* fix(docker-api): migrate to modern datetime library API
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
* Fix examples in README.md
* feat(docker): add user-provided hooks support to Docker API
Implements comprehensive hooks functionality allowing users to provide custom Python
functions as strings that execute at specific points in the crawling pipeline.
Key Features:
- Support for all 8 crawl4ai hook points:
• on_browser_created: Initialize browser settings
• on_page_context_created: Configure page context
• before_goto: Pre-navigation setup
• after_goto: Post-navigation processing
• on_user_agent_updated: User agent modification handling
• on_execution_started: Crawl execution initialization
• before_retrieve_html: Pre-extraction processing
• before_return_html: Final HTML processing
Implementation Details:
- Created UserHookManager for validation, compilation, and safe execution
- Added IsolatedHookWrapper for error isolation and timeout protection
- AST-based validation ensures code structure correctness
- Sandboxed execution with restricted builtins for security
- Configurable timeout (1-120 seconds) prevents infinite loops
- Comprehensive error handling ensures hooks don't crash main process
- Execution tracking with detailed statistics and logging
API Changes:
- Added HookConfig schema with code and timeout fields
- Extended CrawlRequest with optional hooks parameter
- Added /hooks/info endpoint for hook discovery
- Updated /crawl and /crawl/stream endpoints to support hooks
Safety Features:
- Malformed hooks return clear validation errors
- Hook errors are isolated and reported without stopping crawl
- Execution statistics track success/failure/timeout rates
- All hook results are JSON-serializable
Testing:
- Comprehensive test suite covering all 8 hooks
- Error handling and timeout scenarios validated
- Authentication, performance, and content extraction examples
- 100% success rate in production testing
Documentation:
- Added extensive hooks section to docker-deployment.md
- Security warnings about user-provided code risks
- Real-world examples using httpbin.org, GitHub, BBC
- Best practices and troubleshooting guide
ref #1377
* fix(deep-crawl): BestFirst priority inversion; remove pre-scoring truncation. ref #1253
Use negative scores in PQ to visit high-score URLs first and drop link cap prior to scoring; add test for ordering.
* docs: Update URL seeding examples to use proper async context managers
- Wrap all AsyncUrlSeeder usage with async context managers
- Update URL seeding adventure example to use "sitemap+cc" source, focus on course posts, and add stream=True parameter to fix runtime error
* fix(crawler): Removed the incorrect reference in browser_config variable #1310
* docs: update Docker instructions to use the latest release tag
* fix(docker): Fix LLM API key handling for multi-provider support
Previously, the system incorrectly used OPENAI_API_KEY for all LLM providers
due to a hardcoded api_key_env fallback in config.yml. This caused authentication
errors when using non-OpenAI providers like Gemini.
Changes:
- Remove api_key_env from config.yml to let litellm handle provider-specific env vars
- Simplify get_llm_api_key() to return None, allowing litellm to auto-detect keys
- Update validate_llm_provider() to trust litellm's built-in key detection
- Update documentation to reflect the new automatic key handling
The fix leverages litellm's existing capability to automatically find the correct
environment variable for each provider (OPENAI_API_KEY, GEMINI_API_TOKEN, etc.)
without manual configuration.
ref #1291
* docs: update adaptive crawler docs and cache defaults; remove deprecated examples (#1330)
- Replace BaseStrategy with CrawlStrategy in custom strategy examples (DomainSpecificStrategy, HybridStrategy)
- Remove “Custom Link Scoring” and “Caching Strategy” sections no longer aligned with current library
- Revise memory pruning example to use adaptive.get_relevant_content and index-based retention of top 500 docs
- Correct Quickstart note: default cache mode is CacheMode.BYPASS; instruct enabling with CacheMode.ENABLED
* fix(utils): Improve URL normalization by avoiding quote/unquote to preserve '+' signs. ref #1332
* feat: Add comprehensive website to API example with frontend
This commit adds a complete, web scraping API example that demonstrates how to get structured data from any website and use it like an API using the crawl4ai library with a minimalist frontend interface.
Core Functionality
- AI-powered web scraping with plain English queries
- Dual scraping approaches: Schema-based (faster) and LLM-based (flexible)
- Intelligent schema caching for improved performance
- Custom LLM model support with API key management
- Automatic duplicate request prevention
Modern Frontend Interface
- Minimalist black-and-white design inspired by modern web apps
- Responsive layout with smooth animations and transitions
- Three main pages: Scrape Data, Models Management, API Request History
- Real-time results display with JSON formatting
- Copy-to-clipboard functionality for extracted data
- Toast notifications for user feedback
- Auto-scroll to results when scraping starts
Model Management System
- Web-based model configuration interface
- Support for any LLM provider (OpenAI, Gemini, Anthropic, etc.)
