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ChatWithWebsite is a web app that enables users to obtain information from websites through AI-powered chat. Leveraging Retrieval-Augmented Generation (RAG), it provides interactive and intelligent responses based on website content

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ELEF-TQ/ChatWithWebsite

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RAG Application : ChatWithWebsite

Overview

ChatWithWebsite is a dynamic web application that enables users to interact with websites via a chat interface. By appending a website link to localhost:3000, users can query and receive AI-generated responses based on the website's content. This application integrates advanced AI technologies to provide relevant and contextually accurate information.

Features

  • Interactive Chat:: Users can ask questions about the content of any website.
  • AI-Driven Responses: Provides accurate and contextually relevant information based on the website's content.
  • User-Friendly Interface:: Simple and intuitive chat experience.

Technologies Used

  • Next.js: Framework for server-rendered React applications.
  • AI SDK(meta-llama/Meta-Llama-3-8B-Instruct):: For generating AI-driven responses.
  • Upstash: For vector storage and retrieval.

Installation

To run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/your-username/chatwithwebsite.git
    cd chatwithwebsite
  2. Install dependencies:

    yarn install
  3. Start the development server:

    yarn dev

Configuration

Ensure you have the following environment variables set up:

  • UPSTASH_VECTOR_REST_URL: Your Upstash Vector REST API URL.
  • UPSTASH_VECTOR_REST_TOKEN: Your Upstash Vector REST API Token.
  • QSTASH_TOKEN: Your QStash API Token.
  • UPSTASH_REDIS_REST_TOKEN: Your Upstash Redis REST API Token.
  • UPSTASH_REDIS_REST_URL: Your Upstash Redis REST API URL.

You can set these environment variables in a .env file at the root of your project.

Demo video

ChatWithWebsite.mp4

About

ChatWithWebsite is a web app that enables users to obtain information from websites through AI-powered chat. Leveraging Retrieval-Augmented Generation (RAG), it provides interactive and intelligent responses based on website content

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