| # RAG Architecture for Norwegian Chatbot | |
| ## Overview | |
| This document outlines the architecture for a Retrieval-Augmented Generation (RAG) based chatbot optimized for Norwegian language, designed to be hosted on Hugging Face. The architecture leverages open-source models with strong Norwegian language support and integrates with Hugging Face's infrastructure for seamless deployment. | |
| ## System Components | |
| ### 1. Language Model (LLM) | |
| Based on our research, we recommend using one of the following models: | |
| **Primary Option: NorMistral-7b-scratch** | |
| - Strong Norwegian language support | |
| - Apache 2.0 license (allows commercial use) | |
| - 7B parameters (reasonable size for deployment) | |
| - Good performance on Norwegian language tasks | |
| - Available on Hugging Face | |
| **Alternative Option: Viking 7B** | |
| - Specifically designed for Nordic languages | |
| - Apache 2.0 license | |
| - 4K context length | |
| - Good multilingual capabilities (useful if the chatbot needs to handle some English queries) | |
| **Fallback Option: NorskGPT-Mistral** | |
| - Specifically designed for Norwegian | |
| - Note: Non-commercial license (cc-by-nc-sa-4.0) | |
| ### 2. Embedding Model | |
| **Recommended: NbAiLab/nb-sbert-base** | |
| - Specifically trained for Norwegian | |
| - 768-dimensional embeddings | |
| - Good performance on sentence similarity tasks | |
| - Works well with both Norwegian and English content | |
| - Apache 2.0 license | |
| - High download count on Hugging Face (41,370 last month) | |
| ### 3. Vector Database | |
| **Recommended: FAISS** | |
| - Lightweight and efficient | |
| - Easy integration with Hugging Face | |
| - Can be packaged with the application | |
| - Works well for moderate-sized document collections | |
| **Alternative: Milvus** | |
| - More scalable for larger document collections | |
| - Well-documented integration with Hugging Face | |
| - Better for production deployments with large document bases | |
| ### 4. Document Processing Pipeline | |
| 1. **Text Extraction**: Extract text from various document formats (PDF, DOCX, TXT) | |
| 2. **Text Chunking**: Split documents into manageable chunks (recommended chunk size: 512 tokens) | |
| 3. **Text Cleaning**: Remove irrelevant content, normalize text | |
| 4. **Embedding Generation**: Generate embeddings using NbAiLab/nb-sbert-base | |
| 5. **Vector Storage**: Store embeddings in FAISS index | |
| ### 5. Retrieval Mechanism | |
| 1. **Query Processing**: Process user query | |
| 2. **Query Embedding**: Generate embedding for the query using the same embedding model | |
| 3. **Similarity Search**: Find most relevant document chunks using cosine similarity | |
| 4. **Context Assembly**: Assemble retrieved chunks into context for the LLM | |
| ### 6. Generation Component | |
| 1. **Prompt Construction**: Construct prompt with retrieved context and user query | |
| 2. **LLM Inference**: Generate response using the LLM | |
| 3. **Response Post-processing**: Format and clean the response | |
| ### 7. Chat Interface | |
| 1. **Frontend**: Lightweight, responsive web interface | |
| 2. **API Layer**: RESTful API for communication between frontend and backend | |
| 3. **Session Management**: Maintain conversation history | |
| ## Hugging Face Integration | |
| ### Deployment Options | |
| 1. **Hugging Face Spaces**: | |
| - Deploy the entire application as a Gradio or Streamlit app | |
| - Provides a public URL for access | |
| - Supports Git-based deployment | |
| 2. **Model Hosting**: | |
| - Host the fine-tuned LLM on Hugging Face Model Hub | |
| - Use Hugging Face Inference API for model inference | |
| 3. **Datasets**: | |
| - Store and version document collections on Hugging Face Datasets | |
| ### Implementation Approach | |
| 1. **Gradio Interface**: | |
