Your First Postgres RAG

The Whole RAG Pipeline on One Database

This is the beginner capstone. For four days you learned the pieces; today you wire them into a single working RAG (Retrieval-Augmented Generation) pipeline that runs entirely inside Postgres with the pgvector extension. No separate vector database, no extra service — just the Postgres you already operate.

What RAG Actually Is

RAG answers a question by retrieving relevant text from your own data and handing it to an LLM as context, so the model answers from your documents instead of guessing from its training data. Two phases:

• Ingest (offline, once per document): load → chunk → embed → store. You do this when documents arrive or change.
• Retrieve + generate (online, per question): embed the question → similarity-search the chunks → assemble a context → call the LLM. You do this on every user query.

Why Postgres Is Enough for This

A first RAG system rarely has more than a few hundred thousand chunks. At that scale pgvector gives you everything you need and three things a standalone vector DB can't:

• One transaction spans your documents, chunks, embeddings, and application tables. Ingest a document and its chunks atomically — no half-written state.
• One filter language. Metadata filters are just SQL WHERE clauses, joined against any table you already have (users, permissions, tenants).
• One system to operate. Backups, replicas, monitoring, and access control you already run cover your RAG data too.

The Five Days, Assembled

Each earlier day maps to one stage of today's pipeline:

Day	Concept	Stage in today's pipeline
1	Postgres as an AI datastore	The documents / chunks schema
2	pgvector setup	The vector column and <=> operator
3	Similarity search	The KNN ORDER BY embedding <=> $1 query
4	Vector indexing	The HNSW index that keeps retrieval fast
5	(today)	Tying them together into ingest + retrieve + generate

By the end of this lesson you'll be able to read — and write — every line of a small but complete RAG service.