- Simplified configuration requiring only provider and API token
- Add, list, and delete model configurations
- Secure storage of API keys in local JSON files
API Request History
- Automatic saving of all API requests and responses
- Display of request history with URL, query, and cURL commands
- Duplicate prevention (same URL + query combinations)
- Request deletion functionality
- Clean, simplified display focusing on essential information
Technical Implementation
Backend (FastAPI)
- RESTful API with comprehensive endpoints
- Pydantic models for request/response validation
- Async web scraping with crawl4ai library
- Error handling with detailed error messages
- File-based storage for models and request history
Frontend (Vanilla JS/CSS/HTML)
- No framework dependencies - pure HTML, CSS, JavaScript
- Modern CSS Grid and Flexbox layouts
- Custom dropdown styling with SVG arrows
- Responsive design for mobile and desktop
- Smooth scrolling and animations
Core Library Integration
- WebScraperAgent class for orchestration
- ModelConfig class for LLM configuration management
- Schema generation and caching system
- LLM extraction strategy support
- Browser configuration with headless mode
* fix(dependencies): add cssselect to project dependencies
Fixes bug reported in issue #1405
[Bug]: Excluded selector (excluded_selector) doesn't work
This commit reintroduces the cssselect library which was removed by PR (https://github.com/unclecode/crawl4ai/pull/1368) and merged via (437395e490).
Integration tested against 0.7.4 Docker container. Reintroducing cssselector package eliminated errors seen in logs and excluded_selector functionality was restored.
Refs: #1405
* fix(docker): resolve filter serialization and JSON encoding errors in deep crawl strategy (ref #1419)
- Fix URLPatternFilter serialization by preventing private __slots__ from being serialized as constructor params
- Add public attributes to URLPatternFilter to store original constructor parameters for proper serialization
- Handle property descriptors in CrawlResult.model_dump() to prevent JSON serialization errors
- Ensure filter chains work correctly with Docker client and REST API
The issue occurred because:
1. Private implementation details (_simple_suffixes, etc.) were being serialized and passed as constructor arguments during deserialization
2. Property descriptors were being included in the serialized output, causing "Object of type property is not JSON serializable" errors
Changes:
- async_configs.py: Comment out __slots__ serialization logic (lines 100-109)
- filters.py: Add patterns, use_glob, reverse to URLPatternFilter __slots__ and store as public attributes
- models.py: Convert property descriptors to strings in model_dump() instead of including them directly
* fix(logger): ensure logger is a Logger instance in crawling strategies. ref #1437
* feat(docker): Add temperature and base_url parameters for LLM configuration. ref #1035
Implement hierarchical configuration for LLM parameters with support for:
- Temperature control (0.0-2.0) to adjust response creativity
- Custom base_url for proxy servers and alternative endpoints
- 4-tier priority: request params > provider env > global env > defaults
Add helper functions in utils.py, update API schemas and handlers,
support environment variables (LLM_TEMPERATURE, OPENAI_TEMPERATURE, etc.),
and provide comprehensive documentation with examples.
* feat(docker): improve docker error handling
- Return comprehensive error messages along with status codes for api internal errors.
- Fix fit_html property serialization issue in both /crawl and /crawl/stream endpoints
- Add sanitization to ensure fit_html is always JSON-serializable (string or None)
- Add comprehensive error handling test suite.
* #1375 : refactor(proxy) Deprecate 'proxy' parameter in BrowserConfig and enhance proxy string parsing
- Updated ProxyConfig.from_string to support multiple proxy formats, including URLs with credentials.
- Deprecated the 'proxy' parameter in BrowserConfig, replacing it with 'proxy_config' for better flexibility.
- Added warnings for deprecated usage and clarified behavior when both parameters are provided.
- Updated documentation and tests to reflect changes in proxy configuration handling.
* Remove deprecated test for 'proxy' parameter in BrowserConfig and update .gitignore to include test_scripts directory.
* feat: add preserve_https_for_internal_links flag to maintain HTTPS during crawling. Ref #1410
Added a new `preserve_https_for_internal_links` configuration flag that preserves the original HTTPS scheme for same-domain links even when the server redirects to HTTP.
* feat: update documentation for preserve_https_for_internal_links. ref #1410
* fix: drop Python 3.9 support and require Python >=3.10.
The library no longer supports Python 3.9 and so it was important to drop all references to python 3.9.
