chroma

You can store and search embeddings using an open-source vector database with local persistence. It provides a simple API for adding, querying, and filtering documents with metadata, enabling memory-enabled AI workflows and semantic search across local development environments.

Adding metadata filtering to semantic search involves using a vector database that stores metadata alongside embeddings. This allows fast similarity search while narrowing results based on specific document attributes directly within the retrieval workflow.

Yes, this vector database works with LangChain and LlamaIndex through easy plug-and-play integrations. This compatibility allows you to seamlessly incorporate vector storage and similarity search into existing AI tooling workflows for document retrieval and RAG applications.

You can use an open-source vector database for production deployments because it supports scalable document retrieval from notebooks to production. It provides a self-hosted environment for storing embeddings and executing fast similarity search across various deployment scales.

You query documents using a simple 4-function API to add, query, and filter embeddings. This straightforward interface allows you to manage collections of documents and retrieve relevant information quickly through similarity search without complex configuration.