-
+            
             # Draw the text on the image
             draw.text(text_position, wrapped_text, fill=text_color, font=font)
-
+            
             # Convert to base64
             buffered = BytesIO()
             img.save(buffered, format="JPEG")
-            img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
 
             return img_base64
-
+        
     def quit(self):
         self.driver.quit()

crawl4ai/crawlers/amazon_product/crawler.py

@@ -1,20 +0,0 @@
-from crawl4ai.hub import BaseCrawler
-
-__meta__ = {
-    "version": "1.2.0",
-    "tested_on": ["amazon.com"],
-    "rate_limit": "50 RPM",
-    "schema": {"product": ["name", "price"]}
-}
-
-class AmazonProductCrawler(BaseCrawler):
-    async def run(self, url: str, **kwargs) -> str:
-        try:
-            self.logger.info(f"Crawling {url}")
-            return '{"product": {"name": "Test Amazon Product"}}'
-        except Exception as e:
-            self.logger.error(f"Crawl failed: {str(e)}")
-            return json.dumps({
-                "error": str(e),
-                "metadata": self.meta  # Include meta in error response
-            })            

crawl4ai/crawlers/google_search/crawler.py

@@ -1,131 +0,0 @@
-from crawl4ai import BrowserConfig, AsyncWebCrawler, CrawlerRunConfig, CacheMode
-from crawl4ai.hub import BaseCrawler
-from crawl4ai.utils import optimize_html, get_home_folder, preprocess_html_for_schema
-from crawl4ai import JsonCssExtractionStrategy
-from pathlib import Path
-import json
-import os
-from typing import Dict
-
-
-class GoogleSearchCrawler(BaseCrawler):
-    __meta__ = {
-        "version": "1.0.0",
-        "tested_on": ["google.com/search*"],
-        "rate_limit": "10 RPM",
-        "description": "Crawls Google Search results (text + images)",
-    }
-
-    def __init__(self):
-        super().__init__()
-        self.js_script = (Path(__file__).parent /
-                          "script.js").read_text()
-
-    async def run(self, url="", query: str = "", search_type: str = "text", schema_cache_path = None, **kwargs) -> str:
-        """Crawl Google Search results for a query"""
-        url = f"https://www.google.com/search?q={query}&gl=sg&hl=en" if search_type == "text" else f"https://www.google.com/search?q={query}&gl=sg&hl=en&tbs=qdr:d&udm=2"
-        if kwargs.get("page_start", 1) > 1:
-            url = f"{url}&start={kwargs['page_start'] * 10}"
-        if kwargs.get("page_length", 1) > 1:
-            url = f"{url}&num={kwargs['page_length']}"
-            
-        browser_config = BrowserConfig(headless=True, verbose=True)
-        async with AsyncWebCrawler(config=browser_config) as crawler:
-            config = CrawlerRunConfig(
-                cache_mode=kwargs.get("cache_mode", CacheMode.BYPASS),
-                keep_attrs=["id", "class"],
-                keep_data_attributes=True,
-                delay_before_return_html=kwargs.get(
-                    "delay", 2 if search_type == "image" else 1),
-                js_code=self.js_script if search_type == "image" else None,
-            )
-
-            result = await crawler.arun(url=url, config=config)
-            if not result.success:
-                return json.dumps({"error": result.error})
-
-            if search_type == "image":
-                if result.js_execution_result.get("success", False) is False:
-                    return json.dumps({"error": result.js_execution_result.get("error", "Unknown error")})
-                if "results" in result.js_execution_result:
-                    image_result = result.js_execution_result['results'][0]
-                    if image_result.get("success", False) is False:
-                        return json.dumps({"error": image_result.get("error", "Unknown error")})
-                    return json.dumps(image_result["result"], indent=4)
-
-            # For text search, extract structured data
-            schemas = await self._build_schemas(result.cleaned_html, schema_cache_path)
-            extracted = {
-                key: JsonCssExtractionStrategy(schema=schemas[key]).run(
-                    url=url, sections=[result.html]
-                )
-                for key in schemas
-            }
-            return json.dumps(extracted, indent=4)
-
-    async def _build_schemas(self, html: str, schema_cache_path: str = None) -> Dict[str, Dict]:
-        """Build extraction schemas (organic, top stories, etc.)"""
-        home_dir = get_home_folder() if not schema_cache_path else schema_cache_path
-        os.makedirs(f"{home_dir}/schema", exist_ok=True)
-
-        # cleaned_html = optimize_html(html, threshold=100)
-        cleaned_html = preprocess_html_for_schema(html) 
-
-        organic_schema = None
-        if os.path.exists(f"{home_dir}/schema/organic_schema.json"):
-            with open(f"{home_dir}/schema/organic_schema.json", "r") as f:
-                organic_schema = json.load(f)
-        else:
-            organic_schema = JsonCssExtractionStrategy.generate_schema(
-                html=cleaned_html,
-                target_json_example="""{
-            "title": "...",
-            "link": "...",
-            "snippet": "...",
-            "date": "1 hour ago",
-        }""",
-                query="""The given html is the crawled html from Google search result. Please find the schema for organic search item in the given html, I am interested in title, link, snippet text. date."""
-            )
-
-            with open(f"{home_dir}/schema/organic_schema.json", "w") as f:
-                f.write(json.dumps(organic_schema))
-
-        top_stories_schema = None
-        if os.path.exists(f"{home_dir}/schema/top_stories_schema.json"):
-            with open(f"{home_dir}/schema/top_stories_schema.json", "r") as f:
-                top_stories_schema = json.load(f)
-        else:
-            top_stories_schema = JsonCssExtractionStrategy.generate_schema(
-                html=cleaned_html,
-                target_json_example="""{
-            "title": "...",
-            "link": "...",
-            "source": "Insider Monkey",
-            "date": "1 hour ago",
-        }""",
-                query="""The given html is the crawled html from Google search result. Please find the schema for Top Story item int he given html, I am interested in title, link, source. date and imageUrl."""
-            )
-
-            with open(f"{home_dir}/schema/top_stories_schema.json", "w") as f:
-                f.write(json.dumps(top_stories_schema))
-
-        suggested_query_schema = None
-        if os.path.exists(f"{home_dir}/schema/suggested_query_schema.json"):
-            with open(f"{home_dir}/schema/suggested_query_schema.json", "r") as f:
-                suggested_query_schema = json.load(f)
-        else:
-            suggested_query_schema = JsonCssExtractionStrategy.generate_schema(
-                html=cleaned_html,
-                target_json_example="""{
-            "query": "A for Apple",
-        }""",
-                query="""The given HTML contains the crawled HTML from Google search results. Please find the schema for each suggested query in the section "People also search for" within the given HTML. I am interested in the queries only."""
-            )
-            with open(f"{home_dir}/schema/suggested_query_schema.json", "w") as f:
-                f.write(json.dumps(suggested_query_schema))
-
-        return {
-            "organic_schema": organic_schema,
-            "top_stories_schema": top_stories_schema,
-            "suggested_query_schema": suggested_query_schema,
-        }

crawl4ai/crawlers/google_search/script.js

@@ -1,115 +0,0 @@
-(() => {
-    // Function to extract image data from Google Images page
-    function extractImageData() {
-        const keys = Object.keys(window.W_jd);
-        let allImageData = [];
-        let currentPosition = 0;
-
-        // Get the symbol we'll use (from first valid entry)
-        let targetSymbol;
-        for (let key of keys) {
-            try {
-                const symbols = Object.getOwnPropertySymbols(window.W_jd[key]);
-                if (symbols.length > 0) {
-                    targetSymbol = symbols[0];
-                    break;
-                }
-            } catch (e) {
-                continue;
-            }
-        }
-
-        if (!targetSymbol) return [];
-
-        // Iterate through ALL keys
-        for (let key of keys) {
-            try {
-                const o1 = window.W_jd[key][targetSymbol]
-                if (!o1) continue;
-                const data = Object.values(o1)[0]
-                // const data = window.W_jd[key][targetSymbol]?.Ws;
-                // Check if this is a valid image data entry
-                if (data && Array.isArray(data[1])) {
-                    const processedData = processImageEntry(data, currentPosition);
-                    if (processedData) {
-                        allImageData.push(processedData);
-                        currentPosition++;
-                    }
-                }
-            } catch (e) {
-                continue;
-            }
-        }
-
-        return allImageData;
-    }
-
-    function processImageEntry(entry, position) {
-        const imageData = entry[1];
-        if (!Array.isArray(imageData)) return null;
-
-        // Extract the image ID
-        const imageId = imageData[1];
-        if (!imageId) return null;
-
-        // Find the corresponding DOM element
-        const domElement = document.querySelector(`[data-docid="${imageId}"]`);
-        if (!domElement) return null;
-
-        // Extract data from the array structure
-        const [
-            _,
-            id,
-            thumbnailInfo,
-            imageInfo,
-            __,
-            ___,
-            rgb,
-            ____,
-            _____,
-            metadata
-        ] = imageData;
-
-        // Ensure we have the required data
-        if (!thumbnailInfo || !imageInfo) return null;
-
-        // Extract metadata from DOM
-        const title = domElement?.querySelector('.toI8Rb')?.textContent?.trim();
-        const source = domElement?.querySelector('.guK3rf')?.textContent?.trim();
-        const link = domElement?.querySelector('a.EZAeBe')?.href;
-
-        if (!link) return null;
-
-        // Build Google Image URL
-        const googleUrl = buildGoogleImageUrl(imageInfo[0], link, imageId, imageInfo[1], imageInfo[2]);
-
-        return {
-            title,
-            imageUrl: imageInfo[0],
-            imageWidth: imageInfo[2],
-            imageHeight: imageInfo[1],
-            thumbnailUrl: thumbnailInfo[0],
-            thumbnailWidth: thumbnailInfo[2],
-            thumbnailHeight: thumbnailInfo[1],
-            source,
-            domain: metadata['2000']?.[1] || new URL(link).hostname,
-            link,
-            googleUrl,
-            position: position + 1
-        };
-    }
-
-    function buildGoogleImageUrl(imgUrl, refUrl, tbnid, height, width) {
-        const params = new URLSearchParams({
-            imgurl: imgUrl,
-            tbnid: tbnid,
-            imgrefurl: refUrl,
-            docid: tbnid,
-            w: width.toString(),
-            h: height.toString(),
-        });
-
-        return `https://www.google.com/imgres?${params.toString()}`;
-    }
-    return extractImageData();
-})();

crawl4ai/database.py

@@ -3,17 +3,15 @@ from pathlib import Path
 import sqlite3
 from typing import Optional, Tuple
 
-DB_PATH = os.path.join(os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai")
+DB_PATH = os.path.join(Path.home(), ".crawl4ai")
 os.makedirs(DB_PATH, exist_ok=True)
 DB_PATH = os.path.join(DB_PATH, "crawl4ai.db")
 
-
 def init_db():
     global DB_PATH
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
-    cursor.execute(
-        """
+    cursor.execute('''
         CREATE TABLE IF NOT EXISTS crawled_data (
             url TEXT PRIMARY KEY,
             html TEXT,
@@ -26,42 +24,31 @@ def init_db():
             metadata TEXT DEFAULT "{}",
             screenshot TEXT DEFAULT ""
         )
-    """
-    )
+    ''')
     conn.commit()
     conn.close()
 
-
 def alter_db_add_screenshot(new_column: str = "media"):
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute(
-            f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""'
-        )
+        cursor.execute(f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""')
         conn.commit()
         conn.close()
     except Exception as e:
         print(f"Error altering database to add screenshot column: {e}")
 
-
 def check_db_path():
     if not DB_PATH:
         raise ValueError("Database path is not set or is empty.")
 
-
-def get_cached_url(
-    url: str,
-) -> Optional[Tuple[str, str, str, str, str, str, str, bool, str]]:
+def get_cached_url(url: str) -> Optional[Tuple[str, str, str, str, str, str, str, bool, str]]:
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute(
-            "SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?",
-            (url,),
-        )
+        cursor.execute('SELECT url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot FROM crawled_data WHERE url = ?', (url,))
         result = cursor.fetchone()
         conn.close()
         return result
@@ -69,25 +56,12 @@ def get_cached_url(
         print(f"Error retrieving cached URL: {e}")
         return None
 
-
-def cache_url(
-    url: str,
-    html: str,
-    cleaned_html: str,
-    markdown: str,
-    extracted_content: str,
-    success: bool,
-    media: str = "{}",
-    links: str = "{}",
-    metadata: str = "{}",
-    screenshot: str = "",
-):
+def cache_url(url: str, html: str, cleaned_html: str, markdown: str, extracted_content: str, success: bool, media : str = "{}", links : str = "{}", metadata : str = "{}", screenshot: str = ""):
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute(
-            """
+        cursor.execute('''
             INSERT INTO crawled_data (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot)
             VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             ON CONFLICT(url) DO UPDATE SET
@@ -100,32 +74,18 @@ def cache_url(
                 links = excluded.links,    
                 metadata = excluded.metadata,      
                 screenshot = excluded.screenshot
-        """,
-            (
-                url,
-                html,
-                cleaned_html,
-                markdown,
-                extracted_content,
-                success,
-                media,
-                links,
-                metadata,
-                screenshot,
-            ),
-        )
+        ''', (url, html, cleaned_html, markdown, extracted_content, success, media, links, metadata, screenshot))
         conn.commit()
         conn.close()
     except Exception as e:
         print(f"Error caching URL: {e}")
 
-
 def get_total_count() -> int:
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute("SELECT COUNT(*) FROM crawled_data")
+        cursor.execute('SELECT COUNT(*) FROM crawled_data')
         result = cursor.fetchone()
         conn.close()
         return result[0]
@@ -133,48 +93,43 @@ def get_total_count() -> int:
         print(f"Error getting total count: {e}")
         return 0
 
-
 def clear_db():
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute("DELETE FROM crawled_data")
+        cursor.execute('DELETE FROM crawled_data')
         conn.commit()
         conn.close()
     except Exception as e:
         print(f"Error clearing database: {e}")
-
-
+        
 def flush_db():
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute("DROP TABLE crawled_data")
+        cursor.execute('DROP TABLE crawled_data')
         conn.commit()
         conn.close()
     except Exception as e:
         print(f"Error flushing database: {e}")
 
-
 def update_existing_records(new_column: str = "media", default_value: str = "{}"):
     check_db_path()
     try:
         conn = sqlite3.connect(DB_PATH)
         cursor = conn.cursor()
-        cursor.execute(
-            f'UPDATE crawled_data SET {new_column} = "{default_value}" WHERE screenshot IS NULL'
-        )
+        cursor.execute(f'UPDATE crawled_data SET {new_column} = "{default_value}" WHERE screenshot IS NULL')
         conn.commit()
         conn.close()
     except Exception as e:
         print(f"Error updating existing records: {e}")
 
-
 if __name__ == "__main__":
     # Delete the existing database file
     if os.path.exists(DB_PATH):
         os.remove(DB_PATH)
-    init_db()
+    init_db()  
     # alter_db_add_screenshot("COL_NAME")
+    

crawl4ai/deep_crawling/__init__.py

@@ -1,47 +0,0 @@
-# deep_crawling/__init__.py
-from .base_strategy import DeepCrawlDecorator, DeepCrawlStrategy
-from .bfs_strategy import BFSDeepCrawlStrategy
-from .bff_strategy import BestFirstCrawlingStrategy
-from .dfs_strategy import DFSDeepCrawlStrategy
-from .filters import (
-    FilterChain,
-    ContentTypeFilter,
-    DomainFilter,
-    URLFilter,
-    URLPatternFilter,
-    FilterStats,
-    ContentRelevanceFilter,
-    SEOFilter
-)
-from .scorers import (
-    KeywordRelevanceScorer,
-    URLScorer,
-    CompositeScorer,
-    DomainAuthorityScorer,
-    FreshnessScorer,
-    PathDepthScorer,
-    ContentTypeScorer
-)
-
-__all__ = [
-    "DeepCrawlDecorator",
-    "DeepCrawlStrategy",
-    "BFSDeepCrawlStrategy",
-    "BestFirstCrawlingStrategy",
-    "DFSDeepCrawlStrategy",
-    "FilterChain",
-    "ContentTypeFilter",
-    "DomainFilter",
-    "URLFilter",
-    "URLPatternFilter",
-    "FilterStats",
-    "ContentRelevanceFilter",
-    "SEOFilter",
-    "KeywordRelevanceScorer",
-    "URLScorer",
-    "CompositeScorer",
-    "DomainAuthorityScorer",
-    "FreshnessScorer",
-    "PathDepthScorer",
-    "ContentTypeScorer",
-]

crawl4ai/deep_crawling/base_strategy.py

@@ -1,159 +0,0 @@
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from typing import AsyncGenerator, Optional, Set, List, Dict
-from functools import wraps
-from contextvars import ContextVar
-from ..types import AsyncWebCrawler, CrawlerRunConfig, CrawlResult, RunManyReturn
-
-
-class DeepCrawlDecorator:
-    """Decorator that adds deep crawling capability to arun method."""
-    deep_crawl_active = ContextVar("deep_crawl_active", default=False)
-    
-    def __init__(self, crawler: AsyncWebCrawler): 
-        self.crawler = crawler
-
-    def __call__(self, original_arun):
-        @wraps(original_arun)
-        async def wrapped_arun(url: str, config: CrawlerRunConfig = None, **kwargs):
-            # If deep crawling is already active, call the original method to avoid recursion.
-            if config and config.deep_crawl_strategy and not self.deep_crawl_active.get():
-                token = self.deep_crawl_active.set(True)
-                # Await the arun call to get the actual result object.
-                result_obj = await config.deep_crawl_strategy.arun(
-                    crawler=self.crawler,
-                    start_url=url,
-                    config=config
-                )
-                if config.stream:
-                    async def result_wrapper():
-                        try:
-                            async for result in result_obj:
-                                yield result
-                        finally:
-                            self.deep_crawl_active.reset(token)
-                    return result_wrapper()
-                else:
-                    try:
-                        return result_obj
-                    finally:
-                        self.deep_crawl_active.reset(token)
-            return await original_arun(url, config=config, **kwargs)
-        return wrapped_arun
-
-class DeepCrawlStrategy(ABC):
-    """
-    Abstract base class for deep crawling strategies.
-    
-    Core functions:
-      - arun: Main entry point that returns an async generator of CrawlResults.
-      - shutdown: Clean up resources.
-      - can_process_url: Validate a URL and decide whether to process it.
-      - _process_links: Extract and process links from a CrawlResult.
-    """
-
-    @abstractmethod
-    async def _arun_batch(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> List[CrawlResult]:
-        """
-        Batch (non-streaming) mode:
-        Processes one BFS level at a time, then yields all the results.
-        """
-        pass
-
-    @abstractmethod
-    async def _arun_stream(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> AsyncGenerator[CrawlResult, None]:
-        """
-        Streaming mode:
-        Processes one BFS level at a time and yields results immediately as they arrive.
-        """
-        pass
-    
-    async def arun(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: Optional[CrawlerRunConfig] = None,
-    ) -> RunManyReturn:
-        """
-        Traverse the given URL using the specified crawler.
-        
-        Args:
-            start_url (str): The URL from which to start crawling.
-            crawler (AsyncWebCrawler): The crawler instance to use.
-            crawler_run_config (Optional[CrawlerRunConfig]): Crawler configuration.
-        
-        Returns:
-            Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
-        """
-        if config is None:
-            raise ValueError("CrawlerRunConfig must be provided")
-
-        if config.stream:
-            return self._arun_stream(start_url, crawler, config)
-        else:
-            return await self._arun_batch(start_url, crawler, config)
-
-    def __call__(self, start_url: str, crawler: AsyncWebCrawler, config: CrawlerRunConfig):
-        return self.arun(start_url, crawler, config)
-
-    @abstractmethod
-    async def shutdown(self) -> None:
-        """
-        Clean up resources used by the deep crawl strategy.
-        """
-        pass
-
-    @abstractmethod
-    async def can_process_url(self, url: str, depth: int) -> bool:
-        """
-        Validate the URL format and apply custom filtering logic.
-        
-        Args:
-            url (str): The URL to validate.
-            depth (int): The current depth in the crawl.
-        
-        Returns:
-            bool: True if the URL should be processed, False otherwise.
-        """
-        pass
-
-    @abstractmethod
-    async def link_discovery(
-        self,
-        result: CrawlResult,
-        source_url: str,
-        current_depth: int,
-        visited: Set[str],
-        next_level: List[tuple],
-        depths: Dict[str, int],
-    ) -> None:
-        """
-        Extract and process links from the given crawl result.
-        
-        This method should:
-          - Validate each extracted URL using can_process_url.
-          - Optionally score URLs.
-          - Append valid URLs (and their parent references) to the next_level list.
-          - Update the depths dictionary with the new depth for each URL.
-        
-        Args:
-            result (CrawlResult): The result from a crawl operation.
-            source_url (str): The URL from which this result was obtained.
-            current_depth (int): The depth at which the source URL was processed.
-            visited (Set[str]): Set of already visited URLs.
-            next_level (List[tuple]): List of tuples (url, parent_url) for the next BFS level.
-            depths (Dict[str, int]): Mapping of URLs to their current depth.
-        """
-        pass
-

crawl4ai/deep_crawling/bff_strategy.py

@@ -1,271 +0,0 @@
-# best_first_crawling_strategy.py
-import asyncio
-import logging
-from datetime import datetime
-from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
-from urllib.parse import urlparse
-
-from ..models import TraversalStats
-from .filters import FilterChain
-from .scorers import URLScorer
-from . import DeepCrawlStrategy
-
-from ..types import AsyncWebCrawler, CrawlerRunConfig, CrawlResult, RunManyReturn
-from ..utils import normalize_url_for_deep_crawl
-
-from math import inf as infinity
-
-# Configurable batch size for processing items from the priority queue
-BATCH_SIZE = 10
-
-
-class BestFirstCrawlingStrategy(DeepCrawlStrategy):
-    """
-    Best-First Crawling Strategy using a priority queue.
-    
-    This strategy prioritizes URLs based on their score, ensuring that higher-value
-    pages are crawled first. It reimplements the core traversal loop to use a priority
-    queue while keeping URL validation and link discovery consistent with our design.
-    
-    Core methods:
-      - arun: Returns either a list (batch mode) or an async generator (stream mode).
-      - _arun_best_first: Core generator that uses a priority queue to yield CrawlResults.
-      - can_process_url: Validates URLs and applies filtering (inherited behavior).
-      - link_discovery: Extracts and validates links from a CrawlResult.
-    """
-    def __init__(
-        self,
-        max_depth: int,
-        filter_chain: FilterChain = FilterChain(),
-        url_scorer: Optional[URLScorer] = None,
-        include_external: bool = False,
-        max_pages: int = infinity,
-        logger: Optional[logging.Logger] = None,
-    ):
-        self.max_depth = max_depth
-        self.filter_chain = filter_chain
-        self.url_scorer = url_scorer
-        self.include_external = include_external
-        self.max_pages = max_pages
-        # self.logger = logger or logging.getLogger(__name__)
-        # Ensure logger is always a Logger instance, not a dict from serialization
-        if isinstance(logger, logging.Logger):
-            self.logger = logger
-        else:
-            # Create a new logger if logger is None, dict, or any other non-Logger type
-            self.logger = logging.getLogger(__name__)
-        self.stats = TraversalStats(start_time=datetime.now())
-        self._cancel_event = asyncio.Event()
-        self._pages_crawled = 0
-
-    async def can_process_url(self, url: str, depth: int) -> bool:
-        """
-        Validate the URL format and apply filtering.
-        For the starting URL (depth 0), filtering is bypassed.
-        """
-        try:
-            parsed = urlparse(url)
-            if not parsed.scheme or not parsed.netloc:
-                raise ValueError("Missing scheme or netloc")
-            if parsed.scheme not in ("http", "https"):
-                raise ValueError("Invalid scheme")
-            if "." not in parsed.netloc:
-                raise ValueError("Invalid domain")
-        except Exception as e:
-            self.logger.warning(f"Invalid URL: {url}, error: {e}")
-            return False
-
-        if depth != 0 and not await self.filter_chain.apply(url):
-            return False
-
-        return True
-
-    async def link_discovery(
-        self,
-        result: CrawlResult,
-        source_url: str,
-        current_depth: int,
-        visited: Set[str],
-        next_links: List[Tuple[str, Optional[str]]],
-        depths: Dict[str, int],
-    ) -> None:
-        """
-        Extract links from the crawl result, validate them, and append new URLs
-        (with their parent references) to next_links.
-        Also updates the depths dictionary.
-        """
-        new_depth = current_depth + 1
-        if new_depth > self.max_depth:
-            return
-            
-        # If we've reached the max pages limit, don't discover new links
-        remaining_capacity = self.max_pages - self._pages_crawled
-        if remaining_capacity <= 0:
-            self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping link discovery")
-            return
-
-        # Retrieve internal links; include external links if enabled.
-        links = result.links.get("internal", [])
-        if self.include_external:
-            links += result.links.get("external", [])
-
-        # If we have more links than remaining capacity, limit how many we'll process
-        valid_links = []
-        for link in links:
-            url = link.get("href")
-            base_url = normalize_url_for_deep_crawl(url, source_url)
-            if base_url in visited:
-                continue
-            if not await self.can_process_url(url, new_depth):
-                self.stats.urls_skipped += 1
-                continue
-                
-            valid_links.append(base_url)
-            
-        # Record the new depths and add to next_links
-        for url in valid_links:
-            depths[url] = new_depth
-            next_links.append((url, source_url))
-
-    async def _arun_best_first(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> AsyncGenerator[CrawlResult, None]:
-        """
-        Core best-first crawl method using a priority queue.
-        
-        The queue items are tuples of (score, depth, url, parent_url). Lower scores
-        are treated as higher priority. URLs are processed in batches for efficiency.
-        """
-        queue: asyncio.PriorityQueue = asyncio.PriorityQueue()
-        # Push the initial URL with score 0 and depth 0.
-        initial_score = self.url_scorer.score(start_url) if self.url_scorer else 0
-        await queue.put((-initial_score, 0, start_url, None))
-        visited: Set[str] = set()
-        depths: Dict[str, int] = {start_url: 0}
-
-        while not queue.empty() and not self._cancel_event.is_set():
-            # Stop if we've reached the max pages limit
-            if self._pages_crawled >= self.max_pages:
-                self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
-                break
-                
-            # Calculate how many more URLs we can process in this batch
-            remaining = self.max_pages - self._pages_crawled
-            batch_size = min(BATCH_SIZE, remaining)
-            if batch_size <= 0:
-                # No more pages to crawl
-                self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
-                break
-                
-            batch: List[Tuple[float, int, str, Optional[str]]] = []
-            # Retrieve up to BATCH_SIZE items from the priority queue.
-            for _ in range(BATCH_SIZE):
-                if queue.empty():
-                    break
-                item = await queue.get()
-                score, depth, url, parent_url = item
-                if url in visited:
-                    continue
-                visited.add(url)
-                batch.append(item)
-
-            if not batch:
-                continue
-
-            # Process the current batch of URLs.
-            urls = [item[2] for item in batch]
-            batch_config = config.clone(deep_crawl_strategy=None, stream=True)
-            stream_gen = await crawler.arun_many(urls=urls, config=batch_config)
-            async for result in stream_gen:
-                result_url = result.url
-                # Find the corresponding tuple from the batch.
-                corresponding = next((item for item in batch if item[2] == result_url), None)
-                if not corresponding:
-                    continue
-                score, depth, url, parent_url = corresponding
-                result.metadata = result.metadata or {}
-                result.metadata["depth"] = depth
-                result.metadata["parent_url"] = parent_url
-                result.metadata["score"] = -score
-                
-                # Count only successful crawls toward max_pages limit
-                if result.success:
-                    self._pages_crawled += 1
-                    # Check if we've reached the limit during batch processing
-                    if self._pages_crawled >= self.max_pages:
-                        self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
-                        break  # Exit the generator
-                
-                yield result
-                
-                # Only discover links from successful crawls
-                if result.success:
-                    # Discover new links from this result
-                    new_links: List[Tuple[str, Optional[str]]] = []
-                    await self.link_discovery(result, result_url, depth, visited, new_links, depths)
-                    
-                    for new_url, new_parent in new_links:
-                        new_depth = depths.get(new_url, depth + 1)
-                        new_score = self.url_scorer.score(new_url) if self.url_scorer else 0
-                        await queue.put((-new_score, new_depth, new_url, new_parent))
-
-        # End of crawl.
-
-    async def _arun_batch(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> List[CrawlResult]:
-        """
-        Best-first crawl in batch mode.
-        
-        Aggregates all CrawlResults into a list.
-        """
-        results: List[CrawlResult] = []
-        async for result in self._arun_best_first(start_url, crawler, config):
-            results.append(result)
-        return results
-
-    async def _arun_stream(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> AsyncGenerator[CrawlResult, None]:
-        """
-        Best-first crawl in streaming mode.
-        
-        Yields CrawlResults as they become available.
-        """
-        async for result in self._arun_best_first(start_url, crawler, config):
-            yield result
-
-    async def arun(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: Optional[CrawlerRunConfig] = None,
-    ) -> "RunManyReturn":
-        """
-        Main entry point for best-first crawling.
-        
-        Returns either a list (batch mode) or an async generator (stream mode)
-        of CrawlResults.
-        """
-        if config is None:
-            raise ValueError("CrawlerRunConfig must be provided")
-        if config.stream:
-            return self._arun_stream(start_url, crawler, config)
-        else:
-            return await self._arun_batch(start_url, crawler, config)
-
-    async def shutdown(self) -> None:
-        """
-        Signal cancellation and clean up resources.
-        """
-        self._cancel_event.set()
-        self.stats.end_time = datetime.now()

crawl4ai/deep_crawling/bfs_strategy.py

@@ -1,260 +0,0 @@
-# bfs_deep_crawl_strategy.py
-import asyncio
-import logging
-from datetime import datetime
-from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
-from urllib.parse import urlparse
-
-from ..models import TraversalStats
-from .filters import FilterChain
-from .scorers import URLScorer
-from . import DeepCrawlStrategy  
-from ..types import AsyncWebCrawler, CrawlerRunConfig, CrawlResult
-from ..utils import normalize_url_for_deep_crawl, efficient_normalize_url_for_deep_crawl
-from math import inf as infinity
-
-class BFSDeepCrawlStrategy(DeepCrawlStrategy):
-    """
-    Breadth-First Search deep crawling strategy.
-    
-    Core functions:
-      - arun: Main entry point; splits execution into batch or stream modes.
-      - link_discovery: Extracts, filters, and (if needed) scores the outgoing URLs.
-      - can_process_url: Validates URL format and applies the filter chain.
-    """
-    def __init__(
-        self,
-        max_depth: int,
-        filter_chain: FilterChain = FilterChain(),
-        url_scorer: Optional[URLScorer] = None,        
-        include_external: bool = False,
-        score_threshold: float = -infinity,
-        max_pages: int = infinity,
-        logger: Optional[logging.Logger] = None,
-    ):
-        self.max_depth = max_depth
-        self.filter_chain = filter_chain
-        self.url_scorer = url_scorer
-        self.include_external = include_external
-        self.score_threshold = score_threshold
-        self.max_pages = max_pages
-        # self.logger = logger or logging.getLogger(__name__)
-        # Ensure logger is always a Logger instance, not a dict from serialization
-        if isinstance(logger, logging.Logger):
-            self.logger = logger
-        else:
-            # Create a new logger if logger is None, dict, or any other non-Logger type
-            self.logger = logging.getLogger(__name__)
-        self.stats = TraversalStats(start_time=datetime.now())
-        self._cancel_event = asyncio.Event()
-        self._pages_crawled = 0
-
-    async def can_process_url(self, url: str, depth: int) -> bool:
-        """
-        Validates the URL and applies the filter chain.
-        For the start URL (depth 0) filtering is bypassed.
-        """
-        try:
-            parsed = urlparse(url)
-            if not parsed.scheme or not parsed.netloc:
-                raise ValueError("Missing scheme or netloc")
-            if parsed.scheme not in ("http", "https"):
-                raise ValueError("Invalid scheme")
-            if "." not in parsed.netloc:
-                raise ValueError("Invalid domain")
-        except Exception as e:
-            self.logger.warning(f"Invalid URL: {url}, error: {e}")
-            return False
-
-        if depth != 0 and not await self.filter_chain.apply(url):
-            return False
-
-        return True
-
-    async def link_discovery(
-        self,
-        result: CrawlResult,
-        source_url: str,
-        current_depth: int,
-        visited: Set[str],
-        next_level: List[Tuple[str, Optional[str]]],
-        depths: Dict[str, int],
-    ) -> None:
-        """
-        Extracts links from the crawl result, validates and scores them, and
-        prepares the next level of URLs.
-        Each valid URL is appended to next_level as a tuple (url, parent_url)
-        and its depth is tracked.
-        """            
-        next_depth = current_depth + 1
-        if next_depth > self.max_depth:
-            return
-
-        # If we've reached the max pages limit, don't discover new links
-        remaining_capacity = self.max_pages - self._pages_crawled
-        if remaining_capacity <= 0:
-            self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping link discovery")
-            return
-
-        # Get internal links and, if enabled, external links.
-        links = result.links.get("internal", [])
-        if self.include_external:
-            links += result.links.get("external", [])
-
-        valid_links = []
-        
-        # First collect all valid links
-        for link in links:
-            url = link.get("href")
-            # Strip URL fragments to avoid duplicate crawling
-            # base_url = url.split('#')[0] if url else url
-            base_url = normalize_url_for_deep_crawl(url, source_url)
-            if base_url in visited:
-                continue
-            if not await self.can_process_url(url, next_depth):
-                self.stats.urls_skipped += 1
-                continue
-
-            # Score the URL if a scorer is provided
-            score = self.url_scorer.score(base_url) if self.url_scorer else 0
-            
-            # Skip URLs with scores below the threshold
-            if score < self.score_threshold:
-                self.logger.debug(f"URL {url} skipped: score {score} below threshold {self.score_threshold}")
-                self.stats.urls_skipped += 1
-                continue
-
-            visited.add(base_url)
-            valid_links.append((base_url, score))
-        
-        # If we have more valid links than capacity, sort by score and take the top ones
-        if len(valid_links) > remaining_capacity:
-            if self.url_scorer:
-                # Sort by score in descending order
-                valid_links.sort(key=lambda x: x[1], reverse=True)
-            # Take only as many as we have capacity for
-            valid_links = valid_links[:remaining_capacity]
-            self.logger.info(f"Limiting to {remaining_capacity} URLs due to max_pages limit")
-            
-        # Process the final selected links
-        for url, score in valid_links:
-            # attach the score to metadata if needed
-            if score:
-                result.metadata = result.metadata or {}
-                result.metadata["score"] = score
-            next_level.append((url, source_url))
-            depths[url] = next_depth
-
-    async def _arun_batch(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> List[CrawlResult]:
-        """
-        Batch (non-streaming) mode:
-        Processes one BFS level at a time, then yields all the results.
-        """
-        visited: Set[str] = set()
-        # current_level holds tuples: (url, parent_url)
-        current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
-        depths: Dict[str, int] = {start_url: 0}
-
-        results: List[CrawlResult] = []
-
-        while current_level and not self._cancel_event.is_set():
-            # Check if we've already reached max_pages before starting a new level
-            if self._pages_crawled >= self.max_pages:
-                self.logger.info(f"Max pages limit ({self.max_pages}) reached, stopping crawl")
-                break
-            
-            next_level: List[Tuple[str, Optional[str]]] = []
-            urls = [url for url, _ in current_level]
-
-            # Clone the config to disable deep crawling recursion and enforce batch mode.
-            batch_config = config.clone(deep_crawl_strategy=None, stream=False)
-            batch_results = await crawler.arun_many(urls=urls, config=batch_config)
-            
-            # Update pages crawled counter - count only successful crawls
-            successful_results = [r for r in batch_results if r.success]
-            self._pages_crawled += len(successful_results)
-            
-            for result in batch_results:
-                url = result.url
-                depth = depths.get(url, 0)
-                result.metadata = result.metadata or {}
-                result.metadata["depth"] = depth
-                parent_url = next((parent for (u, parent) in current_level if u == url), None)
-                result.metadata["parent_url"] = parent_url
-                results.append(result)
-                
-                # Only discover links from successful crawls
-                if result.success:
-                    # Link discovery will handle the max pages limit internally
-                    await self.link_discovery(result, url, depth, visited, next_level, depths)
-
-            current_level = next_level
-
-        return results
-
-    async def _arun_stream(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> AsyncGenerator[CrawlResult, None]:
-        """
-        Streaming mode:
-        Processes one BFS level at a time and yields results immediately as they arrive.
-        """
-        visited: Set[str] = set()
-        current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
-        depths: Dict[str, int] = {start_url: 0}
-
-        while current_level and not self._cancel_event.is_set():
-            next_level: List[Tuple[str, Optional[str]]] = []
-            urls = [url for url, _ in current_level]
-            visited.update(urls)
-
-            stream_config = config.clone(deep_crawl_strategy=None, stream=True)
-            stream_gen = await crawler.arun_many(urls=urls, config=stream_config)
-            
-            # Keep track of processed results for this batch
-            results_count = 0
-            async for result in stream_gen:
-                url = result.url
-                depth = depths.get(url, 0)
-                result.metadata = result.metadata or {}
-                result.metadata["depth"] = depth
-                parent_url = next((parent for (u, parent) in current_level if u == url), None)
-                result.metadata["parent_url"] = parent_url
-                
-                # Count only successful crawls
-                if result.success:
-                    self._pages_crawled += 1
-                    # Check if we've reached the limit during batch processing
-                    if self._pages_crawled >= self.max_pages:
-                        self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
-                        break  # Exit the generator
-                
-                results_count += 1
-                yield result
-                
-                # Only discover links from successful crawls
-                if result.success:
-                    # Link discovery will handle the max pages limit internally
-                    await self.link_discovery(result, url, depth, visited, next_level, depths)
-            
-            # If we didn't get results back (e.g. due to errors), avoid getting stuck in an infinite loop
-            # by considering these URLs as visited but not counting them toward the max_pages limit
-            if results_count == 0 and urls:
-                self.logger.warning(f"No results returned for {len(urls)} URLs, marking as visited")
-                
-            current_level = next_level
-
-    async def shutdown(self) -> None:
-        """
-        Clean up resources and signal cancellation of the crawl.
-        """
-        self._cancel_event.set()
-        self.stats.end_time = datetime.now()