Following changes have been made:
- pyproject.toml: set requires-python to ">=3.10"; remove 3.9 classifier
- setup.py: set python_requires to ">=3.10"; remove 3.9 classifier
- docs: update Python version mentions
- deploy/docker/c4ai-doc-context.md: options -> 3.10, 3.11, 3.12, 3.13
* issue #1329 refactor(crawler): move unwanted properties to CrawlerRunConfig class
* fix(auth): fixed Docker JWT authentication. ref #1442
* remove: delete unused yoyo snapshot subproject
* fix: raise error on last attempt failure in perform_completion_with_backoff. ref #989
* Commit without API
* fix: update option labels in request builder for clarity
* fix: allow custom LLM providers for adaptive crawler embedding config. ref: #1291
- Change embedding_llm_config from Dict to Union[LLMConfig, Dict] for type safety
- Add backward-compatible conversion property _embedding_llm_config_dict
- Replace all hardcoded OpenAI embedding configs with configurable options
- Fix LLMConfig object attribute access in query expansion logic
- Add comprehensive example demonstrating multiple provider configurations
- Update documentation with both LLMConfig object and dictionary usage patterns
Users can now specify any LLM provider for query expansion in embedding strategy:
- New: embedding_llm_config=LLMConfig(provider='anthropic/claude-3', api_token='key')
- Old: embedding_llm_config={'provider': 'openai/gpt-4', 'api_token': 'key'} (still works)
* refactor(BrowserConfig): change deprecation warning for 'proxy' parameter to UserWarning
* feat(StealthAdapter): fix stealth features for Playwright integration. ref #1481
* #1505 fix(api): update config handling to only set base config if not provided by user
* fix(docker-deployment): replace console.log with print for metadata extraction
* Release v0.7.5: The Update
- Updated version to 0.7.5
- Added comprehensive demo and release notes
- Updated documentation
* refactor(release): remove memory management section for cleaner documentation. ref #1443
* feat(docs): add brand book and page copy functionality
- Add comprehensive brand book with color system, typography, components
- Add page copy dropdown with markdown copy/view functionality
- Update mkdocs.yml with new assets and branding navigation
- Use terminal-style ASCII icons and condensed menu design
* Update gitignore add local scripts folder
* fix: remove this import as it causes python to treat "json" as a variable in the except block
* fix: always return a list, even if we catch an exception
* feat(marketplace): Add Crawl4AI marketplace with secure configuration
- Implement marketplace frontend and admin dashboard
- Add FastAPI backend with environment-based configuration
- Use .env file for secrets management
- Include data generation scripts
- Add proper CORS configuration
- Remove hardcoded password from admin login
- Update gitignore for security
* fix(marketplace): Update URLs to use /marketplace path and relative API endpoints
- Change API_BASE to relative '/api' for production
- Move marketplace to /marketplace instead of /marketplace/frontend
- Update MkDocs navigation
- Fix logo path in marketplace index
* fix(docs): hide copy menu on non-markdown pages
* feat(marketplace): add sponsor logo uploads
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
* feat(docs): add chatgpt quick link to page actions
* fix(marketplace): align admin api with backend endpoints
* fix(marketplace): isolate api under marketplace prefix
* fix(marketplace): resolve app detail page routing and styling issues
- Fixed JavaScript errors from missing HTML elements (install-code, usage-code, integration-code)
- Added missing CSS classes for tabs, overview layout, sidebar, and integration content
- Fixed tab navigation to display horizontally in single line
- Added proper padding to tab content sections (removed from container, added to content)
- Fixed tab selector from .nav-tab to .tab-btn to match HTML structure
- Added sidebar styling with stats grid and metadata display
- Improved responsive design with mobile-friendly tab scrolling
- Fixed code block positioning for copy buttons
- Removed margin from first headings to prevent extra spacing
- Added null checks for DOM elements in JavaScript to prevent errors
These changes resolve the routing issue where clicking on apps caused page redirects,
and fix the broken layout where CSS was not properly applied to the app detail page.
* fix(marketplace): prevent hero image overflow and secondary card stretching
- Fixed hero image to 200px height with min/max constraints
- Added object-fit: cover to hero-image img elements
- Changed secondary-featured align-items from stretch to flex-start
- Fixed secondary-card height to 118px (no flex: 1 stretching)
- Updated responsive grid layouts for wider screens
- Added flex: 1 to hero-content for better content distribution
These changes ensure a rigid, predictable layout that prevents:
1. Large images from pushing text content down
2. Single secondary cards from stretching to fill entire height
* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377
Add hooks_to_string() utility function that converts Python function objects
to string representations for the Docker API, enabling developers to write hooks
as regular Python functions instead of strings.
Core Changes:
- New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
- Docker client now accepts both function objects and strings for hooks
- Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
- New hooks and hooks_timeout parameters in client.crawl() method
Documentation:
- Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
- Updated main Docker deployment guide with comprehensive hooks section
- Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)
* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377
Add hooks_to_string() utility function that converts Python function objects
to string representations for the Docker API, enabling developers to write hooks
as regular Python functions instead of strings.
Core Changes:
- New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
- Docker client now accepts both function objects and strings for hooks
- Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
- New hooks and hooks_timeout parameters in client.crawl() method
Documentation:
- Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
- Updated main Docker deployment guide with comprehensive hooks section
- Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)
* fix(docs): clarify Docker Hooks System with function-based API in README
* docs: Add demonstration files for v0.7.5 release, showcasing the new Docker Hooks System and all other features.
