Enterprise RAG ChatBot

A full-stack AI-powered ChatBot platform built with React, FastAPI, PostgreSQL, and OpenAI.

The platform supports conversational AI, document uploads, Retrieval-Augmented Generation (RAG), semantic search, user authentication, conversation management, and administration features.

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Architecture Overview

┌─────────────────────┐
│     React Frontend  │
│     (Vite + React)  │
└──────────┬──────────┘
           │ REST API
           ▼
┌─────────────────────┐
│    FastAPI Backend  │
│ Authentication      │
│ Chat Services       │
│ File Services       │
│ RAG Services        │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│     PostgreSQL      │
│ Users              │
│ Conversations      │
│ Messages           │
│ Documents          │
│ Embeddings         │
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│      OpenAI API     │
│ GPT Models          │
│ Embeddings          │
└─────────────────────┘

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Features

Authentication

• User Registration
• User Login
• JWT Authentication
• Role-Based Access Control
• Protected API Endpoints

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Conversational AI

• ChatGPT-style Interface
• Streaming AI Responses
• Conversation History
• Multi-Conversation Support
• Persistent Messages

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Retrieval-Augmented Generation (RAG)

• PDF Upload
• TXT Upload
• Markdown Upload
• Text Extraction
• Document Chunking
• OpenAI Embeddings
• Semantic Search
• Context-Aware Responses

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Administration

• User Statistics
• Usage Tracking
• Conversation Analytics
• Admin Dashboard APIs

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Production Features

• FastAPI
• PostgreSQL
• SQLAlchemy ORM
• Alembic Migrations
• OpenAI Integration
• Rate Limiting
• Automated Testing
• Modular Service Architecture

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Technology Stack

Frontend

Technology	Purpose
React	UI Framework
Vite	Build Tool
JavaScript	Frontend Logic
Fetch API	API Communication

---

Backend

Technology	Purpose
FastAPI	API Framework
PostgreSQL	Database
SQLAlchemy	ORM
Alembic	Database Migrations
OpenAI	LLM & Embeddings
JWT	Authentication
SlowAPI	Rate Limiting
PyPDF	PDF Processing
Pytest	Testing

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Project Structure

project-root/
│
├── backend/
│   │
│   ├── app/
│   │   ├── api/
│   │   ├── core/
│   │   ├── db/
│   │   ├── models/
│   │   ├── schemas/
│   │   ├── services/
│   │   └── main.py
│   │
│   ├── alembic/
│   ├── tests/
│   ├── uploads/
│   ├── requirements.txt
│   └── .env
│
├── frontend/
│   │
│   ├── src/
│   │   ├── components/
│   │   ├── services/
│   │   ├── config.js
│   │   ├── App.jsx
│   │   └── main.jsx
│   │
│   ├── public/
│   ├── package.json
│   └── .env
│
└── README.md

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Getting Started

Clone Repository

git clone https://github.com/yourusername/enterprise-rag-chatbot.git

cd enterprise-rag-chatbot

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Backend Setup

Create Virtual Environment

cd backend

python -m venv venv

source venv/bin/activate

Windows:

venv\Scripts\activate

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Install Dependencies

pip install -r requirements.txt

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Configure Environment

Create:

backend/.env

DATABASE_URL=postgresql://postgres:password@localhost:5432/chatbot_db

OPENAI_API_KEY=your_openai_api_key

SECRET_KEY=change-this-secret

ALGORITHM=HS256

ACCESS_TOKEN_EXPIRE_MINUTES=60

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Create Database

CREATE DATABASE chatbot_db;

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Run Migrations

alembic upgrade head

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Start Backend

uvicorn app.main:app --reload

Backend:

http://127.0.0.1:8000

Swagger Docs:

http://127.0.0.1:8000/docs

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Creating a Test User

Before using the application, create a user account.

Register via API

Open Swagger UI:

http://127.0.0.1:8000/docs

Use:

POST /auth/register

Request Body:

{
  "name": "Test User",
  "email": "test@example.com",
  "password": "password123",
  "role": "user"
}

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Frontend Setup

Install Dependencies

cd frontend

npm install

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Configure Environment

Create:

frontend/.env

VITE_API_BASE_URL=http://127.0.0.1:8000

---

Start Frontend

npm run dev

Frontend:

http://localhost:5173

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API Endpoints

Authentication

Method	Endpoint
POST	/auth/register
POST	/auth/login
GET	/auth/me

---

Chat

Method	Endpoint
POST	/chat/stream

---

Files

Method	Endpoint
POST	/files/upload

---

Conversations

Method	Endpoint
GET	/conversations
GET	/conversations/{id}/messages
DELETE	/conversations/{id}

---

Admin

Method	Endpoint
GET	/admin/stats
GET	/admin/users
GET	/admin/usage

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Testing

Run backend tests:

cd backend

python -m pytest

Run coverage:

pytest --cov=app

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RAG Workflow

Upload Document
        │
        ▼
Extract Text
        │
        ▼
Chunk Document
        │
        ▼
Generate Embeddings
        │
        ▼
Store in PostgreSQL
        │
        ▼
Semantic Search
        │
        ▼
Retrieve Relevant Chunks
        │
        ▼
Generate AI Response

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Future Roadmap

• pgvector Integration
• Multi-Model Routing
• AI Agent Workflows
• Citation-Based Responses
• Source Attribution
• Conversation Summarization
• Docker Deployment
• Kubernetes Deployment
• CI/CD Pipelines
• Observability & Monitoring
• Multi-Tenant Architecture

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Security Notes

Never commit:

.env
uploads/
venv/
node_modules/

Use:

.env.example

for sharing configuration templates.

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License

MIT License

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Author

Jay Ram Singh

LinkedIn: https://www.linkedin.com/in/jayram