Tag: rag

Run embeddings on Postgres with pgvector. Skip Chroma/Qdrant, tune IVFFlat vs HNSW indexes, build RAG pipelines locally.

RAG beyond dense embeddings: BM25 lexical search, hybrid retrieval (RRF, weighted fusion), cross-encoder re-rankers (BGE, Cohere, Jina). When pure vector search fails.

Stop guessing if your RAG pipeline works. RAGAS gives you reproducible metrics: faithfulness, answer relevance, context precision and recall.

AnythingLLM is the closest thing to a real private NotebookLM you can self-host. Workspaces, RAG, agents, document chat, running locally on Ollama in 20 minutes.

Most RAG demos look great until you ship them. Ragas measures faithfulness, context precision, answer relevancy, the metrics that actually predict user trust.

Every RAG tutorial says 'just use Chroma.' Then you hit production. Here's what Qdrant, Weaviate, and ChromaDB actually offer and when each one earns its place.

LangChain does everything and LlamaIndex does one thing brilliantly. Here's how to pick the right RAG framework without regretting it at 2 AM.

RAG breaks documents into chunks. But what chunk size? Too small and context is lost. Too large and semantic search fails. Here's how to pick.

Most people use OpenAI's embeddings because it's easy. But local embeddings exist. How to pick and when it actually matters.

Stop paying per-token to ask questions about your docs. Build a local RAG pipeline with Ollama and ChromaDB: Docker Compose, Python code, chunking strategy.