Fix version

.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
-}

.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

@@ -1,11 +1,3 @@
-# Scripts folder (private tools)
-.scripts/
-
-# Local development CLI (private)
-local_dev.py
-dev
-DEV_CLI_README.md
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -234,48 +226,8 @@ tree.md
 .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
-
-tests/**/test_site
-tests/**/reports
-tests/**/benchmark_reports
-
-docs/**/data
-.codecat/
-
-docs/apps/linkdin/debug*/
-docs/apps/linkdin/samples/insights/*
-
-# Production checklist (local, not for version control)
-PRODUCTION_CHECKLIST.md
+# windsurf rules
+.windsurfrules

CHANGELOG.md

@@ -5,411 +5,9 @@ All notable changes to Crawl4AI will be documented in this file.
 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).
 
-## [0.7.3] - 2025-08-09
+Okay, here's a detailed changelog in Markdown format, generated from the provided git diff and commit history. I've focused on user-facing changes, fixes, and features, and grouped them as requested:
 
-### Added
-- **🕵️ Undetected Browser Support**: New browser adapter pattern with stealth capabilities
-  - `browser_adapter.py` with undetected Chrome integration
-  - Bypass sophisticated bot detection systems (Cloudflare, Akamai, custom solutions)
-  - Support for headless stealth mode with anti-detection techniques
-  - Human-like behavior simulation with random mouse movements and scrolling
-  - Comprehensive examples for anti-bot strategies and stealth crawling
-  - Full documentation guide for undetected browser usage
-
-- **🎨 Multi-URL Configuration System**: URL-specific crawler configurations for batch processing
-  - Different crawling strategies for different URL patterns in a single batch
-  - Support for string patterns with wildcards (`"*.pdf"`, `"*/blog/*"`)
-  - Lambda function matchers for complex URL logic
-  - Mixed matchers combining strings and functions with AND/OR logic
-  - Fallback configuration support when no patterns match
-  - First-match-wins configuration selection with optional fallback
-
-- **🧠 Memory Monitoring & Optimization**: Comprehensive memory usage tracking
-  - New `memory_utils.py` module for memory monitoring and optimization
-  - Real-time memory usage tracking during crawl sessions
-  - Memory leak detection and reporting
-  - Performance optimization recommendations
-  - Peak memory usage analysis and efficiency metrics
-  - Automatic cleanup suggestions for memory-intensive operations
-
-- **📊 Enhanced Table Extraction**: Improved table access and DataFrame conversion
-  - Direct `result.tables` interface replacing generic `result.media` approach
-  - Instant pandas DataFrame conversion with `pd.DataFrame(table['data'])`
-  - Enhanced table detection algorithms for better accuracy
-  - Table metadata including source XPath and headers
-  - Improved table structure preservation during extraction
-
-- **💰 GitHub Sponsors Integration**: 4-tier sponsorship system
-  - Supporter ($5/month): Community support + early feature previews
-  - Professional ($25/month): Priority support + beta access
-  - Business ($100/month): Direct consultation + custom integrations
-  - Enterprise ($500/month): Dedicated support + feature development
-  - Custom arrangement options for larger organizations
-
-- **🐳 Docker LLM Provider Flexibility**: Environment-based LLM configuration
-  - `LLM_PROVIDER` environment variable support for dynamic provider switching
-  - `.llm.env` file support for secure configuration management
-  - Per-request provider override capabilities in API endpoints
-  - Support for OpenAI, Groq, and other providers without rebuilding images
-  - Enhanced Docker documentation with deployment examples
-
-### Fixed
-- **URL Matcher Fallback**: Resolved edge cases in URL pattern matching logic
-- **Memory Management**: Fixed memory leaks in long-running crawl sessions
-- **Sitemap Processing**: Improved redirect handling in sitemap fetching
-- **Table Extraction**: Enhanced table detection and extraction accuracy
-- **Error Handling**: Better error messages and recovery from network failures
-
-### Changed
-- **Architecture Refactoring**: Major cleanup and optimization
-  - Moved 2,450+ lines from main `async_crawler_strategy.py` to backup
-  - Cleaner separation of concerns in crawler architecture
-  - Better maintainability and code organization
-  - Preserved backward compatibility while improving performance
-
-### Documentation
-- **Comprehensive Examples**: Added real-world URLs and practical use cases
-- **API Documentation**: Complete CrawlResult field documentation with all available fields
-- **Migration Guides**: Updated table extraction patterns from `result.media` to `result.tables`
-- **Undetected Browser Guide**: Full documentation for stealth mode and anti-bot strategies
-- **Multi-Config Examples**: Detailed examples for URL-specific configurations
-- **Docker Deployment**: Enhanced Docker documentation with LLM provider configuration
-
-## [0.7.x] - 2025-06-29
-
-### Added
-- **Virtual Scroll Support**: New `VirtualScrollConfig` for handling virtualized scrolling on modern websites
-  - Automatically detects and handles three scrolling scenarios:
-    - Content unchanged (continue scrolling)
-    - Content appended (traditional infinite scroll)
-    - Content replaced (true virtual scroll - Twitter/Instagram style)
-  - Captures ALL content from pages that replace DOM elements during scroll
-  - Intelligent deduplication based on normalized text content
-  - Configurable scroll amount, count, and wait times
-  - Seamless integration with existing extraction strategies
-  - Comprehensive examples including Twitter timeline, Instagram grid, and mixed content scenarios
-
-## [Unreleased]
-
-### Added
-- **Flexible LLM Provider Configuration** (Docker): 
-  - Support for `LLM_PROVIDER` environment variable to override default provider
-  - Per-request provider override via optional `provider` parameter in API endpoints
-  - Automatic provider validation with clear error messages
-  - Updated Docker documentation and examples
-
-### Changed
-- **WebScrapingStrategy Refactoring**: Simplified content scraping architecture
-  - `WebScrapingStrategy` is now an alias for `LXMLWebScrapingStrategy` for backward compatibility
-  - Removed redundant BeautifulSoup-based implementation (~1000 lines of code)
-  - `LXMLWebScrapingStrategy` now inherits directly from `ContentScrapingStrategy`
-  - All existing code using `WebScrapingStrategy` continues to work without modification
-  - Default scraping strategy remains `LXMLWebScrapingStrategy` for optimal performance
-
-### Added
-- **AsyncUrlSeeder**: High-performance URL discovery system for intelligent crawling at scale
-  - Discover URLs from sitemaps and Common Crawl index
-  - Extract and analyze page metadata without full crawling
-  - BM25 relevance scoring for query-based URL filtering
-  - Multi-domain parallel discovery with `many_urls()` method
-  - Automatic caching with TTL for discovered URLs
-  - Rate limiting and concurrent request management
-  - Live URL validation with HEAD requests
-  - JSON-LD and Open Graph metadata extraction
-- **SeedingConfig**: Configuration class for URL seeding operations
-  - Support for multiple discovery sources (`sitemap`, `cc`, `sitemap+cc`)
-  - Pattern-based URL filtering with wildcards
-  - Configurable concurrency and rate limiting
-  - Query-based relevance scoring with BM25
-  - Score threshold filtering for quality control
-- Comprehensive documentation for URL seeding feature
-  - Detailed comparison with deep crawling approaches
-  - Complete API reference with examples
-  - Integration guide with AsyncWebCrawler
-  - Performance benchmarks and best practices
-- Example scripts demonstrating URL seeding:
-  - `url_seeder_demo.py`: Interactive Rich-based demonstration
-  - `url_seeder_quick_demo.py`: Screenshot-friendly examples
-- Test suite for URL seeding with BM25 scoring
-
-### Changed
-- Updated `__init__.py` to export AsyncUrlSeeder and SeedingConfig
-- Enhanced documentation with URL seeding integration examples
-
-### Fixed
-- Corrected examples to properly extract URLs from seeder results before passing to `arun_many()`
-- Fixed logger color compatibility issue (changed `lightblack` to `bright_black`)
-
-## [0.6.2] - 2025-05-02
-
-### Added
-- New `RegexExtractionStrategy` for fast pattern-based extraction without requiring LLM
-  - Built-in patterns for emails, URLs, phone numbers, dates, and more
-  - Support for custom regex patterns
-  - `generate_pattern` utility for LLM-assisted pattern creation (one-time use)
-- Added `fit_html` as a top-level field in `CrawlResult` for optimized HTML extraction
-- Added support for network response body capture in network request tracking
-
-### Changed
-- Updated documentation for no-LLM extraction strategies
-- Enhanced API reference to include RegexExtractionStrategy examples and usage
-- Improved HTML preprocessing with optimized performance for extraction strategies
-
-## [0.6.1] - 2025-04-24
-
-### Added
-- New dedicated `tables` field in `CrawlResult` model for better table extraction handling
-- Updated crypto_analysis_example.py to use the new tables field with backward compatibility
-
-### Changed
-- Improved playground UI in Docker deployment with better endpoint handling and UI feedback
-
-## [0.6.0] ‑ 2025‑04‑22
-
-### Added
-- Browser pooling with page pre‑warming and fine‑grained **geolocation, locale, and timezone** controls  
-- Crawler pool manager (SDK + Docker API) for smarter resource allocation  
-- Network & console log capture plus MHTML snapshot export  
-- **Table extractor**: turn HTML `<table>`s into DataFrames or CSV with one flag  
-- High‑volume stress‑test framework in `tests/memory` and API load scripts  
-- MCP protocol endpoints with socket & SSE support; playground UI scaffold  
-- Docs v2 revamp: TOC, GitHub badge, copy‑code buttons, Docker API demo  
-- “Ask AI” helper button *(work‑in‑progress, shipping soon)*  
-- New examples: geo‑location usage, network/console capture, Docker API, markdown source selection, crypto analysis  
-- Expanded automated test suites for browser, Docker, MCP and memory benchmarks  
-
-### Changed
-- Consolidated and renamed browser strategies; legacy docker strategy modules removed  
-- `ProxyConfig` moved to `async_configs`  
-- Server migrated to pool‑based crawler management  
-- FastAPI validators replace custom query validation  
-- Docker build now uses Chromium base image  
-- Large‑scale repo tidy‑up (≈36 k insertions, ≈5 k deletions)  
-
-### Fixed
-- Async crawler session leak, duplicate‑visit handling, URL normalisation  
-- Target‑element regressions in scraping strategies  
-- Logged‑URL readability, encoded‑URL decoding, middle truncation for long URLs  
-- Closed issues: #701, #733, #756, #774, #804, #822, #839, #841, #842, #843, #867, #902, #911  
-
-### Removed
-- Obsolete modules under `crawl4ai/browser/*` superseded by the new pooled browser layer  
-
-### Deprecated
-- Old markdown generator names now alias `DefaultMarkdownGenerator` and emit warnings  
-
----
-
-#### Upgrade notes
-1. Update any direct imports from `crawl4ai/browser/*` to the new pooled browser modules  
-2. If you override `AsyncPlaywrightCrawlerStrategy.get_page`, adopt the new signature  
-3. Rebuild Docker images to pull the new Chromium layer  
-4. Switch to `DefaultMarkdownGenerator` (or silence the deprecation warning)  
-
----
-
-`121 files changed, ≈36 223 insertions, ≈4 975 deletions` :contentReference[oaicite:0]{index=0}&#8203;:contentReference[oaicite:1]{index=1}
-
-
-### [Feature] 2025-04-21
-- Implemented MCP protocol for machine-to-machine communication
-  - Added WebSocket and SSE transport for MCP server
-  - Exposed server endpoints via MCP protocol
-  - Created tests for MCP socket and SSE communication
-- Enhanced Docker server with file handling and intelligent search
-  - Added PDF and screenshot endpoints with file saving capability
-  - Added JavaScript execution endpoint for page interaction
-  - Implemented advanced context search with BM25 and code chunking
-  - Added file path output support for generated assets
-- Improved server endpoints and API surface
-  - Added intelligent context search with query filtering
-  - Added syntax-aware code function chunking
-  - Implemented efficient HTML processing pipeline
-- Added support for controlling browser geolocation via new GeolocationConfig class
-  - Added locale and timezone configuration options to CrawlerRunConfig
-  - Added example script demonstrating geolocation and locale usage
-  - Added documentation for location-based identity features
-
-### [Refactor] 2025-04-20
-- Replaced crawler_manager.py with simpler crawler_pool.py implementation
-- Added global page semaphore for hard concurrency cap
-- Implemented browser pool with idle cleanup
-- Added playground UI for testing and stress testing
-- Updated API handlers to use pooled crawlers
-- Enhanced logging levels and symbols
-- Added memory tests and stress test utilities
-
-### [Added] 2025-04-17
-- Added content source selection feature for markdown generation
-  - New `content_source` parameter allows choosing between `cleaned_html`, `raw_html`, and `fit_html`
-  - Provides flexibility in how HTML content is processed before markdown conversion
-  - Added examples and documentation for the new feature
-  - Includes backward compatibility with default `cleaned_html` behavior
-  
-## Version 0.5.0.post5 (2025-03-14)
-
-### Added
-
-- *(crawler)* Add experimental parameters dictionary to CrawlerRunConfig to support beta features
-- *(tables)* Add comprehensive table detection and extraction functionality with scoring system
-- *(monitor)* Add real-time crawler monitoring system with memory management
-- *(content)* Add target_elements parameter for selective content extraction
-- *(browser)* Add standalone CDP browser launch capability
-- *(schema)* Add preprocess_html_for_schema utility for better HTML cleaning
-- *(api)* Add special handling for single URL requests in Docker API
-
-### Changed
-
-- *(filters)* Add reverse option to URLPatternFilter for inverting filter logic
-- *(browser)* Make CSP nonce headers optional via experimental config
-- *(browser)* Remove default cookie injection from page initialization
-- *(crawler)* Optimize response handling for single-URL processing
-- *(api)* Refactor crawl request handling to streamline processing
-- *(config)* Update default provider to gpt-4o
-- *(cache)* Change default cache_mode from aggressive to bypass in examples
-
-### Fixed
-
-- *(browser)* Clean up browser context creation code
-- *(api)* Improve code formatting in API handler
-
-### Breaking Changes
-
-- WebScrapingStrategy no longer returns 'scraped_html' in its output dictionary
-- Table extraction logic has been modified to better handle thead/tbody structures
-- Default cookie injection has been removed from page initialization
-
-## Version 0.5.0 (2025-03-02)
-
-### Added
-
-- *(profiles)* Add BrowserProfiler class for dedicated browser profile management
-- *(cli)* Add interactive profile management to CLI with rich UI
-- *(profiles)* Add ability to crawl directly from profile management interface
-- *(browser)* Support identity-based browsing with persistent profiles
-- *(deep-crawling)* Add max_pages parameter to limit the number of pages crawled in all deep crawling strategies
-- *(deep-crawling)* Add score_threshold parameter to BFS and DFS strategies to filter URLs by score
-
-### Changed
-
-- *(browser)* Refactor profile management from ManagedBrowser to BrowserProfiler class
-- *(cli)* Enhance CLI with profile selection and status display for crawling
-- *(examples)* Update identity-based browsing example to use BrowserProfiler class
-- *(docs)* Update identity-based crawling documentation
-- *(docs)* Update deep crawling documentation with max_pages and score_threshold parameters
-- *(examples)* Add example demonstrating the use of max_pages and score_threshold parameters
-
-### Fixed
-
-- *(browser)* Fix profile detection and management on different platforms
-- *(cli)* Fix CLI command structure for better user experience
-- *(deep-crawling)* Improve BFS and DFS strategies to handle page count limits more efficiently
-
-
-## Version 0.5.0 (2025-02-21)
-
-### Added
-
-- *(crawler)* [**breaking**] Add memory-adaptive dispatcher with rate limiting
-- *(scraping)* [**breaking**] Add LXML-based scraping mode for improved performance
-- *(content-filter)* Add LLMContentFilter for intelligent markdown generation
-- *(dispatcher)* [**breaking**] Add streaming support for URL processing
-- *(browser)* [**breaking**] Improve browser context management and add shared data support
-- *(config)* [**breaking**] Add streaming support and config cloning
-- *(crawler)* Add URL redirection tracking
-- *(extraction)* Add LLM-powered schema generation utility
-- *(proxy)* Add proxy configuration support to CrawlerRunConfig
-- *(robots)* Add robots.txt compliance support
-- *(release)* [**breaking**] Prepare v0.4.3 beta release
-- *(proxy)* Add proxy rotation support and documentation
-- *(browser)* Add CDP URL configuration support
-- *(demo)* Uncomment feature demos and add fake-useragent dependency
-- *(pdf)* Add PDF processing capabilities
-- *(crawler)* [**breaking**] Enhance JavaScript execution and PDF processing
-- *(docker)* Add Docker deployment configuration and API server
-- *(docker)* Add Docker service integration and config serialization
-- *(docker)* [**breaking**] Enhance Docker deployment setup and configuration
-- *(api)* Improve cache handling and add API tests
-- *(crawler)* [**breaking**] Add deep crawling capabilities with BFS strategy
-- *(proxy)* [**breaking**] Add proxy rotation strategy
-- *(deep-crawling)* Add DFS strategy and update exports; refactor CLI entry point
-- *(cli)* Add command line interface with comprehensive features
-- *(config)* Enhance serialization and add deep crawling exports
-- *(crawler)* Add HTTP crawler strategy for lightweight web scraping
-- *(docker)* [**breaking**] Implement supervisor and secure API endpoints
-- *(docker)* [**breaking**] Add JWT authentication and improve server architecture
-
-### Changed
-
-- *(browser)* Update browser channel default to 'chromium' in BrowserConfig.from_args method
-- *(crawler)* Optimize response handling and default settings
-- *(crawler)* - Update hello_world example with proper content filtering
-- - Update hello_world.py example
-- *(docs)* [**breaking**] Reorganize documentation structure and update styles
-- *(dispatcher)* [**breaking**] Migrate to modular dispatcher system with enhanced monitoring
-- *(scraping)* [**breaking**] Replace ScrapingMode enum with strategy pattern
-- *(browser)* Improve browser path management
-- *(models)* Rename final_url to redirected_url for consistency
-- *(core)* [**breaking**] Improve type hints and remove unused file
-- *(docs)* Improve code formatting in features demo
-- *(user-agent)* Improve user agent generation system
-- *(core)* [**breaking**] Reorganize project structure and remove legacy code
-- *(docker)* Clean up import statements in server.py
-- *(docker)* Remove unused models and utilities for cleaner codebase
-- *(docker)* [**breaking**] Improve server architecture and configuration
-- *(deep-crawl)* [**breaking**] Reorganize deep crawling functionality into dedicated module
-- *(deep-crawling)* [**breaking**] Reorganize deep crawling strategies and add new implementations
-- *(crawling)* [**breaking**] Improve type hints and code cleanup
-- *(crawler)* [**breaking**] Improve HTML handling and cleanup codebase
-- *(crawler)* [**breaking**] Remove content filter functionality
-- *(examples)* Update API usage in features demo
-- *(config)* [**breaking**] Enhance serialization and config handling
-
-### Docs
-
-- Add Code of Conduct for the project (#410)
-
-### Documentation
-
-- *(extraction)* Add clarifying comments for CSS selector behavior
-- *(readme)* Update personal story and project vision
-- *(urls)* [**breaking**] Update documentation URLs to new domain
-- *(api)* Add streaming mode documentation and examples
-- *(readme)* Update version and feature announcements for v0.4.3b1
-- *(examples)* Update demo scripts and fix output formats
-- *(examples)* Update v0.4.3 features demo to v0.4.3b2
-- *(readme)* Update version references and fix links
-- *(multi-url)* [**breaking**] Improve documentation clarity and update examples
-- *(examples)* Update proxy rotation demo and disable other demos
-- *(api)* Improve formatting and readability of API documentation
-- *(examples)* Add SERP API project example
-- *(urls)* Update documentation URLs to new domain
-- *(readme)* Resolve merge conflict and update version info
-
-### Fixed
-
-- *(browser)* Update default browser channel to chromium and simplify channel selection logic
-- *(browser)* [**breaking**] Default to Chromium channel for new headless mode (#387)
-- *(browser)* Resolve merge conflicts in browser channel configuration
-- Prevent memory leaks by ensuring proper closure of Playwright pages
-- Not working long page screenshot (#403)
-- *(extraction)* JsonCss selector and crawler improvements
-- *(models)* [**breaking**] Make model fields optional with default values
-- *(dispatcher)* Adjust memory threshold and fix dispatcher initialization
-- *(install)* Ensure proper exit after running doctor command
-
-### Miscellaneous Tasks
-
-- *(cleanup)* Remove unused files and improve type hints
-- Add .gitattributes file
-
-## License Update
-
-Crawl4AI v0.5.0 updates the license to Apache 2.0 *with a required attribution clause*.  This means you are free to use, modify, and distribute Crawl4AI (even commercially), but you *must* clearly attribute the project in any public use or distribution.  See the updated `LICENSE` file for the full legal text and specific requirements.
-
----
-
-## Version 0.4.3b2 (2025-01-21)
+## Version 0.4.3 (2025-01-21)
 
 This release introduces several powerful new features, including robots.txt compliance, dynamic proxy support, LLM-powered schema generation, and improved documentation.
 
@@ -537,11 +135,9 @@ This release introduces several powerful new features, including robots.txt comp
 - **Multiple Element Selection**: Modified `_get_elements` in `JsonCssExtractionStrategy` to return all matching elements instead of just the first one, ensuring comprehensive extraction. ([#extraction_strategy.py](crawl4ai/extraction_strategy.py))
 - **Error Handling in Scrolling**: Added robust error handling to ensure scrolling proceeds safely even if a configuration is missing. ([#async_crawler_strategy.py](crawl4ai/async_crawler_strategy.py))
 
-## [0.4.267] - 2025 - 01 - 06
+#### Other
+- **Git Ignore Update**: Added `/plans` to `.gitignore` for better development environment consistency. ([#.gitignore](.gitignore))
 
-### Added
-- **Windows Event Loop Configuration**: Introduced a utility function `configure_windows_event_loop` to resolve `NotImplementedError` for asyncio subprocesses on Windows. ([#utils.py](crawl4ai/utils.py), [#tutorials/async-webcrawler-basics.md](docs/md_v3/tutorials/async-webcrawler-basics.md))
-- **`page_need_scroll` Method**: Added a method to determine if a page requires scrolling before taking actions in `AsyncPlaywrightCrawlerStrategy`. ([#async_crawler_strategy.py](crawl4ai/async_crawler_strategy.py))
 
 ## [0.4.24] - 2024-12-31
 
@@ -685,6 +281,12 @@ This release introduces several powerful new features, including robots.txt comp
 - Fixed potential viewport mismatches by ensuring consistent use of `self.viewport_width` and `self.viewport_height` throughout the code.
 - Improved robustness of dynamic content loading to avoid timeouts and failed evaluations.
 
+
+
+
+
+
+
 ## [0.3.75] December 1, 2024
 
 ### PruningContentFilter

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

@@ -24,14 +24,6 @@ We would like to thank the following people for their contributions to Crawl4AI:
 - [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
 
@@ -39,11 +31,6 @@ We would like to thank the following people for their contributions to Crawl4AI:
 - [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 +1,32 @@
-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
+ARG TARGETPLATFORM
+ARG BUILDPLATFORM
 
-# Set build arguments
-ARG APP_HOME=/app
-ARG GITHUB_REPO=https://github.com/unclecode/crawl4ai.git
-ARG GITHUB_BRANCH=main
-ARG USE_LOCAL=true
+# Other build arguments
+ARG PYTHON_VERSION=3.10
 
-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
+# Base stage with system dependencies
+FROM python:${PYTHON_VERSION}-slim as base
 
-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 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 +37,10 @@ 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 +63,30 @@ 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 and architecture is supported
+RUN if [ "$ENABLE_GPU" = "true" ] && [ "$TARGETPLATFORM" = "linux/amd64" ] ; 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
 
-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 [ "$INSTALL_TYPE" = "all" ] ; then \
         pip install --no-cache-dir \
             torch \
@@ -140,61 +99,38 @@ RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
         python -m nltk.downloader punkt stopwords ; \
     fi
 
+# Install the package
 RUN if [ "$INSTALL_TYPE" = "all" ] ; then \
-        pip install "/tmp/project/[all]" && \
+        pip install ".[all]" && \
         python -m crawl4ai.model_loader ; \
     elif [ "$INSTALL_TYPE" = "torch" ] ; then \
-        pip install "/tmp/project/[torch]" ; \
+        pip install ".[torch]" ; \
     elif [ "$INSTALL_TYPE" = "transformer" ] ; then \
-        pip install "/tmp/project/[transformer]" && \
+        pip install ".[transformer]" && \
         python -m crawl4ai.model_loader ; \
     else \
-        pip install "/tmp/project" ; \
+        pip install "." ; \
     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 MkDocs and required plugins
+RUN pip install --no-cache-dir \
+    mkdocs \
+    mkdocs-material \
+    mkdocs-terminal \
+    pymdown-extensions
 
-RUN crawl4ai-setup
+# Build MkDocs documentation
+RUN mkdocs build
 
-RUN playwright install --with-deps
+# Install Playwright and browsers
+RUN if [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
+    playwright install chromium; \
+    elif [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
+    playwright install chromium; \
+    fi
 
-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
+# Expose port
+EXPOSE 8000 11235 9222 8080
 
-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

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.md

@@ -10,48 +10,40 @@
 [![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>
+<!-- [![Documentation Status](https://readthedocs.org/projects/crawl4ai/badge/?version=latest)](https://crawl4ai.readthedocs.io/) -->
+[![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![Security: bandit](https://img.shields.io/badge/security-bandit-yellow.svg)](https://github.com/PyCQA/bandit)
+
 </div>
 
-Crawl4AI turns the web into clean, LLM ready Markdown for RAG, agents, and data pipelines. Fast, controllable, battle tested by a 50k+ star community.
+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 v0.7.3](#-recent-updates)
+[✨ Check out latest update v0.4.3bx](#-recent-updates)
 
-✨ New in v0.7.3: Undetected Browser Support, Multi-URL Configurations, Memory Monitoring, Enhanced Table Extraction, GitHub Sponsors. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
+🎉 **Version 0.4.3bx is out!** This release brings exciting new features like a Memory Dispatcher System, Streaming Support, LLM-Powered Markdown Generation, Schema Generation, and Robots.txt Compliance! [Read the release notes →](https://docs.crawl4ai.com/blog)
 
 <details>
-  <summary>🤓 <strong>My Personal Story</strong></summary>
+<summary>🤓 <strong>My Personal Story</strong></summary>
 
-I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. That’s where I learned how much extraction matters.
+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.
 
-In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now it’s the most-starred crawler on GitHub.
+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 it open source for **availability**, anyone can use it without a gate. Now I’m building the platform for **affordability**, anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing.
+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?
 
