using-weaviate

To build a RAG pipeline, use Weaviate to store and retrieve high-dimensional embeddings, grounding LLM responses with accurate context fetched via semantic search. This Skill manages collections and data operations to fetch context for generative models.

Hybrid search combines traditional BM25 keyword queries with vector-based semantic search to deliver more relevant results. Weaviate enables this by integrating both methods, matching exact terms while understanding overall semantic meaning.

Yes, you can deploy Weaviate locally using Docker for AI-native application development. This Skill supports configurations for both local Docker and cloud deployments, enabling developers to build and scale semantic search and RAG pipelines.

You can use either Python or TypeScript clients to manage vector embeddings in Weaviate. This Skill supports both client languages for creating collections, performing data operations, and executing semantic similarity searches across stored data.

To store and search embeddings for a semantic knowledge base, create a collection in Weaviate to hold your vectorized data. The database facilitates semantic similarity retrieval, allowing users to query using natural language instead of exact keywords.

A limitation of using vector databases for semantic search is that pure vector queries may miss exact keyword matches. Weaviate addresses this constraint by supporting hybrid search, combining vector embeddings with BM25 keyword search to improve relevance.