| # 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 |
| |