-<details>
-  <summary>Why developers pick Crawl4AI</summary>
-
-- **LLM ready output**, smart Markdown with headings, tables, code, citation hints
-- **Fast in practice**, async browser pool, caching, minimal hops
-- **Full control**, sessions, proxies, cookies, user scripts, hooks
-- **Adaptive intelligence**, learns site patterns, explores only what matters
-- **Deploy anywhere**, zero keys, CLI and Docker, cloud friendly
-</details>
-
+1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.  
+2. **Lightning Fast**: Delivers results 6x 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 
 
@@ -75,7 +67,7 @@ If you encounter any browser-related issues, you can install them manually:
 python -m playwright install --with-deps chromium
 ```
 
-2. Run a simple web crawl with Python:
+2. Run a simple web crawl:
 ```python
 import asyncio
 from crawl4ai import *
@@ -91,45 +83,6 @@ 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"
-```
-
-## 💖 Support Crawl4AI
-
-> 🎉 **Sponsorship Program Now Open!** After powering 51K+ developers and 1 year of growth, Crawl4AI is launching dedicated support for **startups** and **enterprises**. Be among the first 50 **Founding Sponsors** for permanent recognition in our Hall of Fame.
-
-Crawl4AI is the #1 trending open-source web crawler on GitHub. Your support keeps it independent, innovative, and free for the community — while giving you direct access to premium benefits.
-
-<div align="">
-  
-[![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)** — Priority support & early access to features  
-- **💼 Growing Team ($500/mo)** — Bi-weekly syncs & optimization help  
-- **🏢 Data Infrastructure Partner ($2000/mo)** — Full partnership with dedicated support  
-  *Custom arrangements available - see [SPONSORS.md](SPONSORS.md) for details & contact*
-
-**Why sponsor?**  
-No rate-limited APIs. No lock-in. Build and own your data pipeline with direct guidance from the creator of Crawl4AI.
-
-[See All Tiers & Benefits →](https://github.com/sponsors/unclecode)
-
-
 ## ✨ Features 
 
 <details>
@@ -158,7 +111,6 @@ No rate-limited APIs. No lock-in. Build and own your data pipeline with direct g
 
 - 🖥️ **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.
@@ -187,11 +139,10 @@ No rate-limited APIs. No lock-in. Build and own your data pipeline with direct g
 <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.
+- 🐳 **Dockerized Setup**: Optimized Docker image with API server for easy deployment.
 - 🔄 **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.
+- ⚙️ **DigitalOcean Deployment**: Ready-to-deploy configurations for DigitalOcean and similar platforms.
 
 </details>
 
@@ -287,27 +238,28 @@ pip install -e ".[all]"             # Install all optional features
 <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.
+> 🚀 **Major Changes Coming!** We're developing a completely new Docker implementation that will make deployment even more efficient and seamless. The current Docker setup is being deprecated in favor of this new solution.
 
-### New Docker Features
+### Current Docker Support
 
-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
+The existing Docker implementation is being deprecated and will be replaced soon. If you still need to use Docker with the current version:
 
-### Getting Started
+- 📚 [Deprecated Docker Setup](./docs/deprecated/docker-deployment.md) - Instructions for the current Docker implementation
+- ⚠️ Note: This setup will be replaced in the next major release
 
-```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
+### What's Coming Next?
 
-# Visit the playground at http://localhost:11235/playground
-```
+Our new Docker implementation will bring:
+- Improved performance and resource efficiency
+- Streamlined deployment process
+- Better integration with Crawl4AI features
+- Enhanced scalability options
+
+Stay connected with our [GitHub repository](https://github.com/unclecode/crawl4ai) for updates!
+
+</details>
+
+---
 
 ### Quick Test
 
@@ -319,31 +271,22 @@ import requests
 # Submit a crawl job
 response = requests.post(
     "http://localhost:11235/crawl",
-    json={"urls": ["https://example.com"], "priority": 10}
+    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}")
+task_id = response.json()["task_id"]
+
+# Continue polling until the task is complete (status="completed")
+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.
+You can check the project structure in the directory [https://github.com/unclecode/crawl4ai/docs/examples](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>
@@ -374,8 +317,9 @@ async def main():
             url="https://docs.micronaut.io/4.7.6/guide/",
             config=run_config
         )
-        print(len(result.markdown.raw_markdown))
-        print(len(result.markdown.fit_markdown))
+        print(len(result.markdown))
+        print(len(result.fit_markdown))
+        print(len(result.markdown_v2.fit_markdown))
 
 if __name__ == "__main__":
     asyncio.run(main())
@@ -389,7 +333,7 @@ if __name__ == "__main__":
 ```python
 import asyncio
 from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
-from crawl4ai import JsonCssExtractionStrategy
+from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
 import json
 
 async def main():
@@ -462,8 +406,8 @@ if __name__ == "__main__":
 ```python
 import os
 import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
-from crawl4ai import LLMExtractionStrategy
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai.extraction_strategy import LLMExtractionStrategy
 from pydantic import BaseModel, Field
 
 class OpenAIModelFee(BaseModel):
@@ -478,7 +422,7 @@ async def main():
         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')), 
+            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. 
@@ -502,7 +446,7 @@ if __name__ == "__main__":
 </details>
 
