README.md

metadata

app_file: ui/streamlit_app.py
sdk: streamlit

Agentic RAG Chatbot

Project Description

This project implements an Agentic RAG (Retrieval Augmented Generation) Chatbot designed to answer questions based on uploaded documents. It leverages various agents for ingestion, retrieval, and LLM response generation, providing a conversational interface through a Streamlit application.

Features

• Document Upload: Supports PDF, PPTX, CSV, DOCX, TXT, and MD file formats.
• Intelligent Retrieval: Retrieves relevant information from uploaded documents to answer user queries.
• Conversational Interface: Interact with the chatbot through a user-friendly Streamlit UI.
• Modular Agentic Architecture: Built with distinct agents for better maintainability and scalability.

Hugging Face Spaces Deployment

To deploy this application on Hugging Face Spaces, follow these steps:

1. Create a New Space: Go to Hugging Face Spaces and create a new Space. Choose Streamlit as the SDK.
2. Upload Files: Upload all project files and directories (agents/, config/, core/, ui/, main.py, requirements.txt, ad.txt, PHILIP_SIMON_DEROCK.pdf) to your Hugging Face Space repository.
3. Entry Point: Ensure your main application file is named app.py. If you have a main.py like in this project, you will need to rename it to app.py or create an app.py that imports and runs your Streamlit app.
   • In this project, app.py has been created to serve as the entry point, which calls StreamlitApp from ui/streamlit_app.py.
4. Dependencies: The requirements.txt file specifies all necessary Python dependencies. Hugging Face Spaces will automatically install these when building your Space.
5. Environment Variables: Set the GOOGLE_API_KEY environment variable in your Space settings.
   • Go to your Space settings (usually Settings tab in your Space).
   • Scroll down to "Repository secrets" or "Environment variables".
   • Add a new secret/variable named GOOGLE_API_KEY with your actual Google API Key.

Your Space should now be ready to deploy and run the Agentic RAG Chatbot.

Local Setup (Optional)

To run this project locally, follow these steps:

1. Clone the repository:

   git clone <repository_url>
   cd Z
   

2. Create a virtual environment (recommended):

   python -m venv virtual_container
   source virtual_container/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set Environment Variable: Set your GOOGLE_API_KEY:

   export GOOGLE_API_KEY="your_google_api_key"
   # For Windows (Command Prompt):
   # set GOOGLE_API_KEY="your_google_api_key"
   # For Windows (PowerShell):
   # $env:GOOGLE_API_KEY="your_google_api_key"
   

5. Run the Streamlit application:

   streamlit run app.py
   

   The application will typically open in your web browser at http://localhost:8501.

Usage

1. Upload Documents: Use the sidebar to upload PDF, PPTX, CSV, DOCX, TXT, or MD files.
2. Ask Questions: Once documents are uploaded, type your questions in the chat input field to get answers based on the document content.

Project Structure

Z/
├── agents/                  # Contains different agents (base, ingestion, llm_response, retrieval)
├── config/                  # Configuration settings
├── core/                    # Core functionalities like document parsing and vector store
├── ui/                      # Streamlit UI components
├── app.py                   # Main entry point for the Streamlit application (for Hugging Face Spaces)
├── main_local.py            # Original main entry point (can be removed or kept for local dev)
├── requirements.txt         # Python dependencies
├── README.md                # Project README and deployment instructions
└── ... other files (ad.txt, PHILIP_SIMON_DEROCK.pdf)