chroma

Store embeddings with metadata for local semantic search by using a local vector database to index documents, enabling fast similarity search for retrieval in AI workflows.

Yes, you can use this local vector database for RAG pipelines in production. It supports semantic search and document retrieval, integrating with frameworks like LangChain and LlamaIndex.

Query vectors locally using sentence-transformers by embedding documents, storing them with metadata in a local collection, and retrieving similar items via a simple four-function API.

Yes, you need Chromadb and sentence-transformers to build this local vector store. Chromadb handles the vector database storage, while sentence-transformers generates the embeddings for indexing.

The best way to add memory to AI workflows using a self-hosted vector store is indexing embeddings with metadata locally, which enables fast similarity search for memory-powered AI applications.

Using a local vector database for document retrieval is suitable for notebooks and production AI workflows, but local storage capacity and compute resources may limit the maximum scale of indexed collections.