 <details>
-<summary>🤖 <strong>Using Your own Browser with Custom User Profile</strong></summary>
+<summary>🤖 <strong>Using You own Browswer with Custome User Profile</strong></summary>
 
 ```python
 import os, sys
@@ -542,161 +486,27 @@ async def test_news_crawl():
 
 ## ✨ Recent Updates
 
-<details>
-<summary><strong>Version 0.7.3 Release Highlights - The Multi-Config Intelligence Update</strong></summary>
+-   **🚀 New Dispatcher System**: Scale to thousands of URLs with intelligent **memory monitoring**, **concurrency control**, and optional **rate limiting**. (See `MemoryAdaptiveDispatcher`, `SemaphoreDispatcher`, `RateLimiter`, `CrawlerMonitor`)
+-   **⚡ Streaming Mode**: Process results **as they arrive** instead of waiting for an entire batch to complete. (Set `stream=True` in `CrawlerRunConfig`)
+-   **🤖 Enhanced LLM Integration**:
+    -   **Automatic schema generation**: Create extraction rules from HTML using OpenAI or Ollama, no manual CSS/XPath needed.
+    -   **LLM-powered Markdown filtering**: Refine your markdown output with a new `LLMContentFilter` that understands content relevance.
+    -   **Ollama Support**: Use open-source or self-hosted models for private or cost-effective extraction.
+-   **🏎️ Faster Scraping Option**: New `LXMLWebScrapingStrategy` offers **10-20x speedup** for large, complex pages (experimental).
+-   **🤖 robots.txt Compliance**: Respect website rules with `check_robots_txt=True` and efficient local caching.
+-   **🔄 Proxy Rotation**: Built-in support for dynamic proxy switching and IP verification, with support for authenticated proxies and session persistence.
+-   **➡️ URL Redirection Tracking**: The `redirected_url` field now captures the final destination after any redirects.
+-   **🪞 Improved Mirroring**: The `LXMLWebScrapingStrategy` now has much greater fidelity, allowing for almost pixel-perfect mirroring of websites.
+-   **📈 Enhanced Monitoring**: Track memory, CPU, and individual crawler status with `CrawlerMonitor`.
+-   **📝 Improved Documentation**: More examples, clearer explanations, and updated tutorials.
 
-- **🕵️ Undetected Browser Support**: Bypass sophisticated bot detection systems:
-  ```python
-  from crawl4ai import AsyncWebCrawler, BrowserConfig
-  
-  browser_config = BrowserConfig(
-      browser_type="undetected",  # Use undetected Chrome
-      headless=True,              # Can run headless with stealth
-      extra_args=[
-          "--disable-blink-features=AutomationControlled",
-          "--disable-web-security"
-      ]
-  )
-  
-  async with AsyncWebCrawler(config=browser_config) as crawler:
-      result = await crawler.arun("https://protected-site.com")
-  # Successfully bypass Cloudflare, Akamai, and custom bot detection
-  ```
-
-- **🎨 Multi-URL Configuration**: Different strategies for different URL patterns in one batch:
-  ```python
-  from crawl4ai import CrawlerRunConfig, MatchMode
-  
-  configs = [
-      # Documentation sites - aggressive caching
-      CrawlerRunConfig(
-          url_matcher=["*docs*", "*documentation*"],
-          cache_mode="write",
-          markdown_generator_options={"include_links": True}
-      ),
-      
-      # News/blog sites - fresh content
-      CrawlerRunConfig(
-          url_matcher=lambda url: 'blog' in url or 'news' in url,
-          cache_mode="bypass"
-      ),
-      
-      # Fallback for everything else
-      CrawlerRunConfig()
-  ]
-  
-  results = await crawler.arun_many(urls, config=configs)
-  # Each URL gets the perfect configuration automatically
-  ```
-
-- **🧠 Memory Monitoring**: Track and optimize memory usage during crawling:
-  ```python
-  from crawl4ai.memory_utils import MemoryMonitor
-  
-  monitor = MemoryMonitor()
-  monitor.start_monitoring()
-  
-  results = await crawler.arun_many(large_url_list)
-  
-  report = monitor.get_report()
-  print(f"Peak memory: {report['peak_mb']:.1f} MB")
-  print(f"Efficiency: {report['efficiency']:.1f}%")
-  # Get optimization recommendations
-  ```
-
-- **📊 Enhanced Table Extraction**: Direct DataFrame conversion from web tables:
-  ```python
-  result = await crawler.arun("https://site-with-tables.com")
-  
-  # New way - direct table access
-  if result.tables:
-      import pandas as pd
-      for table in result.tables:
-          df = pd.DataFrame(table['data'])
-          print(f"Table: {df.shape[0]} rows × {df.shape[1]} columns")
-  ```
-
-- **💰 GitHub Sponsors**: 4-tier sponsorship system for project sustainability
-- **🐳 Docker LLM Flexibility**: Configure providers via environment variables
-
-[Full v0.7.3 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
-
-</details>
-
-<details>
-<summary><strong>Version 0.7.0 Release Highlights - The Adaptive Intelligence Update</strong></summary>
-
-- **🧠 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).
-
-</details>
+Read the full details in our [0.4.3bx Release Notes](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.
 
-<details>
-<summary>📈 <strong>Version Numbers Explained</strong></summary>
+### Version Numbers Explained
 
 Our version numbers follow this pattern: `MAJOR.MINOR.PATCH` (e.g., 0.4.3)
 
@@ -706,7 +516,7 @@ 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
+- `rc` (0.4.3rc1): Release candidates, potential final version
 
 #### Installation
 - Regular installation (stable version):
@@ -733,8 +543,6 @@ We use pre-releases to:
 
 For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the `--pre` flag.
 
-</details>
-
 ## 📖 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!
@@ -747,16 +555,16 @@ To check our development plans and upcoming features, visit our [Roadmap](https:
 <summary>📈 <strong>Development TODOs</strong></summary>
 
 - [x] 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
-- [x] 1. Question-Based Crawler: Natural language driven web discovery and content extraction
-- [x] 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
-- [x] 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
-- [x] 4. Automated Schema Generator: Convert natural language to extraction schemas
-- [x] 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
-- [x] 6. Web Embedding Index: Semantic search infrastructure for crawled content
-- [x] 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
-- [x] 8. Performance Monitor: Real-time insights into crawler operations
+- [ ] 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
-- [x] 10. Sponsorship Program: Structured support system with tiered benefits
+- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
 - [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
 
 </details>
@@ -765,88 +573,9 @@ To check our development plans and upcoming features, visit our [Roadmap](https:
 
 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:
+## 📄 License 
 
-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:
-
-<details>
-<summary>📈 <strong>1. Badge Attribution (Recommended)</strong></summary>
-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>
-```
-
-</details>
-
-<details>
-<summary>📖 <strong>2. Text Attribution</strong></summary>
-Add this line to your documentation:
-```
-This project uses Crawl4AI (https://github.com/unclecode/crawl4ai) for web data extraction.
-```
-</details>
-
-## 📚 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
-```
+Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
 
 ## 📧 Contact 
 

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

@@ -1,158 +1,46 @@
 # __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 .async_configs import BrowserConfig, CrawlerRunConfig
 from .content_scraping_strategy import (
     ContentScrapingStrategy,
+    WebScrapingStrategy,
     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
+    JsonXPathExtractionStrategy
 )
 from .chunking_strategy import ChunkingStrategy, RegexChunking
 from .markdown_generation_strategy import DefaultMarkdownGenerator
-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 .content_filter_strategy import PruningContentFilter, BM25ContentFilter, LLMContentFilter, RelevantContentFilter
+from .models import CrawlResult, MarkdownGenerationResult
 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
+    CrawlerMonitor,
+    DisplayMode,
+    BaseDispatcher
 )
 
 __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",
@@ -165,52 +53,36 @@ __all__ = [
     "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 # noqa
+def is_sync_version_installed():
+    try:
+        import selenium
 
-#         return True
-#     except ImportError:
-#         return False
+        return True
+    except ImportError:
+        return False
 
 
-# if is_sync_version_installed():
-#     try:
-#         from .web_crawler import WebCrawler
+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.")
+        __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.")
+
+import warnings
+from pydantic import warnings as pydantic_warnings
 
 # Disable all Pydantic warnings
 warnings.filterwarnings("ignore", module="pydantic")

crawl4ai/__version__.py

@@ -1,8 +1,3 @@
-# crawl4ai/__version__.py
-
-# This is the version that will be used for stable releases
-__version__ = "0.7.3"
-
-# For nightly builds, this gets set during build process
-__nightly_version__ = None
-
+# crawl4ai/_version.py
+# __version__ = "0.4.3b3"
+__version__ = "0.4.248b3"

crawl4ai/async_database.py

@@ -4,15 +4,20 @@ import aiosqlite
 import asyncio
 from typing import Optional, 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 .models import CrawlResult, MarkdownGenerationResult
+import aiofiles
+from .version_manager import VersionManager
+from .async_logger import AsyncLogger
 from .utils import get_error_context, create_box_message
 
+# Set up logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+# logger.setLevel(logging.INFO)
+
 base_directory = DB_PATH = os.path.join(
     os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
 )
@@ -171,10 +176,7 @@ class AsyncDatabaseManager:
                             f"Code context:\n{error_context['code_context']}"
                         )
                         self.logger.error(
-                            message="{error}",
-                            tag="ERROR",
-                            params={"error": str(error_message)},
-                            boxes=["error"],
+                            message=create_box_message(error_message, type="error"),
                         )
 
                         raise
@@ -192,10 +194,7 @@ class AsyncDatabaseManager:
                 f"Code context:\n{error_context['code_context']}"
             )
             self.logger.error(
-                message="{error}",
-                tag="ERROR",
-                params={"error": str(error_message)},
-                boxes=["error"],
+                message=create_box_message(error_message, type="error"),
             )
             raise
         finally:
@@ -337,17 +336,12 @@ class AsyncDatabaseManager:
                     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="",
-                            )
+                            row_dict[field] = row_dict[field]
                         else:
                             row_dict[field] = {}
 
                 if isinstance(row_dict["markdown"], Dict):
+                    row_dict["markdown_v2"] = row_dict["markdown"]
                     if row_dict["markdown"].get("raw_markdown"):
                         row_dict["markdown"] = row_dict["markdown"]["raw_markdown"]
 
@@ -364,7 +358,7 @@ class AsyncDatabaseManager:
                 # 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)
 
         try:
@@ -390,16 +384,16 @@ class AsyncDatabaseManager:
         }
 
         try:
-            if isinstance(result.markdown, StringCompatibleMarkdown):
-                content_map["markdown"] = (
-                    result.markdown,
-                    "markdown",
-                )
-            elif isinstance(result.markdown, MarkdownGenerationResult):
+            if isinstance(result.markdown, MarkdownGenerationResult):
                 content_map["markdown"] = (
                     result.markdown.model_dump_json(),
                     "markdown",
                 )
+            elif hasattr(result, "markdown_v2"):
+                content_map["markdown"] = (
+                    result.markdown_v2.model_dump_json(),
+                    "markdown",
+                )
             elif isinstance(result.markdown, str):
                 markdown_result = MarkdownGenerationResult(raw_markdown=result.markdown)
                 content_map["markdown"] = (

crawl4ai/async_dispatcher.py

@@ -1,18 +1,20 @@
-from typing import Dict, Optional, List, Tuple, Union
+from typing import Dict, Optional, List, Tuple
 from .async_configs import CrawlerRunConfig
 from .models import (
     CrawlResult,
     CrawlerTaskResult,
     CrawlStatus,
+    DisplayMode,
+    CrawlStats,
     DomainState,
 )
 
-from .components.crawler_monitor import CrawlerMonitor
-
-from .types import AsyncWebCrawler
-
+from rich.live import Live
+from rich.table import Table
+from rich.console import Console
+from rich import box
+from datetime import datetime, timedelta
 from collections.abc import AsyncGenerator
-
 import time
 import psutil
 import asyncio
@@ -22,7 +24,6 @@ from urllib.parse import urlparse
 import random
 from abc import ABC, abstractmethod
 
-from .memory_utils import get_true_memory_usage_percent
 
 
 class RateLimiter:
@@ -85,6 +86,201 @@ class RateLimiter:
         return True
 
 
+class CrawlerMonitor:
+    def __init__(
+        self,
+        max_visible_rows: int = 15,
+        display_mode: DisplayMode = DisplayMode.DETAILED,
+    ):
+        self.console = Console()
+        self.max_visible_rows = max_visible_rows
+        self.display_mode = display_mode
+        self.stats: Dict[str, CrawlStats] = {}
+        self.process = psutil.Process()
+        self.start_time = datetime.now()
+        self.live = Live(self._create_table(), refresh_per_second=2)
+
+    def start(self):
+        self.live.start()
+
+    def stop(self):
+        self.live.stop()
+
+    def add_task(self, task_id: str, url: str):
+        self.stats[task_id] = CrawlStats(
+            task_id=task_id, url=url, status=CrawlStatus.QUEUED
+        )
+        self.live.update(self._create_table())
+
+    def update_task(self, task_id: str, **kwargs):
+        if task_id in self.stats:
+            for key, value in kwargs.items():
+                setattr(self.stats[task_id], key, value)
+            self.live.update(self._create_table())
+
+    def _create_aggregated_table(self) -> Table:
+        """Creates a compact table showing only aggregated statistics"""
+        table = Table(
+            box=box.ROUNDED,
+            title="Crawler Status Overview",
+            title_style="bold magenta",
+            header_style="bold blue",
+            show_lines=True,
+        )
+
+        # Calculate statistics
+        total_tasks = len(self.stats)
+        queued = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.QUEUED
+        )
+        in_progress = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
+        )
+        completed = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
+        )
+        failed = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
+        )
+
+        # Memory statistics
+        current_memory = self.process.memory_info().rss / (1024 * 1024)
+        total_task_memory = sum(stat.memory_usage for stat in self.stats.values())
+        peak_memory = max(
+            (stat.peak_memory for stat in self.stats.values()), default=0.0
+        )
+
+        # Duration
+        duration = datetime.now() - self.start_time
+
+        # Create status row
+        table.add_column("Status", style="bold cyan")
+        table.add_column("Count", justify="right")
+        table.add_column("Percentage", justify="right")
+
+        table.add_row("Total Tasks", str(total_tasks), "100%")
+        table.add_row(
+            "[yellow]In Queue[/yellow]",
+            str(queued),
+            f"{(queued/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[blue]In Progress[/blue]",
+            str(in_progress),
+            f"{(in_progress/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[green]Completed[/green]",
+            str(completed),
+            f"{(completed/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[red]Failed[/red]",
+            str(failed),
+            f"{(failed/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+
+        # Add memory information
+        table.add_section()
+        table.add_row(
+            "[magenta]Current Memory[/magenta]", f"{current_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[magenta]Total Task Memory[/magenta]", f"{total_task_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[magenta]Peak Task Memory[/magenta]", f"{peak_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[yellow]Runtime[/yellow]",
+            str(timedelta(seconds=int(duration.total_seconds()))),
+            "",
+        )
+
+        return table
+
+    def _create_detailed_table(self) -> Table:
+        table = Table(
+            box=box.ROUNDED,
+            title="Crawler Performance Monitor",
+            title_style="bold magenta",
+            header_style="bold blue",
+        )
+
+        # Add columns
+        table.add_column("Task ID", style="cyan", no_wrap=True)
+        table.add_column("URL", style="cyan", no_wrap=True)
+        table.add_column("Status", style="bold")
+        table.add_column("Memory (MB)", justify="right")
+        table.add_column("Peak (MB)", justify="right")
+        table.add_column("Duration", justify="right")
+        table.add_column("Info", style="italic")
+
+        # Add summary row
+        total_memory = sum(stat.memory_usage for stat in self.stats.values())
+        active_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
+        )
+        completed_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
+        )
+        failed_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
+        )
+
+        table.add_row(
+            "[bold yellow]SUMMARY",
+            f"Total: {len(self.stats)}",
+            f"Active: {active_count}",
+            f"{total_memory:.1f}",
+            f"{self.process.memory_info().rss / (1024 * 1024):.1f}",
+            str(
+                timedelta(
+                    seconds=int((datetime.now() - self.start_time).total_seconds())
+                )
+            ),
+            f"✓{completed_count} ✗{failed_count}",
+            style="bold",
+        )
+
+        table.add_section()
+
+        # Add rows for each task
+        visible_stats = sorted(
+            self.stats.values(),
+            key=lambda x: (
+                x.status != CrawlStatus.IN_PROGRESS,
+                x.status != CrawlStatus.QUEUED,
+                x.end_time or datetime.max,
+            ),
+        )[: self.max_visible_rows]
+
+        for stat in visible_stats:
+            status_style = {
+                CrawlStatus.QUEUED: "white",
+                CrawlStatus.IN_PROGRESS: "yellow",
+                CrawlStatus.COMPLETED: "green",
+                CrawlStatus.FAILED: "red",
+            }[stat.status]
+
+            table.add_row(
+                stat.task_id[:8],  # Show first 8 chars of task ID
+                stat.url[:40] + "..." if len(stat.url) > 40 else stat.url,
+                f"[{status_style}]{stat.status.value}[/{status_style}]",
+                f"{stat.memory_usage:.1f}",
+                f"{stat.peak_memory:.1f}",
+                stat.duration,
+                stat.error_message[:40] if stat.error_message else "",
+            )
+
+        return table
+
+    def _create_table(self) -> Table:
+        """Creates the appropriate table based on display mode"""
+        if self.display_mode == DisplayMode.AGGREGATED:
+            return self._create_aggregated_table()
+        return self._create_detailed_table()
+
 
 class BaseDispatcher(ABC):
     def __init__(
@@ -98,37 +294,11 @@ class BaseDispatcher(ABC):
         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]],
+        config: CrawlerRunConfig,
         task_id: str,
         monitor: Optional[CrawlerMonitor] = None,
     ) -> CrawlerTaskResult:
@@ -138,8 +308,8 @@ class BaseDispatcher(ABC):
     async def run_urls(
         self,
         urls: List[str],
-        crawler: AsyncWebCrawler,  # noqa: F821
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
+        crawler: "AsyncWebCrawler",  # noqa: F821
+        config: CrawlerRunConfig,
         monitor: Optional[CrawlerMonitor] = None,
     ) -> List[CrawlerTaskResult]:
         pass
@@ -149,195 +319,71 @@ 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,
+        memory_wait_timeout: float = 300.0,  # 5 minutes default timeout
         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()
+        self.result_queue = asyncio.Queue()  # Queue for storing results
 
-            # 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]],
+        config: CrawlerRunConfig,
         task_id: str,
-        retry_count: int = 0,
     ) -> CrawlerTaskResult:
-        start_time = time.time()
+        start_time = datetime.now()
         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
+                    task_id, status=CrawlStatus.IN_PROGRESS, start_time=start_time
                 )
-                
             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
+
+            process = psutil.Process()
+            start_memory = process.memory_info().rss / (1024 * 1024)
+            result = await self.crawler.arun(url, config=config, session_id=task_id)
             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
+                    result = CrawlerTaskResult(
+                        task_id=task_id,
+                        url=url,
+                        result=result,
+                        memory_usage=memory_usage,
+                        peak_memory=peak_memory,
+                        start_time=start_time,
+                        end_time=datetime.now(),
+                        error_message=error_message,
+                    )
+                    await self.result_queue.put(result)
+                    return 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:
@@ -345,9 +391,9 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
             result = CrawlResult(
                 url=url, html="", metadata={}, success=False, error_message=str(e)
             )
-            
+
         finally:
-            end_time = time.time()
+            end_time = datetime.now()
             if self.monitor:
                 self.monitor.update_task(
                     task_id,
@@ -355,10 +401,9 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
                     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,
@@ -368,257 +413,117 @@ class MemoryAdaptiveDispatcher(BaseDispatcher):
             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 = []
+        crawler: "AsyncWebCrawler",  # noqa: F821
+        config: CrawlerRunConfig,
+        ) -> List[CrawlerTaskResult]:
+            self.crawler = crawler
 
-        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())))
+            if self.monitor:
+                self.monitor.start()
 
-            active_tasks = []
+            try:
+                pending_tasks = []
+                active_tasks = []
+                task_queue = []
 
-            # 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
+                for url in urls:
+                    task_id = str(uuid.uuid4())
+                    if self.monitor:
+                        self.monitor.add_task(task_id, url)
+                    task_queue.append((url, task_id))
 
-                # 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
+                while task_queue or active_tasks:
+                    wait_start_time = time.time()
+                    while len(active_tasks) < self.max_session_permit and task_queue:
+                        if psutil.virtual_memory().percent >= self.memory_threshold_percent:
+                            # Check if we've exceeded the timeout
+                            if time.time() - wait_start_time > self.memory_wait_timeout:
+                                raise MemoryError(
+                                    f"Memory usage above threshold ({self.memory_threshold_percent}%) for more than {self.memory_wait_timeout} seconds"
                                 )
-                            
-                            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()
-                
-            return results
+                            await asyncio.sleep(self.check_interval)
+                            continue
 
-        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()
-                
-    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
+                        url, task_id = task_queue.pop(0)
+                        task = asyncio.create_task(self.crawl_url(url, config, task_id))
+                        active_tasks.append(task)
+
+                    if not active_tasks:
+                        await asyncio.sleep(self.check_interval)
+                        continue
+
+                    done, pending = await asyncio.wait(
+                        active_tasks, return_when=asyncio.FIRST_COMPLETED
                     )
-                    
-                    # 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)
-                
+
+                    pending_tasks.extend(done)
+                    active_tasks = list(pending)
+
+                return await asyncio.gather(*pending_tasks)
+            finally:
+                if self.monitor:
+                    self.monitor.stop()
+
     async def run_urls_stream(
         self,
         urls: List[str],
-        crawler: AsyncWebCrawler,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
+        crawler: "AsyncWebCrawler",
+        config: 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:
+            active_tasks = []
+            task_queue = []
+            completed_count = 0
+            total_urls = len(urls)
+
             # 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)
+                task_queue.append((url, task_id))
 
             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
+                # Start new tasks if memory permits
+                while len(active_tasks) < self.max_session_permit and task_queue:
+                    if psutil.virtual_memory().percent >= self.memory_threshold_percent:
+                        await asyncio.sleep(self.check_interval)
+                        continue
+
+                    url, task_id = task_queue.pop(0)
+                    task = asyncio.create_task(self.crawl_url(url, config, task_id))
+                    active_tasks.append(task)
+
+                if not active_tasks and not task_queue:
+                    break
+
+                # Wait for any task to complete and yield results
                 if active_tasks:
                     done, pending = await asyncio.wait(
-                        active_tasks, timeout=0.1, return_when=asyncio.FIRST_COMPLETED
+                        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
+                        completed_count += 1
+                        yield result
                     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()
-                
+                    await asyncio.sleep(self.check_interval)
+
         finally:
-            # Clean up
-            memory_monitor.cancel()
             if self.monitor:
                 self.monitor.stop()
-                
 
 class SemaphoreDispatcher(BaseDispatcher):
     def __init__(
@@ -635,44 +540,14 @@ class SemaphoreDispatcher(BaseDispatcher):
     async def crawl_url(
         self,
         url: str,
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
+        config: CrawlerRunConfig,
         task_id: str,
         semaphore: asyncio.Semaphore = None,
     ) -> CrawlerTaskResult:
-        start_time = time.time()
+        start_time = datetime.now()
         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(
@@ -685,7 +560,7 @@ class SemaphoreDispatcher(BaseDispatcher):
             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)
+                result = await self.crawler.arun(url, config=config, session_id=task_id)
                 end_memory = process.memory_info().rss / (1024 * 1024)
 
                 memory_usage = peak_memory = end_memory - start_memory
@@ -702,7 +577,7 @@ class SemaphoreDispatcher(BaseDispatcher):
                             memory_usage=memory_usage,
                             peak_memory=peak_memory,
                             start_time=start_time,
-                            end_time=time.time(),
+                            end_time=datetime.now(),
                             error_message=error_message,
                         )
 
@@ -722,7 +597,7 @@ class SemaphoreDispatcher(BaseDispatcher):
             )
 
         finally:
-            end_time = time.time()
+            end_time = datetime.now()
             if self.monitor:
                 self.monitor.update_task(
                     task_id,
@@ -745,9 +620,9 @@ class SemaphoreDispatcher(BaseDispatcher):
 
     async def run_urls(
         self,
-        crawler: AsyncWebCrawler,  # noqa: F821
+        crawler: "AsyncWebCrawler",  # noqa: F821
         urls: List[str],
-        config: Union[CrawlerRunConfig, List[CrawlerRunConfig]],
+        config: CrawlerRunConfig,
     ) -> List[CrawlerTaskResult]:
         self.crawler = crawler
         if self.monitor:
@@ -769,4 +644,4 @@ class SemaphoreDispatcher(BaseDispatcher):
             return await asyncio.gather(*tasks, return_exceptions=True)
         finally:
             if self.monitor:
-                self.monitor.stop()
+                self.monitor.stop()

crawl4ai/async_dispatcher_.py

@@ -0,0 +1,588 @@
+from typing import Dict, Optional, List, Tuple
+from .async_configs import CrawlerRunConfig
+from .models import (
+    CrawlResult,
+    CrawlerTaskResult,
+    CrawlStatus,
+    DisplayMode,
+    CrawlStats,
+    DomainState,
+)
+
+from rich.live import Live
+from rich.table import Table
+from rich.console import Console
+from rich import box
+from datetime import datetime, timedelta
+
+import time
+import psutil
+import asyncio
+import uuid
+
+from urllib.parse import urlparse
+import random
+from abc import ABC, abstractmethod
+
+
+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 CrawlerMonitor:
+    def __init__(
+        self,
+        max_visible_rows: int = 15,
+        display_mode: DisplayMode = DisplayMode.DETAILED,
+    ):
+        self.console = Console()
+        self.max_visible_rows = max_visible_rows
+        self.display_mode = display_mode
+        self.stats: Dict[str, CrawlStats] = {}
+        self.process = psutil.Process()
+        self.start_time = datetime.now()
+        self.live = Live(self._create_table(), refresh_per_second=2)
+
+    def start(self):
+        self.live.start()
+
+    def stop(self):
+        self.live.stop()
+
+    def add_task(self, task_id: str, url: str):
+        self.stats[task_id] = CrawlStats(
+            task_id=task_id, url=url, status=CrawlStatus.QUEUED
+        )
+        self.live.update(self._create_table())
+
+    def update_task(self, task_id: str, **kwargs):
+        if task_id in self.stats:
+            for key, value in kwargs.items():
+                setattr(self.stats[task_id], key, value)
+            self.live.update(self._create_table())
+
+    def _create_aggregated_table(self) -> Table:
+        """Creates a compact table showing only aggregated statistics"""
+        table = Table(
+            box=box.ROUNDED,
+            title="Crawler Status Overview",
+            title_style="bold magenta",
+            header_style="bold blue",
+            show_lines=True,
+        )
+
+        # Calculate statistics
+        total_tasks = len(self.stats)
+        queued = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.QUEUED
+        )
+        in_progress = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
+        )
+        completed = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
+        )
+        failed = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
+        )
+
+        # Memory statistics
+        current_memory = self.process.memory_info().rss / (1024 * 1024)
+        total_task_memory = sum(stat.memory_usage for stat in self.stats.values())
+        peak_memory = max(
+            (stat.peak_memory for stat in self.stats.values()), default=0.0
+        )
+
+        # Duration
+        duration = datetime.now() - self.start_time
+
+        # Create status row
+        table.add_column("Status", style="bold cyan")
+        table.add_column("Count", justify="right")
+        table.add_column("Percentage", justify="right")
+
+        table.add_row("Total Tasks", str(total_tasks), "100%")
+        table.add_row(
+            "[yellow]In Queue[/yellow]",
+            str(queued),
+            f"{(queued/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[blue]In Progress[/blue]",
+            str(in_progress),
+            f"{(in_progress/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[green]Completed[/green]",
+            str(completed),
+            f"{(completed/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+        table.add_row(
+            "[red]Failed[/red]",
+            str(failed),
+            f"{(failed/total_tasks*100):.1f}%" if total_tasks > 0 else "0%",
+        )
+
+        # Add memory information
+        table.add_section()
+        table.add_row(
+            "[magenta]Current Memory[/magenta]", f"{current_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[magenta]Total Task Memory[/magenta]", f"{total_task_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[magenta]Peak Task Memory[/magenta]", f"{peak_memory:.1f} MB", ""
+        )
+        table.add_row(
+            "[yellow]Runtime[/yellow]",
+            str(timedelta(seconds=int(duration.total_seconds()))),
+            "",
+        )
+
+        return table
+
+    def _create_detailed_table(self) -> Table:
+        table = Table(
+            box=box.ROUNDED,
+            title="Crawler Performance Monitor",
+            title_style="bold magenta",
+            header_style="bold blue",
+        )
+
+        # Add columns
+        table.add_column("Task ID", style="cyan", no_wrap=True)
+        table.add_column("URL", style="cyan", no_wrap=True)
+        table.add_column("Status", style="bold")
+        table.add_column("Memory (MB)", justify="right")
+        table.add_column("Peak (MB)", justify="right")
+        table.add_column("Duration", justify="right")
+        table.add_column("Info", style="italic")
+
+        # Add summary row
+        total_memory = sum(stat.memory_usage for stat in self.stats.values())
+        active_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.IN_PROGRESS
+        )
+        completed_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.COMPLETED
+        )
+        failed_count = sum(
