Overview

Privacy-focused RAG system that runs entirely offline:

• using Ollama LLMs.
• Implemented LangGraph to orchestrate an intelligent workflow that switches between
• Vector Search (PDF/TXT) and SQL generation based on query intent.

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Tools

• Python (FastAPI,LangChain, LangGraph, DSPy)
• JavaScript, TailwindCSS, REACT.JS
• Ollama Local Models
• PostMan

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API's

1. /api/query
2. /api/document
3. /api/upload
4. /api//tables
5. /api/health

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Workflow (LangGraph)

Screenshots

Setup

Download OLLAMA and PULL Model

1. Download and install Ollama Locally from here
2. Pull The Model llama3.1:8b

ollama pull llama3.1:8b

Setup Environment Variable.

1. Rename .env.example to .env.

cp .env.example .env

2. Setup Your Variables.

• You can pull any model you want, but you will have to change LLM_MODEL = Your Model Name
• You can direct DATABASE_PATH to your local database by using the absolute path.

Install Requirements

1. Make a virtual environment and activate it.

python -m venv .venv
./.venv/Scripts/activate

2. Install requirements inside the venv.

pip install -r requirements.txt

Install Node.js

1. Download and install Node.Js from here
2. Make sure you installed it by using this command.

npm --version

Install React.JS

cd frontent
npm i

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Run the system.

Open Two Terminals

1. Run the FASTAPI Services.

cd backend
uvicorn main:app --reload --host 0.0.0.0 --port 5000

2. Run the REACT Frontend

cd frontend
npm run

3. Access Application from here http://localhost:5173/