chroma

To store embeddings and run semantic search, you can create persistent collections, add documents with metadata, and query by similarity with metadata filters for precise document retrieval.

Yes, Chroma vector database supports self-hosted retrieval for RAG pipelines by storing embeddings and metadata to enable similarity search and filtered document lookup across local or server deployments.

Yes, this semantic search tool integrates with LangChain and LlamaIndex, allowing you to connect custom embedding functions and query stored document collections within existing RAG application frameworks.

The best way to index product documents for a RAG application is to store text passages and metadata in persistent vector collections, then use similarity queries to retrieve the most relevant content.

You do not strictly need custom embedding functions for document retrieval in notebooks, as you can use default options, but Chroma supports custom functions to tailor semantic search to your specific content.

Local vector collections for notebook prototyping are designed for development and self-hosted retrieval, meaning limitations arise primarily from your own hardware constraints when scaling persistent collections and similarity queries.