crawl4ai/deep_crawling/crazy.py

@@ -1,432 +0,0 @@
-from __future__ import annotations
-# I just got crazy, trying to wrute K&R C but in Python. Right now I feel like I'm in a quantum state.
-# I probably won't use this; I just want to leave it here. A century later, the future human race will be like, "WTF?"
-
-# ------ Imports That Will Make You Question Reality ------ #
-from functools import wraps
-from contextvars import ContextVar
-import inspect
-
-from crawl4ai import CacheMode
-from crawl4ai.async_configs import CrawlerRunConfig
-from crawl4ai.models import CrawlResult, TraversalStats
-from crawl4ai.deep_crawling.filters import FilterChain
-from crawl4ai.async_webcrawler import AsyncWebCrawler
-import time
-import logging
-from urllib.parse import urlparse
-
-from abc import ABC, abstractmethod
-from collections import deque
-import asyncio
-from typing import (
-    AsyncGenerator,
-    Dict,
-    List,
-    TypeVar,
-    Generic,
-    Tuple,
-    Callable,
-    Awaitable,
-    Union,
-)
-from functools import lru_cache
-import mmh3
-from bitarray import bitarray
-import numpy as np
-from heapq import heappush, heappop
-
-# ------ Type Algebra Mastery ------ #
-CrawlResultT = TypeVar("CrawlResultT", bound="CrawlResult")
-PriorityT = TypeVar("PriorityT")
-P = TypeVar("P")
-
-# ------ Hyperscalar Context Management ------ #
-deep_crawl_ctx = ContextVar("deep_crawl_stack", default=deque())
-
-# ------ Algebraic Crawler Monoid ------ #
-class TraversalContext:
-    __slots__ = ('visited', 'frontier', 'depths', 'priority_fn', 'current_depth')
-    
-    def __init__(self,
-                 priority_fn: Callable[[str], Awaitable[float]] = lambda _: 1.0):
-        self.visited: BloomFilter = BloomFilter(10**6, 0.01)  # 1M items, 1% FP
-        self.frontier: PriorityQueue = PriorityQueue()
-        self.depths: Dict[str, int] = {}
-        self.priority_fn = priority_fn
-        self.current_depth = 0
-
-    def clone_for_level(self) -> TraversalContext:
-        """Monadic context propagation"""
-        new_ctx = TraversalContext(self.priority_fn)
-        new_ctx.visited = self.visited.copy()
-        new_ctx.depths = self.depths.copy()
-        new_ctx.current_depth = self.current_depth
-        return new_ctx
-
-class PriorityQueue(Generic[PriorityT]):
-    """Fibonacci heap-inspired priority queue with O(1) amortized operations"""
-    __slots__ = ('_heap', '_index')
-
-    def __init__(self):
-        self._heap: List[Tuple[PriorityT, float, P]] = []
-        self._index: Dict[P, int] = {}
-
-    def insert(self, priority: PriorityT, item: P) -> None:
-        tiebreaker = time.time()  # Ensure FIFO for equal priorities
-        heappush(self._heap, (priority, tiebreaker, item))
-        self._index[item] = len(self._heap) - 1
-
-    def extract(self, top_n = 1) -> P:
-        items = []
-        for _ in range(top_n):
-            if not self._heap:
-                break
-            priority, _, item = heappop(self._heap)
-            del self._index[item]
-            items.append(item)
-        if not items:
-            raise IndexError("Priority queue empty")
-        return items
-        # while self._heap:
-        #     _, _, item = heappop(self._heap)
-        #     if item in self._index:
-        #         del self._index[item]
-        #         return item
-        raise IndexError("Priority queue empty")
-
-
-    def is_empty(self) -> bool:
-        return not bool(self._heap)
-
-class BloomFilter:
-    """Optimal Bloom filter using murmur3 hash avalanche"""
-    __slots__ = ('size', 'hashes', 'bits')
-
-    def __init__(self, capacity: int, error_rate: float):
-        self.size = self._optimal_size(capacity, error_rate)
-        self.hashes = self._optimal_hashes(capacity, self.size)
-        self.bits = bitarray(self.size)
-        self.bits.setall(False)
-
-    @staticmethod
-    def _optimal_size(n: int, p: float) -> int:
-        m = - (n * np.log(p)) / (np.log(2) ** 2)
-        return int(np.ceil(m))
-
-    @staticmethod
-    def _optimal_hashes(n: int, m: int) -> int:
-        k = (m / n) * np.log(2)
-        return int(np.ceil(k))
-
-    def add(self, item: str) -> None:
-        for seed in range(self.hashes):
-            digest = mmh3.hash(item, seed) % self.size
-            self.bits[digest] = True
-
-    def __contains__(self, item: str) -> bool:
-        return all(
-            self.bits[mmh3.hash(item, seed) % self.size]
-            for seed in range(self.hashes)
-        )
-
-    def copy(self) -> BloomFilter:
-        new = object.__new__(BloomFilter)
-        new.size = self.size
-        new.hashes = self.hashes
-        new.bits = self.bits.copy()
-        return new
-    
-    def __len__(self) -> int:
-        """
-        Estimates the number of items in the filter using the 
-        count of set bits and the formula:
-        n = -m/k * ln(1 - X/m)
-        where:
-            m = size of bit array
-            k = number of hash functions
-            X = count of set bits
-        """
-        set_bits = self.bits.count(True)
-        if set_bits == 0:
-            return 0
-            
-        # Use the inverse bloom filter formula to estimate cardinality
-        return int(
-            -(self.size / self.hashes) * 
-            np.log(1 - set_bits / self.size)
-        )
-    
-    def bit_count(self) -> int:
-        """Returns the raw count of set bits in the filter"""
-        return self.bits.count(True)
-        
-    def __repr__(self) -> str:
-        return f"BloomFilter(est_items={len(self)}, bits={self.bit_count()}/{self.size})"
-
-# ------ Hyper-Optimal Deep Crawl Core ------ #
-class DeepCrawlDecorator:
-    """Metaprogramming marvel: Zero-cost deep crawl abstraction"""
-    def __init__(self, crawler: AsyncWebCrawler):
-        self.crawler = crawler
-
-    def __call__(self, original_arun: Callable) -> Callable:
-        @wraps(original_arun)
-        async def quantum_arun(url: str, config: CrawlerRunConfig = None, **kwargs):
-            stack = deep_crawl_ctx.get()
-            if config and config.deep_crawl_strategy and not stack:
-                stack.append(self.crawler)
-                try:
-                    deep_crawl_ctx.set(stack)
-                    async for result in config.deep_crawl_strategy.traverse(
-                        start_url=url,
-                        crawler=self.crawler,
-                        config=config
-                    ):
-                        yield result
-                finally:
-                    stack.pop()
-                    deep_crawl_ctx.set(stack)
-            else:
-                result = await original_arun(url, config=config, **kwargs)
-                yield result
-        return quantum_arun
-
-
-async def collect_results(url, crawler, config):
-    if id(getattr(crawler, "arun")) != id(getattr(crawler, "original_arun")):
-        setattr(crawler, "arun", getattr(crawler, "original_arun"))
-
-    ret = crawler.arun(url, config=config)
-    # If arun is an async generator, iterate over it
-    if inspect.isasyncgen(ret):
-        return [r async for r in ret]
-    # Otherwise, await the coroutine and normalize to a list
-    result = await ret
-    return result if isinstance(result, list) else [result]
-
-async def collect_many_results(url, crawler, config):
-    # Replace back arun to its original implementation
-    if id(getattr(crawler, "arun")) != id(getattr(crawler, "original_arun")):
-        setattr(crawler, "arun", getattr(crawler, "original_arun"))
-    ret = crawler.arun_many(url, config=config)
-    # If arun is an async generator, iterate over it
-    if inspect.isasyncgen(ret):
-        return [r async for r in ret]
-    # Otherwise, await the coroutine and normalize to a list
-    result = await ret
-    return result if isinstance(result, list) else [result]
-
-
-# ------ Deep Crawl Strategy Interface ------ #
-CrawlResultT = TypeVar("CrawlResultT", bound=CrawlResult)
-# In batch mode we return List[CrawlResult] and in stream mode an AsyncGenerator.
-RunManyReturn = Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
-
-
-class DeepCrawlStrategy(ABC):
-    """Abstract base class that will make Dijkstra smile"""
-    @abstractmethod
-    async def traverse(self,
-                      start_url: str,
-                      crawler: AsyncWebCrawler,
-                      config: CrawlerRunConfig) -> RunManyReturn:
-        """Traverse with O(1) memory complexity via generator fusion"""
-        ...
-
-    @abstractmethod
-    def precompute_priority(self, url: str) -> Awaitable[float]:
-        """Quantum-inspired priority precomputation"""
-        pass
-
-    @abstractmethod
-    async def link_hypercube(self, result: CrawlResult) -> AsyncGenerator[str, None]:
-        """Hilbert-curve optimized link generation"""
-        pass
-
-# ------ BFS That Would Make Knuth Proud ------ #
-
-def calculate_quantum_batch_size(
-    depth: int,
-    max_depth: int,
-    frontier_size: int,
-    visited_size: int
-) -> int:
-    """
-    Calculates optimal batch size for URL processing using quantum-inspired mathematical principles.
-    
-    This function implements a sophisticated batch size calculation using:
-    1. Golden Ratio (φ) based scaling for optimal irrationality
-    2. Depth-aware amplitude modulation
-    3. Harmonic series dampening
-    4. Logarithmic growth control
-    5. Dynamic frontier adaptation
-    
-    The formula follows the quantum harmonic oscillator principle:
-        N = ⌈φ^(2d) * log₂(|V|) * H(d)⁻¹ * min(20, |F|/10)⌉
-    where:
-        φ = Golden Ratio ((1 + √5) / 2)
-        d = depth factor (normalized remaining depth)
-        |V| = size of visited set
-        H(d) = d-th harmonic number
-        |F| = frontier size
-    
-    Args:
-        depth (int): Current traversal depth
-        max_depth (int): Maximum allowed depth
-        frontier_size (int): Current size of frontier queue
-        visited_size (int): Number of URLs visited so far
-    
-    Returns:
-        int: Optimal batch size bounded between 1 and 100
-        
-    Mathematical Properties:
-        - Maintains O(log n) growth with respect to visited size
-        - Provides φ-optimal distribution of resources
-        - Ensures quantum-like state transitions between depths
-        - Harmonically dampened to prevent exponential explosion
-    """
-    # Golden ratio φ = (1 + √5) / 2
-    φ = (1 + 5 ** 0.5) / 2
-    
-    # Calculate normalized depth factor [0, 1]
-    depth_factor = (max_depth - depth) / max_depth if depth < max_depth else 0
-    
-    # Compute harmonic number for current depth
-    harmonic = sum(1/k for k in range(1, depth + 2))
-    
-    # Calculate quantum batch size
-    batch_size = int(np.ceil(
-        (φ ** (depth_factor * 2)) *          # Golden ratio scaling
-        np.log2(visited_size + 2) *          # Logarithmic growth factor
-        (1 / harmonic) *                     # Harmonic dampening
-        max(1, min(20, frontier_size / 10))  # Frontier-aware scaling
-    ))
-    
-    # Enforce practical bounds
-    return max(1, min(100, batch_size))
-
-
-class BFSDeepCrawlStrategy(DeepCrawlStrategy):
-    """Breadth-First Search with Einstein-Rosen bridge optimization"""
-    __slots__ = ('max_depth', 'filter_chain', 'priority_fn', 'stats', '_cancel')
-
-    def __init__(self,
-                 max_depth: int,
-                 filter_chain: FilterChain = FilterChain(),
-                 priority_fn: Callable[[str], Awaitable[float]] = lambda url: 1.0,
-                 logger: logging.Logger = None):
-        self.max_depth = max_depth
-        self.filter_chain = filter_chain
-        self.priority_fn = priority_fn
-        self.stats = TraversalStats()
-        self._cancel = asyncio.Event()
-        self.semaphore = asyncio.Semaphore(1000)
-
-    async def traverse(self,
-                      start_url: str,
-                      crawler: AsyncWebCrawler,
-                      config: CrawlerRunConfig) -> RunManyReturn:
-        """Non-blocking BFS with O(b^d) time complexity awareness"""
-        ctx = TraversalContext(self.priority_fn)
-        ctx.frontier.insert(self.priority_fn(start_url), (start_url, None, 0))
-        ctx.visited.add(start_url)
-        ctx.depths[start_url] = 0
-
-        while not ctx.frontier.is_empty() and not self._cancel.is_set():
-            # Use the best algorith, to find top_n value
-            top_n = calculate_quantum_batch_size(
-                depth=ctx.current_depth,
-                max_depth=self.max_depth,
-                frontier_size=len(ctx.frontier._heap),
-                visited_size=len(ctx.visited)
-            )
-
-            urls = ctx.frontier.extract(top_n=top_n)
-            # url, parent, depth = ctx.frontier.extract(top_n=top_n)
-            if urls:
-                ctx.current_depth = urls[0][2]
-
-            async with self.semaphore:
-                results = await collect_many_results([url for (url, parent, depth) in urls], crawler, config)
-                # results = await asyncio.gather(*[
-                #     collect_results(url, crawler, config) for (url, parent, depth) in urls
-                # ])
-                # result = _result[0]
-                for ix, result in enumerate(results):
-                    url, parent, depth = result.url, urls[ix][1], urls[ix][2]
-                    result.metadata['depth'] = depth
-                    result.metadata['parent'] = parent
-                    yield result
-
-                    if depth < self.max_depth:
-                        async for link in self.link_hypercube(result):
-                            if link not in ctx.visited:
-                                priority = self.priority_fn(link)
-                                ctx.frontier.insert(priority, (link, url, depth + 1))
-                                ctx.visited.add(link)
-                                ctx.depths[link] = depth + 1
-
-    @lru_cache(maxsize=65536)
-    async def validate_url(self, url: str) -> bool:
-        """Memoized URL validation with λ-calculus purity"""
-        try:
-            parsed = urlparse(url)
-            return (parsed.scheme in {'http', 'https'}
-                    and '.' in parsed.netloc
-                    and await self.filter_chain.apply(url))
-        except Exception:
-            return False
-
-    async def link_hypercube(self, result: CrawlResult) -> AsyncGenerator[str, None]:
-        """Hilbert-ordered link generation with O(1) yield latency"""
-        links = (link['href'] for link in result.links.get('internal', []))
-        validated = filter(self.validate_url, links)
-        for link in sorted(validated, key=lambda x: -self.priority_fn(x)):
-            yield link
-
-    def __aiter__(self) -> AsyncGenerator[CrawlResult, None]:
-        """Native async iterator interface"""
-        return self.traverse()
-
-    async def __anext__(self) -> CrawlResult:
-        """True async iterator protocol implementation"""
-        result = await self.traverse().__anext__()
-        if result:
-            return result
-        raise StopAsyncIteration
-
-    async def precompute_priority(self, url):
-        return super().precompute_priority(url)
-
-    async def shutdown(self):
-        self._cancel.set()
-
-# ------ Usage That Will Drop Jaws ------ #
-async def main():
-    """Quantum crawl example"""
-    strategy = BFSDeepCrawlStrategy(
-        max_depth=2,
-        priority_fn=lambda url: 1.0 / (len(url) + 1e-9),  # Inverse length priority
-        # filter_chain=FilterChain(...)
-    )
-
-    config: CrawlerRunConfig = CrawlerRunConfig(
-        deep_crawl_strategy=strategy,
-        stream=False,
-        verbose=True,
-        cache_mode=CacheMode.BYPASS
-    )
-
-    async with AsyncWebCrawler() as crawler:
-        run_decorator = DeepCrawlDecorator(crawler)
-        setattr(crawler, "original_arun", crawler.arun)
-        crawler.arun = run_decorator(crawler.arun)
-        start_time = time.perf_counter()
-        async for result in crawler.arun("https://docs.crawl4ai.com", config=config):
-            print(f"🌀 {result.url} (Depth: {result.metadata['depth']})")
-        print(f"Deep crawl completed in {time.perf_counter() - start_time:.2f}s")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

crawl4ai/deep_crawling/dfs_strategy.py

@@ -1,110 +0,0 @@
-# dfs_deep_crawl_strategy.py
-from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
-
-from ..models import CrawlResult
-from .bfs_strategy import BFSDeepCrawlStrategy  # noqa
-from ..types import AsyncWebCrawler, CrawlerRunConfig
-
-class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
-    """
-    Depth-First Search (DFS) deep crawling strategy.
-
-    Inherits URL validation and link discovery from BFSDeepCrawlStrategy.
-    Overrides _arun_batch and _arun_stream to use a stack (LIFO) for DFS traversal.
-    """
-    async def _arun_batch(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> List[CrawlResult]:
-        """
-        Batch (non-streaming) DFS mode.
-        Uses a stack to traverse URLs in DFS order, aggregating CrawlResults into a list.
-        """
-        visited: Set[str] = set()
-        # Stack items: (url, parent_url, depth)
-        stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
-        depths: Dict[str, int] = {start_url: 0}
-        results: List[CrawlResult] = []
-
-        while stack and not self._cancel_event.is_set():
-            url, parent, depth = stack.pop()
-            if url in visited or depth > self.max_depth:
-                continue
-            visited.add(url)
-
-            # Clone config to disable recursive deep crawling.
-            batch_config = config.clone(deep_crawl_strategy=None, stream=False)
-            url_results = await crawler.arun_many(urls=[url], config=batch_config)
-            
-            for result in url_results:
-                result.metadata = result.metadata or {}
-                result.metadata["depth"] = depth
-                result.metadata["parent_url"] = parent
-                if self.url_scorer:
-                    result.metadata["score"] = self.url_scorer.score(url)
-                results.append(result)
-                
-                # Count only successful crawls toward max_pages limit
-                if result.success:
-                    self._pages_crawled += 1
-                    # Check if we've reached the limit during batch processing
-                    if self._pages_crawled >= self.max_pages:
-                        self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
-                        break  # Exit the generator
-                    
-                    # Only discover links from successful crawls
-                    new_links: List[Tuple[str, Optional[str]]] = []
-                    await self.link_discovery(result, url, depth, visited, new_links, depths)
-                    
-                    # Push new links in reverse order so the first discovered is processed next.
-                    for new_url, new_parent in reversed(new_links):
-                        new_depth = depths.get(new_url, depth + 1)
-                        stack.append((new_url, new_parent, new_depth))
-        return results
-
-    async def _arun_stream(
-        self,
-        start_url: str,
-        crawler: AsyncWebCrawler,
-        config: CrawlerRunConfig,
-    ) -> AsyncGenerator[CrawlResult, None]:
-        """
-        Streaming DFS mode.
-        Uses a stack to traverse URLs in DFS order and yields CrawlResults as they become available.
-        """
-        visited: Set[str] = set()
-        stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
-        depths: Dict[str, int] = {start_url: 0}
-
-        while stack and not self._cancel_event.is_set():
-            url, parent, depth = stack.pop()
-            if url in visited or depth > self.max_depth:
-                continue
-            visited.add(url)
-
-            stream_config = config.clone(deep_crawl_strategy=None, stream=True)
-            stream_gen = await crawler.arun_many(urls=[url], config=stream_config)
-            async for result in stream_gen:
-                result.metadata = result.metadata or {}
-                result.metadata["depth"] = depth
-                result.metadata["parent_url"] = parent
-                if self.url_scorer:
-                    result.metadata["score"] = self.url_scorer.score(url)
-                yield result
-
-                # Only count successful crawls toward max_pages limit
-                # and only discover links from successful crawls
-                if result.success:
-                    self._pages_crawled += 1
-                    # Check if we've reached the limit during batch processing
-                    if self._pages_crawled >= self.max_pages:
-                        self.logger.info(f"Max pages limit ({self.max_pages}) reached during batch, stopping crawl")
-                        break  # Exit the generator
-                    
-                    new_links: List[Tuple[str, Optional[str]]] = []
-                    await self.link_discovery(result, url, depth, visited, new_links, depths)
-                    for new_url, new_parent in reversed(new_links):
-                        new_depth = depths.get(new_url, depth + 1)
-                        stack.append((new_url, new_parent, new_depth))

crawl4ai/deep_crawling/filters.py

@@ -1,694 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Pattern, Set, Union
-from urllib.parse import urlparse
-from array import array
-import re
-import logging
-from functools import lru_cache
-import fnmatch
-from dataclasses import dataclass
-import weakref
-import math
-from collections import defaultdict
-from typing import Dict
-from ..utils import HeadPeekr
-import asyncio
-import inspect
-
-
-@dataclass
-class FilterStats:
-    __slots__ = ("_counters",)
-
-    def __init__(self):
-        # Use array of unsigned ints for atomic operations
-        self._counters = array("I", [0, 0, 0])  # total, passed, rejected
-
-    @property
-    def total_urls(self):
-        return self._counters[0]
-
-    @property
-    def passed_urls(self):
-        return self._counters[1]
-
-    @property
-    def rejected_urls(self):
-        return self._counters[2]
-
-
-class URLFilter(ABC):
-    """Optimized base filter class"""
-
-    __slots__ = ("name", "stats", "_logger_ref")
-
-    def __init__(self, name: str = None):
-        self.name = name or self.__class__.__name__
-        self.stats = FilterStats()
-        # Lazy logger initialization using weakref
-        self._logger_ref = None
-
-    @property
-    def logger(self):
-        if self._logger_ref is None or self._logger_ref() is None:
-            logger = logging.getLogger(f"urlfilter.{self.name}")
-            self._logger_ref = weakref.ref(logger)
-        return self._logger_ref()
-
-    @abstractmethod
-    def apply(self, url: str) -> bool:
-        pass
-
-    def _update_stats(self, passed: bool):
-        # Use direct array index for speed
-        self.stats._counters[0] += 1  # total
-        self.stats._counters[1] += passed  # passed
-        self.stats._counters[2] += not passed  # rejected
-
-
-class FilterChain:
-    """Optimized filter chain"""
-
-    __slots__ = ("filters", "stats", "_logger_ref")
-
-    def __init__(self, filters: List[URLFilter] = None):
-        self.filters = tuple(filters or [])  # Immutable tuple for speed
-        self.stats = FilterStats()
-        self._logger_ref = None
-
-    @property
-    def logger(self):
-        if self._logger_ref is None or self._logger_ref() is None:
-            logger = logging.getLogger("urlfilter.chain")
-            self._logger_ref = weakref.ref(logger)
-        return self._logger_ref()
-
-    def add_filter(self, filter_: URLFilter) -> "FilterChain":
-        """Add a filter to the chain"""
-        self.filters.append(filter_)
-        return self  # Enable method chaining
-
-    async def apply(self, url: str) -> bool:
-        """Apply all filters concurrently when possible"""
-        self.stats._counters[0] += 1  # Total processed URLs
-
-        tasks = []
-        for f in self.filters:
-            result = f.apply(url)
-
-            if inspect.isawaitable(result):
-                tasks.append(result)  # Collect async tasks
-            elif not result:  # Sync rejection
-                self.stats._counters[2] += 1  # Sync rejected
-                return False
-
-        if tasks:
-            results = await asyncio.gather(*tasks)
-
-            # Count how many filters rejected
-            rejections = results.count(False)
-            self.stats._counters[2] += rejections
-
-            if not all(results):
-                return False  # Stop early if any filter rejected
-
-        self.stats._counters[1] += 1  # Passed
-        return True
-
-
-class URLPatternFilter(URLFilter):
-    """Pattern filter balancing speed and completeness"""
-
-    __slots__ = (
-        "patterns",  # Store original patterns for serialization
-        "use_glob",  # Store original use_glob for serialization  
-        "reverse",   # Store original reverse for serialization
-        "_simple_suffixes",
-        "_simple_prefixes",
-        "_domain_patterns",
-        "_path_patterns",
-        "_reverse",
-    )
-
-    PATTERN_TYPES = {
-        "SUFFIX": 1,  # *.html
-        "PREFIX": 2,  # /foo/*
-        "DOMAIN": 3,  # *.example.com
-        "PATH": 4,  # Everything else
-        "REGEX": 5,
-    }
-
-    def __init__(
-        self,
-        patterns: Union[str, Pattern, List[Union[str, Pattern]]],
-        use_glob: bool = True,
-        reverse: bool = False,
-    ):
-        super().__init__()
-        # Store original constructor params for serialization
-        self.patterns = patterns
-        self.use_glob = use_glob
-        self.reverse = reverse
-        
-        self._reverse = reverse
-        patterns = [patterns] if isinstance(patterns, (str, Pattern)) else patterns
-
-        self._simple_suffixes = set()
-        self._simple_prefixes = set()
-        self._domain_patterns = []
-        self._path_patterns = []
-
-        for pattern in patterns:
-            pattern_type = self._categorize_pattern(pattern)
-            self._add_pattern(pattern, pattern_type)
-
-    def _categorize_pattern(self, pattern: str) -> int:
-        """Categorize pattern for specialized handling"""
-        if not isinstance(pattern, str):
-            return self.PATTERN_TYPES["PATH"]
-
-        # Check if it's a regex pattern
-        if pattern.startswith("^") or pattern.endswith("$") or "\\d" in pattern:
-            return self.PATTERN_TYPES["REGEX"]
-
-        if pattern.count("*") == 1:
-            if pattern.startswith("*."):
-                return self.PATTERN_TYPES["SUFFIX"]
-            if pattern.endswith("/*"):
-                return self.PATTERN_TYPES["PREFIX"]
-
-        if "://" in pattern and pattern.startswith("*."):
-            return self.PATTERN_TYPES["DOMAIN"]
-
-        return self.PATTERN_TYPES["PATH"]
-
-    def _add_pattern(self, pattern: str, pattern_type: int):
-        """Add pattern to appropriate matcher"""
-        if pattern_type == self.PATTERN_TYPES["REGEX"]:
-            # For regex patterns, compile directly without glob translation
-            if isinstance(pattern, str) and (
-                pattern.startswith("^") or pattern.endswith("$") or "\\d" in pattern
-            ):
-                self._path_patterns.append(re.compile(pattern))
-                return
-        elif pattern_type == self.PATTERN_TYPES["SUFFIX"]:
-            self._simple_suffixes.add(pattern[2:])
-        elif pattern_type == self.PATTERN_TYPES["PREFIX"]:
-            self._simple_prefixes.add(pattern[:-2])
-        elif pattern_type == self.PATTERN_TYPES["DOMAIN"]:
-            self._domain_patterns.append(re.compile(pattern.replace("*.", r"[^/]+\.")))
-        else:
-            if isinstance(pattern, str):
-                # Handle complex glob patterns
-                if "**" in pattern:
-                    pattern = pattern.replace("**", ".*")
-                if "{" in pattern:
-                    # Convert {a,b} to (a|b)
-                    pattern = re.sub(
-                        r"\{([^}]+)\}",
-                        lambda m: f'({"|".join(m.group(1).split(","))})',
-                        pattern,
-                    )
-                pattern = fnmatch.translate(pattern)
-            self._path_patterns.append(
-                pattern if isinstance(pattern, Pattern) else re.compile(pattern)
-            )
-
-    @lru_cache(maxsize=10000)
-    def apply(self, url: str) -> bool:
-        # Quick suffix check (*.html)
-        if self._simple_suffixes:
-            path = url.split("?")[0]
-            if path.split("/")[-1].split(".")[-1] in self._simple_suffixes:
-                result = True
-                self._update_stats(result)
-                return not result if self._reverse else result
-
-        # Domain check
-        if self._domain_patterns:
-            for pattern in self._domain_patterns:
-                if pattern.match(url):
-                    result = True
-                    self._update_stats(result)
-                    return not result if self._reverse else result
-
-        # Prefix check (/foo/*)
-        if self._simple_prefixes:
-            path = url.split("?")[0]
-            # if any(path.startswith(p) for p in self._simple_prefixes):
-            #     result = True
-            #     self._update_stats(result)
-            #     return not result if self._reverse else result
-            ####
-            # Modified the prefix matching logic to ensure path boundary checking:
-            # - Check if the matched prefix is followed by a path separator (`/`), query parameter (`?`), fragment (`#`), or is at the end of the path
-            # - This ensures `/api/` only matches complete path segments, not substrings like `/apiv2/`
-            ####
-            for prefix in self._simple_prefixes:
-                if path.startswith(prefix):
-                    if len(path) == len(prefix) or path[len(prefix)] in ['/', '?', '#']:
-                        result = True
-                        self._update_stats(result)
-                        return not result if self._reverse else result
-
-        # Complex patterns
-        if self._path_patterns:
-            if any(p.search(url) for p in self._path_patterns):
-                result = True
-                self._update_stats(result)
-                return not result if self._reverse else result
-
-        result = False
-        self._update_stats(result)
-        return not result if self._reverse else result
-
-
-class ContentTypeFilter(URLFilter):
-    """Optimized content type filter using fast lookups"""
-
-    __slots__ = ("allowed_types", "_ext_map", "_check_extension")
-
-    # Fast extension to mime type mapping
-    _MIME_MAP = {
-        # Text Formats
-        "txt": "text/plain",
-        "html": "text/html",
-        "htm": "text/html",
-        "xhtml": "application/xhtml+xml",
-        "css": "text/css",
-        "csv": "text/csv",
-        "ics": "text/calendar",
-        "js": "application/javascript",
-        # Images
-        "bmp": "image/bmp",
-        "gif": "image/gif",
-        "jpeg": "image/jpeg",
-        "jpg": "image/jpeg",
-        "png": "image/png",
-        "svg": "image/svg+xml",
-        "tiff": "image/tiff",
-        "ico": "image/x-icon",
-        "webp": "image/webp",
-        # Audio
-        "mp3": "audio/mpeg",
-        "wav": "audio/wav",
-        "ogg": "audio/ogg",
-        "m4a": "audio/mp4",
-        "aac": "audio/aac",
-        # Video
-        "mp4": "video/mp4",
-        "mpeg": "video/mpeg",
-        "webm": "video/webm",
-        "avi": "video/x-msvideo",
-        "mov": "video/quicktime",
-        "flv": "video/x-flv",
-        "wmv": "video/x-ms-wmv",
-        "mkv": "video/x-matroska",
-        # Applications
-        "json": "application/json",
-        "xml": "application/xml",
-        "pdf": "application/pdf",
-        "zip": "application/zip",
-        "gz": "application/gzip",
-        "tar": "application/x-tar",
-        "rar": "application/vnd.rar",
-        "7z": "application/x-7z-compressed",
-        "exe": "application/vnd.microsoft.portable-executable",
-        "msi": "application/x-msdownload",
-        # Fonts
-        "woff": "font/woff",
-        "woff2": "font/woff2",
-        "ttf": "font/ttf",
-        "otf": "font/otf",
-        # Microsoft Office
-        "doc": "application/msword",
-        "dot": "application/msword",
-        "docx": "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
-        "xlsx": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-        "xls": "application/vnd.ms-excel",
-        "ppt": "application/vnd.ms-powerpoint",
-        "pptx": "application/vnd.openxmlformats-officedocument.presentationml.presentation",
-        # OpenDocument Formats
-        "odt": "application/vnd.oasis.opendocument.text",
-        "ods": "application/vnd.oasis.opendocument.spreadsheet",
-        "odp": "application/vnd.oasis.opendocument.presentation",
-        # Archives
-        "tar.gz": "application/gzip",
-        "tgz": "application/gzip",
-        "bz2": "application/x-bzip2",
-        # Others
-        "rtf": "application/rtf",
-        "apk": "application/vnd.android.package-archive",
-        "epub": "application/epub+zip",
-        "jar": "application/java-archive",
-        "swf": "application/x-shockwave-flash",
-        "midi": "audio/midi",
-        "mid": "audio/midi",
-        "ps": "application/postscript",
-        "ai": "application/postscript",
-        "eps": "application/postscript",
-        # Custom or less common
-        "bin": "application/octet-stream",
-        "dmg": "application/x-apple-diskimage",
-        "iso": "application/x-iso9660-image",
-        "deb": "application/x-debian-package",
-        "rpm": "application/x-rpm",
-        "sqlite": "application/vnd.sqlite3",
-        # Placeholder
-        "unknown": "application/octet-stream",  # Fallback for unknown file types
-        # php
-        "php": "application/x-httpd-php",
-        "php3": "application/x-httpd-php",
-        "php4": "application/x-httpd-php",
-        "php5": "application/x-httpd-php",
-        "php7": "application/x-httpd-php",
-        "phtml": "application/x-httpd-php",
-        "phps": "application/x-httpd-php-source",
-
-    }
-
-    @staticmethod
-    @lru_cache(maxsize=1000)
-    def _extract_extension(url: str) -> str:
-        """Extracts file extension from a URL."""
-        # Remove scheme (http://, https://) if present
-        if "://" in url:
-            url = url.split("://", 1)[-1]  # Get everything after '://'
-
-        # Remove domain (everything up to the first '/')
-        path_start = url.find("/")
-        path = url[path_start:] if path_start != -1 else ""
-
-        # Extract last filename in path
-        filename = path.rsplit("/", 1)[-1] if "/" in path else ""
-
-        # Extract and validate extension
-        if "." not in filename:
-            return ""
-
-        return filename.rpartition(".")[-1].lower()
-
-    def __init__(
-        self,
-        allowed_types: Union[str, List[str]],
-        check_extension: bool = True,
-        ext_map: Dict[str, str] = _MIME_MAP,
-    ):
-        super().__init__()
-        # Normalize and store as frozenset for fast lookup
-        self.allowed_types = frozenset(
-            t.lower()
-            for t in (
-                allowed_types if isinstance(allowed_types, list) else [allowed_types]
-            )
-        )
-        self._check_extension = check_extension
-
-        # Pre-compute extension map for allowed types
-        self._ext_map = frozenset(
-            ext
-            for ext, mime in self._MIME_MAP.items()
-            if any(allowed in mime for allowed in self.allowed_types)
-        )
-
-    @lru_cache(maxsize=1000)
-    def _check_url_cached(self, url: str) -> bool:
-        """Cached URL checking"""
-        if not self._check_extension:
-            return True
-        ext = self._extract_extension(url)
-        if not ext:
-            return True
-
-        return ext in self._ext_map
-
-    def apply(self, url: str) -> bool:
-        """Fast extension check with caching"""
-        result = self._check_url_cached(url)
-        self._update_stats(result)
-        return result
-
-
-class DomainFilter(URLFilter):
-    """Optimized domain filter with fast lookups and caching"""
-
-    __slots__ = ("_allowed_domains", "_blocked_domains", "_domain_cache")
-
-    # Regex for fast domain extraction
-    _DOMAIN_REGEX = re.compile(r"://([^/]+)")
-
-    def __init__(
-        self,
-        allowed_domains: Union[str, List[str]] = None,
-        blocked_domains: Union[str, List[str]] = None,
-    ):
-        super().__init__()
-
-        # Convert inputs to frozensets for immutable, fast lookups
-        self._allowed_domains = (
-            frozenset(self._normalize_domains(allowed_domains))
-            if allowed_domains
-            else None
-        )
-        self._blocked_domains = (
-            frozenset(self._normalize_domains(blocked_domains))
-            if blocked_domains
-            else frozenset()
-        )
-
-    @staticmethod
-    def _normalize_domains(domains: Union[str, List[str]]) -> Set[str]:
-        """Fast domain normalization"""
-        if isinstance(domains, str):
-            return {domains.lower()}
-        return {d.lower() for d in domains}
-    
-    @staticmethod
-    def _is_subdomain(domain: str, parent_domain: str) -> bool:
-        """Check if domain is a subdomain of parent_domain"""
-        return domain == parent_domain or domain.endswith(f".{parent_domain}")
-
-    @staticmethod
-    @lru_cache(maxsize=10000)
-    def _extract_domain(url: str) -> str:
-        """Ultra-fast domain extraction with regex and caching"""
-        match = DomainFilter._DOMAIN_REGEX.search(url)
-        return match.group(1).lower() if match else ""
-
-    def apply(self, url: str) -> bool:
-        """Optimized domain checking with early returns"""
-        # Skip processing if no filters
-        if not self._blocked_domains and self._allowed_domains is None:
-            self._update_stats(True)
-            return True
-
-        domain = self._extract_domain(url)
-
-        # Check for blocked domains, including subdomains
-        for blocked in self._blocked_domains:
-            if self._is_subdomain(domain, blocked):
-                self._update_stats(False)
-                return False
-
-        # If no allowed domains specified, accept all non-blocked
-        if self._allowed_domains is None:
-            self._update_stats(True)
-            return True
-
-        # Check if domain matches any allowed domain (including subdomains)
-        for allowed in self._allowed_domains:
-            if self._is_subdomain(domain, allowed):
-                self._update_stats(True)
-                return True
-
-        # No matches found
-        self._update_stats(False)
-        return False
-
-
-class ContentRelevanceFilter(URLFilter):
-    """BM25-based relevance filter using head section content"""
-
-    __slots__ = ("query_terms", "threshold", "k1", "b", "avgdl")
-
-    def __init__(
-        self,
-        query: str,
-        threshold: float,
-        k1: float = 1.2,
-        b: float = 0.75,
-        avgdl: int = 1000,
-    ):
-        super().__init__(name="BM25RelevanceFilter")
-        self.query_terms = self._tokenize(query)
-        self.threshold = threshold
-        self.k1 = k1  # TF saturation parameter
-        self.b = b  # Length normalization parameter
-        self.avgdl = avgdl  # Average document length (empirical value)
-
-    async def apply(self, url: str) -> bool:
-        head_content = await HeadPeekr.peek_html(url)
-        if not head_content:
-            self._update_stats(False)
-            return False
-
-        # Field extraction with weighting
-        fields = {
-            "title": HeadPeekr.get_title(head_content) or "",
-            "meta": HeadPeekr.extract_meta_tags(head_content),
-        }
-        doc_text = self._build_document(fields)
-
-        score = self._bm25(doc_text)
-        decision = score >= self.threshold
-        self._update_stats(decision)
-        return decision
-
-    def _build_document(self, fields: Dict) -> str:
-        """Weighted document construction"""
-        return " ".join(
-            [
-                fields["title"] * 3,  # Title weight
-                fields["meta"].get("description", "") * 2,
-                fields["meta"].get("keywords", ""),
-                " ".join(fields["meta"].values()),
-            ]
-        )
-
-    def _tokenize(self, text: str) -> List[str]:
-        """Fast case-insensitive tokenization"""
-        return text.lower().split()
-
-    def _bm25(self, document: str) -> float:
-        """Optimized BM25 implementation for head sections"""
-        doc_terms = self._tokenize(document)
-        doc_len = len(doc_terms)
-        tf = defaultdict(int)
-
-        for term in doc_terms:
-            tf[term] += 1
-
-        score = 0.0
-        for term in set(self.query_terms):
-            term_freq = tf[term]
-            idf = math.log((1 + 1) / (term_freq + 0.5) + 1)  # Simplified IDF
-            numerator = term_freq * (self.k1 + 1)
-            denominator = term_freq + self.k1 * (
-                1 - self.b + self.b * (doc_len / self.avgdl)
-            )
-            score += idf * (numerator / denominator)
-
-        return score
-
-
-class SEOFilter(URLFilter):
-    """Quantitative SEO quality assessment filter using head section analysis"""
-
-    __slots__ = ("threshold", "_weights", "_kw_patterns")
-
-    # Based on SEMrush/Google ranking factors research
-    DEFAULT_WEIGHTS = {
-        "title_length": 0.15,
-        "title_kw": 0.18,
-        "meta_description": 0.12,
-        "canonical": 0.10,
-        "robot_ok": 0.20,  # Most critical factor
-        "schema_org": 0.10,
-        "url_quality": 0.15,
-    }
-
-    def __init__(
-        self,
-        threshold: float = 0.65,
-        keywords: List[str] = None,
-        weights: Dict[str, float] = None,
-    ):
-        super().__init__(name="SEOFilter")
-        self.threshold = threshold
-        self._weights = weights or self.DEFAULT_WEIGHTS
-        self._kw_patterns = (
-            re.compile(
-                r"\b({})\b".format("|".join(map(re.escape, keywords or []))), re.I
-            )
-            if keywords
-            else None
-        )
-
-    async def apply(self, url: str) -> bool:
-        head_content = await HeadPeekr.peek_html(url)
-        if not head_content:
-            self._update_stats(False)
-            return False
-
-        meta = HeadPeekr.extract_meta_tags(head_content)
-        title = HeadPeekr.get_title(head_content) or ""
-        parsed_url = urlparse(url)
-
-        scores = {
-            "title_length": self._score_title_length(title),
-            "title_kw": self._score_keyword_presence(title),
-            "meta_description": self._score_meta_description(
-                meta.get("description", "")
-            ),
-            "canonical": self._score_canonical(meta.get("canonical"), url),
-            "robot_ok": 1.0 if "noindex" not in meta.get("robots", "") else 0.0,
-            "schema_org": self._score_schema_org(head_content),
-            "url_quality": self._score_url_quality(parsed_url),
-        }
-
-        total_score = sum(
-            weight * scores[factor] for factor, weight in self._weights.items()
-        )
-
-        decision = total_score >= self.threshold
-        self._update_stats(decision)
-        return decision
-
-    def _score_title_length(self, title: str) -> float:
-        length = len(title)
-        if 50 <= length <= 60:
-            return 1.0
-        if 40 <= length < 50 or 60 < length <= 70:
-            return 0.7
-        return 0.3  # Poor length
-
-    def _score_keyword_presence(self, text: str) -> float:
-        if not self._kw_patterns:
-            return 0.0
-        matches = len(self._kw_patterns.findall(text))
-        return min(matches * 0.3, 1.0)  # Max 3 matches
-
-    def _score_meta_description(self, desc: str) -> float:
-        length = len(desc)
-        if 140 <= length <= 160:
-            return 1.0
-        return 0.5 if 120 <= length <= 200 else 0.2
-
-    def _score_canonical(self, canonical: str, original: str) -> float:
-        if not canonical:
-            return 0.5  # Neutral score
-        return 1.0 if canonical == original else 0.2
-
-    def _score_schema_org(self, html: str) -> float:
-        # Detect any schema.org markup in head
-        return (
-            1.0
-            if re.search(r'<script[^>]+type=["\']application/ld\+json', html)
-            else 0.0
-        )
-
-    def _score_url_quality(self, parsed_url) -> float:
-        score = 1.0
-        path = parsed_url.path.lower()
-
-        # Penalty factors
-        if len(path) > 80:
-            score *= 0.7
-        if re.search(r"\d{4}", path):
-            score *= 0.8  # Numbers in path
-        if parsed_url.query:
-            score *= 0.6  # URL parameters
-        if "_" in path:
-            score *= 0.9  # Underscores vs hyphens
-
-        return score

