chroma

To store and query vector embeddings for a RAG pipeline, you need a vector database to index embeddings with metadata, enabling semantic search and document retrieval via a simple API. This skill provides exactly that using Chromadb.

Yes, you can use a self-hosted vector database for semantic search in production. This approach allows you to store embeddings locally and scale seamlessly from notebooks to production workloads while maintaining data privacy.

You can filter vector similarity search results using metadata by applying metadata conditions to your queries. This database supports storing embeddings with IDs and metadata, allowing precise filtering during document retrieval.

Yes, this vector database supports full-text search alongside semantic vector search. You can perform both query types to enhance document retrieval workflows and improve RAG pipeline accuracy.

The best way to manage embeddings locally for AI applications is using a lightweight, self-hosted database. This avoids the complexity of building scalable private stores while providing a simple 4-function API for embedding management.

Yes, you need sentence-transformers to generate the vector embeddings required for document retrieval. Once generated, this database stores and indexes those embeddings with metadata for efficient querying.