src/project_structure.md

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