📄 Gemini-RAG: PDF Q&A Chatbot

Gemini-RAG is a Python-based application that enables users to interactively query PDF documents using Retrieval-Augmented Generation (RAG) techniques. It leverages Google Generative AI models and LangChain to process PDF files, create vector embeddings, and respond to user queries via a Gradio interface.

---

🚀 Features

• PDF Document Processing
  Load, split, and process PDF documents into retrievable chunks for efficient querying.

• Embedding and Retrieval
  Creates embeddings using GoogleGenerativeAIEmbeddings and stores them in a Chroma vector database for efficient similarity-based document retrieval.

• Interactive Q&A
  Query the document using a chatbot powered by Google Generative AI (Gemini) and retrieve relevant information.

• Gradio Interface
  A user-friendly Gradio-based UI for real-time question-answering.

---

🛠️ Technologies Used

• LangChain: For managing document loading, splitting, and retrieval QA chains.
• Google Generative AI: To generate embeddings and handle the LLM-based chatbot.
• Chroma: As a vector database for efficient document similarity search.
• Gradio: For building an interactive web-based user interface.

---

📥 Installation

Prerequisites

• Python 3.8 or higher
• A Google Cloud account with access to Google Generative AI (for embedding and LLM models)
• Install required Python packages using pip:

  pip install -r requirements.txt

Environment Setup

• Create a .env file in the project directory with your Google Generative AI API Key:

  GOOGLE_API_KEY=<your_api_key>
  

---

▶️ Usage

1. Place the PDF document you want to query in the project directory (e.g., rep_dl.pdf).
2. Run the chatbot:

   python rag.py

3. Open the Gradio interface in your browser and start asking questions about the document.

Optional Parameters

• pdf_path: Path to the PDF file (default: ./rep_dl.pdf).
• retrieve_source_documents: Set to True to retrieve the most relevant chunks along with the response (default: False).

Example:

python rag.py --pdf_path ./your_file.pdf --retrieve_source_documents True

---

📂 Project Structure

Gemini-RAG/
│
├── rag.py                     # Main script for chatbot functionality
├── requirements.txt           # Required Python dependencies
├── .env                       # API keys (to be created)
├── LICENSE                    # MIT License
└── README.md                  # Project documentation

---

📜 License

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