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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.

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)

Requirements

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

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

Usage

1️⃣ CLI Version

python run_langgraph.py

2️⃣ Streamlit UI

streamlit run streamlit_app.py

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

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

Credits

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

License

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