| - Create a Gradio app for the chat interface | |
| - Deploy to Hugging Face Spaces | |
| 2. **Backend Processing**: | |
| - Use Hugging Face Transformers and Sentence-Transformers libraries | |
| - Implement document processing pipeline | |
| - Set up FAISS for vector storage and retrieval | |
| 3. **Model Integration**: | |
| - Load models from Hugging Face Model Hub | |
| - Implement caching for better performance | |
| ## Technical Architecture Diagram | |
| ``` | |
| ┌─────────────────────────────────────────────────────────────────┐ | |
| │ Hugging Face Spaces │ | |
| └─────────────────────────────────────────────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────────────────────────────┐ | |
| │ Web Interface │ | |
| │ │ | |
| │ ┌─────────────┐ ┌────────────┐ │ | |
| │ │ Gradio │ │ Session │ │ | |
| │ │ Interface │◄──────────────────────────────┤ Manager │ │ | |
| │ └─────────────┘ └────────────┘ │ | |
| └─────────────────────────────────────────────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────────────────────────────┐ | |
| │ Backend Processing │ | |
| │ │ | |
| │ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │ | |
| │ │ Query │ │ Retrieval │ │ Generation │ │ | |
| │ │ Processing │───►│ Engine │───►│ Engine │ │ | |
| │ └─────────────┘ └─────────────┘ └─────────────────────┘ │ | |
| │ │ ▲ │ | |
| │ ▼ │ │ | |
| │ ┌─────────────┐ │ │ | |
| │ │ FAISS │ │ │ | |
| │ │ Vector │ │ │ | |
| │ │ Store │ │ │ | |
| │ └─────────────┘ │ │ | |
| │ ▲ │ │ | |
| │ │ │ │ | |
| │ ┌─────────────────────────┴──────────────────────┴───────────┐ │ | |
| │ │ Document Processor │ │ | |
| │ └──────────────────────────────────────────────────────────────┘ | |
| └─────────────────────────────────────────────────────────────────┘ | |
| │ | |
| ▼ | |
| ┌─────────────────────────────────────────────────────────────────┐ | |
| │ Hugging Face Model Hub │ | |
| │ │ | |
| │ ┌─────────────────┐ ┌───────────────────┐ │ | |
| │ │ NbAiLab/ │ │ NorMistral- │ │ | |
| │ │ nb-sbert-base │ │ 7b-scratch │ │ | |
| │ │ (Embeddings) │ │ (LLM) │ │ | |
| │ └─────────────────┘ └───────────────────┘ │ | |
| └─────────────────────────────────────────────────────────────────┘ | |
| ``` | |
| ## Implementation Considerations | |
| ### 1. Performance Optimization | |
| - **Model Quantization**: Use GGUF or GPTQ quantized versions of the LLM to reduce memory requirements | |
| - **Batch Processing**: Implement batch processing for document embedding generation | |
| - **Caching**: Cache frequent queries and responses | |
| - **Progressive Loading**: Implement progressive loading for large document collections | |
| ### 2. Norwegian Language Optimization | |
| - **Tokenization**: Ensure proper tokenization for Norwegian-specific characters and word structures | |
| - **Text Normalization**: Implement Norwegian-specific text normalization (handling of "æ", "ø", "å") | |
| - **Stopword Removal**: Use Norwegian stopword list for improved retrieval | |
| ### 3. Embedding Functionality | |
| - **iFrame Integration**: Provide code snippets for embedding the chatbot in iFrames | |
| - **JavaScript Widget**: Create a JavaScript widget for easy integration into any website | |
| - **API Access**: Provide API endpoints for programmatic access | |
| ### 4. Security and Privacy | |
| - **Data Handling**: Implement proper data handling practices | |
| - **User Authentication**: Add optional user authentication for personalized experiences | |
| - **Rate Limiting**: Implement rate limiting to prevent abuse | |
| ## Next Steps | |
| 1. Set up the development environment | |
| 2. Implement the document processing pipeline | |
| 3. Integrate the LLM and embedding models | |
| 4. Create the chat interface | |
| 5. Develop the embedding functionality | |
| 6. Deploy to Hugging Face | |
| 7. Test and optimize the solution | |