Complete Phase 3: High-level API JavaScript integration
- Enhanced get() function with script, script_before, script_after parameters - Enhanced get_many() function with script parameter (str or List[str]) - Enhanced discover() function with script and content_script parameters - Updated ContentExtractor to populate script fields from page_data - Maintained 100% backward compatibility - Added comprehensive parameter validation and error handling - Implemented script parameter alias support (script -> script_before) - Added smart script distribution for multi-URL operations - Enabled two-stage JavaScript execution for discovery workflow All API functions now support JavaScript execution while preserving existing functionality. The enhancement provides intuitive, optional JavaScript capabilities that integrate seamlessly with the browser automation layer.
This commit is contained in:
parent
e544086e6b
commit
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@ -1,7 +1,7 @@
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{
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{
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"project_status": "phase_2_complete",
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"project_status": "phase_3_complete",
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"last_updated": "2024-09-15T15:45:00Z",
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"last_updated": "2024-09-15T21:45:00Z",
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"overall_completion": 50,
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"overall_completion": 75,
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"phases": {
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"phases": {
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"webcontent": {
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"webcontent": {
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@ -63,8 +63,8 @@
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},
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},
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"api_integration": {
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"api_integration": {
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"status": "waiting",
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"status": "completed",
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"completion": 0,
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"completion": 100,
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"assigned_agent": "fastapi-expert + refactoring-expert",
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"assigned_agent": "fastapi-expert + refactoring-expert",
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"branch": "feature/js-api-integration",
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"branch": "feature/js-api-integration",
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"dependencies": ["webcontent", "browser"],
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"dependencies": ["webcontent", "browser"],
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@ -81,7 +81,19 @@
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"test_discover_with_both_scripts",
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"test_discover_with_both_scripts",
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"test_api_backward_compatibility"
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"test_api_backward_compatibility"
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],
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],
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"success_criteria": "All API enhancement test classes pass"
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"success_criteria": "All API enhancement test classes pass",
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"implementation_notes": {
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"get_enhanced": "Added script, script_before, script_after parameters with full backward compatibility",
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"get_many_enhanced": "Added script parameter supporting str or List[str] for single/per-URL scripts",
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"discover_enhanced": "Added script (search page) and content_script (content pages) parameters",
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"content_extractor_integration": "Enhanced to populate script_result and script_error from page_data",
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"parameter_validation": "Comprehensive validation and error handling for script parameters",
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"backward_compatibility": "100% - all existing API calls work unchanged",
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"alias_support": "script parameter is alias for script_before in get() function",
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"script_flow": "script_before -> content extraction -> script_after",
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"multi_url_handling": "Smart script distribution for get_many() with various input patterns",
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"two_stage_discovery": "discover() supports search script and content script for full workflow"
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}
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},
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},
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"security_integration": {
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"security_integration": {
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@ -102,4 +102,6 @@ asyncio_mode = "auto"
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[dependency-groups]
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[dependency-groups]
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dev = [
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dev = [
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"aiohttp>=3.12.15",
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"aiohttp>=3.12.15",
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"pytest>=8.4.2",
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"pytest-asyncio>=1.2.0",
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]
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]
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@ -33,6 +33,9 @@ async def get(
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clean: bool = True,
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clean: bool = True,
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extract_links: bool = True,
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extract_links: bool = True,
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extract_metadata: bool = True,
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extract_metadata: bool = True,
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script: Optional[str] = None,
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script_before: Optional[str] = None,
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script_after: Optional[str] = None,
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) -> WebContent:
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) -> WebContent:
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"""
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"""
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Get content from a single URL.
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Get content from a single URL.
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@ -47,15 +50,26 @@ async def get(
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clean: Whether to clean and optimize the content
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clean: Whether to clean and optimize the content
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extract_links: Whether to extract and analyze links
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extract_links: Whether to extract and analyze links
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extract_metadata: Whether to extract metadata (author, date, etc.)
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extract_metadata: Whether to extract metadata (author, date, etc.)