+            1 for stat in self.stats.values() if stat.status == CrawlStatus.FAILED
+        )
+
+        table.add_row(
+            "[bold yellow]SUMMARY",
+            f"Total: {len(self.stats)}",
+            f"Active: {active_count}",
+            f"{total_memory:.1f}",
+            f"{self.process.memory_info().rss / (1024 * 1024):.1f}",
+            str(
+                timedelta(
+                    seconds=int((datetime.now() - self.start_time).total_seconds())
+                )
+            ),
+            f"✓{completed_count} ✗{failed_count}",
+            style="bold",
+        )
+
+        table.add_section()
+
+        # Add rows for each task
+        visible_stats = sorted(
+            self.stats.values(),
+            key=lambda x: (
+                x.status != CrawlStatus.IN_PROGRESS,
+                x.status != CrawlStatus.QUEUED,
+                x.end_time or datetime.max,
+            ),
+        )[: self.max_visible_rows]
+
+        for stat in visible_stats:
+            status_style = {
+                CrawlStatus.QUEUED: "white",
+                CrawlStatus.IN_PROGRESS: "yellow",
+                CrawlStatus.COMPLETED: "green",
+                CrawlStatus.FAILED: "red",
+            }[stat.status]
+
+            table.add_row(
+                stat.task_id[:8],  # Show first 8 chars of task ID
+                stat.url[:40] + "..." if len(stat.url) > 40 else stat.url,
+                f"[{status_style}]{stat.status.value}[/{status_style}]",
+                f"{stat.memory_usage:.1f}",
+                f"{stat.peak_memory:.1f}",
+                stat.duration,
+                stat.error_message[:40] if stat.error_message else "",
+            )
+
+        return table
+
+    def _create_table(self) -> Table:
+        """Creates the appropriate table based on display mode"""
+        if self.display_mode == DisplayMode.AGGREGATED:
+            return self._create_aggregated_table()
+        return self._create_detailed_table()
+
+
+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
+
+    @abstractmethod
+    async def crawl_url(
+        self,
+        url: str,
+        config: CrawlerRunConfig,
+        task_id: str,
+        monitor: Optional[CrawlerMonitor] = None,
+    ) -> CrawlerTaskResult:
+        pass
+
+    @abstractmethod
+    async def run_urls(
+        self,
+        urls: List[str],
+        crawler: "AsyncWebCrawler",  # noqa: F821
+        config: CrawlerRunConfig,
+        monitor: Optional[CrawlerMonitor] = None,
+    ) -> List[CrawlerTaskResult]:
+        pass
+
+
+class MemoryAdaptiveDispatcher(BaseDispatcher):
+    def __init__(
+        self,
+        memory_threshold_percent: float = 90.0,
+        check_interval: float = 1.0,
+        max_session_permit: int = 20,
+        memory_wait_timeout: float = 300.0,  # 5 minutes default timeout
+        rate_limiter: Optional[RateLimiter] = None,
+        monitor: Optional[CrawlerMonitor] = None,
+    ):
+        super().__init__(rate_limiter, monitor)
+        self.memory_threshold_percent = memory_threshold_percent
+        self.check_interval = check_interval
+        self.max_session_permit = max_session_permit
+        self.memory_wait_timeout = memory_wait_timeout
+
+    async def crawl_url(
+        self,
+        url: str,
+        config: CrawlerRunConfig,
+        task_id: str,
+    ) -> CrawlerTaskResult:
+        start_time = datetime.now()
+        error_message = ""
+        memory_usage = peak_memory = 0.0
+
+        try:
+            if self.monitor:
+                self.monitor.update_task(
+                    task_id, status=CrawlStatus.IN_PROGRESS, start_time=start_time
+                )
+            self.concurrent_sessions += 1
+
+            if self.rate_limiter:
+                await self.rate_limiter.wait_if_needed(url)
+
+            process = psutil.Process()
+            start_memory = process.memory_info().rss / (1024 * 1024)
+            result = await self.crawler.arun(url, config=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=datetime.now(),
+                        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 = datetime.now()
+            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,
+                )
+            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,
+        )
+
+    async def run_urls(
+        self,
+        urls: List[str],
+        crawler: "AsyncWebCrawler",  # noqa: F821
+        config: CrawlerRunConfig,
+    ) -> List[CrawlerTaskResult]:
+        self.crawler = crawler
+
+        if self.monitor:
+            self.monitor.start()
+
+        try:
+            pending_tasks = []
+            active_tasks = []
+            task_queue = []
+
+            for url in urls:
+                task_id = str(uuid.uuid4())
+                if self.monitor:
+                    self.monitor.add_task(task_id, url)
+                task_queue.append((url, task_id))
+
+            while task_queue or active_tasks:
+                wait_start_time = time.time()
+                while len(active_tasks) < self.max_session_permit and task_queue:
+                    if psutil.virtual_memory().percent >= self.memory_threshold_percent:
+                        # Check if we've exceeded the timeout
+                        if time.time() - wait_start_time > self.memory_wait_timeout:
+                            raise MemoryError(
+                                f"Memory usage above threshold ({self.memory_threshold_percent}%) for more than {self.memory_wait_timeout} seconds"
+                            )
+                        await asyncio.sleep(self.check_interval)
+                        continue
+
+                    url, task_id = task_queue.pop(0)
+                    task = asyncio.create_task(self.crawl_url(url, config, task_id))
+                    active_tasks.append(task)
+
+                if not active_tasks:
+                    await asyncio.sleep(self.check_interval)
+                    continue
+
+                done, pending = await asyncio.wait(
+                    active_tasks, return_when=asyncio.FIRST_COMPLETED
+                )
+
+                pending_tasks.extend(done)
+                active_tasks = list(pending)
+
+            return await asyncio.gather(*pending_tasks)
+        finally:
+            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: CrawlerRunConfig,
+        task_id: str,
+        semaphore: asyncio.Semaphore = None,
+    ) -> CrawlerTaskResult:
+        start_time = datetime.now()
+        error_message = ""
+        memory_usage = peak_memory = 0.0
+
+        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=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=datetime.now(),
+                            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 = datetime.now()
+            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: 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_logger.py

@@ -1,82 +1,19 @@
-from abc import ABC, abstractmethod
 from enum import Enum
-from typing import Optional, Dict, Any, List
+from typing import Optional, Dict, Any
+from colorama import Fore, Style, init
 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):
+class AsyncLogger:
     """
     Asynchronous logger with support for colored console output and file logging.
     Supports templated messages with colored components.
@@ -93,21 +30,14 @@ class AsyncLogger(AsyncLoggerBase):
         "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,
+        LogLevel.DEBUG: Fore.LIGHTBLACK_EX,
+        LogLevel.INFO: Fore.CYAN,
+        LogLevel.SUCCESS: Fore.GREEN,
+        LogLevel.WARNING: Fore.YELLOW,
+        LogLevel.ERROR: Fore.RED,
     }
 
     def __init__(
@@ -116,7 +46,7 @@ class AsyncLogger(AsyncLoggerBase):
         log_level: LogLevel = LogLevel.DEBUG,
         tag_width: int = 10,
         icons: Optional[Dict[str, str]] = None,
-        colors: Optional[Dict[LogLevel, LogColor]] = None,
+        colors: Optional[Dict[LogLevel, str]] = None,
         verbose: bool = True,
     ):
         """
@@ -130,13 +60,13 @@ class AsyncLogger(AsyncLoggerBase):
             colors: Custom colors for different log levels
             verbose: Whether to output to console
         """
+        init()  # Initialize colorama
         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:
@@ -149,23 +79,20 @@ class AsyncLogger(AsyncLoggerBase):
     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")
+                # Strip ANSI color codes for file output
+                clean_message = message.replace(Fore.RESET, "").replace(
+                    Style.RESET_ALL, ""
+                )
+                for color in vars(Fore).values():
+                    if isinstance(color, str):
+                        clean_message = clean_message.replace(color, "")
+                f.write(f"[{timestamp}] {clean_message}\n")
 
     def _log(
         self,
@@ -173,9 +100,8 @@ class AsyncLogger(AsyncLoggerBase):
         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,
+        colors: Optional[Dict[str, str]] = None,
+        base_color: Optional[str] = None,
         **kwargs,
     ):
         """
@@ -187,44 +113,42 @@ class AsyncLogger(AsyncLoggerBase):
             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("]", "]]")
+        # Format the message with parameters if provided
         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
+            try:
+                # First format the message with raw parameters
+                formatted_message = message.format(**params)
 
-                # 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)))
+                # Then apply colors if specified
+                if colors:
+                    for key, color in colors.items():
+                        # Find the formatted value in the message and wrap it with color
+                        if key in params:
+                            value_str = str(params[key])
+                            formatted_message = formatted_message.replace(
+                                value_str, f"{color}{value_str}{Style.RESET_ALL}"
+                            )
 
+            except KeyError as e:
+                formatted_message = (
+                    f"LOGGING ERROR: Missing parameter {e} in message template"
+                )
+                level = LogLevel.ERROR
         else:
-            formatted_message = parsed_message
+            formatted_message = 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}]"
+        color = base_color or self.colors[level]
+        log_line = f"{color}{self._format_tag(tag)} {self._get_icon(tag)} {formatted_message}{Style.RESET_ALL}"
 
         # Output to console if verbose
         if self.verbose or kwargs.get("force_verbose", False):
-            self.console.print(log_line)
+            print(log_line)
 
         # Write to file if configured
         self._write_to_file(log_line)
@@ -244,22 +168,6 @@ class AsyncLogger(AsyncLoggerBase):
     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."""
@@ -271,7 +179,7 @@ class AsyncLogger(AsyncLoggerBase):
         success: bool,
         timing: float,
         tag: str = "FETCH",
-        url_length: int = 100,
+        url_length: int = 50,
     ):
         """
         Convenience method for logging URL fetch status.
@@ -283,20 +191,19 @@ class AsyncLogger(AsyncLoggerBase):
             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",
+            message="{url:.{url_length}}... | Status: {status} | Time: {timing:.2f}s",
             tag=tag,
             params={
-                "url": readable_url,
-                "status": "✓" if success else "✗",
+                "url": url,
+                "url_length": url_length,
+                "status": success,
                 "timing": timing,
             },
             colors={
-                "status": LogColor.SUCCESS if success else LogColor.ERROR,
-                "timing": LogColor.WARNING,
+                "status": Fore.GREEN if success else Fore.RED,
+                "timing": Fore.YELLOW,
             },
         )
 
@@ -312,63 +219,9 @@ class AsyncLogger(AsyncLoggerBase):
             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}",
+            message="{url:.{url_length}}... | Error: {error}",
             tag=tag,
-            params={"url": readable_url, "error": error},
+            params={"url": url, "url_length": url_length, "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

@@ -1,293 +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 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/chunking_strategy.py

@@ -4,6 +4,7 @@ from collections import Counter
 import string
 from .model_loader import load_nltk_punkt
 
+
 # Define the abstract base class for chunking strategies
 class ChunkingStrategy(ABC):
     """
@@ -71,7 +72,6 @@ class NlpSentenceChunking(ChunkingStrategy):
         """
         Initialize the NlpSentenceChunking object.
         """
-        from crawl4ai.le.legacy.model_loader import load_nltk_punkt
         load_nltk_punkt()
 
     def chunk(self, text: str) -> list:

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

@@ -4,8 +4,7 @@ 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 = {
@@ -16,26 +15,10 @@ PROVIDER_MODELS = {
     "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
@@ -101,46 +84,3 @@ 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"]
-    }
-}

crawl4ai/content_filter_strategy.py

@@ -1,4 +1,3 @@
-import inspect
 import re
 import time
 from bs4 import BeautifulSoup, Tag
@@ -6,46 +5,25 @@ from typing import List, Tuple, Dict, Optional
 from rank_bm25 import BM25Okapi
 from collections import deque
 from bs4 import NavigableString, Comment
-
-from .utils import (
-    clean_tokens,
-    perform_completion_with_backoff,
-    escape_json_string,
-    sanitize_html,
-    get_home_folder,
-    extract_xml_data,
-    merge_chunks,
-)
-from .types import LLMConfig
-from .config import DEFAULT_PROVIDER, OVERLAP_RATE, WORD_TOKEN_RATE
+from .utils import clean_tokens, perform_completion_with_backoff, escape_json_string, sanitize_html, get_home_folder, extract_xml_data
 from abc import ABC, abstractmethod
 import math
 from snowballstemmer import stemmer
+from .config import DEFAULT_PROVIDER, OVERLAP_RATE, WORD_TOKEN_RATE
 from .models import TokenUsage
 from .prompts import PROMPT_FILTER_CONTENT
+import os
 import json
 import hashlib
 from pathlib import Path
-from concurrent.futures import ThreadPoolExecutor
-from .async_logger import AsyncLogger, LogLevel, LogColor
-
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from .async_logger import AsyncLogger, LogLevel
+from colorama import Fore, Style, init
 
 class RelevantContentFilter(ABC):
     """Abstract base class for content filtering strategies"""
 
-    def __init__(
-        self,
-        user_query: str = None,
-        verbose: bool = False,
-        logger: Optional[AsyncLogger] = None,
-    ):
-        """
-        Initializes the RelevantContentFilter class with optional user query.
-
-        Args:
-            user_query (str): User query for filtering (optional).
-            verbose (bool): Enable verbose logging (default: False).
-        """
+    def __init__(self, user_query: str = None):
         self.user_query = user_query
         self.included_tags = {
             # Primary structure
@@ -114,8 +92,6 @@ class RelevantContentFilter(ABC):
             r"nav|footer|header|sidebar|ads|comment|promo|advert|social|share", re.I
         )
         self.min_word_count = 2
-        self.verbose = False
-        self.logger = logger
 
     @abstractmethod
     def filter_content(self, html: str) -> List[str]:
@@ -377,7 +353,6 @@ class RelevantContentFilter(ABC):
         except Exception:
             return str(tag)  # Fallback to original if anything fails
 
-
 class BM25ContentFilter(RelevantContentFilter):
     """
     Content filtering using BM25 algorithm with priority tag handling.
@@ -405,7 +380,6 @@ class BM25ContentFilter(RelevantContentFilter):
         user_query: str = None,
         bm25_threshold: float = 1.0,
         language: str = "english",
-        use_stemming: bool = True,
     ):
         """
         Initializes the BM25ContentFilter class, if not provided, falls back to page metadata.
@@ -417,11 +391,9 @@ class BM25ContentFilter(RelevantContentFilter):
             user_query (str): User query for filtering (optional).
             bm25_threshold (float): BM25 threshold for filtering (default: 1.0).
             language (str): Language for stemming (default: 'english').
-            use_stemming (bool): Whether to apply stemming (default: True).
         """
         super().__init__(user_query=user_query)
         self.bm25_threshold = bm25_threshold
-        self.use_stemming = use_stemming
         self.priority_tags = {
             "h1": 5.0,
             "h2": 4.0,
@@ -435,7 +407,7 @@ class BM25ContentFilter(RelevantContentFilter):
             "pre": 1.5,
             "th": 1.5,  # Table headers
         }
-        self.stemmer = stemmer(language) if use_stemming else None
+        self.stemmer = stemmer(language)
 
     def filter_content(self, html: str, min_word_threshold: int = None) -> List[str]:
         """
@@ -482,19 +454,13 @@ class BM25ContentFilter(RelevantContentFilter):
         #                 for _, chunk, _, _ in candidates]
         # tokenized_query = [ps.stem(word) for word in query.lower().split()]
 
-        if self.use_stemming:
-            tokenized_corpus = [
-                [self.stemmer.stemWord(word) for word in chunk.lower().split()]
-                for _, chunk, _, _ in candidates
-            ]
-            tokenized_query = [
-                self.stemmer.stemWord(word) for word in query.lower().split()
-            ]
-        else:
-            tokenized_corpus = [
-                chunk.lower().split() for _, chunk, _, _ in candidates
-            ]
-            tokenized_query = query.lower().split()
+        tokenized_corpus = [
+            [self.stemmer.stemWord(word) for word in chunk.lower().split()]
+            for _, chunk, _, _ in candidates
+        ]
+        tokenized_query = [
+            self.stemmer.stemWord(word) for word in query.lower().split()
+        ]
 
         # tokenized_corpus = [[self.stemmer.stemWord(word) for word in tokenize_text(chunk.lower())]
         #            for _, chunk, _, _ in candidates]
@@ -529,7 +495,6 @@ class BM25ContentFilter(RelevantContentFilter):
 
         return [self.clean_element(tag) for _, _, tag in selected_candidates]
 
-
 class PruningContentFilter(RelevantContentFilter):
     """
     Content filtering using pruning algorithm with dynamic threshold.
@@ -776,130 +741,110 @@ class PruningContentFilter(RelevantContentFilter):
                 class_id_score -= 0.5
         return class_id_score
 
-
 class LLMContentFilter(RelevantContentFilter):
-    """Content filtering using LLMs to generate relevant markdown.
-
-    How it works:
-    1. Extracts page metadata with fallbacks.
-    2. Extracts text chunks from the body element.
-    3. Applies LLMs to generate markdown for each chunk.
-    4. Filters out chunks below the threshold.
-    5. Sorts chunks by score in descending order.
-    6. Returns the top N chunks.
-
-    Attributes:
-        llm_config (LLMConfig): LLM configuration object.
-        instruction (str): Instruction for LLM markdown generation
-        chunk_token_threshold (int): Chunk token threshold for splitting (default: 1e9).
-        overlap_rate (float): Overlap rate for chunking (default: 0.5).
-        word_token_rate (float): Word token rate for chunking (default: 0.2).
-        verbose (bool): Enable verbose logging (default: False).
-        logger (AsyncLogger): Custom logger for LLM operations (optional).
-    """
-    _UNWANTED_PROPS = {
-        'provider' : 'Instead, use llm_config=LLMConfig(provider="...")',
-        'api_token' : 'Instead, use llm_config=LlMConfig(api_token="...")',
-        'base_url' : 'Instead, use llm_config=LLMConfig(base_url="...")',
-        'api_base' : 'Instead, use llm_config=LLMConfig(base_url="...")',
-    }
+    """Content filtering using LLMs to generate relevant markdown."""
 
     def __init__(
         self,
-        llm_config: "LLMConfig" = None,
+        provider: str = DEFAULT_PROVIDER,
+        api_token: Optional[str] = None,
         instruction: str = None,
         chunk_token_threshold: int = int(1e9),
         overlap_rate: float = OVERLAP_RATE,
         word_token_rate: float = WORD_TOKEN_RATE,
-        # char_token_rate: float = WORD_TOKEN_RATE * 5,
-        # chunk_mode: str = "char",
-        verbose: bool = False,
-        logger: Optional[AsyncLogger] = None,
-        ignore_cache: bool = True,
-        # Deprecated properties
-        provider: str = DEFAULT_PROVIDER,
-        api_token: Optional[str] = None,
         base_url: Optional[str] = None,
         api_base: Optional[str] = None,
         extra_args: Dict = None,
+        verbose: bool = False,
+        logger: Optional[AsyncLogger] = None,
     ):
         super().__init__(None)
         self.provider = provider
-        self.api_token = api_token
-        self.base_url = base_url or api_base
-        self.llm_config = llm_config
+        self.api_token = (
+            api_token
+            or PROVIDER_MODELS.get(provider, "no-token")
+            or os.getenv("OPENAI_API_KEY")
+        )
         self.instruction = instruction
         self.chunk_token_threshold = chunk_token_threshold
         self.overlap_rate = overlap_rate
-        self.word_token_rate = word_token_rate or WORD_TOKEN_RATE
-        # self.chunk_mode: str = chunk_mode
-        # self.char_token_rate = char_token_rate or word_token_rate / 5
-        # self.token_rate = word_token_rate if chunk_mode == "word" else self.char_token_rate
-        self.token_rate = word_token_rate or WORD_TOKEN_RATE
+        self.word_token_rate = word_token_rate
+        self.base_url = base_url
+        self.api_base = api_base or base_url
         self.extra_args = extra_args or {}
-        self.ignore_cache = ignore_cache
         self.verbose = verbose
-
+        
         # Setup logger with custom styling for LLM operations
         if logger:
             self.logger = logger
         elif verbose:
             self.logger = AsyncLogger(
-                verbose=verbose,
+                verbose=True,
                 icons={
                     **AsyncLogger.DEFAULT_ICONS,
                     "LLM": "★",  # Star for LLM operations
                     "CHUNK": "◈",  # Diamond for chunks
-                    "CACHE": "⚡",  # Lightning for cache operations
+                    "CACHE": "⚡", # Lightning for cache operations
                 },
                 colors={
                     **AsyncLogger.DEFAULT_COLORS,
-                    LogLevel.INFO: LogColor.DIM_MAGENTA  # Dimmed purple for LLM ops
-                },
+                    LogLevel.INFO: Fore.MAGENTA + Style.DIM,  # Dimmed purple for LLM ops
+                }
             )
         else:
             self.logger = None
-
+        
         self.usages = []
         self.total_usage = TokenUsage()
-    
-    def __setattr__(self, name, value):
-        """Handle attribute setting."""
-        # TODO: Planning to set properties dynamically based on the __init__ signature
-        sig = inspect.signature(self.__init__)
-        all_params = sig.parameters  # Dictionary of parameter names and their details
 
-        if name in self._UNWANTED_PROPS and value is not all_params[name].default:
-            raise AttributeError(f"Setting '{name}' is deprecated. {self._UNWANTED_PROPS[name]}")
-        
-        super().__setattr__(name, value)  
-        
     def _get_cache_key(self, html: str, instruction: str) -> str:
         """Generate a unique cache key based on HTML and instruction"""
         content = f"{html}{instruction}"
         return hashlib.md5(content.encode()).hexdigest()
 
     def _merge_chunks(self, text: str) -> List[str]:
-        """Split text into chunks with overlap using char or word mode."""
-        ov = int(self.chunk_token_threshold * self.overlap_rate)
-        sections = merge_chunks(
-            docs=[text],
-            target_size=self.chunk_token_threshold,
-            overlap=ov,
-            word_token_ratio=self.word_token_rate,
-        )
-        return sections
+        """Split text into chunks with overlap"""
+        # Calculate tokens and sections
+        total_tokens = len(text.split()) * self.word_token_rate
+        num_sections = max(1, math.floor(total_tokens / self.chunk_token_threshold))
+        adjusted_chunk_threshold = total_tokens / num_sections
 
-    def filter_content(self, html: str, ignore_cache: bool = True) -> List[str]:
+        # Split into words
+        words = text.split()
+        chunks = []
+        current_chunk = []
+        current_token_count = 0
+
+        for word in words:
+            word_tokens = len(word) * self.word_token_rate
+            if current_token_count + word_tokens <= adjusted_chunk_threshold:
+                current_chunk.append(word)
+                current_token_count += word_tokens
+            else:
+                # Add overlap if not the last chunk
+                if chunks and self.overlap_rate > 0:
+                    overlap_size = int(len(current_chunk) * self.overlap_rate)
+                    current_chunk.extend(current_chunk[-overlap_size:])
+                
+                chunks.append(" ".join(current_chunk))
+                current_chunk = [word]
+                current_token_count = word_tokens
+
+        if current_chunk:
+            chunks.append(" ".join(current_chunk))
+
+        return chunks
+
+    def filter_content(self, html: str, ignore_cache: bool = False) -> List[str]:
         if not html or not isinstance(html, str):
             return []
 
         if self.logger:
             self.logger.info(
-                "Starting LLM markdown content filtering process",
+                "Starting LLM content filtering process", 
                 tag="LLM",
-                params={"provider": self.llm_config.provider},
-                colors={"provider": LogColor.CYAN},
+                params={"provider": self.provider},
+                colors={"provider": Fore.CYAN}
             )
 
         # Cache handling
@@ -908,88 +853,65 @@ class LLMContentFilter(RelevantContentFilter):
         cache_key = self._get_cache_key(html, self.instruction or "")
         cache_file = cache_dir / f"{cache_key}.json"
 
-        # if ignore_cache == None:
-        ignore_cache = self.ignore_cache
-
         if not ignore_cache and cache_file.exists():
             if self.logger:
-                self.logger.info("Found  cached markdown result", tag="CACHE")
+                self.logger.info("Found cached result", tag="CACHE")
             try:
-                with cache_file.open("r") as f:
+                with cache_file.open('r') as f:
                     cached_data = json.load(f)
-                    usage = TokenUsage(**cached_data["usage"])
+                    usage = TokenUsage(**cached_data['usage'])
                     self.usages.append(usage)
                     self.total_usage.completion_tokens += usage.completion_tokens
                     self.total_usage.prompt_tokens += usage.prompt_tokens
                     self.total_usage.total_tokens += usage.total_tokens
-                    return cached_data["blocks"]
+                    return cached_data['blocks']
             except Exception as e:
                 if self.logger:
-                    self.logger.error(
-                        f"LLM markdown: Cache read error: {str(e)}", tag="CACHE"
-                    )
+                    self.logger.error(f"Cache read error: {str(e)}", tag="CACHE")
 
         # Split into chunks
         html_chunks = self._merge_chunks(html)
         if self.logger:
             self.logger.info(
-                "LLM markdown: Split content into {chunk_count} chunks",
+                "Split content into {chunk_count} chunks", 
                 tag="CHUNK",
                 params={"chunk_count": len(html_chunks)},
-                colors={"chunk_count": LogColor.YELLOW},
+                colors={"chunk_count": Fore.YELLOW}
             )
-
+        
+        extracted_content = []
         start_time = time.time()
-
+        
         # Process chunks in parallel
         with ThreadPoolExecutor(max_workers=4) as executor:
             futures = []
             for i, chunk in enumerate(html_chunks):
                 if self.logger:
                     self.logger.debug(
-                        "LLM markdown: Processing chunk {chunk_num}/{total_chunks}",
+                        "Processing chunk {chunk_num}/{total_chunks}", 
                         tag="CHUNK",
-                        params={"chunk_num": i + 1, "total_chunks": len(html_chunks)},
+                        params={
+                            "chunk_num": i + 1,
+                            "total_chunks": len(html_chunks)
+                        }
                     )
 
                 prompt_variables = {
                     "HTML": escape_json_string(sanitize_html(chunk)),
-                    "REQUEST": self.instruction
-                    or "Convert this HTML into clean, relevant markdown, removing any noise or irrelevant content.",
+                    "REQUEST": self.instruction or "Convert this HTML into clean, relevant markdown, removing any noise or irrelevant content."
                 }
 
                 prompt = PROMPT_FILTER_CONTENT
                 for var, value in prompt_variables.items():
                     prompt = prompt.replace("{" + var + "}", value)
 
-                def _proceed_with_chunk(
-                    provider: str,
-                    prompt: str,
-                    api_token: str,
-                    base_url: Optional[str] = None,
-                    extra_args: Dict = {},
-                ) -> List[str]:
-                    if self.logger:
-                        self.logger.info(
-                            "LLM Markdown: Processing chunk {chunk_num}",
-                            tag="CHUNK",
-                            params={"chunk_num": i + 1},
-                        )
-                    return perform_completion_with_backoff(
-                        provider,
-                        prompt,
-                        api_token,
-                        base_url=base_url,
-                        extra_args=extra_args,
-                    )
-
                 future = executor.submit(
-                    _proceed_with_chunk,
-                    self.llm_config.provider,
+                    perform_completion_with_backoff,
+                    self.provider,
                     prompt,
-                    self.llm_config.api_token,
-                    self.llm_config.base_url,
-                    self.extra_args,
+                    self.api_token,
+                    base_url=self.api_base,
+                    extra_args=self.extra_args
                 )
                 futures.append((i, future))
 
@@ -998,61 +920,59 @@ class LLMContentFilter(RelevantContentFilter):
             for i, future in sorted(futures):
                 try:
                     response = future.result()
-
+                    
                     # Track usage
                     usage = TokenUsage(
                         completion_tokens=response.usage.completion_tokens,
                         prompt_tokens=response.usage.prompt_tokens,
                         total_tokens=response.usage.total_tokens,
-                        completion_tokens_details=(
-                            response.usage.completion_tokens_details.__dict__
-                            if response.usage.completion_tokens_details
-                            else {}
-                        ),
-                        prompt_tokens_details=(
-                            response.usage.prompt_tokens_details.__dict__
-                            if response.usage.prompt_tokens_details
-                            else {}
-                        ),
+                        completion_tokens_details=response.usage.completion_tokens_details.__dict__ 
+                        if response.usage.completion_tokens_details else {},
+                        prompt_tokens_details=response.usage.prompt_tokens_details.__dict__
+                        if response.usage.prompt_tokens_details else {},
                     )
                     self.usages.append(usage)
                     self.total_usage.completion_tokens += usage.completion_tokens
                     self.total_usage.prompt_tokens += usage.prompt_tokens
                     self.total_usage.total_tokens += usage.total_tokens
 
-                    blocks = extract_xml_data(
-                        ["content"], response.choices[0].message.content
-                    )["content"]
+                    blocks = extract_xml_data(["content"], response.choices[0].message.content)["content"]
                     if blocks:
                         ordered_results.append(blocks)
                         if self.logger:
                             self.logger.success(
-                                "LLM markdown: Successfully processed chunk {chunk_num}",
+                                "Successfully processed chunk {chunk_num}", 
                                 tag="CHUNK",
-                                params={"chunk_num": i + 1},
+                                params={"chunk_num": i + 1}
                             )
                 except Exception as e:
                     if self.logger:
                         self.logger.error(
-                            "LLM markdown: Error processing chunk {chunk_num}: {error}",
+                            "Error processing chunk {chunk_num}: {error}", 
                             tag="CHUNK",
-                            params={"chunk_num": i + 1, "error": str(e)},
+                            params={
+                                "chunk_num": i + 1,
+                                "error": str(e)
+                            }
                         )
 
         end_time = time.time()
         if self.logger:
             self.logger.success(
-                "LLM markdown: Completed processing in {time:.2f}s",
+                "Completed processing in {time:.2f}s", 
                 tag="LLM",
                 params={"time": end_time - start_time},
-                colors={"time": LogColor.YELLOW},
+                colors={"time": Fore.YELLOW}
             )
 
         result = ordered_results if ordered_results else []
 
         # Cache the final result
-        cache_data = {"blocks": result, "usage": self.total_usage.__dict__}
-        with cache_file.open("w") as f:
+        cache_data = {
+            'blocks': result,
+            'usage': self.total_usage.__dict__
+        }
+        with cache_file.open('w') as f:
             json.dump(cache_data, f)
             if self.logger:
                 self.logger.info("Cached results for future use", tag="CACHE")
@@ -1076,4 +996,4 @@ class LLMContentFilter(RelevantContentFilter):
