🤖 CKsFinBot - AI-Powered Financial Analysis Assistant

Your intelligent companion for financial document analysis, market insights, and data-driven decision making

🌟 Overview

CKsFinBot is a sophisticated AI-powered financial analysis platform that combines the power of Large Language Models (LLMs) with advanced document processing capabilities. Built with a modern microservices architecture, it provides intelligent financial insights, document analysis, and conversational AI features tailored for finance professionals and enthusiasts.

✨ Key Features

• 🧠 Multi-Modal AI Chat - Text and image support with Google Gemini integration
• 📄 Advanced Document Analysis - PDF, Excel, and financial report processing
• 📊 Analytical Insights - Financial ratio calculations and trend analysis
• 🔍 Multi-Document Search - Cross-document comparison and analysis
• 💬 Conversational Interface - Natural language financial Q&A
• ☁️ Large File Support - S3 presigned URLs for files >10MB
• 🎨 Modern UI/UX - Google AI Studio inspired interface
• 🔐 Secure Authentication - JWT-based user management
• 📱 Responsive Design - Works seamlessly across all devices

🏗️ Architecture

graph TB
    A[React Frontend] --> B[Node.js Backend]
    B --> C[Python AI Service]
    B --> D[MongoDB Database]
    B --> E[AWS S3 Storage]
    C --> F[Pinecone Vector DB]
    C --> G[Google Gemini API]
    C --> H[LangChain Framework]

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🔧 Tech Stack

Frontend

• React 19 - Modern UI framework
• Tailwind CSS 4 - Utility-first styling
• Framer Motion - Smooth animations
• Lucide React - Beautiful icons
• Axios - HTTP client
• Vite - Fast build tool

Backend (Node.js)

• Express.js - Web framework
• MongoDB - Document database
• Mongoose - ODM for MongoDB
• JWT - Authentication
• Multer + AWS S3 - File uploads
• Serverless Framework - Deployment

AI Service (Python)

• FastAPI - High-performance API framework
• LangChain - LLM orchestration
• Google Gemini - Advanced AI model
• Pinecone - Vector database
• PyPDF - Document processing
• Sentence Transformers - Text embeddings

Cloud & Infrastructure

• AWS S3 - File storage
• AWS Lambda - Serverless functions
• MongoDB Atlas - Cloud database
• Pinecone - Vector search
• Vercel - Frontend deployment

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Project Structure

Python-Backend/
│
├── app/
│   ├── api/
│   │   └── endpoints.py
│   │
│   ├── core/
│   │   └── config.py
│   │
│   ├── schemas/
│   │   └── models.py
│   │
│   └── services/
│       ├── document_processor.py
│       ├── llm_service.py
│       ├── multi_modal_processor.py
│       ├── pinecone_service.py
│       ├── s3_service.py
│       ├── main.py
│       └── prompts.py
│
├── src/
│   └── Function/
│       └── index.js
│
├── .gitignore
├── handler.py
├── package-lock.json
├── package.json
├── requirements.txt
├── serverless.yml
└── README.md



Node-Backend/
│
├── .esbuild/
│   └── .build/
│
├── src/
│   ├── Controllers/
│   ├── Middlewares/
│   ├── Models/
│   ├── Routes/
│   ├── Utils/
│   └── db/
│
├── Constants.js
├── app.js
├── index.js
├── lambda.js
├── .gitignore
├── package-lock.json
├── package.json
└── serverless.yml


Frontend/
│
├── public/
│   ├── index.html
│   └── ...
│
├── src/
│   ├── Config/
│   ├── JSX/
│   ├── assets/
│   ├── services/
│   │
│   ├── App.css
│   ├── App.jsx
│   ├── index.css
│   └── main.jsx
│
├── .env
├── .gitignore
├── README.md
├── eslint.config.js
├── package-lock.json
└── package.json

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🚀 Quick Start

Prerequisites

• Node.js 18+ and npm
• Python 3.11.9 and pip
• MongoDB instance
• AWS account (for S3)
• Google AI API key
• Pinecone account

1. Clone the Repository

git clone https://github.com/KrishChothani/CKsFinBot.git
cd CKsFinBot

2. Environment Setup

Create .env files in each service directory:

Frontend (.env)

VITE_API_URL=http://localhost:2590/api/v1

Node Backend (.env)

PORT=2590
MONGODB_URI=mongodb://localhost:27017/cksfinbot
CORS_ORIGIN=http://localhost:5173

# JWT Secrets
ACCESS_TOKEN_SECRET=your_access_token_secret
ACCESS_TOKEN_EXPIRY=1d
REFRESH_TOKEN_SECRET=your_refresh_token_secret
REFRESH_TOKEN_EXPIRY=10d

# AWS Configuration
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-1
AWS_S3_BUCKET_NAME=your-s3-bucket

# Python Service
PYTHON_SERVICE_URL=http://127.0.0.1:8000/api/v1

# Email (Optional)
EMAIL_ID_FOR_VERIFICATION=your_email@gmail.com
EMAIL_PASSWORD_FOR_VERIFICATION=your_app_password

Python Backend (.env)

# Google AI
GOOGLE_API_KEY=your_google_ai_api_key

# Pinecone
PINECONE_API_KEY=your_pinecone_api_key
PINECONE_ENVIRONMENT=your_pinecone_environment

# AWS (for document access)
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-1

# Database
MONGODB_URI=mongodb://localhost:27017/cksfinbot

3. Installation & Setup

Install Frontend Dependencies

cd Frontend
npm install

Install Node Backend Dependencies

cd ../Node-Backend
npm install

Install Python Backend Dependencies

cd ../Python-Backend
pip install -r requirements.txt

4. Database Setup

1. Install MongoDB locally or use MongoDB Atlas
2. Create a database named cksfinbot
3. The application will automatically create required collections

