vector-db-rag

To build a multi-tenant RAG pipeline with PostgreSQL, you use pgvector as the vector store and enforce Row-Level Security for tenant isolation. This Skill standardizes that process by applying per-document-type chunking policies and HNSW indexing.

The best way to isolate vector embeddings in pgvector is by enforcing Row-Level Security policies on the embedding storage tables. This ensures multi-tenant isolation so queries only retrieve chunks belonging to the specific tenant.

You configure hybrid search for a RAG application by combining semantic vector retrieval with keyword matching within the PostgreSQL database. This approach uses HNSW indexing to optimize vector similarity searches alongside traditional text queries.

Yes, this approach supports configurable chunking strategies tailored per document type. Whether processing documents, code, or knowledge bases, the pipeline applies specific chunking policies to optimize embedding generation and retrieval accuracy.

Yes, you can track runtime evaluation metrics for your RAG pipeline. The pipeline includes metrics tracking to measure retrieval effectiveness and overall performance during the embedding, chunking, and retrieval phases.

You should use HNSW indexing for vector similarity search when you need high-performance approximate nearest neighbor queries in PostgreSQL. It is enforced in this pipeline to ensure fast hybrid semantic and keyword retrieval across large embedding datasets.