                 print(
                     f"{i:<10} {usage.completion_tokens:>12,} "
                     f"{usage.prompt_tokens:>12,} {usage.total_tokens:>12,}"
-                )
+                )

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/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

@@ -1,269 +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__)
-        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)
-            
-        # If we have more valid links than capacity, limit them
-        if len(valid_links) > remaining_capacity:
-            valid_links = valid_links[:remaining_capacity]
-            self.logger.info(f"Limiting to {remaining_capacity} URLs due to max_pages limit")
-            
-        # 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.
-        await queue.put((0, 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

@@ -1,261 +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
-        # Type check for logger
-        if isinstance(logger, dict):
-            logging.getLogger(__name__).warning(
-                "BFSDeepCrawlStrategy received a dict as logger; falling back to default logger."
-            )
-            self.logger = logging.getLogger(__name__)
-        else:
-            self.logger = logger or 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

@@ -1,686 +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__ = (
-        "_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__()
-        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

@@ -1,188 +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 = 600.0,  # Increased to 10 minutes for crawling operations
-        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:
-            # For streaming, we need to return the async generator properly
-            # The caller should be able to do: async for result in await client.crawl(...)
-            async def streaming_wrapper():
-                async for result in self._stream_crawl_results(data):
-                    yield result
-            return streaming_wrapper()
-        
-        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 _stream_crawl_results(self, data: Dict[str, Any]) -> AsyncGenerator[CrawlResult, None]:
-        """Internal method to handle streaming crawl results."""
-        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():
-                    try:
-                        result = json.loads(line)
-                        if "error" in result:
-                            self.logger.error_status(url=result.get("url", "unknown"), error=result["error"])
-                            continue
-                        
-                        # Check if this is a crawl result (has required fields)
-                        if "url" in result and "success" in result:
-                            self.logger.url_status(url=result.get("url", "unknown"), success=result.get("success", False), timing=result.get("timing", 0.0))
-                            
-                            # Create CrawlResult object properly
-                            crawl_result = CrawlResult(**result)
-                            yield crawl_result
-                        # Skip status-only messages
-                        elif result.get("status") == "completed":
-                            continue
-                    except json.JSONDecodeError as e:
-                        self.logger.error(f"Failed to parse streaming response: {e}", tag="STREAM")
-                        continue
-                    except Exception as e:
-                        self.logger.error(f"Error processing streaming result: {e}", tag="STREAM")
-                        continue
-
-    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

@@ -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
@@ -1037,7 +1026,6 @@ class CustomHTML2Text(HTML2Text):
         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 = []
@@ -1117,17 +1105,11 @@ class CustomHTML2Text(HTML2Text):
                 # 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.o("`")  # Markdown inline code start
                 self.inside_code = True
             else:
-                if not self.inside_link:
-                    self.o("`")  # Only output backtick if not inside a link
+                self.o("`")  # Markdown inline code end
                 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)
 

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

@@ -2,93 +2,17 @@ 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.
-    """
+    """Run all post-installation tasks"""
     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()
-
+    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():
@@ -119,32 +43,6 @@ def install_playwright():
         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():
@@ -208,5 +106,4 @@ def doctor():
     """Entry point for the doctor command"""
     import asyncio
 
-    asyncio.run(run_doctor())
-    sys.exit(0)
+    return asyncio.run(run_doctor())

crawl4ai/js_snippet/remove_overlay_elements.js

@@ -115,6 +115,5 @@ async () => {
     document.body.style.overflow = "auto";
 
     // Wait a bit for any animations to complete
-    document.body.scrollIntoView(false);
-    await new Promise((resolve) => setTimeout(resolve, 50));
+    await new Promise((resolve) => setTimeout(resolve, 100));
 };

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/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

@@ -2,7 +2,6 @@ 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
@@ -30,25 +29,21 @@ class MarkdownGenerationStrategy(ABC):
         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,
+        cleaned_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."""
+        """Generate markdown from cleaned HTML."""
         pass
 
 
@@ -65,7 +60,6 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
     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.
@@ -75,9 +69,8 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
         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)
+        super().__init__(content_filter, options)
 
     def convert_links_to_citations(
         self, markdown: str, base_url: str = ""
@@ -147,7 +140,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
 
     def generate_markdown(
         self,
-        input_html: str,
+        cleaned_html: str,
         base_url: str = "",
         html2text_options: Optional[Dict[str, Any]] = None,
         options: Optional[Dict[str, Any]] = None,
@@ -156,16 +149,16 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
         **kwargs,
     ) -> MarkdownGenerationResult:
         """
-        Generate markdown with citations from the provided input HTML.
+        Generate markdown with citations from cleaned HTML.
 
         How it works:
-        1. Generate raw markdown from the input HTML.
+        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:
-            input_html (str): The HTML content to process (selected based on content_source).
+            cleaned_html (str): Cleaned HTML content.
             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.
@@ -183,7 +176,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
                 "ignore_emphasis": False,
                 "ignore_links": False,
                 "ignore_images": False,
-                "protect_links": False,
+                "protect_links": True,
                 "single_line_break": True,
                 "mark_code": True,
                 "escape_snob": False,
@@ -200,14 +193,14 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
             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)
+            if not cleaned_html:
+                cleaned_html = ""
+            elif not isinstance(cleaned_html, str):
+                cleaned_html = str(cleaned_html)
 
             # Generate raw markdown
             try:
-                raw_markdown = h.handle(input_html)
+                raw_markdown = h.handle(cleaned_html)
             except Exception as e:
                 raw_markdown = f"Error converting HTML to markdown: {str(e)}"
 
@@ -232,7 +225,7 @@ class DefaultMarkdownGenerator(MarkdownGenerationStrategy):
             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 = content_filter.filter_content(cleaned_html)
                     filtered_html = "\n".join(
                         "<div>{}</div>".format(s) for s in filtered_html
                     )

crawl4ai/memory_utils.py

@@ -1,79 +0,0 @@
-import psutil
-import platform
-import subprocess
-from typing import Tuple
-
-
-def get_true_available_memory_gb() -> float:
-    """Get truly available memory including inactive pages (cross-platform)"""
-    vm = psutil.virtual_memory()
-
-    if platform.system() == 'Darwin':  # macOS
-        # On macOS, we need to include inactive memory too
-        try:
-            # Use vm_stat to get accurate values
-            result = subprocess.run(['vm_stat'], capture_output=True, text=True)
-            lines = result.stdout.split('\n')
-
-            page_size = 16384  # macOS page size
-            pages = {}
-
-            for line in lines:
-                if 'Pages free:' in line:
-                    pages['free'] = int(line.split()[-1].rstrip('.'))
-                elif 'Pages inactive:' in line:
-                    pages['inactive'] = int(line.split()[-1].rstrip('.'))
-                elif 'Pages speculative:' in line:
-                    pages['speculative'] = int(line.split()[-1].rstrip('.'))
-                elif 'Pages purgeable:' in line:
-                    pages['purgeable'] = int(line.split()[-1].rstrip('.'))
-
-            # Calculate total available (free + inactive + speculative + purgeable)
-            total_available_pages = (
-                pages.get('free', 0) + 
-                pages.get('inactive', 0) + 
-                pages.get('speculative', 0) + 
-                pages.get('purgeable', 0)
-            )
-            available_gb = (total_available_pages * page_size) / (1024**3)
-
-            return available_gb
-        except:
-            # Fallback to psutil
-            return vm.available / (1024**3)
-    else:
-        # For Windows and Linux, psutil.available is accurate
-        return vm.available / (1024**3)
-
-
-def get_true_memory_usage_percent() -> float:
-    """
-    Get memory usage percentage that accounts for platform differences.
-    
-    Returns:
-        float: Memory usage percentage (0-100)
-    """
-    vm = psutil.virtual_memory()
-    total_gb = vm.total / (1024**3)
-    available_gb = get_true_available_memory_gb()
-    
-    # Calculate used percentage based on truly available memory
-    used_percent = 100.0 * (total_gb - available_gb) / total_gb
-    
-    # Ensure it's within valid range
-    return max(0.0, min(100.0, used_percent))
-
-
-def get_memory_stats() -> Tuple[float, float, float]:
-    """
-    Get comprehensive memory statistics.
-    
-    Returns:
-        Tuple[float, float, float]: (used_percent, available_gb, total_gb)
-    """
-    vm = psutil.virtual_memory()
-    total_gb = vm.total / (1024**3)
-    available_gb = get_true_available_memory_gb()
-    used_percent = get_true_memory_usage_percent()
-    
-    return used_percent, available_gb, total_gb

crawl4ai/models.py

@@ -1,46 +1,12 @@
-
-"""
-Crawl4AI Models Module
-
-This module contains Pydantic models used throughout the Crawl4AI library.
-
-Key Features:
-- ORJSONModel: Base model with ORJSON serialization support
-- DeprecatedPropertiesMixin: Global system for handling deprecated properties
-- CrawlResult: Main result model with backward compatibility support
-
-Deprecated Properties System:
-The DeprecatedPropertiesMixin provides a global way to handle deprecated properties
-across all models. Instead of manually excluding deprecated properties in each
-model_dump() call, you can simply override the get_deprecated_properties() method:
-
-Example:
-    class MyModel(ORJSONModel):
-        name: str
-        old_field: Optional[str] = None
-        
-        def get_deprecated_properties(self) -> set[str]:
-            return {'old_field', 'another_deprecated_field'}
-        
-        @property
-        def old_field(self):
-            raise AttributeError("old_field is deprecated, use name instead")
-
-The system automatically excludes these properties from serialization, preventing
-property objects from appearing in JSON output.
-"""
-
-from pydantic import BaseModel, ConfigDict,HttpUrl, PrivateAttr, Field
+from pydantic import BaseModel, HttpUrl
 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
 
-import orjson
+
 ###############################
 # Dispatcher Models
 ###############################
@@ -58,15 +24,10 @@ class CrawlerTaskResult:
     result: "CrawlResult"
     memory_usage: float
     peak_memory: float
-    start_time: Union[datetime, float]
-    end_time: Union[datetime, float]
+    start_time: datetime
+    end_time: datetime
     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"
@@ -74,39 +35,27 @@ class CrawlStatus(Enum):
     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
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = 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
+        duration = end - self.start_time
         return str(timedelta(seconds=int(duration.total_seconds())))
 
+
 class DisplayMode(Enum):
     DETAILED = "DETAILED"
     AGGREGATED = "AGGREGATED"
@@ -124,169 +73,42 @@ class TokenUsage:
     prompt_tokens_details: Optional[dict] = None
 
 
-def orjson_default(obj):
-    # Handle datetime (if not already handled by orjson)
-    if isinstance(obj, datetime):
-        return obj.isoformat()
-
-    # Handle property objects (convert to string or something else)
-    if isinstance(obj, property):
-        return str(obj)
-
-    # Last resort: convert to string
-    return str(obj)
-
-
-class DeprecatedPropertiesMixin:
-    """
-    Mixin to handle deprecated properties in Pydantic models.
-    
-    Classes that inherit from this mixin can define deprecated properties
-    that will be automatically excluded from serialization.
-    
-    Usage:
-    1. Override the get_deprecated_properties() method to return a set of deprecated property names
-    2. The model_dump method will automatically exclude these properties
-    
-    Example:
-        class MyModel(ORJSONModel):
-            def get_deprecated_properties(self) -> set[str]:
-                return {'old_field', 'legacy_property'}
-            
-            name: str
-            old_field: Optional[str] = None  # Field definition
-            
-            @property
-            def old_field(self):  # Property that overrides the field
-                raise AttributeError("old_field is deprecated, use name instead")
-    """
-    
-    def get_deprecated_properties(self) -> set[str]:
-        """
-        Get deprecated property names for this model.
-        Override this method in subclasses to define deprecated properties.
-        
-        Returns:
-            set[str]: Set of deprecated property names
-        """
-        return set()
-    
-    @classmethod
-    def get_all_deprecated_properties(cls) -> set[str]:
-        """
-        Get all deprecated properties from this class and all parent classes.
-        
-        Returns:
-            set[str]: Set of all deprecated property names
-        """
-        deprecated_props = set()
-        # Create an instance to call the instance method
-        try:
-            # Try to create a dummy instance to get deprecated properties
-            dummy_instance = cls.__new__(cls)
-            deprecated_props.update(dummy_instance.get_deprecated_properties())
-        except Exception:
-            # If we can't create an instance, check for class-level definitions
-            pass
-            
-        # Also check parent classes
-        for klass in cls.__mro__:
-            if hasattr(klass, 'get_deprecated_properties') and klass != DeprecatedPropertiesMixin:
-                try:
-                    dummy_instance = klass.__new__(klass)
-                    deprecated_props.update(dummy_instance.get_deprecated_properties())
-                except Exception:
-                    pass
-        return deprecated_props
-    
-    def model_dump(self, *args, **kwargs):
-        """
-        Override model_dump to automatically exclude deprecated properties.
-        
-        This method:
-        1. Gets the existing exclude set from kwargs
-        2. Adds all deprecated properties defined in get_deprecated_properties()
-        3. Calls the parent model_dump with the updated exclude set
-        """
-        # Get the default exclude set, or create empty set if None
-        exclude = kwargs.get('exclude', set())
-        if exclude is None:
-            exclude = set()
-        elif not isinstance(exclude, set):
-            exclude = set(exclude) if exclude else set()
-        
-        # Add deprecated properties for this instance
-        exclude.update(self.get_deprecated_properties())
-        kwargs['exclude'] = exclude
-        
-        return super().model_dump(*args, **kwargs)
-
-
-class ORJSONModel(DeprecatedPropertiesMixin, BaseModel):
-    model_config = ConfigDict(
-        ser_json_timedelta="iso8601",  # Optional: format timedelta
-        ser_json_bytes="utf8",         # Optional: bytes → UTF-8 string
-    )
-    
-    def model_dump_json(self, **kwargs) -> bytes:
-        """Custom JSON serialization using orjson"""
-        return orjson.dumps(self.model_dump(**kwargs), default=orjson_default)
-    
-    @classmethod
-    def model_validate_json(cls, json_data: Union[str, bytes], **kwargs):
-        """Custom JSON deserialization using orjson"""
-        if isinstance(json_data, str):
-            json_data = json_data.encode()
-        return cls.model_validate(orjson.loads(json_data), **kwargs)
-class UrlModel(ORJSONModel):
+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(ORJSONModel):
-    task_id: str
-    memory_usage: float
-    peak_memory: float
-    start_time: Union[datetime, float]
-    end_time: Union[datetime, float]
-    error_message: str = ""
-
-class MarkdownGenerationResult(ORJSONModel):
+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(ORJSONModel):
+
+class DispatchResult(BaseModel):
+    task_id: str
+    memory_usage: float
+    peak_memory: float
+    start_time: datetime
+    end_time: datetime
+    error_message: str = ""
+
+
+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[Union[str, MarkdownGenerationResult]] = None
+    markdown_v2: Optional[MarkdownGenerationResult] = 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
@@ -296,194 +118,31 @@ class CrawlResult(ORJSONModel):
     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
 
-    def get_deprecated_properties(self) -> set[str]:
-        """Define deprecated properties that should be excluded from serialization."""
-        return {'fit_html', 'fit_markdown', 'markdown_v2'}
 
-# 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. Uses the DeprecatedPropertiesMixin to automatically exclude deprecated properties
-        
-        Future developers: This method ensures that the markdown content is properly
-        serialized despite being stored in a private attribute. The deprecated properties
-        are automatically handled by the mixin.
-        """
-        # Use the parent class method which handles deprecated properties automatically
-        result = super().model_dump(*args, **kwargs)
-        
-        # Add the markdown content if it exists
-        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(ORJSONModel):
+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(ORJSONModel):
+class MediaItem(BaseModel):
     src: Optional[str] = ""
-    data: Optional[str] = ""
     alt: Optional[str] = ""
     desc: Optional[str] = ""
     score: Optional[int] = 0
@@ -493,20 +152,14 @@ class MediaItem(ORJSONModel):
     width: Optional[int] = None
 
 
-class Link(ORJSONModel):
+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(ORJSONModel):
+class Media(BaseModel):
     images: List[MediaItem] = []
     videos: List[
         MediaItem
@@ -514,15 +167,14 @@ class Media(ORJSONModel):
     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(ORJSONModel):
+class Links(BaseModel):
     internal: List[Link] = []
     external: List[Link] = []
 
 
-class ScrapingResult(ORJSONModel):
+class ScrapingResult(BaseModel):
     cleaned_html: str
     success: bool
     media: Media = Media()

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/prompts.py

@@ -198,70 +198,25 @@ Avoid Common Mistakes:
 - Do NOT add any comments using "//" or "#" in the JSON output. It causes parsing errors.
 - Make sure the JSON is properly formatted with curly braces, square brackets, and commas in the right places.
 - Do not miss closing </blocks> tag at the end of the JSON output.
-- Do not generate the Python code show me how to do the task, this is your task to extract the information and return it in JSON format.
+- Do not generate the Python coee show me how to do the task, this is your task to extract the information and return it in JSON format.
 
 Result
 Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly."""
 
-PROMPT_EXTRACT_INFERRED_SCHEMA = """Here is the content from the URL:
-<url>{URL}</url>
-
-<url_content>
-{HTML}
-</url_content>
-
-Please carefully read the URL content and the user's request. Analyze the page structure and infer the most appropriate JSON schema based on the content and request.
-
-Extraction Strategy:
-1. First, determine if the page contains repetitive items (like multiple products, articles, etc.) or a single content item (like a single article or page).
-2. For repetitive items: Identify the common pattern and extract each instance as a separate JSON object in an array.
-3. For single content: Extract the key information into a comprehensive JSON object that captures the essential details.
-
-Extraction instructions:
-Return the extracted information as a list of JSON objects. For repetitive content, each object in the list should correspond to a distinct item. For single content, you may return just one detailed JSON object. Wrap the entire JSON list in <blocks>...</blocks> XML tags.
-
-Schema Design Guidelines:
-- Create meaningful property names that clearly describe the data they contain
-- Use nested objects for hierarchical information
-- Use arrays for lists of related items
-- Include all information requested by the user
-- Maintain consistency in property names and data structures
-- Only include properties that are actually present in the content
-- For dates, prefer ISO format (YYYY-MM-DD)
-- For prices or numeric values, extract them without currency symbols when possible
-
-Quality Reflection:
-Before outputting your final answer, double check that:
-1. The inferred schema makes logical sense for the type of content
-2. All requested information is included
-3. The JSON is valid and could be parsed without errors
-4. Property names are consistent and descriptive
-5. The structure is optimal for the type of data being represented
-
-Avoid Common Mistakes:
-- Do NOT add any comments using "//" or "#" in the JSON output. It causes parsing errors.
-- Make sure the JSON is properly formatted with curly braces, square brackets, and commas in the right places.
-- Do not miss closing </blocks> tag at the end of the JSON output.
-- Do not generate Python code showing how to do the task; this is your task to extract the information and return it in JSON format.
-- Ensure consistency in property names across all objects
-- Don't include empty properties or null values unless they're meaningful
-- For repetitive content, ensure all objects follow the same schema
-
-Important: If user specific instruction is provided, then stress significantly on what user is requesting and describing about the schema of end result (if any). If user is requesting to extract specific information, then focus on that and ignore the rest of the content.
-<user_request>
-{REQUEST}
-</user_request>
-
-Result:
-Output the final list of JSON objects, wrapped in <blocks>...</blocks> XML tags. Make sure to close the tag properly.
-
-DO NOT ADD ANY PRE OR POST COMMENTS. JUST RETURN THE JSON OBJECTS INSIDE <blocks>...</blocks> TAGS.
-
-CRITICAL: The content inside the <blocks> tags MUST be a direct array of JSON objects (starting with '[' and ending with ']'), not a dictionary/object containing an array. For example, use <blocks>[{...}, {...}]</blocks> instead of <blocks>{"items": [{...}, {...}]}</blocks>. This is essential for proper parsing.
-"""
 
 PROMPT_FILTER_CONTENT = """Your task is to filter and convert HTML content into clean, focused markdown that's optimized for use with LLMs and information retrieval systems.
 
+INPUT HTML: 
+<|HTML_CONTENT_START|>
+{HTML}
+<|HTML_CONTENT_END|>
+
+
+SPECIFIC INSTRUCTION: 
+<|USER_INSTRUCTION_START|>
+{REQUEST}
+<|USER_INSTRUCTION_END|>
+
 TASK DETAILS:
 1. Content Selection
 - DO: Keep essential information, main content, key details
@@ -285,7 +240,15 @@ TASK DETAILS:
 - DON'T: Fragment related content
 - DON'T: Duplicate information
 
-IMPORTANT: If user specific instruction is provided, ignore above guideline and prioritize those requirements over these general guidelines.
+Example Input:
+<div class="main-content"><h1>Setup Guide</h1><p>Follow these steps...</p></div>
+<div class="sidebar">Related articles...</div>
+
+Example Output:
+# Setup Guide
+Follow these steps...
+
+IMPORTANT: If specific instruction is provided above, prioritize those requirements over these general guidelines.
 
 OUTPUT FORMAT: 
 Wrap your response in <content> tags. Use proper markdown throughout.
@@ -293,18 +256,7 @@ Wrap your response in <content> tags. Use proper markdown throughout.
 [Your markdown content here]
 </content>
 
-Begin filtering now.
-
---------------------------------------------
-
-<|HTML_CONTENT_START|>
-{HTML}
-<|HTML_CONTENT_END|>
-
-<|USER_INSTRUCTION_START|>
-{REQUEST}
-<|USER_INSTRUCTION_END|>
-"""
+Begin filtering now."""
 
 JSON_SCHEMA_BUILDER= """
 # HTML Schema Generation Instructions
@@ -1054,525 +1006,4 @@ Your output must:
 5. Include all required fields
 6. Use valid XPath selectors
 </output_requirements>
-"""
-
-GENERATE_SCRIPT_PROMPT = r"""You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** script possible to prepare a web page for data extraction.
-
-Your scripts run **before the crawl** to handle dynamic content, user interactions, and other obstacles. You are a master of two tools: raw **JavaScript** and the high-level **Crawl4ai Script (c4a)**.
-
-────────────────────────────────────────────────────────
-## Your Core Philosophy: "Efficiency, Robustness, Simplicity"
-
-This is your mantra. Every line of code you write must adhere to it.
-
-1.  **Efficiency (Shortest Path):** Generate the absolute minimum number of steps to achieve the goal. Do not include redundant actions. If a `CLICK` on one button achieves the goal, don't also scroll and wait unnecessarily.
-2.  **Robustness (Will Not Break):** Prioritize selectors and methods that are resistant to cosmetic site changes. `data-*` attributes are gold. Dynamic, auto-generated class names (`.class-a8B_x3`) are poison. Always prefer waiting for a state change (`WAIT \`#results\``) over a blind delay (`WAIT 5`).
-3.  **Simplicity (Right Tool for the Job):** Use the simplest tool that works. Prefer a direct `c4a` command over `EVAL` with JavaScript. Only use `EVAL` when the task is impossible with standard commands (e.g., accessing Shadow DOM, complex array filtering).
-
-────────────────────────────────────────────────────────
-## Output Mode Selection Logic
-
-Your choice of output mode is a critical strategic decision.
-
-*   **Use `crawl4ai_script` for:**
-    *   Standard, sequential browser actions: login forms, clicking "next page," simple "load more" buttons, accepting cookie banners.
-    *   When the user's goal maps clearly to the available `c4a` commands.
-    *   When you need to define reusable macros with `PROC`.
-
-*   **Use `javascript` for:**
-    *   Complex DOM manipulation that has no `c4a` equivalent (e.g., transforming data, complex filtering).
-    *   Interacting with web components inside **Shadow DOM** or **iFrames**.
-    *   Implementing sophisticated logic like custom scrolling patterns or handling non-standard events.
-    *   When the goal is a fine-grained DOM tweak, not a full user journey.
-
-**If the user specifies a mode, you MUST respect it.** If not, you must choose the mode that best embodies your core philosophy.
-
-────────────────────────────────────────────────────────
-## Available Crawl4ai Commands
-
-| Command                | Arguments / Notes                                            |
-|------------------------|--------------------------------------------------------------|
-| GO `<url>`             | Navigate to absolute URL                                     |
-| RELOAD                 | Hard refresh                                                |
-| BACK / FORWARD         | Browser history nav                                          |
-| WAIT `<seconds>`       | **Avoid!** Passive delay. Use only as a last resort.         |
-| WAIT \`<css>\` `<t>`   | **Preferred wait.** Poll selector until found, timeout in seconds. |
-| WAIT "<text>" `<t>`    | Poll page text until found, timeout in seconds.              |
-| CLICK \`<css>\`        | Single click on element                                     |
-| CLICK `<x>` `<y>`      | Viewport click                                              |
-| DOUBLE_CLICK …         | Two rapid clicks                                            |
-| RIGHT_CLICK …          | Context-menu click                                          |
-| MOVE `<x>` `<y>`       | Mouse move                                                  |
-| DRAG `<x1>` `<y1>` `<x2>` `<y2>` | Click-drag gesture                              |
-| SCROLL UP|DOWN|LEFT|RIGHT `[px]` | Viewport scroll                                 |
-| TYPE "<text>"          | Type into focused element                                   |
-| CLEAR \`<css>\`        | Empty input                                                 |
-| SET \`<css>\` "<val>"  | Set element value and dispatch events                       |
-| PRESS `<Key>`          | Keydown + keyup                                             |
-| KEY_DOWN `<Key>` / KEY_UP `<Key>` | Separate key events                          |
-| EVAL \`<js>\`          | **Your fallback.** Run JS when no direct command exists.     |
-| SETVAR $name = <val>   | Store constant for reuse                                    |
-| PROC name … ENDPROC    | Define macro                                                |
-| IF / ELSE / REPEAT     | Flow control                                                |
-| USE "<file.c4a>"       | Include another script, avoid circular includes             |
-
-────────────────────────────────────────────────────────
-## Strategic Principles & Anti-Patterns
-
-These are your commandments. Do not deviate.
-
-1.  **Selector Quality is Paramount:**
-    *   **GOOD:** `[data-testid="submit-button"]`, `#main-content`, `[aria-label="Close dialog"]`
-    *   **BAD:** `div > span:nth-child(3)`, `.button-gR3xY_s`, `//div[contains(@class, 'button')]`
-
-2.  **Wait for State, Not for Time:**
-    *   **DO:** `CLICK \`#load-more\`` followed by `WAIT \`div.new-item\` 10`. This waits for the *result* of the action.
-    *   **DON'T:** `CLICK \`#load-more\`` followed by `WAIT 5`. This is a guess and it will fail.
-
-3.  **Target the Action, Not the Artifact:** If you need to reveal content, click the button that reveals it. Don't try to manually change CSS `display` properties, as this can break the page's internal state.
-
-4.  **DOM-Awareness is Non-Negotiable:**
-    *   **Shadow DOM:** `c4a` commands CANNOT pierce the Shadow DOM. If you see a `#shadow-root (open)` in the HTML, you MUST use `EVAL` and `element.shadowRoot.querySelector(...)`.
-    *   **iFrames:** Likewise, you MUST use `EVAL` and `iframe.contentDocument.querySelector(...)` to interact with elements inside an iframe.
-
-5.  **Be Idempotent:** Your script must be harmless if run multiple times. Use `IF EXISTS` to check for states before acting (e.g., don't try to log in if already logged in).
-
-6.  **Forbidden Techniques:** Never use `document.write()`. It is destructive. Avoid overly complex JS in `EVAL` that could be simplified into a few `c4a` commands.
-
-────────────────────────────────────────────────────────
-## From Vague Goals to Robust Scripts: Your Duty to Infer and Ensure Reliability
-
-This is your most important responsibility. Users are not automation experts. They will provide incomplete or vague instructions. Your job is to be the expert—to infer their true goal and build a script that is reliable by default. You must add the "invisible scaffolding" of checks and waits to ensure the page is stable and ready for the crawler. **A vague user prompt must still result in a robust, complete script.**
-
-Study these examples. No matter which query is given, your output must be the single, robust solution.
-
-### 1. Scenario: Basic Search Query
-
-*   **High Detail Query:** "Find the search box and search button. Wait for the search box to be visible, click it, clear it, type 'r2d2', click the search button, and then wait for the search results to appear."
-*   **Medium Detail Query:** "Find the search box and search for 'r2d2', click the search button until you get a list of items."
-*   **Low Detail Query:** "Search for r2d2."
-
-**THE CORRECT, ROBUST OUTPUT (for all three queries):**
-```
-WAIT `input[type="search"]` 10
-SET `input[type="search"]` "r2d2"
-CLICK `button[aria-label="Search"]`
-WAIT `div.search-results-container` 15
-```
-**Rationale:** You correctly infer the need to `WAIT` for the input first. You use the more efficient `SET` command. Most importantly, you **infer the crucial final step**: waiting for a results container to appear, confirming the search action was successful.
-
-### 2. Scenario: Clicking a "Load More" Button
-
-*   **High Detail Query:** "Click the button with the text 'Load More'. Afterward, wait for a new item with the class '.product-tile' to show up on the page."
-*   **Medium Detail Query:** "Click the load more button to see more products."
-*   **Low Detail Query:** "Load more items."
-
-**THE CORRECT, ROBUST OUTPUT:**
-```
-IF EXISTS `button.load-more` THEN
-  CLICK `button.load-more`
-  WAIT `div.new-item-indicator` 8
-ENDIF
-```
-**Rationale:** You wrap the action in `IF EXISTS` to prevent errors if the button is not present (e.g., on the last page). You correctly infer the need to wait for the *consequence* of the click—a new item appearing—rather than a blind `WAIT 2`.
-
-### 3. Scenario: Applying a Filter from a Dropdown
-
-*   **High Detail Query:** "First, click the dropdown with id 'color-filter'. Then, from the open menu, click the option that says 'Blue'. Finally, wait for the product grid to update."
-*   **Medium Detail Query:** "Filter the products by the color Blue."