* docs: Update 0.7.5 video walkthrough
* docs: add complete SDK reference documentation
Add comprehensive single-page SDK reference combining:
- Installation & Setup
- Quick Start
- Core API (AsyncWebCrawler, arun, arun_many, CrawlResult)
- Configuration (BrowserConfig, CrawlerConfig, Parameters)
- Crawling Patterns
- Content Processing (Markdown, Fit Markdown, Selection, Interaction, Link & Media)
- Extraction Strategies (LLM and No-LLM)
- Advanced Features (Session Management, Hooks & Auth)
Generated using scripts/generate_sdk_docs.py in ultra-dense mode
optimized for AI assistant consumption.
Stats: 23K words, 185 code blocks, 220KB
* feat: add AI assistant skill package for Crawl4AI
- Create comprehensive skill package for AI coding assistants
- Include complete SDK reference (23K words, v0.7.4)
- Add three extraction scripts (basic, batch, pipeline)
- Implement version tracking in skill and scripts
- Add prominent download section on homepage
- Place skill in docs/assets for web distribution
The skill enables AI assistants like Claude, Cursor, and Windsurf
to effectively use Crawl4AI with optimized workflows for markdown
generation and data extraction.
* fix: remove non-existent wiki link and clarify skill usage instructions
* fix: update Crawl4AI skill with corrected parameters and examples
- Fixed CrawlerConfig → CrawlerRunConfig throughout
- Fixed parameter names (timeout → page_timeout, store_html removed)
- Fixed schema format (selector → baseSelector)
- Corrected proxy configuration (in BrowserConfig, not CrawlerRunConfig)
- Fixed fit_markdown usage with content filters
- Added comprehensive references to docs/examples/ directory
- Created safe packaging script to avoid root directory pollution
- All scripts tested and verified working
* fix: thoroughly verify and fix all Crawl4AI skill examples
- Cross-checked every section against actual docs
- Fixed BM25ContentFilter parameters (user_query, bm25_threshold)
- Removed incorrect wait_for selector from basic example
- Added comprehensive test suite (4 test files)
- All examples now tested and verified working
- Tests validate: basic crawling, markdown generation, data extraction, advanced patterns
- Package size: 76.6 KB (includes tests for future validation)
* feat(ci): split release pipeline and add Docker caching
- Split release.yml into PyPI/GitHub release and Docker workflows
- Add GitHub Actions cache for Docker builds (10-15x faster rebuilds)
- Implement dual-trigger for docker-release.yml (auto + manual)
- Add comprehensive workflow documentation in .github/workflows/docs/
- Backup original workflow as release.yml.backup
* feat: add webhook notifications for crawl job completion
Implements webhook support for the crawl job API to eliminate polling requirements.
Changes:
- Added WebhookConfig and WebhookPayload schemas to schemas.py
- Created webhook.py with WebhookDeliveryService class
- Integrated webhook notifications in api.py handle_crawl_job
- Updated job.py CrawlJobPayload to accept webhook_config
- Added webhook configuration section to config.yml
- Included comprehensive usage examples in WEBHOOK_EXAMPLES.md
Features:
- Webhook notifications on job completion (success/failure)
- Configurable data inclusion in webhook payload
- Custom webhook headers support
- Global default webhook URL configuration
- Exponential backoff retry logic (5 attempts: 1s, 2s, 4s, 8s, 16s)
- 30-second timeout per webhook call
Usage:
POST /crawl/job with optional webhook_config:
- webhook_url: URL to receive notifications
- webhook_data_in_payload: include full results (default: false)
- webhook_headers: custom headers for authentication
Generated with Claude Code https://claude.com/claude-code
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: add webhook documentation to Docker README
Added comprehensive webhook section to README.md including:
- Overview of asynchronous job queue with webhooks
- Benefits and use cases
- Quick start examples
- Webhook authentication
- Global webhook configuration
- Job status polling alternative
Updated table of contents and summary to include webhook feature.
Maintains consistent tone and style with rest of README.
Generated with Claude Code https://claude.com/claude-code
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: add webhook example for Docker deployment
Added docker_webhook_example.py demonstrating:
- Submitting crawl jobs with webhook configuration
- Flask-based webhook receiver implementation
- Three usage patterns:
1. Webhook notification only (fetch data separately)
2. Webhook with full data in payload
3. Traditional polling approach for comparison
Includes comprehensive comments explaining:
- Webhook payload structure
- Authentication headers setup
- Error handling
- Production deployment tips
Example is fully functional and ready to run with Flask installed.
