chroma

To build semantic search with metadata filtering, you store document embeddings and per-document metadata in a persistent vector database, then run nearest-neighbor similarity queries with optional logical operators to retrieve relevant text.

A vector database supports RAG pipelines by persisting embeddings and document metadata, enabling fast similarity and filtered retrieval of relevant text to feed into LLM frameworks like LangChain and LlamaIndex for question answering.

Yes, you can use local persistent storage for embeddings and connect to common LLM frameworks like LangChain and LlamaIndex to build RAG pipelines and manage document retrieval workflows.

You need sentence-transformers to generate embeddings for Chroma collections, as it is a required dependency for converting unstructured text into vectors before storing them for similarity search and metadata filtering.

The best way to run filtered similarity queries is to use a vector database that supports nearest-neighbor search alongside per-document metadata filters with logical operators, enabling precise retrieval over unstructured text.

You need a persistent collection setup for semantic search to ensure stored embeddings and document metadata remain available across notebook sessions and production services for consistent retrieval.