---
title: Postgres pgvector RAG Pipeline
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/postgres-pgvector-rag-pipeline/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/postgres-pgvector-rag-pipeline/
last_updated: 2026-06-20
---

# Postgres pgvector RAG Pipeline

> PostgreSQL + pgvector uzerine uctan uca RAG pipeline chunking (recursive 1024/256 overlap vs…

An end-to-end RAG pipeline on PostgreSQL + pgvector covering every stage: chunking, embedding, indexing, retrieval, and quality evaluation. It makes the hard engineering decisions concrete: HNSW vs IVFFLAT, recursive vs semantic chunking, OpenAI vs multilingual embeddings: with cost ceilings, PII masking, and re-embed migration plans baked in. You get a production-grade semantic search layer instead of a fragile prototype.

## Use cases
- Building a semantic search layer for docs, blog, or support content
- Standing up a RAG chatbot retrieval backend
- Migrating an index from IVFFLAT to HNSW to raise recall
- Upgrading embedding models with zero-downtime re-embed
- Tuning recall vs latency with HNSW ef_search
- Evaluating pipeline quality with recall@k, MRR, and nDCG

## Benefits
- Higher retrieval accuracy through deliberate chunking, indexing, and diversity (MMR) choices
- Predictable cost with embedding-token budgets and ceiling alarms
- Privacy-safe ingestion that masks personal data before embedding, when it can no longer be removed
- Confidence that quality is measured, not assumed, via scheduled evaluation

## What’s included
- pgvector schema with HNSW index, metadata GIN index, and embedding versioning
- Ingestion pipeline: recursive chunking, PII masking, batched async embedding, bulk insert
- Retrieval with cosine similarity, relevance threshold, and MMR diversity selection
- Evaluation harness computing recall@k, MRR, and nDCG against ground-truth queries
- Blue-green re-embed migration plan for model upgrades without downtime
- Cost-ceiling alarms and row-level security partitioning by source and visibility

## Who it’s for
Backend and AI engineers building reliable, cost-controlled semantic search or RAG retrieval on PostgreSQL.

## How it runs
Your RAG stack can live inside the Postgres you already run. From HNSW indexing to a weekly recall cron, retrieval gets built as a measured production system, not a demo notebook.
1. Schema first: vector extension enabled, a documents table carrying an embedding_version column and JSONB metadata, an HNSW index at m=16 and ef_construction=64, a GIN index on metadata so filters never full-scan, and row-level security policies splitting admin from public chunks.
2. Ingestion runs as chunk, mask, embed, upsert: recursive splitting at 1024 tokens with 256 overlap (semantic-boundary splitting for legal or financial documents), then PII masking BEFORE embedding, because once personal data is embedded it lives inside the vector space and cannot be removed. Embedding goes in batches of 64, insert is an upsert keyed on source file, chunk index and version.
3. Retrieval applies three controls: cosine similarity with a 0.7 relevance threshold (below it the user gets an honest 'not found' instead of noise), ef_search set per query to trade latency against recall, and MMR diversity so one source file cannot dominate the top 5.
4. A weekly evaluation cron scores recall@k, MRR and nDCG against 100+ human-labeled query pairs. A 10 percent recall drop fires an alarm, so quality drift is measured, not felt.
5. Embedding model upgrades run blue-green: a parallel v2 column and index, dual-write on new inserts, background re-embedding in batches, and cutover only when v2 recall meets or beats v1. Zero downtime, full rollback path.
6. Cost ceilings are wired in: storage at 80 percent of plan and monthly embedding spend over budget both trigger alerts before the bill does.

## FAQ
### Do I need Supabase, or does any PostgreSQL work?
Any PostgreSQL with the pgvector extension works: the schema, HNSW indexing, ingestion pipeline, and evaluation harness are plain Postgres. Supabase appears in the cost-ceiling examples because it's a common managed host, not because anything depends on it.

### How do I know the retrieval actually works instead of just hoping?
Quality is measured, not assumed: the evaluation harness computes recall@k, MRR, and nDCG against ground-truth queries on a schedule, and HNSW ef_search gives you an explicit recall-versus-latency dial. Index and chunking decisions are framed as measurable trade-offs, including the IVFFLAT-to-HNSW migration path.

### Does it include the chatbot or LLM answer layer?
No. It ends where retrieval ends: chunking, PII masking, embedding, indexing, similarity search with MMR diversity, and evaluation. The generation layer that turns retrieved chunks into answers sits on top and is a separate concern.

## Price
$15, one-time, no subscription. VAT included.

Related guide: [AI for data analytics](https://forgehouse.ai/guides/ai-data-analytics/)