Generated with Claude Code https://claude.com/claude-code
Co-Authored-By: Claude <noreply@anthropic.com>
* test: add webhook implementation validation tests
Added comprehensive test suite to validate webhook implementation:
- Module import verification
- WebhookDeliveryService initialization
- Pydantic model validation (WebhookConfig)
- Payload construction logic
- Exponential backoff calculation
- API integration checks
All tests pass (6/6), confirming implementation is correct.
Generated with Claude Code https://claude.com/claude-code
Co-Authored-By: Claude <noreply@anthropic.com>
* test: add comprehensive webhook feature test script
Added end-to-end test script that automates webhook feature testing:
Script Features (test_webhook_feature.sh):
- Automatic branch switching and dependency installation
- Redis and server startup/shutdown management
- Webhook receiver implementation
- Integration test for webhook notifications
- Comprehensive cleanup and error handling
- Returns to original branch after completion
Test Flow:
1. Fetch and checkout webhook feature branch
2. Activate venv and install dependencies
3. Start Redis and Crawl4AI server
4. Submit crawl job with webhook config
5. Verify webhook delivery and payload
6. Clean up all processes and return to original branch
Documentation:
- WEBHOOK_TEST_README.md with usage instructions
- Troubleshooting guide
- Exit codes and safety features
Usage: ./tests/test_webhook_feature.sh
Generated with Claude Code https://claude.com/claude-code
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: properly serialize Pydantic HttpUrl in webhook config
Use model_dump(mode='json') instead of deprecated dict() method to ensure
Pydantic special types (HttpUrl, UUID, etc.) are properly serialized to
JSON-compatible native Python types.
This fixes webhook delivery failures caused by HttpUrl objects remaining
as Pydantic types in the webhook_config dict, which caused JSON
serialization errors and httpx request failures.
Also update mcp requirement to >=1.18.0 for compatibility.
* feat: add webhook support for /llm/job endpoint
Add comprehensive webhook notification support for the /llm/job endpoint,
following the same pattern as the existing /crawl/job implementation.
Changes:
- Add webhook_config field to LlmJobPayload model (job.py)
- Implement webhook notifications in process_llm_extraction() with 4
notification points: success, provider validation failure, extraction
failure, and general exceptions (api.py)
- Store webhook_config in Redis task data for job tracking
- Initialize WebhookDeliveryService with exponential backoff retry logic
Documentation:
- Add Example 6 to WEBHOOK_EXAMPLES.md showing LLM extraction with webhooks
- Update Flask webhook handler to support both crawl and llm_extraction tasks
- Add TypeScript client examples for LLM jobs
- Add comprehensive examples to docker_webhook_example.py with schema support
- Clarify data structure differences between webhook and API responses
Testing:
- Add test_llm_webhook_feature.py with 7 validation tests (all passing)
- Verify pattern consistency with /crawl/job implementation
- Add implementation guide (WEBHOOK_LLM_JOB_IMPLEMENTATION.md)
* fix: remove duplicate comma in webhook_config parameter
* fix: update Crawl4AI Docker container port from 11234 to 11235
* Release v0.7.6: The 0.7.6 Update
- Updated version to 0.7.6
- Added comprehensive demo and release notes
- Updated all documentation
- Update the veriosn in Dockerfile to 0.7.6
---------
Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Nezar Ali <abu5sohaib@gmail.com>
Co-authored-by: Soham Kukreti <kukretisoham@gmail.com>
Co-authored-by: James T. Wood <jamesthomaswood@gmail.com>
Co-authored-by: AHMET YILMAZ <tawfik@kidocode.com>
Co-authored-by: nafeqq-1306 <nafiquee@yahoo.com>
Co-authored-by: unclecode <unclecode@kidocode.com>
Co-authored-by: Martin Sjöborg <martin.sjoborg@quartr.se>
Co-authored-by: Martin Sjöborg <martin@sjoborg.org>
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
402 lines
15 KiB
Python
402 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Test script to validate webhook implementation for /llm/job endpoint.
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This tests that the /llm/job endpoint now supports webhooks
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following the same pattern as /crawl/job.
