Retrieval-augmented generation, vector databases, embeddings and data tooling: guides to building and evaluating RAG, plus releases.
Pick the best embedding model for RAG by language, context length, vector size, hosting and price, then test it on your own documents first.
6 min read
Choose a vector database for RAG or search by comparing pgvector, Qdrant, Chroma and Milvus on deployment, filtering, index types and licence.
7 min read
Set up RAG with pgvector in your existing Postgres: a chunks table, an HNSW or IVFFlat index, the right distance operator and a filtered retrieval query.
6 min read