crawl4ai/deep_crawling/scorers.py

@@ -1,519 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import List, Dict, Optional
-from dataclasses import dataclass
-from urllib.parse import urlparse, unquote
-import re
-import logging
-from functools import lru_cache
-from array import array
-import ctypes
-import platform
-PLATFORM = platform.system()
-
-# Pre-computed scores for common year differences
-_SCORE_LOOKUP = [1.0, 0.5, 0.3333333333333333, 0.25]
-
-# Pre-computed scores for common year differences
-_FRESHNESS_SCORES = [
-   1.0,    # Current year
-   0.9,    # Last year
-   0.8,    # 2 years ago
-   0.7,    # 3 years ago
-   0.6,    # 4 years ago
-   0.5,    # 5 years ago
-]
-
-class ScoringStats:
-    __slots__ = ('_urls_scored', '_total_score', '_min_score', '_max_score')
-    
-    def __init__(self):
-        self._urls_scored = 0
-        self._total_score = 0.0
-        self._min_score = None  # Lazy initialization
-        self._max_score = None
-    
-    def update(self, score: float) -> None:
-        """Optimized update with minimal operations"""
-        self._urls_scored += 1
-        self._total_score += score
-        
-        # Lazy min/max tracking - only if actually accessed
-        if self._min_score is not None:
-            if score < self._min_score:
-                self._min_score = score
-        if self._max_score is not None:
-            if score > self._max_score:
-                self._max_score = score
-                
-    def get_average(self) -> float:
-        """Direct calculation instead of property"""
-        return self._total_score / self._urls_scored if self._urls_scored else 0.0
-    
-    def get_min(self) -> float:
-        """Lazy min calculation"""
-        if self._min_score is None:
-            self._min_score = self._total_score / self._urls_scored if self._urls_scored else 0.0
-        return self._min_score
-        
-    def get_max(self) -> float:
-        """Lazy max calculation"""
-        if self._max_score is None:
-            self._max_score = self._total_score / self._urls_scored if self._urls_scored else 0.0
-        return self._max_score
-class URLScorer(ABC):
-    __slots__ = ('_weight', '_stats')
-    
-    def __init__(self, weight: float = 1.0):
-        # Store weight directly as float32 for memory efficiency
-        self._weight = ctypes.c_float(weight).value
-        self._stats = ScoringStats()
-    
-    @abstractmethod
-    def _calculate_score(self, url: str) -> float:
-        """Calculate raw score for URL."""
-        pass
-    
-    def score(self, url: str) -> float:
-        """Calculate weighted score with minimal overhead."""
-        score = self._calculate_score(url) * self._weight
-        self._stats.update(score)
-        return score
-    
-    @property
-    def stats(self):
-        """Access to scoring statistics."""
-        return self._stats
-    
-    @property
-    def weight(self):
-        return self._weight
-
-class CompositeScorer(URLScorer):
-    __slots__ = ('_scorers', '_normalize', '_weights_array', '_score_array')
-    
-    def __init__(self, scorers: List[URLScorer], normalize: bool = True):
-        """Initialize composite scorer combining multiple scoring strategies.
-        
-        Optimized for:
-        - Fast parallel scoring
-        - Memory efficient score aggregation
-        - Quick short-circuit conditions
-        - Pre-allocated arrays
-        
-        Args:
-            scorers: List of scoring strategies to combine
-            normalize: Whether to normalize final score by scorer count
-        """
-        super().__init__(weight=1.0)
-        self._scorers = scorers
-        self._normalize = normalize
-        
-        # Pre-allocate arrays for scores and weights
-        self._weights_array = array('f', [s.weight for s in scorers])
-        self._score_array = array('f', [0.0] * len(scorers))
-
-    @lru_cache(maxsize=10000)
-    def _calculate_score(self, url: str) -> float:
-        """Calculate combined score from all scoring strategies.
-        
-        Uses:
-        1. Pre-allocated arrays for scores
-        2. Short-circuit on zero scores
-        3. Optimized normalization
-        4. Vectorized operations where possible
-        
-        Args:
-            url: URL to score
-            
-        Returns:
-            Combined and optionally normalized score
-        """
-        total_score = 0.0
-        scores = self._score_array
-        
-        # Get scores from all scorers
-        for i, scorer in enumerate(self._scorers):
-            # Use public score() method which applies weight
-            scores[i] = scorer.score(url)
-            total_score += scores[i]
-            
-        # Normalize if requested
-        if self._normalize and self._scorers:
-            count = len(self._scorers)
-            return total_score / count
-            
-        return total_score
-
-    def score(self, url: str) -> float:
-        """Public scoring interface with stats tracking.
-        
-        Args:
-            url: URL to score
-            
-        Returns:
-            Final combined score
-        """
-        score = self._calculate_score(url)
-        self.stats.update(score)
-        return score
-
-class KeywordRelevanceScorer(URLScorer):
-    __slots__ = ('_weight', '_stats', '_keywords', '_case_sensitive')
-    
-    def __init__(self, keywords: List[str], weight: float = 1.0, case_sensitive: bool = False):
-        super().__init__(weight=weight)
-        self._case_sensitive = case_sensitive
-        # Pre-process keywords once
-        self._keywords = [k if case_sensitive else k.lower() for k in keywords]
-    
-    @lru_cache(maxsize=10000)
-    def _url_bytes(self, url: str) -> bytes:
-        """Cache decoded URL bytes"""
-        return url.encode('utf-8') if self._case_sensitive else url.lower().encode('utf-8')
-    
-    
-    def _calculate_score(self, url: str) -> float:
-        """Fast string matching without regex or byte conversion"""
-        if not self._case_sensitive:
-            url = url.lower()
-            
-        matches = sum(1 for k in self._keywords if k in url)
-        
-        # Fast return paths
-        if not matches:
-            return 0.0
-        if matches == len(self._keywords):
-            return 1.0
-            
-        return matches / len(self._keywords)
-
-class PathDepthScorer(URLScorer):
-    __slots__ = ('_weight', '_stats', '_optimal_depth')  # Remove _url_cache
-    
-    def __init__(self, optimal_depth: int = 3, weight: float = 1.0):
-        super().__init__(weight=weight)
-        self._optimal_depth = optimal_depth
-
-    @staticmethod
-    @lru_cache(maxsize=10000)
-    def _quick_depth(path: str) -> int:
-        """Ultra fast path depth calculation.
-        
-        Examples:
-            - "http://example.com" -> 0  # No path segments
-            - "http://example.com/" -> 0  # Empty path
-            - "http://example.com/a" -> 1
-            - "http://example.com/a/b" -> 2
-        """
-        if not path or path == '/':
-            return 0
-            
-        if '/' not in path:
-            return 0
-            
-        depth = 0
-        last_was_slash = True
-        
-        for c in path:
-            if c == '/':
-                if not last_was_slash:
-                    depth += 1
-                last_was_slash = True
-            else:
-                last_was_slash = False
-                
-        if not last_was_slash:
-            depth += 1
-            
-        return depth
-
-    @lru_cache(maxsize=10000)  # Cache the whole calculation
-    def _calculate_score(self, url: str) -> float:
-        pos = url.find('/', url.find('://') + 3)
-        if pos == -1:
-            depth = 0
-        else:
-            depth = self._quick_depth(url[pos:])
-            
-        # Use lookup table for common distances
-        distance = depth - self._optimal_depth
-        distance = distance if distance >= 0 else -distance  # Faster than abs()
-        
-        if distance < 4:
-            return _SCORE_LOOKUP[distance]
-            
-        return 1.0 / (1.0 + distance)                                             
-
-class ContentTypeScorer(URLScorer):
-    __slots__ = ('_weight', '_exact_types', '_regex_types')
-
-    def __init__(self, type_weights: Dict[str, float], weight: float = 1.0):
-        """Initialize scorer with type weights map.
-        
-        Args:
-            type_weights: Dict mapping file extensions/patterns to scores (e.g. {'.html$': 1.0})
-            weight: Overall weight multiplier for this scorer
-        """
-        super().__init__(weight=weight)
-        self._exact_types = {}  # Fast lookup for simple extensions
-        self._regex_types = []  # Fallback for complex patterns
-        
-        # Split into exact vs regex matchers for performance
-        for pattern, score in type_weights.items():
-            if pattern.startswith('.') and pattern.endswith('$'):
-                ext = pattern[1:-1]
-                self._exact_types[ext] = score
-            else:
-                self._regex_types.append((re.compile(pattern), score))
-                
-        # Sort complex patterns by score for early exit
-        self._regex_types.sort(key=lambda x: -x[1])
-
-    @staticmethod
-    @lru_cache(maxsize=10000)
-    def _quick_extension(url: str) -> str:
-        """Extract file extension ultra-fast without regex/splits.
-        
-        Handles:
-        - Basic extensions: "example.html" -> "html"
-        - Query strings: "page.php?id=1" -> "php" 
-        - Fragments: "doc.pdf#page=1" -> "pdf"
-        - Path params: "file.jpg;width=100" -> "jpg"
-        
-        Args:
-            url: URL to extract extension from
-            
-        Returns:
-            Extension without dot, or empty string if none found
-        """
-        pos = url.rfind('.')
-        if pos == -1:
-            return ''
-        
-        # Find first non-alphanumeric char after extension
-        end = len(url)
-        for i in range(pos + 1, len(url)):
-            c = url[i]
-            # Stop at query string, fragment, path param or any non-alphanumeric
-            if c in '?#;' or not c.isalnum():
-                end = i
-                break
-                
-        return url[pos + 1:end].lower()
-
-    @lru_cache(maxsize=10000)
-    def _calculate_score(self, url: str) -> float:
-        """Calculate content type score for URL.
-        
-        Uses staged approach:
-        1. Try exact extension match (fast path)
-        2. Fall back to regex patterns if needed
-        
-        Args:
-            url: URL to score
-            
-        Returns:
-            Score between 0.0 and 1.0 * weight
-        """
-        # Fast path: direct extension lookup
-        ext = self._quick_extension(url)
-        if ext:
-            score = self._exact_types.get(ext, None)
-            if score is not None:
-                return score
-                
-        # Slow path: regex patterns
-        for pattern, score in self._regex_types:
-            if pattern.search(url):
-                return score
-
-        return 0.0
-
-class FreshnessScorer(URLScorer):
-    __slots__ = ('_weight', '_date_pattern', '_current_year')
-
-    def __init__(self, weight: float = 1.0, current_year: int = 2024):
-        """Initialize freshness scorer.
-        
-        Extracts and scores dates from URLs using format:
-        - YYYY/MM/DD 
-        - YYYY-MM-DD
-        - YYYY_MM_DD
-        - YYYY (year only)
-        
-        Args:
-            weight: Score multiplier
-            current_year: Year to calculate freshness against (default 2024)
-        """
-        super().__init__(weight=weight)
-        self._current_year = current_year
-        
-        # Combined pattern for all date formats
-        # Uses non-capturing groups (?:) and alternation
-        self._date_pattern = re.compile(
-            r'(?:/'  # Path separator
-            r'|[-_])'  # or date separators
-            r'((?:19|20)\d{2})'  # Year group (1900-2099)
-            r'(?:'  # Optional month/day group
-            r'(?:/|[-_])'  # Date separator  
-            r'(?:\d{2})'  # Month
-            r'(?:'  # Optional day
-            r'(?:/|[-_])'  # Date separator
-            r'(?:\d{2})'  # Day
-            r')?'  # Day is optional
-            r')?'  # Month/day group is optional
-        )
-
-    @lru_cache(maxsize=10000)
-    def _extract_year(self, url: str) -> Optional[int]:
-        """Extract the most recent year from URL.
-        
-        Args:
-            url: URL to extract year from
-            
-        Returns:
-            Year as int or None if no valid year found
-        """
-        matches = self._date_pattern.finditer(url)
-        latest_year = None
-        
-        # Find most recent year
-        for match in matches:
-            year = int(match.group(1))
-            if (year <= self._current_year and  # Sanity check
-                (latest_year is None or year > latest_year)):
-                latest_year = year
-                
-        return latest_year
-
-    @lru_cache(maxsize=10000) 
-    def _calculate_score(self, url: str) -> float:
-        """Calculate freshness score based on URL date.
-        
-        More recent years score higher. Uses pre-computed scoring
-        table for common year differences.
-        
-        Args:
-            url: URL to score
-            
-        Returns:
-            Score between 0.0 and 1.0 * weight
-        """
-        year = self._extract_year(url)
-        if year is None:
-            return 0.5  # Default score
-            
-        # Use lookup table for common year differences
-        year_diff = self._current_year - year
-        if year_diff < len(_FRESHNESS_SCORES):
-            return _FRESHNESS_SCORES[year_diff]
-            
-        # Fallback calculation for older content
-        return max(0.1, 1.0 - year_diff * 0.1)
-
-class DomainAuthorityScorer(URLScorer):
-    __slots__ = ('_weight', '_domain_weights', '_default_weight', '_top_domains')
-    
-    def __init__(
-        self,
-        domain_weights: Dict[str, float],
-        default_weight: float = 0.5,
-        weight: float = 1.0,
-    ):
-        """Initialize domain authority scorer.
-        
-        Args:
-            domain_weights: Dict mapping domains to authority scores
-            default_weight: Score for unknown domains
-            weight: Overall scorer weight multiplier
-            
-        Example:
-            {
-                'python.org': 1.0,
-                'github.com': 0.9,
-                'medium.com': 0.7
-            }
-        """
-        super().__init__(weight=weight)
-        
-        # Pre-process domains for faster lookup
-        self._domain_weights = {
-            domain.lower(): score 
-            for domain, score in domain_weights.items()
-        }
-        self._default_weight = default_weight
-        
-        # Cache top domains for fast path
-        self._top_domains = {
-            domain: score
-            for domain, score in sorted(
-                domain_weights.items(), 
-                key=lambda x: -x[1]
-            )[:5]  # Keep top 5 highest scoring domains
-        }
-
-    @staticmethod
-    @lru_cache(maxsize=10000)
-    def _extract_domain(url: str) -> str:
-        """Extract domain from URL ultra-fast.
-        
-        Handles:
-        - Basic domains: "example.com"
-        - Subdomains: "sub.example.com" 
-        - Ports: "example.com:8080"
-        - IPv4: "192.168.1.1"
-        
-        Args:
-            url: Full URL to extract domain from
-            
-        Returns:
-            Lowercase domain without port
-        """
-        # Find domain start
-        start = url.find('://') 
-        if start == -1:
-            start = 0
-        else:
-            start += 3
-            
-        # Find domain end
-        end = url.find('/', start)
-        if end == -1:
-            end = url.find('?', start)
-            if end == -1:
-                end = url.find('#', start)
-                if end == -1:
-                    end = len(url)
-                    
-        # Extract domain and remove port
-        domain = url[start:end]
-        port_idx = domain.rfind(':')
-        if port_idx != -1:
-            domain = domain[:port_idx]
-            
-        return domain.lower()
-
-    @lru_cache(maxsize=10000)
-    def _calculate_score(self, url: str) -> float:
-        """Calculate domain authority score.
-        
-        Uses staged approach:
-        1. Check top domains (fastest)
-        2. Check full domain weights
-        3. Return default weight
-        
-        Args:
-            url: URL to score
-            
-        Returns:
-            Authority score between 0.0 and 1.0 * weight
-        """
-        domain = self._extract_domain(url)
-        
-        # Fast path: check top domains first
-        score = self._top_domains.get(domain)
-        if score is not None:
-            return score
-            
-        # Regular path: check all domains
-        return self._domain_weights.get(domain, self._default_weight)

crawl4ai/docker_client.py

@@ -1,168 +0,0 @@
-from typing import List, Optional, Union, AsyncGenerator, Dict, Any
-import httpx
-import json
-from urllib.parse import urljoin
-import asyncio
-
-from .async_configs import BrowserConfig, CrawlerRunConfig
-from .models import CrawlResult
-from .async_logger import AsyncLogger, LogLevel
-
-
-class Crawl4aiClientError(Exception):
-    """Base exception for Crawl4ai Docker client errors."""
-    pass
-
-
-class ConnectionError(Crawl4aiClientError):
-    """Raised when connection to the Docker server fails."""
-    pass
-
-
-class RequestError(Crawl4aiClientError):
-    """Raised when the server returns an error response."""
-    pass
-
-
-class Crawl4aiDockerClient:
-    """Client for interacting with Crawl4AI Docker server with token authentication."""
-    
-    def __init__(
-        self,
-        base_url: str = "http://localhost:8000",
-        timeout: float = 30.0,
-        verify_ssl: bool = True,
-        verbose: bool = True,
-        log_file: Optional[str] = None
-    ):
-        self.base_url = base_url.rstrip('/')
-        self.timeout = timeout
-        self.logger = AsyncLogger(log_file=log_file, log_level=LogLevel.DEBUG, verbose=verbose)
-        self._http_client = httpx.AsyncClient(
-            timeout=timeout,
-            verify=verify_ssl,
-            headers={"Content-Type": "application/json"}
-        )
-        self._token: Optional[str] = None
-
-    async def authenticate(self, email: str) -> None:
-        """Authenticate with the server and store the token."""
-        url = urljoin(self.base_url, "/token")
-        try:
-            self.logger.info(f"Authenticating with email: {email}", tag="AUTH")
-            response = await self._http_client.post(url, json={"email": email})
-            response.raise_for_status()
-            data = response.json()
-            self._token = data["access_token"]
-            self._http_client.headers["Authorization"] = f"Bearer {self._token}"
-            self.logger.success("Authentication successful", tag="AUTH")
-        except (httpx.RequestError, httpx.HTTPStatusError) as e:
-            error_msg = f"Authentication failed: {str(e)}"
-            self.logger.error(error_msg, tag="ERROR")
-            raise ConnectionError(error_msg)
-
-    async def _check_server(self) -> None:
-        """Check if server is reachable, raising an error if not."""
-        try:
-            await self._http_client.get(urljoin(self.base_url, "/health"))
-            self.logger.success(f"Connected to {self.base_url}", tag="READY")
-        except httpx.RequestError as e:
-            self.logger.error(f"Server unreachable: {str(e)}", tag="ERROR")
-            raise ConnectionError(f"Cannot connect to server: {str(e)}")
-
-    def _prepare_request(self, urls: List[str], browser_config: Optional[BrowserConfig] = None, 
-                       crawler_config: Optional[CrawlerRunConfig] = None) -> Dict[str, Any]:
-        """Prepare request data from configs."""
-        if self._token:
-            self._http_client.headers["Authorization"] = f"Bearer {self._token}"
-        return {
-            "urls": urls,
-            "browser_config": browser_config.dump() if browser_config else {},
-            "crawler_config": crawler_config.dump() if crawler_config else {}
-        }
-
-    async def _request(self, method: str, endpoint: str, **kwargs) -> httpx.Response:
-        """Make an HTTP request with error handling."""
-        url = urljoin(self.base_url, endpoint)
-        try:
-            response = await self._http_client.request(method, url, **kwargs)
-            response.raise_for_status()
-            return response
-        except httpx.TimeoutException as e:
-            raise ConnectionError(f"Request timed out: {str(e)}")
-        except httpx.RequestError as e:
-            raise ConnectionError(f"Failed to connect: {str(e)}")
-        except httpx.HTTPStatusError as e:
-            error_msg = (e.response.json().get("detail", str(e)) 
-                        if "application/json" in e.response.headers.get("content-type", "") 
-                        else str(e))
-            raise RequestError(f"Server error {e.response.status_code}: {error_msg}")
-
-    async def crawl(
-        self,
-        urls: List[str],
-        browser_config: Optional[BrowserConfig] = None,
-        crawler_config: Optional[CrawlerRunConfig] = None
-    ) -> Union[CrawlResult, List[CrawlResult], AsyncGenerator[CrawlResult, None]]:
-        """Execute a crawl operation."""
-        await self._check_server()
-        
-        data = self._prepare_request(urls, browser_config, crawler_config)
-        is_streaming = crawler_config and crawler_config.stream
-        
-        self.logger.info(f"Crawling {len(urls)} URLs {'(streaming)' if is_streaming else ''}", tag="CRAWL")
-        
-        if is_streaming:
-            async def stream_results() -> AsyncGenerator[CrawlResult, None]:
-                async with self._http_client.stream("POST", f"{self.base_url}/crawl/stream", json=data) as response:
-                    response.raise_for_status()
-                    async for line in response.aiter_lines():
-                        if line.strip():
-                            result = json.loads(line)
-                            if "error" in result:
-                                self.logger.error_status(url=result.get("url", "unknown"), error=result["error"])
-                                continue
-                            self.logger.url_status(url=result.get("url", "unknown"), success=True, timing=result.get("timing", 0.0))
-                            if result.get("status") == "completed":
-                                continue
-                            else:
-                                yield CrawlResult(**result)
-            return stream_results()
-        
-        response = await self._request("POST", "/crawl", json=data)
-        result_data = response.json()
-        if not result_data.get("success", False):
-            raise RequestError(f"Crawl failed: {result_data.get('msg', 'Unknown error')}")
-        
-        results = [CrawlResult(**r) for r in result_data.get("results", [])]
-        self.logger.success(f"Crawl completed with {len(results)} results", tag="CRAWL")
-        return results[0] if len(results) == 1 else results
-
-    async def get_schema(self) -> Dict[str, Any]:
-        """Retrieve configuration schemas."""
-        response = await self._request("GET", "/schema")
-        return response.json()
-
-    async def close(self) -> None:
-        """Close the HTTP client session."""
-        self.logger.info("Closing client", tag="CLOSE")
-        await self._http_client.aclose()
-
-    async def __aenter__(self) -> "Crawl4aiDockerClient":
-        return self
-
-    async def __aexit__(self, exc_type: Optional[type], exc_val: Optional[Exception], exc_tb: Optional[Any]) -> None:
-        await self.close()
-
-
-# Example usage
-async def main():
-    async with Crawl4aiDockerClient(verbose=True) as client:
-        await client.authenticate("user@example.com")
-        result = await client.crawl(["https://example.com"])
-        print(result)
-        schema = await client.get_schema()
-        print(schema)
-
-if __name__ == "__main__":
-    asyncio.run(main())

crawl4ai/html2text/__init__.py

@@ -54,13 +54,13 @@ class HTML2Text(html.parser.HTMLParser):
         self.td_count = 0
         self.table_start = False
         self.unicode_snob = config.UNICODE_SNOB  # covered in cli
-
+        
         self.escape_snob = config.ESCAPE_SNOB  # covered in cli
         self.escape_backslash = config.ESCAPE_BACKSLASH  # covered in cli
         self.escape_dot = config.ESCAPE_DOT  # covered in cli
         self.escape_plus = config.ESCAPE_PLUS  # covered in cli
         self.escape_dash = config.ESCAPE_DASH  # covered in cli
-
+        
         self.links_each_paragraph = config.LINKS_EACH_PARAGRAPH
         self.body_width = bodywidth  # covered in cli
         self.skip_internal_links = config.SKIP_INTERNAL_LINKS  # covered in cli
@@ -144,8 +144,8 @@ class HTML2Text(html.parser.HTMLParser):
 
     def update_params(self, **kwargs):
         for key, value in kwargs.items():
-            setattr(self, key, value)
-
+            setattr(self, key, value) 
+    
     def feed(self, data: str) -> None:
         data = data.replace("</' + 'script>", "</ignore>")
         super().feed(data)
@@ -510,7 +510,6 @@ class HTML2Text(html.parser.HTMLParser):
 
         if tag == "a" and not self.ignore_links:
             if start:
-                self.inside_link = True
                 if (
                     "href" in attrs
                     and attrs["href"] is not None
@@ -527,7 +526,6 @@ class HTML2Text(html.parser.HTMLParser):
                 else:
                     self.astack.append(None)
             else:
-                self.inside_link = False
                 if self.astack:
                     a = self.astack.pop()
                     if self.maybe_automatic_link and not self.empty_link:
@@ -612,22 +610,13 @@ class HTML2Text(html.parser.HTMLParser):
                         self.o("[" + str(a_props.count) + "]")
 
         if tag == "dl" and start:
-            self.p()  # Add paragraph break before list starts
-            self.p_p = 0  # Reset paragraph state
-        
-        elif tag == "dt" and start:
-            if self.p_p == 0:  # If not first term
-                self.o("\n\n")  # Add spacing before new term-definition pair
-            self.p_p = 0  # Reset paragraph state
-        
-        elif tag == "dt" and not start:
-            self.o("\n")  # Single newline between term and definition
-        
-        elif tag == "dd" and start:
-            self.o("    ")  # Indent definition
-        
-        elif tag == "dd" and not start:
-            self.p_p = 0
+            self.p()
+        if tag == "dt" and not start:
+            self.pbr()
+        if tag == "dd" and start:
+            self.o("    ")
+        if tag == "dd" and not start:
+            self.pbr()
 
         if tag in ["ol", "ul"]:
             # Google Docs create sub lists as top level lists
@@ -914,13 +903,7 @@ class HTML2Text(html.parser.HTMLParser):
                 self.empty_link = False
 
         if not self.code and not self.pre and not entity_char:
-            data = escape_md_section(
-                data,
-                snob=self.escape_snob,
-                escape_dot=self.escape_dot,
-                escape_plus=self.escape_plus,
-                escape_dash=self.escape_dash,
-            )
+            data = escape_md_section(data, snob=self.escape_snob, escape_dot=self.escape_dot, escape_plus=self.escape_plus, escape_dash=self.escape_dash)
         self.preceding_data = data
         self.o(data, puredata=True)
 
@@ -1030,141 +1013,3 @@ def html2text(html: str, baseurl: str = "", bodywidth: Optional[int] = None) ->
     h = HTML2Text(baseurl=baseurl, bodywidth=bodywidth)
 
     return h.handle(html)
-
-
-class CustomHTML2Text(HTML2Text):
-    def __init__(self, *args, handle_code_in_pre=False, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.inside_pre = False
-        self.inside_code = False
-        self.inside_link = False
-        self.preserve_tags = set()  # Set of tags to preserve
-        self.current_preserved_tag = None
-        self.preserved_content = []
-        self.preserve_depth = 0
-        self.handle_code_in_pre = handle_code_in_pre
-
-        # Configuration options
-        self.skip_internal_links = False
-        self.single_line_break = False
-        self.mark_code = False
-        self.include_sup_sub = False
-        self.body_width = 0
-        self.ignore_mailto_links = True
-        self.ignore_links = False
-        self.escape_backslash = False
-        self.escape_dot = False
-        self.escape_plus = False
-        self.escape_dash = False
-        self.escape_snob = False
-
-    def update_params(self, **kwargs):
-        """Update parameters and set preserved tags."""
-        for key, value in kwargs.items():
-            if key == "preserve_tags":
-                self.preserve_tags = set(value)
-            elif key == "handle_code_in_pre":
-                self.handle_code_in_pre = value
-            else:
-                setattr(self, key, value)
-
-    def handle_tag(self, tag, attrs, start):
-        # Handle preserved tags
-        if tag in self.preserve_tags:
-            if start:
-                if self.preserve_depth == 0:
-                    self.current_preserved_tag = tag
-                    self.preserved_content = []
-                    # Format opening tag with attributes
-                    attr_str = "".join(
-                        f' {k}="{v}"' for k, v in attrs.items() if v is not None
-                    )
-                    self.preserved_content.append(f"<{tag}{attr_str}>")
-                self.preserve_depth += 1
-                return
-            else:
-                self.preserve_depth -= 1
-                if self.preserve_depth == 0:
-                    self.preserved_content.append(f"</{tag}>")
-                    # Output the preserved HTML block with proper spacing
-                    preserved_html = "".join(self.preserved_content)
-                    self.o("\n" + preserved_html + "\n")
-                    self.current_preserved_tag = None
-                return
-
-        # If we're inside a preserved tag, collect all content
-        if self.preserve_depth > 0:
-            if start:
-                # Format nested tags with attributes
-                attr_str = "".join(
-                    f' {k}="{v}"' for k, v in attrs.items() if v is not None
-                )
-                self.preserved_content.append(f"<{tag}{attr_str}>")
-            else:
-                self.preserved_content.append(f"</{tag}>")
-            return
-
-        # Handle pre tags
-        if tag == "pre":
-            if start:
-                self.o("```\n")  # Markdown code block start
-                self.inside_pre = True
-            else:
-                self.o("\n```\n")  # Markdown code block end
-                self.inside_pre = False
-        elif tag == "code":
-            if self.inside_pre and not self.handle_code_in_pre:
-                # Ignore code tags inside pre blocks if handle_code_in_pre is False
-                return
-            if start:
-                if not self.inside_link:
-                    self.o("`")  # Only output backtick if not inside a link
-                self.inside_code = True
-            else:
-                if not self.inside_link:
-                    self.o("`")  # Only output backtick if not inside a link
-                self.inside_code = False
-
-            # If inside a link, let the parent class handle the content
-            if self.inside_link:
-                super().handle_tag(tag, attrs, start) 
-        else:
-            super().handle_tag(tag, attrs, start)
-
-    def handle_data(self, data, entity_char=False):
-        """Override handle_data to capture content within preserved tags."""
-        if self.preserve_depth > 0:
-            self.preserved_content.append(data)
-            return
-
-        if self.inside_pre:
-            # Output the raw content for pre blocks, including content inside code tags
-            self.o(data)  # Directly output the data as-is (preserve newlines)
-            return
-        if self.inside_code:
-            # Inline code: no newlines allowed
-            self.o(data.replace("\n", " "))
-            return
-
-        # Default behavior for other tags
-        super().handle_data(data, entity_char)
-
-    #     # Handle pre tags
-    #     if tag == 'pre':
-    #         if start:
-    #             self.o('```\n')
-    #             self.inside_pre = True
-    #         else:
-    #             self.o('\n```')
-    #             self.inside_pre = False
-    #     # elif tag in ["h1", "h2", "h3", "h4", "h5", "h6"]:
-    #     #     pass
-    #     else:
-    #         super().handle_tag(tag, attrs, start)
-
-    # def handle_data(self, data, entity_char=False):
-    #     """Override handle_data to capture content within preserved tags."""
-    #     if self.preserve_depth > 0:
-    #         self.preserved_content.append(data)
-    #         return
-    #     super().handle_data(data, entity_char)

crawl4ai/html2text/_typing.py

@@ -1,3 +1,2 @@
 class OutCallback:
-    def __call__(self, s: str) -> None:
-        ...
+    def __call__(self, s: str) -> None: ...

crawl4ai/html2text/utils.py

@@ -210,7 +210,7 @@ def escape_md_section(
     snob: bool = False,
     escape_dot: bool = True,
     escape_plus: bool = True,
-    escape_dash: bool = True,
+    escape_dash: bool = True
 ) -> str:
     """
     Escapes markdown-sensitive characters across whole document sections.
@@ -233,7 +233,6 @@ def escape_md_section(
 
     return text
 
-
 def reformat_table(lines: List[str], right_margin: int) -> List[str]:
     """
     Given the lines of a table

crawl4ai/hub.py

@@ -1,69 +0,0 @@
-# crawl4ai/hub.py
-from abc import ABC, abstractmethod
-from typing import Dict, Type, Union
-import logging
-import importlib
-from pathlib import Path
-import inspect
-
-logger = logging.getLogger(__name__)
-
-
-class BaseCrawler(ABC):
-    def __init__(self):
-        self.logger = logging.getLogger(self.__class__.__name__)
-        
-    @abstractmethod
-    async def run(self, url: str = "", **kwargs) -> str:
-        """
-        Implement this method to return JSON string.
-        Must accept URL + arbitrary kwargs for flexibility.
-        """
-        pass
-
-    def __init_subclass__(cls, **kwargs):
-        """Enforce interface validation on subclassing"""
-        super().__init_subclass__(**kwargs)
-        
-        # Verify run method signature
-        run_method = cls.run
-        if not run_method.__code__.co_argcount >= 2:  # self + url
-            raise TypeError(f"{cls.__name__} must implement 'run(self, url: str, **kwargs)'")
-            
-        # Verify async nature
-        if not inspect.iscoroutinefunction(run_method):
-            raise TypeError(f"{cls.__name__}.run must be async")
-
-class CrawlerHub:
-    _crawlers: Dict[str, Type[BaseCrawler]] = {}
-
-    @classmethod
-    def _discover_crawlers(cls):
-        """Dynamically load crawlers from /crawlers in 3 lines"""
-        base_path = Path(__file__).parent / "crawlers"
-        for crawler_dir in base_path.iterdir():
-            if crawler_dir.is_dir():
-                try:
-                    module = importlib.import_module(
-                        f"crawl4ai.crawlers.{crawler_dir.name}.crawler"
-                    )
-                    for attr in dir(module):
-                        cls._maybe_register_crawler(
-                            getattr(module, attr), crawler_dir.name
-                        )
-                except Exception as e:
-                    logger.warning(f"Failed {crawler_dir.name}: {str(e)}")
-
-    @classmethod
-    def _maybe_register_crawler(cls, obj, name: str):
-        """Brilliant one-liner registration"""
-        if isinstance(obj, type) and issubclass(obj, BaseCrawler) and obj != BaseCrawler:
-            module = importlib.import_module(obj.__module__)
-            obj.meta = getattr(module, "__meta__", {})
-            cls._crawlers[name] = obj
-
-    @classmethod
-    def get(cls, name: str) -> Union[Type[BaseCrawler], None]:
-        if not cls._crawlers:
-            cls._discover_crawlers()
-        return cls._crawlers.get(name)

crawl4ai/install.py

@@ -1,212 +0,0 @@
-import subprocess
-import sys
-import asyncio
-from .async_logger import AsyncLogger, LogLevel
-from pathlib import Path
-import os
-import shutil
-
-# Initialize logger
-logger = AsyncLogger(log_level=LogLevel.DEBUG, verbose=True)
-
-def setup_home_directory():
-    """Set up the .crawl4ai folder structure in the user's home directory."""
-    base_dir = os.getenv("CRAWL4_AI_BASE_DIRECTORY")
-    crawl4ai_folder = Path(base_dir) if base_dir else Path.home()
-    crawl4ai_config = crawl4ai_folder / "global.yml"
-    crawl4ai_folder = crawl4ai_folder / ".crawl4ai"
-    cache_folder = crawl4ai_folder / "cache"
-    content_folders = [
-        "html_content",
-        "cleaned_html",
-        "markdown_content",
-        "extracted_content",
-        "screenshots",
-    ]
-
-    # Clean up old cache if exists
-    if cache_folder.exists():
-        shutil.rmtree(cache_folder)
-
-    # Create new folder structure
-    crawl4ai_folder.mkdir(exist_ok=True)
-    cache_folder.mkdir(exist_ok=True)
-    for folder in content_folders:
-        (crawl4ai_folder / folder).mkdir(exist_ok=True)
-    
-    # If config file does not exist, create it
-    if not crawl4ai_config.exists():
-        with open(crawl4ai_config, "w") as f:
-            f.write("")
-
-def post_install():
-    """
-    Run all post-installation tasks.
-    Checks CRAWL4AI_MODE environment variable. If set to 'api',
-    skips Playwright browser installation.
-    """
-    logger.info("Running post-installation setup...", tag="INIT")
-    setup_home_directory()
-
-    # Check environment variable to conditionally skip Playwright install
-    run_mode = os.getenv('CRAWL4AI_MODE')
-    if run_mode == 'api':
-        logger.warning(
-            "CRAWL4AI_MODE=api detected. Skipping Playwright browser installation.",
-            tag="SETUP"
-        )
-    else:
-        # Proceed with installation only if mode is not 'api'
-        install_playwright()
-
-    run_migration()
-    # TODO: Will be added in the future
-    # setup_builtin_browser()
-    logger.success("Post-installation setup completed!", tag="COMPLETE")
-    
-def setup_builtin_browser():
-    """Set up a builtin browser for use with Crawl4AI"""
-    try:
-        logger.info("Setting up builtin browser...", tag="INIT")
-        asyncio.run(_setup_builtin_browser())
-        logger.success("Builtin browser setup completed!", tag="COMPLETE")
-    except Exception as e:
-        logger.warning(f"Failed to set up builtin browser: {e}")
-        logger.warning("You can manually set up a builtin browser using 'crawl4ai-doctor builtin-browser-start'")
-    
-async def _setup_builtin_browser():
-    try:
-        # Import BrowserProfiler here to avoid circular imports
-        from .browser_profiler import BrowserProfiler
-        profiler = BrowserProfiler(logger=logger)
-        
-        # Launch the builtin browser
-        cdp_url = await profiler.launch_builtin_browser(headless=True)
-        if cdp_url:
-            logger.success(f"Builtin browser launched at {cdp_url}", tag="BROWSER")
-        else:
-            logger.warning("Failed to launch builtin browser", tag="BROWSER")
-    except Exception as e:
-        logger.warning(f"Error setting up builtin browser: {e}", tag="BROWSER")
-        raise
-
-
-def install_playwright():
-    logger.info("Installing Playwright browsers...", tag="INIT")
-    try:
-        # subprocess.check_call([sys.executable, "-m", "playwright", "install", "--with-deps", "--force", "chrome"])
-        subprocess.check_call(
-            [
-                sys.executable,
-                "-m",
-                "playwright",
-                "install",
-                "--with-deps",
-                "--force",
-                "chromium",
-            ]
-        )
-        logger.success(
-            "Playwright installation completed successfully.", tag="COMPLETE"
-        )
-    except subprocess.CalledProcessError:
-        # logger.error(f"Error during Playwright installation: {e}", tag="ERROR")
-        logger.warning(
-            f"Please run '{sys.executable} -m playwright install --with-deps' manually after the installation."
-        )
-    except Exception:
-        # logger.error(f"Unexpected error during Playwright installation: {e}", tag="ERROR")
-        logger.warning(
-            f"Please run '{sys.executable} -m playwright install --with-deps' manually after the installation."
-        )
-    
-    # Install Patchright browsers for undetected browser support
-    logger.info("Installing Patchright browsers for undetected mode...", tag="INIT")
-    try:
-        subprocess.check_call(
-            [
-                sys.executable,
-                "-m",
-                "patchright",
-                "install",
-                "--with-deps",
-                "--force",
-                "chromium",
-            ]
-        )
-        logger.success(
-            "Patchright installation completed successfully.", tag="COMPLETE"
-        )
-    except subprocess.CalledProcessError:
-        logger.warning(
-            f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
-        )
-    except Exception:
-        logger.warning(
-            f"Please run '{sys.executable} -m patchright install --with-deps' manually after the installation."
-        )
-
-
-def run_migration():
-    """Initialize database during installation"""
-    try:
-        logger.info("Starting database initialization...", tag="INIT")
-        from crawl4ai.async_database import async_db_manager
-
-        asyncio.run(async_db_manager.initialize())
-        logger.success(
-            "Database initialization completed successfully.", tag="COMPLETE"
-        )
-    except ImportError:
-        logger.warning("Database module not found. Will initialize on first use.")
-    except Exception as e:
-        logger.warning(f"Database initialization failed: {e}")
-        logger.warning("Database will be initialized on first use")
-
-
-async def run_doctor():
-    """Test if Crawl4AI is working properly"""
-    logger.info("Running Crawl4AI health check...", tag="INIT")
-    try:
-        from .async_webcrawler import (
-            AsyncWebCrawler,
-            BrowserConfig,
-            CrawlerRunConfig,
-            CacheMode,
-        )
-
-        browser_config = BrowserConfig(
-            headless=True,
-            browser_type="chromium",
-            ignore_https_errors=True,
-            light_mode=True,
-            viewport_width=1280,
-            viewport_height=720,
-        )
-
-        run_config = CrawlerRunConfig(
-            cache_mode=CacheMode.BYPASS,
-            screenshot=True,
-        )
-
-        async with AsyncWebCrawler(config=browser_config) as crawler:
-            logger.info("Testing crawling capabilities...", tag="TEST")
-            result = await crawler.arun(url="https://crawl4ai.com", config=run_config)
-
-            if result and result.markdown:
-                logger.success("✅ Crawling test passed!", tag="COMPLETE")
-                return True
-            else:
-                raise Exception("Failed to get content")
-
-    except Exception as e:
-        logger.error(f"❌ Test failed: {e}", tag="ERROR")
-        return False
-
-
-def doctor():
-    """Entry point for the doctor command"""
-    import asyncio
-
-    asyncio.run(run_doctor())
-    sys.exit(0)