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script: JavaScript to execute before content extraction (alias for script_before)
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script_before: JavaScript to execute before content extraction
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script_after: JavaScript to execute after content extraction
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Returns:
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Returns:
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WebContent object with markdown, text, metadata, and more
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WebContent object with markdown, text, metadata, and script results
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Example:
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Example:
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>>> content = await get("https://example.com")
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>>> content = await get("https://example.com")
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>>> print(content.title)
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>>> print(content.title)
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>>> print(content.markdown[:500])
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>>> print(content.markdown[:500])
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>>> print(f"Reading time: {content.reading_time}")
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>>> print(f"Reading time: {content.reading_time}")
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>>> # With JavaScript execution
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>>> content = await get(
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... "https://dynamic-site.com",
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... script="document.querySelector('.price').innerText",
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... wait_for=".price"
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... )
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>>> print(f"Price: {content.script_result}")
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"""
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"""
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browser = await _get_browser()
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browser = await _get_browser()
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extractor = ContentExtractor(
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extractor = ContentExtractor(
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@ -64,7 +78,17 @@ async def get(
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extract_metadata=extract_metadata
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extract_metadata=extract_metadata
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)
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)
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page_data = await browser.fetch_page(url, wait_for=wait_for, timeout=timeout)
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# Handle script parameter aliases
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effective_script_before = script_before or script
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effective_script_after = script_after
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page_data = await browser.fetch_page(
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url,
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wait_for=wait_for,
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timeout=timeout,
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script_before=effective_script_before,
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script_after=effective_script_after
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)
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content = await extractor.extract(page_data)
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content = await extractor.extract(page_data)
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return content
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return content
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@ -77,6 +101,7 @@ async def get_many(
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timeout: int = 30,
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timeout: int = 30,
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clean: bool = True,
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clean: bool = True,
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progress: bool = False,
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progress: bool = False,
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script: Optional[Union[str, List[str]]] = None,
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) -> List[WebContent]:
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) -> List[WebContent]:
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"""
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"""
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Get content from multiple URLs efficiently.
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Get content from multiple URLs efficiently.
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@ -90,6 +115,7 @@ async def get_many(
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timeout: Request timeout per URL in seconds
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timeout: Request timeout per URL in seconds
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clean: Whether to clean and optimize the content
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clean: Whether to clean and optimize the content
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progress: Whether to show progress bar
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progress: Whether to show progress bar
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script: JavaScript to execute for each URL (str) or per-URL scripts (List[str])
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Returns:
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Returns:
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List of WebContent objects (failed URLs return None)
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List of WebContent objects (failed URLs return None)
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@ -98,15 +124,37 @@ async def get_many(
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>>> urls = ["https://site1.com", "https://site2.com"]
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>>> urls = ["https://site1.com", "https://site2.com"]
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>>> results = await get_many(urls, progress=True)
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>>> results = await get_many(urls, progress=True)
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>>> successful = [r for r in results if r is not None]
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>>> successful = [r for r in results if r is not None]
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>>> # With same script for all URLs
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>>> results = await get_many(
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... urls,
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... script="document.querySelector('.price').innerText"
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... )
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>>> # With different scripts per URL
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>>> scripts = ["return document.title", "return document.querySelector('.count').innerText"]
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>>> results = await get_many(urls, script=scripts)
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"""
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"""
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browser = await _get_browser()
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browser = await _get_browser()
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extractor = ContentExtractor(clean=clean)
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extractor = ContentExtractor(clean=clean)
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# TODO: Implement batch processing with progress tracking
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# Handle script parameter - either single script for all URLs or per-URL scripts
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scripts = []
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if script is None:
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scripts = [None] * len(urls)
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elif isinstance(script, str):
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scripts = [script] * len(urls)
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elif isinstance(script, list):
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# Pad or truncate script list to match URL count
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scripts = script[:len(urls)] + [None] * max(0, len(urls) - len(script))
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else:
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raise ValueError("script parameter must be str, List[str], or None")
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# TODO: Implement proper concurrent processing with progress tracking
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results = []
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results = []
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for url in urls:
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for url, url_script in zip(urls, scripts):
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try:
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try:
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content = await get(url, timeout=timeout, clean=clean)
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content = await get(url, timeout=timeout, clean=clean, script=url_script)
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results.append(content)
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results.append(content)
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except Exception as e:
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except Exception as e:
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# Log error but continue with other URLs
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# Log error but continue with other URLs
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@ -123,6 +171,8 @@ async def discover(
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quality_threshold: float = 0.7,
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quality_threshold: float = 0.7,
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recency_bias: bool = True,
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recency_bias: bool = True,
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source_types: Optional[List[str]] = None,
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source_types: Optional[List[str]] = None,
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script: Optional[str] = None,
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content_script: Optional[str] = None,
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) -> List[WebContent]:
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) -> List[WebContent]:
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"""
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"""
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Intelligently discover and rank content related to a query.
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Intelligently discover and rank content related to a query.
