jolibrain / colette

What it does

• Vision RAG (V-RAG). treats every document page as an image; preserves tables, figures, diagrams and spatial layout that text extraction loses. Powered by vision embedding models (e.g. gme-Qwen2-VL) and VLMs (e.g. Qwen3-VL).
• Hybrid retrieval. runs vector embedding search and BM25 lexical search (Tantivy) in parallel. Especially effective for technical documents with product codes, part numbers, or rare identifiers.
• HyDE (Hypothetical Document Embeddings). optionally generates a short hypothetical answer passage before retrieval (llm.use_hyde: true). The passage is embedded instead of the raw question, landing closer in vector space to real document c
• Text RAG. classical text-extraction pipeline using unstructured + langchain for text-only documents.
• Multi-backend support. huggingface, vllm, ollama, vllm_client for LLM inference; chromadb or coldb for vector storage.