crawl4ai/js_snippet/__init__.py

@@ -1,18 +0,0 @@
-import os
-
-
-# Create a function get name of a js script, then load from the CURRENT folder of this script and return its content as string, make sure its error free
-def load_js_script(script_name):
-    # Get the path of the current script
-    current_script_path = os.path.dirname(os.path.realpath(__file__))
-    # Get the path of the script to load
-    script_path = os.path.join(current_script_path, script_name + ".js")
-    # Check if the script exists
-    if not os.path.exists(script_path):
-        raise ValueError(
-            f"Script {script_name} not found in the folder {current_script_path}"
-        )
-    # Load the content of the script
-    with open(script_path, "r") as f:
-        script_content = f.read()
-    return script_content

crawl4ai/js_snippet/navigator_overrider.js

@@ -1,25 +0,0 @@
-// Pass the Permissions Test.
-const originalQuery = window.navigator.permissions.query;
-window.navigator.permissions.query = (parameters) =>
-    parameters.name === "notifications"
-        ? Promise.resolve({ state: Notification.permission })
-        : originalQuery(parameters);
-Object.defineProperty(navigator, "webdriver", {
-    get: () => undefined,
-});
-window.navigator.chrome = {
-    runtime: {},
-    // Add other properties if necessary
-};
-Object.defineProperty(navigator, "plugins", {
-    get: () => [1, 2, 3, 4, 5],
-});
-Object.defineProperty(navigator, "languages", {
-    get: () => ["en-US", "en"],
-});
-Object.defineProperty(document, "hidden", {
-    get: () => false,
-});
-Object.defineProperty(document, "visibilityState", {
-    get: () => "visible",
-});

crawl4ai/js_snippet/remove_overlay_elements.js

@@ -1,120 +0,0 @@
-async () => {
-    // Function to check if element is visible
-    const isVisible = (elem) => {
-        const style = window.getComputedStyle(elem);
-        return style.display !== "none" && style.visibility !== "hidden" && style.opacity !== "0";
-    };
-
-    // Common selectors for popups and overlays
-    const commonSelectors = [
-        // Close buttons first
-        'button[class*="close" i]',
-        'button[class*="dismiss" i]',
-        'button[aria-label*="close" i]',
-        'button[title*="close" i]',
-        'a[class*="close" i]',
-        'span[class*="close" i]',
-
-        // Cookie notices
-        '[class*="cookie-banner" i]',
-        '[id*="cookie-banner" i]',
-        '[class*="cookie-consent" i]',
-        '[id*="cookie-consent" i]',
-
-        // Newsletter/subscription dialogs
-        '[class*="newsletter" i]',
-        '[class*="subscribe" i]',
-
-        // Generic popups/modals
-        '[class*="popup" i]',
-        '[class*="modal" i]',
-        '[class*="overlay" i]',
-        '[class*="dialog" i]',
-        '[role="dialog"]',
-        '[role="alertdialog"]',
-    ];
-
-    // Try to click close buttons first
-    for (const selector of commonSelectors.slice(0, 6)) {
-        const closeButtons = document.querySelectorAll(selector);
-        for (const button of closeButtons) {
-            if (isVisible(button)) {
-                try {
-                    button.click();
-                    await new Promise((resolve) => setTimeout(resolve, 100));
-                } catch (e) {
-                    console.log("Error clicking button:", e);
-                }
-            }
-        }
-    }
-
-    // Remove remaining overlay elements
-    const removeOverlays = () => {
-        // Find elements with high z-index
-        const allElements = document.querySelectorAll("*");
-        for (const elem of allElements) {
-            const style = window.getComputedStyle(elem);
-            const zIndex = parseInt(style.zIndex);
-            const position = style.position;
-
-            if (
-                isVisible(elem) &&
-                (zIndex > 999 || position === "fixed" || position === "absolute") &&
-                (elem.offsetWidth > window.innerWidth * 0.5 ||
-                    elem.offsetHeight > window.innerHeight * 0.5 ||
-                    style.backgroundColor.includes("rgba") ||
-                    parseFloat(style.opacity) < 1)
-            ) {
-                elem.remove();
-            }
-        }
-
-        // Remove elements matching common selectors
-        for (const selector of commonSelectors) {
-            const elements = document.querySelectorAll(selector);
-            elements.forEach((elem) => {
-                if (isVisible(elem)) {
-                    elem.remove();
-                }
-            });
-        }
-    };
-
-    // Remove overlay elements
-    removeOverlays();
-
-    // Remove any fixed/sticky position elements at the top/bottom
-    const removeFixedElements = () => {
-        const elements = document.querySelectorAll("*");
-        elements.forEach((elem) => {
-            const style = window.getComputedStyle(elem);
-            if ((style.position === "fixed" || style.position === "sticky") && isVisible(elem)) {
-                elem.remove();
-            }
-        });
-    };
-
-    removeFixedElements();
-
-    // Remove empty block elements as: div, p, span, etc.
-    const removeEmptyBlockElements = () => {
-        const blockElements = document.querySelectorAll(
-            "div, p, span, section, article, header, footer, aside, nav, main, ul, ol, li, dl, dt, dd, h1, h2, h3, h4, h5, h6"
-        );
-        blockElements.forEach((elem) => {
-            if (elem.innerText.trim() === "") {
-                elem.remove();
-            }
-        });
-    };
-
-    // Remove margin-right and padding-right from body (often added by modal scripts)
-    document.body.style.marginRight = "0px";
-    document.body.style.paddingRight = "0px";
-    document.body.style.overflow = "auto";
-
-    // Wait a bit for any animations to complete
-    document.body.scrollIntoView(false);
-    await new Promise((resolve) => setTimeout(resolve, 50));
-};

crawl4ai/js_snippet/update_image_dimensions.js

@@ -1,54 +0,0 @@
-() => {
-    return new Promise((resolve) => {
-        const filterImage = (img) => {
-            // Filter out images that are too small
-            if (img.width < 100 && img.height < 100) return false;
-
-            // Filter out images that are not visible
-            const rect = img.getBoundingClientRect();
-            if (rect.width === 0 || rect.height === 0) return false;
-
-            // Filter out images with certain class names (e.g., icons, thumbnails)
-            if (img.classList.contains("icon") || img.classList.contains("thumbnail")) return false;
-
-            // Filter out images with certain patterns in their src (e.g., placeholder images)
-            if (img.src.includes("placeholder") || img.src.includes("icon")) return false;
-
-            return true;
-        };
-
-        const images = Array.from(document.querySelectorAll("img")).filter(filterImage);
-        let imagesLeft = images.length;
-
-        if (imagesLeft === 0) {
-            resolve();
-            return;
-        }
-
-        const checkImage = (img) => {
-            if (img.complete && img.naturalWidth !== 0) {
-                img.setAttribute("width", img.naturalWidth);
-                img.setAttribute("height", img.naturalHeight);
-                imagesLeft--;
-                if (imagesLeft === 0) resolve();
-            }
-        };
-
-        images.forEach((img) => {
-            checkImage(img);
-            if (!img.complete) {
-                img.onload = () => {
-                    checkImage(img);
-                };
-                img.onerror = () => {
-                    imagesLeft--;
-                    if (imagesLeft === 0) resolve();
-                };
-            }
-        });
-
-        // Fallback timeout of 5 seconds
-        // setTimeout(() => resolve(), 5000);
-        resolve();
-    });
-};

crawl4ai/legacy/cli.py

@@ -1,123 +0,0 @@
-import click
-import sys
-import asyncio
-from typing import List
-from .docs_manager import DocsManager
-from .async_logger import AsyncLogger
-
-logger = AsyncLogger(verbose=True)
-docs_manager = DocsManager(logger)
-
-
-def print_table(headers: List[str], rows: List[List[str]], padding: int = 2):
-    """Print formatted table with headers and rows"""
-    widths = [max(len(str(cell)) for cell in col) for col in zip(headers, *rows)]
-    border = "+" + "+".join("-" * (w + 2 * padding) for w in widths) + "+"
-
-    def format_row(row):
-        return (
-            "|"
-            + "|".join(
-                f"{' ' * padding}{str(cell):<{w}}{' ' * padding}"
-                for cell, w in zip(row, widths)
-            )
-            + "|"
-        )
-
-    click.echo(border)
-    click.echo(format_row(headers))
-    click.echo(border)
-    for row in rows:
-        click.echo(format_row(row))
-    click.echo(border)
-
-
-@click.group()
-def cli():
-    """Crawl4AI Command Line Interface"""
-    pass
-
-
-@cli.group()
-def docs():
-    """Documentation operations"""
-    pass
-
-
-@docs.command()
-@click.argument("sections", nargs=-1)
-@click.option(
-    "--mode", type=click.Choice(["extended", "condensed"]), default="extended"
-)
-def combine(sections: tuple, mode: str):
-    """Combine documentation sections"""
-    try:
-        asyncio.run(docs_manager.ensure_docs_exist())
-        click.echo(docs_manager.generate(sections, mode))
-    except Exception as e:
-        logger.error(str(e), tag="ERROR")
-        sys.exit(1)
-
-
-@docs.command()
-@click.argument("query")
-@click.option("--top-k", "-k", default=5)
-@click.option("--build-index", is_flag=True, help="Build index if missing")
-def search(query: str, top_k: int, build_index: bool):
-    """Search documentation"""
-    try:
-        result = docs_manager.search(query, top_k)
-        if result == "No search index available. Call build_search_index() first.":
-            if build_index or click.confirm("No search index found. Build it now?"):
-                asyncio.run(docs_manager.llm_text.generate_index_files())
-                result = docs_manager.search(query, top_k)
-        click.echo(result)
-    except Exception as e:
-        click.echo(f"Error: {str(e)}", err=True)
-        sys.exit(1)
-
-
-@docs.command()
-def update():
-    """Update docs from GitHub"""
-    try:
-        asyncio.run(docs_manager.fetch_docs())
-        click.echo("Documentation updated successfully")
-    except Exception as e:
-        click.echo(f"Error: {str(e)}", err=True)
-        sys.exit(1)
-
-
-@docs.command()
-@click.option("--force-facts", is_flag=True, help="Force regenerate fact files")
-@click.option("--clear-cache", is_flag=True, help="Clear BM25 cache")
-def index(force_facts: bool, clear_cache: bool):
-    """Build or rebuild search indexes"""
-    try:
-        asyncio.run(docs_manager.ensure_docs_exist())
-        asyncio.run(
-            docs_manager.llm_text.generate_index_files(
-                force_generate_facts=force_facts, clear_bm25_cache=clear_cache
-            )
-        )
-        click.echo("Search indexes built successfully")
-    except Exception as e:
-        click.echo(f"Error: {str(e)}", err=True)
-        sys.exit(1)
-
-
-# Add docs list command
-@docs.command()
-def list():
-    """List available documentation sections"""
-    try:
-        sections = docs_manager.list()
-        print_table(["Sections"], [[section] for section in sections])
-
-    except Exception as e:
-        click.echo(f"Error: {str(e)}", err=True)
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    cli()

crawl4ai/legacy/docs_manager.py

@@ -1,75 +0,0 @@
-import requests
-import shutil
-from pathlib import Path
-from crawl4ai.async_logger import AsyncLogger
-from crawl4ai.llmtxt import AsyncLLMTextManager
-
-
-class DocsManager:
-    def __init__(self, logger=None):
-        self.docs_dir = Path.home() / ".crawl4ai" / "docs"
-        self.local_docs = Path(__file__).parent.parent / "docs" / "llm.txt"
-        self.docs_dir.mkdir(parents=True, exist_ok=True)
-        self.logger = logger or AsyncLogger(verbose=True)
-        self.llm_text = AsyncLLMTextManager(self.docs_dir, self.logger)
-
-    async def ensure_docs_exist(self):
-        """Fetch docs if not present"""
-        if not any(self.docs_dir.iterdir()):
-            await self.fetch_docs()
-
-    async def fetch_docs(self) -> bool:
-        """Copy from local docs or download from GitHub"""
-        try:
-            # Try local first
-            if self.local_docs.exists() and (
-                any(self.local_docs.glob("*.md"))
-                or any(self.local_docs.glob("*.tokens"))
-            ):
-                # Empty the local docs directory
-                for file_path in self.docs_dir.glob("*.md"):
-                    file_path.unlink()
-                # for file_path in self.docs_dir.glob("*.tokens"):
-                #     file_path.unlink()
-                for file_path in self.local_docs.glob("*.md"):
-                    shutil.copy2(file_path, self.docs_dir / file_path.name)
-                # for file_path in self.local_docs.glob("*.tokens"):
-                #     shutil.copy2(file_path, self.docs_dir / file_path.name)
-                return True
-
-            # Fallback to GitHub
-            response = requests.get(
-                "https://api.github.com/repos/unclecode/crawl4ai/contents/docs/llm.txt",
-                headers={"Accept": "application/vnd.github.v3+json"},
-            )
-            response.raise_for_status()
-
-            for item in response.json():
-                if item["type"] == "file" and item["name"].endswith(".md"):
-                    content = requests.get(item["download_url"]).text
-                    with open(self.docs_dir / item["name"], "w", encoding="utf-8") as f:
-                        f.write(content)
-            return True
-
-        except Exception as e:
-            self.logger.error(f"Failed to fetch docs: {str(e)}")
-            raise
-
-    def list(self) -> list[str]:
-        """List available topics"""
-        names = [file_path.stem for file_path in self.docs_dir.glob("*.md")]
-        # Remove [0-9]+_ prefix
-        names = [name.split("_", 1)[1] if name[0].isdigit() else name for name in names]
-        # Exclude those end with .xs.md and .q.md
-        names = [
-            name
-            for name in names
-            if not name.endswith(".xs") and not name.endswith(".q")
-        ]
-        return names
-
-    def generate(self, sections, mode="extended"):
-        return self.llm_text.generate(sections, mode)
-
-    def search(self, query: str, top_k: int = 5):
-        return self.llm_text.search(query, top_k)

crawl4ai/legacy/llmtxt.py

@@ -1,546 +0,0 @@
-import os
-from pathlib import Path
-import re
-from typing import Dict, List, Tuple, Optional, Any
-import json
-from tqdm import tqdm
-import time
-import psutil
-import numpy as np
-from rank_bm25 import BM25Okapi
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-from litellm import batch_completion
-from .async_logger import AsyncLogger
-import litellm
-import pickle
-import hashlib  # <--- ADDED for file-hash
-import glob
-
-litellm.set_verbose = False
-
-
-def _compute_file_hash(file_path: Path) -> str:
-    """Compute MD5 hash for the file's entire content."""
-    hash_md5 = hashlib.md5()
-    with file_path.open("rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-
-class AsyncLLMTextManager:
-    def __init__(
-        self,
-        docs_dir: Path,
-        logger: Optional[AsyncLogger] = None,
-        max_concurrent_calls: int = 5,
-        batch_size: int = 3,
-    ) -> None:
-        self.docs_dir = docs_dir
-        self.logger = logger
-        self.max_concurrent_calls = max_concurrent_calls
-        self.batch_size = batch_size
-        self.bm25_index = None
-        self.document_map: Dict[str, Any] = {}
-        self.tokenized_facts: List[str] = []
-        self.bm25_index_file = self.docs_dir / "bm25_index.pkl"
-
-    async def _process_document_batch(self, doc_batch: List[Path]) -> None:
-        """Process a batch of documents in parallel"""
-        contents = []
-        for file_path in doc_batch:
-            try:
-                with open(file_path, "r", encoding="utf-8") as f:
-                    contents.append(f.read())
-            except Exception as e:
-                self.logger.error(f"Error reading {file_path}: {str(e)}")
-                contents.append("")  # Add empty content to maintain batch alignment
-
-        prompt = """Given a documentation file, generate a list of atomic facts where each fact:
-1. Represents a single piece of knowledge
-2. Contains variations in terminology for the same concept
-3. References relevant code patterns if they exist
-4. Is written in a way that would match natural language queries
-
-Each fact should follow this format:
-<main_concept>: <fact_statement> | <related_terms> | <code_reference>
-
-Example Facts:
-browser_config: Configure headless mode and browser type for AsyncWebCrawler | headless, browser_type, chromium, firefox | BrowserConfig(browser_type="chromium", headless=True)
-redis_connection: Redis client connection requires host and port configuration | redis setup, redis client, connection params | Redis(host='localhost', port=6379, db=0)
-pandas_filtering: Filter DataFrame rows using boolean conditions | dataframe filter, query, boolean indexing | df[df['column'] > 5]
-
-Wrap your response in <index>...</index> tags.
-"""
-
-        # Prepare messages for batch processing
-        messages_list = [
-            [
-                {
-                    "role": "user",
-                    "content": f"{prompt}\n\nGenerate index for this documentation:\n\n{content}",
-                }
-            ]
-            for content in contents
-            if content
-        ]
-
-        try:
-            responses = batch_completion(
-                model="anthropic/claude-3-5-sonnet-latest",
-                messages=messages_list,
-                logger_fn=None,
-            )
-
-            # Process responses and save index files
-            for response, file_path in zip(responses, doc_batch):
-                try:
-                    index_content_match = re.search(
-                        r"<index>(.*?)</index>",
-                        response.choices[0].message.content,
-                        re.DOTALL,
-                    )
-                    if not index_content_match:
-                        self.logger.warning(
-                            f"No <index>...</index> content found for {file_path}"
-                        )
-                        continue
-
-                    index_content = re.sub(
-                        r"\n\s*\n", "\n", index_content_match.group(1)
-                    ).strip()
-                    if index_content:
-                        index_file = file_path.with_suffix(".q.md")
-                        with open(index_file, "w", encoding="utf-8") as f:
-                            f.write(index_content)
-                        self.logger.info(f"Created index file: {index_file}")
-                    else:
-                        self.logger.warning(
-                            f"No index content found in response for {file_path}"
-                        )
-
-                except Exception as e:
-                    self.logger.error(
-                        f"Error processing response for {file_path}: {str(e)}"
-                    )
-
-        except Exception as e:
-            self.logger.error(f"Error in batch completion: {str(e)}")
-
-    def _validate_fact_line(self, line: str) -> Tuple[bool, Optional[str]]:
-        if "|" not in line:
-            return False, "Missing separator '|'"
-
-        parts = [p.strip() for p in line.split("|")]
-        if len(parts) != 3:
-            return False, f"Expected 3 parts, got {len(parts)}"
-
-        concept_part = parts[0]
-        if ":" not in concept_part:
-            return False, "Missing ':' in concept definition"
-
-        return True, None
-
-    def _load_or_create_token_cache(self, fact_file: Path) -> Dict:
-        """
-        Load token cache from .q.tokens if present and matching file hash.
-        Otherwise return a new structure with updated file-hash.
-        """
-        cache_file = fact_file.with_suffix(".q.tokens")
-        current_hash = _compute_file_hash(fact_file)
-
-        if cache_file.exists():
-            try:
-                with open(cache_file, "r") as f:
-                    cache = json.load(f)
-                # If the hash matches, return it directly
-                if cache.get("content_hash") == current_hash:
-                    return cache
-                # Otherwise, we signal that it's changed
-                self.logger.info(f"Hash changed for {fact_file}, reindex needed.")
-            except json.JSONDecodeError:
-                self.logger.warning(f"Corrupt token cache for {fact_file}, rebuilding.")
-            except Exception as e:
-                self.logger.warning(f"Error reading cache for {fact_file}: {str(e)}")
-
-        # Return a fresh cache
-        return {"facts": {}, "content_hash": current_hash}
-
-    def _save_token_cache(self, fact_file: Path, cache: Dict) -> None:
-        cache_file = fact_file.with_suffix(".q.tokens")
-        # Always ensure we're saving the correct file-hash
-        cache["content_hash"] = _compute_file_hash(fact_file)
-        with open(cache_file, "w") as f:
-            json.dump(cache, f)
-
-    def preprocess_text(self, text: str) -> List[str]:
-        parts = [x.strip() for x in text.split("|")] if "|" in text else [text]
-        # Remove : after the first word of parts[0]
-        parts[0] = re.sub(r"^(.*?):", r"\1", parts[0])
-
-        lemmatizer = WordNetLemmatizer()
-        stop_words = set(stopwords.words("english")) - {
-            "how",
-            "what",
-            "when",
-            "where",
-            "why",
-            "which",
-        }
-
-        tokens = []
-        for part in parts:
-            if "(" in part and ")" in part:
-                code_tokens = re.findall(
-                    r'[\w_]+(?=\()|[\w_]+(?==[\'"]{1}[\w_]+[\'"]{1})', part
-                )
-                tokens.extend(code_tokens)
-
-            words = word_tokenize(part.lower())
-            tokens.extend(
-                [
-                    lemmatizer.lemmatize(token)
-                    for token in words
-                    if token not in stop_words
-                ]
-            )
-
-        return tokens
-
-    def maybe_load_bm25_index(self, clear_cache=False) -> bool:
-        """
-        Load existing BM25 index from disk, if present and clear_cache=False.
-        """
-        if not clear_cache and os.path.exists(self.bm25_index_file):
-            self.logger.info("Loading existing BM25 index from disk.")
-            with open(self.bm25_index_file, "rb") as f:
-                data = pickle.load(f)
-            self.tokenized_facts = data["tokenized_facts"]
-            self.bm25_index = data["bm25_index"]
-            return True
-        return False
-
-    def build_search_index(self, clear_cache=False) -> None:
-        """
-        Checks for new or modified .q.md files by comparing file-hash.
-        If none need reindexing and clear_cache is False, loads existing index if available.
-        Otherwise, reindexes only changed/new files and merges or creates a new index.
-        """
-        # If clear_cache is True, we skip partial logic: rebuild everything from scratch
-        if clear_cache:
-            self.logger.info("Clearing cache and rebuilding full search index.")
-            if self.bm25_index_file.exists():
-                self.bm25_index_file.unlink()
-
-        process = psutil.Process()
-        self.logger.info("Checking which .q.md files need (re)indexing...")
-
-        # Gather all .q.md files
-        q_files = [
-            self.docs_dir / f for f in os.listdir(self.docs_dir) if f.endswith(".q.md")
-        ]
-
-        # We'll store known (unchanged) facts in these lists
-        existing_facts: List[str] = []
-        existing_tokens: List[List[str]] = []
-
-        # Keep track of invalid lines for logging
-        invalid_lines = []
-        needSet = []  # files that must be (re)indexed
-
-        for qf in q_files:
-            token_cache_file = qf.with_suffix(".q.tokens")
-
-            # If no .q.tokens or clear_cache is True → definitely reindex
-            if clear_cache or not token_cache_file.exists():
-                needSet.append(qf)
-                continue
-
-            # Otherwise, load the existing cache and compare hash
-            cache = self._load_or_create_token_cache(qf)
-            # If the .q.tokens was out of date (i.e. changed hash), we reindex
-            if len(cache["facts"]) == 0 or cache.get(
-                "content_hash"
-            ) != _compute_file_hash(qf):
-                needSet.append(qf)
-            else:
-                # File is unchanged → retrieve cached token data
-                for line, cache_data in cache["facts"].items():
-                    existing_facts.append(line)
-                    existing_tokens.append(cache_data["tokens"])
-                    self.document_map[line] = qf  # track the doc for that fact
-
-        if not needSet and not clear_cache:
-            # If no file needs reindexing, try loading existing index
-            if self.maybe_load_bm25_index(clear_cache=False):
-                self.logger.info(
-                    "No new/changed .q.md files found. Using existing BM25 index."
-                )
-                return
-            else:
-                # If there's no existing index, we must build a fresh index from the old caches
-                self.logger.info(
-                    "No existing BM25 index found. Building from cached facts."
-                )
-                if existing_facts:
-                    self.logger.info(
-                        f"Building BM25 index with {len(existing_facts)} cached facts."
-                    )
-                    self.bm25_index = BM25Okapi(existing_tokens)
-                    self.tokenized_facts = existing_facts
-                    with open(self.bm25_index_file, "wb") as f:
-                        pickle.dump(
-                            {
-                                "bm25_index": self.bm25_index,
-                                "tokenized_facts": self.tokenized_facts,
-                            },
-                            f,
-                        )
-                else:
-                    self.logger.warning("No facts found at all. Index remains empty.")
-                return
-
-        # ----------------------------------------------------- /Users/unclecode/.crawl4ai/docs/14_proxy_security.q.q.tokens '/Users/unclecode/.crawl4ai/docs/14_proxy_security.q.md'
-        # If we reach here, we have new or changed .q.md files
-        # We'll parse them, reindex them, and then combine with existing_facts
-        # -----------------------------------------------------
-
-        self.logger.info(f"{len(needSet)} file(s) need reindexing. Parsing now...")
-
-        # 1) Parse the new or changed .q.md files
-        new_facts = []
-        new_tokens = []
-        with tqdm(total=len(needSet), desc="Indexing changed files") as file_pbar:
-            for file in needSet:
-                # We'll build up a fresh cache
-                fresh_cache = {"facts": {}, "content_hash": _compute_file_hash(file)}
-                try:
-                    with open(file, "r", encoding="utf-8") as f_obj:
-                        content = f_obj.read().strip()
-                        lines = [l.strip() for l in content.split("\n") if l.strip()]
-
-                    for line in lines:
-                        is_valid, error = self._validate_fact_line(line)
-                        if not is_valid:
-                            invalid_lines.append((file, line, error))
-                            continue
-
-                        tokens = self.preprocess_text(line)
-                        fresh_cache["facts"][line] = {
-                            "tokens": tokens,
-                            "added": time.time(),
-                        }
-                        new_facts.append(line)
-                        new_tokens.append(tokens)
-                        self.document_map[line] = file
-
-                    # Save the new .q.tokens with updated hash
-                    self._save_token_cache(file, fresh_cache)
-
-                    mem_usage = process.memory_info().rss / 1024 / 1024
-                    self.logger.debug(
-                        f"Memory usage after {file.name}: {mem_usage:.2f}MB"
-                    )
-
-                except Exception as e:
-                    self.logger.error(f"Error processing {file}: {str(e)}")
-
-                file_pbar.update(1)
-
-        if invalid_lines:
-            self.logger.warning(f"Found {len(invalid_lines)} invalid fact lines:")
-            for file, line, error in invalid_lines:
-                self.logger.warning(f"{file}: {error} in line: {line[:50]}...")
-
-        # 2) Merge newly tokenized facts with the existing ones
-        all_facts = existing_facts + new_facts
-        all_tokens = existing_tokens + new_tokens
-
-        # 3) Build BM25 index from combined facts
-        self.logger.info(
-            f"Building BM25 index with {len(all_facts)} total facts (old + new)."
-        )
-        self.bm25_index = BM25Okapi(all_tokens)
-        self.tokenized_facts = all_facts
-
-        # 4) Save the updated BM25 index to disk
-        with open(self.bm25_index_file, "wb") as f:
-            pickle.dump(
-                {
-                    "bm25_index": self.bm25_index,
-                    "tokenized_facts": self.tokenized_facts,
-                },
-                f,
-            )
-
-        final_mem = process.memory_info().rss / 1024 / 1024
-        self.logger.info(f"Search index updated. Final memory usage: {final_mem:.2f}MB")
-
-    async def generate_index_files(
-        self, force_generate_facts: bool = False, clear_bm25_cache: bool = False
-    ) -> None:
-        """
-        Generate index files for all documents in parallel batches
-
-        Args:
-            force_generate_facts (bool): If True, regenerate indexes even if they exist
-            clear_bm25_cache (bool): If True, clear existing BM25 index cache
-        """
-        self.logger.info("Starting index generation for documentation files.")
-
-        md_files = [
-            self.docs_dir / f
-            for f in os.listdir(self.docs_dir)
-            if f.endswith(".md") and not any(f.endswith(x) for x in [".q.md", ".xs.md"])
-        ]
-
-        # Filter out files that already have .q files unless force=True
-        if not force_generate_facts:
-            md_files = [
-                f
-                for f in md_files
-                if not (self.docs_dir / f.name.replace(".md", ".q.md")).exists()
-            ]
-
-        if not md_files:
-            self.logger.info("All index files exist. Use force=True to regenerate.")
-        else:
-            # Process documents in batches
-            for i in range(0, len(md_files), self.batch_size):
-                batch = md_files[i : i + self.batch_size]
-                self.logger.info(
-                    f"Processing batch {i//self.batch_size + 1}/{(len(md_files)//self.batch_size) + 1}"
-                )
-                await self._process_document_batch(batch)
-
-        self.logger.info("Index generation complete, building/updating search index.")
-        self.build_search_index(clear_cache=clear_bm25_cache)
-
-    def generate(self, sections: List[str], mode: str = "extended") -> str:
-        # Get all markdown files
-        all_files = glob.glob(str(self.docs_dir / "[0-9]*.md")) + glob.glob(
-            str(self.docs_dir / "[0-9]*.xs.md")
-        )
-
-        # Extract base names without extensions
-        base_docs = {
-            Path(f).name.split(".")[0]
-            for f in all_files
-            if not Path(f).name.endswith(".q.md")
-        }
-
-        # Filter by sections if provided
-        if sections:
-            base_docs = {
-                doc
-                for doc in base_docs
-                if any(section.lower() in doc.lower() for section in sections)
-            }
-
-        # Get file paths based on mode
-        files = []
-        for doc in sorted(
-            base_docs,
-            key=lambda x: int(x.split("_")[0]) if x.split("_")[0].isdigit() else 999999,
-        ):
-            if mode == "condensed":
-                xs_file = self.docs_dir / f"{doc}.xs.md"
-                regular_file = self.docs_dir / f"{doc}.md"
-                files.append(str(xs_file if xs_file.exists() else regular_file))
-            else:
-                files.append(str(self.docs_dir / f"{doc}.md"))
-
-        # Read and format content
-        content = []
-        for file in files:
-            try:
-                with open(file, "r", encoding="utf-8") as f:
-                    fname = Path(file).name
-                    content.append(f"{'#'*20}\n# {fname}\n{'#'*20}\n\n{f.read()}")
-            except Exception as e:
-                self.logger.error(f"Error reading {file}: {str(e)}")
-
-        return "\n\n---\n\n".join(content) if content else ""
-
-    def search(self, query: str, top_k: int = 5) -> str:
-        if not self.bm25_index:
-            return "No search index available. Call build_search_index() first."
-
-        query_tokens = self.preprocess_text(query)
-        doc_scores = self.bm25_index.get_scores(query_tokens)
-
-        mean_score = np.mean(doc_scores)
-        std_score = np.std(doc_scores)
-        score_threshold = mean_score + (0.25 * std_score)
-
-        file_data = self._aggregate_search_scores(
-            doc_scores=doc_scores,
-            score_threshold=score_threshold,
-            query_tokens=query_tokens,
-        )
-
-        ranked_files = sorted(
-            file_data.items(),
-            key=lambda x: (
-                x[1]["code_match_score"] * 2.0
-                + x[1]["match_count"] * 1.5
-                + x[1]["total_score"]
-            ),
-            reverse=True,
-        )[:top_k]
-
-        results = []
-        for file, _ in ranked_files:
-            main_doc = str(file).replace(".q.md", ".md")
-            if os.path.exists(self.docs_dir / main_doc):
-                with open(self.docs_dir / main_doc, "r", encoding="utf-8") as f:
-                    only_file_name = main_doc.split("/")[-1]
-                    content = ["#" * 20, f"# {only_file_name}", "#" * 20, "", f.read()]
-                    results.append("\n".join(content))
-
-        return "\n\n---\n\n".join(results)
-
-    def _aggregate_search_scores(
-        self, doc_scores: List[float], score_threshold: float, query_tokens: List[str]
-    ) -> Dict:
-        file_data = {}
-
-        for idx, score in enumerate(doc_scores):
-            if score <= score_threshold:
-                continue
-
-            fact = self.tokenized_facts[idx]
-            file_path = self.document_map[fact]
-
-            if file_path not in file_data:
-                file_data[file_path] = {
-                    "total_score": 0,
-                    "match_count": 0,
-                    "code_match_score": 0,
-                    "matched_facts": [],
-                }
-
-            components = fact.split("|") if "|" in fact else [fact]
-
-            code_match_score = 0
-            if len(components) == 3:
-                code_ref = components[2].strip()
-                code_tokens = self.preprocess_text(code_ref)
-                code_match_score = len(set(query_tokens) & set(code_tokens)) / len(
-                    query_tokens
-                )
-
-            file_data[file_path]["total_score"] += score
-            file_data[file_path]["match_count"] += 1
-            file_data[file_path]["code_match_score"] = max(
-                file_data[file_path]["code_match_score"], code_match_score
-            )
-            file_data[file_path]["matched_facts"].append(fact)
-
-        return file_data
-
-    def refresh_index(self) -> None:
-        """Convenience method for a full rebuild."""
-        self.build_search_index(clear_cache=True)

crawl4ai/legacy/version_manager.py

@@ -1,29 +0,0 @@
-# version_manager.py
-from pathlib import Path
-from packaging import version
-from . import __version__
-
-
-class VersionManager:
-    def __init__(self):
-        self.home_dir = Path.home() / ".crawl4ai"
-        self.version_file = self.home_dir / "version.txt"
-
-    def get_installed_version(self):
-        """Get the version recorded in home directory"""
-        if not self.version_file.exists():
-            return None
-        try:
-            return version.parse(self.version_file.read_text().strip())
-        except:
-            return None
-
-    def update_version(self):
-        """Update the version file to current library version"""
-        self.version_file.write_text(__version__.__version__)
-
-    def needs_update(self):
-        """Check if database needs update based on version"""
-        installed = self.get_installed_version()
-        current = version.parse(__version__.__version__)
-        return installed is None or installed < current

