README.md

metadata

license: mit
title: Iver
sdk: gradio
emoji: 📚
colorFrom: blue
colorTo: red
short_description: Verifiseringsspesialisten Iver
sdk_version: 5.23.3

Iver

A Retrieval-Augmented Generation (RAG) based chatbot with excellent Norwegian language support, built using Hugging Face's Inference API.

Features

• Norwegian Language Support: Leverages state-of-the-art Norwegian language models like NorMistral, Viking, and NorskGPT
• Document Processing: Upload and process documents in various formats (PDF, TXT, HTML)
• RAG Implementation: Retrieves relevant context from documents to generate accurate responses
• Embeddable Interface: Easily embed the chatbot in any website using iframe or JavaScript widget
• Lightweight Architecture: Uses Hugging Face's Inference API instead of running models locally

Architecture

This chatbot uses a lightweight architecture that leverages Hugging Face's hosted models:

1. Document Processing: Documents are processed locally, extracting text and splitting into chunks
2. Embedding Generation: Document chunks are embedded using Hugging Face's Inference API
3. Retrieval: When a query is received, the most relevant document chunks are retrieved
4. Response Generation: The LLM generates a response based on the retrieved context

Getting Started

Prerequisites

• Python 3.10+
• A Hugging Face account (for API access)

Installation

1. Clone the repository:

git clone https://huggingface.co/spaces/username/norwegian-rag-chatbot
cd norwegian-rag-chatbot

2. Install dependencies:

pip install -r requirements-ultra-light.txt

3. Set up your Hugging Face API key:

export HF_API_KEY="your_api_key_here"

Running the Chatbot

python app.py

The chatbot will be available at http://localhost:7860

Usage

Chat Interface

The main chat interface allows you to:

• Ask questions in Norwegian
• Receive responses based on your uploaded documents
• Adjust temperature and other settings

Document Upload

You can upload documents to provide context for the chatbot:

• Supported formats: PDF, TXT, HTML
• Documents are automatically processed and indexed
• The chatbot will use these documents to provide more accurate responses

Embedding

You can embed the chatbot in your website using:

• iFrame embedding
• JavaScript widget
• Direct link

Deployment

The chatbot is designed to be deployed to Hugging Face Spaces:

1. Create a new Space on Hugging Face
2. Upload the code to the Space
3. Set the HF_API_KEY secret in the Space settings
4. The Space will automatically build and deploy the chatbot

Models

The chatbot can use various Norwegian language models:

• NorMistral-7b-scratch: A large Norwegian language model pretrained from scratch
• Viking 7B: A multilingual model for Nordic languages
• NorskGPT: A Norwegian language model based on Mistral or LLAMA2

For embeddings, it uses:

• NbAiLab/nb-sbert-base: A Norwegian sentence embedding model

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Hugging Face for hosting the models and providing the Inference API
• Gradio for the web interface framework
• The creators of the Norwegian language models used in this project