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# 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
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