v0.9.0 - Contextual Retrieval & Configurable Embeddings

Added

• Contextual Retrieval - Anthropic's approach for ~40-50% better retrieval accuracy

  • LLM-generated context prepended to each chunk before embedding
  • Background processing mode - index usable immediately while contexts generate
  • Progress bar with tqdm for context generation
  • Pause/resume support - yields to user requests during background processing
  • Graceful shutdown with stop_background_contextualization()
  • Resumable - progress saved every 50 chunks, can interrupt and resume
  • Uses same backend (LM Studio/Ollama) as main server
  • Reference: https://www.anthropic.com/news/contextual-retrieval

• Configurable Embedding Models - Speed vs quality tradeoff

  • sentence-transformers/all-MiniLM-L6-v2: Fast (22M params), good quality (default)
  • BAAI/bge-base-en-v1.5: Medium (110M params), better quality
  • BAAI/bge-large-en-v1.5: Slow (335M params), best quality
  • Hot-swap without re-crawling - automatically detects model change and rebuilds embeddings only
  • New rebuild_embeddings() method for explicit embedding rebuild

• Progress Bars for RAG Operations

  • tqdm progress during embedding generation
  • Progress bar for contextual retrieval with failed count display
  • Crawling progress with tqdm (sitemap, recursive, fetching)

• LLMServer RAG Integration

  • New optional rag_index parameter for automatic pause/resume during requests
  • Background RAG processing pauses when user request starts, resumes when complete
  • Ensures user requests get priority access to LLM backend

Changed

• DocSearchIndex now accepts optional server_config parameter for contextual retrieval backend settings
• ChunkContextualizer supports both LM Studio (OpenAI API) and Ollama backends
• FAISS index building now shows progress bar during embedding generation

Fixed

• stop_background_contextualization() now silently exits if no background thread running