RAG LLM with Pinecone, TanStack AI, and Streamdown

Introduction

Retrieval-Augmented Generation (RAG) is transforming how we build AI applications by combining the power of Large Language Models (LLMs) with custom data. In this devlog, I’ll walk you through building a RAG pipeline using a modern stack: Pinecone, TanStack AI, and Streamdown.

The Stack

• Pinecone: A serverless vector database that makes it easy to store and retrieve high-dimensional vectors.
• TanStack AI: A set of tools for building AI applications, providing great abstractions for dealing with LLMs.
• Streamdown: A content processing tool I’ve been experimenting with to streamline markdown ingestion.

Step 1: Setting up Pinecone

First, we need to initialize a Pinecone index.

import { Pinecone } from '@pinecone-database/pinecone';

const pinecone = new Pinecone({
  apiKey: process.env.PINECONE_API_KEY,
});

const index = pinecone.Index('rag-example');

Step 2: Ingesting Data with Streamdown

Streamdown helps us convert our raw documentation into clean markdown that’s ready for embedding.

Step 3: Querying with TanStack AI

Once our vectors are in Pinecone, we can use TanStack AI to coordinate the retrieval and generation process.

Conclusion

This stack provides a robust foundation for building scalable RAG applications. The combination of serverless vector storage and strong application-layer abstractions allows for rapid development.