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CSRC-Car-Manual-RAG
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---

title: CSRC Car Manual RAG System
emoji: 🚗
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 6.0.0
app_file: app.py
pinned: false
license: mit
---


# CSRC Car Manual RAG System

An intelligent RAG (Retrieval-Augmented Generation) system for querying car manual documents using OpenAI and vector stores.

## 🚀 Features

- **RAG-based Q&A**: Ask questions about car manual content
- **Vector Store**: Fast and accurate document retrieval
- **Knowledge Graph**: Visualize document relationships
- **Personalized Learning**: Adaptive learning paths (optional)
- **Scenario Contextualization**: Context-aware responses (optional)

## 📋 Setup Instructions

### 1. Clone or Upload to Hugging Face Spaces

- **Option A**: Create a new Space on Hugging Face and upload files
- **Option B**: Connect your GitHub repository to Spaces

### 2. Set Environment Variables (Secrets)

Go to **Settings > Secrets** in your Space and add:

```

OPENAI_API_KEY=your-openai-api-key-here

```

⚠️ **Important**: Never commit API keys to the repository. Always use Spaces Secrets.

### 3. Upload PDF Files

Ensure your PDF files are in the `car_manual/` directory:

```

car_manual/

├── Function of Active Distance Assist DISTRONIC.pdf

├── Function of Active Lane Change Assist.pdf

├── Function of Active Steering Assist.pdf

└── Function of Active Stop-and-Go Assist.pdf

```

### 4. Wait for Build

Spaces will automatically:
- Install dependencies from `requirements.txt`
- Run `app.py`
- Start the Gradio interface

## 📁 Project Structure

```

.

├── app.py                 # Hugging Face Spaces entry point

├── main.py               # Local development entry point

├── requirements.txt      # Python dependencies

├── src/                  # Core modules

├── modules/              # Feature modules

├── car_manual/           # PDF files directory

├── config/               # Configuration files

└── output/               # Output directory (auto-created)

```

## 🔧 Configuration

### Required

- **OPENAI_API_KEY**: Your OpenAI API key (set in Spaces Secrets)

### Optional

- **PDF Files**: Place in `car_manual/` directory
- **Vector Store**: Automatically created on first run

## 📖 Usage

1. Wait for the Space to build (check the logs)
2. Open the Gradio interface
3. Enter your question in the input field
4. Get answers with source citations

## 🐛 Troubleshooting

### Error: OPENAI_API_KEY not found

- Go to Settings > Secrets
- Add `OPENAI_API_KEY` with your actual API key
- Restart the Space

### Error: No PDF files found

- Ensure PDF files are in the `car_manual/` directory
- Check file permissions
- Verify file names (case-sensitive)

### Build Fails

- Check the logs for error messages
- Verify `requirements.txt` is correct
- Ensure all Python dependencies are compatible

## 📝 Notes

- Vector store is created automatically on first run
- Vector store ID is saved in `config/vector_store_config.json`
- First initialization may take time (uploading PDFs to OpenAI)

## 🔗 Links

- [OpenAI API Keys](https://platform.openai.com/api-keys)
- [Hugging Face Spaces Documentation](https://huggingface.co/docs/hub/spaces)

## 📄 License

MIT License