---
license: mit
title: CRAWLGPT
sdk: docker
emoji: 💻
colorFrom: pink
colorTo: blue
pinned: true
short_description: A powerful web content crawler with LLM-powered RAG.
---
# CrawlGPT 🤖

A powerful web content crawler with LLM-powered RAG (Retrieval Augmented Generation) capabilities. CrawlGPT extracts content from URLs, processes it through intelligent summarization, and enables natural language interactions using modern LLM technology.

## 🌟 Key Features

### Core Features
- **Intelligent Web Crawling**
  - Async web content extraction using Playwright
  - Smart rate limiting and validation
  - Configurable crawling strategies
  
- **Advanced Content Processing** 
  - Automatic text chunking and summarization
  - Vector embeddings via FAISS
  - Context-aware response generation

- **Streamlit Chat Interface**
  - Clean, responsive UI
  - Real-time content processing
  - Conversation history
  - User authentication

### Technical Features
- **Vector Database**
  - FAISS-powered similarity search
  - Efficient content retrieval
  - Persistent storage

- **User Management**
  - SQLite database backend
  - Secure password hashing
  - Chat history tracking

- **Monitoring & Utils**
  - Request metrics collection
  - Progress tracking
  - Data import/export
  - Content validation

## 🎥 Demo
### [Deployed APP 🚀🤖](https://huggingface.co/spaces/jatinmehra/CRAWL-GPT-CHAT)

[streamlit-chat_app video.webm](https://github.com/user-attachments/assets/ae1ddca0-9e3e-4b00-bf21-e73bb8e6cfdf)
  
_Example of CRAWLGPT in action!_

## 🔧 Requirements

-   Python >= 3.8
-   Operating System: OS Independent
-   Required packages are handled by the setup script.


## 🚀 Quick Start

1.  Clone the Repository:
    
    ```git clone https://github.com/Jatin-Mehra119/CRAWLGPT.git
    cd CRAWLGPT
    ```
    
2.  Run the Setup Script:

    ```
    python -m setup_env
    ``` 
    
    _This script installs dependencies, creates a virtual environment, and prepares the project._
    
3.  Update Your Environment Variables:
    
    -   Create or modify the `.env` file.
    -   Add your Groq API key and Ollama API key. Learn how to get API keys.
    
    
    ```
    GROQ_API_KEY=your_groq_api_key_here
    OLLAMA_API_TOKEN=your_ollama_api_key_here
    ```
    
4.  Activate the Virtual Environment:
    
    ```
    source .venv/bin/activate  # On Unix/macOS
    .venv\Scripts\activate  # On Windows
    ``` 
    
5.  Run the Application:
	```
	python -m streamlit run src/crawlgpt/ui/chat_app.py
	```

## 📦 Dependencies

### Core Dependencies

-   `streamlit==1.41.1`
-   `groq==0.15.0`
-   `sentence-transformers==3.3.1`
-   `faiss-cpu==1.9.0.post1`
-   `crawl4ai==0.4.247`
-   `python-dotenv==1.0.1`
-   `pydantic==2.10.5`
-   `aiohttp==3.11.11`
-   `beautifulsoup4==4.12.3`
-   `numpy==2.2.0`
-   `tqdm==4.67.1`
-   `playwright>=1.41.0`
-   `asyncio>=3.4.3`

### Development Dependencies

-   `pytest==8.3.4`
-   `pytest-mockito==0.0.4`
-   `black==24.2.0`
-   `isort==5.13.0`
-   `flake8==7.0.0`

## 🏗️ Project Structure


```
crawlgpt/
├── src/
│   └── crawlgpt/
│       ├── core/                           # Core functionality
│       │   ├── database.py                 # SQL database handling
│       │   ├── LLMBasedCrawler.py         # Main crawler implementation
│       │   ├── DatabaseHandler.py          # Vector database (FAISS)
│       │   └── SummaryGenerator.py         # Text summarization
│       ├── ui/                            # User Interface
│       │   ├── chat_app.py                # Main Streamlit app
│       │   ├── chat_ui.py                 # Development UI
│       │   └── login.py                   # Authentication UI
│       └── utils/                         # Utilities
│           ├── content_validator.py        # URL/content validation
│           ├── data_manager.py            # Import/export handling
│           ├── helper_functions.py         # General helpers
│           ├── monitoring.py              # Metrics collection
│           └── progress.py                # Progress tracking
├── tests/                                # Test suite
│   └── test_core/
│       ├── test_database_handler.py       # Vector DB tests
│       ├── test_integration.py           # Integration tests
│       ├── test_llm_based_crawler.py     # Crawler tests
│       └── test_summary_generator.py     # Summarizer tests
├── .github/                             # CI/CD
│   └── workflows/
│       └── Push_to_hf.yaml              # HuggingFace sync
├── Docs/
│   └── MiniDoc.md                       # Documentation
├── .dockerignore                        # Docker exclusions
├── .gitignore                          # Git exclusions
├── Dockerfile                          # Container config
├── LICENSE                             # MIT License
├── README.md                          # Project documentation
├── README_hf.md                       # HuggingFace README
├── pyproject.toml                     # Project metadata
├── pytest.ini                         # Test configuration
└── setup_env.py                       # Environment setup
``` 

## 🧪 Testing

Run all tests
```
python -m pytest
```
_The tests include unit tests for core functionality and integration tests for end-to-end workflows._

## 📝 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🔗 Links

-   [Bug Tracker](https://github.com/Jatin-Mehra119/crawlgpt/issues)
-   [Documentation](https://github.com/Jatin-Mehra119/crawlgpt/wiki)
-   [Source Code](https://github.com/Jatin-Mehra119/crawlgpt)

## 🧡 Acknowledgments

-   Inspired by the potential of GPT models for intelligent content processing.
-   Special thanks to the creators of Crawl4ai, Groq, FAISS, and Playwright for their powerful tools.

## 👨‍💻 Author

-   Jatin Mehra (jatinmehra@outlook.in)

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, open an issue first to discuss your proposal.

1.  Fork the Project.
2.  Create your Feature Branch:
    ```
    git checkout -b feature/AmazingFeature`
    ```
3.  Commit your Changes:
    ```
    git commit -m 'Add some AmazingFeature
    ```
4.  Push to the Branch:
    ```
    git push origin feature/AmazingFeature
    ```
5.  Open a Pull Request.