Building Production-Grade RAG Systems with LangChain and PostgreSQL

Introduction

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building LLM applications that need access to private or up-to-date data. In this post, we'll walk through everything we've learned building production RAG systems for enterprise clients.

What is RAG?

RAG combines a retrieval system (like vector search) with a generative model. When a user asks a question, the system first retrieves relevant documents, then passes them as context to the LLM to generate a grounded answer.

Architecture Overview

Our production RAG stack uses:

LangChain for orchestration

pgvector (PostgreSQL extension) for vector storage

OpenAI text-embedding-3-small for embeddings

FastAPI for the backend

Redis for caching frequent queries

Chunking Strategy

The most underrated part of RAG is chunking. We've tested:

1Fixed-size chunks — Simple but loses context at boundaries

2Sentence-aware chunks — Better coherence, our default

3Semantic chunking — Best quality, higher compute cost

For most use cases, 512-token sentence-aware chunks with 50-token overlap works well.

Evaluation

Never ship a RAG system without evaluation. We use:

RAGAS for faithfulness and answer relevancy scoring

LangSmith for tracing and debugging

Custom golden dataset tests on every deployment

Key Lessons

1Invest in your retrieval quality first — garbage in, garbage out

2Hybrid search (BM25 + vector) consistently outperforms pure vector search

3Re-ranking with a cross-encoder adds ~15% accuracy at reasonable cost

4Cache your embeddings — they don't change unless your documents do

Conclusion

Building production RAG is 20% LLM and 80% data engineering. Get your chunking, retrieval, and evaluation right, and the generation quality follows.

PythonLangChainPostgreSQLRAGAI

Winnoventures AI Team

AI Engineering · Winnoventures

Expert insights from the Winnoventures engineering team — sharing what we learn building real products for global clients.