How to Build RAG With pgvector and Postgres

pgvector lets you build RAG inside the database you already run

Building RAG with pgvector and Postgres means your embeddings, your metadata, and your similarity search all live in one database you already operate, back up, and secure. The pgvector extension adds a vector column type and approximate-nearest-neighbour indexes to PostgreSQL, so a retrieval-augmented pipeline becomes ordinary SQL plus an embedding step. For teams already on Postgres, this consolidation is a real advantage — one fewer system to run, monitor, and reason about when something breaks at three in the morning.

This guide covers the parts that actually decide whether a pgvector RAG system is fast and accurate: schema design, the right index, hybrid search, and metadata filtering that does not fall apart under load. None of it is exotic, but each has a sharp edge that quietly degrades quality if you get it wrong, so it pays to be deliberate.

Step 1: Schema and the embedding column

Design the table so each chunk carries its vector, its source text, and the metadata you will filter and cite on. A good schema makes everything downstream easier; a careless one forces awkward workarounds later.

• Add a vector column sized to your embedding model's dimension, since the column type fixes the length.
• Keep the original chunk text in a column so answers can quote and verify it directly.
• Store source path, page, document id, and any access tags as ordinary columns you can index and filter.

Step 2: Choose and build the index

pgvector supports HNSW and IVFFlat indexes, and the choice affects both speed and memory. HNSW gives excellent recall and speed at the cost of memory and longer build time; IVFFlat is lighter and faster to build but needs its lists tuned and the data present before you build it, because it learns the partitions from existing rows.

Build the index after loading a representative amount of data, and match the index's operator class to the distance metric your embedding model expects, or recall quietly suffers.

Step 3: Get the distance metric right

pgvector offers cosine distance, inner product, and Euclidean (L2). The metric must match how your embedding model was trained — cosine is the common default for text embeddings, but some models expect inner product. Use the matching operator in your query and the matching operator class on your index, or recall degrades with nothing erroring to warn you that anything is wrong.

• Confirm the recommended metric in your embedding model's documentation before you commit to an index.
• Use the same metric for the index and the query so they agree on what "close" means.
• Validate with a few known-good pairs before trusting the index in anything real.

Step 4: Add hybrid search and filtering

Pure vector search is great at meaning and surprisingly weak at exact tokens — error codes, names, identifiers. Postgres already has full-text search built in, so you can run keyword search and vector search together and fuse the rankings, all in the same query against the same data. This is a quiet superpower of the pgvector approach: hybrid retrieval without standing up a second system to maintain.

• Combine pgvector similarity with Postgres full-text search for the best of meaning and exact match.
• Apply metadata filters — team, date, document type — in the same SQL so you never scan the whole table needlessly.
• Add a reranking pass over the top candidates for the single biggest quality jump in a struggling system.

Step 5: Operate it like a database

Because it is just Postgres, your existing operational muscle applies directly. Back it up, monitor it, and scale it the way you already do, which is a real saving in both effort and risk. The one RAG-specific discipline to add is re-embedding: when you change embedding models, every stored vector must be regenerated, because vectors from different models live in incompatible spaces. Version your embeddings so you know which model produced what, keep the source text so you can rebuild from scratch, and treat a model upgrade as a planned re-index rather than a surprise outage.

Prefer it built and managed for you?

BSH Technologies builds production RAG on pgvector and Postgres — schema and indexes tuned to your recall and latency targets, hybrid search, correct metrics, and a re-embedding plan that keeps model upgrades from becoming outages. If you are already on Postgres, talk to BSH Technologies or explore our AI & automation services.