Building RAG with pgvector on PostgreSQL

PostgreSQL with pgvector is a practical backbone for Retrieval-Augmented Generation. It keeps your embeddings in a familiar, auditable database while supporting fast similarity search. Here is a production-friendly pattern we use when building RAG for public-sector and enterprise teams.

1. Model your data and metadata

Store content chunks, embeddings, and metadata in a single table. Keep doc IDs, section numbers, and security tags in JSONB so you can filter before retrieval.

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE rag_chunks (
  id BIGSERIAL PRIMARY KEY,
  doc_id TEXT NOT NULL,
  chunk_index INT NOT NULL,
  content TEXT NOT NULL,
  embedding VECTOR(1536),
  metadata JSONB DEFAULT '{}'::jsonb
);

2. Add the right index

Start with an IVF index for scale. Tune list counts and probes based on dataset size and latency targets.

CREATE INDEX rag_chunks_embedding_ivf
  ON rag_chunks USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

SET ivfflat.probes = 10;

3. Retrieve with filters

Apply metadata filters before vector search. This keeps results aligned to permissions and context.

SELECT id, content, 1 - (embedding <=> $1) AS score
FROM rag_chunks
WHERE metadata->>'department' = 'public-sector'
ORDER BY embedding <=> $1
LIMIT 5;

4. Build an ingestion pipeline

• Normalize documents into clean text with stable chunk sizes.
• Store source metadata (version, author, file path).
• Re-embed only the changed chunks when documents update.
• Keep a deletion workflow for revoked or outdated content.

5. Observability and QA

Log queries, retrieved chunks, and user feedback. Use this data to improve chunking and adjust filters. A small QA loop makes a big difference in retrieval quality.

How Pipeline-e helps

We design and implement RAG pipelines with pgvector, secure ingestion services, and governance controls. If you want AI that cites reliable sources and respects permissions, we can build it.