-*   **Low Detail Query:** "Show blue products."
-
-**THE CORRECT, ROBUST OUTPUT:**
-```
-CLICK `#color-filter`
-WAIT `[data-value="blue"]` 3
-CLICK `[data-value="blue"]`
-WAIT `#product-grid[data-status="updated"]` 10
-```
-**Rationale:** You infer the need for two waits: one for the menu options to appear after the first click, and a second for the main content to update after the filter is applied. This prevents race conditions.
-
-### 4. Scenario: User Authentication (Login)
-
-*   **High Detail Query:** "Fill username with 'USER_EMAIL', fill password with 'USER_PASS', click login, and wait for the dashboard to appear."
-*   **Medium Detail Query:** "Log in as USER_EMAIL with password USER_PASS."
-*   **Low Detail Query:** "Log in."
-
-**THE CORRECT, ROBUST OUTPUT:**
-```
-IF EXISTS `[data-testid="logout-button"]` THEN
-  EVAL `console.log("Already logged in.")`
-ELSE
-  WAIT `input[name="username"]` 10
-  SET `input[name="username"]` "USER_EMAIL"
-  SET `input[name="password"]` "USER_PASS"
-  CLICK `button[type="submit"]`
-  WAIT `[data-testid="user-dashboard"]` 15
-ENDIF
-```
-**Rationale:** You build an **idempotent** script. You first check if the user is *already* logged in. If not, you proceed with the login and then, critically, `WAIT` for a post-login element to confirm success. You use placeholders when credentials are not provided in low-detail queries.
-
-### 5. Scenario: Dismissing an Interstitial Modal
-
-*   **High Detail Query:** "Check if a popup with id '#promo-modal' exists. If it does, click the close button inside it with class '.close-x'."
-*   **Medium Detail Query:** "Close the promotional popup."
-*   **Low Detail Query:** "Get rid of the popup."
-
-**THE CORRECT, ROBUST OUTPUT:**
-```
-IF EXISTS `div#promo-modal` THEN
-  CLICK `div#promo-modal button.close-x`
-ENDIF
-```
-**Rationale:** You correctly identify this as a conditional action. The script must not fail if the popup doesn't appear. The `IF EXISTS` block is the perfect, robust way to handle this optional interaction.
-
-────────────────────────────────────────────────────────
-## Advanced Scenarios & Master-Level Examples
-
-Study these solutions. Understand the *why* behind each choice.
-
-### Scenario: Interacting with a Web Component (Shadow DOM)
-**Goal:** Click a button inside a custom element `<user-card>`.
-**HTML Snippet:** `<user-card><#shadow-root (open)><button>Details</button></#shadow-root></user-card>`
-**Correct Mode:** `javascript` (or `c4a` with `EVAL`)
-**Rationale:** Standard selectors can't cross the shadow boundary. JavaScript is mandatory.
-
-```javascript
-// Solution in pure JS mode
-const card = document.querySelector('user-card');
-if (card && card.shadowRoot) {
-  const button = card.shadowRoot.querySelector('button');
-  if (button) button.click();
-}
-```
-```
-# Solution in c4a mode (using EVAL as the weapon of choice)
-EVAL `
-  const card = document.querySelector('user-card');
-  if (card && card.shadowRoot) {
-    const button = card.shadowRoot.querySelector('button');
-    if (button) button.click();
-  }
-`
-```
-
-### Scenario: Handling a Cookie Banner
-**Goal:** Accept the cookies to dismiss the modal.
-**HTML Snippet:** `<div id="cookie-consent-modal"><button id="accept-cookies">Accept All</button></div>`
-**Correct Mode:** `crawl4ai_script`
-**Rationale:** A simple, direct action. `c4a` is cleaner and more declarative.
-
-```
-# The most efficient solution
-IF EXISTS `#cookie-consent-modal` THEN
-  CLICK `#accept-cookies`
-  WAIT `div.content-loaded` 5
-ENDIF
-```
-
-### Scenario: Infinite Scroll Page
-**Goal:** Scroll down 5 times to load more content.
-**HTML Snippet:** `(A page with a long body and no "load more" button)`
-**Correct Mode:** `crawl4ai_script`
-**Rationale:** `REPEAT` is designed for exactly this. It's more readable than a JS loop for this simple task.
-
-```
-REPEAT (
-  SCROLL DOWN 1000,
-  5
-)
-WAIT 2
-```
-
-### Scenario: Hover-to-Reveal Menu
-**Goal:** Hover over "Products" to open the menu, then click "Laptops".
-**HTML Snippet:** `<a href="/products" id="products-menu">Products</a> <div class="menu-dropdown"><a href="/laptops">Laptops</a></div>`
-**Correct Mode:** `crawl4ai_script` (with `EVAL`)
-**Rationale:** `c4a` has no `HOVER` command. `EVAL` is the perfect tool to dispatch the `mouseover` event.
-
-```
-EVAL `document.querySelector('#products-menu').dispatchEvent(new MouseEvent('mouseover', { bubbles: true }))`
-WAIT `div.menu-dropdown a[href="/laptops"]` 3
-CLICK `div.menu-dropdown a[href="/laptops"]`
-```
-
-### Scenario: Login Form
-**Goal:** Fill and submit a login form.
-**HTML Snippet:** `<form><input name="email"><input name="password" type="password"><button type="submit"></button></form>`
-**Correct Mode:** `crawl4ai_script`
-**Rationale:** This is the canonical use case for `c4a`. The commands map 1:1 to the user journey.
-
-```
-WAIT `form` 10
-SET `input[name="email"]` "USER_EMAIL"
-SET `input[name="password"]` "USER_PASS"
-CLICK `button[type="submit"]`
-WAIT `[data-testid="user-dashboard"]` 12
-```
-
-────────────────────────────────────────────────────────
-## Final Output Mandate
-
-1.  **CODE ONLY.** Your entire response must be the script body.
-2.  **NO CHAT.** Do not say "Here is the script" or "This should work."
-3.  **NO MARKDOWN.** Do not wrap your code in ` ``` ` fences.
-4.  **NO COMMENTS.** Do not add comments to the final code output.
-5.  **SYNTACTICALLY PERFECT.** The script must be immediately executable.
-6.  **UTF-8, STANDARD QUOTES.** Use `"` for string literals, not `“` or `”`.
-
-You are an engine of automation. Now, receive the user's request and produce the optimal script."""
-
-
-GENERATE_JS_SCRIPT_PROMPT = """# The World-Class JavaScript Automation Scripter
-
-You are a world-class browser automation specialist. Your sole purpose is to convert a natural language objective and a snippet of HTML into the most **efficient, robust, and simple** pure JavaScript script possible to prepare a web page for data extraction.
-
-Your scripts will be executed directly in the browser (e.g., via Playwright's `page.evaluate()`) to handle dynamic content, user interactions, and other obstacles before the page is crawled. You are a master of browser-native JavaScript APIs.
-
-────────────────────────────────────────────────────────
-## Your Core Philosophy: "Efficiency, Robustness, Simplicity"
-
-This is your mantra. Every line of JavaScript you write must adhere to it.
-
-1.  **Efficiency (Shortest Path):** Generate the absolute minimum number of steps to achieve the goal. Do not include redundant actions. Your code should be concise and direct.
-2.  **Robustness (Will Not Break):** Prioritize selectors that are resistant to cosmetic site changes. `data-*` attributes are gold. Dynamic, auto-generated class names (`.class-a8B_x3`) are poison. Always prefer waiting for a state change over a blind `setTimeout`.
-3.  **Simplicity (Right Tool for the Job):** Use simple, direct DOM methods (`.querySelector`, `.click()`) whenever possible. Avoid overly complex or fragile logic when a simpler approach exists.
-
-────────────────────────────────────────────────────────
-## Essential JavaScript Automation Patterns & Toolkit
-
-All code should be wrapped in an `async` Immediately Invoked Function Expression `(async () => { ... })();` to allow for top-level `await` and to avoid polluting the global scope.
-
-| Task                 | Best-Practice JavaScript Implementation                                                                                                                                                                                                                                                                                                      |
-| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Wait for Element** | Create and use a robust `waitForElement` helper function. This is your most important tool. <br> `const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });` |
-| **Click Element**    | `const el = await waitForElement('selector'); if (el) el.click();`                                                                                                                                                                                                                                                                           |
-| **Set Input Value**  | `const input = await waitForElement('selector'); if (input) { input.value = 'new value'; input.dispatchEvent(new Event('input', { bubbles: true })); input.dispatchEvent(new Event('change', { bubbles: true })); }` <br> *Crucially, always dispatch `input` and `change` events to trigger framework reactivity.* |
-| **Check Existence**  | `const el = document.querySelector('selector'); if (el) { /* ... it exists */ }`                                                                                                                                                                                                                                                            |
-| **Scroll**           | `window.scrollBy(0, window.innerHeight);`                                                                                                                                                                                                                                                                                                    |
-| **Deal with Time**   | Use `await new Promise(r => setTimeout(r, 500));` for short, unavoidable pauses after an action. **Avoid long, blind waits.**                                                                                                                                                                                                                |
-
-REMEMBER: Make sure to generate very deterministic css selector. If you refer to a specific button, then be specific, otherwise you may capture elements you do not need, be very specific about the element you want to interact with.
-
-────────────────────────────────────────────────────────
-## The Art of High-Specificity Selectors: Your Defense Against Ambiguity
-
-This is your most critical skill for ensuring robustness. **You must assume the provided HTML is only a small fragment of the entire page.** A selector that looks unique in the fragment could be disastrously generic on the full page. Your primary defense is to **anchor your selectors to the most specific, stable parent element available in the given HTML context.**
-
-Think of it as creating a "sandbox" for your selectors.
-
-**Your Guiding Principle:** Start from a unique parent, then find the child.
-
-### Scenario: Selecting a Submit Button within a Login Form
-
-**HTML Snippet Provided:**
-```html
-<div class="user-auth-module" id="login-widget">
-  <h2>Member Login</h2>
-  <form action="/login">
-    <input name="email" type="email">
-    <input name="password" type="password">
-    <button type="submit">Sign In</button>
-  </form>
-</div>
-```
-
-*   **TERRIBLE (High Risk):** `button[type="submit"]`
-    *   **Why it's bad:** There could be dozens of other forms on the full page (e.g., a newsletter signup, a search bar in the header). This selector is a shot in the dark.
-
-*   **BETTER (Lower Risk):** `#login-widget button[type="submit"]`
-    *   **Why it's better:** It's anchored to a unique ID (`#login-widget`). This dramatically reduces the chance of ambiguity.
-
-*   **EXCELLENT (Minimal Risk):** `div[id="login-widget"] form button[type="submit"]`
-    *   **Why it's best:** This is a highly specific, descriptive path. It says, "Find the login widget, then the form inside it, and then the submit button inside *that* form." It is virtually guaranteed to be unique and is resilient to minor layout changes within the form.
-
-### Scenario: Selecting a "Add to Cart" Button
-
-**HTML Snippet Provided:**
-```html
-<section data-testid="product-details-main">
-  <h1>Awesome T-Shirt</h1>
-  <div class="product-actions">
-    <button class="add-to-cart-btn">Add to Cart</button>
-  </div>
-</section>
-```
-
-*   **TERRIBLE (High Risk):** `.add-to-cart-btn`
-    *   **Why it's bad:** A "related products" section outside this snippet might also use the same class name.
-
-*   **EXCELLENT (Minimal Risk):** `[data-testid="product-details-main"] .add-to-cart-btn`
-    *   **Why it's best:** It uses the stable `data-testid` attribute of the parent section as an anchor. This is the most robust pattern.
-
-**Your Mandate:** Always examine the provided HTML for a stable, unique parent (like an element with an `id`, a `data-testid`, or a highly specific combination of classes) and use it as the root of your selectors. **NEVER generate a generic, un-anchored selector if a better, more specific parent is available in the context.**
-
-
-────────────────────────────────────────────────────────
-## Strategic Principles & Anti-Patterns
-
-These are your commandments. Do not deviate.
-
-1.  **Selector Quality is Paramount:**
-    *   **GOOD:** `[data-testid="submit-button"]`, `#main-content`, `[aria-label="Close dialog"]`
-    *   **BAD:** `div > span:nth-child(3)`, `.button-gR3xY_s`, `//div[contains(@class, 'button')]`
-
-2.  **Wait for State, Not for Time:**
-    *   **DO:** `(await waitForElement('#load-more')).click(); await waitForElement('div.new-item');` This waits for the *result* of the action.
-    *   **DON'T:** `document.querySelector('#load-more').click(); await new Promise(r => setTimeout(r, 5000));` This is a guess and it will fail.
-
-3.  **Target the Action, Not the Artifact:** If you need to reveal content, click the button that reveals it. Don't try to manually change CSS `display` properties, as this can break the page's internal state.
-
-4.  **DOM-Awareness is Non-Negotiable:**
-    *   **Shadow DOM:** You MUST use `element.shadowRoot.querySelector(...)` to access elements inside a `#shadow-root (open)`.
-    *   **iFrames:** You MUST use `iframe.contentDocument.querySelector(...)` to interact with elements inside an iframe.
-
-5.  **Be Idempotent:** Your script must be harmless if run multiple times. Use `if (document.querySelector(...))` checks to avoid re-doing actions unnecessarily.
-
-6.  **Forbidden Techniques:** Never use `document.write()`. It is destructive.
-
-────────────────────────────────────────────────────────
-## From Vague Goals to Robust Scripts: Your Duty to Infer and Ensure Reliability
-
-This is your most important responsibility. Users are not automation experts. They will provide incomplete or vague instructions. Your job is to be the expert—to infer their true goal and build a script that is reliable by default. **A vague user prompt must still result in a robust, complete script.**
-
-Study these examples. No matter which query is given, your output must be the single, robust solution.
-
-### 1. Scenario: Basic Search Query
-
-*   **High Detail Query:** "Find the search box and search button. Wait for the search box to be visible, click it, clear it, type 'r2d2', click the search button, and then wait for the search results to appear."
-*   **Medium Detail Query:** "Find the search box and search for 'r2d2'."
-*   **Low Detail Query:** "Search for r2d2."
-
-**THE CORRECT, ROBUST JAVASCRIPT OUTPUT (for all three queries):**
-```javascript
-(async () => {
-  const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });
-  try {
-    const searchInput = await waitForElement('input[type="search"], input[aria-label*="search"]');
-    searchInput.value = 'r2d2';
-    searchInput.dispatchEvent(new Event('input', { bubbles: true }));
-    const searchButton = await waitForElement('button[type="submit"], button[aria-label*="search"]');
-    searchButton.click();
-    await waitForElement('div.search-results-container, #search-results');
-  } catch (e) {
-    console.error('Search script failed:', e.message);
-  }
-})();
-```
-
-### 2. Scenario: Clicking a "Load More" Button
-
-*   **High Detail Query:** "Click the button with the text 'Load More'. Afterward, wait for a new item with the class '.product-tile' to show up."
-*   **Medium Detail Query:** "Click the load more button."
-*   **Low Detail Query:** "Load more items."
-
-**THE CORRECT, ROBUST JAVASCRIPT OUTPUT:**
-```javascript
-(async () => {
-  const loadMoreButton = document.querySelector('button.load-more, [data-testid="load-more"]');
-  if (loadMoreButton) {
-    const initialItemCount = document.querySelectorAll('.product-tile').length;
-    loadMoreButton.click();
-    const waitForNewItem = (timeout = 8000) => new Promise((resolve, reject) => { const t0 = Date.now(); const check = () => { if (document.querySelectorAll('.product-tile').length > initialItemCount) return resolve(); if (Date.now() - t0 > timeout) return reject(new Error('Timeout waiting for new items to load.')); setTimeout(check, 200); }; check(); });
-    await waitForNewItem();
-  }
-})();
-```
-
-### 3. Scenario: User Authentication (Login)
-
-*   **High Detail Query:** "Fill username with 'USER_EMAIL', password with 'USER_PASS', click login, and wait for the dashboard."
-*   **Medium Detail Query:** "Log in as USER_EMAIL."
-*   **Low Detail Query:** "Log in."
-
-**THE CORRECT, ROBUST JAVASCRIPT OUTPUT:**
-```javascript
-(async () => {
-  if (document.querySelector('[data-testid="logout-button"]')) {
-    console.log('Already logged in.');
-    return;
-  }
-  const waitForElement = (selector, timeout = 10000) => new Promise((resolve, reject) => { const el = document.querySelector(selector); if (el) return resolve(el); const observer = new MutationObserver(() => { const el = document.querySelector(selector); if (el) { observer.disconnect(); resolve(el); } }); observer.observe(document.body, { childList: true, subtree: true }); setTimeout(() => { observer.disconnect(); reject(new Error(`Timeout waiting for ${selector}`)); }, timeout); });
-  try {
-    const userInput = await waitForElement('input[name*="user"], input[name*="email"]');
-    userInput.value = 'USER_EMAIL';
-    userInput.dispatchEvent(new Event('input', { bubbles: true }));
-    const passInput = await waitForElement('input[name*="pass"], input[type="password"]');
-    passInput.value = 'USER_PASS';
-    passInput.dispatchEvent(new Event('input', { bubbles: true }));
-    const submitButton = await waitForElement('button[type="submit"]');
-    submitButton.click();
-    await waitForElement('[data-testid="user-dashboard"], #dashboard, .account-page');
-  } catch (e) {
-    console.error('Login script failed:', e.message);
-  }
-})();
-```
-
-────────────────────────────────────────────────────────
-## The Art of High-Specificity Selectors: Your Defense Against Ambiguity
-
-This is your most critical skill for ensuring robustness. **You must assume the provided HTML is only a small fragment of the entire page.** A selector that looks unique in the fragment could be disastrously generic on the full page. Your primary defense is to **anchor your selectors to the most specific, stable parent element available in the given HTML context.**
-
-Think of it as creating a "sandbox" for your selectors.
-
-**Your Guiding Principle:** Start from a unique parent, then find the child.
-
-### Scenario: Selecting a Submit Button within a Login Form
-
-**HTML Snippet Provided:**
-```html
-<div class="user-auth-module" id="login-widget">
-  <h2>Member Login</h2>
-  <form action="/login">
-    <input name="email" type="email">
-    <input name="password" type="password">
-    <button type="submit">Sign In</button>
-  </form>
-</div>
-```
-
-*   **TERRIBLE (High Risk):** `button[type="submit"]`
-    *   **Why it's bad:** There could be dozens of other forms on the full page (e.g., a newsletter signup, a search bar in the header). This selector is a shot in the dark.
-
-*   **BETTER (Lower Risk):** `#login-widget button[type="submit"]`
-    *   **Why it's better:** It's anchored to a unique ID (`#login-widget`). This dramatically reduces the chance of ambiguity.
-
-*   **EXCELLENT (Minimal Risk):** `div[id="login-widget"] form button[type="submit"]`
-    *   **Why it's best:** This is a highly specific, descriptive path. It says, "Find the login widget, then the form inside it, and then the submit button inside *that* form." It is virtually guaranteed to be unique and is resilient to minor layout changes within the form.
-
-### Scenario: Selecting a "Add to Cart" Button
-
-**HTML Snippet Provided:**
-```html
-<section data-testid="product-details-main">
-  <h1>Awesome T-Shirt</h1>
-  <div class="product-actions">
-    <button class="add-to-cart-btn">Add to Cart</button>
-  </div>
-</section>
-```
-
-*   **TERRIBLE (High Risk):** `.add-to-cart-btn`
-    *   **Why it's bad:** A "related products" section outside this snippet might also use the same class name.
-
-*   **EXCELLENT (Minimal Risk):** `[data-testid="product-details-main"] .add-to-cart-btn`
-    *   **Why it's best:** It uses the stable `data-testid` attribute of the parent section as an anchor. This is the most robust pattern.
-
-**Your Mandate:** Always examine the provided HTML for a stable, unique parent (like an element with an `id`, a `data-testid`, or a highly specific combination of classes) and use it as the root of your selectors. **NEVER generate a generic, un-anchored selector if a better, more specific parent is available in the context.**
-
-
-────────────────────────────────────────────────────────
-## Final Output Mandate
-
-1.  **CODE ONLY.** Your entire response must be the script body.
-2.  **NO CHAT.** Do not say "Here is the script" or "This should work."
-3.  **NO MARKDOWN.** Do not wrap your code in ` ``` ` fences.
-4.  **NO COMMENTS.** Do not add comments to the final code output, except within the logic where it's a best practice.
-5.  **SYNTACTICALLY PERFECT.** The script must be a single, self-contained block, immediately executable. Wrap it in `(async () => { ... })();`.
-6.  **UTF-8, STANDARD QUOTES.** Use `'` for string literals, not `“` or `”`.
-
-You are an engine of automation. Now, receive the user's request and produce the optimal JavaScript."""
-
-
-
-
+"""

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

@@ -9,44 +9,83 @@ 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.
+class SSLCertificate:
     """
+    A class representing an SSL certificate with methods to export in various formats.
 
-    # 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
+    Attributes:
+        cert_info (Dict[str, Any]): The certificate information.
+
+        Methods:
+            from_url(url: str, timeout: int = 10) -> Optional['SSLCertificate']: Create SSLCertificate instance from a URL.
+            from_file(file_path: str) -> Optional['SSLCertificate']: Create SSLCertificate instance from a file.
+            from_binary(binary_data: bytes) -> Optional['SSLCertificate']: Create SSLCertificate instance from binary data.
+            export_as_pem() -> str: Export the certificate as PEM format.
+            export_as_der() -> bytes: Export the certificate as DER format.
+            export_as_json() -> Dict[str, Any]: Export the certificate as JSON format.
+            export_as_text() -> str: Export the certificate as text format.
+    """
 
     def __init__(self, cert_info: Dict[str, Any]):
+        self._cert_info = self._decode_cert_data(cert_info)
+
+    @staticmethod
+    def from_url(url: str, timeout: int = 10) -> Optional["SSLCertificate"]:
         """
-        Initializes the SSLCertificate object.
+        Create SSLCertificate instance from a URL.
 
         Args:
-            cert_info (Dict[str, Any]): The raw certificate dictionary.
+            url (str): URL of the website.
+            timeout (int): Timeout for the connection (default: 10).
+
+        Returns:
+            Optional[SSLCertificate]: SSLCertificate instance if successful, None otherwise.
         """
-        # 1. Decode the data (handle bytes -> str)
-        decoded_info = self._decode_cert_data(cert_info)
+        try:
+            hostname = urlparse(url).netloc
+            if ":" in hostname:
+                hostname = hostname.split(":")[0]
 
-        # 2. Store the decoded info internally (optional but good practice)
-        # self._cert_info = decoded_info # You can keep this if methods rely on it
+            context = ssl.create_default_context()
+            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)
+                    x509 = OpenSSL.crypto.load_certificate(
+                        OpenSSL.crypto.FILETYPE_ASN1, cert_binary
+                    )
 
-        # 3. Initialize the dictionary part of the object with the decoded data
-        super().__init__(decoded_info)
+                    cert_info = {
+                        "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(),
+                        "not_after": x509.get_notAfter(),
+                        "fingerprint": x509.digest("sha256").hex(),
+                        "signature_algorithm": x509.get_signature_algorithm(),
+                        "raw_cert": base64.b64encode(cert_binary),
+                    }
+
+                    # Add extensions
+                    extensions = []
+                    for i in range(x509.get_extension_count()):
+                        ext = x509.get_extension(i)
+                        extensions.append(
+                            {"name": ext.get_short_name(), "value": str(ext)}
+                        )
+                    cert_info["extensions"] = extensions
+
+                    return SSLCertificate(cert_info)
+
+        except Exception:
+            return None
 
     @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
+            return data.decode("utf-8")
         elif isinstance(data, dict):
             return {
                 (
@@ -58,119 +97,36 @@ class SSLCertificate(dict):
             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)
+        """
+        Export certificate as JSON.
+
+        Args:
+            filepath (Optional[str]): Path to save the JSON file (default: None).
+
+        Returns:
+            Optional[str]: JSON string if successful, None otherwise.
+        """
+        json_str = json.dumps(self._cert_info, 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."""
+        """
+        Export certificate as PEM.
+
+        Args:
+            filepath (Optional[str]): Path to save the PEM file (default: None).
+
+        Returns:
+            Optional[str]: PEM string if successful, None otherwise.
+        """
         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
+                OpenSSL.crypto.FILETYPE_ASN1,
+                base64.b64decode(self._cert_info["raw_cert"]),
             )
             pem_data = OpenSSL.crypto.dump_certificate(
                 OpenSSL.crypto.FILETYPE_PEM, x509
@@ -180,25 +136,49 @@ class SSLCertificate(dict):
                 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
+        except Exception:
+            return None
 
     def to_der(self, filepath: Optional[str] = None) -> Optional[bytes]:
-        """Export certificate as DER."""
+        """
+        Export certificate as DER.
+
+        Args:
+            filepath (Optional[str]): Path to save the DER file (default: None).
+
+        Returns:
+            Optional[bytes]: DER bytes if successful, None otherwise.
+        """
         try:
-            # Decode the raw_cert (which should be string due to _decode)
-            der_data = base64.b64decode(self.get("raw_cert", ""))
+            der_data = base64.b64decode(self._cert_info["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
+        except Exception:
+            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}'>"
+    @property
+    def issuer(self) -> Dict[str, str]:
+        """Get certificate issuer information."""
+        return self._cert_info.get("issuer", {})
+
+    @property
+    def subject(self) -> Dict[str, str]:
+        """Get certificate subject information."""
+        return self._cert_info.get("subject", {})
+
+    @property
+    def valid_from(self) -> str:
+        """Get certificate validity start date."""
+        return self._cert_info.get("not_before", "")
+
+    @property
+    def valid_until(self) -> str:
+        """Get certificate validity end date."""
+        return self._cert_info.get("not_after", "")
+
+    @property
+    def fingerprint(self) -> str:
+        """Get certificate fingerprint."""
+        return self._cert_info.get("fingerprint", "")

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)

crawl4ai/user_agent_generator.py

@@ -3,11 +3,12 @@ from typing import Optional, Literal, List, Dict, Tuple
 import re
 
 from abc import ABC, abstractmethod
+import random
 from fake_useragent import UserAgent
 import requests
 from lxml import html
 import json
-from typing import Union
+from typing import Optional, List, Union, Dict
 
 class UAGen(ABC):
    @abstractmethod

crawl4ai/web_crawler.py

@@ -11,7 +11,7 @@ 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 .content_scraping_strategy import WebScrapingStrategy
 from .config import *
 import warnings
 import json

deploy/docker/.dockerignore

@@ -1,31 +0,0 @@
-# .dockerignore
-*
-
-# Allow specific files and directories when using local installation
-!crawl4ai/
-!docs/
-!deploy/docker/
-!setup.py
-!pyproject.toml
-!README.md
-!LICENSE
-!MANIFEST.in
-!setup.cfg
-!mkdocs.yml
-
-.git/
-__pycache__/
-*.pyc
-*.pyo
-*.pyd
-.DS_Store
-.env
-.venv
-venv/
-tests/
-coverage.xml
-*.log
-*.swp
-*.egg-info/
-dist/
-build/

deploy/docker/.llm.env.example

@@ -1,13 +0,0 @@
-# LLM Provider Keys
-OPENAI_API_KEY=your_openai_key_here
-DEEPSEEK_API_KEY=your_deepseek_key_here
-ANTHROPIC_API_KEY=your_anthropic_key_here
-GROQ_API_KEY=your_groq_key_here
-TOGETHER_API_KEY=your_together_key_here
-MISTRAL_API_KEY=your_mistral_key_here
-GEMINI_API_TOKEN=your_gemini_key_here
-
-# Optional: Override the default LLM provider
-# Examples: "openai/gpt-4", "anthropic/claude-3-opus", "deepseek/chat", etc.
-# If not set, uses the provider specified in config.yml (default: openai/gpt-4o-mini)
-# LLM_PROVIDER=anthropic/claude-3-opus

deploy/docker/README.md

@@ -1,846 +0,0 @@
-# Crawl4AI Docker Guide 🐳
-
-## Table of Contents
-- [Prerequisites](#prerequisites)
-- [Installation](#installation)
-  - [Option 1: Using Pre-built Docker Hub Images (Recommended)](#option-1-using-pre-built-docker-hub-images-recommended)
-  - [Option 2: Using Docker Compose](#option-2-using-docker-compose)
-  - [Option 3: Manual Local Build & Run](#option-3-manual-local-build--run)
-- [Dockerfile Parameters](#dockerfile-parameters)
-- [Using the API](#using-the-api)
-  - [Playground Interface](#playground-interface)
-  - [Python SDK](#python-sdk)
-  - [Understanding Request Schema](#understanding-request-schema)
-  - [REST API Examples](#rest-api-examples)
-- [Additional API Endpoints](#additional-api-endpoints)
-  - [HTML Extraction Endpoint](#html-extraction-endpoint)
-  - [Screenshot Endpoint](#screenshot-endpoint)
-  - [PDF Export Endpoint](#pdf-export-endpoint)
-  - [JavaScript Execution Endpoint](#javascript-execution-endpoint)
-  - [Library Context Endpoint](#library-context-endpoint)
-- [MCP (Model Context Protocol) Support](#mcp-model-context-protocol-support)
-  - [What is MCP?](#what-is-mcp)
-  - [Connecting via MCP](#connecting-via-mcp)
-  - [Using with Claude Code](#using-with-claude-code)
-  - [Available MCP Tools](#available-mcp-tools)
-  - [Testing MCP Connections](#testing-mcp-connections)
-  - [MCP Schemas](#mcp-schemas)
-- [Metrics & Monitoring](#metrics--monitoring)
-- [Deployment Scenarios](#deployment-scenarios)
-- [Complete Examples](#complete-examples)
-- [Server Configuration](#server-configuration)
-  - [Understanding config.yml](#understanding-configyml)
-  - [JWT Authentication](#jwt-authentication)
-  - [Configuration Tips and Best Practices](#configuration-tips-and-best-practices)
-  - [Customizing Your Configuration](#customizing-your-configuration)
-  - [Configuration Recommendations](#configuration-recommendations)
-- [Getting Help](#getting-help)
-- [Summary](#summary)
-
-## Prerequisites
-
-Before we dive in, make sure you have:
-- Docker installed and running (version 20.10.0 or higher), including `docker compose` (usually bundled with Docker Desktop).
-- `git` for cloning the repository.
-- At least 4GB of RAM available for the container (more recommended for heavy use).
-- Python 3.10+ (if using the Python SDK).
-- Node.js 16+ (if using the Node.js examples).
-
-> 💡 **Pro tip**: Run `docker info` to check your Docker installation and available resources.
-
-## Installation
-
-We offer several ways to get the Crawl4AI server running. The quickest way is to use our pre-built Docker Hub images.
-
-### Option 1: Using Pre-built Docker Hub Images (Recommended)
-
-Pull and run images directly from Docker Hub without building locally.
-
-#### 1. Pull the Image
-
-Our latest release candidate is `0.7.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
-
-> ⚠️ **Important Note**: The `latest` tag currently points to the stable `0.6.0` version. After testing and validation, `0.7.0` (without -r1) will be released and `latest` will be updated. For now, please use `0.7.0-r1` to test the new features.
-
-```bash
-# Pull the release candidate (for testing new features)
-docker pull unclecode/crawl4ai:0.7.0-r1
-
-# Or pull the current stable version (0.6.0)
-docker pull unclecode/crawl4ai:latest
-```
-
-#### 2. Setup Environment (API Keys)
-
-If you plan to use LLMs, create a `.llm.env` file in your working directory:
-
-```bash
-# Create a .llm.env file with your API keys
-cat > .llm.env << EOL
-# OpenAI
-OPENAI_API_KEY=sk-your-key
-
-# Anthropic
-ANTHROPIC_API_KEY=your-anthropic-key
-
-# Other providers as needed
-# DEEPSEEK_API_KEY=your-deepseek-key
-# GROQ_API_KEY=your-groq-key
-# TOGETHER_API_KEY=your-together-key
-# MISTRAL_API_KEY=your-mistral-key
-# GEMINI_API_TOKEN=your-gemini-token
-EOL
-```
-> 🔑 **Note**: Keep your API keys secure! Never commit `.llm.env` to version control.
-
-#### 3. Run the Container
-
-*   **Basic run:**
-    ```bash
-    docker run -d \
-      -p 11235:11235 \
-      --name crawl4ai \
-      --shm-size=1g \
-      unclecode/crawl4ai:0.7.0-r1
-    ```
-
-*   **With LLM support:**
-    ```bash
-    # Make sure .llm.env is in the current directory
-    docker run -d \
-      -p 11235:11235 \
-      --name crawl4ai \
-      --env-file .llm.env \
-      --shm-size=1g \
-      unclecode/crawl4ai:0.7.0-r1
-    ```
-
-> The server will be available at `http://localhost:11235`. Visit `/playground` to access the interactive testing interface.
-
-#### 4. Stopping the Container
-
-```bash
-docker stop crawl4ai && docker rm crawl4ai
-```
-
-#### Docker Hub Versioning Explained
-
-*   **Image Name:** `unclecode/crawl4ai`
-*   **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.0-r1`)
-    *   `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
-    *   `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
-*   **`latest` Tag:** Points to the most recent stable version
-*   **Multi-Architecture Support:** All images support both `linux/amd64` and `linux/arm64` architectures through a single tag
-
-### Option 2: Using Docker Compose
-
-Docker Compose simplifies building and running the service, especially for local development and testing.
