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--- |
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license: mit |
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title: Iver |
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sdk: gradio |
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emoji: 📚 |
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colorFrom: blue |
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colorTo: red |
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short_description: Verifiseringsspesialisten Iver |
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sdk_version: 5.23.3 |
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--- |
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# Iver |
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A Retrieval-Augmented Generation (RAG) based chatbot with excellent Norwegian language support, built using Hugging Face's Inference API. |
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## Features |
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- **Norwegian Language Support**: Leverages state-of-the-art Norwegian language models like NorMistral, Viking, and NorskGPT |
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- **Document Processing**: Upload and process documents in various formats (PDF, TXT, HTML) |
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- **RAG Implementation**: Retrieves relevant context from documents to generate accurate responses |
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- **Embeddable Interface**: Easily embed the chatbot in any website using iframe or JavaScript widget |
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- **Lightweight Architecture**: Uses Hugging Face's Inference API instead of running models locally |
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## Architecture |
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This chatbot uses a lightweight architecture that leverages Hugging Face's hosted models: |
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1. **Document Processing**: Documents are processed locally, extracting text and splitting into chunks |
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2. **Embedding Generation**: Document chunks are embedded using Hugging Face's Inference API |
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3. **Retrieval**: When a query is received, the most relevant document chunks are retrieved |
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4. **Response Generation**: The LLM generates a response based on the retrieved context |
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## Getting Started |
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### Prerequisites |
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- Python 3.10+ |
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- A Hugging Face account (for API access) |
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### Installation |
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1. Clone the repository: |
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```bash |
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git clone https://huggingface.co/spaces/username/norwegian-rag-chatbot |
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cd norwegian-rag-chatbot |
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``` |
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2. Install dependencies: |
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```bash |
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pip install -r requirements-ultra-light.txt |
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``` |
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3. Set up your Hugging Face API key: |
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```bash |
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export HF_API_KEY="your_api_key_here" |
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``` |
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### Running the Chatbot |
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```bash |
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python app.py |
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``` |
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The chatbot will be available at http://localhost:7860 |
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## Usage |
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### Chat Interface |
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The main chat interface allows you to: |
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- Ask questions in Norwegian |
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- Receive responses based on your uploaded documents |
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- Adjust temperature and other settings |
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### Document Upload |
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You can upload documents to provide context for the chatbot: |
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- Supported formats: PDF, TXT, HTML |
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- Documents are automatically processed and indexed |
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- The chatbot will use these documents to provide more accurate responses |
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### Embedding |
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You can embed the chatbot in your website using: |
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- iFrame embedding |
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- JavaScript widget |
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- Direct link |
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## Deployment |
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The chatbot is designed to be deployed to Hugging Face Spaces: |
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1. Create a new Space on Hugging Face |
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2. Upload the code to the Space |
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3. Set the HF_API_KEY secret in the Space settings |
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4. The Space will automatically build and deploy the chatbot |
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## Models |
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The chatbot can use various Norwegian language models: |
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- **NorMistral-7b-scratch**: A large Norwegian language model pretrained from scratch |
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- **Viking 7B**: A multilingual model for Nordic languages |
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- **NorskGPT**: A Norwegian language model based on Mistral or LLAMA2 |
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For embeddings, it uses: |
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- **NbAiLab/nb-sbert-base**: A Norwegian sentence embedding model |
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## License |
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This project is licensed under the MIT License - see the LICENSE file for details. |
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## Acknowledgements |
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- [Hugging Face](https://huggingface.co/) for hosting the models and providing the Inference API |
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- [Gradio](https://gradio.app/) for the web interface framework |
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- The creators of the Norwegian language models used in this project |
