chroma

Store and query embeddings for semantic search using a self-hosted vector database by creating a persistent collection, adding documents with metadata, and running vector similarity queries to retrieve nearest neighbors.

Build a RAG pipeline locally by storing document embeddings and metadata in a self-hosted vector database, running similarity queries to fetch relevant context, and integrating the results into LLM workflows like LangChain or LlamaIndex.

Yes, you can combine vector similarity with exact metadata filters to narrow search results. This hybrid search approach allows precise document retrieval by filtering collections before applying nearest neighbor queries.

Chroma integrates with LangChain and LlamaIndex to support RAG pipelines and document retrieval workflows. You can use these frameworks to manage embeddings, query collections, and build knowledge assistants for market research.

Persistent storage is optional. You can configure a persistent storage path to save embeddings locally, or run in-memory for notebook development and temporary semantic search tasks without saving data permanently.

Use a self-hosted vector database when you need local development, notebook testing, or self-hosted production vector stores for semantic search and document retrieval without the cost or external dependencies of managed cloud services.