using-vector-databases

To build a scalable RAG pipeline, select a vector database and an embedding service to store document chunks and retrieve relevant context. This skill handles vector storage, embedding integration, and chunking strategies for long documents to enable high-quality retrieval workflows.

Hybrid search combines vector similarity with keyword matching using BM25 to improve relevance. You should use it when pure semantic search misses exact keywords, ensuring higher accuracy across large document corpora.

Yes, you can implement multi-tenant knowledge bases using vector storage. This skill supports enterprise-grade retrieval workflows, allowing isolated data access and scalable context-aware retrieval for AI assistants and chatbots.

Effective chunking strategies for long documents break text into manageable segments before embedding. This skill integrates chunking directly with vector storage and embedding models to maintain context and optimize semantic search accuracy.

Yes, this approach includes deployment considerations for monitoring and scaling enterprise search. It supports multi-language environments and ensures vector databases maintain performance across large-scale retrieval workflows.

Vector databases enable context-aware semantic search rather than exact keyword matching. By storing embeddings, they retrieve relevant information across large corpora even when query terms differ from document text, powering high-quality RAG systems.