5. Start the Services

Terminal 1: Python AI Service

cd Python-Backend
uvicorn app.main:app --reload --port 8000

Terminal 2: Node.js Backend

cd Node-Backend
npm run dev

Terminal 3: React Frontend

cd Frontend
npm run dev

6. Access the Application

Open your browser and navigate to http://localhost:5173

📖 Usage Guide

🎯 Feature Modes

CKsFinBot offers 5 distinct modes for different use cases:

1. 🤖 Smart Chat

• Purpose: Intelligent conversations with text and image support
• Use Cases: General financial questions, image analysis, multi-modal queries
• Example: "Analyze this chart and explain the trends"

2. 📄 Document Analysis

• Purpose: Deep analysis of financial documents and reports
• Use Cases: PDF analysis, financial statement review, report summarization
• Example: "Summarize the key insights from this quarterly report"

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3. 📈 Analytical Insights

• Purpose: Advanced financial calculations and trend analysis
• Use Cases: Ratio calculations, performance metrics, trend analysis
• Example: "Calculate the debt-to-equity ratio trends over the last 3 years"

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3. 📈 Financial Templete Generation

• Purpose: Advanced financial calculations and trend analysis
• Use Cases: Ratio calculations, performance metrics, trend analysis
• Example: "Calculate the debt-to-equity ratio trends over the last 3 years"

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4. 💬 General Conversation

• Purpose: Casual finance discussions and educational Q&A
• Use Cases: Learning financial concepts, basic questions, definitions
• Example: "What's the difference between stocks and bonds?"

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5. 🔍 Multi-Document Search

• Purpose: Search and compare across multiple uploaded documents
• Use Cases: Cross-document analysis, comparative studies, research
• Example: "Compare revenue growth across all uploaded annual reports" =======

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📁 File Upload Features

Supported File Types

• PDF: Financial reports, statements, research papers
• Excel/CSV: Financial data, spreadsheets, datasets
• Images: Charts, graphs, financial diagrams

Upload Methods

1. Drag & Drop: Simply drag files into the chat interface
2. Click to Browse: Use the paperclip icon to select files
3. Large File Support: Files >10MB automatically use S3 direct upload

File Processing

• Automatic text extraction and indexing
• Vector embeddings for semantic search
• Real-time processing status updates
• Secure cloud storage with public access URLs

💬 Chat Interface

Welcome Screen

• Feature mode selection with visual cards
• Example prompts for quick start
• Quick action buttons for common tasks
• Professional Google AI Studio inspired design

Chat Features

• Real-time messaging with typing indicators
• File attachment with preview and removal
• Message history with conversation persistence
• Export/Share functionality for conversations
• Responsive design for mobile and desktop

🔧 API Documentation

Authentication Endpoints

POST /api/v1/users/register
POST /api/v1/users/login
POST /api/v1/users/logout
GET  /api/v1/users/current-user
POST /api/v1/users/refresh-token

Conversation Management

GET    /api/v1/conversations
POST   /api/v1/conversations
GET    /api/v1/conversations/:id
DELETE /api/v1/conversations/:id
PATCH  /api/v1/conversations/:id/feature
PATCH  /api/v1/conversations/:id/title

Messaging

POST /api/v1/conversations/:id/messages

Document Upload

POST /api/v1/documents/upload          # Regular upload (<10MB)
POST /api/v1/documents/upload-s3       # S3 direct upload (>10MB)
POST /api/v1/s3/generate-presigned-url # Generate S3 upload URL

AI Processing (Python Service)

POST /api/v1/query              # Process chat queries
POST /api/v1/process-document   # Process uploaded documents
POST /api/v1/delete-documents   # Cleanup documents

🚀 Deployment

Frontend Deployment (Vercel/Netlify)

1. Build the frontend:

cd Frontend
npm run build

2. Deploy to Vercel:

vercel --prod

Backend Deployment (AWS Lambda)

1. Configure AWS credentials
2. Deploy Node.js backend:

cd Node-Backend
serverless deploy

3. Deploy Python backend:

cd Python-Backend
serverless deploy

Environment Variables for Production

Update your production environment variables with:

• Production database URLs
• Production API endpoints
• Production AWS credentials
• Production API keys

🔒 Security Features

• JWT Authentication with access and refresh tokens
• CORS Protection with configurable origins
• Input Validation using express-validator
• File Upload Security with type and size restrictions
• Environment Variable Protection for sensitive data
• AWS IAM Roles for secure cloud resource access

🧪 Testing

Run Frontend Tests

cd Frontend
npm run test

Run Backend Tests

cd Node-Backend
npm run test

Run Python Tests

cd Python-Backend
pytest

📊 Monitoring & Analytics

• Error Tracking: Comprehensive error handling and logging
• Performance Monitoring: Response time tracking
• Usage Analytics: User interaction metrics
• Document Processing Stats: Upload and processing statistics

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Development Guidelines

• Follow ESLint configuration for JavaScript/React
• Use Black formatter for Python code
• Write comprehensive tests for new features
• Update documentation for API changes
• Follow conventional commit messages

📝 License

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

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👥 Team

Team CKsDev

• Full-stack development
• AI/ML integration
• Cloud architecture
• UI/UX design

🆘 Support

For support and questions:

• 📧 Email: support@cksfinbot.com
• 💬 Discord: Join our community
• 📖 Documentation: docs.cksfinbot.com
• 🐛 Issues: GitHub Issues =======

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---

Made with ❤️ by Team CKsDev

⭐ Star this repo | 🐛 Report Bug | 💡 Request Feature

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