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How to Build Your Own NotebookLM: RAG with PostgreSQL + OpenAI

How to Build Your Own NotebookLM: RAG with PostgreSQL + OpenAI

Introduction

NotebookLM is a strong example of where modern AI systems are heading: away from generic answers and toward grounded answers based on your own sources. Under the hood, this usually is not a magical custom model. It is a well-designed RAG system (Retrieval-Augmented Generation). :contentReference[oaicite:15]{index=15}

The good news: you can build a similar system yourself with a practical stack:

  • PostgreSQL as the main database
  • pgvector for semantic search
  • OpenAI embeddings for vector generation
  • OpenAI Responses API for answer generation

This is often a better fit than a generic chatbot for internal knowledge bases, contracts, support documentation, CMS content, and industry-specific AI tools. OpenAI currently recommends text-embedding-3-small and text-embedding-3-large as its latest embedding models, and current text models are exposed through the Responses API. :contentReference[oaicite:16]{index=16}

What you are really building

The goal is not an all-knowing model. It is a pipeline with four clear layers:

Documents
→ parsing & chunking
→ embeddings
→ PostgreSQL + pgvector
→ retrieval
→ LLM answer with source context

The key difference from a standard chatbot is that answers come primarily from your documents, retrieved semantically and then passed into the model as context. That is exactly what embeddings are for: they convert text into vectors so semantically similar content ends up close together. :contentReference[oaicite:17]{index=17}

Architecture: the practical version

1) Document ingestion

Start by importing PDFs, markdown files, HTML, contracts, wiki pages, or CMS content. Extract clean text plus metadata such as:

  • file name / source
  • document type
  • language
  • section / heading
  • timestamp / version

A good RAG system stores not just text, but also origin and structure. Otherwise source references will be weak later on.

2) Chunking

Next, split documents into chunks. They should be large enough to preserve meaning, but small enough to keep retrieval precise.

Good practice:

  • avoid giant full-text blocks
  • keep chunks topically coherent
  • allow slight overlap between chunks

Poor chunking is one of the biggest reasons RAG systems feel inaccurate.

3) Generate embeddings

Create one embedding per chunk. OpenAI currently offers text-embedding-3-small as an efficient default and text-embedding-3-large for higher quality retrieval. The large model supports up to 3072 dimensions, and OpenAI documents a dimensions parameter that can shorten embeddings to trade off quality for lower storage and compute cost. :contentReference[oaicite:18]{index=18}

For many business systems, text-embedding-3-small is an excellent starting point. If retrieval quality matters more than cost, text-embedding-3-large is worth testing. :contentReference[oaicite:19]{index=19}

4) Store everything in PostgreSQL

pgvector extends Postgres with vector similarity search. According to the project, it supports exact and approximate nearest neighbor search, multiple distance functions, and HNSW / IVFFlat indexing. This makes PostgreSQL attractive when you want relational data, metadata, and vectors in one place. :contentReference[oaicite:20]{index=20}

A typical schema looks like this:

documents
- id
- title
- source
- language
- created_at
- updated_at

document_chunks
- id
- document_id
- chunk_index
- content
- token_count
- embedding vector(...)
- metadata jsonb

The major benefit is that you can combine standard SQL filters with semantic search, such as restricting results to English support docs or a specific customer workspace. pgvector explicitly positions this as “store your vectors with the rest of your data.” :contentReference[oaicite:21]{index=21}

Retrieval: the most important layer

Many developers focus too much on the model. In production, the real bottleneck is often retrieval quality.

The flow usually works like this:

  1. User asks a question
  2. The question is embedded
  3. The nearest chunks are found via vector search
  4. Top matches are ranked and filtered
  5. Those chunks are passed into the model as context

OpenAI’s embeddings-based question answering examples follow this same pattern: embed the query, rank text by distance, and answer using the best-matching content. :contentReference[oaicite:22]{index=22}

Useful improvements for real systems:

  • metadata filtering
  • hybrid search (keyword + vector)
  • deduplication of similar chunks
  • re-ranking the top hits
  • strict context budget control

Answer generation with OpenAI

After retrieval, send the best chunks together with the user question into an OpenAI text model. OpenAI’s current model docs state that the latest models are available through the Responses API, and the model guide suggests gpt-5.4 as a strong starting point for complex reasoning and coding use cases. :contentReference[oaicite:23]{index=23}

Your prompt should clearly separate:

  • system rules
  • user question
  • retrieved context

For example:

Answer only using the provided context.
If the answer is not contained in the context, say so clearly.
Cite the source document and section.

This does not remove hallucinations entirely, but it reduces them significantly.

Why PostgreSQL instead of an external vector database?

For many teams, PostgreSQL is the more pragmatic choice:

  • one system instead of extra infrastructure
  • SQL + metadata + relations + vectors together
  • simpler backups, replication, and operations
  • great fit for CMS, SaaS, and internal business apps

If you already use Postgres, pgvector is often the fastest route to a production-capable RAG prototype. The project specifically highlights JOINs, ACID compliance, and point-in-time recovery as benefits. :contentReference[oaicite:24]{index=24}

A solid MVP stack

A practical MVP could look like this:

  • Backend: Next.js, Node.js, or Go
  • Database: PostgreSQL + pgvector
  • Embeddings: text-embedding-3-small
  • Answer model: current text model via Responses API
  • Storage: S3 / object storage
  • Queue: for parsing and re-embedding
  • UI: chat + citations panel + source viewer

The citations panel matters a lot. A NotebookLM-like experience only feels trustworthy when users can see where an answer came from.

What usually goes wrong

  • chunks are too large or too small
  • metadata is missing or inconsistent
  • vector search has no re-ranking
  • the frontend shows no sources
  • the prompt is too loose
  • document updates do not trigger re-indexing

When RAG feels weak, the model often is not the main issue. The pipeline is.

Advanced features

Once the basics work, you can move closer to a NotebookLM-style product:

  • document-linked notes
  • auto summaries
  • topic clustering
  • suggested questions
  • cross-document reasoning
  • audio briefings
  • workspace-level permissions

At that point, it stops being a simple chatbot and becomes a real knowledge system.

Conclusion

Building your own NotebookLM-style product is much more realistic today than many teams assume. With OpenAI embeddings, the Responses API, and PostgreSQL plus pgvector, you can build a solid RAG system that works on your own data and produces grounded, source-aware answers. :contentReference[oaicite:25]{index=25}

The real challenge is not just the model. It is the quality of ingestion, chunking, retrieval, and source attribution.

If those four parts are well designed, you get surprisingly close to the user experience people associate with NotebookLM:

  • your own sources
  • semantic retrieval
  • context-based answers
  • higher trust through citations