Build a RAG Pipeline with Next.js and pgvector

Overview

Retrieval-augmented generation works best when the retrieval layer is treated as product infrastructure rather than an afterthought. You are not just adding vector search. You are designing a context-delivery pipeline.

In this tutorial, the core system has four stages:

1. content ingestion
2. embedding generation
3. vector retrieval
4. prompt assembly and answer generation

Prerequisites

• working knowledge of Next.js App Router
• PostgreSQL with pgvector
• API access for embeddings and chat completions

Project Shape

Step 1: Define the ingestion model

Good RAG systems begin with consistent chunk metadata. That is what makes retrieval debuggable later.

1type DocumentChunk = {
2  id: string
3  source: string
4  title: string
5  section: string
6  content: string
7  tokenCount: number
8}

Step 2: Store vectors in PostgreSQL

pgvector keeps the storage model simple for an MVP and leaves room for stronger filtering and relational joins later.

1create extension if not exists vector;
2
3create table knowledge_chunks (
4  id text primary key,
5  source text not null,
6  title text not null,
7  section text not null,
8  content text not null,
9  embedding vector(1536) not null
10);

Step 3: Query by similarity

Retrieval is where most RAG quality issues surface first. Keep the retrieval function isolated so you can evaluate it separately from generation.

1export async function searchKnowledgeBase(embedding: number[]) {
2  return db.execute(sql`
3    select id, source, title, section, content
4    from knowledge_chunks
5    order by embedding <=> ${embedding}
6    limit 5
7  `)
8}

Step 4: Build the final prompt

The answer quality depends heavily on how you format retrieved context. The model needs constraints, not just more text.

Retrieved context should be explicit, bounded, and source-aware. Dumping raw chunks into a giant prompt usually lowers answer quality instead of improving it.

Evaluation ideas

• log retrieved chunk IDs with every answer
• compare retrieval quality separately from answer quality
• test failure cases where the answer should be “not enough context”

Summary

The durable pattern is simple: predictable chunking, traceable retrieval, and disciplined prompt assembly. Once that exists, model upgrades become much less risky.