AI Agent for Tailwind CSS Documentation

This project implements an AI agent that utilizes Retrieval-Augmented Generation (RAG) to access the full documentation of Tailwind CSS version 3 and version 4. The agent can efficiently retrieve and provide information from the documentation based on user queries. It also uses web scraping to get the latest information from the documentation.

Overview

The AI agent is designed to read and process the Tailwind CSS documentation, allowing users to ask questions and receive accurate responses based on the official documentation. The project leverages embeddings to enhance the retrieval process, ensuring that the agent can quickly access relevant information.

The project is deployed on Vercel and can be accessed at https://tailwind-css-ai-agent.vercel.app/

Features

• RAG Implementation: Combines retrieval and generation techniques to provide accurate responses.
• Embeddings: Each URL from the Tailwind CSS documentation is stored as a resource in the database, with corresponding embeddings for efficient retrieval.
• API Endpoint: A GET endpoint that reads urls_v3.md and urls_v4.md, which contains all URLs for the Tailwind CSS v3 and v4 documentation.
• Web Scraping: The webscrapping API is available at http://localhost:3000/api/tailwind.

  Getting Started

Prerequisites

• Node.js (version >= v18.17.0)
• npm or yarn
• A database (e.g., PostgreSQL) for storing resources and embeddings
• A Firecrawl API key (https://www.firecrawl.dev/)
• A OpenAI API key (https://openai.com/index/openai-api/)

Environment Variables

To run the application, you need to create a .env file in the root directory of the project with the following environment variables:

DATABASE_URL=<your_postgresql_database_url>
OPENAI_API_KEY=<your_openai_api_key>
FIRECRAWL_API_KEY=<your_firecrawl_api_key>

• DATABASE_URL: The connection string for your PostgreSQL database.
• OPENAI_API_KEY: Your API key for accessing OpenAI services for chat and embeddings.
• FIRECRAWL_API_KEY: The API key for the Firecrawl service used for web scraping.

Installation

1. Clone the repository:

   git clone https://github.com/Saraceni/TailwindCSSAiAgent.git
   cd your-repo-name
   

2. Install dependencies:

   pnpm install
   # or
   yarn install
   

3. Set up your database and configure the connection settings in your environment variables.

Usage

1. Run the Application:

   pnpm run dev
   # or
   yarn dev
   

2. Access the web scrapping API:

   • The webscrapping API is available at http://localhost:3000/api/tailwind.
   • The GET endpoint reads urls_v3.md or urls_v4.md and creates embeddings for each page.
   • You need to pass a query parameter version with the value v3 or v4. For example: http://localhost:3000/api/tailwind?version=v3.

API Endpoint

• GET /api/tailwind
  • Reads urls_v3.md or urls_v4.md, which contains all URLs for the Tailwind CSS v3 or v4 documentation.
  • Creates embeddings for each resource in the database.

Database Schema

Each resource in the database represents a URL from the Tailwind CSS documentation. The schema includes:

• Resource ID: Unique identifier for each resource.
• URL: The URL of the Tailwind CSS documentation.
• Embeddings: Vector representations of the resource for efficient retrieval.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Tailwind CSS for their excellent documentation.
• OpenAI for the inspiration behind the RAG approach.

This project was bootsrapped with the Vercel AI SDK RAG Guide Starter Project

This is the starter project for the Vercel AI SDK Retrieval-Augmented Generation (RAG) guide.

In this project, you will build a chatbot that will only respond with information that it has within its knowledge base. The chatbot will be able to both store and retrieve information. This project has many interesting use cases from customer support through to building your own second brain!

This project will use the following stack:

• Next.js 14 (App Router)
• Vercel AI SDK
• OpenAI
• Drizzle ORM
• Postgres with pgvector
• shadcn-ui and TailwindCSS for styling