🎆 Comprehensive End-to-End QA GenAI Workflow 🎆

Welcome to our End-to-End QA GenAI Workflow, where state-of-the-art technology merges with an intuitive user interface! 🚀 This solution implements a Retrieval-Augmented Generation (RAG) technique, coupled with a Streamlit-based interface to manage PDF document uploads and queries. We incorporate the Google Gemini API—featuring the robust Gemini Pro model—to deliver high-quality natural language comprehension and response generation. Leveraging the LlamaIndex framework, we enable efficient document indexing and streamlined retrieval, ensuring timely and accurate responses to user queries. 🔍💬

With this setup, you can upload, interact, and retrieve insightful answers from your documents in a completely new way! 🎉

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✨ Key Features ✨

• 🖥️ Streamlit Interface for smooth PDF uploads and user engagement.
• 🤖 Google Gemini API integration, utilizing the Gemini Pro model for top-tier language generation.
• 🗂️ LlamaIndex support for rapid and scalable content indexing and retrieval.
• 🛠️ Modular Architecture for straightforward maintenance and adaptability.
• 🚀 High-Efficiency QA processes that harness the power of your uploaded documents.

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🏗️ System Architecture 🏗️

This solution follows a modular structure designed for scalability and maintainability. Its main building blocks include:

1. Streamlit Frontend: Provides a friendly interface for PDF uploads and queries.
2. Document Processing: Extracts text from uploaded PDFs for indexing.
3. LlamaIndex Integration: Indexes the extracted content to facilitate swift, accurate searches.
4. Google Gemini API: Leverages the Gemini Pro model to handle queries and generate human-like responses.
5. Modular Code Design: Keeps components separate, making it easier to update or extend.

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⚙️ Setup Instructions ⚙️

Ready to get started with this cutting-edge GenAI solution? Follow these steps to set up your local environment:

Prerequisites

• Python 3.10 or newer
• Valid Google Gemini API credentials
• LlamaIndex library installed

Installation Steps

1. Clone the Repository:

   git clone https://github.com/RohmaButt/RAG.git
   cd End-to-End-QA-GenAI-Project

2. Create and activate a Python virtual environment:

   python3.10 -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install the required Python packages:

   pip install -r requirements.txt

4. Set up your Google Gemini API credentials. Follow Google's official documentation for setting up the API credentials.

5. Install LlamaIndex:

   pip install llama-index

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🚀 Running the Application 🚀

Now that you're all set up, let’s get the app running:

1. Start the Streamlit app:

   streamlit run app.py

2. Open your browser and visit http://localhost:8501 to interact with the app. 🎉

3. Upload a PDF file and query it for intelligent responses generated using Gemini Pro!

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🔄 Workflow 🔄

Here’s how the magic happens:

1. Upload PDF: The user uploads a PDF file through the Streamlit UI.
2. Text Extraction: The system extracts text content from the uploaded PDF.
3. Document Indexing: The text is indexed using LlamaIndex for quick retrieval.
4. Query Generation: The user submits a query, which is processed by the Gemini Pro model.
5. Response Generation: The system retrieves the relevant information and generates a natural language response using the Gemini API. 🎯

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🔌 API Integration 🔌

This project utilizes the Google Gemini API (with the Gemini Pro model) for natural language generation. To interact with the API, you must set up your Google Gemini API credentials in the config.py file.

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⭐ Star the Project ⭐

If you love this project, don’t forget to star it on GitHub! It helps us keep the project alive and motivates us to keep improving it. 🌟🚀

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