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"""
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import sys
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import os
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# Add deploy/docker to path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'deploy', 'docker'))
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def test_llm_job_payload_model():
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"""Test that LlmJobPayload includes webhook_config field"""
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print("=" * 60)
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print("TEST 1: LlmJobPayload Model")
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print("=" * 60)
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try:
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from job import LlmJobPayload
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from schemas import WebhookConfig
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from pydantic import ValidationError
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# Test with webhook_config
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payload_dict = {
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"url": "https://example.com",
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"q": "Extract main content",
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"schema": None,
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"cache": False,
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"provider": None,
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"webhook_config": {
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"webhook_url": "https://myapp.com/webhook",
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"webhook_data_in_payload": True,
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"webhook_headers": {"X-Secret": "token"}
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}
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}
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payload = LlmJobPayload(**payload_dict)
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print(f"✅ LlmJobPayload accepts webhook_config")
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print(f" - URL: {payload.url}")
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print(f" - Query: {payload.q}")
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print(f" - Webhook URL: {payload.webhook_config.webhook_url}")
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print(f" - Data in payload: {payload.webhook_config.webhook_data_in_payload}")
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# Test without webhook_config (should be optional)
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minimal_payload = {
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"url": "https://example.com",
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"q": "Extract content"
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}
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payload2 = LlmJobPayload(**minimal_payload)
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assert payload2.webhook_config is None, "webhook_config should be optional"
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print(f"✅ LlmJobPayload works without webhook_config (optional)")
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return True
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except Exception as e:
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print(f"❌ Failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_handle_llm_request_signature():
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"""Test that handle_llm_request accepts webhook_config parameter"""
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print("\n" + "=" * 60)
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print("TEST 2: handle_llm_request Function Signature")
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print("=" * 60)
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try:
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from api import handle_llm_request
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import inspect
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sig = inspect.signature(handle_llm_request)
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params = list(sig.parameters.keys())
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print(f"Function parameters: {params}")
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if 'webhook_config' in params:
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print(f"✅ handle_llm_request has webhook_config parameter")
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# Check that it's optional with default None
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webhook_param = sig.parameters['webhook_config']
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if webhook_param.default is None or webhook_param.default == inspect.Parameter.empty:
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print(f"✅ webhook_config is optional (default: {webhook_param.default})")
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else:
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print(f"⚠️ webhook_config default is: {webhook_param.default}")
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return True
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else:
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print(f"❌ handle_llm_request missing webhook_config parameter")
|
|
return False
|
|
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def test_process_llm_extraction_signature():
|
|
"""Test that process_llm_extraction accepts webhook_config parameter"""
|
|
print("\n" + "=" * 60)
|
|
print("TEST 3: process_llm_extraction Function Signature")
|
|
print("=" * 60)
|
|
|
|
try:
|
|
from api import process_llm_extraction
|
|
import inspect
|
|
|
|
sig = inspect.signature(process_llm_extraction)
|
|
params = list(sig.parameters.keys())
|
|
|
|
print(f"Function parameters: {params}")
|
|
|
|
if 'webhook_config' in params:
|
|
print(f"✅ process_llm_extraction has webhook_config parameter")
|
|
|
|
webhook_param = sig.parameters['webhook_config']
|
|
if webhook_param.default is None or webhook_param.default == inspect.Parameter.empty:
|
|
print(f"✅ webhook_config is optional (default: {webhook_param.default})")
|
|
else:
|
|
print(f"⚠️ webhook_config default is: {webhook_param.default}")