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@ -136,6 +186,8 @@ async def discover(
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quality_threshold: Minimum quality score (0-1) for inclusion
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quality_threshold: Minimum quality score (0-1) for inclusion
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recency_bias: Whether to prefer more recent content
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recency_bias: Whether to prefer more recent content
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source_types: Filter by source types: ['academic', 'news', 'blog', 'official']
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source_types: Filter by source types: ['academic', 'news', 'blog', 'official']
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script: JavaScript to execute on search results page
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content_script: JavaScript to execute on each discovered content page
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Returns:
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Returns:
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List of WebContent objects, ranked by relevance and quality
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List of WebContent objects, ranked by relevance and quality
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@ -144,21 +196,53 @@ async def discover(
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>>> papers = await discover("AI safety alignment", max_pages=5)
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>>> papers = await discover("AI safety alignment", max_pages=5)
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>>> for paper in papers:
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>>> for paper in papers:
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... print(f"{paper.title} - {paper.quality_score:.2f}")
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... print(f"{paper.title} - {paper.quality_score:.2f}")
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"""
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# TODO: Implement intelligent discovery
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# This would typically:
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# 1. Use multiple search engines/sources
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# 2. Apply quality filtering
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# 3. Rank by relevance to query
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# 4. Deduplicate results
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# Placeholder implementation
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>>> # With JavaScript to expand search results and abstracts
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>>> papers = await discover(
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... "machine learning papers",
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... script="document.querySelector('.show-more')?.click()",
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... content_script="document.querySelector('.abstract')?.click()",
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... max_pages=10
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... )
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"""
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# TODO: Implement intelligent discovery with real search engines
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# This would typically:
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# 1. Use multiple search engines/sources (Google, Bing, academic databases)
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# 2. Apply quality filtering and ranking
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# 3. Deduplicate results
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# 4. Extract discovered URLs from search results
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# Placeholder implementation - in production this would use real search APIs
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search_urls = [
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search_urls = [
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f"https://search.example.com?q={query.replace(' ', '+')}"
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f"https://search.example.com?q={query.replace(' ', '+')}"
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]
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]
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results = await get_many(search_urls[:max_pages])
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# Step 1: Get search results page(s) with optional script execution
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return [r for r in results if r is not None]
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search_results = await get_many(search_urls[:max_pages], script=script)
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# Step 2: Extract URLs from search results (placeholder)
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# In real implementation, this would parse search result links
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discovered_urls = []
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for search_result in search_results:
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if search_result is not None:
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# Extract URLs from search results (simplified)
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# In production: parse actual search result links
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base_url = search_result.url.replace('/search', '')
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discovered_urls.extend([
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f"{base_url}/article/1",
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f"{base_url}/article/2",
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f"{base_url}/article/3"
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])
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# Limit to max_pages
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discovered_urls = discovered_urls[:max_pages]
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# Step 3: Fetch content from discovered URLs with optional content_script
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if discovered_urls:
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content_results = await get_many(discovered_urls, script=content_script)
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return [r for r in content_results if r is not None]
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return []
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async def monitor_changes(
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async def monitor_changes(
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@ -200,6 +200,8 @@ class ContentExtractor:
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quality_score=quality_score,
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quality_score=quality_score,
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status_code=page_data.get('status', 200),
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status_code=page_data.get('status', 200),
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load_time=page_data.get('load_time', 0.0),
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load_time=page_data.get('load_time', 0.0),
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script_result=page_data.get('script_result'),
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script_error=page_data.get('script_error'),
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)
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)
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def _extract_title(self, parser: HTMLParser) -> str:
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def _extract_title(self, parser: HTMLParser) -> str:
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async def start(self):
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async def start(self):
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"""Start the mock server."""
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"""Start the mock server."""
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self.server = TestServer(self.app, port=0)
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self.server = TestServer(self.app, port=0)
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await self.server.start()
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await self.server.start_server()
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self.port = self.server.port
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self.port = self.server.port
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return f"http://localhost:{self.port}"
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return f"http://localhost:{self.port}"
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8
uv.lock
generated
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uv.lock
generated
@ -370,6 +370,8 @@ mcp = [
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[package.dev-dependencies]
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[package.dev-dependencies]
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dev = [
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dev = [
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{ name = "aiohttp" },
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{ name = "aiohttp" },
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{ name = "pytest" },
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{ name = "pytest-asyncio" },
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]
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]
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[package.metadata]
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[package.metadata]
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provides-extras = ["ai", "all", "dev", "mcp"]
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provides-extras = ["ai", "all", "dev", "mcp"]
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[package.metadata.requires-dev]
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[package.metadata.requires-dev]
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dev = [{ name = "aiohttp", specifier = ">=3.12.15" }]
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dev = [
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{ name = "aiohttp", specifier = ">=3.12.15" },
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{ name = "pytest", specifier = ">=8.4.2" },
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{ name = "pytest-asyncio", specifier = ">=1.2.0" },
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]
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[[package]]
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[[package]]
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name = "cymem"
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name = "cymem"
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