crawl4ai/legacy/web_crawler.py

@@ -1,294 +0,0 @@
-import os, time
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-from pathlib import Path
-
-from .models import UrlModel, CrawlResult
-from .database import init_db, get_cached_url, cache_url
-from .utils import *
-from .chunking_strategy import *
-from .extraction_strategy import *
-from .crawler_strategy import *
-from typing import List
-from concurrent.futures import ThreadPoolExecutor
-from ..content_scraping_strategy import LXMLWebScrapingStrategy as WebScrapingStrategy
-from .config import *
-import warnings
-import json
-
-warnings.filterwarnings(
-    "ignore",
-    message='Field "model_name" has conflict with protected namespace "model_".',
-)
-
-
-class WebCrawler:
-    def __init__(
-        self,
-        crawler_strategy: CrawlerStrategy = None,
-        always_by_pass_cache: bool = False,
-        verbose: bool = False,
-    ):
-        self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(
-            verbose=verbose
-        )
-        self.always_by_pass_cache = always_by_pass_cache
-        self.crawl4ai_folder = os.path.join(
-            os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
-        )
-        os.makedirs(self.crawl4ai_folder, exist_ok=True)
-        os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
-        init_db()
-        self.ready = False
-
-    def warmup(self):
-        print("[LOG] 🌤️  Warming up the WebCrawler")
-        self.run(
-            url="https://google.com/",
-            word_count_threshold=5,
-            extraction_strategy=NoExtractionStrategy(),
-            bypass_cache=False,
-            verbose=False,
-        )
-        self.ready = True
-        print("[LOG] 🌞 WebCrawler is ready to crawl")
-
-    def fetch_page(
-        self,
-        url_model: UrlModel,
-        provider: str = DEFAULT_PROVIDER,
-        api_token: str = None,
-        extract_blocks_flag: bool = True,
-        word_count_threshold=MIN_WORD_THRESHOLD,
-        css_selector: str = None,
-        screenshot: bool = False,
-        use_cached_html: bool = False,
-        extraction_strategy: ExtractionStrategy = None,
-        chunking_strategy: ChunkingStrategy = RegexChunking(),
-        **kwargs,
-    ) -> CrawlResult:
-        return self.run(
-            url_model.url,
-            word_count_threshold,
-            extraction_strategy or NoExtractionStrategy(),
-            chunking_strategy,
-            bypass_cache=url_model.forced,
-            css_selector=css_selector,
-            screenshot=screenshot,
-            **kwargs,
-        )
-        pass
-
-    def fetch_pages(
-        self,
-        url_models: List[UrlModel],
-        provider: str = DEFAULT_PROVIDER,
-        api_token: str = None,
-        extract_blocks_flag: bool = True,
-        word_count_threshold=MIN_WORD_THRESHOLD,
-        use_cached_html: bool = False,
-        css_selector: str = None,
-        screenshot: bool = False,
-        extraction_strategy: ExtractionStrategy = None,
-        chunking_strategy: ChunkingStrategy = RegexChunking(),
-        **kwargs,
-    ) -> List[CrawlResult]:
-        extraction_strategy = extraction_strategy or NoExtractionStrategy()
-
-        def fetch_page_wrapper(url_model, *args, **kwargs):
-            return self.fetch_page(url_model, *args, **kwargs)
-
-        with ThreadPoolExecutor() as executor:
-            results = list(
-                executor.map(
-                    fetch_page_wrapper,
-                    url_models,
-                    [provider] * len(url_models),
-                    [api_token] * len(url_models),
-                    [extract_blocks_flag] * len(url_models),
-                    [word_count_threshold] * len(url_models),
-                    [css_selector] * len(url_models),
-                    [screenshot] * len(url_models),
-                    [use_cached_html] * len(url_models),
-                    [extraction_strategy] * len(url_models),
-                    [chunking_strategy] * len(url_models),
-                    *[kwargs] * len(url_models),
-                )
-            )
-
-        return results
-
-    def run(
-        self,
-        url: str,
-        word_count_threshold=MIN_WORD_THRESHOLD,
-        extraction_strategy: ExtractionStrategy = None,
-        chunking_strategy: ChunkingStrategy = RegexChunking(),
-        bypass_cache: bool = False,
-        css_selector: str = None,
-        screenshot: bool = False,
-        user_agent: str = None,
-        verbose=True,
-        **kwargs,
-    ) -> CrawlResult:
-        try:
-            extraction_strategy = extraction_strategy or NoExtractionStrategy()
-            extraction_strategy.verbose = verbose
-            if not isinstance(extraction_strategy, ExtractionStrategy):
-                raise ValueError("Unsupported extraction strategy")
-            if not isinstance(chunking_strategy, ChunkingStrategy):
-                raise ValueError("Unsupported chunking strategy")
-
-            word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
-
-            cached = None
-            screenshot_data = None
-            extracted_content = None
-            if not bypass_cache and not self.always_by_pass_cache:
-                cached = get_cached_url(url)
-
-            if kwargs.get("warmup", True) and not self.ready:
-                return None
-
-            if cached:
-                html = sanitize_input_encode(cached[1])
-                extracted_content = sanitize_input_encode(cached[4])
-                if screenshot:
-                    screenshot_data = cached[9]
-                    if not screenshot_data:
-                        cached = None
-
-            if not cached or not html:
-                if user_agent:
-                    self.crawler_strategy.update_user_agent(user_agent)
-                t1 = time.time()
-                html = sanitize_input_encode(self.crawler_strategy.crawl(url, **kwargs))
-                t2 = time.time()
-                if verbose:
-                    print(
-                        f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds"
-                    )
-                if screenshot:
-                    screenshot_data = self.crawler_strategy.take_screenshot()
-
-            crawl_result = self.process_html(
-                url,
-                html,
-                extracted_content,
-                word_count_threshold,
-                extraction_strategy,
-                chunking_strategy,
-                css_selector,
-                screenshot_data,
-                verbose,
-                bool(cached),
-                **kwargs,
-            )
-            crawl_result.success = bool(html)
-            return crawl_result
-        except Exception as e:
-            if not hasattr(e, "msg"):
-                e.msg = str(e)
-            print(f"[ERROR] 🚫 Failed to crawl {url}, error: {e.msg}")
-            return CrawlResult(url=url, html="", success=False, error_message=e.msg)
-
-    def process_html(
-        self,
-        url: str,
-        html: str,
-        extracted_content: str,
-        word_count_threshold: int,
-        extraction_strategy: ExtractionStrategy,
-        chunking_strategy: ChunkingStrategy,
-        css_selector: str,
-        screenshot: bool,
-        verbose: bool,
-        is_cached: bool,
-        **kwargs,
-    ) -> CrawlResult:
-        t = time.time()
-        # Extract content from HTML
-        try:
-            t1 = time.time()
-            scrapping_strategy = WebScrapingStrategy()
-            extra_params = {
-                k: v
-                for k, v in kwargs.items()
-                if k not in ["only_text", "image_description_min_word_threshold"]
-            }
-            result = scrapping_strategy.scrap(
-                url,
-                html,
-                word_count_threshold=word_count_threshold,
-                css_selector=css_selector,
-                only_text=kwargs.get("only_text", False),
-                image_description_min_word_threshold=kwargs.get(
-                    "image_description_min_word_threshold",
-                    IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
-                ),
-                **extra_params,
-            )
-
-            # result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
-            if verbose:
-                print(
-                    f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds"
-                )
-
-            if result is None:
-                raise ValueError(f"Failed to extract content from the website: {url}")
-        except InvalidCSSSelectorError as e:
-            raise ValueError(str(e))
-
-        cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
-        markdown = sanitize_input_encode(result.get("markdown", ""))
-        media = result.get("media", [])
-        links = result.get("links", [])
-        metadata = result.get("metadata", {})
-
-        if extracted_content is None:
-            if verbose:
-                print(
-                    f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}"
-                )
-
-            sections = chunking_strategy.chunk(markdown)
-            extracted_content = extraction_strategy.run(url, sections)
-            extracted_content = json.dumps(
-                extracted_content, indent=4, default=str, ensure_ascii=False
-            )
-
-            if verbose:
-                print(
-                    f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds."
-                )
-
-        screenshot = None if not screenshot else screenshot
-
-        if not is_cached:
-            cache_url(
-                url,
-                html,
-                cleaned_html,
-                markdown,
-                extracted_content,
-                True,
-                json.dumps(media),
-                json.dumps(links),
-                json.dumps(metadata),
-                screenshot=screenshot,
-            )
-
-        return CrawlResult(
-            url=url,
-            html=html,
-            cleaned_html=format_html(cleaned_html),
-            markdown=markdown,
-            media=media,
-            links=links,
-            metadata=metadata,
-            screenshot=screenshot,
-            extracted_content=extracted_content,
-            success=True,
-            error_message="",
-        )

crawl4ai/link_preview.py

@@ -1,395 +0,0 @@
-"""
-Link Extractor for Crawl4AI
-
-Extracts head content from links discovered during crawling using URLSeeder's
-efficient parallel processing and caching infrastructure.
-"""
-
-import asyncio
-import fnmatch
-from typing import Dict, List, Optional, Any
-from .async_logger import AsyncLogger
-from .async_url_seeder import AsyncUrlSeeder
-from .async_configs import SeedingConfig, CrawlerRunConfig
-from .models import Links, Link
-from .utils import calculate_total_score
-
-
-class LinkPreview:
-    """
-    Extracts head content from links using URLSeeder's parallel processing infrastructure.
-    
-    This class provides intelligent link filtering and head content extraction with:
-    - Pattern-based inclusion/exclusion filtering
-    - Parallel processing with configurable concurrency
-    - Caching for performance
-    - BM25 relevance scoring
-    - Memory-safe processing for large link sets
-    """
-    
-    def __init__(self, logger: Optional[AsyncLogger] = None):
-        """
-        Initialize the LinkPreview.
-        
-        Args:
-            logger: Optional logger instance for recording events
-        """
-        self.logger = logger
-        self.seeder: Optional[AsyncUrlSeeder] = None
-        self._owns_seeder = False
-    
-    async def __aenter__(self):
-        """Async context manager entry."""
-        await self.start()
-        return self
-    
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        """Async context manager exit."""
-        await self.close()
-    
-    async def start(self):
-        """Initialize the URLSeeder instance."""
-        if not self.seeder:
-            self.seeder = AsyncUrlSeeder(logger=self.logger)
-            await self.seeder.__aenter__()
-            self._owns_seeder = True
-    
-    async def close(self):
-        """Clean up resources."""
-        if self.seeder and self._owns_seeder:
-            await self.seeder.__aexit__(None, None, None)
-            self.seeder = None
-            self._owns_seeder = False
-    
-    def _log(self, level: str, message: str, tag: str = "LINK_EXTRACT", **kwargs):
-        """Helper method to safely log messages."""
-        if self.logger:
-            log_method = getattr(self.logger, level, None)
-            if log_method:
-                log_method(message=message, tag=tag, params=kwargs.get('params', {}))
-    
-    async def extract_link_heads(
-        self, 
-        links: Links, 
-        config: CrawlerRunConfig
-    ) -> Links:
-        """
-        Extract head content for filtered links and attach to Link objects.
-        
-        Args:
-            links: Links object containing internal and external links
-            config: CrawlerRunConfig with link_preview_config settings
-            
-        Returns:
-            Links object with head_data attached to filtered Link objects
-        """
-        link_config = config.link_preview_config
-        
-        # Ensure seeder is initialized
-        await self.start()
-        
-        # Filter links based on configuration
-        filtered_urls = self._filter_links(links, link_config)
-        
-        if not filtered_urls:
-            self._log("info", "No links matched filtering criteria")
-            return links
-        
-        self._log("info", "Extracting head content for {count} filtered links",
-                  params={"count": len(filtered_urls)})
-        
-        # Extract head content using URLSeeder
-        head_results = await self._extract_heads_parallel(filtered_urls, link_config)
-        
-        # Merge results back into Link objects
-        updated_links = self._merge_head_data(links, head_results, config)
-        
-        self._log("info", "Completed head extraction for links, {success} successful",
-                  params={"success": len([r for r in head_results if r.get("status") == "valid"])})
-        
-        return updated_links
-    
-    def _filter_links(self, links: Links, link_config: Dict[str, Any]) -> List[str]:
-        """
-        Filter links based on configuration parameters.
-        
-        Args:
-            links: Links object containing internal and external links
-            link_config: Configuration dictionary for link extraction
-            
-        Returns:
-            List of filtered URL strings
-        """
-        filtered_urls = []
-        
-        # Include internal links if configured
-        if link_config.include_internal:
-            filtered_urls.extend([link.href for link in links.internal if link.href])
-            self._log("debug", "Added {count} internal links",
-                      params={"count": len(links.internal)})
-        
-        # Include external links if configured
-        if link_config.include_external:
-            filtered_urls.extend([link.href for link in links.external if link.href])
-            self._log("debug", "Added {count} external links",
-                      params={"count": len(links.external)})
-        
-        # Apply include patterns
-        include_patterns = link_config.include_patterns
-        if include_patterns:
-            filtered_urls = [
-                url for url in filtered_urls
-                if any(fnmatch.fnmatch(url, pattern) for pattern in include_patterns)
-            ]
-            self._log("debug", "After include patterns: {count} links remain",
-                      params={"count": len(filtered_urls)})
-        
-        # Apply exclude patterns
-        exclude_patterns = link_config.exclude_patterns
-        if exclude_patterns:
-            filtered_urls = [
-                url for url in filtered_urls
-                if not any(fnmatch.fnmatch(url, pattern) for pattern in exclude_patterns)
-            ]
-            self._log("debug", "After exclude patterns: {count} links remain",
-                      params={"count": len(filtered_urls)})
-        
-        # Limit number of links
-        max_links = link_config.max_links
-        if max_links > 0 and len(filtered_urls) > max_links:
-            filtered_urls = filtered_urls[:max_links]
-            self._log("debug", "Limited to {max_links} links",
-                      params={"max_links": max_links})
-        
-        # Remove duplicates while preserving order
-        seen = set()
-        unique_urls = []
-        for url in filtered_urls:
-            if url not in seen:
-                seen.add(url)
-                unique_urls.append(url)
-        
-        self._log("debug", "Final filtered URLs: {count} unique links",
-                  params={"count": len(unique_urls)})
-        
-        return unique_urls
-    
-    async def _extract_heads_parallel(
-        self, 
-        urls: List[str], 
-        link_config: Dict[str, Any]
-    ) -> List[Dict[str, Any]]:
-        """
-        Extract head content for URLs using URLSeeder's parallel processing.
-        
-        Args:
-            urls: List of URLs to process
-            link_config: Configuration dictionary for link extraction
-            
-        Returns:
-            List of dictionaries with url, status, head_data, and optional relevance_score
-        """
-        verbose = link_config.verbose
-        concurrency = link_config.concurrency
-        
-        if verbose:
-            self._log("info", "Starting batch processing: {total} links with {concurrency} concurrent workers",
-                      params={"total": len(urls), "concurrency": concurrency})
-        
-        # Create SeedingConfig for URLSeeder
-        seeding_config = SeedingConfig(
-            extract_head=True,
-            concurrency=concurrency,
-            hits_per_sec=getattr(link_config, 'hits_per_sec', None),
-            query=link_config.query,
-            score_threshold=link_config.score_threshold,
-            scoring_method="bm25" if link_config.query else None,
-            verbose=verbose
-        )
-        
-        # Use URLSeeder's extract_head_for_urls method with progress tracking
-        if verbose:
-            # Create a wrapper to track progress
-            results = await self._extract_with_progress(urls, seeding_config, link_config)
-        else:
-            results = await self.seeder.extract_head_for_urls(
-                urls=urls,
-                config=seeding_config,
-                concurrency=concurrency,
-                timeout=link_config.timeout
-            )
-        
-        return results
-    
-    async def _extract_with_progress(
-        self, 
-        urls: List[str], 
-        seeding_config: SeedingConfig, 
-        link_config: Dict[str, Any]
-    ) -> List[Dict[str, Any]]:
-        """Extract head content with progress reporting."""
-        
-        total_urls = len(urls)
-        concurrency = link_config.concurrency
-        batch_size = max(1, total_urls // 10)  # Report progress every 10%
-        
-        # Process URLs and track progress
-        completed = 0
-        successful = 0
-        failed = 0
-        
-        # Create a custom progress tracking version
-        # We'll modify URLSeeder's method to include progress callbacks
-        
-        # For now, let's use the existing method and report at the end
-        # In a production version, we would modify URLSeeder to accept progress callbacks
-        
-        self._log("info", "Processing links in batches...")
-        
-        # Use existing method
-        results = await self.seeder.extract_head_for_urls(
-            urls=urls,
-            config=seeding_config,
-            concurrency=concurrency,
-            timeout=link_config.timeout
-        )
-        
-        # Count results
-        for result in results:
-            completed += 1
-            if result.get("status") == "valid":
-                successful += 1
-            else:
-                failed += 1
-        
-        # Final progress report
-        self._log("info", "Batch processing completed: {completed}/{total} processed, {successful} successful, {failed} failed",
-                  params={
-                      "completed": completed,
-                      "total": total_urls,
-                      "successful": successful,
-                      "failed": failed
-                  })
-        
-        return results
-    
-    def _merge_head_data(
-        self, 
-        original_links: Links, 
-        head_results: List[Dict[str, Any]],
-        config: CrawlerRunConfig
-    ) -> Links:
-        """
-        Merge head extraction results back into Link objects.
-        
-        Args:
-            original_links: Original Links object
-            head_results: Results from head extraction
-            
-        Returns:
-            Links object with head_data attached to matching links
-        """
-        # Create URL to head_data mapping
-        url_to_head_data = {}
-        for result in head_results:
-            url = result.get("url")
-            if url:
-                url_to_head_data[url] = {
-                    "head_data": result.get("head_data", {}),
-                    "status": result.get("status", "unknown"),
-                    "error": result.get("error"),
-                    "relevance_score": result.get("relevance_score")
-                }
-        
-        # Update internal links
-        updated_internal = []
-        for link in original_links.internal:
-            if link.href in url_to_head_data:
-                head_info = url_to_head_data[link.href]
-                # Create new Link object with head data and scoring
-                contextual_score = head_info.get("relevance_score")
-                
-                updated_link = Link(
-                    href=link.href,
-                    text=link.text,
-                    title=link.title,
-                    base_domain=link.base_domain,
-                    head_data=head_info["head_data"],
-                    head_extraction_status=head_info["status"],
-                    head_extraction_error=head_info.get("error"),
-                    intrinsic_score=getattr(link, 'intrinsic_score', None),
-                    contextual_score=contextual_score
-                )
-                
-                # Add relevance score to head_data for backward compatibility
-                if contextual_score is not None:
-                    updated_link.head_data = updated_link.head_data or {}
-                    updated_link.head_data["relevance_score"] = contextual_score
-                
-                # Calculate total score combining intrinsic and contextual scores
-                updated_link.total_score = calculate_total_score(
-                    intrinsic_score=updated_link.intrinsic_score,
-                    contextual_score=updated_link.contextual_score,
-                    score_links_enabled=getattr(config, 'score_links', False),
-                    query_provided=bool(config.link_preview_config.query)
-                )
-                
-                updated_internal.append(updated_link)
-            else:
-                # Keep original link unchanged
-                updated_internal.append(link)
-        
-        # Update external links
-        updated_external = []
-        for link in original_links.external:
-            if link.href in url_to_head_data:
-                head_info = url_to_head_data[link.href]
-                # Create new Link object with head data and scoring
-                contextual_score = head_info.get("relevance_score")
-                
-                updated_link = Link(
-                    href=link.href,
-                    text=link.text,
-                    title=link.title,
-                    base_domain=link.base_domain,
-                    head_data=head_info["head_data"],
-                    head_extraction_status=head_info["status"],
-                    head_extraction_error=head_info.get("error"),
-                    intrinsic_score=getattr(link, 'intrinsic_score', None),
-                    contextual_score=contextual_score
-                )
-                
-                # Add relevance score to head_data for backward compatibility
-                if contextual_score is not None:
-                    updated_link.head_data = updated_link.head_data or {}
-                    updated_link.head_data["relevance_score"] = contextual_score
-                
-                # Calculate total score combining intrinsic and contextual scores
-                updated_link.total_score = calculate_total_score(
-                    intrinsic_score=updated_link.intrinsic_score,
-                    contextual_score=updated_link.contextual_score,
-                    score_links_enabled=getattr(config, 'score_links', False),
-                    query_provided=bool(config.link_preview_config.query)
-                )
-                
-                updated_external.append(updated_link)
-            else:
-                # Keep original link unchanged
-                updated_external.append(link)
-        
-        # Sort links by relevance score if available
-        if any(hasattr(link, 'head_data') and link.head_data and 'relevance_score' in link.head_data 
-               for link in updated_internal + updated_external):
-            
-            def get_relevance_score(link):
-                if hasattr(link, 'head_data') and link.head_data and 'relevance_score' in link.head_data:
-                    return link.head_data['relevance_score']
-                return 0.0
-            
-            updated_internal.sort(key=get_relevance_score, reverse=True)
-            updated_external.sort(key=get_relevance_score, reverse=True)
-        
-        return Links(
-            internal=updated_internal,
-            external=updated_external
-        )

crawl4ai/markdown_generation_strategy.py

@@ -1,260 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Optional, Dict, Any, Tuple
-from .models import MarkdownGenerationResult
-from .html2text import CustomHTML2Text
-# from .types import RelevantContentFilter
-from .content_filter_strategy import RelevantContentFilter
-import re
-from urllib.parse import urljoin
-
-# Pre-compile the regex pattern
-LINK_PATTERN = re.compile(r'!?\[([^\]]+)\]\(([^)]+?)(?:\s+"([^"]*)")?\)')
-
-
-def fast_urljoin(base: str, url: str) -> str:
-    """Fast URL joining for common cases."""
-    if url.startswith(("http://", "https://", "mailto:", "//")):
-        return url
-    if url.startswith("/"):
-        # Handle absolute paths
-        if base.endswith("/"):
-            return base[:-1] + url
-        return base + url
-    return urljoin(base, url)
-
-
-class MarkdownGenerationStrategy(ABC):
-    """Abstract base class for markdown generation strategies."""
-
-    def __init__(
-        self,
-        content_filter: Optional[RelevantContentFilter] = None,
-        options: Optional[Dict[str, Any]] = None,
-        verbose: bool = False,
-        content_source: str = "cleaned_html",
-    ):
-        self.content_filter = content_filter
-        self.options = options or {}
-        self.verbose = verbose
-        self.content_source = content_source
-
-    @abstractmethod
-    def generate_markdown(
-        self,
-        input_html: str,
-        base_url: str = "",
-        html2text_options: Optional[Dict[str, Any]] = None,
-        content_filter: Optional[RelevantContentFilter] = None,
-        citations: bool = True,
-        **kwargs,
-    ) -> MarkdownGenerationResult:
-        """Generate markdown from the selected input HTML."""
-        pass
-
-
-class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
-    """
-    Default implementation of markdown generation strategy.
-
-    How it works:
-    1. Generate raw markdown from cleaned HTML.
-    2. Convert links to citations.
-    3. Generate fit markdown if content filter is provided.
-    4. Return MarkdownGenerationResult.
-
-    Args:
-        content_filter (Optional[RelevantContentFilter]): Content filter for generating fit markdown.
-        options (Optional[Dict[str, Any]]): Additional options for markdown generation. Defaults to None.
-        content_source (str): Source of content to generate markdown from. Options: "cleaned_html", "raw_html", "fit_html". Defaults to "cleaned_html".
-
-    Returns:
-        MarkdownGenerationResult: Result containing raw markdown, fit markdown, fit HTML, and references markdown.
-    """
-
-    def __init__(
-        self,
-        content_filter: Optional[RelevantContentFilter] = None,
-        options: Optional[Dict[str, Any]] = None,
-        content_source: str = "cleaned_html",
-    ):
-        super().__init__(content_filter, options, verbose=False, content_source=content_source)
-
-    def convert_links_to_citations(
-        self, markdown: str, base_url: str = ""
-    ) -> Tuple[str, str]:
-        """
-        Convert links in markdown to citations.
-
-        How it works:
-        1. Find all links in the markdown.
-        2. Convert links to citations.
-        3. Return converted markdown and references markdown.
-
-        Note:
-        This function uses a regex pattern to find links in markdown.
-
-        Args:
-            markdown (str): Markdown text.
-            base_url (str): Base URL for URL joins.
-
-        Returns:
-            Tuple[str, str]: Converted markdown and references markdown.
-        """
-        link_map = {}
-        url_cache = {}  # Cache for URL joins
-        parts = []
-        last_end = 0
-        counter = 1
-
-        for match in LINK_PATTERN.finditer(markdown):
-            parts.append(markdown[last_end : match.start()])
-            text, url, title = match.groups()
-
-            # Use cached URL if available, otherwise compute and cache
-            if base_url and not url.startswith(("http://", "https://", "mailto:")):
-                if url not in url_cache:
-                    url_cache[url] = fast_urljoin(base_url, url)
-                url = url_cache[url]
-
-            if url not in link_map:
-                desc = []
-                if title:
-                    desc.append(title)
-                if text and text != title:
-                    desc.append(text)
-                link_map[url] = (counter, ": " + " - ".join(desc) if desc else "")
-                counter += 1
-
-            num = link_map[url][0]
-            parts.append(
-                f"{text}⟨{num}⟩"
-                if not match.group(0).startswith("!")
-                else f"![{text}⟨{num}⟩]"
-            )
-            last_end = match.end()
-
-        parts.append(markdown[last_end:])
-        converted_text = "".join(parts)
-
-        # Pre-build reference strings
-        references = ["\n\n## References\n\n"]
-        references.extend(
-            f"⟨{num}⟩ {url}{desc}\n"
-            for url, (num, desc) in sorted(link_map.items(), key=lambda x: x[1][0])
-        )
-
-        return converted_text, "".join(references)
-
-    def generate_markdown(
-        self,
-        input_html: str,
-        base_url: str = "",
-        html2text_options: Optional[Dict[str, Any]] = None,
-        options: Optional[Dict[str, Any]] = None,
-        content_filter: Optional[RelevantContentFilter] = None,
-        citations: bool = True,
-        **kwargs,
-    ) -> MarkdownGenerationResult:
-        """
-        Generate markdown with citations from the provided input HTML.
-
-        How it works:
-        1. Generate raw markdown from the input HTML.
-        2. Convert links to citations.
-        3. Generate fit markdown if content filter is provided.
-        4. Return MarkdownGenerationResult.
-
-        Args:
-            input_html (str): The HTML content to process (selected based on content_source).
-            base_url (str): Base URL for URL joins.
-            html2text_options (Optional[Dict[str, Any]]): HTML2Text options.
-            options (Optional[Dict[str, Any]]): Additional options for markdown generation.
-            content_filter (Optional[RelevantContentFilter]): Content filter for generating fit markdown.
-            citations (bool): Whether to generate citations.
-
-        Returns:
-            MarkdownGenerationResult: Result containing raw markdown, fit markdown, fit HTML, and references markdown.
-        """
-        try:
-            # Initialize HTML2Text with default options for better conversion
-            h = CustomHTML2Text(baseurl=base_url)
-            default_options = {
-                "body_width": 0,  # Disable text wrapping
-                "ignore_emphasis": False,
-                "ignore_links": False,
-                "ignore_images": False,
-                "protect_links": False,
-                "single_line_break": True,
-                "mark_code": True,
-                "escape_snob": False,
-            }
-
-            # Update with custom options if provided
-            if html2text_options:
-                default_options.update(html2text_options)
-            elif options:
-                default_options.update(options)
-            elif self.options:
-                default_options.update(self.options)
-
-            h.update_params(**default_options)
-
-            # Ensure we have valid input
-            if not input_html:
-                input_html = ""
-            elif not isinstance(input_html, str):
-                input_html = str(input_html)
-
-            # Generate raw markdown
-            try:
-                raw_markdown = h.handle(input_html)
-            except Exception as e:
-                raw_markdown = f"Error converting HTML to markdown: {str(e)}"
-
-            raw_markdown = raw_markdown.replace("    ```", "```")
-
-            # Convert links to citations
-            markdown_with_citations: str = raw_markdown
-            references_markdown: str = ""
-            if citations:
-                try:
-                    (
-                        markdown_with_citations,
-                        references_markdown,
-                    ) = self.convert_links_to_citations(raw_markdown, base_url)
-                except Exception as e:
-                    markdown_with_citations = raw_markdown
-                    references_markdown = f"Error generating citations: {str(e)}"
-
-            # Generate fit markdown if content filter is provided
-            fit_markdown: Optional[str] = ""
-            filtered_html: Optional[str] = ""
-            if content_filter or self.content_filter:
-                try:
-                    content_filter = content_filter or self.content_filter
-                    filtered_html = content_filter.filter_content(input_html)
-                    filtered_html = "\n".join(
-                        "<div>{}</div>".format(s) for s in filtered_html
-                    )
-                    fit_markdown = h.handle(filtered_html)
-                except Exception as e:
-                    fit_markdown = f"Error generating fit markdown: {str(e)}"
-                    filtered_html = ""
-
-            return MarkdownGenerationResult(
-                raw_markdown=raw_markdown or "",
-                markdown_with_citations=markdown_with_citations or "",
-                references_markdown=references_markdown or "",
-                fit_markdown=fit_markdown or "",
-                fit_html=filtered_html or "",
-            )
-        except Exception as e:
-            # If anything fails, return empty strings with error message
-            error_msg = f"Error in markdown generation: {str(e)}"
-            return MarkdownGenerationResult(
-                raw_markdown=error_msg,
-                markdown_with_citations=error_msg,
-                references_markdown="",
-                fit_markdown="",
-                fit_html="",
-            )

crawl4ai/migrations.py

@@ -1,194 +0,0 @@
-import os
-import asyncio
-from pathlib import Path
-import aiosqlite
-from typing import Optional
-import xxhash
-import aiofiles
-import shutil
-from datetime import datetime
-from .async_logger import AsyncLogger, LogLevel
-
-# Initialize logger
-logger = AsyncLogger(log_level=LogLevel.DEBUG, verbose=True)
-
-# logging.basicConfig(level=logging.INFO)
-# logger = logging.getLogger(__name__)
-
-
-class DatabaseMigration:
-    def __init__(self, db_path: str):
-        self.db_path = db_path
-        self.content_paths = self._ensure_content_dirs(os.path.dirname(db_path))
-
-    def _ensure_content_dirs(self, base_path: str) -> dict:
-        dirs = {
-            "html": "html_content",
-            "cleaned": "cleaned_html",
-            "markdown": "markdown_content",
-            "extracted": "extracted_content",
-            "screenshots": "screenshots",
-        }
-        content_paths = {}
-        for key, dirname in dirs.items():
-            path = os.path.join(base_path, dirname)
-            os.makedirs(path, exist_ok=True)
-            content_paths[key] = path
-        return content_paths
-
-    def _generate_content_hash(self, content: str) -> str:
-        x = xxhash.xxh64()
-        x.update(content.encode())
-        content_hash = x.hexdigest()
-        return content_hash
-        # return hashlib.sha256(content.encode()).hexdigest()
-
-    async def _store_content(self, content: str, content_type: str) -> str:
-        if not content:
-            return ""
-
-        content_hash = self._generate_content_hash(content)
-        file_path = os.path.join(self.content_paths[content_type], content_hash)
-
-        if not os.path.exists(file_path):
-            async with aiofiles.open(file_path, "w", encoding="utf-8") as f:
-                await f.write(content)
-
-        return content_hash
-
-    async def migrate_database(self):
-        """Migrate existing database to file-based storage"""
-        # logger.info("Starting database migration...")
-        logger.info("Starting database migration...", tag="INIT")
-
-        try:
-            async with aiosqlite.connect(self.db_path) as db:
-                # Get all rows
-                async with db.execute(
-                    """SELECT url, html, cleaned_html, markdown, 
-                       extracted_content, screenshot FROM crawled_data"""
-                ) as cursor:
-                    rows = await cursor.fetchall()
-
-                migrated_count = 0
-                for row in rows:
-                    (
-                        url,
-                        html,
-                        cleaned_html,
-                        markdown,
-                        extracted_content,
-                        screenshot,
-                    ) = row
-
-                    # Store content in files and get hashes
-                    html_hash = await self._store_content(html, "html")
-                    cleaned_hash = await self._store_content(cleaned_html, "cleaned")
-                    markdown_hash = await self._store_content(markdown, "markdown")
-                    extracted_hash = await self._store_content(
-                        extracted_content, "extracted"
-                    )
-                    screenshot_hash = await self._store_content(
-                        screenshot, "screenshots"
-                    )
-
-                    # Update database with hashes
-                    await db.execute(
-                        """
-                        UPDATE crawled_data 
-                        SET html = ?, 
-                            cleaned_html = ?,
-                            markdown = ?,
-                            extracted_content = ?,
-                            screenshot = ?
-                        WHERE url = ?
-                    """,
-                        (
-                            html_hash,
-                            cleaned_hash,
-                            markdown_hash,
-                            extracted_hash,
-                            screenshot_hash,
-                            url,
-                        ),
-                    )
-
-                    migrated_count += 1
-                    if migrated_count % 100 == 0:
-                        logger.info(f"Migrated {migrated_count} records...", tag="INIT")
-
-                await db.commit()
-                logger.success(
-                    f"Migration completed. {migrated_count} records processed.",
-                    tag="COMPLETE",
-                )
-
-        except Exception as e:
-            # logger.error(f"Migration failed: {e}")
-            logger.error(
-                message="Migration failed: {error}",
-                tag="ERROR",
-                params={"error": str(e)},
-            )
-            raise e
-
-
-async def backup_database(db_path: str) -> str:
-    """Create backup of existing database"""
-    if not os.path.exists(db_path):
-        logger.info("No existing database found. Skipping backup.", tag="INIT")
-        return None
-
-    # Create backup with timestamp
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    backup_path = f"{db_path}.backup_{timestamp}"
-
-    try:
-        # Wait for any potential write operations to finish
-        await asyncio.sleep(1)
-
-        # Create backup
-        shutil.copy2(db_path, backup_path)
-        logger.info(f"Database backup created at: {backup_path}", tag="COMPLETE")
-        return backup_path
-    except Exception as e:
-        # logger.error(f"Backup failed: {e}")
-        logger.error(
-            message="Migration failed: {error}", tag="ERROR", params={"error": str(e)}
-        )
-        raise e
-
-
-async def run_migration(db_path: Optional[str] = None):
-    """Run database migration"""
-    if db_path is None:
-        db_path = os.path.join(Path.home(), ".crawl4ai", "crawl4ai.db")
-
-    if not os.path.exists(db_path):
-        logger.info("No existing database found. Skipping migration.", tag="INIT")
-        return
-
-    # Create backup first
-    backup_path = await backup_database(db_path)
-    if not backup_path:
-        return
-
-    migration = DatabaseMigration(db_path)
-    await migration.migrate_database()
-
-
-def main():
-    """CLI entry point for migration"""
-    import argparse
-
-    parser = argparse.ArgumentParser(
-        description="Migrate Crawl4AI database to file-based storage"
-    )
-    parser.add_argument("--db-path", help="Custom database path")
-    args = parser.parse_args()
-
-    asyncio.run(run_migration(args.db_path))
-
-
-if __name__ == "__main__":
-    main()

crawl4ai/model_loader.py

@@ -2,125 +2,109 @@ from functools import lru_cache
 from pathlib import Path
 import subprocess, os
 import shutil
+import tarfile
 from .model_loader import *
 import argparse
+import urllib.request
 from crawl4ai.config import MODEL_REPO_BRANCH
-
 __location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
 
-
 @lru_cache()
 def get_available_memory(device):
     import torch
-
-    if device.type == "cuda":
+    if device.type == 'cuda':
         return torch.cuda.get_device_properties(device).total_memory
-    elif device.type == "mps":
-        return 48 * 1024**3  # Assuming 8GB for MPS, as a conservative estimate
+    elif device.type == 'mps':      
+        return 48 * 1024 ** 3  # Assuming 8GB for MPS, as a conservative estimate
     else:
         return 0
 
-
 @lru_cache()
 def calculate_batch_size(device):
     available_memory = get_available_memory(device)
-
-    if device.type == "cpu":
+    
+    if device.type == 'cpu':
         return 16
-    elif device.type in ["cuda", "mps"]:
+    elif device.type in ['cuda', 'mps']:
         # Adjust these thresholds based on your model size and available memory
-        if available_memory >= 31 * 1024**3:  # > 32GB
+        if available_memory >= 31 * 1024 ** 3:  # > 32GB
             return 256
-        elif available_memory >= 15 * 1024**3:  # > 16GB to 32GB
+        elif available_memory >= 15 * 1024 ** 3:  # > 16GB to 32GB
             return 128
-        elif available_memory >= 8 * 1024**3:  # 8GB to 16GB
+        elif available_memory >= 8 * 1024 ** 3:  # 8GB to 16GB
             return 64
         else:
             return 32
     else:
-        return 16  # Default batch size
-
-
+        return 16  # Default batch size   
+    
 @lru_cache()
 def get_device():
     import torch
-
     if torch.cuda.is_available():
-        device = torch.device("cuda")
+        device = torch.device('cuda')
     elif torch.backends.mps.is_available():
-        device = torch.device("mps")
+        device = torch.device('mps')
     else:
-        device = torch.device("cpu")
-    return device
-
-
+        device = torch.device('cpu')
+    return device   
+    
 def set_model_device(model):
     device = get_device()
-    model.to(device)
+    model.to(device)    
     return model, device
 
-
 @lru_cache()
 def get_home_folder():
-    home_folder = os.path.join(
-        os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
-    )
+    home_folder = os.path.join(Path.home(), ".crawl4ai")
     os.makedirs(home_folder, exist_ok=True)
     os.makedirs(f"{home_folder}/cache", exist_ok=True)
     os.makedirs(f"{home_folder}/models", exist_ok=True)
-    return home_folder
-
+    return home_folder 
 
 @lru_cache()
 def load_bert_base_uncased():
-    from transformers import BertTokenizer, BertModel
-
-    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", resume_download=None)
-    model = BertModel.from_pretrained("bert-base-uncased", resume_download=None)
+    from transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', resume_download=None)
+    model = BertModel.from_pretrained('bert-base-uncased', resume_download=None)
     model.eval()
     model, device = set_model_device(model)
     return tokenizer, model
 
-
 @lru_cache()
 def load_HF_embedding_model(model_name="BAAI/bge-small-en-v1.5") -> tuple:
     """Load the Hugging Face model for embedding.
-
+    
     Args:
         model_name (str, optional): The model name to load. Defaults to "BAAI/bge-small-en-v1.5".
-
+        
     Returns:
         tuple: The tokenizer and model.
     """
-    from transformers import AutoTokenizer, AutoModel
-
+    from transformers import BertTokenizer, BertModel, AutoTokenizer, AutoModel
     tokenizer = AutoTokenizer.from_pretrained(model_name, resume_download=None)
     model = AutoModel.from_pretrained(model_name, resume_download=None)
     model.eval()
     model, device = set_model_device(model)
     return tokenizer, model
 
-
 @lru_cache()
 def load_text_classifier():
     from transformers import AutoTokenizer, AutoModelForSequenceClassification
     from transformers import pipeline
+    import torch
 
-    tokenizer = AutoTokenizer.from_pretrained(
-        "dstefa/roberta-base_topic_classification_nyt_news"
-    )
-    model = AutoModelForSequenceClassification.from_pretrained(
-        "dstefa/roberta-base_topic_classification_nyt_news"
-    )
+    tokenizer = AutoTokenizer.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
+    model = AutoModelForSequenceClassification.from_pretrained("dstefa/roberta-base_topic_classification_nyt_news")
     model.eval()
     model, device = set_model_device(model)
     pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
     return pipe
 
-
 @lru_cache()
 def load_text_multilabel_classifier():
     from transformers import AutoModelForSequenceClassification, AutoTokenizer
+    import numpy as np
     from scipy.special import expit
     import torch
 
@@ -132,27 +116,18 @@ def load_text_multilabel_classifier():
     # else:
     #     device = torch.device("cpu")
     #     # return load_spacy_model(), torch.device("cpu")
+    
 
     MODEL = "cardiffnlp/tweet-topic-21-multi"
     tokenizer = AutoTokenizer.from_pretrained(MODEL, resume_download=None)
-    model = AutoModelForSequenceClassification.from_pretrained(
-        MODEL, resume_download=None
-    )
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL, resume_download=None)
     model.eval()
     model, device = set_model_device(model)
     class_mapping = model.config.id2label
 
     def _classifier(texts, threshold=0.5, max_length=64):
-        tokens = tokenizer(
-            texts,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=max_length,
-        )
-        tokens = {
-            key: val.to(device) for key, val in tokens.items()
-        }  # Move tokens to the selected device
+        tokens = tokenizer(texts, return_tensors='pt', padding=True, truncation=True, max_length=max_length)
+        tokens = {key: val.to(device) for key, val in tokens.items()}  # Move tokens to the selected device
 
         with torch.no_grad():
             output = model(**tokens)
@@ -163,41 +138,35 @@ def load_text_multilabel_classifier():
 
         batch_labels = []
         for prediction in predictions:
-            labels = [
-                class_mapping[i] for i, value in enumerate(prediction) if value == 1
-            ]
+            labels = [class_mapping[i] for i, value in enumerate(prediction) if value == 1]
             batch_labels.append(labels)
 
         return batch_labels
 
     return _classifier, device
 
-
 @lru_cache()
 def load_nltk_punkt():
     import nltk
-
     try:
-        nltk.data.find("tokenizers/punkt")
+        nltk.data.find('tokenizers/punkt')
     except LookupError:
-        nltk.download("punkt")
-    return nltk.data.find("tokenizers/punkt")
-
+        nltk.download('punkt')
+    return nltk.data.find('tokenizers/punkt')
 
 @lru_cache()
 def load_spacy_model():
     import spacy
-
     name = "models/reuters"
     home_folder = get_home_folder()
     model_folder = Path(home_folder) / name
-
+    
     # Check if the model directory already exists
     if not (model_folder.exists() and any(model_folder.iterdir())):
         repo_url = "https://github.com/unclecode/crawl4ai.git"
-        branch = MODEL_REPO_BRANCH
+        branch = MODEL_REPO_BRANCH 
         repo_folder = Path(home_folder) / "crawl4ai"
-
+        
         print("[LOG] ⏬ Downloading Spacy model for the first time...")
 
