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# Norwegian RAG Chatbot Project Structure
## Overview
This document outlines the project structure for our lightweight Norwegian RAG chatbot implementation that uses Hugging Face's Inference API instead of running models locally.
## Directory Structure
```
chatbot_project/
├── design/ # Design documents
│ ├── rag_architecture.md
│ ├── document_processing.md
│ └── chat_interface.md
├── research/ # Research findings
│ └── norwegian_llm_research.md
├── src/ # Source code
│ ├── api/ # API integration
│ │ ├── __init__.py
│ │ ├── huggingface_api.py # HF Inference API integration
│ │ └── config.py # API configuration
│ ├── document_processing/ # Document processing
│ │ ├── __init__.py
│ │ ├── extractor.py # Text extraction from documents
│ │ ├── chunker.py # Text chunking
│ │ └── processor.py # Main document processor
│ ├── rag/ # RAG implementation
│ │ ├── __init__.py
│ │ ├── retriever.py # Document retrieval
│ │ └── generator.py # Response generation
│ ├── web/ # Web interface
│ │ ├── __init__.py
│ │ ├── app.py # Gradio app
│ │ └── embed.py # Embedding functionality
│ ├── utils/ # Utilities
│ │ ├── __init__.py
│ │ └── helpers.py # Helper functions
│ └── main.py # Main application entry point
├── data/ # Data storage
│ ├── documents/ # Original documents
│ └── processed/ # Processed documents and embeddings
├── tests/ # Tests
│ ├── test_api.py
│ ├── test_document_processing.py
│ └── test_rag.py
├── venv/ # Virtual environment
├── requirements-ultra-light.txt # Lightweight dependencies
├── requirements.txt # Original requirements (for reference)
└── README.md # Project documentation
```
## Key Components
### 1. API Integration (`src/api/`)
- `huggingface_api.py`: Integration with Hugging Face Inference API for both LLM and embedding models
- `config.py`: Configuration for API endpoints, model IDs, and API keys
### 2. Document Processing (`src/document_processing/`)
- `extractor.py`: Extract text from various document formats
- `chunker.py`: Split documents into manageable chunks
- `processor.py`: Orchestrate the document processing pipeline
### 3. RAG Implementation (`src/rag/`)
- `retriever.py`: Retrieve relevant document chunks based on query
- `generator.py`: Generate responses using retrieved context
### 4. Web Interface (`src/web/`)
- `app.py`: Gradio web interface for the chatbot
- `embed.py`: Generate embedding code for website integration
### 5. Main Application (`src/main.py`)
- Entry point for the application
- Orchestrates the different components
## Implementation Approach
1. **Remote Model Execution**: Use Hugging Face's Inference API for both LLM and embedding models
2. **Lightweight Document Processing**: Process documents locally but use remote APIs for embedding generation
3. **Simple Vector Storage**: Store embeddings in simple file-based format rather than dedicated vector database
4. **Gradio Interface**: Create a simple but effective chat interface using Gradio
5. **Hugging Face Spaces Deployment**: Deploy the final solution to Hugging Face Spaces
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