vector-database

To build a knowledge base for RAG, use a vector database to store document embeddings and retrieve relevant context for LLM prompts. This Skill manages vector collections and facilitates semantic search to fetch precise information for augmented generation.

Yes, ChromaDB supports custom embedding functions for semantic search. You can integrate various embedding models, including OpenAI and Sentence Transformers, to generate vector representations tailored to your specific data retrieval needs.

The best way to perform CRUD operations on vector collections is using a dedicated vector database manager. This Skill enables you to create, read, update, and delete embeddings within collections like ChromaDB efficiently for AI applications.

Yes, ChromaDB supports metadata filtering for precise data retrieval. You can apply advanced metadata and document content filters to your semantic queries to restrict search results to specific subsets of your stored embeddings.

You need a vector database for your AI application when managing large corpora of documents for semantic search or RAG. It enables efficient storage and retrieval of embeddings to provide relevant context to LLM prompts.