Build Powerful AI Apps with Cloudflare AutoRAG – A Step-by-Step Tutorial

AI chatbots and search assistants are everywhere—but building one that’s fast, scalable, and doesn’t break the bank can be tricky. That’s where Cloudflare AutoRAG steps in.

In this tutorial, you’ll learn what AutoRAG is, why it’s awesome, and how to use it to build your own AI-powered search or Q&A experience—without worrying about infrastructure or vector database complexity.

What is Cloudflare AutoRAG?

AutoRAG is an open-source framework by Cloudflare that makes it dead simple to build Retrieval-Augmented Generation (RAG) applications using:

• ✨ Cloudflare Workers (serverless compute)
• 🧠 Cloudflare Vectorize (for storing and searching embeddings)
• 🤖 OpenAI or local LLMs (for generating answers)
• 📄 Automatic document parsing and chunking
• 🛠️ Built-in tools for scraping, indexing, and chat handling

How AutoRAG Works (The Flow)

Here's how AutoRAG simplifies RAG:

1. Ingest Content
   • Point AutoRAG to a URL (like a blog or GitHub repo).
   • It downloads the content, splits it into chunks, and creates vector embeddings.
2. Store in Vectorize
   • Those embeddings are stored in Cloudflare Vectorize, a managed vector store.
3. Chat API (Ask Questions)
   • Send a question via API.
   • AutoRAG fetches the most relevant chunks from Vectorize and feeds them to the LLM.
   • You get a smart, context-aware answer.

What’s Included Out of the Box

AutoRAG comes with:

• index.mjs: Entry point for Cloudflare Worker (your chat API).
• lib/: All core logic for document loading, chunking, embedding, and querying.
• tools/: CLI for scraping and uploading documents.
• Ready-to-deploy wrangler.toml config.

Setting Up AutoRAG (Step-by-Step)

1. 📦 Clone the Repo

git clone https://github.com/cloudflare/autorag
cd autorag

2. 🔐 Set Up Environment Variables

Create a .dev.vars file:

OPENAI_API_KEY=your_openai_key
VECTORIZE_INDEX_NAME=autorag-index
VECTORIZE_NAMESPACE=autorag

(If you’re using local embeddings, you can skip OpenAI and use HuggingFace models.)

3. 🔧 Install Wrangler & Deploy Worker

Install Cloudflare Wrangler:

npm install -g wrangler

Publish your chat endpoint:

wrangler publish

4. 🧠 Index Content (Optional)

Want to feed your chatbot real content? Use:

npm run scrape -- https://developers.cloudflare.com/autorag/

This fetches the docs, chunks the content, and uploads it to your vector index.

Try Asking Questions

You now have a fully working API that can answer questions about the documents you've indexed!

Example:

curl -X POST https://your-worker-name.workers.dev/ \
-H "Content-Type: application/json" \
-d '{"question": "What is AutoRAG?", "chat_id": "test"}'

You’ll get a smart answer based on real docs you provided.

Use Cases for AutoRAG

• ✅ Company-specific documentation bots
• ✅ AI search for knowledge bases
• ✅ Educational content assistants
• ✅ Intranet chatbots

Extend It Further

AutoRAG is modular, so you can:

• Use HuggingFace models for local inference
• Add new chunking or embedding strategies
• Customize how results are ranked or filtered

Final Thoughts

Cloudflare AutoRAG takes the hard parts out of building RAG-based chat apps. It gives you:

• ⚡ Fast inference and search
• 🔒 Secure by design
• 🧩 Fully customizable
• 🌍 Runs on Cloudflare’s global edge network