SmartDoc LangGraph Agent

SmartDoc LangGraph Agent is a simple prototype demonstrating how to combine:

• LLM Agent (TinyLlama)
• PDF Querying using LangChain's vector store
• Calculator Tool
• LangGraph for orchestrating tool selection
• Streamlit UI

This agent can answer:

• PDF-based questions
• General LLM questions
• Simple arithmetic calculations

developed as part of a learning exercise.

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Features

✅ PDF Document Question Answering

✅ Calculator for Simple Math Queries

✅ General Purpose Question Answering

✅ Dynamic Routing via LangGraph

✅ Streamlit-based simple UI

✅ Compatible with HuggingFace Transformers

✅ CPU Friendly (runs on normal laptops)

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Requirements

• Python 3.10+
• Anaconda (recommended)
• git
• Hugging Face Token (required to download TinyLlama)

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Installation

# Clone this repo
https://github.com/rajantripathi/smartdoc-langgraph-agent.git
cd smartdoc-langgraph-agent

# Create virtual environment (Recommended)
conda env create -f environment.yml
conda activate agentenv

# Or install manually
pip install -r requirements.txt

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Usage

1️⃣ CLI Version

python run_langgraph.py

2️⃣ Streamlit UI

streamlit run streamlit_app.py

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Folder Structure

smartdoc-langgraph-agent/
├── agent.py
├── langgraph_agent.py
├── pdf_reader.py
├── pdf_qa_tool.py
├── tools.py
├── tool_selector.py
├── run_langgraph.py
├── streamlit_app.py
├── requirements.txt
├── environment.yml
├── data/
│   └── sample.pdf
└── .env.example

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Notes

• The project demonstrates how you can create simple agents using LangGraph
• You can swap TinyLlama with any small HF model easily
• This is a base for further experimentation

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Credits

Built by Rajan Tripathi with support from LangGraph, LangChain, and HuggingFace libraries.

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License

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