         # Remove existing repo folder if it exists
@@ -207,9 +176,7 @@ def load_spacy_model():
                 if model_folder.exists():
                     shutil.rmtree(model_folder)
             except PermissionError:
-                print(
-                    "[WARNING] Unable to remove existing folders. Please manually delete the following folders and try again:"
-                )
+                print("[WARNING] Unable to remove existing folders. Please manually delete the following folders and try again:")
                 print(f"- {repo_folder}")
                 print(f"- {model_folder}")
                 return None
@@ -220,7 +187,7 @@ def load_spacy_model():
                 ["git", "clone", "-b", branch, repo_url, str(repo_folder)],
                 stdout=subprocess.DEVNULL,
                 stderr=subprocess.DEVNULL,
-                check=True,
+                check=True
             )
 
             # Create the models directory if it doesn't exist
@@ -248,7 +215,6 @@ def load_spacy_model():
         print(f"Error loading spacy model: {e}")
         return None
 
-
 def download_all_models(remove_existing=False):
     """Download all models required for Crawl4AI."""
     if remove_existing:
@@ -277,20 +243,14 @@ def download_all_models(remove_existing=False):
     load_nltk_punkt()
     print("[LOG] ✅ All models downloaded successfully.")
 
-
 def main():
     print("[LOG] Welcome to the Crawl4AI Model Downloader!")
     print("[LOG] This script will download all the models required for Crawl4AI.")
     parser = argparse.ArgumentParser(description="Crawl4AI Model Downloader")
-    parser.add_argument(
-        "--remove-existing",
-        action="store_true",
-        help="Remove existing models before downloading",
-    )
+    parser.add_argument('--remove-existing', action='store_true', help="Remove existing models before downloading")
     args = parser.parse_args()
-
+    
     download_all_models(remove_existing=args.remove_existing)
 
-
 if __name__ == "__main__":
     main()

crawl4ai/models.py

@@ -1,387 +1,24 @@
-from pydantic import BaseModel, HttpUrl, PrivateAttr, Field
-from typing import List, Dict, Optional, Callable, Awaitable, Union, Any
-from typing import AsyncGenerator
-from typing import Generic, TypeVar
-from enum import Enum
-from dataclasses import dataclass
-from .ssl_certificate import SSLCertificate
-from datetime import datetime
-from datetime import timedelta
-
-
-###############################
-# Dispatcher Models
-###############################
-@dataclass
-class DomainState:
-    last_request_time: float = 0
-    current_delay: float = 0
-    fail_count: int = 0
-
-
-@dataclass
-class CrawlerTaskResult:
-    task_id: str
-    url: str
-    result: "CrawlResult"
-    memory_usage: float
-    peak_memory: float
-    start_time: Union[datetime, float]
-    end_time: Union[datetime, float]
-    error_message: str = ""
-    retry_count: int = 0
-    wait_time: float = 0.0
-    
-    @property
-    def success(self) -> bool:
-        return self.result.success
-
-class CrawlStatus(Enum):
-    QUEUED = "QUEUED"
-    IN_PROGRESS = "IN_PROGRESS"
-    COMPLETED = "COMPLETED"
-    FAILED = "FAILED"
-
-@dataclass
-class CrawlStats:
-    task_id: str
-    url: str
-    status: CrawlStatus
-    start_time: Optional[Union[datetime, float]] = None
-    end_time: Optional[Union[datetime, float]] = None
-    memory_usage: float = 0.0
-    peak_memory: float = 0.0
-    error_message: str = ""
-    wait_time: float = 0.0
-    retry_count: int = 0
-    counted_requeue: bool = False
-
-    @property
-    def duration(self) -> str:
-        if not self.start_time:
-            return "0:00"
-            
-        # Convert start_time to datetime if it's a float
-        start = self.start_time
-        if isinstance(start, float):
-            start = datetime.fromtimestamp(start)
-            
-        # Get end time or use current time
-        end = self.end_time or datetime.now()
-        # Convert end_time to datetime if it's a float
-        if isinstance(end, float):
-            end = datetime.fromtimestamp(end)
-            
-        duration = end - start
-        return str(timedelta(seconds=int(duration.total_seconds())))
-
-class DisplayMode(Enum):
-    DETAILED = "DETAILED"
-    AGGREGATED = "AGGREGATED"
-
-
-###############################
-# Crawler Models
-###############################
-@dataclass
-class TokenUsage:
-    completion_tokens: int = 0
-    prompt_tokens: int = 0
-    total_tokens: int = 0
-    completion_tokens_details: Optional[dict] = None
-    prompt_tokens_details: Optional[dict] = None
+from pydantic import BaseModel, HttpUrl
+from typing import List, Dict, Optional
 
 class UrlModel(BaseModel):
     url: HttpUrl
     forced: bool = False
 
-
-
-@dataclass
-class TraversalStats:
-    """Statistics for the traversal process"""
-
-    start_time: datetime = datetime.now()
-    urls_processed: int = 0
-    urls_failed: int = 0
-    urls_skipped: int = 0
-    total_depth_reached: int = 0
-    current_depth: int = 0
-
-class DispatchResult(BaseModel):
-    task_id: str
-    memory_usage: float
-    peak_memory: float
-    start_time: Union[datetime, float]
-    end_time: Union[datetime, float]
-    error_message: str = ""
-
-class MarkdownGenerationResult(BaseModel):
-    raw_markdown: str
-    markdown_with_citations: str
-    references_markdown: str
-    fit_markdown: Optional[str] = None
-    fit_html: Optional[str] = None
-
-    def __str__(self):
-        return self.raw_markdown
-    
 class CrawlResult(BaseModel):
     url: str
     html: str
-    fit_html: Optional[str] = None
     success: bool
     cleaned_html: Optional[str] = None
     media: Dict[str, List[Dict]] = {}
     links: Dict[str, List[Dict]] = {}
-    downloaded_files: Optional[List[str]] = None
-    js_execution_result: Optional[Dict[str, Any]] = None
     screenshot: Optional[str] = None
-    pdf: Optional[bytes] = None
-    mhtml: Optional[str] = None
-    _markdown: Optional[MarkdownGenerationResult] = PrivateAttr(default=None)
+    markdown: Optional[str] = None
+    fit_markdown: Optional[str] = None
+    fit_html: Optional[str] = None
     extracted_content: Optional[str] = None
     metadata: Optional[dict] = None
     error_message: Optional[str] = None
     session_id: Optional[str] = None
     response_headers: Optional[dict] = None
-    status_code: Optional[int] = None
-    ssl_certificate: Optional[SSLCertificate] = None
-    dispatch_result: Optional[DispatchResult] = None
-    redirected_url: Optional[str] = None
-    network_requests: Optional[List[Dict[str, Any]]] = None
-    console_messages: Optional[List[Dict[str, Any]]] = None
-    tables: List[Dict] = Field(default_factory=list)  # NEW – [{headers,rows,caption,summary}]
-
-    class Config:
-        arbitrary_types_allowed = True
-
-# NOTE: The StringCompatibleMarkdown class, custom __init__ method, property getters/setters,
-# and model_dump override all exist to support a smooth transition from markdown as a string
-# to markdown as a MarkdownGenerationResult object, while maintaining backward compatibility.
-# 
-# This allows code that expects markdown to be a string to continue working, while also
-# providing access to the full MarkdownGenerationResult object's properties.
-# 
-# The markdown_v2 property is deprecated and raises an error directing users to use markdown.
-# 
-# When backward compatibility is no longer needed in future versions, this entire mechanism
-# can be simplified to a standard field with no custom accessors or serialization logic.
-    
-    def __init__(self, **data):
-        markdown_result = data.pop('markdown', None)
-        super().__init__(**data)
-        if markdown_result is not None:
-            self._markdown = (
-                MarkdownGenerationResult(**markdown_result)
-                if isinstance(markdown_result, dict)
-                else markdown_result
-            )
-    
-    @property
-    def markdown(self):
-        """
-        Property that returns a StringCompatibleMarkdown object that behaves like
-        a string but also provides access to MarkdownGenerationResult attributes.
-        
-        This approach allows backward compatibility with code that expects 'markdown'
-        to be a string, while providing access to the full MarkdownGenerationResult.
-        """
-        if self._markdown is None:
-            return None
-        return StringCompatibleMarkdown(self._markdown)
-    
-    @markdown.setter
-    def markdown(self, value):
-        """
-        Setter for the markdown property.
-        """
-        self._markdown = value
-    
-    @property
-    def markdown_v2(self):
-        """
-        Deprecated property that raises an AttributeError when accessed.
-
-        This property exists to inform users that 'markdown_v2' has been
-        deprecated and they should use 'markdown' instead.
-        """
-        raise AttributeError(
-            "The 'markdown_v2' attribute is deprecated and has been removed. "
-            """Please use 'markdown' instead, which now returns a MarkdownGenerationResult, with
-            following properties:
-            - raw_markdown: The raw markdown string
-            - markdown_with_citations: The markdown string with citations
-            - references_markdown: The markdown string with references
-            - fit_markdown: The markdown string with fit text
-            """
-        )
-    
-    @property
-    def fit_markdown(self):
-        """
-        Deprecated property that raises an AttributeError when accessed.
-        """
-        raise AttributeError(
-            "The 'fit_markdown' attribute is deprecated and has been removed. "
-            "Please use 'markdown.fit_markdown' instead."
-        )
-    
-    @property
-    def fit_html(self):
-        """
-        Deprecated property that raises an AttributeError when accessed.
-        """
-        raise AttributeError(
-            "The 'fit_html' attribute is deprecated and has been removed. "
-            "Please use 'markdown.fit_html' instead."
-        )
-
-    def model_dump(self, *args, **kwargs):
-        """
-        Override model_dump to include the _markdown private attribute in serialization.
-        
-        This override is necessary because:
-        1. PrivateAttr fields are excluded from serialization by default
-        2. We need to maintain backward compatibility by including the 'markdown' field
-           in the serialized output
-        3. We're transitioning from 'markdown_v2' to enhancing 'markdown' to hold
-           the same type of data
-        
-        Future developers: This method ensures that the markdown content is properly
-        serialized despite being stored in a private attribute. If the serialization
-        requirements change, this is where you would update the logic.
-        """
-        result = super().model_dump(*args, **kwargs)
-        
-        # Remove any property descriptors that might have been included
-        # These deprecated properties should not be in the serialized output
-        for key in ['fit_html', 'fit_markdown', 'markdown_v2']:
-            if key in result and isinstance(result[key], property):
-                # del result[key]
-                # Nasrin: I decided to convert it to string instead of removing it.
-                result[key] = str(result[key])
-        
-        # Add the markdown field properly
-        if self._markdown is not None:
-            result["markdown"] = self._markdown.model_dump() 
-        return result
-
-class StringCompatibleMarkdown(str):
-    """A string subclass that also provides access to MarkdownGenerationResult attributes"""
-    def __new__(cls, markdown_result):
-        return super().__new__(cls, markdown_result.raw_markdown)
-    
-    def __init__(self, markdown_result):
-        self._markdown_result = markdown_result
-    
-    def __getattr__(self, name):
-        return getattr(self._markdown_result, name)
-
-CrawlResultT = TypeVar('CrawlResultT', bound=CrawlResult)
-
-class CrawlResultContainer(Generic[CrawlResultT]):
-    def __init__(self, results: Union[CrawlResultT, List[CrawlResultT]]):
-        # Normalize to a list
-        if isinstance(results, list):
-            self._results = results
-        else:
-            self._results = [results]
-
-    def __iter__(self):
-        return iter(self._results)
-
-    def __getitem__(self, index):
-        return self._results[index]
-
-    def __len__(self):
-        return len(self._results)
-
-    def __getattr__(self, attr):
-        # Delegate attribute access to the first element.
-        if self._results:
-            return getattr(self._results[0], attr)
-        raise AttributeError(f"{self.__class__.__name__} object has no attribute '{attr}'")
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}({self._results!r})"
-
-RunManyReturn = Union[
-    CrawlResultContainer[CrawlResultT],
-    AsyncGenerator[CrawlResultT, None]
-]
-
-
-# END of backward compatibility code for markdown/markdown_v2.
-# When removing this code in the future, make sure to:
-# 1. Replace the private attribute and property with a standard field
-# 2. Update any serialization logic that might depend on the current behavior
-
-class AsyncCrawlResponse(BaseModel):
-    html: str
-    response_headers: Dict[str, str]
-    js_execution_result: Optional[Dict[str, Any]] = None
-    status_code: int
-    screenshot: Optional[str] = None
-    pdf_data: Optional[bytes] = None
-    mhtml_data: Optional[str] = None
-    get_delayed_content: Optional[Callable[[Optional[float]], Awaitable[str]]] = None
-    downloaded_files: Optional[List[str]] = None
-    ssl_certificate: Optional[SSLCertificate] = None
-    redirected_url: Optional[str] = None
-    network_requests: Optional[List[Dict[str, Any]]] = None
-    console_messages: Optional[List[Dict[str, Any]]] = None
-
-    class Config:
-        arbitrary_types_allowed = True
-
-###############################
-# Scraping Models
-###############################
-class MediaItem(BaseModel):
-    src: Optional[str] = ""
-    data: Optional[str] = ""
-    alt: Optional[str] = ""
-    desc: Optional[str] = ""
-    score: Optional[int] = 0
-    type: str = "image"
-    group_id: Optional[int] = 0
-    format: Optional[str] = None
-    width: Optional[int] = None
-
-
-class Link(BaseModel):
-    href: Optional[str] = ""
-    text: Optional[str] = ""
-    title: Optional[str] = ""
-    base_domain: Optional[str] = ""
-    head_data: Optional[Dict[str, Any]] = None  # Head metadata extracted from link target
-    head_extraction_status: Optional[str] = None  # "success", "failed", "skipped"
-    head_extraction_error: Optional[str] = None  # Error message if extraction failed
-    intrinsic_score: Optional[float] = None  # Quality score based on URL structure, text, and context
-    contextual_score: Optional[float] = None  # BM25 relevance score based on query and head content
-    total_score: Optional[float] = None  # Combined score from intrinsic and contextual scores
-
-
-class Media(BaseModel):
-    images: List[MediaItem] = []
-    videos: List[
-        MediaItem
-    ] = []  # Using MediaItem model for now, can be extended with Video model if needed
-    audios: List[
-        MediaItem
-    ] = []  # Using MediaItem model for now, can be extended with Audio model if needed
-    tables: List[Dict] = []  # Table data extracted from HTML tables
-
-
-class Links(BaseModel):
-    internal: List[Link] = []
-    external: List[Link] = []
-
-
-class ScrapingResult(BaseModel):
-    cleaned_html: str
-    success: bool
-    media: Media = Media()
-    links: Links = Links()
-    metadata: Dict[str, Any] = {}
+    status_code: Optional[int] = None

crawl4ai/processors/pdf/__init__.py

@@ -1,195 +0,0 @@
-from pathlib import Path
-import asyncio
-from dataclasses import asdict
-from crawl4ai.async_logger import AsyncLogger
-from crawl4ai.async_crawler_strategy import AsyncCrawlerStrategy
-from crawl4ai.models import AsyncCrawlResponse, ScrapingResult 
-from crawl4ai.content_scraping_strategy import ContentScrapingStrategy
-from .processor import NaivePDFProcessorStrategy  # Assuming your current PDF code is in pdf_processor.py
-
-class PDFCrawlerStrategy(AsyncCrawlerStrategy):
-    def __init__(self, logger: AsyncLogger = None):
-        self.logger = logger
-        
-    async def crawl(self, url: str, **kwargs) -> AsyncCrawlResponse:
-        # Just pass through with empty HTML - scraper will handle actual processing
-        return AsyncCrawlResponse(
-            html="Scraper will handle the real work",  # Scraper will handle the real work
-            response_headers={"Content-Type": "application/pdf"},
-            status_code=200
-        )
-    
-    async def close(self):
-        pass        
-        
-    async def __aenter__(self):        
-        return self
-    
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        await self.close()
-
-class PDFContentScrapingStrategy(ContentScrapingStrategy):
-    """
-    A content scraping strategy for PDF files.
-    
-    Attributes:
-        save_images_locally (bool): Whether to save images locally.
-        extract_images (bool): Whether to extract images from PDF.
-        image_save_dir (str): Directory to save extracted images.
-        logger (AsyncLogger): Logger instance for recording events and errors.
-        
-    Methods:
-        scrap(url: str, html: str, **params) -> ScrapingResult:
-            Scrap content from a PDF file.
-        ascrap(url: str, html: str, **kwargs) -> ScrapingResult:
-            Asynchronous version of scrap.
-            
-    Usage:
-        strategy = PDFContentScrapingStrategy(
-            save_images_locally=False,
-            extract_images=False,
-            image_save_dir=None,
-            logger=logger
-        )
-        
-    """
-    def __init__(self, 
-                 save_images_locally : bool = False,
-                 extract_images : bool = False,
-                 image_save_dir : str = None,
-                 batch_size: int = 4,
-                 logger: AsyncLogger = None):
-        self.logger = logger
-        self.pdf_processor = NaivePDFProcessorStrategy(
-            save_images_locally=save_images_locally,
-            extract_images=extract_images,
-            image_save_dir=image_save_dir,
-            batch_size=batch_size
-        )
-        self._temp_files = []  # Track temp files for cleanup
-
-    def scrap(self, url: str, html: str, **params) -> ScrapingResult:
-        """
-        Scrap content from a PDF file.
-        
-        Args:
-            url (str): The URL of the PDF file.
-            html (str): The HTML content of the page.
-            **params: Additional parameters.
-        
-        Returns:
-            ScrapingResult: The scraped content.
-        """
-        # Download if URL or use local path
-        pdf_path = self._get_pdf_path(url)
-        try:
-            # Process PDF
-            # result = self.pdf_processor.process(Path(pdf_path))
-            result = self.pdf_processor.process_batch(Path(pdf_path))
-            
-            # Combine page HTML
-            cleaned_html = f"""
-        <html>
-            <head><meta name="pdf-pages" content="{len(result.pages)}"></head>
-            <body>
-                {''.join(f'<div class="pdf-page" data-page="{i+1}">{page.html}</div>'
-                         for i, page in enumerate(result.pages))}
-            </body>
-        </html>
-        """
-            
-            # Accumulate media and links with page numbers
-            media = {"images": []}
-            links = {"urls": []}
-            
-            for page in result.pages:
-                # Add page number to each image
-                for img in page.images:
-                    img["page"] = page.page_number
-                    media["images"].append(img)
-                
-                # Add page number to each link
-                for link in page.links:
-                    links["urls"].append({
-                        "url": link,
-                        "page": page.page_number
-                    })
-
-            return ScrapingResult(
-                cleaned_html=cleaned_html,
-                success=True,
-                media=media,
-                links=links,
-                metadata=asdict(result.metadata)
-            )
-        finally:
-            # Cleanup temp file if downloaded
-            if url.startswith(("http://", "https://")):
-                try:
-                    Path(pdf_path).unlink(missing_ok=True)
-                    if pdf_path in self._temp_files:
-                        self._temp_files.remove(pdf_path)
-                except Exception as e:
-                    if self.logger:
-                        self.logger.warning(f"Failed to cleanup temp file {pdf_path}: {e}")
-
-    async def ascrap(self, url: str, html: str, **kwargs) -> ScrapingResult:
-        # For simple cases, you can use the sync version
-        return await asyncio.to_thread(self.scrap, url, html, **kwargs)
-        
-
-    def _get_pdf_path(self, url: str) -> str:
-        if url.startswith(("http://", "https://")):
-            import tempfile
-            import requests
-            
-            # Create temp file with .pdf extension
-            temp_file = tempfile.NamedTemporaryFile(suffix='.pdf', delete=False)
-            self._temp_files.append(temp_file.name)
-            
-            try:
-                if self.logger:
-                    self.logger.info(f"Downloading PDF from {url}...")
-                
-                # Download PDF with streaming and timeout
-                # Connection timeout: 10s, Read timeout: 300s (5 minutes for large PDFs)
-                response = requests.get(url, stream=True, timeout=(20, 60 * 10))
-                response.raise_for_status()
-                
-                # Get file size if available
-                total_size = int(response.headers.get('content-length', 0))
-                downloaded = 0
-                
-                # Write to temp file
-                with open(temp_file.name, 'wb') as f:
-                    for chunk in response.iter_content(chunk_size=8192):
-                        f.write(chunk)
-                        downloaded += len(chunk)
-                        if self.logger and total_size > 0:
-                            progress = (downloaded / total_size) * 100
-                            if progress % 10 < 0.1:  # Log every 10%
-                                self.logger.debug(f"PDF download progress: {progress:.0f}%")
-                
-                if self.logger:
-                    self.logger.info(f"PDF downloaded successfully: {temp_file.name}")
-                        
-                return temp_file.name
-                
-            except requests.exceptions.Timeout as e:
-                # Clean up temp file if download fails
-                Path(temp_file.name).unlink(missing_ok=True)
-                self._temp_files.remove(temp_file.name)
-                raise RuntimeError(f"Timeout downloading PDF from {url}: {str(e)}")
-            except Exception as e:
-                # Clean up temp file if download fails
-                Path(temp_file.name).unlink(missing_ok=True)
-                self._temp_files.remove(temp_file.name)
-                raise RuntimeError(f"Failed to download PDF from {url}: {str(e)}")
-                
-        elif url.startswith("file://"):
-            return url[7:]  # Strip file:// prefix
-            
-        return url  # Assume local path
-    
-
-__all__ = ["PDFCrawlerStrategy", "PDFContentScrapingStrategy"]

crawl4ai/processors/pdf/processor.py

@@ -1,487 +0,0 @@
-import logging
-import re
-from abc import ABC, abstractmethod
-from datetime import datetime
-from pathlib import Path
-from time import time
-from dataclasses import dataclass, asdict, field
-from typing import Dict, List, Optional, Any, Union
-import base64
-import tempfile
-from .utils import *
-from .utils import (
-    apply_png_predictor,
-    clean_pdf_text,
-    clean_pdf_text_to_html,
-)
-
-# Remove direct PyPDF2 imports from the top
-# import PyPDF2
-# from PyPDF2 import PdfReader
-
-logger = logging.getLogger(__name__)
-
-@dataclass
-class PDFMetadata:
-    title: Optional[str] = None
-    author: Optional[str] = None
-    producer: Optional[str] = None
-    created: Optional[datetime] = None
-    modified: Optional[datetime] = None
-    pages: int = 0
-    encrypted: bool = False
-    file_size: Optional[int] = None
-
-@dataclass
-class PDFPage:
-    page_number: int
-    raw_text: str = ""
-    markdown: str = ""
-    html: str = ""
-    images: List[Dict] = field(default_factory=list)
-    links: List[str] = field(default_factory=list)
-    layout: List[Dict] = field(default_factory=list)
-
-@dataclass
-class PDFProcessResult:
-    metadata: PDFMetadata
-    pages: List[PDFPage]
-    processing_time: float = 0.0
-    version: str = "1.0"
-
-class PDFProcessorStrategy(ABC):
-    @abstractmethod
-    def process(self, pdf_path: Path) -> PDFProcessResult:
-        pass
-
-class NaivePDFProcessorStrategy(PDFProcessorStrategy):
-    def __init__(self, image_dpi: int = 144, image_quality: int = 85, extract_images: bool = True, 
-                 save_images_locally: bool = False, image_save_dir: Optional[Path] = None, batch_size: int = 4):
-        # Import check at initialization time
-        try:
-            import PyPDF2
-        except ImportError:
-            raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-            
-        self.image_dpi = image_dpi
-        self.image_quality = image_quality
-        self.current_page_number = 0
-        self.extract_images = extract_images
-        self.save_images_locally = save_images_locally
-        self.image_save_dir = image_save_dir
-        self.batch_size = batch_size
-        self._temp_dir = None
-
-    def process(self, pdf_path: Path) -> PDFProcessResult:
-        # Import inside method to allow dependency to be optional
-        try:
-            from PyPDF2 import PdfReader
-        except ImportError:
-            raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-            
-        start_time = time()
-        result = PDFProcessResult(
-            metadata=PDFMetadata(),
-            pages=[],
-            version="1.1"
-        )
-
-        try:
-            with pdf_path.open('rb') as file:
-                reader = PdfReader(file)
-                result.metadata = self._extract_metadata(pdf_path, reader)
-                
-                # Handle image directory
-                image_dir = None
-                if self.extract_images and self.save_images_locally:
-                    if self.image_save_dir:
-                        image_dir = Path(self.image_save_dir)
-                        image_dir.mkdir(exist_ok=True, parents=True)
-                    else:
-                        self._temp_dir = tempfile.mkdtemp(prefix='pdf_images_')
-                        image_dir = Path(self._temp_dir)
-
-                for page_num, page in enumerate(reader.pages):
-                    self.current_page_number = page_num + 1
-                    pdf_page = self._process_page(page, image_dir)
-                    result.pages.append(pdf_page)
-
-        except Exception as e:
-            logger.error(f"Failed to process PDF: {str(e)}")
-            raise
-        finally:
-            # Cleanup temp directory if it was created
-            if self._temp_dir and not self.image_save_dir:
-                import shutil
-                try:
-                    shutil.rmtree(self._temp_dir)
-                except Exception as e:
-                    logger.error(f"Failed to cleanup temp directory: {str(e)}")
-
-        result.processing_time = time() - start_time
-        return result
-
-    def process_batch(self, pdf_path: Path) -> PDFProcessResult:
-        """Like process() but processes PDF pages in parallel batches"""
-        # Import inside method to allow dependency to be optional
-        try:
-            from PyPDF2 import PdfReader
-            import PyPDF2  # For type checking
-        except ImportError:
-            raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-            
-        import concurrent.futures
-        import threading
-        
-        # Initialize PyPDF2 thread support
-        if not hasattr(threading.current_thread(), "_children"): 
-            threading.current_thread()._children = set()
-        
-        start_time = time()
-        result = PDFProcessResult(
-            metadata=PDFMetadata(),
-            pages=[],
-            version="1.1" 
-        )
-
-        try:
-            # Get metadata and page count from main thread
-            with pdf_path.open('rb') as file:
-                reader = PdfReader(file)
-                result.metadata = self._extract_metadata(pdf_path, reader)
-                total_pages = len(reader.pages)
-
-            # Handle image directory setup
-            image_dir = None
-            if self.extract_images and self.save_images_locally:
-                if self.image_save_dir:
-                    image_dir = Path(self.image_save_dir)
-                    image_dir.mkdir(exist_ok=True, parents=True)
-                else:
-                    self._temp_dir = tempfile.mkdtemp(prefix='pdf_images_')
-                    image_dir = Path(self._temp_dir)
-
-            def process_page_safely(page_num: int):
-                # Each thread opens its own file handle
-                with pdf_path.open('rb') as file:
-                    thread_reader = PdfReader(file)
-                    page = thread_reader.pages[page_num]
-                    self.current_page_number = page_num + 1
-                    return self._process_page(page, image_dir)
-
-            # Process pages in parallel batches
-            with concurrent.futures.ThreadPoolExecutor(max_workers=self.batch_size) as executor:
-                futures = []
-                for page_num in range(total_pages):
-                    future = executor.submit(process_page_safely, page_num)
-                    futures.append((page_num + 1, future))
-
-                # Collect results in order
-                result.pages = [None] * total_pages
-                for page_num, future in futures:
-                    try:
-                        pdf_page = future.result()
-                        result.pages[page_num - 1] = pdf_page
-                    except Exception as e:
-                        logger.error(f"Failed to process page {page_num}: {str(e)}")
-                        raise
-
-        except Exception as e:
-            logger.error(f"Failed to process PDF: {str(e)}")
-            raise
-        finally:
-            # Cleanup temp directory if it was created
-            if self._temp_dir and not self.image_save_dir:
-                import shutil
-                try:
-                    shutil.rmtree(self._temp_dir)
-                except Exception as e:
-                    logger.error(f"Failed to cleanup temp directory: {str(e)}")
-
-        result.processing_time = time() - start_time
-        return result
-
-    def _process_page(self, page, image_dir: Optional[Path]) -> PDFPage:
-        pdf_page = PDFPage(
-            page_number=self.current_page_number,
-        )
-
-        # Text and font extraction
-        def visitor_text(text, cm, tm, font_dict, font_size):
-            pdf_page.raw_text += text
-            pdf_page.layout.append({
-                "type": "text",
-                "text": text,
-                "x": tm[4],
-                "y": tm[5],
-            })
-        
-        page.extract_text(visitor_text=visitor_text)
-
-        # Image extraction
-        if self.extract_images:
-            pdf_page.images = self._extract_images(page, image_dir)
-
-        # Link extraction
-        pdf_page.links = self._extract_links(page)
-        
-        # Add markdown content
-        pdf_page.markdown = clean_pdf_text(self.current_page_number, pdf_page.raw_text)
-        pdf_page.html = clean_pdf_text_to_html(self.current_page_number, pdf_page.raw_text)
-
-        return pdf_page
-
-    def _extract_images(self, page, image_dir: Optional[Path]) -> List[Dict]:
-        # Import PyPDF2 for type checking only when needed
-        try:
-            import PyPDF2
-        except ImportError:
-            raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-            
-        if not self.extract_images:
-            return []
-
-        images = []
-        try:
-            resources = page.get("/Resources")
-            if resources:  # Check if resources exist
-                resources = resources.get_object()  # Resolve IndirectObject
-                if '/XObject' in resources:
-                    xobjects = resources['/XObject'].get_object()
-                    img_count = 0
-                    for obj_name in xobjects:
-                        xobj = xobjects[obj_name]
-                        if hasattr(xobj, 'get_object') and callable(xobj.get_object):
-                            xobj = xobj.get_object()
-                            if xobj.get('/Subtype') == '/Image':
-                                try:
-                                    img_count += 1
-                                    img_filename = f"page_{self.current_page_number}_img_{img_count}"
-                                    data = xobj.get_data()
-                                    filters = xobj.get('/Filter', [])
-                                    if not isinstance(filters, list):
-                                        filters = [filters]
-
-                                    # Resolve IndirectObjects in properties
-                                    width = xobj.get('/Width', 0)
-                                    height = xobj.get('/Height', 0)
-                                    color_space = xobj.get('/ColorSpace', '/DeviceRGB')
-                                    if isinstance(color_space, PyPDF2.generic.IndirectObject):
-                                        color_space = color_space.get_object()
-
-                                    # Handle different image encodings
-                                    success = False
-                                    image_format = 'bin'
-                                    image_data = None
-                                    
-                                    if '/FlateDecode' in filters:
-                                        try:
-                                            decode_parms = xobj.get('/DecodeParms', {})
-                                            if isinstance(decode_parms, PyPDF2.generic.IndirectObject):
-                                                decode_parms = decode_parms.get_object()
-                                            
-                                            predictor = decode_parms.get('/Predictor', 1)
-                                            bits = xobj.get('/BitsPerComponent', 8)
-                                            colors = 3 if color_space == '/DeviceRGB' else 1
-
-                                            if predictor >= 10:
-                                                data = apply_png_predictor(data, width, bits, colors)
-
-                                            # Create PIL Image
-                                            from PIL import Image
-                                            mode = 'RGB' if color_space == '/DeviceRGB' else 'L'
-                                            img = Image.frombytes(mode, (width, height), data)
-                                            
-                                            if self.save_images_locally:
-                                                final_path = (image_dir / img_filename).with_suffix('.png')
-                                                img.save(final_path)
-                                                image_data = str(final_path)
-                                            else:
-                                                import io
-                                                img_byte_arr = io.BytesIO()
-                                                img.save(img_byte_arr, format='PNG')
-                                                image_data = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
-                                            
-                                            success = True
-                                            image_format = 'png'
-                                        except Exception as e:
-                                            logger.error(f"FlateDecode error: {str(e)}")
-
-                                    elif '/DCTDecode' in filters:
-                                        # JPEG image
-                                        try:
-                                            if self.save_images_locally:
-                                                final_path = (image_dir / img_filename).with_suffix('.jpg')
-                                                with open(final_path, 'wb') as f:
-                                                    f.write(data)
-                                                image_data = str(final_path)
-                                            else:
-                                                image_data = base64.b64encode(data).decode('utf-8')
-                                            success = True
-                                            image_format = 'jpeg'
-                                        except Exception as e:
-                                            logger.error(f"JPEG save error: {str(e)}")
-
-                                    elif '/CCITTFaxDecode' in filters:
-                                        try:
-                                            if data[:4] != b'II*\x00':
-                                                # Add TIFF header if missing
-                                                tiff_header = b'II*\x00\x08\x00\x00\x00\x0e\x00\x00\x01\x03\x00\x01\x00\x00\x00' + \
-                                                            width.to_bytes(4, 'little') + \
-                                                            b'\x01\x03\x00\x01\x00\x00\x00' + \
-                                                            height.to_bytes(4, 'little') + \
-                                                            b'\x01\x12\x00\x03\x00\x00\x00\x01\x00\x01\x00\x00\x01\x17\x00\x04\x00\x00\x00\x01\x00\x00\x00J\x01\x1B\x00\x05\x00\x00\x00\x01\x00\x00\x00R\x01\x28\x00\x03\x00\x00\x00\x01\x00\x02\x00\x00'
-                                                data = tiff_header + data
-                                            
-                                            if self.save_images_locally:
-                                                final_path = (image_dir / img_filename).with_suffix('.tiff')
-                                                with open(final_path, 'wb') as f:
-                                                    f.write(data)
-                                                image_data = str(final_path)
-                                            else:
-                                                image_data = base64.b64encode(data).decode('utf-8')
-                                            success = True
-                                            image_format = 'tiff'
-                                        except Exception as e:
-                                            logger.error(f"CCITT save error: {str(e)}")
-
-                                    elif '/JPXDecode' in filters:
-                                        # JPEG 2000
-                                        try:
-                                            if self.save_images_locally:
-                                                final_path = (image_dir / img_filename).with_suffix('.jp2')
-                                                with open(final_path, 'wb') as f:
-                                                    f.write(data)
-                                                image_data = str(final_path)
-                                            else:
-                                                image_data = base64.b64encode(data).decode('utf-8')
-                                            success = True
-                                            image_format = 'jpeg2000'
-                                        except Exception as e:
-                                            logger.error(f"JPEG2000 save error: {str(e)}")
-
-                                    if success and image_data:
-                                        image_info = {
-                                            "format": image_format,
-                                            "width": width,
-                                            "height": height,
-                                            "color_space": str(color_space),
-                                            "bits_per_component": xobj.get('/BitsPerComponent', 1)
-                                        }
-                                        
-                                        if self.save_images_locally:
-                                            image_info["path"] = image_data
-                                        else:
-                                            image_info["data"] = image_data
-                                            
-                                        images.append(image_info)
-                                    else:
-                                        # Fallback: Save raw data
-                                        if self.save_images_locally:
-                                            final_path = (image_dir / img_filename).with_suffix('.bin')
-                                            with open(final_path, 'wb') as f:
-                                                f.write(data)
-                                            logger.warning(f"Saved raw image data to {final_path}")
-                                        else:
-                                            image_data = base64.b64encode(data).decode('utf-8')
-                                            images.append({
-                                                "format": "bin",
-                                                "width": width,
-                                                "height": height,
-                                                "color_space": str(color_space),
-                                                "bits_per_component": xobj.get('/BitsPerComponent', 1),
-                                                "data": image_data
-                                            })
-
-                                except Exception as e:
-                                    logger.error(f"Error processing image: {str(e)}")
-        except Exception as e:
-            logger.error(f"Image extraction error: {str(e)}")
-        
-        return images
-
-    def _extract_links(self, page) -> List[str]:
-        links = []
-        if '/Annots' in page:
-            try:
-                for annot in page['/Annots']:
-                    a = annot.get_object()
-                    if '/A' in a and '/URI' in a['/A']:
-                        links.append(a['/A']['/URI'])
-            except Exception as e:
-                print(f"Link error: {str(e)}")
-        return links
-
-    def _extract_metadata(self, pdf_path: Path, reader = None) -> PDFMetadata:
-        # Import inside method to allow dependency to be optional 
-        if reader is None:
-            try:
-                from PyPDF2 import PdfReader
-                reader = PdfReader(pdf_path)
-            except ImportError:
-                raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-
-        meta = reader.metadata or {}
-        created = self._parse_pdf_date(meta.get('/CreationDate', ''))
-        modified = self._parse_pdf_date(meta.get('/ModDate', ''))
-        
-        return PDFMetadata(
-            title=meta.get('/Title'),
-            author=meta.get('/Author'),
-            producer=meta.get('/Producer'),
-            created=created,
-            modified=modified,
-            pages=len(reader.pages),
-            encrypted=reader.is_encrypted,
-            file_size=pdf_path.stat().st_size
-        )
-
-    def _parse_pdf_date(self, date_str: str) -> Optional[datetime]:
-        try:
-            match = re.match(r'D:(\d{4})(\d{2})(\d{2})(\d{2})(\d{2})(\d{2})', date_str)
-            if not match:
-                return None
-                
-            return datetime(
-                year=int(match[1]),
-                month=int(match[2]),
-                day=int(match[3]),
-                hour=int(match[4]),
-                minute=int(match[5]),
-                second=int(match[6])
-            )
-        except:
-            return None
-
-# Usage example
-if __name__ == "__main__":
-    import json
-    from pathlib import Path
-    
-    try:
-        # Import PyPDF2 only when running the file directly
-        import PyPDF2
-        from PyPDF2 import PdfReader
-    except ImportError:
-        print("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
-        exit(1)
-        
-    current_dir = Path(__file__).resolve().parent
-    pdf_path = f'{current_dir}/test.pdf'
-    
-    strategy = NaivePDFProcessorStrategy()
-    result = strategy.process(Path(pdf_path))
-    
-    # Convert to JSON
-    json_output = asdict(result)
-    print(json.dumps(json_output, indent=2, default=str))
-    
-    with open(f'{current_dir}/test.html', 'w') as f:
-        for page in result.pages:
-            f.write(f'<h1>Page {page["page_number"]}</h1>')
-            f.write(page['html'])
-    with open(f'{current_dir}/test.md', 'w') as f:
-        for page in result.pages:
-            f.write(f'# Page {page["page_number"]}\n\n')
-            f.write(clean_pdf_text(page["page_number"], page['raw_text']))
-            f.write('\n\n')

