The "Chat with Website" project is designed to facilitate interaction with a website through a chat interface. This Python-based project allows users to interact with a website and retrieve information dynamically.

• User-Friendly Interface: Chat with the website using a simple and intuitive chat interface.
• Website Interaction: The chatbot uses the latest version of LangChain to interact with and extract information from various websites.
• Large Language Model Integration: Compatibility with models like GPT-4, Mistral, Llama2, and ollama. In this code I am using GPT-4, but you can change it to any other model.
• Streamlit GUI: A clean and intuitive user interface built with Streamlit, making it accessible for users with varying levels of technical expertise.
• Python-based: Entirely coded in Python.

The app.py file implements a web-based chat interface using Streamlit, allowing users to interact with a website by entering its URL. The core functionality is powered by Langchain and OpenAI. Here’s a brief explanation of how it works:

• Document Loading: The website content is fetched and split into smaller text chunks.
• Vector Store Creation: These chunks are embedded into a vector store using OpenAI embeddings.
• Retrieval-Augmented Generation (RAG): The user's queries are processed, and relevant information is retrieved and combined with the chat history to generate accurate responses.
• Interactive Chat: The conversation is maintained and displayed, enabling a dynamic and contextual interaction with the website.

• Retrieval-Augmented Generation (RAG) RAG combines large language models (LLMs) with a retrieval mechanism to generate accurate and context-aware responses by pulling in relevant information from external data sources during the generation process.

• Large Language Model (LLM) LLMs are AI models trained on vast amounts of text data, enabling them to generate and understand human-like text, making them useful in natural language processing tasks.

• Langchain Langchain is a framework designed to simplify the development of applications that use LLMs by providing tools for chaining together different components like prompts, retrievers, and generators.

1. Clone the repository:

   git clone https://github.com/Mahek-05/Chat-with-Website.git

2. Navigate to the project directory:

   cd Chat-with-Website

3. Install dependencies:

   pip install -r requirements.txt

To use this project, you'll need an OpenAI API key. Follow these steps:

1. Go to the OpenAI API Keys page.
2. Sign up or log in to your OpenAI account.
3. Click on "Create new secret key" to generate an API key.
4. Copy the API key and store it securely.

Create your own .env file with the following variables:

OPENAI_API_KEY=[your-openai-api-key]

To run the Streamlit app:

streamlit run app.py

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

This README.md provides an overview, installation instructions, and other essential details about this project.

• @Mahek-05