Local AI RAG agent built using Langgraph, DeepSeek R1 and Ollama

StudyBuddy: Local RAG Agent built with Deepseek R1 and Langgraph

I built a corrective AI RAG agent using LangGraph and DeepSeek R1 model running on Ollama. This agent acts like a study buddy, designed to help students summarize their notes and answer their queries.

RAG Architecture

How to run

Ensure you have Python 3.10.9 or higher, pip 24.0 or higher installed.

Clone repository

git clone https://github.com/S3annnyyy/local-rag-study-buddy.git
cd local-rag-study-buddy 

Configuring environment.

1. Obtain Tavily API Key by creating free-tier account from Tavily AI
2. Create .env,local file under the root folder in this manner:

TAVILY_API_KEY=XXX

3. Create virtual environment in root folder

python -m venv .venv

4. Activate environment by running .venv/Scripts/Activate.ps1 for Windows Powershell or source .venv/bin/activate for MacOs/Linux
5. Install ollama and download deepseek-R1 model by running the following in command prompt: (You can choose bigger models)

ollama pull deepseek-r1:1.5b
ollama run deepseek-r1:1.5b
~Should take about 5 minutes unless your laptop trashy af 

Install dependencies with:

pip install -r requirements.txt

Once it's done start up by running this command in the terminal:

streamlit run app.py

Visualizing workflow with LangGraph Studio

1. Sign up for an account here and create an API key
2. Add API key to .env.local

LANGSMITH_API_KEY=lsv2_***

3. Run the following commands

pip install -U "langgraph-cli[inmem]"
langgraph dev

4. An interface will popup and you can test the worklow throug