- Pass all registered tools to LLM during chat completion
- Handle tool_calls from LLM response
- Execute tools and feed results back to LLM
- Loop until LLM returns final response
- Updated system prompt to encourage tool use
- Updated streaming to handle tool calls
- Increased MAX_TOOL_ITERATIONS to 5
Key changes:
- Add URL extraction and detection functions
- Download websites BEFORE RAG retrieval (not after)
- Expand trigger keywords to include common phrases like 'go to', 'headlines', etc.
- Update system prompt to tell LLM it CAN access websites
- Improve streaming response handling
Now when user asks 'go to orovillemr.com and give me the headlines':
1. System detects URL and access intent
2. Downloads and ingests website content
3. RAG retrieves relevant content
4. LLM generates response with actual website content
Features:
- RAG system now uses website_downloader_tool as primary content ingestion method
- download_and_ingest_website() method for complete website processing
- Stores page pointers (source_url, page_url, local_path) in vector store
- Site registry tracks all downloaded websites with metadata
- New API endpoints for website management:
- POST /v1/documents/website - Download and ingest a website
- GET /v1/documents/sites - List all downloaded sites
- GET /v1/documents/sites/{url} - Get site info
- DELETE /v1/documents/sites/{url} - Delete a site and its content
Changes:
- rag/__init__.py: Added download_and_ingest_website(), site registry
- rag/document_processor.py: Added extract_text_from_html() public method
- rag/vector_store.py: Added delete_by_source_url(), get_stats()
- main.py: New website endpoints, integrated tool with RAG system
Features:
- FastAPI server with OpenAI-compatible endpoints (/v1/chat/completions, /v1/models)
- RAG system with document processing and vector storage
- Support for multiple document formats (PDF, DOCX, HTML, text, code)
- Streaming response support
- Tool integration with website_downloader
- Document management API endpoints
- GLM-4.7-Flash integration via z-ai-web-dev-sdk
- Works transparently with Open WebUI and other OpenAI clients
Components:
- main.py: FastAPI application with OpenAI-compatible API
- rag/: RAG system (document processor, vector store, retriever)
- tools/: Tool manager with website_downloader integration
- .env.example: Configuration template
