chroma

To store and query vector embeddings for semantic search, you can use a self-hosted vector database to manage high-dimensional vectors and metadata, performing similarity searches to retrieve relevant information.

A vector database works for RAG applications by providing a memory layer for LLMs, storing document embeddings and metadata so you can retrieve contextually relevant information to augment generation.

Yes, you can use chroma with LangChain and LlamaIndex to implement Retrieval-Augmented Generation and memory functionalities, embedding documents and retrieving them based on semantic similarity.

The best way to filter semantic search results by metadata is to use a vector database that supports metadata filtering, allowing you to narrow down similarity searches based on specific document attributes.

Yes, chroma vector database scales from local development to production clusters, allowing you to start with local embedding storage and scale up to handle production-level semantic search and document retrieval.