https://github.com/abu14/chat-with-website-langchain
An interactive chatbot built using LangChain & deployed on Stramlit.
https://github.com/abu14/chat-with-website-langchain
generative-ai huggingface langchain mistral-ai streamlit
Last synced: over 1 year ago
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An interactive chatbot built using LangChain & deployed on Stramlit.
- Host: GitHub
- URL: https://github.com/abu14/chat-with-website-langchain
- Owner: abu14
- License: mit
- Created: 2025-03-02T03:53:51.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-03T21:47:33.000Z (over 1 year ago)
- Last Synced: 2025-03-03T22:20:36.104Z (over 1 year ago)
- Topics: generative-ai, huggingface, langchain, mistral-ai, streamlit
- Language: Python
- Homepage:
- Size: 2.32 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# WebChat+ - Chat with Websites from URL
[](https://www.linkedin.com/in/abenezer-tesfaye-191579214/)
WebChat+ is an AI-powered web assistant that I built that allows anyone to interact with and extract meaningful insights from website and it's content. Built using Streamlit, LangChain, and Huggingface, WebChat+ leverages state-of-the-art language model from Mistral (Mistral-7B-Instruct-v0.2) and embeddings to facilitate intelligent conversations based on retrieved website data.
## 🚀 Features
- **Website Content Processing**: Extracts and tokenizes website content for efficient retrieval.
- **Conversational AI**: Uses a retrieval-augmented generation (RAG) model to answer user queries based on website data.
- **Hugging Face Integration**: Utilizes models from Hugging Face Hub for intelligent response generation.
- **Vector Store for Retrieval**: Employs ChromaDB for semantic search and fast document retrieval.
- **Streamlit UI**: Provides a user-friendly and interactive chat experience.
- **Real-Time API Status Check**: Displays system and API connection status.
- **Customizable Settings**: Supports dynamic website input and personalized interaction.
## 🛠️ Installation & Setup
#### Prerequisites
```
Python 3.8+
pip
A Hugging Face API token
```
#### Clone the Repository
```
git clone https://github.com/abu14/chat-with-website-langchain
cd WebChat+
```
#### Requirements
```
pip install -r requirements.txt
```
#### Set Up Environment Variables
Create a ```.env``` file in the root directory and add your Hugging Face API token:
```
HUGGINGFACEHUB_API_TOKEN=your_api_token_here
```
## Tech Stack
```
Frontend: Streamlit
Language Models: Hugging Face Hub (e.g., Mistral-7B-Instruct-v0.2)
Embeddings: Hugging Face Embeddings (e.g., sentence-transformers/all-MiniLM-L6-v2)
Vector Store: Chroma
Text Processing: LangChain, BeautifulSoup, RecursiveCharacterTextSplitter
Environment Management: dotenv
```
## 🔧 Usage
#### Run the Streamlit app
Once the application starts, follow these steps:
```
1. Enter a website URL in the sidebar.
2. Wait for content processing to complete.
3. Ask questions related to the website content.
```
## 📌 How It Works
- **Content Loading**: Extracts text from the given website URL.
- **Text Splitting**: Breaks the content into manageable chunks for better retrieval.
- **Embedding Generation**: Converts text into vector embeddings using Hugging Face models.
- **Vector Storage & Retrieval**: Stores the embeddings in ChromaDB for quick semantic search.
- **Conversational RAG Model**: Uses LangChain to retrieve relevant content and generate responses.
- **Response Processing**: Filters and cleans responses before displaying them in chat
## 📷 Screenshot
## 📞 Contact
Developed by Abenezer Tesfaye
- GitHub: [abu14](https://github.com/abu14/chat-with-website-langchain)
- Email: tesfayeabenezer64@gmail.com