-
-#### 1. Clone Repository
-
-```bash
-git clone https://github.com/unclecode/crawl4ai.git
-cd crawl4ai
-```
-
-#### 2. Environment Setup (API Keys)
-
-If you plan to use LLMs, copy the example environment file and add your API keys. This file should be in the **project root directory**.
-
-```bash
-# Make sure you are in the 'crawl4ai' root directory
-cp deploy/docker/.llm.env.example .llm.env
-
-# Now edit .llm.env and add your API keys
-```
-
-**Flexible LLM Provider Configuration:**
-
-The Docker setup now supports flexible LLM provider configuration through three methods:
-
-1. **Environment Variable** (Highest Priority): Set `LLM_PROVIDER` to override the default
-   ```bash
-   export LLM_PROVIDER="anthropic/claude-3-opus"
-   # Or in your .llm.env file:
-   # LLM_PROVIDER=anthropic/claude-3-opus
-   ```
-
-2. **API Request Parameter**: Specify provider per request
-   ```json
-   {
-     "url": "https://example.com",
-     "provider": "groq/mixtral-8x7b"
-   }
-   ```
-
-3. **Config File Default**: Falls back to `config.yml` (default: `openai/gpt-4o-mini`)
-
-The system automatically selects the appropriate API key based on the provider.
-
-#### 3. Build and Run with Compose
-
-The `docker-compose.yml` file in the project root provides a simplified approach that automatically handles architecture detection using buildx.
-
-*   **Run Pre-built Image from Docker Hub:**
-    ```bash
-    # Pulls and runs the release candidate from Docker Hub
-    # Automatically selects the correct architecture
-    IMAGE=unclecode/crawl4ai:0.7.0-r1 docker compose up -d
-    ```
-
-*   **Build and Run Locally:**
-    ```bash
-    # Builds the image locally using Dockerfile and runs it
-    # Automatically uses the correct architecture for your machine
-    docker compose up --build -d
-    ```
-
-*   **Customize the Build:**
-    ```bash
-    # Build with all features (includes torch and transformers)
-    INSTALL_TYPE=all docker compose up --build -d
-    
-    # Build with GPU support (for AMD64 platforms)
-    ENABLE_GPU=true docker compose up --build -d
-    ```
-
-> The server will be available at `http://localhost:11235`.
-
-#### 4. Stopping the Service
-
-```bash
-# Stop the service
-docker compose down
-```
-
-### Option 3: Manual Local Build & Run
-
-If you prefer not to use Docker Compose for direct control over the build and run process.
-
-#### 1. Clone Repository & Setup Environment
-
-Follow steps 1 and 2 from the Docker Compose section above (clone repo, `cd crawl4ai`, create `.llm.env` in the root).
-
-#### 2. Build the Image (Multi-Arch)
-
-Use `docker buildx` to build the image. Crawl4AI now uses buildx to handle multi-architecture builds automatically.
-
-```bash
-# Make sure you are in the 'crawl4ai' root directory
-# Build for the current architecture and load it into Docker
-docker buildx build -t crawl4ai-local:latest --load .
-
-# Or build for multiple architectures (useful for publishing)
-docker buildx build --platform linux/amd64,linux/arm64 -t crawl4ai-local:latest --load .
-
-# Build with additional options
-docker buildx build \
-  --build-arg INSTALL_TYPE=all \
-  --build-arg ENABLE_GPU=false \
-  -t crawl4ai-local:latest --load .
-```
-
-#### 3. Run the Container
-
-*   **Basic run (no LLM support):**
-    ```bash
-    docker run -d \
-      -p 11235:11235 \
-      --name crawl4ai-standalone \
-      --shm-size=1g \
-      crawl4ai-local:latest
-    ```
-
-*   **With LLM support:**
-    ```bash
-    # Make sure .llm.env is in the current directory (project root)
-    docker run -d \
-      -p 11235:11235 \
-      --name crawl4ai-standalone \
-      --env-file .llm.env \
-      --shm-size=1g \
-      crawl4ai-local:latest
-    ```
-
-> The server will be available at `http://localhost:11235`.
-
-#### 4. Stopping the Manual Container
-
-```bash
-docker stop crawl4ai-standalone && docker rm crawl4ai-standalone
-```
-
----
-
-## MCP (Model Context Protocol) Support
-
-Crawl4AI server includes support for the Model Context Protocol (MCP), allowing you to connect the server's capabilities directly to MCP-compatible clients like Claude Code.
-
-### What is MCP?
-
-MCP is an open protocol that standardizes how applications provide context to LLMs. It allows AI models to access external tools, data sources, and services through a standardized interface.
-
-### Connecting via MCP
-
-The Crawl4AI server exposes two MCP endpoints:
-
-- **Server-Sent Events (SSE)**: `http://localhost:11235/mcp/sse`
-- **WebSocket**: `ws://localhost:11235/mcp/ws`
-
-### Using with Claude Code
-
-You can add Crawl4AI as an MCP tool provider in Claude Code with a simple command:
-
-```bash
-# Add the Crawl4AI server as an MCP provider
-claude mcp add --transport sse c4ai-sse http://localhost:11235/mcp/sse
-
-# List all MCP providers to verify it was added
-claude mcp list
-```
-
-Once connected, Claude Code can directly use Crawl4AI's capabilities like screenshot capture, PDF generation, and HTML processing without having to make separate API calls.
-
-### Available MCP Tools
-
-When connected via MCP, the following tools are available:
-
-- `md` - Generate markdown from web content
-- `html` - Extract preprocessed HTML
-- `screenshot` - Capture webpage screenshots
-- `pdf` - Generate PDF documents
-- `execute_js` - Run JavaScript on web pages
-- `crawl` - Perform multi-URL crawling
-- `ask` - Query the Crawl4AI library context
-
-### Testing MCP Connections
-
-You can test the MCP WebSocket connection using the test file included in the repository:
-
-```bash
-# From the repository root
-python tests/mcp/test_mcp_socket.py
-```
-
-### MCP Schemas
-
-Access the MCP tool schemas at `http://localhost:11235/mcp/schema` for detailed information on each tool's parameters and capabilities.
-
----
-
-## Additional API Endpoints
-
-In addition to the core `/crawl` and `/crawl/stream` endpoints, the server provides several specialized endpoints:
-
-### HTML Extraction Endpoint
-
-```
-POST /html
-```
-
-Crawls the URL and returns preprocessed HTML optimized for schema extraction.
-
-```json
-{
-  "url": "https://example.com"
-}
-```
-
-### Screenshot Endpoint
-
-```
-POST /screenshot
-```
-
-Captures a full-page PNG screenshot of the specified URL.
-
-```json
-{
-  "url": "https://example.com",
-  "screenshot_wait_for": 2,
-  "output_path": "/path/to/save/screenshot.png"
-}
-```
-
-- `screenshot_wait_for`: Optional delay in seconds before capture (default: 2)
-- `output_path`: Optional path to save the screenshot (recommended)
-
-### PDF Export Endpoint
-
-```
-POST /pdf
-```
-
-Generates a PDF document of the specified URL.
-
-```json
-{
-  "url": "https://example.com",
-  "output_path": "/path/to/save/document.pdf"
-}
-```
-
-- `output_path`: Optional path to save the PDF (recommended)
-
-### JavaScript Execution Endpoint
-
-```
-POST /execute_js
-```
-
-Executes JavaScript snippets on the specified URL and returns the full crawl result.
-
-```json
-{
-  "url": "https://example.com",
-  "scripts": [
-    "return document.title",
-    "return Array.from(document.querySelectorAll('a')).map(a => a.href)"
-  ]
-}
-```
-
-- `scripts`: List of JavaScript snippets to execute sequentially
-
----
-
-## Dockerfile Parameters
-
-You can customize the image build process using build arguments (`--build-arg`). These are typically used via `docker buildx build` or within the `docker-compose.yml` file.
-
-```bash
-# Example: Build with 'all' features using buildx
-docker buildx build \
-  --platform linux/amd64,linux/arm64 \
-  --build-arg INSTALL_TYPE=all \
-  -t yourname/crawl4ai-all:latest \
-  --load \
-  . # Build from root context
-```
-
-### Build Arguments Explained
-
-| Argument     | Description                              | Default   | Options                            |
-| :----------- | :--------------------------------------- | :-------- | :--------------------------------- |
-| INSTALL_TYPE | Feature set                              | `default` | `default`, `all`, `torch`, `transformer` |
-| ENABLE_GPU   | GPU support (CUDA for AMD64)           | `false`   | `true`, `false`                    |
-| APP_HOME     | Install path inside container (advanced) | `/app`    | any valid path                   |
-| USE_LOCAL    | Install library from local source        | `true`    | `true`, `false`                    |
-| GITHUB_REPO  | Git repo to clone if USE_LOCAL=false   | *(see Dockerfile)* | any git URL                  |
-| GITHUB_BRANCH| Git branch to clone if USE_LOCAL=false   | `main`    | any branch name                  |
-
-*(Note: PYTHON_VERSION is fixed by the `FROM` instruction in the Dockerfile)*
-
-### Build Best Practices
-
-1.  **Choose the Right Install Type**
-    *   `default`: Basic installation, smallest image size. Suitable for most standard web scraping and markdown generation.
-    *   `all`: Full features including `torch` and `transformers` for advanced extraction strategies (e.g., CosineStrategy, certain LLM filters). Significantly larger image. Ensure you need these extras.
-2.  **Platform Considerations**
-    *   Use `buildx` for building multi-architecture images, especially for pushing to registries.
-    *   Use `docker compose` profiles (`local-amd64`, `local-arm64`) for easy platform-specific local builds.
-3.  **Performance Optimization**
-    *   The image automatically includes platform-specific optimizations (OpenMP for AMD64, OpenBLAS for ARM64).
-
----
-
-## Using the API
-
-Communicate with the running Docker server via its REST API (defaulting to `http://localhost:11235`). You can use the Python SDK or make direct HTTP requests.
-
-### Playground Interface
-
-A built-in web playground is available at `http://localhost:11235/playground` for testing and generating API requests. The playground allows you to:
-
-1. Configure `CrawlerRunConfig` and `BrowserConfig` using the main library's Python syntax
-2. Test crawling operations directly from the interface
-3. Generate corresponding JSON for REST API requests based on your configuration
-
-This is the easiest way to translate Python configuration to JSON requests when building integrations.
-
-### Python SDK
-
-Install the SDK: `pip install crawl4ai`
-
-```python
-import asyncio
-from crawl4ai.docker_client import Crawl4aiDockerClient
-from crawl4ai import BrowserConfig, CrawlerRunConfig, CacheMode # Assuming you have crawl4ai installed
-
-async def main():
-    # Point to the correct server port
-    async with Crawl4aiDockerClient(base_url="http://localhost:11235", verbose=True) as client:
-        # If JWT is enabled on the server, authenticate first:
-        # await client.authenticate("user@example.com") # See Server Configuration section
-
-        # Example Non-streaming crawl
-        print("--- Running Non-Streaming Crawl ---")
-        results = await client.crawl(
-            ["https://httpbin.org/html"],
-            browser_config=BrowserConfig(headless=True), # Use library classes for config aid
-            crawler_config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
-        )
-        if results: # client.crawl returns None on failure
-          print(f"Non-streaming results success: {results.success}")
-          if results.success:
-              for result in results: # Iterate through the CrawlResultContainer
-                  print(f"URL: {result.url}, Success: {result.success}")
-        else:
-            print("Non-streaming crawl failed.")
-
-
-        # Example Streaming crawl
-        print("\n--- Running Streaming Crawl ---")
-        stream_config = CrawlerRunConfig(stream=True, cache_mode=CacheMode.BYPASS)
-        try:
-            async for result in await client.crawl( # client.crawl returns an async generator for streaming
-                ["https://httpbin.org/html", "https://httpbin.org/links/5/0"],
-                browser_config=BrowserConfig(headless=True),
-                crawler_config=stream_config
-            ):
-                print(f"Streamed result: URL: {result.url}, Success: {result.success}")
-        except Exception as e:
-            print(f"Streaming crawl failed: {e}")
-
-
-        # Example Get schema
-        print("\n--- Getting Schema ---")
-        schema = await client.get_schema()
-        print(f"Schema received: {bool(schema)}") # Print whether schema was received
-
-if __name__ == "__main__":
-    asyncio.run(main())
-```
-
-*(SDK parameters like timeout, verify_ssl etc. remain the same)*
-
-### Second Approach: Direct API Calls
-
-Crucially, when sending configurations directly via JSON, they **must** follow the `{"type": "ClassName", "params": {...}}` structure for any non-primitive value (like config objects or strategies). Dictionaries must be wrapped as `{"type": "dict", "value": {...}}`.
-
-*(Keep the detailed explanation of Configuration Structure, Basic Pattern, Simple vs Complex, Strategy Pattern, Complex Nested Example, Quick Grammar Overview, Important Rules, Pro Tip)*
-
-#### More Examples *(Ensure Schema example uses type/value wrapper)*
-
-**Advanced Crawler Configuration**
-*(Keep example, ensure cache_mode uses valid enum value like "bypass")*
-
-**Extraction Strategy**
-```json
-{
-    "crawler_config": {
-        "type": "CrawlerRunConfig",
-        "params": {
-            "extraction_strategy": {
-                "type": "JsonCssExtractionStrategy",
-                "params": {
-                    "schema": {
-                        "type": "dict",
-                        "value": {
-                           "baseSelector": "article.post",
-                           "fields": [
-                               {"name": "title", "selector": "h1", "type": "text"},
-                               {"name": "content", "selector": ".content", "type": "html"}
-                           ]
-                         }
-                    }
-                }
-            }
-        }
-    }
-}
-```
-
-**LLM Extraction Strategy** *(Keep example, ensure schema uses type/value wrapper)*
-*(Keep Deep Crawler Example)*
-
-### REST API Examples
-
-Update URLs to use port `11235`.
-
-#### Simple Crawl
-
-```python
-import requests
-
-# Configuration objects converted to the required JSON structure
-browser_config_payload = {
-    "type": "BrowserConfig",
-    "params": {"headless": True}
-}
-crawler_config_payload = {
-    "type": "CrawlerRunConfig",
-    "params": {"stream": False, "cache_mode": "bypass"} # Use string value of enum
-}
-
-crawl_payload = {
-    "urls": ["https://httpbin.org/html"],
-    "browser_config": browser_config_payload,
-    "crawler_config": crawler_config_payload
-}
-response = requests.post(
-    "http://localhost:11235/crawl", # Updated port
-    # headers={"Authorization": f"Bearer {token}"},  # If JWT is enabled
-    json=crawl_payload
-)
-print(f"Status Code: {response.status_code}")
-if response.ok:
-    print(response.json())
-else:
-    print(f"Error: {response.text}")
-
-```
-
-#### Streaming Results
-
-```python
-import json
-import httpx # Use httpx for async streaming example
-
-async def test_stream_crawl(token: str = None): # Made token optional
-    """Test the /crawl/stream endpoint with multiple URLs."""
-    url = "http://localhost:11235/crawl/stream" # Updated port
-    payload = {
-        "urls": [
-            "https://httpbin.org/html",
-            "https://httpbin.org/links/5/0",
-        ],
-        "browser_config": {
-            "type": "BrowserConfig",
-            "params": {"headless": True, "viewport": {"type": "dict", "value": {"width": 1200, "height": 800}}} # Viewport needs type:dict
-        },
-        "crawler_config": {
-            "type": "CrawlerRunConfig",
-            "params": {"stream": True, "cache_mode": "bypass"}
-        }
-    }
-
-    headers = {}
-    # if token:
-    #    headers = {"Authorization": f"Bearer {token}"} # If JWT is enabled
-
-    try:
-        async with httpx.AsyncClient() as client:
-            async with client.stream("POST", url, json=payload, headers=headers, timeout=120.0) as response:
-                print(f"Status: {response.status_code} (Expected: 200)")
-                response.raise_for_status() # Raise exception for bad status codes
-
-                # Read streaming response line-by-line (NDJSON)
-                async for line in response.aiter_lines():
-                    if line:
-                        try:
-                            data = json.loads(line)
-                            # Check for completion marker
-                            if data.get("status") == "completed":
-                                print("Stream completed.")
-                                break
-                            print(f"Streamed Result: {json.dumps(data, indent=2)}")
-                        except json.JSONDecodeError:
-                            print(f"Warning: Could not decode JSON line: {line}")
-
-    except httpx.HTTPStatusError as e:
-         print(f"HTTP error occurred: {e.response.status_code} - {e.response.text}")
-    except Exception as e:
-        print(f"Error in streaming crawl test: {str(e)}")
-
-# To run this example:
-# import asyncio
-# asyncio.run(test_stream_crawl())
-```
-
----
-
-## Metrics & Monitoring
-
-Keep an eye on your crawler with these endpoints:
-
-- `/health` - Quick health check
-- `/metrics` - Detailed Prometheus metrics
-- `/schema` - Full API schema
-
-Example health check:
-```bash
-curl http://localhost:11235/health
-```
-
----
-
-*(Deployment Scenarios and Complete Examples sections remain the same, maybe update links if examples moved)*
-
----
-
-## Server Configuration
-
-The server's behavior can be customized through the `config.yml` file.
-
-### Understanding config.yml
-
-The configuration file is loaded from `/app/config.yml` inside the container. By default, the file from `deploy/docker/config.yml` in the repository is copied there during the build.
-
-Here's a detailed breakdown of the configuration options (using defaults from `deploy/docker/config.yml`):
-
-```yaml
-# Application Configuration
-app:
-  title: "Crawl4AI API"
-  version: "1.0.0" # Consider setting this to match library version, e.g., "0.5.1"
-  host: "0.0.0.0"
-  port: 8020 # NOTE: This port is used ONLY when running server.py directly. Gunicorn overrides this (see supervisord.conf).
-  reload: False # Default set to False - suitable for production
-  timeout_keep_alive: 300
-
-# Default LLM Configuration
-llm:
-  provider: "openai/gpt-4o-mini"  # Can be overridden by LLM_PROVIDER env var
-  api_key_env: "OPENAI_API_KEY"
-  # api_key: sk-...  # If you pass the API key directly then api_key_env will be ignored
-
-# Redis Configuration (Used by internal Redis server managed by supervisord)
-redis:
-  host: "localhost"
-  port: 6379
-  db: 0
-  password: ""
-  # ... other redis options ...
-
-# Rate Limiting Configuration
-rate_limiting:
-  enabled: True
-  default_limit: "1000/minute"
-  trusted_proxies: []
-  storage_uri: "memory://"  # Use "redis://localhost:6379" if you need persistent/shared limits
-
-# Security Configuration
-security:
-  enabled: false # Master toggle for security features
-  jwt_enabled: false # Enable JWT authentication (requires security.enabled=true)
-  https_redirect: false # Force HTTPS (requires security.enabled=true)
-  trusted_hosts: ["*"] # Allowed hosts (use specific domains in production)
-  headers: # Security headers (applied if security.enabled=true)
-    x_content_type_options: "nosniff"
-    x_frame_options: "DENY"
-    content_security_policy: "default-src 'self'"
-    strict_transport_security: "max-age=63072000; includeSubDomains"
-
-# Crawler Configuration
-crawler:
-  memory_threshold_percent: 95.0
-  rate_limiter:
-    base_delay: [1.0, 2.0] # Min/max delay between requests in seconds for dispatcher
-  timeouts:
-    stream_init: 30.0  # Timeout for stream initialization
-    batch_process: 300.0 # Timeout for non-streaming /crawl processing
-
-# Logging Configuration
-logging:
-  level: "INFO"
-  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-
-# Observability Configuration
-observability:
-  prometheus:
-    enabled: True
-    endpoint: "/metrics"
-  health_check:
-    endpoint: "/health"
-```
-
-*(JWT Authentication section remains the same, just note the default port is now 11235 for requests)*
-
-*(Configuration Tips and Best Practices remain the same)*
-
-### Customizing Your Configuration
-
-You can override the default `config.yml`.
-
-#### Method 1: Modify Before Build
-
-1.  Edit the `deploy/docker/config.yml` file in your local repository clone.
-2.  Build the image using `docker buildx` or `docker compose --profile local-... up --build`. The modified file will be copied into the image.
-
-#### Method 2: Runtime Mount (Recommended for Custom Deploys)
-
-1.  Create your custom configuration file, e.g., `my-custom-config.yml` locally. Ensure it contains all necessary sections.
-2.  Mount it when running the container:
-
-    *   **Using `docker run`:**
-        ```bash
-        # Assumes my-custom-config.yml is in the current directory
-        docker run -d -p 11235:11235 \
-          --name crawl4ai-custom-config \
-          --env-file .llm.env \
-          --shm-size=1g \
-          -v $(pwd)/my-custom-config.yml:/app/config.yml \
-          unclecode/crawl4ai:latest # Or your specific tag
-        ```
-
-    *   **Using `docker-compose.yml`:** Add a `volumes` section to the service definition:
-        ```yaml
-        services:
-          crawl4ai-hub-amd64: # Or your chosen service
-            image: unclecode/crawl4ai:latest
-            profiles: ["hub-amd64"]
-            <<: *base-config
-            volumes:
-              # Mount local custom config over the default one in the container
-              - ./my-custom-config.yml:/app/config.yml
-              # Keep the shared memory volume from base-config
-              - /dev/shm:/dev/shm
-        ```
-        *(Note: Ensure `my-custom-config.yml` is in the same directory as `docker-compose.yml`)*
-
-> 💡 When mounting, your custom file *completely replaces* the default one. Ensure it's a valid and complete configuration.
-
-### Configuration Recommendations
-
-1. **Security First** 🔒
-   - Always enable security in production
-   - Use specific trusted_hosts instead of wildcards
-   - Set up proper rate limiting to protect your server
-   - Consider your environment before enabling HTTPS redirect
-
-2. **Resource Management** 💻
-   - Adjust memory_threshold_percent based on available RAM
-   - Set timeouts according to your content size and network conditions
-   - Use Redis for rate limiting in multi-container setups
-
-3. **Monitoring** 📊
-   - Enable Prometheus if you need metrics
-   - Set DEBUG logging in development, INFO in production
-   - Regular health check monitoring is crucial
-
-4. **Performance Tuning** ⚡
-   - Start with conservative rate limiter delays
-   - Increase batch_process timeout for large content
-   - Adjust stream_init timeout based on initial response times
-
-## Getting Help
-
-We're here to help you succeed with Crawl4AI! Here's how to get support:
-
-- 📖 Check our [full documentation](https://docs.crawl4ai.com)
-- 🐛 Found a bug? [Open an issue](https://github.com/unclecode/crawl4ai/issues)
-- 💬 Join our [Discord community](https://discord.gg/crawl4ai)
-- ⭐ Star us on GitHub to show support!
-
-## Summary
-
-In this guide, we've covered everything you need to get started with Crawl4AI's Docker deployment:
-- Building and running the Docker container
-- Configuring the environment  
-- Using the interactive playground for testing
-- Making API requests with proper typing
-- Using the Python SDK
-- Leveraging specialized endpoints for screenshots, PDFs, and JavaScript execution
-- Connecting via the Model Context Protocol (MCP)
-- Monitoring your deployment
-
-The new playground interface at `http://localhost:11235/playground` makes it much easier to test configurations and generate the corresponding JSON for API requests.
-
-For AI application developers, the MCP integration allows tools like Claude Code to directly access Crawl4AI's capabilities without complex API handling.
-
-Remember, the examples in the `examples` folder are your friends - they show real-world usage patterns that you can adapt for your needs.
-
-Keep exploring, and don't hesitate to reach out if you need help! We're building something amazing together. 🚀
-
-Happy crawling! 🕷️

deploy/docker/api.py

@@ -1,699 +0,0 @@
-import os
-import json
-import orjson
-import asyncio
-from typing import List, Tuple, Dict
-from functools import partial
-from uuid import uuid4
-from datetime import datetime
-from base64 import b64encode
-
-import logging
-from typing import Optional, AsyncGenerator
-from urllib.parse import unquote
-from fastapi import HTTPException, Request, status
-from fastapi.background import BackgroundTasks
-from fastapi.responses import JSONResponse
-from redis import asyncio as aioredis
-
-from crawl4ai import (
-    AsyncWebCrawler,
-    CrawlerRunConfig,
-    LLMExtractionStrategy,
-    CacheMode,
-    BrowserConfig,
-    MemoryAdaptiveDispatcher,
-    RateLimiter, 
-    LLMConfig
-)
-from crawl4ai.utils import perform_completion_with_backoff
-from crawl4ai.content_filter_strategy import (
-    PruningContentFilter,
-    BM25ContentFilter,
-    LLMContentFilter
-)
-from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
-from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
-
-from utils import (
-    TaskStatus,
-    FilterType,
-    get_base_url,
-    is_task_id,
-    should_cleanup_task,
-    decode_redis_hash,
-    get_llm_api_key,
-    validate_llm_provider
-)
-
-import psutil, time
-
-logger = logging.getLogger(__name__)
-
-# --- Helper to get memory ---
-def _get_memory_mb():
-    try:
-        return psutil.Process().memory_info().rss / (1024 * 1024)
-    except Exception as e:
-        logger.warning(f"Could not get memory info: {e}")
-        return None
-
-
-async def handle_llm_qa(
-    url: str,
-    query: str,
-    config: dict
-) -> str:
-    """Process QA using LLM with crawled content as context."""
-    try:
-        if not url.startswith(('http://', 'https://')) and not url.startswith(("raw:", "raw://")):
-            url = 'https://' + url
-        # Extract base URL by finding last '?q=' occurrence
-        last_q_index = url.rfind('?q=')
-        if last_q_index != -1:
-            url = url[:last_q_index]
-
-        # Get markdown content
-        async with AsyncWebCrawler() as crawler:
-            result = await crawler.arun(url)
-            if not result.success:
-                raise HTTPException(
-                    status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                    detail=result.error_message
-                )
-            content = result.markdown.fit_markdown or result.markdown.raw_markdown
-
-        # Create prompt and get LLM response
-        prompt = f"""Use the following content as context to answer the question.
-    Content:
-    {content}
-
-    Question: {query}
-
-    Answer:"""
-
-        # api_token=os.environ.get(config["llm"].get("api_key_env", ""))
-
-        response = perform_completion_with_backoff(
-            provider=config["llm"]["provider"],
-            prompt_with_variables=prompt,
-            api_token=get_llm_api_key(config)
-        )
-
-        return response.choices[0].message.content
-    except Exception as e:
-        logger.error(f"QA processing error: {str(e)}", exc_info=True)
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=str(e)
-        )
-
-async def process_llm_extraction(
-    redis: aioredis.Redis,
-    config: dict,
-    task_id: str,
-    url: str,
-    instruction: str,
-    schema: Optional[str] = None,
-    cache: str = "0",
-    provider: Optional[str] = None
-) -> None:
-    """Process LLM extraction in background."""
-    try:
-        # Validate provider
-        is_valid, error_msg = validate_llm_provider(config, provider)
-        if not is_valid:
-            await redis.hset(f"task:{task_id}", mapping={
-                "status": TaskStatus.FAILED,
-                "error": error_msg
-            })
-            return
-        api_key = get_llm_api_key(config, provider)
-        llm_strategy = LLMExtractionStrategy(
-            llm_config=LLMConfig(
-                provider=provider or config["llm"]["provider"],
-                api_token=api_key
-            ),
-            instruction=instruction,
-            schema=json.loads(schema) if schema else None,
-        )
-
-        cache_mode = CacheMode.ENABLED if cache == "1" else CacheMode.WRITE_ONLY
-
-        async with AsyncWebCrawler() as crawler:
-            result = await crawler.arun(
-                url=url,
-                config=CrawlerRunConfig(
-                    extraction_strategy=llm_strategy,
-                    scraping_strategy=LXMLWebScrapingStrategy(),
-                    cache_mode=cache_mode
-                )
-            )
-
-        if not result.success:
-            await redis.hset(f"task:{task_id}", mapping={
-                "status": TaskStatus.FAILED,
-                "error": result.error_message
-            })
-            return
-
-        try:
-            content = json.loads(result.extracted_content)
-        except json.JSONDecodeError:
-            content = result.extracted_content
-        await redis.hset(f"task:{task_id}", mapping={
-            "status": TaskStatus.COMPLETED,
-            "result": json.dumps(content)
-        })
-
-    except Exception as e:
-        logger.error(f"LLM extraction error: {str(e)}", exc_info=True)
-        await redis.hset(f"task:{task_id}", mapping={
-            "status": TaskStatus.FAILED,
-            "error": str(e)
-        })
-
-async def handle_markdown_request(
-    url: str,
-    filter_type: FilterType,
-    query: Optional[str] = None,
-    cache: str = "0",
-    config: Optional[dict] = None,
-    provider: Optional[str] = None
-) -> str:
-    """Handle markdown generation requests."""
-    try:
-        # Validate provider if using LLM filter
-        if filter_type == FilterType.LLM:
-            is_valid, error_msg = validate_llm_provider(config, provider)
-            if not is_valid:
-                raise HTTPException(
-                    status_code=status.HTTP_400_BAD_REQUEST,
-                    detail=error_msg
-                )
-        decoded_url = unquote(url)
-        if not decoded_url.startswith(('http://', 'https://')) and not decoded_url.startswith(("raw:", "raw://")):
-            decoded_url = 'https://' + decoded_url
-
-        if filter_type == FilterType.RAW:
-            md_generator = DefaultMarkdownGenerator()
-        else:
-            content_filter = {
-                FilterType.FIT: PruningContentFilter(),
-                FilterType.BM25: BM25ContentFilter(user_query=query or ""),
-                FilterType.LLM: LLMContentFilter(
-                    llm_config=LLMConfig(
-                        provider=provider or config["llm"]["provider"],
-                        api_token=get_llm_api_key(config, provider),
-                    ),
-                    instruction=query or "Extract main content"
-                )
-            }[filter_type]
-            md_generator = DefaultMarkdownGenerator(content_filter=content_filter)
-
-        cache_mode = CacheMode.ENABLED if cache == "1" else CacheMode.WRITE_ONLY
-
-        async with AsyncWebCrawler() as crawler:
-            result = await crawler.arun(
-                url=decoded_url,
-                config=CrawlerRunConfig(
-                    markdown_generator=md_generator,
-                    scraping_strategy=LXMLWebScrapingStrategy(),
-                    cache_mode=cache_mode
-                )
-            )
-            
-            if not result.success:
-                raise HTTPException(
-                    status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-                    detail=result.error_message
-                )
-
-            return (result.markdown.raw_markdown 
-                   if filter_type == FilterType.RAW 
-                   else result.markdown.fit_markdown)
-
-    except Exception as e:
-        logger.error(f"Markdown error: {str(e)}", exc_info=True)
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=str(e)
-        )
-
-async def handle_llm_request(
-    redis: aioredis.Redis,
-    background_tasks: BackgroundTasks,
-    request: Request,
-    input_path: str,
-    query: Optional[str] = None,
-    schema: Optional[str] = None,
-    cache: str = "0",
-    config: Optional[dict] = None,
-    provider: Optional[str] = None
-) -> JSONResponse:
-    """Handle LLM extraction requests."""
-    base_url = get_base_url(request)
-    
-    try:
-        if is_task_id(input_path):
-            return await handle_task_status(
-                redis, input_path, base_url
-            )
-
-        if not query:
-            return JSONResponse({
-                "message": "Please provide an instruction",
-                "_links": {
-                    "example": {
-                        "href": f"{base_url}/llm/{input_path}?q=Extract+main+content",
-                        "title": "Try this example"
-                    }
-                }
-            })
-
-        return await create_new_task(
-            redis,
-            background_tasks,
-            input_path,
-            query,
-            schema,
-            cache,
-            base_url,
-            config,
-            provider
-        )
-
-    except Exception as e:
-        logger.error(f"LLM endpoint error: {str(e)}", exc_info=True)
-        return JSONResponse({
-            "error": str(e),
-            "_links": {
-                "retry": {"href": str(request.url)}
-            }
-        }, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
-
-async def handle_task_status(
-    redis: aioredis.Redis,
-    task_id: str,
-    base_url: str,
-    *,
-    keep: bool = False
-) -> JSONResponse:
-    """Handle task status check requests."""
-    task = await redis.hgetall(f"task:{task_id}")
-    if not task:
-        raise HTTPException(
-            status_code=status.HTTP_404_NOT_FOUND,
-            detail="Task not found"
-        )
-
-    task = decode_redis_hash(task)
-    response = create_task_response(task, task_id, base_url)
-
-    if task["status"] in [TaskStatus.COMPLETED, TaskStatus.FAILED]:
-        if not keep and should_cleanup_task(task["created_at"]):
-            await redis.delete(f"task:{task_id}")
-
-    return JSONResponse(response)
-
-async def create_new_task(
-    redis: aioredis.Redis,
-    background_tasks: BackgroundTasks,
-    input_path: str,
-    query: str,
-    schema: Optional[str],
-    cache: str,
-    base_url: str,
-    config: dict,
-    provider: Optional[str] = None
-) -> JSONResponse:
-    """Create and initialize a new task."""
-    decoded_url = unquote(input_path)
-    if not decoded_url.startswith(('http://', 'https://')) and not decoded_url.startswith(("raw:", "raw://")):
-        decoded_url = 'https://' + decoded_url
-
-    from datetime import datetime
-    task_id = f"llm_{int(datetime.now().timestamp())}_{id(background_tasks)}"
-    
-    await redis.hset(f"task:{task_id}", mapping={
-        "status": TaskStatus.PROCESSING,
