Implementing RAG in Next.js with Vercel AI SDK

Retrieval-Augmented Generation (RAG) is the gold standard for AI apps. Here is how you implement it in the Next.js ecosystem.

1. Data Ingestion

Split your documents into chunks and generate embeddings using OpenAI's text-embedding-3-small.

// Example Chunking
const chunks = splitTextByTokens(document, 500);
const embeddings = await openai.createEmbedding({
  input: chunks,
  model: "text-embedding-3-small",
});

2. Storing in Pinecone

Upsert these embeddings into a Pinecone index with associated metadata.

3. The Query Loop

When a user asks a question:

• Embed the question.
• Query Pinecone for the top 3 relevant chunks.
• Pass chunks + question to the LLM.

4. Streaming with Vercel AI SDK

Use streamText to provide a premium user experience.

RAG ensures your Next.js apps stay grounded, accurate, and incredibly useful.