ashishpatel26/Retrieval-and-Generation-of-Clinical-Insights-from-PDF

The RAFT project integrates Retrieval-Augmented Generation (RAG) with fine-tuning approaches to provide a versatile question-answering system. Leveraging the strengths of OpenAI GPT-3.5 Turbo and Google MedPaLM 2, this framework aims to enhance information retrieval and generation capabilities across various domains.

Retrieval and Generation of Clinical Insights from PDF

Table of Contents

• Overview
• Features
  • Unified Vector Store
  • Dynamic LLM Integration
  • Interactive Streamlit UI
• System Requirements
• Setup
• Usage
• Acknowledgments
• Contributions

Overview

The RAFT project integrates Retrieval-Augmented Generation (RAG) with fine-tuning approaches to provide a versatile question-answering system. Leveraging the strengths of OpenAI GPT-3.5 Turbo and Google MedPaLM 2, this framework aims to enhance information retrieval and generation capabilities across various domains.

Features

1. Unified Vector Store

A single vector store is used for document retrieval, ensuring efficiency and speed regardless of the selected generation model.

2. Dynamic LLM Integration

Seamlessly switch between OpenAI GPT-3.5 Turbo and MedPaLM 2 depending on the specific needs of the query and the context provided by the user.

3. Interactive Streamlit UI

A user-friendly interface allows for real-time uploading of PDF documents and querying, making the system accessible to users with minimal technical background.

System Requirements

• Python 3.11.5 or higher
• Dependencies as listed in the requirements.txt file

Setup

To get started with the RAFT project, follow these steps:

1. Clone the repository:

git clone <repository_url>

2. Navigate to the project directory:

cd RAFT

3. Create a Virtual Environment:

python -m venv venv

4. Activate the Virtual Environment:

source venv/bin/activate  # Mac/Linux
venv\Scripts\activate     # Windows

5. Install Dependencies:

pip install -r requirements.txt

6. Set Environment Variables: Create a .env file in the project root with the following variables (Please refer the example.env file):

OPENAI_API_KEY=your_openai_api_key_here
FINE_TUNED_MODEL_NAME=ft:gpt-3.5-turbo-0125:your_fine_tuned_model_name
GOOGLE_API_KEY=your_google_api_key_here

Usage

To run the Streamlit application, use the following command:

streamlit run app.py

This will launch the Streamlit server, allowing you to upload PDF files and ask questions in real-time.

Uploading PDF Files

• Use the file uploader in the Streamlit app to upload one or more PDF files.
• The system will process the PDFs and create a vector store for retrieval.

Asking Questions

• After uploading PDF files, enter your question in the text area and click the "Ask" button.
• The system will return an answer based on the selected language model.

Acknowledgments

• LangChain: A framework for building applications with large language models and data.
• Chroma: An open-source embedding database for retrieval.
• OpenAI: Provides the GPT-3.5 Turbo language model.
• Google Palm: A generative language model from Google.

Contributing

Contributions to this project are welcome. Please fork the repository and submit a pull request for any changes or improvements.