|
|
|
|
return True
|
|
else:
|
|
print(f"❌ process_llm_extraction missing webhook_config parameter")
|
|
return False
|
|
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def test_webhook_integration_in_api():
|
|
"""Test that api.py properly integrates webhook notifications"""
|
|
print("\n" + "=" * 60)
|
|
print("TEST 4: Webhook Integration in process_llm_extraction")
|
|
print("=" * 60)
|
|
|
|
try:
|
|
api_file = os.path.join(os.path.dirname(__file__), 'deploy', 'docker', 'api.py')
|
|
|
|
with open(api_file, 'r') as f:
|
|
api_content = f.read()
|
|
|
|
# Check for WebhookDeliveryService initialization
|
|
if 'webhook_service = WebhookDeliveryService(config)' in api_content:
|
|
print("✅ process_llm_extraction initializes WebhookDeliveryService")
|
|
else:
|
|
print("❌ Missing WebhookDeliveryService initialization in process_llm_extraction")
|
|
return False
|
|
|
|
# Check for notify_job_completion calls with llm_extraction
|
|
if 'task_type="llm_extraction"' in api_content:
|
|
print("✅ Uses correct task_type='llm_extraction' for notifications")
|
|
else:
|
|
print("❌ Missing task_type='llm_extraction' in webhook notifications")
|
|
return False
|
|
|
|
# Count webhook notification calls (should have at least 3: success + 2 failure paths)
|
|
notification_count = api_content.count('await webhook_service.notify_job_completion')
|
|
# Find only in process_llm_extraction function
|
|
llm_func_start = api_content.find('async def process_llm_extraction')
|
|
llm_func_end = api_content.find('\nasync def ', llm_func_start + 1)
|
|
if llm_func_end == -1:
|
|
llm_func_end = len(api_content)
|
|
|
|
llm_func_content = api_content[llm_func_start:llm_func_end]
|
|
llm_notification_count = llm_func_content.count('await webhook_service.notify_job_completion')
|
|
|
|
print(f"✅ Found {llm_notification_count} webhook notification calls in process_llm_extraction")
|
|
|
|
if llm_notification_count >= 3:
|
|
print(f"✅ Sufficient notification points (success + failure paths)")
|
|
else:
|
|
print(f"⚠️ Expected at least 3 notification calls, found {llm_notification_count}")
|
|
|
|
return True
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def test_job_endpoint_integration():
|
|
"""Test that /llm/job endpoint extracts and passes webhook_config"""
|
|
print("\n" + "=" * 60)
|
|
print("TEST 5: /llm/job Endpoint Integration")
|
|
print("=" * 60)
|
|
|
|
try:
|
|
job_file = os.path.join(os.path.dirname(__file__), 'deploy', 'docker', 'job.py')
|
|
|
|
with open(job_file, 'r') as f:
|
|
job_content = f.read()
|
|
|
|
# Find the llm_job_enqueue function
|
|
llm_job_start = job_content.find('async def llm_job_enqueue')
|
|
llm_job_end = job_content.find('\n\n@router', llm_job_start + 1)
|
|
if llm_job_end == -1:
|
|
llm_job_end = job_content.find('\n\nasync def', llm_job_start + 1)
|
|
|
|
llm_job_func = job_content[llm_job_start:llm_job_end]
|
|
|
|
# Check for webhook_config extraction
|
|
if 'webhook_config = None' in llm_job_func:
|
|
print("✅ llm_job_enqueue initializes webhook_config variable")
|
|
else:
|
|
print("❌ Missing webhook_config initialization")
|
|
return False
|
|
|
|
if 'if payload.webhook_config:' in llm_job_func:
|
|
print("✅ llm_job_enqueue checks for payload.webhook_config")
|
|
else:
|
|
print("❌ Missing webhook_config check")
|
|
return False
|
|
|
|
if 'webhook_config = payload.webhook_config.model_dump(mode=\'json\')' in llm_job_func:
|
|
print("✅ llm_job_enqueue converts webhook_config to dict")
|
|
else:
|
|
print("❌ Missing webhook_config.model_dump conversion")
|
|
return False
|
|
|
|
if 'webhook_config=webhook_config' in llm_job_func:
|
|
print("✅ llm_job_enqueue passes webhook_config to handle_llm_request")
|
|
else:
|
|
print("❌ Missing webhook_config parameter in handle_llm_request call")
|
|
return False
|
|
|
|
return True
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def test_create_new_task_integration():
|
|
"""Test that create_new_task stores webhook_config in Redis"""
|
|
print("\n" + "=" * 60)
|
|
print("TEST 6: create_new_task Webhook Storage")
|
|
print("=" * 60)
|
|
|
|
try:
|
|
api_file = os.path.join(os.path.dirname(__file__), 'deploy', 'docker', 'api.py')
|
|
|
|
with open(api_file, 'r') as f:
|
|
api_content = f.read()
|
|
|
|
# Find create_new_task function
|
|
create_task_start = api_content.find('async def create_new_task')
|
|
create_task_end = api_content.find('\nasync def ', create_task_start + 1)
|
|
if create_task_end == -1:
|
|
create_task_end = len(api_content)
|
|
|
|
create_task_func = api_content[create_task_start:create_task_end]
|
|
|
|
# Check for webhook_config storage
|
|
if 'if webhook_config:' in create_task_func:
|
|
print("✅ create_new_task checks for webhook_config")
|
|
else:
|
|
print("❌ Missing webhook_config check in create_new_task")
|
|
return False
|
|
|
|
if 'task_data["webhook_config"] = json.dumps(webhook_config)' in create_task_func:
|
|
print("✅ create_new_task stores webhook_config in Redis task data")
|
|
else:
|
|
print("❌ Missing webhook_config storage in task_data")
|
|
return False
|
|
|
|
# Check that webhook_config is passed to process_llm_extraction
|
|
if 'webhook_config' in create_task_func and 'background_tasks.add_task' in create_task_func:
|
|
print("✅ create_new_task passes webhook_config to background task")
|
|
else:
|
|
print("⚠️ Could not verify webhook_config passed to background task")
|
|
|
|
return True
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def test_pattern_consistency():
|
|
"""Test that /llm/job follows the same pattern as /crawl/job"""
|
|
print("\n" + "=" * 60)
|
|
print("TEST 7: Pattern Consistency with /crawl/job")
|
|
print("=" * 60)
|
|
|
|
try:
|
|
api_file = os.path.join(os.path.dirname(__file__), 'deploy', 'docker', 'api.py')
|
|
|
|
with open(api_file, 'r') as f:
|
|
api_content = f.read()
|
|
|
|
# Find handle_crawl_job to compare pattern
|
|
crawl_job_start = api_content.find('async def handle_crawl_job')
|
|
crawl_job_end = api_content.find('\nasync def ', crawl_job_start + 1)
|
|
if crawl_job_end == -1:
|
|
crawl_job_end = len(api_content)
|
|
crawl_job_func = api_content[crawl_job_start:crawl_job_end]
|
|
|
|
# Find process_llm_extraction
|
|
llm_extract_start = api_content.find('async def process_llm_extraction')
|
|
llm_extract_end = api_content.find('\nasync def ', llm_extract_start + 1)
|
|
if llm_extract_end == -1:
|
|
llm_extract_end = len(api_content)
|
|
llm_extract_func = api_content[llm_extract_start:llm_extract_end]
|
|
|
|
print("Checking pattern consistency...")
