QA Chat App for Numerical Methods and Computational Fluid Dynamics

This is an interactive web application for context-aware question answering over documents. It leverages semantic search, LangGraph workflow, and ChatGroq LLM to provide concise, source-backed answers to user queries.

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Features

• Contextual Answers: Retrieves the most relevant passages from your documents and generates answers using an LLM.
• Conversation Memory: Maintains chat history for follow-up questions using LangGraph's in-memory checkpointer.
• Source Referencing: Displays the source document and page numbers for transparency.
• Easy-to-Use UI: Built with Streamlit for a responsive chat experience.
• Robust Error Handling: Gracefully handles missing API keys, empty vector DBs, and other runtime errors.

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Demo

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Prerequisites

This project uses uv as the python package manager

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Verify the installation:

echo 'export PATH="$HOME/snap/code/221/.local/bin:$PATH"' >> ~/.bashrc && source ~/.bashrc

uv --version

Clone the repository or Download as zip file

https://github.com/yoursrealkiran/RAG.git

cd RAG

Environment Setup

Create a Virual Environment

In the terminal, run the below command to create a virtual environment

uv venv

Activate the environment

source .venv/bin/activate

Install Dependencies

uv sync

Running the Application

1. Data Ingestion

Run the data ingestion script to prepare your data:

uv run document_processing.py

2. Start the Application

Paste your Groq API key in the .env file

Launch the Streamlit application:

uv run streamlit run chatbot.py

Note:

• Make sure you have the required pdf files in place before running the ingestion script (document_processing.py) and change the config.py accordingly.
• The web app will be available in your browser once Streamlit starts.
• To deactivate the virtual environment simply run deactivate.