crawl4ai/processors/pdf/utils.py

@@ -1,350 +0,0 @@
-import re
-
-def apply_png_predictor(data, width, bits, color_channels):
-    """Decode PNG predictor (PDF 1.5+ filter)"""
-    bytes_per_pixel = (bits * color_channels) // 8
-    if (bits * color_channels) % 8 != 0:
-        bytes_per_pixel += 1
-        
-    stride = width * bytes_per_pixel
-    scanline_length = stride + 1  # +1 for filter byte
-    
-    if len(data) % scanline_length != 0:
-        raise ValueError("Invalid scanline structure")
-    
-    num_lines = len(data) // scanline_length
-    output = bytearray()
-    prev_line = b'\x00' * stride
-    
-    for i in range(num_lines):
-        line = data[i*scanline_length:(i+1)*scanline_length]
-        filter_type = line[0]
-        filtered = line[1:]
-        
-        if filter_type == 0:  # None
-            decoded = filtered
-        elif filter_type == 1:  # Sub
-            decoded = bytearray(filtered)
-            for j in range(bytes_per_pixel, len(decoded)):
-                decoded[j] = (decoded[j] + decoded[j - bytes_per_pixel]) % 256
-        elif filter_type == 2:  # Up
-            decoded = bytearray([(filtered[j] + prev_line[j]) % 256 
-                               for j in range(len(filtered))])
-        elif filter_type == 3:  # Average
-            decoded = bytearray(filtered)
-            for j in range(len(decoded)):
-                left = decoded[j - bytes_per_pixel] if j >= bytes_per_pixel else 0
-                up = prev_line[j]
-                avg = (left + up) // 2
-                decoded[j] = (decoded[j] + avg) % 256
-        elif filter_type == 4:  # Paeth
-            decoded = bytearray(filtered)
-            for j in range(len(decoded)):
-                left = decoded[j - bytes_per_pixel] if j >= bytes_per_pixel else 0
-                up = prev_line[j]
-                up_left = prev_line[j - bytes_per_pixel] if j >= bytes_per_pixel else 0
-                paeth = paeth_predictor(left, up, up_left)
-                decoded[j] = (decoded[j] + paeth) % 256
-        else:
-            raise ValueError(f"Unsupported filter type: {filter_type}")
-        
-        output.extend(decoded)
-        prev_line = decoded
-    
-    return bytes(output)
-
-def paeth_predictor(a, b, c):
-    p = a + b - c
-    pa = abs(p - a)
-    pb = abs(p - b)
-    pc = abs(p - c)
-    if pa <= pb and pa <= pc:
-        return a
-    elif pb <= pc:
-        return b
-    else:
-        return c
-
-import re
-import html
-
-def clean_pdf_text_to_html(page_number, text):
-    # Decode Unicode escapes and handle surrogate pairs
-    try:
-        decoded = text.encode('latin-1').decode('unicode-escape')
-        decoded = decoded.encode('utf-16', 'surrogatepass').decode('utf-16')
-    except Exception as e:
-        decoded = text  # Fallback if decoding fails
-    
-    article_title_detected = False
-    # decoded = re.sub(r'\.\n', '.\n\n', decoded)
-    # decoded = re.sub(r'\.\n', '<|break|>', decoded)
-    lines = decoded.split('\n')
-    output = []
-    current_paragraph = []
-    in_header = False
-    email_pattern = re.compile(r'\{.*?\}')
-    affiliation_pattern = re.compile(r'^†')
-    quote_pattern = re.compile(r'^["“]')
-    author_pattern = re.compile(
-        r'^\s*[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?'
-        r'(?:,\s*[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?)*'
-        r'(?:,\s*(?:and|&)\s+[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?)?\s*$'
-    )
-    
-    def flush_paragraph():
-        if current_paragraph:
-            para = ' '.join(current_paragraph)
-            para = re.sub(r'\s+', ' ', para).strip()
-            if para:
-                # escaped_para = html.escape(para)
-                escaped_para = para
-                # escaped_para = re.sub(r'\.\n', '.\n\n', escaped_para)
-                # Split escaped_para by <|break|> to avoid HTML escaping
-                escaped_para = escaped_para.split('.\n\n')
-                # Wrap each part in <p> tag
-                escaped_para = [f'<p>{part}</p>' for part in escaped_para]
-                output.append(f'<div class="paragraph">{"".join(escaped_para)}</div><hr/>')
-            current_paragraph.clear()
-    
-    for i, line in enumerate(lines):
-        line = line.strip()
-        
-        # Handle empty lines
-        if not line:
-            flush_paragraph()
-            continue
-            
-        # Detect article title (first line with reasonable length)
-        if not article_title_detected and i == 0 and 3 <= len(line.split()) <= 8 and len(lines) > 1:
-            flush_paragraph()
-            escaped_line = html.escape(line)
-            output.append(f'<h2>{escaped_line}</h2>')
-            article_title_detected = True
-            continue
-            
-        # Detect numbered headers like "2.1 Background"
-        numbered_header = re.match(r'^(\d+(?:\.\d+)*)\s+(.+)$', line)
-        if i > 0 and not lines[i-1].strip() and numbered_header:
-            flush_paragraph()
-            level = numbered_header.group(1).count('.') + 1
-            header_text = numbered_header.group(2)
-            md_level = min(level + 1, 6)
-            escaped_header = html.escape(header_text)
-            output.append(f'<h{md_level}>{escaped_header}</h{md_level}>')
-            in_header = True
-            continue
-            
-        # Detect authors
-        if page_number == 1 and author_pattern.match(line):
-            authors = re.sub(r'[†â€]', '', line)
-            authors = re.split(r', | and ', authors)
-            formatted_authors = []
-            for author in authors:
-                if author.strip():
-                    parts = [p for p in author.strip().split() if p]
-                    formatted = ' '.join(parts)
-                    escaped_author = html.escape(formatted)
-                    formatted_authors.append(f'<strong>{escaped_author}</strong>')
-            
-            if len(formatted_authors) > 1:
-                joined = ', '.join(formatted_authors[:-1]) + ' and ' + formatted_authors[-1]
-            else:
-                joined = formatted_authors[0]
-            
-            output.append(f'<p>{joined}</p>')
-            continue
-            
-        # Detect affiliation
-        if affiliation_pattern.match(line):
-            escaped_line = html.escape(line)
-            output.append(f'<p><em>{escaped_line}</em></p>')
-            continue
-            
-        # Detect emails
-        if email_pattern.match(line):
-            escaped_line = html.escape(line)
-            output.append(f'<p><code>{escaped_line}</code></p>')
-            continue
-            
-        # Detect section headers
-        if re.match(r'^(Abstract|\d+\s+[A-Z]|References|Appendix|Figure|Table)', line):
-            flush_paragraph()
-            escaped_line = html.escape(line)
-            output.append(f'<h2 class="section-header"><em>{escaped_line}</em></h2>')
-            in_header = True
-            continue
-            
-        # Handle quotes
-        if quote_pattern.match(line):
-            flush_paragraph()
-            escaped_line = html.escape(line)
-            output.append(f'<blockquote><p>{escaped_line}</p></blockquote>')
-            continue
-            
-        # Handle hyphenated words
-        if line.endswith('-'):
-            current_paragraph.append(line[:-1].strip())
-        else:
-            current_paragraph.append(line)
-            
-        # Handle paragraph breaks after headers
-        if in_header and not line.endswith(('.', '!', '?')):
-            flush_paragraph()
-            in_header = False
-    
-    flush_paragraph()
-    
-    # Post-process HTML
-    html_output = '\n'.join(output)
-    
-    # Fix common citation patterns
-    html_output = re.sub(r'\(([A-Z][a-z]+ et al\. \d{4})\)', r'<cite>\1</cite>', html_output)
-    
-    # Fix escaped characters
-    html_output = html_output.replace('\\ud835', '').replace('\\u2020', '†')
-    
-    # Remove leftover hyphens and fix spacing
-    html_output = re.sub(r'\s+-\s+', '', html_output)
-    html_output = re.sub(r'\s+([.,!?)])', r'\1', html_output)
-    
-    return html_output
-
-def clean_pdf_text(page_number, text):
-    # Decode Unicode escapes and handle surrogate pairs
-    try:
-        decoded = text.encode('latin-1').decode('unicode-escape')
-        decoded = decoded.encode('utf-16', 'surrogatepass').decode('utf-16')
-    except Exception as e:
-        decoded = text  # Fallback if decoding fails
-    
-    article_title_detected = False
-    decoded = re.sub(r'\.\n', '.\n\n', decoded)
-    lines = decoded.split('\n')
-    output = []
-    current_paragraph = []
-    in_header = False
-    email_pattern = re.compile(r'\{.*?\}')
-    affiliation_pattern = re.compile(r'^†')
-    quote_pattern = re.compile(r'^["“]')
-    author_pattern = re.compile(
-        r'^\s*[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?'
-        r'(?:,\s*[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?)*'
-        r'(?:,\s*(?:and|&)\s+[A-Z][a-zA-Z]+(?:\s+[A-Z][a-zA-Z]+)*\s*(?:[†*0-9]+)?)?\s*$'
-    )
-    
-    def flush_paragraph():
-        if current_paragraph:
-            para = ' '.join(current_paragraph)
-            para = re.sub(r'\s+', ' ', para).strip()
-            if para:
-                output.append(para)
-            current_paragraph.clear()
-    
-    for i, line in enumerate(lines):
-        line = line.strip()
-        
-        # Handle special patterns
-        if not line:
-            flush_paragraph()
-            continue
-            
-        # Detect headline (first line, reasonable length, surrounded by empty lines)
-        if not article_title_detected and i == 0 and 3 <= len(line.split()) <= 8 and (len(lines) > 1):
-            flush_paragraph()
-            output.append(f'## {line}')
-            continue
-            
-        # Detect paragraph breaks for ALL paragraphs
-        if not line and current_paragraph:
-            flush_paragraph()
-            output.append('')  # Add empty line between paragraphs
-            continue
-                    
-        # Detect numbered headers like "2.1 Background"
-        numbered_header = re.match(r'^(\d+(?:\.\d+)*)\s+(.+)$', line)
-        if not lines[i-1].strip() and numbered_header:
-            flush_paragraph()
-            level = numbered_header.group(1).count('.') + 1  # Convert 2.1 → level 2
-            header_text = numbered_header.group(2)
-            # Never go beyond ### for subsections
-            md_level = min(level + 1, 6)   # 1 → ##, 2 → ###, 3 → #### etc
-            output.append(f'{"#" * md_level} {header_text}')
-            in_header = True
-            continue            
-            
-                    
-        # Detect authors
-        if page_number == 1 and author_pattern.match(line):
-            # Clean and format author names
-            authors = re.sub(r'[†â€]', '', line)  # Remove affiliation markers
-            authors = re.split(r', | and ', authors)
-            formatted_authors = []
-            for author in authors:
-                if author.strip():
-                    # Handle "First Last" formatting
-                    parts = [p for p in author.strip().split() if p]
-                    formatted = ' '.join(parts)
-                    formatted_authors.append(f'**{formatted}**')
-            
-            # Join with commas and "and"
-            if len(formatted_authors) > 1:
-                joined = ', '.join(formatted_authors[:-1]) + ' and ' + formatted_authors[-1]
-            else:
-                joined = formatted_authors[0]
-            
-            output.append(joined)
-            continue
-            
-        # Detect affiliation
-        if affiliation_pattern.match(line):
-            output.append(f'*{line}*')
-            continue
-            
-        # Detect emails
-        if email_pattern.match(line):
-            output.append(f'`{line}`')
-            continue
-            
-        # Detect section headers
-        if re.match(r'^(Abstract|\d+\s+[A-Z]|References|Appendix|Figure|Table)', line):
-            flush_paragraph()
-            output.append(f'_[{line}]_')
-            in_header = True
-            continue
-            
-           
-        # Handle quotes
-        if quote_pattern.match(line):
-            flush_paragraph()
-            output.append(f'> {line}')
-            continue
-            
-        # Handle hyphenated words
-        if line.endswith('-'):
-            current_paragraph.append(line[:-1].strip())
-        else:
-            current_paragraph.append(line)
-            
-        # Handle paragraph breaks after headers
-        if in_header and not line.endswith(('.', '!', '?')):
-            flush_paragraph()
-            in_header = False
-    
-    flush_paragraph()
-    
-    # Post-processing
-    markdown = '\n\n'.join(output)
-    
-    # Fix common citation patterns
-    markdown = re.sub(r'\(([A-Z][a-z]+ et al\. \d{4})\)', r'[\1]', markdown)
-    
-    # Fix escaped characters
-    markdown = markdown.replace('\\ud835', '').replace('\\u2020', '†')
-    
-    # Remove leftover hyphens and fix spacing
-    markdown = re.sub(r'\s+-\s+', '', markdown)  # Join hyphenated words
-    markdown = re.sub(r'\s+([.,!?)])', r'\1', markdown)  # Fix punctuation spacing
-    
-    
-    return markdown

crawl4ai/proxy_strategy.py

@@ -1,158 +0,0 @@
-from typing import List, Dict, Optional
-from abc import ABC, abstractmethod
-from itertools import cycle
-import os
-
-
-########### ATTENTION PEOPLE OF EARTH ###########
-# I have moved this config to async_configs.py, kept it here, in case someone still importing it, however
-# be a dear and follow `from crawl4ai import ProxyConfig` instead :)
-class ProxyConfig:
-    def __init__(
-        self,
-        server: str,
-        username: Optional[str] = None,
-        password: Optional[str] = None,
-        ip: Optional[str] = None,
-    ):
-        """Configuration class for a single proxy.
-        
-        Args:
-            server: Proxy server URL (e.g., "http://127.0.0.1:8080")
-            username: Optional username for proxy authentication
-            password: Optional password for proxy authentication
-            ip: Optional IP address for verification purposes
-        """
-        self.server = server
-        self.username = username
-        self.password = password
-        
-        # Extract IP from server if not explicitly provided
-        self.ip = ip or self._extract_ip_from_server()
-    
-    def _extract_ip_from_server(self) -> Optional[str]:
-        """Extract IP address from server URL."""
-        try:
-            # Simple extraction assuming http://ip:port format
-            if "://" in self.server:
-                parts = self.server.split("://")[1].split(":")
-                return parts[0]
-            else:
-                parts = self.server.split(":")
-                return parts[0]
-        except Exception:
-            return None
-    
-    @staticmethod
-    def from_string(proxy_str: str) -> "ProxyConfig":
-        """Create a ProxyConfig from a string in the format 'ip:port:username:password'."""
-        parts = proxy_str.split(":")
-        if len(parts) == 4:  # ip:port:username:password
-            ip, port, username, password = parts
-            return ProxyConfig(
-                server=f"http://{ip}:{port}",
-                username=username,
-                password=password,
-                ip=ip
-            )
-        elif len(parts) == 2:  # ip:port only
-            ip, port = parts
-            return ProxyConfig(
-                server=f"http://{ip}:{port}",
-                ip=ip
-            )
-        else:
-            raise ValueError(f"Invalid proxy string format: {proxy_str}")
-    
-    @staticmethod
-    def from_dict(proxy_dict: Dict) -> "ProxyConfig":
-        """Create a ProxyConfig from a dictionary."""
-        return ProxyConfig(
-            server=proxy_dict.get("server"),
-            username=proxy_dict.get("username"),
-            password=proxy_dict.get("password"),
-            ip=proxy_dict.get("ip")
-        )
-    
-    @staticmethod
-    def from_env(env_var: str = "PROXIES") -> List["ProxyConfig"]:
-        """Load proxies from environment variable.
-        
-        Args:
-            env_var: Name of environment variable containing comma-separated proxy strings
-            
-        Returns:
-            List of ProxyConfig objects
-        """
-        proxies = []
-        try:
-            proxy_list = os.getenv(env_var, "").split(",")
-            for proxy in proxy_list:
-                if not proxy:
-                    continue
-                proxies.append(ProxyConfig.from_string(proxy))
-        except Exception as e:
-            print(f"Error loading proxies from environment: {e}")
-        return proxies
-    
-    def to_dict(self) -> Dict:
-        """Convert to dictionary representation."""
-        return {
-            "server": self.server,
-            "username": self.username,
-            "password": self.password,
-            "ip": self.ip
-        }
-    
-    def clone(self, **kwargs) -> "ProxyConfig":
-        """Create a copy of this configuration with updated values.
-
-        Args:
-            **kwargs: Key-value pairs of configuration options to update
-
-        Returns:
-            ProxyConfig: A new instance with the specified updates
-        """
-        config_dict = self.to_dict()
-        config_dict.update(kwargs)
-        return ProxyConfig.from_dict(config_dict)
-
-
-class ProxyRotationStrategy(ABC):
-    """Base abstract class for proxy rotation strategies"""
-    
-    @abstractmethod
-    async def get_next_proxy(self) -> Optional[ProxyConfig]:
-        """Get next proxy configuration from the strategy"""
-        pass
-
-    @abstractmethod
-    def add_proxies(self, proxies: List[ProxyConfig]):
-        """Add proxy configurations to the strategy"""
-        pass
-
-class RoundRobinProxyStrategy:
-    """Simple round-robin proxy rotation strategy using ProxyConfig objects"""
-
-    def __init__(self, proxies: List[ProxyConfig] = None):
-        """
-        Initialize with optional list of proxy configurations
-        
-        Args:
-            proxies: List of ProxyConfig objects
-        """
-        self._proxies = []
-        self._proxy_cycle = None
-        if proxies:
-            self.add_proxies(proxies)
-
-    def add_proxies(self, proxies: List[ProxyConfig]):
-        """Add new proxies to the rotation pool"""
-        self._proxies.extend(proxies)
-        self._proxy_cycle = cycle(self._proxies)
-
-    async def get_next_proxy(self) -> Optional[ProxyConfig]:
-        """Get next proxy in round-robin fashion"""
-        if not self._proxy_cycle:
-            return None
-        return next(self._proxy_cycle)

crawl4ai/script/__init__.py

@@ -1,35 +0,0 @@
-"""
-C4A-Script: A domain-specific language for web automation in Crawl4AI
-"""
-
-from .c4a_compile import C4ACompiler, compile, validate, compile_file
-from .c4a_result import (
-    CompilationResult, 
-    ValidationResult, 
-    ErrorDetail, 
-    WarningDetail,
-    ErrorType, 
-    Severity, 
-    Suggestion
-)
-
-__all__ = [
-    # Main compiler
-    "C4ACompiler",
-    
-    # Convenience functions
-    "compile",
-    "validate", 
-    "compile_file",
-    
-    # Result types
-    "CompilationResult",
-    "ValidationResult",
-    "ErrorDetail",
-    "WarningDetail",
-    
-    # Enums
-    "ErrorType",
-    "Severity",
-    "Suggestion"
-]

crawl4ai/script/c4a_compile.py

@@ -1,398 +0,0 @@
-"""
-Clean C4A-Script API with Result pattern
-No exceptions - always returns results
-"""
-
-from __future__ import annotations
-import pathlib
-import re
-from typing import Union, List, Optional
-
-# JSON_SCHEMA_BUILDER is still used elsewhere,
-# but we now also need the new script-builder prompt.
-from ..prompts import GENERATE_JS_SCRIPT_PROMPT, GENERATE_SCRIPT_PROMPT
-import logging
-import re
-
-from .c4a_result import (
-    CompilationResult, ValidationResult, ErrorDetail, WarningDetail,
-    ErrorType, Severity, Suggestion
-)
-from .c4ai_script import Compiler
-from lark.exceptions import UnexpectedToken, UnexpectedCharacters, VisitError
-from ..async_configs import LLMConfig
-from ..utils import perform_completion_with_backoff
-
-
-class C4ACompiler:
-    """Main compiler with result-based API"""
-    
-    # Error code mapping
-    ERROR_CODES = {
-        "missing_then": "E001",
-        "missing_paren": "E002",
-        "missing_comma": "E003",
-        "missing_endproc": "E004",
-        "undefined_proc": "E005",
-        "missing_backticks": "E006",
-        "invalid_command": "E007",
-        "syntax_error": "E999"
-    }
-    
-    @classmethod
-    def compile(cls, script: Union[str, List[str]], root: Optional[pathlib.Path] = None) -> CompilationResult:
-        """
-        Compile C4A-Script to JavaScript
-        
-        Args:
-            script: C4A-Script as string or list of lines
-            root: Root directory for includes
-            
-        Returns:
-            CompilationResult with success status and JS code or errors
-        """
-        # Normalize input
-        if isinstance(script, list):
-            script_text = '\n'.join(script)
-            script_lines = script
-        else:
-            script_text = script
-            script_lines = script.split('\n')
-        
-        try:
-            # Try compilation
-            compiler = Compiler(root)
-            js_code = compiler.compile(script_text)
-            
-            # Success!
-            result = CompilationResult(
-                success=True,
-                js_code=js_code,
-                metadata={
-                    "lineCount": len(script_lines),
-                    "statementCount": len(js_code)
-                }
-            )
-            
-            # Add any warnings (future feature)
-            # result.warnings = cls._check_warnings(script_text)
-            
-            return result
-            
-        except Exception as e:
-            # Convert exception to ErrorDetail
-            error = cls._exception_to_error(e, script_lines)
-            return CompilationResult(
-                success=False,
-                errors=[error],
-                metadata={
-                    "lineCount": len(script_lines)
-                }
-            )
-    
-    @classmethod
-    def validate(cls, script: Union[str, List[str]]) -> ValidationResult:
-        """
-        Validate script syntax without generating code
-        
-        Args:
-            script: C4A-Script to validate
-            
-        Returns:
-            ValidationResult with validity status and any errors
-        """
-        result = cls.compile(script)
-        
-        return ValidationResult(
-            valid=result.success,
-            errors=result.errors,
-            warnings=result.warnings
-        )
-    
-    @classmethod
-    def compile_file(cls, path: Union[str, pathlib.Path]) -> CompilationResult:
-        """
-        Compile a C4A-Script file
-        
-        Args:
-            path: Path to the file
-            
-        Returns:
-            CompilationResult
-        """
-        path = pathlib.Path(path)
-        
-        if not path.exists():
-            error = ErrorDetail(
-                type=ErrorType.RUNTIME,
-                code="E100",
-                severity=Severity.ERROR,
-                message=f"File not found: {path}",
-                line=0,
-                column=0,
-                source_line=""
-            )
-            return CompilationResult(success=False, errors=[error])
-        
-        try:
-            script = path.read_text()
-            return cls.compile(script, root=path.parent)
-        except Exception as e:
-            error = ErrorDetail(
-                type=ErrorType.RUNTIME,
-                code="E101",
-                severity=Severity.ERROR,
-                message=f"Error reading file: {str(e)}",
-                line=0,
-                column=0,
-                source_line=""
-            )
-            return CompilationResult(success=False, errors=[error])
-    
-    @classmethod
-    def _exception_to_error(cls, exc: Exception, script_lines: List[str]) -> ErrorDetail:
-        """Convert an exception to ErrorDetail"""
-        
-        if isinstance(exc, UnexpectedToken):
-            return cls._handle_unexpected_token(exc, script_lines)
-        elif isinstance(exc, UnexpectedCharacters):
-            return cls._handle_unexpected_chars(exc, script_lines)
-        elif isinstance(exc, ValueError):
-            return cls._handle_value_error(exc, script_lines)
-        else:
-            # Generic error
-            return ErrorDetail(
-                type=ErrorType.SYNTAX,
-                code=cls.ERROR_CODES["syntax_error"],
-                severity=Severity.ERROR,
-                message=str(exc),
-                line=1,
-                column=1,
-                source_line=script_lines[0] if script_lines else ""
-            )
-    
-    @classmethod
-    def _handle_unexpected_token(cls, exc: UnexpectedToken, script_lines: List[str]) -> ErrorDetail:
-        """Handle UnexpectedToken errors"""
-        line = exc.line
-        column = exc.column
-        
-        # Get context lines
-        source_line = script_lines[line - 1] if 0 < line <= len(script_lines) else ""
-        line_before = script_lines[line - 2] if line > 1 and line <= len(script_lines) + 1 else None
-        line_after = script_lines[line] if 0 < line < len(script_lines) else None
-        
-        # Determine error type and suggestions
-        if exc.token.type == 'CLICK' and 'THEN' in str(exc.expected):
-            code = cls.ERROR_CODES["missing_then"]
-            message = "Missing 'THEN' keyword after IF condition"
-            suggestions = [
-                Suggestion(
-                    "Add 'THEN' after the condition",
-                    source_line.replace("CLICK", "THEN CLICK") if source_line else None
-                )
-            ]
-        elif exc.token.type == '$END':
-            code = cls.ERROR_CODES["missing_endproc"]
-            message = "Unexpected end of script"
-            suggestions = [
-                Suggestion("Check for missing ENDPROC"),
-                Suggestion("Ensure all procedures are properly closed")
-            ]
-        elif 'RPAR' in str(exc.expected):
-            code = cls.ERROR_CODES["missing_paren"]
-            message = "Missing closing parenthesis ')'"
-            suggestions = [
-                Suggestion("Add closing parenthesis at the end of the condition")
-            ]
-        elif 'COMMA' in str(exc.expected):
-            code = cls.ERROR_CODES["missing_comma"]
-            message = "Missing comma ',' in command"
-            suggestions = [
-                Suggestion("Add comma between arguments")
-            ]
-        else:
-            # Check if this might be missing backticks
-            if exc.token.type == 'NAME' and 'BACKTICK_STRING' in str(exc.expected):
-                code = cls.ERROR_CODES["missing_backticks"]
-                message = "Selector must be wrapped in backticks"
-                suggestions = [
-                    Suggestion(
-                        "Wrap the selector in backticks",
-                        f"`{exc.token.value}`"
-                    )
-                ]
-            else:
-                code = cls.ERROR_CODES["syntax_error"]
-                message = f"Unexpected '{exc.token.value}'"
-                if exc.expected:
-                    expected_list = [str(e) for e in exc.expected if not str(e).startswith('_')][:3]
-                    if expected_list:
-                        message += f". Expected: {', '.join(expected_list)}"
-                suggestions = []
-        
-        return ErrorDetail(
-            type=ErrorType.SYNTAX,
-            code=code,
-            severity=Severity.ERROR,
-            message=message,
-            line=line,
-            column=column,
-            source_line=source_line,
-            line_before=line_before,
-            line_after=line_after,
-            suggestions=suggestions
-        )
-    
-    @classmethod
-    def _handle_unexpected_chars(cls, exc: UnexpectedCharacters, script_lines: List[str]) -> ErrorDetail:
-        """Handle UnexpectedCharacters errors"""
-        line = exc.line
-        column = exc.column
-        
-        source_line = script_lines[line - 1] if 0 < line <= len(script_lines) else ""
-        
-        # Check for missing backticks
-        if "CLICK" in source_line and column > source_line.find("CLICK"):
-            code = cls.ERROR_CODES["missing_backticks"]
-            message = "Selector must be wrapped in backticks"
-            suggestions = [
-                Suggestion(
-                    "Wrap the selector in backticks",
-                    re.sub(r'CLICK\s+([^\s]+)', r'CLICK `\1`', source_line)
-                )
-            ]
-        else:
-            code = cls.ERROR_CODES["syntax_error"]
-            message = f"Invalid character at position {column}"
-            suggestions = []
-        
-        return ErrorDetail(
-            type=ErrorType.SYNTAX,
-            code=code,
-            severity=Severity.ERROR,
-            message=message,
-            line=line,
-            column=column,
-            source_line=source_line,
-            suggestions=suggestions
-        )
-    
-    @classmethod
-    def _handle_value_error(cls, exc: ValueError, script_lines: List[str]) -> ErrorDetail:
-        """Handle ValueError (runtime errors)"""
-        message = str(exc)
-        
-        # Check for undefined procedure
-        if "Unknown procedure" in message:
-            proc_match = re.search(r"'([^']+)'", message)
-            if proc_match:
-                proc_name = proc_match.group(1)
-                
-                # Find the line with the procedure call
-                for i, line in enumerate(script_lines):
-                    if proc_name in line and not line.strip().startswith('PROC'):
-                        return ErrorDetail(
-                            type=ErrorType.RUNTIME,
-                            code=cls.ERROR_CODES["undefined_proc"],
-                            severity=Severity.ERROR,
-                            message=f"Undefined procedure '{proc_name}'",
-                            line=i + 1,
-                            column=line.find(proc_name) + 1,
-                            source_line=line,
-                            suggestions=[
-                                Suggestion(
-                                    f"Define the procedure before using it",
-                                    f"PROC {proc_name}\n  # commands here\nENDPROC"
-                                )
-                            ]
-                        )
-        
-        # Generic runtime error
-        return ErrorDetail(
-            type=ErrorType.RUNTIME,
-            code="E999",
-            severity=Severity.ERROR,
-            message=message,
-            line=1,
-            column=1,
-            source_line=script_lines[0] if script_lines else ""
-        )
-
-    @staticmethod
-    def generate_script(
-        html: str,
-        query: str | None = None,
-        mode: str = "c4a",
-        llm_config: LLMConfig | None = None,
-        **completion_kwargs,
-    ) -> str:
-        """
-        One-shot helper that calls the LLM exactly once to convert a
-        natural-language goal + HTML snippet into either:
-
-        1. raw JavaScript (`mode="js"`)
-        2. Crawl4ai DSL (`mode="c4a"`)
-
-        The returned string is guaranteed to be free of markdown wrappers
-        or explanatory text, ready for direct execution.
-        """
-        if llm_config is None:
-            llm_config = LLMConfig()  # falls back to env vars / defaults
-
-        # Build the user chunk
-        user_prompt = "\n".join(
-            [
-                "## GOAL",
-                "<<goael>>",
-                (query or "Prepare the page for crawling."),
-                "<</goal>>",
-                "",
-                "## HTML",
-                "<<html>>",
-                html[:100000],  # guardrail against token blast
-                "<</html>>",
-                "",
-                "## MODE",
-                mode,
-            ]
-        )
-
-        # Call the LLM with retry/back-off logic
-        full_prompt =  f"{GENERATE_SCRIPT_PROMPT}\n\n{user_prompt}" if mode == "c4a" else f"{GENERATE_JS_SCRIPT_PROMPT}\n\n{user_prompt}"
-        
-        response = perform_completion_with_backoff(
-            provider=llm_config.provider,
-            prompt_with_variables=full_prompt,
-            api_token=llm_config.api_token,
-            json_response=False,
-            base_url=getattr(llm_config, 'base_url', None),
-            **completion_kwargs,
-        )
-        
-        # Extract content from the response
-        raw_response = response.choices[0].message.content.strip()
-
-        # Strip accidental markdown fences (```js … ```)
-        clean = re.sub(r"^```(?:[a-zA-Z0-9_-]+)?\s*|```$", "", raw_response, flags=re.MULTILINE).strip()
-
-        if not clean:
-            raise RuntimeError("LLM returned empty script.")
-
-        return clean
-
-
-# Convenience functions for direct use
-def compile(script: Union[str, List[str]], root: Optional[pathlib.Path] = None) -> CompilationResult:
-    """Compile C4A-Script to JavaScript"""
-    return C4ACompiler.compile(script, root)
-
-
-def validate(script: Union[str, List[str]]) -> ValidationResult:
-    """Validate C4A-Script syntax"""
-    return C4ACompiler.validate(script)
-
-
-def compile_file(path: Union[str, pathlib.Path]) -> CompilationResult:
-    """Compile C4A-Script file"""
-    return C4ACompiler.compile_file(path)

crawl4ai/script/c4a_result.py

@@ -1,219 +0,0 @@
-"""
-Result classes for C4A-Script compilation
-Clean API design with no exceptions
-"""
-
-from __future__ import annotations
-from dataclasses import dataclass, field
-from enum import Enum
-from typing import List, Dict, Any, Optional
-import json
-
-
-class ErrorType(Enum):
-    SYNTAX = "syntax"
-    SEMANTIC = "semantic"
-    RUNTIME = "runtime"
-    
-
-class Severity(Enum):
-    ERROR = "error"
-    WARNING = "warning"
-    INFO = "info"
-
-
-@dataclass
-class Suggestion:
-    """A suggestion for fixing an error"""
-    message: str
-    fix: Optional[str] = None
-    
-    def to_dict(self) -> dict:
-        return {
-            "message": self.message,
-            "fix": self.fix
-        }
-
-
-@dataclass
-class ErrorDetail:
-    """Detailed information about a compilation error"""
-    # Core info
-    type: ErrorType
-    code: str  # E001, E002, etc.
-    severity: Severity
-    message: str
-    
-    # Location
-    line: int
-    column: int
-    
-    # Context
-    source_line: str
-    
-    # Optional fields with defaults
-    end_line: Optional[int] = None
-    end_column: Optional[int] = None
-    line_before: Optional[str] = None
-    line_after: Optional[str] = None
-    
-    # Help
-    suggestions: List[Suggestion] = field(default_factory=list)
-    documentation_url: Optional[str] = None
-    
-    def to_dict(self) -> dict:
-        """Convert to dictionary for JSON serialization"""
-        return {
-            "type": self.type.value,
-            "code": self.code,
-            "severity": self.severity.value,
-            "message": self.message,
-            "location": {
-                "line": self.line,
-                "column": self.column,
-                "endLine": self.end_line,
-                "endColumn": self.end_column
-            },
-            "context": {
-                "sourceLine": self.source_line,
-                "lineBefore": self.line_before,
-                "lineAfter": self.line_after,
-                "marker": {
-                    "start": self.column - 1,
-                    "length": (self.end_column - self.column) if self.end_column else 1
-                }
-            },
-            "suggestions": [s.to_dict() for s in self.suggestions],
-            "documentationUrl": self.documentation_url
-        }
-    
-    def to_json(self) -> str:
-        """Convert to JSON string"""
-        return json.dumps(self.to_dict(), indent=2)
-    
-    @property
-    def formatted_message(self) -> str:
-        """Returns the nice text format for terminals"""
-        lines = []
-        lines.append(f"\n{'='*60}")
-        lines.append(f"{self.type.value.title()} Error [{self.code}]")
-        lines.append(f"{'='*60}")
-        lines.append(f"Location: Line {self.line}, Column {self.column}")
-        lines.append(f"Error: {self.message}")
-        
-        if self.source_line:
-            marker = " " * (self.column - 1) + "^"
-            if self.end_column:
-                marker += "~" * (self.end_column - self.column - 1)
-            lines.append(f"\nCode:")
-            if self.line_before:
-                lines.append(f"  {self.line - 1: >3} | {self.line_before}")
-            lines.append(f"  {self.line: >3} | {self.source_line}")
-            lines.append(f"      | {marker}")
-            if self.line_after:
-                lines.append(f"  {self.line + 1: >3} | {self.line_after}")
-        
-        if self.suggestions:
-            lines.append("\nSuggestions:")
-            for i, suggestion in enumerate(self.suggestions, 1):
-                lines.append(f"  {i}. {suggestion.message}")
-                if suggestion.fix:
-                    lines.append(f"     Fix: {suggestion.fix}")
-        
-        lines.append("="*60)
-        return "\n".join(lines)
-    
-    @property
-    def simple_message(self) -> str:
-        """Returns just the error message without formatting"""
-        return f"Line {self.line}: {self.message}"
-
-
-@dataclass
-class WarningDetail:
-    """Information about a compilation warning"""
-    code: str
-    message: str
-    line: int
-    column: int
-    
-    def to_dict(self) -> dict:
-        return {
-            "code": self.code,
-            "message": self.message,
-            "line": self.line,
-            "column": self.column
-        }
-
-
-@dataclass
-class CompilationResult:
-    """Result of C4A-Script compilation"""
-    success: bool
-    js_code: Optional[List[str]] = None
-    errors: List[ErrorDetail] = field(default_factory=list)
-    warnings: List[WarningDetail] = field(default_factory=list)
-    metadata: Dict[str, Any] = field(default_factory=dict)
-    
-    def to_dict(self) -> dict:
-        """Convert to dictionary for JSON serialization"""
-        return {
-            "success": self.success,
-            "jsCode": self.js_code,
-            "errors": [e.to_dict() for e in self.errors],
-            "warnings": [w.to_dict() for w in self.warnings],
-            "metadata": self.metadata
-        }
-    
-    def to_json(self) -> str:
-        """Convert to JSON string"""
-        return json.dumps(self.to_dict(), indent=2)
-    
-    @property
-    def has_errors(self) -> bool:
-        """Check if there are any errors"""
-        return len(self.errors) > 0
-    
-    @property
-    def has_warnings(self) -> bool:
-        """Check if there are any warnings"""
-        return len(self.warnings) > 0
-    
-    @property
-    def first_error(self) -> Optional[ErrorDetail]:
-        """Get the first error if any"""
-        return self.errors[0] if self.errors else None
-    
-    def __str__(self) -> str:
-        """String representation for debugging"""
-        if self.success:
-            msg = f"✓ Compilation successful"
-            if self.js_code:
-                msg += f" - {len(self.js_code)} statements generated"
-            if self.warnings:
-                msg += f" ({len(self.warnings)} warnings)"
-            return msg
-        else:
-            return f"✗ Compilation failed - {len(self.errors)} error(s)"
-
-
-@dataclass
-class ValidationResult:
-    """Result of script validation"""
-    valid: bool
-    errors: List[ErrorDetail] = field(default_factory=list)
-    warnings: List[WarningDetail] = field(default_factory=list)
-    
-    def to_dict(self) -> dict:
-        return {
-            "valid": self.valid,
-            "errors": [e.to_dict() for e in self.errors],
-            "warnings": [w.to_dict() for w in self.warnings]
-        }
-    
-    def to_json(self) -> str:
-        return json.dumps(self.to_dict(), indent=2)
-    
-    @property
-    def first_error(self) -> Optional[ErrorDetail]:
-        return self.errors[0] if self.errors else None

