cloudflare-retrieval-augmented-generation-example

This repo shows how to build a Retrieval Augmented Generation (RAG) application using Cloudflare Workers AI. It uses Cloudflare Workflows, D1, and Vectorize to store notes that can be used to generate context for the RAG model.

Installation/Setup

You must have a Cloudflare account and the wrangler CLI installed (or use npx wrangler).

Clone the repo and install dependencies:

$ git clone https://github.com/cloudflare/cloudflare-retrieval-augmented-generation-example.git
$ cd cloudflare-retrieval-augmented-generation-example
$ npm install

Generate a new database and vector index:

$ wrangler d1 create DATABASE
$ wrangler vectorize:index create VECTOR_INDEX --preset "@cf/baai/bge-base-en-v1.5"

Apply the migration to create the notes table in D1:

$ wrangler d1 migrations apply DATABASE

# Apply in production
$ wrangler d1 migrations apply DATABASE --remote

Add the configuration to wrangler.toml, replacing my values with your own:

[[d1_databases]]
binding = "DATABASE"
database_name = "<your database name>"
database_id = "<your database id>"

[[vectorize]]
binding = "VECTOR_INDEX"
index_name = "<your vector index name>"

Deploy the application:

$ npm run deploy

Usage

After deploying, you can use the following routes:

• / is an endpoint that accepts a ?text query param and returns a response from the model.
• /ui is a form UI that allows you to ask the AI a question and get a response.
• /write is a form UI that allows you to add a note to the AI's knowledge base.
• /notes is a list of all the notes in the AI's knowledge base.
• /notes.json is a JSON endpoint that returns all the notes in the AI's knowledge base.

Tutorial

This project was created as part of a tutorial on Building a Retrieval Augmented Generation (RAG) Application with Cloudflare Workers AI.