Vector Database
A vector database is a system that stores embedding vectors with metadata and supports fast approximate nearest-neighbour search, forming the retrieval backbone of a RAG system.
What is Vector Database?
A vector database is a system that stores embedding vectors with metadata and supports fast approximate nearest-neighbour search, forming the retrieval backbone of a RAG system.
A vector database is a system that stores embedding vectors with metadata and supports fast approximate nearest-neighbour search, forming the retrieval backbone of a RAG system.
Where is it used?
Pinecone, Weaviate, Milvus, Qdrant, and pgvector are used in production RAG; FAISS and Chroma are popular for local prototyping with LangChain.
How to build it
Use `chromadb.Client().create_collection("docs")`, call `.add(ids=..., embeddings=..., documents=...)`, then `.query(query_embeddings=..., n_results=5)` to retrieve top chunks.