solnone / Document-Genie-using-RAG-Framwork

Document Q& A using RAG - GEMINI PRO

# Document Genie: Your AI-powered Insight Generator from PDFs

Document Genie is a powerful Streamlit application designed to extract and analyze text from PDF documents, leveraging the advanced capabilities of Google's Generative AI, specifically the Gemini-PRO model. This tool uses a Retrieval-Augmented Generation (RAG) framework to offer precise, context-aware answers to user queries based on the content of uploaded documents.

## Features

- **Instant Insights**: Extracts and analyses text from uploaded PDF documents to provide instant insights.
- **Retrieval-Augmented Generation**: Utilizes Google's Generative AI model Gemini-PRO for high-quality, contextually relevant answers.
- **Secure API Key Input**: Ensures secure entry of Google API keys for accessing generative AI models.

## Getting Started

### Prerequisites

- Google API Key: Obtain a Google API key to interact with Google's Generative AI models. Visit [Google API Key Setup](https://makersuite.google.com/app/apikey) to get your key.
- Streamlit: This application is built with Streamlit. Ensure you have Streamlit installed in your environment.

### Installation

Clone this repository or download the source code to your local machine. Navigate to the application directory and install the required Python packages:

```bash
pip install -r requirements.txt

How to Use

1. Start the Application: Launch the Streamlit application by running the command:

   streamlit run <path_to_script.py>

   Replace <path_to_script.py> with the path to the script file.

2. Enter Your Google API Key: Securely enter your Google API key when prompted. This key enables the application to access Google's Generative AI models.

3. Upload PDF Documents: You can upload one or multiple PDF documents. The application will analyze the content of these documents to respond to queries.

4. Ask Questions: Once your documents are processed, you can ask any question related to the content of your uploaded documents.

Technical Overview

• PDF Processing: Utilizes PyPDF2 for extracting text from PDF documents.
• Text Chunking: Employs the RecursiveCharacterTextSplitter from LangChain for dividing the extracted text into manageable chunks.
• Vector Store Creation: Uses FAISS for creating a searchable vector store from text chunks.
• Answer Generation: Leverages ChatGoogleGenerativeAI from LangChain for generating answers to user queries using the context provided by the uploaded documents.

Support

For issues, questions, or contributions, please refer to the GitHub repository issues section or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.