-        "created_at": datetime.now().isoformat(),
-        "url": decoded_url
-    })
-
-    background_tasks.add_task(
-        process_llm_extraction,
-        redis,
-        config,
-        task_id,
-        decoded_url,
-        query,
-        schema,
-        cache,
-        provider
-    )
-
-    return JSONResponse({
-        "task_id": task_id,
-        "status": TaskStatus.PROCESSING,
-        "url": decoded_url,
-        "_links": {
-            "self": {"href": f"{base_url}/llm/{task_id}"},
-            "status": {"href": f"{base_url}/llm/{task_id}"}
-        }
-    })
-
-def create_task_response(task: dict, task_id: str, base_url: str) -> dict:
-    """Create response for task status check."""
-    response = {
-        "task_id": task_id,
-        "status": task["status"],
-        "created_at": task["created_at"],
-        "url": task["url"],
-        "_links": {
-            "self": {"href": f"{base_url}/llm/{task_id}"},
-            "refresh": {"href": f"{base_url}/llm/{task_id}"}
-        }
-    }
-
-    if task["status"] == TaskStatus.COMPLETED:
-        response["result"] = json.loads(task["result"])
-    elif task["status"] == TaskStatus.FAILED:
-        response["error"] = task["error"]
-
-    return response
-
-async def stream_results(crawler: AsyncWebCrawler, results_gen: AsyncGenerator) -> AsyncGenerator[bytes, None]:
-    """Stream results with heartbeats and completion markers."""
-    import orjson
-    from datetime import datetime
-    import inspect
-    
-    def orjson_default(obj):
-        # Handle datetime (if not already handled by orjson)
-        if isinstance(obj, datetime):
-            return obj.isoformat()
-        # Handle property objects (convert to string or something else)
-        if isinstance(obj, property):
-            return str(obj)
-        # Last resort: convert to string
-        return str(obj)
-
-    try:
-        logger.info(f"Starting streaming with results_gen type: {type(results_gen)}")
-        logger.info(f"Is results_gen async generator: {inspect.isasyncgen(results_gen)}")
-        
-        # Check if results_gen is actually an async generator vs another type
-        if inspect.isasyncgen(results_gen):
-            logger.info("Processing as async generator")
-            async for result in results_gen:
-                try:
-                    logger.info(f"Processing streaming result of type: {type(result)}")
-                    
-                    # Check if this result is actually a CrawlResult
-                    if hasattr(result, 'model_dump_json'):
-                        server_memory_mb = _get_memory_mb()
-                        result_json = result.model_dump_json()
-                        result_dict = orjson.loads(result_json)
-                        result_dict['server_memory_mb'] = server_memory_mb
-                        
-                        if result_dict.get('pdf') is not None:
-                            result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
-                        
-                        logger.info(f"Streaming result for {result_dict.get('url', 'unknown')}")
-                        data = orjson.dumps(result_dict, default=orjson_default).decode('utf-8') + "\n"
-                        yield data.encode('utf-8')
-                    else:
-                        logger.error(f"Result doesn't have model_dump_json method: {type(result)}")
-                        error_response = {"error": f"Invalid result type: {type(result)}", "url": "unknown"}
-                        yield (orjson.dumps(error_response).decode('utf-8') + "\n").encode('utf-8')
-                        
-                except Exception as e:
-                    logger.error(f"Serialization error: {e}")
-                    logger.error(f"Result type was: {type(result)}")
-                    error_response = {"error": str(e), "url": getattr(result, 'url', 'unknown')}
-                    yield (orjson.dumps(error_response).decode('utf-8') + "\n").encode('utf-8')
-        else:
-            logger.error(f"results_gen is not an async generator: {type(results_gen)}")
-            error_response = {"error": f"Invalid results_gen type: {type(results_gen)}"}
-            yield (orjson.dumps(error_response).decode('utf-8') + "\n").encode('utf-8')
-
-        yield orjson.dumps({"status": "completed"}).decode('utf-8').encode('utf-8')
-        
-    except asyncio.CancelledError:
-        logger.warning("Client disconnected during streaming")
-    finally:
-        # try:
-        #     await crawler.close()
-        # except Exception as e:
-        #     logger.error(f"Crawler cleanup error: {e}")
-        pass
-
-async def handle_crawl_request(
-    urls: List[str],
-    browser_config: dict,
-    crawler_config: dict,
-    config: dict
-) -> dict:
-    """Handle non-streaming crawl requests."""
-    start_mem_mb = _get_memory_mb() # <--- Get memory before
-    start_time = time.time()
-    mem_delta_mb = None
-    peak_mem_mb = start_mem_mb
-    
-    try:
-        urls = [('https://' + url) if not url.startswith(('http://', 'https://')) and not url.startswith(("raw:", "raw://")) else url for url in urls]
-        browser_config = BrowserConfig.load(browser_config)
-        crawler_config = CrawlerRunConfig.load(crawler_config)
-
-        dispatcher = MemoryAdaptiveDispatcher(
-            memory_threshold_percent=config["crawler"]["memory_threshold_percent"],
-            rate_limiter=RateLimiter(
-                base_delay=tuple(config["crawler"]["rate_limiter"]["base_delay"])
-            ) if config["crawler"]["rate_limiter"]["enabled"] else None
-        )
-        
-        from crawler_pool import get_crawler
-        crawler = await get_crawler(browser_config)
-
-        # crawler: AsyncWebCrawler = AsyncWebCrawler(config=browser_config)
-        # await crawler.start()
-        
-        base_config = config["crawler"]["base_config"]
-        # Iterate on key-value pairs in global_config then use haseattr to set them 
-        for key, value in base_config.items():
-            if hasattr(crawler_config, key):
-                setattr(crawler_config, key, value)
-
-        results = []
-        func = getattr(crawler, "arun" if len(urls) == 1 else "arun_many")
-        partial_func = partial(func, 
-                                urls[0] if len(urls) == 1 else urls, 
-                                config=crawler_config, 
-                                dispatcher=dispatcher)
-        results = await partial_func()
-
-        # await crawler.close()
-        
-        end_mem_mb = _get_memory_mb() # <--- Get memory after
-        end_time = time.time()
-        
-        if start_mem_mb is not None and end_mem_mb is not None:
-            mem_delta_mb = end_mem_mb - start_mem_mb # <--- Calculate delta
-            peak_mem_mb = max(peak_mem_mb if peak_mem_mb else 0, end_mem_mb) # <--- Get peak memory
-        logger.info(f"Memory usage: Start: {start_mem_mb} MB, End: {end_mem_mb} MB, Delta: {mem_delta_mb} MB, Peak: {peak_mem_mb} MB")
-
-        # Process results to handle PDF bytes
-        processed_results = []
-        for result in results:
-            # Use ORJSON serialization to handle property objects properly
-            result_json = result.model_dump_json()
-            result_dict = orjson.loads(result_json)
-            # If PDF exists, encode it to base64
-            if result_dict.get('pdf') is not None:
-                result_dict['pdf'] = b64encode(result_dict['pdf']).decode('utf-8')
-            processed_results.append(result_dict)
-            
-        return {
-            "success": True,
-            "results": processed_results,
-            "server_processing_time_s": end_time - start_time,
-            "server_memory_delta_mb": mem_delta_mb,
-            "server_peak_memory_mb": peak_mem_mb
-        }
-
-    except Exception as e:
-        logger.error(f"Crawl error: {str(e)}", exc_info=True)
-        if 'crawler' in locals() and crawler.ready: # Check if crawler was initialized and started
-            #  try:
-            #      await crawler.close()
-            #  except Exception as close_e:
-            #       logger.error(f"Error closing crawler during exception handling: {close_e}")
-            logger.error(f"Error closing crawler during exception handling: {str(e)}")
-
-        # Measure memory even on error if possible
-        end_mem_mb_error = _get_memory_mb()
-        if start_mem_mb is not None and end_mem_mb_error is not None:
-            mem_delta_mb = end_mem_mb_error - start_mem_mb
-
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=json.dumps({ # Send structured error
-                "error": str(e),
-                "server_memory_delta_mb": mem_delta_mb,
-                "server_peak_memory_mb": max(peak_mem_mb if peak_mem_mb else 0, end_mem_mb_error or 0)
-            })
-        )
-
-async def handle_stream_crawl_request(
-    urls: List[str],
-    browser_config: dict,
-    crawler_config: dict,
-    config: dict
-) -> Tuple[AsyncWebCrawler, AsyncGenerator]:
-    """Handle streaming crawl requests."""
-    try:
-        browser_config = BrowserConfig.load(browser_config)
-        # browser_config.verbose = True # Set to False or remove for production stress testing
-        browser_config.verbose = False
-        crawler_config = CrawlerRunConfig.load(crawler_config)
-        crawler_config.scraping_strategy = LXMLWebScrapingStrategy()
-        # Don't force stream=True here - let the deep crawl strategy control its own streaming behavior
-
-        # Apply global base config (this was missing!)
-        base_config = config["crawler"]["base_config"]
-        for key, value in base_config.items():
-            if hasattr(crawler_config, key):
-                print(f"[DEBUG] Applying base_config: {key} = {value}")
-                setattr(crawler_config, key, value)
-
-        print(f"[DEBUG] Deep crawl strategy: {type(crawler_config.deep_crawl_strategy).__name__ if crawler_config.deep_crawl_strategy else 'None'}")
-        print(f"[DEBUG] Stream mode: {crawler_config.stream}")
-        print(f"[DEBUG] Simulate user: {getattr(crawler_config, 'simulate_user', 'Not set')}")
-        
-        dispatcher = MemoryAdaptiveDispatcher(
-            memory_threshold_percent=config["crawler"]["memory_threshold_percent"],
-            rate_limiter=RateLimiter(
-                base_delay=tuple(config["crawler"]["rate_limiter"]["base_delay"])
-            )
-        )
-
-        from crawler_pool import get_crawler
-        crawler = await get_crawler(browser_config)
-
-        # crawler = AsyncWebCrawler(config=browser_config)
-        # await crawler.start()
-
-        # Use correct method based on URL count (same as regular endpoint)
-        if len(urls) == 1:
-            # For single URL, use arun to get CrawlResult, then wrap in async generator
-            single_result_container = await crawler.arun(
-                url=urls[0],
-                config=crawler_config,
-                dispatcher=dispatcher
-            )
-            
-            async def single_result_generator():
-                # Handle CrawlResultContainer - extract the actual results
-                if hasattr(single_result_container, '_results'):
-                    # It's a CrawlResultContainer - iterate over the internal results
-                    for result in single_result_container._results:
-                        # Check if the result is an async generator (from deep crawl)
-                        if hasattr(result, '__aiter__'):
-                            async for sub_result in result:
-                                yield sub_result
-                        else:
-                            yield result
-                elif hasattr(single_result_container, '__aiter__'):
-                    # It's an async generator (from streaming deep crawl)
-                    async for result in single_result_container:
-                        yield result
-                elif hasattr(single_result_container, '__iter__') and not hasattr(single_result_container, 'url'):
-                    # It's iterable but not a CrawlResult itself
-                    for result in single_result_container:
-                        # Check if each result is an async generator
-                        if hasattr(result, '__aiter__'):
-                            async for sub_result in result:
-                                yield sub_result
-                        else:
-                            yield result
-                else:
-                    # It's a single CrawlResult
-                    yield single_result_container
-                    
-            results_gen = single_result_generator()
-        else:
-            # For multiple URLs, use arun_many
-            results_gen = await crawler.arun_many(
-                urls=urls,
-                config=crawler_config,
-                dispatcher=dispatcher
-            )
-            
-            # If results_gen is a list (e.g., from deep crawl), convert to async generator
-            if isinstance(results_gen, list):
-                async def convert_list_to_generator():
-                    for result in results_gen:
-                        yield result
-                results_gen = convert_list_to_generator()
-
-        return crawler, results_gen
-
-    except Exception as e:
-        # Make sure to close crawler if started during an error here
-        if 'crawler' in locals() and crawler.ready:
-            #  try:
-            #       await crawler.close()
-            #  except Exception as close_e:
-            #       logger.error(f"Error closing crawler during stream setup exception: {close_e}")
-            logger.error(f"Error closing crawler during stream setup exception: {str(e)}")
-        logger.error(f"Stream crawl error: {str(e)}", exc_info=True)
-        # Raising HTTPException here will prevent streaming response
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=str(e)
-        )
-        
-async def handle_crawl_job(
-    redis,
-    background_tasks: BackgroundTasks,
-    urls: List[str],
-    browser_config: Dict,
-    crawler_config: Dict,
-    config: Dict,
-) -> Dict:
-    """
-    Fire-and-forget version of handle_crawl_request.
-    Creates a task in Redis, runs the heavy work in a background task,
-    lets /crawl/job/{task_id} polling fetch the result.
-    """
-    task_id = f"crawl_{uuid4().hex[:8]}"
-    await redis.hset(f"task:{task_id}", mapping={
-        "status": TaskStatus.PROCESSING,         # <-- keep enum values consistent
-        "created_at": datetime.utcnow().isoformat(),
-        "url": json.dumps(urls),                 # store list as JSON string
-        "result": "",
-        "error": "",
-    })
-
-    async def _runner():
-        try:
-            result = await handle_crawl_request(
-                urls=urls,
-                browser_config=browser_config,
-                crawler_config=crawler_config,
-                config=config,
-            )
-            await redis.hset(f"task:{task_id}", mapping={
-                "status": TaskStatus.COMPLETED,
-                "result": json.dumps(result),
-            })
-            await asyncio.sleep(5)  # Give Redis time to process the update
-        except Exception as exc:
-            await redis.hset(f"task:{task_id}", mapping={
-                "status": TaskStatus.FAILED,
-                "error": str(exc),
-            })
-
-    background_tasks.add_task(_runner)
-    return {"task_id": task_id}

deploy/docker/auth.py

@@ -1,55 +0,0 @@
-import os
-from datetime import datetime, timedelta, timezone
-from typing import Dict, Optional
-from jwt import JWT, jwk_from_dict
-from jwt.utils import get_int_from_datetime
-from fastapi import Depends, HTTPException
-from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
-from pydantic import EmailStr
-from pydantic.main import BaseModel
-import base64
-
-instance = JWT()
-security = HTTPBearer(auto_error=False)
-SECRET_KEY = os.environ.get("SECRET_KEY", "mysecret")
-ACCESS_TOKEN_EXPIRE_MINUTES = 60
-
-def get_jwk_from_secret(secret: str):
-    """Convert a secret string into a JWK object."""
-    secret_bytes = secret.encode('utf-8')
-    b64_secret = base64.urlsafe_b64encode(secret_bytes).rstrip(b'=').decode('utf-8')
-    return jwk_from_dict({"kty": "oct", "k": b64_secret})
-
-def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -> str:
-    """Create a JWT access token with an expiration."""
-    to_encode = data.copy()
-    expire = datetime.now(timezone.utc) + (expires_delta or timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES))
-    to_encode.update({"exp": get_int_from_datetime(expire)})
-    signing_key = get_jwk_from_secret(SECRET_KEY)
-    return instance.encode(to_encode, signing_key, alg='HS256')
-
-def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)) -> Dict:
-    """Verify the JWT token from the Authorization header."""
-
-    if credentials is None:
-        return None
-    token = credentials.credentials
-    verifying_key = get_jwk_from_secret(SECRET_KEY)
-    try:
-        payload = instance.decode(token, verifying_key, do_time_check=True, algorithms='HS256')
-        return payload
-    except Exception:
-        raise HTTPException(status_code=401, detail="Invalid or expired token")
-
-
-def get_token_dependency(config: Dict):
-    """Return the token dependency if JWT is enabled, else a function that returns None."""
-
-    if config.get("security", {}).get("jwt_enabled", False):
-        return verify_token
-    else:
-        return lambda: None
-
-
-class TokenRequest(BaseModel):
-    email: EmailStr

deploy/docker/config.yml

@@ -1,91 +0,0 @@
-# Application Configuration
-app:
-  title: "Crawl4AI API"
-  version: "1.0.0"
-  host: "0.0.0.0"
-  port: 11234
-  reload: False
-  workers: 1
-  timeout_keep_alive: 300
-
-# Default LLM Configuration
-llm:
-  provider: "openai/gpt-4o-mini"
-  api_key_env: "OPENAI_API_KEY"
-  # api_key: sk-...  # If you pass the API key directly then api_key_env will be ignored
-
-# Redis Configuration
-redis:
-  host: "localhost"
-  port: 6379
-  db: 0
-  password: ""
-  ssl: False
-  ssl_cert_reqs: None
-  ssl_ca_certs: None
-  ssl_certfile: None
-  ssl_keyfile: None
-  ssl_cert_reqs: None
-  ssl_ca_certs: None
-  ssl_certfile: None
-  ssl_keyfile: None
-
-# Rate Limiting Configuration
-rate_limiting:
-  enabled: True
-  default_limit: "1000/minute"
-  trusted_proxies: []
-  storage_uri: "memory://"  # Use "redis://localhost:6379" for production
-
-# Security Configuration
-security:
-  enabled: false 
-  jwt_enabled: false 
-  https_redirect: false
-  trusted_hosts: ["*"]
-  headers:
-    x_content_type_options: "nosniff"
-    x_frame_options: "DENY"
-    content_security_policy: "default-src 'self'"
-    strict_transport_security: "max-age=63072000; includeSubDomains"
-
-# Crawler Configuration
-crawler:
-  base_config:
-    simulate_user: true
-  memory_threshold_percent: 95.0
-  rate_limiter:
-    enabled: true
-    base_delay: [1.0, 2.0]
-  timeouts:
-    stream_init: 30.0  # Timeout for stream initialization
-    batch_process: 300.0  # Timeout for batch processing
-  pool:
-    max_pages: 40                          # ← GLOBAL_SEM permits
-    idle_ttl_sec: 1800                     # ← 30 min janitor cutoff
-  browser:
-    kwargs:
-      headless: true
-      text_mode: true
-    extra_args:
-      # - "--single-process"
-      - "--no-sandbox"
-      - "--disable-dev-shm-usage"
-      - "--disable-gpu"
-      - "--disable-software-rasterizer"
-      - "--disable-web-security"
-      - "--allow-insecure-localhost"
-      - "--ignore-certificate-errors"
-
-# Logging Configuration
-logging:
-  level: "INFO"
-  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
-
-# Observability Configuration
-observability:
-  prometheus:
-    enabled: True
-    endpoint: "/metrics"
-  health_check:
-    endpoint: "/health"

deploy/docker/crawler_pool.py

@@ -1,60 +0,0 @@
-# crawler_pool.py  (new file)
-import asyncio, json, hashlib, time, psutil
-from contextlib import suppress
-from typing import Dict
-from crawl4ai import AsyncWebCrawler, BrowserConfig
-from typing import Dict
-from utils import load_config 
-
-CONFIG = load_config()
-
-POOL: Dict[str, AsyncWebCrawler] = {}
-LAST_USED: Dict[str, float] = {}
-LOCK = asyncio.Lock()
-
-MEM_LIMIT  = CONFIG.get("crawler", {}).get("memory_threshold_percent", 95.0)   # % RAM – refuse new browsers above this
-IDLE_TTL  = CONFIG.get("crawler", {}).get("pool", {}).get("idle_ttl_sec", 1800)   # close if unused for 30 min
-
-def _sig(cfg: BrowserConfig) -> str:
-    payload = json.dumps(cfg.to_dict(), sort_keys=True, separators=(",",":"))
-    return hashlib.sha1(payload.encode()).hexdigest()
-
-async def get_crawler(cfg: BrowserConfig) -> AsyncWebCrawler:
-    try:
-        sig = _sig(cfg)
-        async with LOCK:
-            if sig in POOL:
-                LAST_USED[sig] = time.time();  
-                return POOL[sig]
-            if psutil.virtual_memory().percent >= MEM_LIMIT:
-                raise MemoryError("RAM pressure – new browser denied")
-            crawler = AsyncWebCrawler(config=cfg, thread_safe=False)
-            await crawler.start()
-            POOL[sig] = crawler; LAST_USED[sig] = time.time()
-            return crawler
-    except MemoryError as e:
-        raise MemoryError(f"RAM pressure – new browser denied: {e}")
-    except Exception as e:
-        raise RuntimeError(f"Failed to start browser: {e}")
-    finally:
-        if sig in POOL:
-            LAST_USED[sig] = time.time()
-        else:
-            # If we failed to start the browser, we should remove it from the pool
-            POOL.pop(sig, None)
-            LAST_USED.pop(sig, None)
-        # If we failed to start the browser, we should remove it from the pool
-async def close_all():
-    async with LOCK:
-        await asyncio.gather(*(c.close() for c in POOL.values()), return_exceptions=True)
-        POOL.clear(); LAST_USED.clear()
-
-async def janitor():
-    while True:
-        await asyncio.sleep(60)
-        now = time.time()
-        async with LOCK:
-            for sig, crawler in list(POOL.items()):
-                if now - LAST_USED[sig] > IDLE_TTL:
-                    with suppress(Exception): await crawler.close()
-                    POOL.pop(sig, None); LAST_USED.pop(sig, None)

deploy/docker/job.py

@@ -1,101 +0,0 @@
-"""
-Job endpoints (enqueue + poll) for long-running LL​M extraction and raw crawl.
-Relies on the existing Redis task helpers in api.py
-"""
-
-from typing import Dict, Optional, Callable
-from fastapi import APIRouter, BackgroundTasks, Depends, Request
-from pydantic import BaseModel, HttpUrl
-
-from api import (
-    handle_llm_request,
-    handle_crawl_job,
-    handle_task_status,
-)
-
-# ------------- dependency placeholders -------------
-_redis = None        # will be injected from server.py
-_config = None
-_token_dep: Callable = lambda: None  # dummy until injected
-
-# public router
-router = APIRouter()
-
-
-# === init hook called by server.py =========================================
-def init_job_router(redis, config, token_dep) -> APIRouter:
-    """Inject shared singletons and return the router for mounting."""
-    global _redis, _config, _token_dep
-    _redis, _config, _token_dep = redis, config, token_dep
-    return router
-
-
-# ---------- payload models --------------------------------------------------
-class LlmJobPayload(BaseModel):
-    url:    HttpUrl
-    q:      str
-    schema: Optional[str] = None
-    cache:  bool = False
-    provider: Optional[str] = None
-
-
-class CrawlJobPayload(BaseModel):
-    urls:           list[HttpUrl]
-    browser_config: Dict = {}
-    crawler_config: Dict = {}
-
-
-# ---------- LL​M job ---------------------------------------------------------
-@router.post("/llm/job", status_code=202)
-async def llm_job_enqueue(
-        payload: LlmJobPayload,
-        background_tasks: BackgroundTasks,
-        request: Request,
-        _td: Dict = Depends(lambda: _token_dep()),   # late-bound dep
-):
-    return await handle_llm_request(
-        _redis,
-        background_tasks,
-        request,
-        str(payload.url),
-        query=payload.q,
-        schema=payload.schema,
-        cache=payload.cache,
-        config=_config,
-        provider=payload.provider,
-    )
-
-
-@router.get("/llm/job/{task_id}")
-async def llm_job_status(
-    request: Request,
-    task_id: str,
-    _td: Dict = Depends(lambda: _token_dep())
-):
-    return await handle_task_status(_redis, task_id)
-
-
-# ---------- CRAWL job -------------------------------------------------------
-@router.post("/crawl/job", status_code=202)
-async def crawl_job_enqueue(
-        payload: CrawlJobPayload,
-        background_tasks: BackgroundTasks,
-        _td: Dict = Depends(lambda: _token_dep()),
-):
-    return await handle_crawl_job(
-        _redis,
-        background_tasks,
-        [str(u) for u in payload.urls],
-        payload.browser_config,
-        payload.crawler_config,
-        config=_config,
-    )
-
-
-@router.get("/crawl/job/{task_id}")
-async def crawl_job_status(
-    request: Request,
-    task_id: str,
-    _td: Dict = Depends(lambda: _token_dep())
-):
-    return await handle_task_status(_redis, task_id, base_url=str(request.base_url))

deploy/docker/mcp_bridge.py

@@ -1,252 +0,0 @@
-# deploy/docker/mcp_bridge.py
-
-from __future__ import annotations
-import inspect, json, re, anyio
-from contextlib import suppress
-from typing import Any, Callable, Dict, List, Tuple
-import httpx
-
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException
-from fastapi.responses import JSONResponse
-from fastapi import Request
-from sse_starlette.sse import EventSourceResponse
-from pydantic import BaseModel
-from mcp.server.sse import SseServerTransport
-
-import mcp.types as t
-from mcp.server.lowlevel.server import Server, NotificationOptions
-from mcp.server.models import InitializationOptions
-
-# ── opt‑in decorators ───────────────────────────────────────────
-def mcp_resource(name: str | None = None):
-    def deco(fn):
-        fn.__mcp_kind__, fn.__mcp_name__ = "resource", name
-        return fn
-    return deco
-
-def mcp_template(name: str | None = None):
-    def deco(fn):
-        fn.__mcp_kind__, fn.__mcp_name__ = "template", name
-        return fn
-    return deco
-
-def mcp_tool(name: str | None = None):
-    def deco(fn):
-        fn.__mcp_kind__, fn.__mcp_name__ = "tool", name
-        return fn
-    return deco
-
-# ── HTTP‑proxy helper for FastAPI endpoints ─────────────────────
-def _make_http_proxy(base_url: str, route):
-    method = list(route.methods - {"HEAD", "OPTIONS"})[0]
-    async def proxy(**kwargs):
-        # replace `/items/{id}` style params first
-        path = route.path
-        for k, v in list(kwargs.items()):
-            placeholder = "{" + k + "}"
-            if placeholder in path:
-                path = path.replace(placeholder, str(v))
-                kwargs.pop(k)
-        url = base_url.rstrip("/") + path
-
-        async with httpx.AsyncClient() as client:
-            try:
-                r = (
-                    await client.get(url, params=kwargs)
-                    if method == "GET"
-                    else await client.request(method, url, json=kwargs)
-                )
-                r.raise_for_status()
-                return r.text if method == "GET" else r.json()
-            except httpx.HTTPStatusError as e:
-                # surface FastAPI error details instead of plain 500
-                raise HTTPException(e.response.status_code, e.response.text)
-    return proxy
-
-# ── main entry point ────────────────────────────────────────────
-def attach_mcp(
-    app: FastAPI,
-    *,                          # keyword‑only
-    base: str = "/mcp",
-    name: str | None = None,
-    base_url: str,              # eg. "http://127.0.0.1:8020"
-) -> None:
-    """Call once after all routes are declared to expose WS+SSE MCP endpoints."""
-    server_name = name or app.title or "FastAPI-MCP"
-    mcp = Server(server_name)
-
-    # tools: Dict[str, Callable] = {}
-    tools: Dict[str, Tuple[Callable, Callable]] = {}
-    resources: Dict[str, Callable] = {}
-    templates: Dict[str, Callable] = {}
-
-    # register decorated FastAPI routes
-    for route in app.routes:
-        fn = getattr(route, "endpoint", None)
-        kind = getattr(fn, "__mcp_kind__", None)
-        if not kind:
-            continue
-
-        key = fn.__mcp_name__ or re.sub(r"[/{}}]", "_", route.path).strip("_")
-
-        # if kind == "tool":
-        #     tools[key] = _make_http_proxy(base_url, route)
-        if kind == "tool":
-            proxy = _make_http_proxy(base_url, route)
-            tools[key] = (proxy, fn)
-            continue
-        if kind == "resource":
-            resources[key] = fn
-        if kind == "template":
-            templates[key] = fn
-
-    # helpers for JSON‑Schema
-    def _schema(model: type[BaseModel] | None) -> dict:
-        return {"type": "object"} if model is None else model.model_json_schema()
-
-    def _body_model(fn: Callable) -> type[BaseModel] | None:
-        for p in inspect.signature(fn).parameters.values():
-            a = p.annotation
-            if inspect.isclass(a) and issubclass(a, BaseModel):
-                return a
-        return None
-
-    # MCP handlers
-    @mcp.list_tools()
-    async def _list_tools() -> List[t.Tool]:
-        out = []
-        for k, (proxy, orig_fn) in tools.items():
-            desc   = getattr(orig_fn, "__mcp_description__", None) or inspect.getdoc(orig_fn) or ""
-            schema = getattr(orig_fn, "__mcp_schema__", None) or _schema(_body_model(orig_fn))
-            out.append(
-                t.Tool(name=k, description=desc, inputSchema=schema)
-            )
-        return out
-             
-
-    @mcp.call_tool()
-    async def _call_tool(name: str, arguments: Dict | None) -> List[t.TextContent]:
-        if name not in tools:
-            raise HTTPException(404, "tool not found")
-        
-        proxy, _ = tools[name]
-        try:
-            res = await proxy(**(arguments or {}))
-        except HTTPException as exc:
-            # map server‑side errors into MCP "text/error" payloads
-            err = {"error": exc.status_code, "detail": exc.detail}
-            return [t.TextContent(type = "text", text=json.dumps(err))]
-        return [t.TextContent(type = "text", text=json.dumps(res, default=str))]
-
-    @mcp.list_resources()
-    async def _list_resources() -> List[t.Resource]:
-        return [
-            t.Resource(name=k, description=inspect.getdoc(f) or "", mime_type="application/json")
-            for k, f in resources.items()
-        ]
-
-    @mcp.read_resource()
-    async def _read_resource(name: str) -> List[t.TextContent]:
-        if name not in resources:
-            raise HTTPException(404, "resource not found")
-        res = resources[name]()
-        return [t.TextContent(type = "text", text=json.dumps(res, default=str))]
-
-    @mcp.list_resource_templates()
-    async def _list_templates() -> List[t.ResourceTemplate]:
-        return [
-            t.ResourceTemplate(
-                name=k,
-                description=inspect.getdoc(f) or "",
-                parameters={
-                    p: {"type": "string"} for p in _path_params(app, f)
-                },
-            )
-            for k, f in templates.items()
-        ]
-
-    init_opts = InitializationOptions(
-        server_name=server_name,
-        server_version="0.1.0",
-        capabilities=mcp.get_capabilities(
-            notification_options=NotificationOptions(),
-            experimental_capabilities={},
-        ),
-    )
-
-    # ── WebSocket transport ────────────────────────────────────
-    @app.websocket_route(f"{base}/ws")
-    async def _ws(ws: WebSocket):
-        await ws.accept()
-        c2s_send, c2s_recv = anyio.create_memory_object_stream(100)
-        s2c_send, s2c_recv = anyio.create_memory_object_stream(100)
-
-        from pydantic import TypeAdapter
-        from mcp.types import JSONRPCMessage
-        adapter = TypeAdapter(JSONRPCMessage)
-
-        init_done = anyio.Event()
-
-        async def srv_to_ws():
-            first = True 
-            try:
-                async for msg in s2c_recv:
-                    await ws.send_json(msg.model_dump())
-                    if first:
-                        init_done.set()
-                        first = False
-            finally:
-                # make sure cleanup survives TaskGroup cancellation
-                with anyio.CancelScope(shield=True):
-                    with suppress(RuntimeError):       # idempotent close
-                        await ws.close()
-
-        async def ws_to_srv():
-            try:
-                # 1st frame is always "initialize"
-                first = adapter.validate_python(await ws.receive_json())
-                await c2s_send.send(first)
-                await init_done.wait()          # block until server ready
-                while True:
-                    data = await ws.receive_json()
-                    await c2s_send.send(adapter.validate_python(data))
-            except WebSocketDisconnect:
-                await c2s_send.aclose()
-
-        async with anyio.create_task_group() as tg:
-            tg.start_soon(mcp.run, c2s_recv, s2c_send, init_opts)
-            tg.start_soon(ws_to_srv)
-            tg.start_soon(srv_to_ws)
-
-    # ── SSE transport (official) ─────────────────────────────
-    sse = SseServerTransport(f"{base}/messages/")
-
-    @app.get(f"{base}/sse")
-    async def _mcp_sse(request: Request):
-        async with sse.connect_sse(
-            request.scope, request.receive, request._send  # starlette ASGI primitives
-        ) as (read_stream, write_stream):
-            await mcp.run(read_stream, write_stream, init_opts)
-
-    # client → server frames are POSTed here
-    app.mount(f"{base}/messages", app=sse.handle_post_message)
-
-    # ── schema endpoint ───────────────────────────────────────
-    @app.get(f"{base}/schema")
-    async def _schema_endpoint():
-        return JSONResponse({
-            "tools": [x.model_dump() for x in await _list_tools()],
-            "resources": [x.model_dump() for x in await _list_resources()],
-            "resource_templates": [x.model_dump() for x in await _list_templates()],
-        })
-
-
-# ── helpers ────────────────────────────────────────────────────
-def _route_name(path: str) -> str:
-    return re.sub(r"[/{}}]", "_", path).strip("_")
-
-def _path_params(app: FastAPI, fn: Callable) -> List[str]:
-    for r in app.routes:
-        if r.endpoint is fn:
-            return list(r.param_convertors.keys())
-    return []

deploy/docker/requirements.txt

@@ -1,17 +0,0 @@
-fastapi>=0.115.12
-uvicorn>=0.34.2
-gunicorn>=23.0.0
-slowapi==0.1.9
-prometheus-fastapi-instrumentator>=7.1.0
-redis>=5.2.1
-jwt>=1.3.1
-dnspython>=2.7.0
-email-validator==2.2.0
-sse-starlette==2.2.1
-pydantic>=2.11
-rank-bm25==0.2.2
-anyio==4.9.0
-PyJWT==2.10.1
-mcp>=1.6.0
-websockets>=15.0.1
-httpx[http2]>=0.27.2