Valve Handbook RAG Chatbot

A Retrieval-Augmented Generation (RAG) chatbot application built with Streamlit, LangGraph, and Google Gemini. This application allows users to ask questions about a provided document (defaulting to the Valve New Employee Handbook) or upload their own PDF documents to dynamically build a knowledge base and chat with their data.

Features

• Interactive Chat Interface: Ask questions and get context-aware answers directly referencing the documents.
• PDF Upload Support: Upload your own PDF documents to instantly build a temporary vector database and query against your own data.
• Multiple LLM Support: Choose from various Google Gemini models (e.g., Gemini 3.1 Flash Lite, Gemini 3.5 Flash) via a simple dropdown menu.
• LangGraph Backend: Features a robust, graph-based workflow for document retrieval, context formulation, and text generation.
• Chroma Vector Store: Uses ChromaDB for fast and local document embedding storage.

Live Demo

Check out the live application running on Streamlit Community Cloud: Launch Streamlit App

Local Setup

1. Clone the repository:

   git clone https://github.com/jonid89/rag-documentation_llm.git
   cd rag-documentation_llm

2. Install Dependencies: Install the required Python packages using pip:

   pip install -r requirements.txt

3. Configure Environment Variables: Create a .env file in the root directory and add your Google API key:

   GOOGLE_API_KEY=your_google_api_key_here

4. Run the Application:

   streamlit run app.py