ClawGPT 🦞

AI-Powered Documentation Assistant for OpenClaw - Get instant, intelligent answers about OpenClaw features, installation, configuration, and troubleshooting.

✨ Features

• 🧠 RAG-Powered Q&A - Retrieval-Augmented Generation for accurate, context-aware answers
• ⚡ Instant Responses - Pre-built embeddings for lightning-fast query processing
• 🎯 Source Attribution - See exactly which documentation pages inform each answer
• 💬 Chat Interface - Natural conversation with chat history
• 🔍 Semantic Search - Find relevant information even with imprecise queries
• 📱 Responsive Design - Works beautifully on desktop and mobile

🛠️ Tech Stack

• LLM: Google Gemini 2.0 Flash
• Embeddings: Gemini Embedding 001
• Vector Database: ChromaDB
• Frontend: Streamlit
• Framework: LangChain

🚀 Quick Start

Prerequisites

• Python 3.9+
• Gemini API Key (Get one here)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/ClawGPT-.git
   cd ClawGPT-

2. Create virtual environment

   python -m venv venv
   
   # Windows
   venv\Scripts\activate
   
   # macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment

   cp .env.example .env
   # Edit .env and add your GEMINI_API_KEY

5. Generate embeddings (one-time setup)

   python create_embeddings.py

6. Run the application

   streamlit run app.py

Visit http://localhost:8501 to start chatting!

📁 Project Structure

ClawGPT/
├── app.py                 # Main Streamlit application
├── create_embeddings.py   # Script to generate vector embeddings
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (not tracked)
├── .env.example           # Environment template
├── .gitignore             # Git ignore rules
├── assets/                # Static assets
│   └── logo.png           # Application logo
├── pdfs/                  # OpenClaw documentation (markdown files)
├── chroma_db/             # Vector store (generated)
└── README.md              # This file

🔧 Configuration

Environment Variables

Variable	Description	Required
GEMINI_API_KEY	Google Gemini API key	Yes

Customization

• Adjust context chunks: Use the sidebar slider (3-10)
• Toggle sources: Show/hide source documents
• Add logo: Place your logo at assets/logo.png

📊 How It Works

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   User Query    │───▶│  Semantic Search │───▶│ Relevant Chunks │
└─────────────────┘    │   (ChromaDB)     │    └────────┬────────┘
                       └──────────────────┘             │
                                                        ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│    Response     │◀───│   Gemini 2.0     │◀───│  Context + Query│
└─────────────────┘    │     Flash        │    └─────────────────┘
                       └──────────────────┘

1. Document Processing: Markdown files are loaded and split into chunks
2. Embedding Generation: Each chunk is converted to a vector using Gemini Embedding
3. Vector Storage: Embeddings are stored in ChromaDB for fast retrieval
4. Query Processing: User questions are embedded and matched against stored vectors
5. Response Generation: Relevant chunks + query are sent to Gemini 2.0 Flash for response

🌐 Deployment

Streamlit Cloud

1. Push to GitHub
2. Connect to Streamlit Cloud
3. Add GEMINI_API_KEY to secrets
4. Deploy!

Docker

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["streamlit", "run", "app.py", "--server.port=8501"]

📈 SEO & Discoverability

ClawGPT is optimized for search engines with:

• Semantic HTML structure
• OpenGraph meta tags
• Twitter Card support
• Descriptive page titles
• Canonical URLs

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is for educational purposes.

🔗 Links

• OpenClaw GitHub
• OpenClaw Documentation
• Discord Community

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Made with ❤️ | Powered by Google Gemini 2.0