chroma

You can store embeddings and metadata for local RAG applications using a local embedding database that enables fast vector-based retrieval and semantic search. This approach provides persistent storage with metadata filtering for document retrieval.

Yes, Chroma works with LangChain and LlamaIndex for semantic search. It offers seamless integration with these frameworks, enabling document retrieval and RAG workflows from notebook prototyping through to production deployment.

The best way to manage documents in a local vector database is using a simple 4-function API. This API allows you to easily manage documents, embeddings, and queries, providing persistent storage and multiple embedding options for local development.

Yes, you can use an open-source vector database for both notebook prototyping and production. Chroma provides local, self-hosted vector storage with metadata filtering that scales seamlessly from development environments to production deployment.

Metadata filtering for semantic search works by storing metadata alongside embeddings in a local database. This allows you to apply precise filters to your vector queries, enabling fast and scalable retrieval of specific documents within RAG applications.

Limitations of self-hosted vector storage for local development typically revolve around scaling infrastructure manually. While Chroma offers persistent storage and a simple API, extremely large-scale production deployments may require additional engineering to manage distributed resources.