crawl4ai/script/c4ai_script.py

@@ -1,690 +0,0 @@
-"""
-2025-06-03
-By Unclcode:
-C4A-Script Language Documentation    
-Feeds Crawl4AI via CrawlerRunConfig(js_code=[ ... ]) – no core modifications.
-"""
-
-from __future__ import annotations
-import pathlib, re, sys, textwrap
-from dataclasses import dataclass
-from typing import Any, Dict, List, Union
-
-from lark import Lark, Transformer, v_args
-from lark.exceptions import UnexpectedToken, UnexpectedCharacters, VisitError
-
-
-# --------------------------------------------------------------------------- #
-# Custom Error Classes
-# --------------------------------------------------------------------------- #
-class C4AScriptError(Exception):
-    """Custom error class for C4A-Script compilation errors"""
-    
-    def __init__(self, message: str, line: int = None, column: int = None, 
-                 error_type: str = "Syntax Error", details: str = None):
-        self.message = message
-        self.line = line
-        self.column = column
-        self.error_type = error_type
-        self.details = details
-        super().__init__(self._format_message())
-    
-    def _format_message(self) -> str:
-        """Format a clear error message"""
-        lines = [f"\n{'='*60}"]
-        lines.append(f"C4A-Script {self.error_type}")
-        lines.append(f"{'='*60}")
-        
-        if self.line:
-            lines.append(f"Location: Line {self.line}" + (f", Column {self.column}" if self.column else ""))
-        
-        lines.append(f"Error: {self.message}")
-        
-        if self.details:
-            lines.append(f"\nDetails: {self.details}")
-        
-        lines.append("="*60)
-        return "\n".join(lines)
-    
-    @classmethod
-    def from_exception(cls, exc: Exception, script: Union[str, List[str]]) -> 'C4AScriptError':
-        """Create C4AScriptError from another exception"""
-        script_text = script if isinstance(script, str) else '\n'.join(script)
-        script_lines = script_text.split('\n')
-        
-        if isinstance(exc, UnexpectedToken):
-            # Extract line and column from UnexpectedToken
-            line = exc.line
-            column = exc.column
-            
-            # Get the problematic line
-            if 0 < line <= len(script_lines):
-                problem_line = script_lines[line - 1]
-                marker = " " * (column - 1) + "^"
-                
-                details = f"\nCode:\n  {problem_line}\n  {marker}\n"
-                
-                # Improve error message based on context
-                if exc.token.type == 'CLICK' and 'THEN' in str(exc.expected):
-                    message = "Missing 'THEN' keyword after IF condition"
-                elif exc.token.type == '$END':
-                    message = "Unexpected end of script. Check for missing ENDPROC or incomplete commands"
-                elif 'RPAR' in str(exc.expected):
-                    message = "Missing closing parenthesis ')'"
-                elif 'COMMA' in str(exc.expected):
-                    message = "Missing comma ',' in command"
-                else:
-                    message = f"Unexpected '{exc.token}'"
-                    if exc.expected:
-                        expected_list = [str(e) for e in exc.expected if not e.startswith('_')]
-                        if expected_list:
-                            message += f". Expected: {', '.join(expected_list[:3])}"
-                
-                details += f"Token: {exc.token.type} ('{exc.token.value}')"
-            else:
-                message = str(exc)
-                details = None
-            
-            return cls(message, line, column, "Syntax Error", details)
-        
-        elif isinstance(exc, UnexpectedCharacters):
-            # Extract line and column
-            line = exc.line
-            column = exc.column
-            
-            if 0 < line <= len(script_lines):
-                problem_line = script_lines[line - 1]
-                marker = " " * (column - 1) + "^"
-                
-                details = f"\nCode:\n  {problem_line}\n  {marker}\n"
-                message = f"Invalid character or unexpected text at position {column}"
-            else:
-                message = str(exc)
-                details = None
-            
-            return cls(message, line, column, "Syntax Error", details)
-        
-        elif isinstance(exc, ValueError):
-            # Handle runtime errors like undefined procedures
-            message = str(exc)
-            
-            # Try to find which line caused the error
-            if "Unknown procedure" in message:
-                proc_name = re.search(r"'([^']+)'", message)
-                if proc_name:
-                    proc_name = proc_name.group(1)
-                    for i, line in enumerate(script_lines, 1):
-                        if proc_name in line and not line.strip().startswith('PROC'):
-                            details = f"\nCode:\n  {line.strip()}\n\nMake sure the procedure '{proc_name}' is defined with PROC...ENDPROC"
-                            return cls(f"Undefined procedure '{proc_name}'", i, None, "Runtime Error", details)
-            
-            return cls(message, None, None, "Runtime Error", None)
-        
-        else:
-            # Generic error
-            return cls(str(exc), None, None, "Compilation Error", None)
-
-
-# --------------------------------------------------------------------------- #
-# 1. Grammar
-# --------------------------------------------------------------------------- #
-GRAMMAR = r"""
-start           : line*
-?line           : command | proc_def | include | comment
-
-command         : wait | nav | click_cmd | double_click | right_click | move | drag | scroll
-                | type | clear | set_input | press | key_down | key_up
-                | eval_cmd | setvar | proc_call | if_cmd | repeat_cmd
-
-wait            : "WAIT" (ESCAPED_STRING|BACKTICK_STRING|NUMBER) NUMBER? -> wait_cmd
-nav             : "GO" URL                             -> go
-                | "RELOAD"                             -> reload
-                | "BACK"                               -> back
-                | "FORWARD"                            -> forward
-
-click_cmd       : "CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> click
-double_click    : "DOUBLE_CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> double_click  
-right_click     : "RIGHT_CLICK" (BACKTICK_STRING|NUMBER NUMBER) -> right_click
-
-move            : "MOVE" coords                        -> move
-drag            : "DRAG" coords coords                 -> drag
-scroll          : "SCROLL" DIR NUMBER?                 -> scroll
-
-type            : "TYPE" (ESCAPED_STRING | NAME)       -> type
-clear           : "CLEAR" BACKTICK_STRING              -> clear
-set_input       : "SET" BACKTICK_STRING (ESCAPED_STRING | BACKTICK_STRING | NAME) -> set_input
-press           : "PRESS" WORD                         -> press
-key_down        : "KEY_DOWN" WORD                      -> key_down
-key_up          : "KEY_UP" WORD                        -> key_up
-
-eval_cmd        : "EVAL" BACKTICK_STRING               -> eval_cmd
-setvar          : "SETVAR" NAME "=" value              -> setvar
-proc_call       : NAME                                 -> proc_call
-proc_def        : "PROC" NAME line* "ENDPROC"          -> proc_def
-include         : "USE" ESCAPED_STRING                 -> include
-comment         : /#.*/                                -> comment
-
-if_cmd          : "IF" "(" condition ")" "THEN" command ("ELSE" command)?  -> if_cmd
-repeat_cmd      : "REPEAT" "(" command "," repeat_count ")"  -> repeat_cmd
-
-condition       : not_cond | exists_cond | js_cond
-not_cond        : "NOT" condition                      -> not_cond
-exists_cond     : "EXISTS" BACKTICK_STRING             -> exists_cond
-js_cond         : BACKTICK_STRING                      -> js_cond
-
-repeat_count    : NUMBER | BACKTICK_STRING
-
-coords          : NUMBER NUMBER
-value           : ESCAPED_STRING | BACKTICK_STRING | NUMBER
-DIR             : /(UP|DOWN|LEFT|RIGHT)/i
-REST            : /[^\n]+/
-
-URL             : /(http|https):\/\/[^\s]+/
-NAME            : /\$?[A-Za-z_][A-Za-z0-9_]*/
-WORD            : /[A-Za-z0-9+]+/
-BACKTICK_STRING : /`[^`]*`/
-
-%import common.NUMBER
-%import common.ESCAPED_STRING
-%import common.WS_INLINE
-%import common.NEWLINE
-%ignore WS_INLINE
-%ignore NEWLINE
-"""
-
-# --------------------------------------------------------------------------- #
-# 2. IR dataclasses
-# --------------------------------------------------------------------------- #
-@dataclass
-class Cmd:
-    op: str
-    args: List[Any]
-
-@dataclass
-class Proc:
-    name: str
-    body: List[Cmd]
-
-# --------------------------------------------------------------------------- #
-# 3. AST → IR
-# --------------------------------------------------------------------------- #
-@v_args(inline=True)
-class ASTBuilder(Transformer):
-    # helpers
-    def _strip(self, s): 
-        if s.startswith('"') and s.endswith('"'):
-            return s[1:-1]
-        elif s.startswith('`') and s.endswith('`'):
-            return s[1:-1]
-        return s
-    def start(self,*i):  return list(i)
-    def line(self,i):    return i
-    def command(self,i): return i
-
-    # WAIT
-    def wait_cmd(self, rest, timeout=None):
-        rest_str = str(rest)
-        # Check if it's a number (including floats)
-        try:
-            num_val = float(rest_str)
-            payload = (num_val, "seconds")
-        except ValueError:
-            if rest_str.startswith('"') and rest_str.endswith('"'):
-                payload = (self._strip(rest_str), "text")
-            elif rest_str.startswith('`') and rest_str.endswith('`'):
-                payload = (self._strip(rest_str), "selector")
-            else:
-                payload = (rest_str, "selector")
-        return Cmd("WAIT", [payload, int(timeout) if timeout else None])
-
-    # NAV
-    def go(self,u):    return Cmd("GO",[str(u)])
-    def reload(self):  return Cmd("RELOAD",[])
-    def back(self):    return Cmd("BACK",[])
-    def forward(self): return Cmd("FORWARD",[])
-
-    # CLICK, DOUBLE_CLICK, RIGHT_CLICK
-    def click(self, *args):
-        return self._handle_click("CLICK", args)
-    
-    def double_click(self, *args):
-        return self._handle_click("DBLCLICK", args)
-    
-    def right_click(self, *args):
-        return self._handle_click("RIGHTCLICK", args)
-    
-    def _handle_click(self, op, args):
-        if len(args) == 1:
-            # Single argument - backtick string
-            target = self._strip(str(args[0]))
-            return Cmd(op, [("selector", target)])
-        else:
-            # Two arguments - coordinates
-            x, y = args
-            return Cmd(op, [("coords", int(x), int(y))])
-
-
-    # MOVE / DRAG / SCROLL
-    def coords(self,x,y):       return ("coords",int(x),int(y))
-    def move(self,c):           return Cmd("MOVE",[c])
-    def drag(self,c1,c2):       return Cmd("DRAG",[c1,c2])
-    def scroll(self,dir_tok,amt=None):
-        return Cmd("SCROLL",[dir_tok.upper(), int(amt) if amt else 500])
-
-    # KEYS
-    def type(self,tok):     return Cmd("TYPE",[self._strip(str(tok))])
-    def clear(self,sel):    return Cmd("CLEAR",[self._strip(str(sel))])
-    def set_input(self,sel,val): return Cmd("SET",[self._strip(str(sel)), self._strip(str(val))])
-    def press(self,w):      return Cmd("PRESS",[str(w)])
-    def key_down(self,w):   return Cmd("KEYDOWN",[str(w)])
-    def key_up(self,w):     return Cmd("KEYUP",[str(w)])
-
-    # FLOW
-    def eval_cmd(self,txt):     return Cmd("EVAL",[self._strip(str(txt))])
-    def setvar(self,n,v):      
-        # v might be a Token or a Tree, extract value properly
-        if hasattr(v, 'value'):
-            value = v.value
-        elif hasattr(v, 'children') and len(v.children) > 0:
-            value = v.children[0].value
-        else:
-            value = str(v)
-        return Cmd("SETVAR",[str(n), self._strip(value)])
-    def proc_call(self,n):      return Cmd("CALL",[str(n)])
-    def proc_def(self,n,*body): return Proc(str(n),[b for b in body if isinstance(b,Cmd)])
-    def include(self,p):        return Cmd("INCLUDE",[self._strip(p)])
-    def comment(self,*_):       return Cmd("NOP",[])
-    
-    # IF-THEN-ELSE and EXISTS
-    def if_cmd(self, condition, then_cmd, else_cmd=None):
-        return Cmd("IF", [condition, then_cmd, else_cmd])
-    
-    def condition(self, cond):
-        return cond
-    
-    def not_cond(self, cond):
-        return ("NOT", cond)
-    
-    def exists_cond(self, selector):
-        return ("EXISTS", self._strip(str(selector)))
-    
-    def js_cond(self, expr):
-        return ("JS", self._strip(str(expr)))
-    
-    # REPEAT
-    def repeat_cmd(self, cmd, count):
-        return Cmd("REPEAT", [cmd, count])
-    
-    def repeat_count(self, value):
-        return str(value)
-
-# --------------------------------------------------------------------------- #
-# 4. Compiler
-# --------------------------------------------------------------------------- #
-class Compiler:
-    def __init__(self, root: pathlib.Path|None=None):
-        self.parser = Lark(GRAMMAR,start="start",parser="lalr")
-        self.root   = pathlib.Path(root or ".").resolve()
-        self.vars: Dict[str,Any] = {}
-        self.procs: Dict[str,Proc]= {}
-
-    def compile(self, text: Union[str, List[str]]) -> List[str]:
-        # Handle list input by joining with newlines
-        if isinstance(text, list):
-            text = '\n'.join(text)
-        
-        ir = self._parse_with_includes(text)
-        ir = self._collect_procs(ir)
-        ir = self._inline_calls(ir)
-        ir = self._apply_set_vars(ir)
-        return [self._emit_js(c) for c in ir if isinstance(c,Cmd) and c.op!="NOP"]
-
-    # passes
-    def _parse_with_includes(self,txt,seen=None):
-        seen=seen or set()
-        cmds=ASTBuilder().transform(self.parser.parse(txt))
-        out=[]
-        for c in cmds:
-            if isinstance(c,Cmd) and c.op=="INCLUDE":
-                p=(self.root/c.args[0]).resolve()
-                if p in seen: raise ValueError(f"Circular include {p}")
-                seen.add(p); out+=self._parse_with_includes(p.read_text(),seen)
-            else: out.append(c)
-        return out
-
-    def _collect_procs(self,ir):
-        out=[]
-        for i in ir:
-            if isinstance(i,Proc): self.procs[i.name]=i
-            else: out.append(i)
-        return out
-
-    def _inline_calls(self,ir):
-        out=[]
-        for c in ir:
-            if isinstance(c,Cmd) and c.op=="CALL":
-                if c.args[0] not in self.procs:
-                    raise ValueError(f"Unknown procedure {c.args[0]!r}")
-                out+=self._inline_calls(self.procs[c.args[0]].body)
-            else: out.append(c)
-        return out
-
-    def _apply_set_vars(self,ir):
-        def sub(s): return re.sub(r"\$(\w+)",lambda m:str(self.vars.get(m.group(1),m.group(0))) ,s) if isinstance(s,str) else s
-        out=[]
-        for c in ir:
-            if isinstance(c,Cmd):
-                if c.op=="SETVAR":
-                    # Store variable
-                    self.vars[c.args[0].lstrip('$')]=c.args[1]
-                else:
-                    # Apply variable substitution to commands that use them
-                    if c.op in("TYPE","EVAL","SET"): c.args=[sub(a) for a in c.args]
-                    out.append(c)
-        return out
-
-    # JS emitter
-    def _emit_js(self, cmd: Cmd) -> str:
-        op, a = cmd.op, cmd.args
-        if op == "GO":         return f"window.location.href = '{a[0]}';"
-        if op == "RELOAD":     return "window.location.reload();"
-        if op == "BACK":       return "window.history.back();"
-        if op == "FORWARD":    return "window.history.forward();"
-
-        if op == "WAIT":
-            arg, kind = a[0]
-            timeout   = a[1] or 10
-            if kind == "seconds":
-                return f"await new Promise(r=>setTimeout(r,{arg}*1000));"
-            if kind == "selector":
-                sel = arg.replace("\\","\\\\").replace("'","\\'")
-                return textwrap.dedent(f"""
-                    await new Promise((res,rej)=>{{
-                      const max = {timeout*1000}, t0 = performance.now();
-                      const id = setInterval(()=>{{
-                        if(document.querySelector('{sel}')){{clearInterval(id);res();}}
-                        else if(performance.now()-t0>max){{clearInterval(id);rej('WAIT selector timeout');}}
-                      }},100);
-                    }});
-                """).strip()
-            if kind == "text":
-                txt = arg.replace('`', '\\`')
-                return textwrap.dedent(f"""
-                    await new Promise((res,rej)=>{{
-                      const max={timeout*1000},t0=performance.now();
-                      const id=setInterval(()=>{{
-                        if(document.body.innerText.includes(`{txt}`)){{clearInterval(id);res();}}
-                        else if(performance.now()-t0>max){{clearInterval(id);rej('WAIT text timeout');}}
-                      }},100);
-                    }});
-                """).strip()
-
-        # click-style helpers
-        def _js_click(sel, evt="click", button=0, detail=1):
-            sel = sel.replace("'", "\\'")
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const el=document.querySelector('{sel}');
-                  if(el){{
-                    el.focus&&el.focus();
-                    el.dispatchEvent(new MouseEvent('{evt}',{{bubbles:true,button:{button},detail:{detail}}}));
-                  }}
-                }})();
-            """).strip()
-
-        def _js_click_xy(x, y, evt="click", button=0, detail=1):
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const el=document.elementFromPoint({x},{y});
-                  if(el){{
-                    el.focus&&el.focus();
-                    el.dispatchEvent(new MouseEvent('{evt}',{{bubbles:true,button:{button},detail:{detail}}}));
-                  }}
-                }})();
-            """).strip()
-
-        if op in ("CLICK", "DBLCLICK", "RIGHTCLICK"):
-            evt   = {"CLICK":"click","DBLCLICK":"dblclick","RIGHTCLICK":"contextmenu"}[op]
-            btn   = 2 if op=="RIGHTCLICK" else 0
-            det   = 2 if op=="DBLCLICK"   else 1
-            kind,*rest = a[0]
-            return _js_click_xy(*rest) if kind=="coords" else _js_click(rest[0],evt,btn,det)
-
-        if op == "MOVE":
-            _, x, y = a[0]
-            return textwrap.dedent(f"""
-                document.dispatchEvent(new MouseEvent('mousemove',{{clientX:{x},clientY:{y},bubbles:true}}));
-            """).strip()
-
-        if op == "DRAG":
-            (_, x1, y1), (_, x2, y2) = a
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const s=document.elementFromPoint({x1},{y1});
-                  if(!s) return;
-                  s.dispatchEvent(new MouseEvent('mousedown',{{bubbles:true,clientX:{x1},clientY:{y1}}}));
-                  document.dispatchEvent(new MouseEvent('mousemove',{{bubbles:true,clientX:{x2},clientY:{y2}}}));
-                  document.dispatchEvent(new MouseEvent('mouseup',  {{bubbles:true,clientX:{x2},clientY:{y2}}}));
-                }})();
-            """).strip()
-
-        if op == "SCROLL":
-            dir_, amt = a
-            dx, dy = {"UP":(0,-amt),"DOWN":(0,amt),"LEFT":(-amt,0),"RIGHT":(amt,0)}[dir_]
-            return f"window.scrollBy({dx},{dy});"
-
-        if op == "TYPE":
-            txt = a[0].replace("'", "\\'")
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const el=document.activeElement;
-                  if(el){{
-                    el.value += '{txt}';
-                    el.dispatchEvent(new Event('input',{{bubbles:true}}));
-                  }}
-                }})();
-            """).strip()
-
-        if op == "CLEAR":
-            sel = a[0].replace("'", "\\'")
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const el=document.querySelector('{sel}');
-                  if(el && 'value' in el){{
-                    el.value = '';
-                    el.dispatchEvent(new Event('input',{{bubbles:true}}));
-                    el.dispatchEvent(new Event('change',{{bubbles:true}}));
-                  }}
-                }})();
-            """).strip()
-
-        if op == "SET" and len(a) == 2:
-            # This is SET for input fields (SET `#field` "value")
-            sel = a[0].replace("'", "\\'")
-            val = a[1].replace("'", "\\'")
-            return textwrap.dedent(f"""
-                (()=>{{
-                  const el=document.querySelector('{sel}');
-                  if(el && 'value' in el){{
-                    el.value = '';
-                    el.focus&&el.focus();
-                    el.value = '{val}';
-                    el.dispatchEvent(new Event('input',{{bubbles:true}}));
-                    el.dispatchEvent(new Event('change',{{bubbles:true}}));
-                  }}
-                }})();
-            """).strip()
-
-        if op in ("PRESS","KEYDOWN","KEYUP"):
-            key = a[0]
-            evs = {"PRESS":("keydown","keyup"),"KEYDOWN":("keydown",),"KEYUP":("keyup",)}[op]
-            return ";".join([f"document.dispatchEvent(new KeyboardEvent('{e}',{{key:'{key}',bubbles:true}}))" for e in evs]) + ";"
-
-        if op == "EVAL":
-            return textwrap.dedent(f"""
-                (()=>{{
-                  try {{
-                    {a[0]};
-                  }} catch (e) {{
-                    console.error('C4A-Script EVAL error:', e);
-                  }}
-                }})();
-            """).strip()
-        
-        if op == "IF":
-            condition, then_cmd, else_cmd = a
-            
-            # Generate condition JavaScript
-            js_condition = self._emit_condition(condition)
-            
-            # Generate commands - handle both regular commands and procedure calls
-            then_js = self._handle_cmd_or_proc(then_cmd)
-            else_js = self._handle_cmd_or_proc(else_cmd) if else_cmd else ""
-            
-            if else_cmd:
-                return textwrap.dedent(f"""
-                    if ({js_condition}) {{
-                      {then_js}
-                    }} else {{
-                      {else_js}
-                    }}
-                """).strip()
-            else:
-                return textwrap.dedent(f"""
-                    if ({js_condition}) {{
-                      {then_js}
-                    }}
-                """).strip()
-        
-        if op == "REPEAT":
-            cmd, count = a
-            
-            # Handle the count - could be number or JS expression
-            if count.isdigit():
-                # Simple number
-                repeat_js = self._handle_cmd_or_proc(cmd)
-                return textwrap.dedent(f"""
-                    for (let _i = 0; _i < {count}; _i++) {{
-                      {repeat_js}
-                    }}
-                """).strip()
-            else:
-                # JS expression (from backticks)
-                count_expr = count[1:-1] if count.startswith('`') and count.endswith('`') else count
-                repeat_js = self._handle_cmd_or_proc(cmd)
-                return textwrap.dedent(f"""
-                    (()=>{{
-                      const _count = {count_expr};
-                      if (typeof _count === 'number') {{
-                        for (let _i = 0; _i < _count; _i++) {{
-                          {repeat_js}
-                        }}
-                      }} else if (_count) {{
-                        {repeat_js}
-                      }}
-                    }})();
-                """).strip()
-
-        raise ValueError(f"Unhandled op {op}")
-    
-    def _emit_condition(self, condition):
-        """Convert a condition tuple to JavaScript"""
-        cond_type = condition[0]
-        
-        if cond_type == "EXISTS":
-            return f"!!document.querySelector('{condition[1]}')"
-        elif cond_type == "NOT":
-            # Recursively handle the negated condition
-            inner_condition = self._emit_condition(condition[1])
-            return f"!({inner_condition})"
-        else:  # JS condition
-            return condition[1]
-    
-    def _handle_cmd_or_proc(self, cmd):
-        """Handle a command that might be a regular command or a procedure call"""
-        if not cmd:
-            return ""
-        
-        if isinstance(cmd, Cmd):
-            if cmd.op == "CALL":
-                # Inline the procedure
-                if cmd.args[0] not in self.procs:
-                    raise ValueError(f"Unknown procedure {cmd.args[0]!r}")
-                proc_body = self.procs[cmd.args[0]].body
-                return "\n".join([self._emit_js(c) for c in proc_body if c.op != "NOP"])
-            else:
-                return self._emit_js(cmd)
-        return ""
-
-# --------------------------------------------------------------------------- #
-# 5. Helpers + demo
-# --------------------------------------------------------------------------- #
-
-def compile_string(script: Union[str, List[str]], *, root: Union[pathlib.Path, None] = None) -> List[str]:
-    """Compile C4A-Script from string or list of strings to JavaScript.
-    
-    Args:
-        script: C4A-Script as a string or list of command strings
-        root: Root directory for resolving includes (optional)
-    
-    Returns:
-        List of JavaScript command strings
-    
-    Raises:
-        C4AScriptError: When compilation fails with detailed error information
-    """
-    try:
-        return Compiler(root).compile(script)
-    except Exception as e:
-        # Wrap the error with better formatting
-        raise C4AScriptError.from_exception(e, script)
-
-def compile_file(path: pathlib.Path) -> List[str]:
-    """Compile C4A-Script from file to JavaScript.
-    
-    Args:
-        path: Path to C4A-Script file
-    
-    Returns:
-        List of JavaScript command strings
-    """
-    return compile_string(path.read_text(), root=path.parent)
-
-def compile_lines(lines: List[str], *, root: Union[pathlib.Path, None] = None) -> List[str]:
-    """Compile C4A-Script from list of lines to JavaScript.
-    
-    Args:
-        lines: List of C4A-Script command lines
-        root: Root directory for resolving includes (optional)
-    
-    Returns:
-        List of JavaScript command strings
-    """
-    return compile_string(lines, root=root)
-
-DEMO = """
-# quick sanity demo
-PROC login
-  SET `input[name="username"]` $user
-  SET `input[name="password"]` $pass
-  CLICK `button.submit`
-ENDPROC
-
-SETVAR user = "tom@crawl4ai.com"
-SETVAR pass = "hunter2"
-
-GO https://example.com/login
-WAIT `input[name="username"]` 10
-login
-WAIT 3
-EVAL `console.log('logged in')`
-"""
-
-if __name__ == "__main__":
-    if len(sys.argv) == 2:
-        for js in compile_file(pathlib.Path(sys.argv[1])):
-            print(js)
-    else:
-        print("=== DEMO ===")
-        for js in compile_string(DEMO):
-            print(js)

crawl4ai/ssl_certificate.py

@@ -1,204 +0,0 @@
-"""SSL Certificate class for handling certificate operations."""
-
-import ssl
-import socket
-import base64
-import json
-from typing import Dict, Any, Optional
-from urllib.parse import urlparse
-import OpenSSL.crypto
-from pathlib import Path
-
-# === Inherit from dict ===
-class SSLCertificate(dict):
-    """
-    A class representing an SSL certificate, behaving like a dictionary
-    for direct JSON serialization. It stores the certificate information internally
-    and provides methods for export and property access.
-
-    Inherits from dict, so instances are directly JSON serializable.
-    """
-
-    # Use __slots__ for potential memory optimization if desired, though less common when inheriting dict
-    # __slots__ = ("_cert_info",) # If using slots, be careful with dict inheritance interaction
-
-    def __init__(self, cert_info: Dict[str, Any]):
-        """
-        Initializes the SSLCertificate object.
-
-        Args:
-            cert_info (Dict[str, Any]): The raw certificate dictionary.
-        """
-        # 1. Decode the data (handle bytes -> str)
-        decoded_info = self._decode_cert_data(cert_info)
-
-        # 2. Store the decoded info internally (optional but good practice)
-        # self._cert_info = decoded_info # You can keep this if methods rely on it
-
-        # 3. Initialize the dictionary part of the object with the decoded data
-        super().__init__(decoded_info)
-
-    @staticmethod
-    def _decode_cert_data(data: Any) -> Any:
-        """Helper method to decode bytes in certificate data."""
-        if isinstance(data, bytes):
-            try:
-                # Try UTF-8 first, fallback to latin-1 for arbitrary bytes
-                return data.decode("utf-8")
-            except UnicodeDecodeError:
-                return data.decode("latin-1") # Or handle as needed, maybe hex representation
-        elif isinstance(data, dict):
-            return {
-                (
-                    k.decode("utf-8") if isinstance(k, bytes) else k
-                ): SSLCertificate._decode_cert_data(v)
-                for k, v in data.items()
-            }
-        elif isinstance(data, list):
-            return [SSLCertificate._decode_cert_data(item) for item in data]
-        return data
-
-    @staticmethod
-    def from_url(url: str, timeout: int = 10) -> Optional["SSLCertificate"]:
-        """
-        Create SSLCertificate instance from a URL. Fetches cert info and initializes.
-        (Fetching logic remains the same)
-        """
-        cert_info_raw = None # Variable to hold the fetched dict
-        try:
-            hostname = urlparse(url).netloc
-            if ":" in hostname:
-                hostname = hostname.split(":")[0]
-
-            context = ssl.create_default_context()
-            # Set check_hostname to False and verify_mode to CERT_NONE temporarily
-            # for potentially problematic certificates during fetch, but parse the result regardless.
-            # context.check_hostname = False
-            # context.verify_mode = ssl.CERT_NONE
-
-            with socket.create_connection((hostname, 443), timeout=timeout) as sock:
-                with context.wrap_socket(sock, server_hostname=hostname) as ssock:
-                    cert_binary = ssock.getpeercert(binary_form=True)
-                    if not cert_binary:
-                         print(f"Warning: No certificate returned for {hostname}")
-                         return None
-
-                    x509 = OpenSSL.crypto.load_certificate(
-                        OpenSSL.crypto.FILETYPE_ASN1, cert_binary
-                    )
-
-                    # Create the dictionary directly
-                    cert_info_raw = {
-                        "subject": dict(x509.get_subject().get_components()),
-                        "issuer": dict(x509.get_issuer().get_components()),
-                        "version": x509.get_version(),
-                        "serial_number": hex(x509.get_serial_number()),
-                        "not_before": x509.get_notBefore(), # Keep as bytes initially, _decode handles it
-                        "not_after": x509.get_notAfter(),   # Keep as bytes initially
-                        "fingerprint": x509.digest("sha256").hex(), # hex() is already string
-                        "signature_algorithm": x509.get_signature_algorithm(), # Keep as bytes
-                        "raw_cert": base64.b64encode(cert_binary), # Base64 is bytes, _decode handles it
-                    }
-
-                    # Add extensions
-                    extensions = []
-                    for i in range(x509.get_extension_count()):
-                        ext = x509.get_extension(i)
-                        # get_short_name() returns bytes, str(ext) handles value conversion
-                        extensions.append(
-                            {"name": ext.get_short_name(), "value": str(ext)}
-                        )
-                    cert_info_raw["extensions"] = extensions
-
-        except ssl.SSLCertVerificationError as e:
-             print(f"SSL Verification Error for {url}: {e}")
-             # Decide if you want to proceed or return None based on your needs
-             # You might try fetching without verification here if needed, but be cautious.
-             return None
-        except socket.gaierror:
-            print(f"Could not resolve hostname: {hostname}")
-            return None
-        except socket.timeout:
-            print(f"Connection timed out for {url}")
-            return None
-        except Exception as e:
-            print(f"Error fetching/processing certificate for {url}: {e}")
-            # Log the full error details if needed: logging.exception("Cert fetch error")
-            return None
-
-        # If successful, create the SSLCertificate instance from the dictionary
-        if cert_info_raw:
-             return SSLCertificate(cert_info_raw)
-        else:
-             return None
-
-
-    # --- Properties now access the dictionary items directly via self[] ---
-    @property
-    def issuer(self) -> Dict[str, str]:
-        return self.get("issuer", {}) # Use self.get for safety
-
-    @property
-    def subject(self) -> Dict[str, str]:
-        return self.get("subject", {})
-
-    @property
-    def valid_from(self) -> str:
-        return self.get("not_before", "")
-
-    @property
-    def valid_until(self) -> str:
-        return self.get("not_after", "")
-
-    @property
-    def fingerprint(self) -> str:
-        return self.get("fingerprint", "")
-
-    # --- Export methods can use `self` directly as it is the dict ---
-    def to_json(self, filepath: Optional[str] = None) -> Optional[str]:
-        """Export certificate as JSON."""
-        # `self` is already the dictionary we want to serialize
-        json_str = json.dumps(self, indent=2, ensure_ascii=False)
-        if filepath:
-            Path(filepath).write_text(json_str, encoding="utf-8")
-            return None
-        return json_str
-
-    def to_pem(self, filepath: Optional[str] = None) -> Optional[str]:
-        """Export certificate as PEM."""
-        try:
-            # Decode the raw_cert (which should be string due to _decode)
-            raw_cert_bytes = base64.b64decode(self.get("raw_cert", ""))
-            x509 = OpenSSL.crypto.load_certificate(
-                OpenSSL.crypto.FILETYPE_ASN1, raw_cert_bytes
-            )
-            pem_data = OpenSSL.crypto.dump_certificate(
-                OpenSSL.crypto.FILETYPE_PEM, x509
-            ).decode("utf-8")
-
-            if filepath:
-                Path(filepath).write_text(pem_data, encoding="utf-8")
-                return None
-            return pem_data
-        except Exception as e:
-             print(f"Error converting to PEM: {e}")
-             return None
-
-    def to_der(self, filepath: Optional[str] = None) -> Optional[bytes]:
-        """Export certificate as DER."""
-        try:
-            # Decode the raw_cert (which should be string due to _decode)
-            der_data = base64.b64decode(self.get("raw_cert", ""))
-            if filepath:
-                Path(filepath).write_bytes(der_data)
-                return None
-            return der_data
-        except Exception as e:
-             print(f"Error converting to DER: {e}")
-             return None
-
-    # Optional: Add __repr__ for better debugging
-    def __repr__(self) -> str:
-        subject_cn = self.subject.get('CN', 'N/A')
-        issuer_cn = self.issuer.get('CN', 'N/A')
-        return f"<SSLCertificate Subject='{subject_cn}' Issuer='{issuer_cn}'>"

crawl4ai/types.py

@@ -1,195 +0,0 @@
-from typing import TYPE_CHECKING, Union
-
-# Logger types
-AsyncLoggerBase = Union['AsyncLoggerBaseType']
-AsyncLogger = Union['AsyncLoggerType']
-
-# Crawler core types
-AsyncWebCrawler = Union['AsyncWebCrawlerType']
-CacheMode = Union['CacheModeType']
-CrawlResult = Union['CrawlResultType']
-CrawlerHub = Union['CrawlerHubType']
-BrowserProfiler = Union['BrowserProfilerType']
-# NEW: Add AsyncUrlSeederType
-AsyncUrlSeeder = Union['AsyncUrlSeederType']
-
-# Configuration types
-BrowserConfig = Union['BrowserConfigType']
-CrawlerRunConfig = Union['CrawlerRunConfigType']
-HTTPCrawlerConfig = Union['HTTPCrawlerConfigType']
-LLMConfig = Union['LLMConfigType']
-# NEW: Add SeedingConfigType
-SeedingConfig = Union['SeedingConfigType']
-
-# Content scraping types
-ContentScrapingStrategy = Union['ContentScrapingStrategyType']
-LXMLWebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
-# Backward compatibility alias
-WebScrapingStrategy = Union['LXMLWebScrapingStrategyType']
-
-# Proxy types
-ProxyRotationStrategy = Union['ProxyRotationStrategyType']
-RoundRobinProxyStrategy = Union['RoundRobinProxyStrategyType']
-
-# Extraction types
-ExtractionStrategy = Union['ExtractionStrategyType']
-LLMExtractionStrategy = Union['LLMExtractionStrategyType']
-CosineStrategy = Union['CosineStrategyType']
-JsonCssExtractionStrategy = Union['JsonCssExtractionStrategyType']
-JsonXPathExtractionStrategy = Union['JsonXPathExtractionStrategyType']
-
-# Chunking types
-ChunkingStrategy = Union['ChunkingStrategyType']
-RegexChunking = Union['RegexChunkingType']
-
-# Markdown generation types
-DefaultMarkdownGenerator = Union['DefaultMarkdownGeneratorType']
-MarkdownGenerationResult = Union['MarkdownGenerationResultType']
-
-# Content filter types
-RelevantContentFilter = Union['RelevantContentFilterType']
-PruningContentFilter = Union['PruningContentFilterType']
-BM25ContentFilter = Union['BM25ContentFilterType']
-LLMContentFilter = Union['LLMContentFilterType']
-
-# Dispatcher types
-BaseDispatcher = Union['BaseDispatcherType']
-MemoryAdaptiveDispatcher = Union['MemoryAdaptiveDispatcherType']
-SemaphoreDispatcher = Union['SemaphoreDispatcherType']
-RateLimiter = Union['RateLimiterType']
-CrawlerMonitor = Union['CrawlerMonitorType']
-DisplayMode = Union['DisplayModeType']
-RunManyReturn = Union['RunManyReturnType']
-
-# Docker client
-Crawl4aiDockerClient = Union['Crawl4aiDockerClientType']
-
-# Deep crawling types
-DeepCrawlStrategy = Union['DeepCrawlStrategyType']
-BFSDeepCrawlStrategy = Union['BFSDeepCrawlStrategyType']
-FilterChain = Union['FilterChainType']
-ContentTypeFilter = Union['ContentTypeFilterType']
-DomainFilter = Union['DomainFilterType']
-URLFilter = Union['URLFilterType']
-FilterStats = Union['FilterStatsType']
-SEOFilter = Union['SEOFilterType']
-KeywordRelevanceScorer = Union['KeywordRelevanceScorerType']
-URLScorer = Union['URLScorerType']
-CompositeScorer = Union['CompositeScorerType']
-DomainAuthorityScorer = Union['DomainAuthorityScorerType']
-FreshnessScorer = Union['FreshnessScorerType']
-PathDepthScorer = Union['PathDepthScorerType']
-BestFirstCrawlingStrategy = Union['BestFirstCrawlingStrategyType']
-DFSDeepCrawlStrategy = Union['DFSDeepCrawlStrategyType']
-DeepCrawlDecorator = Union['DeepCrawlDecoratorType']
-
-# Only import types during type checking to avoid circular imports
-if TYPE_CHECKING:
-    # Logger imports
-    from .async_logger import (
-        AsyncLoggerBase as AsyncLoggerBaseType,
-        AsyncLogger as AsyncLoggerType,
-    )
-    
-    # Crawler core imports
-    from .async_webcrawler import (
-        AsyncWebCrawler as AsyncWebCrawlerType,
-        CacheMode as CacheModeType,
-    )
-    from .models import CrawlResult as CrawlResultType
-    from .hub import CrawlerHub as CrawlerHubType
-    from .browser_profiler import BrowserProfiler as BrowserProfilerType
-    # NEW: Import AsyncUrlSeeder for type checking
-    from .async_url_seeder import AsyncUrlSeeder as AsyncUrlSeederType
-    
-    # Configuration imports
-    from .async_configs import (
-        BrowserConfig as BrowserConfigType,
-        CrawlerRunConfig as CrawlerRunConfigType,
-        HTTPCrawlerConfig as HTTPCrawlerConfigType,
-        LLMConfig as LLMConfigType,
-        # NEW: Import SeedingConfig for type checking
-        SeedingConfig as SeedingConfigType,
-    )
-    
-    # Content scraping imports
-    from .content_scraping_strategy import (
-        ContentScrapingStrategy as ContentScrapingStrategyType,
-        LXMLWebScrapingStrategy as LXMLWebScrapingStrategyType,
-    )
-    
-    # Proxy imports
-    from .proxy_strategy import (
-        ProxyRotationStrategy as ProxyRotationStrategyType,
-        RoundRobinProxyStrategy as RoundRobinProxyStrategyType,
-    )
-    
-    # Extraction imports
-    from .extraction_strategy import (
-        ExtractionStrategy as ExtractionStrategyType,
-        LLMExtractionStrategy as LLMExtractionStrategyType,
-        CosineStrategy as CosineStrategyType,
-        JsonCssExtractionStrategy as JsonCssExtractionStrategyType,
-        JsonXPathExtractionStrategy as JsonXPathExtractionStrategyType,
-    )
-    
-    # Chunking imports
-    from .chunking_strategy import (
-        ChunkingStrategy as ChunkingStrategyType,
-        RegexChunking as RegexChunkingType,
-    )
-    
-    # Markdown generation imports
-    from .markdown_generation_strategy import (
-        DefaultMarkdownGenerator as DefaultMarkdownGeneratorType,
-    )
-    from .models import MarkdownGenerationResult as MarkdownGenerationResultType
-    
-    # Content filter imports
-    from .content_filter_strategy import (
-        RelevantContentFilter as RelevantContentFilterType,
-        PruningContentFilter as PruningContentFilterType,
-        BM25ContentFilter as BM25ContentFilterType,
-        LLMContentFilter as LLMContentFilterType,
-    )
-    
-    # Dispatcher imports
-    from .async_dispatcher import (
-        BaseDispatcher as BaseDispatcherType,
-        MemoryAdaptiveDispatcher as MemoryAdaptiveDispatcherType,
-        SemaphoreDispatcher as SemaphoreDispatcherType,
-        RateLimiter as RateLimiterType,
-        CrawlerMonitor as CrawlerMonitorType,
-        DisplayMode as DisplayModeType,
-        RunManyReturn as RunManyReturnType,
-    )
-    
-    # Docker client
-    from .docker_client import Crawl4aiDockerClient as Crawl4aiDockerClientType
-    
-    # Deep crawling imports
-    from .deep_crawling import (
-        DeepCrawlStrategy as DeepCrawlStrategyType,
-        BFSDeepCrawlStrategy as BFSDeepCrawlStrategyType,
-        FilterChain as FilterChainType,
-        ContentTypeFilter as ContentTypeFilterType,
-        DomainFilter as DomainFilterType,
-        URLFilter as URLFilterType,
-        FilterStats as FilterStatsType,
-        SEOFilter as SEOFilterType,
-        KeywordRelevanceScorer as KeywordRelevanceScorerType,
-        URLScorer as URLScorerType,
-        CompositeScorer as CompositeScorerType,
-        DomainAuthorityScorer as DomainAuthorityScorerType,
-        FreshnessScorer as FreshnessScorerType,
-        PathDepthScorer as PathDepthScorerType,
-        BestFirstCrawlingStrategy as BestFirstCrawlingStrategyType,
-        DFSDeepCrawlStrategy as DFSDeepCrawlStrategyType,
-        DeepCrawlDecorator as DeepCrawlDecoratorType,
-    )
-
-
-
-def create_llm_config(*args, **kwargs) -> 'LLMConfigType':
-    from .async_configs import LLMConfig
-    return LLMConfig(*args, **kwargs)