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Atlas Whoff
Atlas Whoff

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Building RAG with Next.js: Pinecone, Embeddings, and Retrieval Augmented Generation

#ai #nextjs #typescript #javascript

Why Your AI Feature Needs a Vector Database

LLMs have a context window. Your knowledge base doesn't fit in it.
Vector databases solve this: embed your documents, store the embeddings, retrieve only what's relevant to the user's query, and feed that into the model.

This is Retrieval Augmented Generation (RAG).

The RAG Pipeline 

1. Ingest: document -> chunk -> embed -> store in vector DB
2. Query: user question -> embed -> similarity search -> top-k chunks
3. Generate: [system prompt] + [top-k chunks] + [user question] -> LLM -> answer
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Setup with Pinecone 

npm install @pinecone-database/pinecone openai
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// lib/pinecone.ts
import { Pinecone } from '@pinecone-database/pinecone'

export const pinecone = new Pinecone({
  apiKey: process.env.PINECONE_API_KEY!
})

export const index = pinecone.index('knowledge-base')
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Embedding Documents 

import OpenAI from 'openai'
import { index } from '@/lib/pinecone'

const openai = new OpenAI()

async function embedText(text: string): Promise<number[]> {
  const response = await openai.embeddings.create({
    model: 'text-embedding-3-small', // 1536 dimensions, cheap
    input: text
  })
  return response.data[0].embedding
}

async function ingestDocument(doc: {
  id: string
  text: string
  metadata: Record<string, any>
}) {
  // Chunk large documents
  const chunks = chunkText(doc.text, 1000, 200) // 1000 chars, 200 overlap

  const vectors = await Promise.all(
    chunks.map(async (chunk, i) => ({
      id: `${doc.id}-chunk-${i}`,
      values: await embedText(chunk),
      metadata: {
        ...doc.metadata,
        text: chunk,
        chunkIndex: i
      }
    }))
  )

  // Upsert in batches of 100
  for (let i = 0; i < vectors.length; i += 100) {
    await index.upsert(vectors.slice(i, i + 100))
  }
}

function chunkText(text: string, size: number, overlap: number): string[] {
  const chunks: string[] = []
  let start = 0
  while (start < text.length) {
    chunks.push(text.slice(start, start + size))
    start += size - overlap
  }
  return chunks
}
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Retrieval + Generation 

import Anthropic from '@anthropic-ai/sdk'
import { index } from '@/lib/pinecone'

const anthropic = new Anthropic()

export async function ragQuery(question: string, userId?: string) {
  // 1. Embed the question
  const queryEmbedding = await embedText(question)

  // 2. Find similar chunks
  const searchResults = await index.query({
    vector: queryEmbedding,
    topK: 5,
    includeMetadata: true,
    filter: userId ? { userId } : undefined // Namespace per user
  })

  // 3. Build context from results
  const context = searchResults.matches
    .map(m => m.metadata?.text as string)
    .filter(Boolean)
    .join('\n\n---\n\n')

  // 4. Generate answer with context
  const response = await anthropic.messages.create({
    model: 'claude-sonnet-4-6',
    system: [
      'Answer questions based only on the provided context.',
      'If the answer is not in the context, say "I don\'t have information about that."',
      'Be concise and cite which part of the context you used.'
    ].join(' '),
    messages: [{
      role: 'user',
      content: `Context:\n${context}\n\nQuestion: ${question}`
    }],
    max_tokens: 1024
  })

  return {
    answer: response.content[0].type === 'text' ? response.content[0].text : '',
    sources: searchResults.matches.map(m => m.metadata)
  }
}
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API Route 

// app/api/chat/route.ts
import { NextRequest, NextResponse } from 'next/server'
import { getServerSession } from 'next-auth'
import { authOptions } from '@/lib/auth'
import { ragQuery } from '@/lib/rag'

export async function POST(req: NextRequest) {
  const session = await getServerSession(authOptions)
  if (!session) return NextResponse.json({ error: 'Unauthorized' }, { status: 401 })

  const { question } = await req.json()
  const result = await ragQuery(question, session.user.id)

  return NextResponse.json(result)
}
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Free Alternative: pgvector

If you're already on PostgreSQL, avoid Pinecone costs with pgvector:

CREATE EXTENSION vector;

CREATE TABLE embeddings (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  content TEXT,
  embedding VECTOR(1536),
  metadata JSONB,
  created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE INDEX ON embeddings USING ivfflat (embedding vector_cosine_ops);
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// Query with pgvector via Prisma raw SQL
const results = await db.$queryRaw`
  SELECT content, metadata, 1 - (embedding <=> ${queryVector}::vector) AS similarity
  FROM embeddings
  ORDER BY embedding <=> ${queryVector}::vector
  LIMIT 5
`
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Build It Faster

The AI SaaS Starter Kit includes RAG infrastructure: Pinecone integration, document ingestion, chunking utilities, and a working chat interface.

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