📚 RAG Pipeline Project Structure

Directory Structure

rag-pdf-pipeline/
├── README.md
├── STEPS.md
├── pyproject.toml
├── requirements.txt
├── .env.example
├── .gitignore
├── src/
│   ├── __init__.py
│   ├── config.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── llm_handler.py
│   │   └── embedding_handler.py
│   ├── storage/
│   │   ├── __init__.py
│   │   ├── vector_store.py
│   │   └── document_store.py
│   ├── processing/
│   │   ├── __init__.py
│   │   ├── pdf_processor.py
│   │   └── text_chunker.py
│   ├── rag/
│   │   ├── __init__.py
│   │   ├── retriever.py
│   │   └── generator.py
│   └── utils/
│       ├── __init__.py
│       └── logger.py
├── ui/
│   ├── __init__.py
│   ├── streamlit_app.py
│   ├── components/
│   │   ├── __init__.py
│   │   ├── file_upload.py
│   │   ├── chat_interface.py
│   │   └── settings.py
│   └── styles/
│       └── main.css
├── data/
│   ├── uploads/
│   ├── vector_db/
│   └── chat_history/
├── tests/
│   ├── __init__.py
│   ├── test_pdf_processor.py
│   ├── test_vector_store.py
│   └── test_rag_pipeline.py
└── scripts/
    ├── setup.bat
    └── run.bat

🚀 Installation/Setup

To set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-repo/rag-pdf-pipeline.git
   cd rag-pdf-pipeline

2. Install dependencies:

   This project uses uv for dependency management. If you don't have uv installed, you can install it via pip:

   pip install uv

   Then, install the project dependencies:

   uv sync

3. Environment Variables:

   Create a .env file in the root directory based on .env.example and fill in your API keys and other configurations.

   # .env example
   OPENAI_API_KEY=your_openai_api_key_here
   # Add other necessary environment variables
   

✨ Features

This RAG pipeline project provides the following key features:

• PDF Document Processing: Utilizes pdf_processor.py to extract text from PDF documents.
• Text Chunking: Efficiently breaks down large texts into manageable chunks for processing.
• Embedding Generation: Generates vector embeddings for text chunks using a dedicated embedding_handler.py.
• Vector Storage and Retrieval: Stores and retrieves document embeddings using a vector_store.py for efficient similarity search.
• LLM Integration: Integrates with Large Language Models via llm_handler.py for generating responses based on retrieved information.
• Streamlit User Interface: Provides an interactive web interface for uploading PDFs, chatting with the RAG model, and configuring settings.

🏗️ Project Modules/Architecture

The project is organized into several modules, each responsible for a specific part of the RAG pipeline:

• src/: Contains the core logic of the RAG pipeline.
  • src/models/: Handles interactions with LLMs and embedding models.
  • src/processing/: Manages document processing, such as PDF parsing and text chunking.
  • src/rag/: Implements the retriever and generator components of the RAG system.
  • src/storage/: Deals with document storage and vector database operations.
  • src/utils/: Provides utility functions like logging.
• ui/: Contains the Streamlit web application and its components.
• static/: Stores static assets like images and example documents.
• scripts/: Holds various utility scripts for setup and running the application.
• logs/: Directory for application logs.

▶️ Usage

To use the RAG pipeline, follow these steps:

1. Start the Streamlit Application:

   streamlit run ui/app.py

2. Interact with the UI:

   • Upload PDF: Use the file uploader to select a PDF document. The application will process the document, extract text, chunk it, and generate embeddings.
   • Chat: Once the document is processed, you can type your questions into the chat input field. The RAG model will retrieve relevant information from the document and generate a response.
   • Settings: Adjust various parameters like the chunk size, overlap, and model settings through the sidebar.

System Diagram