|
|
|
|
# Both should initialize WebhookDeliveryService
|
|
crawl_has_service = 'webhook_service = WebhookDeliveryService(config)' in crawl_job_func
|
|
llm_has_service = 'webhook_service = WebhookDeliveryService(config)' in llm_extract_func
|
|
|
|
if crawl_has_service and llm_has_service:
|
|
print("✅ Both initialize WebhookDeliveryService")
|
|
else:
|
|
print(f"❌ Service initialization mismatch (crawl: {crawl_has_service}, llm: {llm_has_service})")
|
|
return False
|
|
|
|
# Both should call notify_job_completion on success
|
|
crawl_notifies_success = 'status="completed"' in crawl_job_func and 'notify_job_completion' in crawl_job_func
|
|
llm_notifies_success = 'status="completed"' in llm_extract_func and 'notify_job_completion' in llm_extract_func
|
|
|
|
if crawl_notifies_success and llm_notifies_success:
|
|
print("✅ Both notify on success")
|
|
else:
|
|
print(f"❌ Success notification mismatch (crawl: {crawl_notifies_success}, llm: {llm_notifies_success})")
|
|
return False
|
|
|
|
# Both should call notify_job_completion on failure
|
|
crawl_notifies_failure = 'status="failed"' in crawl_job_func and 'error=' in crawl_job_func
|
|
llm_notifies_failure = 'status="failed"' in llm_extract_func and 'error=' in llm_extract_func
|
|
|
|
if crawl_notifies_failure and llm_notifies_failure:
|
|
print("✅ Both notify on failure")
|
|
else:
|
|
print(f"❌ Failure notification mismatch (crawl: {crawl_notifies_failure}, llm: {llm_notifies_failure})")
|
|
return False
|
|
|
|
print("✅ /llm/job follows the same pattern as /crawl/job")
|
|
return True
|
|
|
|
except Exception as e:
|
|
print(f"❌ Failed: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return False
|
|
|
|
def main():
|
|
"""Run all tests"""
|
|
print("\n🧪 LLM Job Webhook Feature Validation")
|
|
print("=" * 60)
|
|
print("Testing that /llm/job now supports webhooks like /crawl/job")
|
|
print("=" * 60 + "\n")
|
|
|
|
results = []
|
|
|
|
# Run all tests
|
|
results.append(("LlmJobPayload Model", test_llm_job_payload_model()))
|
|
results.append(("handle_llm_request Signature", test_handle_llm_request_signature()))
|
|
results.append(("process_llm_extraction Signature", test_process_llm_extraction_signature()))
|
|
results.append(("Webhook Integration", test_webhook_integration_in_api()))
|
|
results.append(("/llm/job Endpoint", test_job_endpoint_integration()))
|
|
results.append(("create_new_task Storage", test_create_new_task_integration()))
|
|
results.append(("Pattern Consistency", test_pattern_consistency()))
|
|
|
|
# Print summary
|
|
print("\n" + "=" * 60)
|
|
print("TEST SUMMARY")
|
|
print("=" * 60)
|
|
|
|
passed = sum(1 for _, result in results if result)
|
|
total = len(results)
|
|
|
|
for test_name, result in results:
|
|
status = "✅ PASS" if result else "❌ FAIL"
|
|
print(f"{status} - {test_name}")
|
|
|
|
print(f"\n{'=' * 60}")
|
|
print(f"Results: {passed}/{total} tests passed")
|
|
print(f"{'=' * 60}")
|
|
|
|
if passed == total:
|
|
print("\n🎉 All tests passed! /llm/job webhook feature is correctly implemented.")
|
|
print("\n📝 Summary of changes:")
|
|
print(" 1. LlmJobPayload model includes webhook_config field")
|
|
print(" 2. /llm/job endpoint extracts and passes webhook_config")
|
|
print(" 3. handle_llm_request accepts webhook_config parameter")
|
|
print(" 4. create_new_task stores webhook_config in Redis")
|
|
print(" 5. process_llm_extraction sends webhook notifications")
|
|
print(" 6. Follows the same pattern as /crawl/job")
|
|
return 0
|
|
else:
|
|
print(f"\n⚠️ {total - passed} test(s) failed. Please review the output above.")
|
|
return 1
|
|
|
|
if __name__ == "__main__":
|
|
exit(main())
|