LLM-Based Business Chat & Data Validation System

📌 Project Overview

LLM-Based Business Chat & Data Validation System is an AI-powered platform that automates the verification of business information and allows users to query the validated data using an interactive chatbot. This system uses GPT-4 Turbo with Custom Fine-Tuning to validate business details and provide conversational insights.

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🎯 Features

• Business Data Validation using LLM (Large Language Models)
• Fine-Tuned LLM for Business Data Validation
• Interactive Chatbot to query business information
• CSV File Upload Support
• Automatic Data Storage in Neon PostgreSQL Database
• Real-time Validation Status Display
• Verified Data History View

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🛠️ Tech Stack

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🔑 Installation & Setup

Prerequisites

• Python 3.9+
• Neon PostgreSQL Account
• OpenAI API Key (Fine-Tuned Model Access)
• Streamlit

Clone Repository

git clone https://github.com/your-repo/llm-business-chat.git
cd llm-business-chat

Install Dependencies

pip install -r requirements.txt

Configure Environment Variables

Create a .env file and add the following keys:

OPENAI_API_KEY=your_openai_api_key
NEON_DB_URL=your_postgresql_url
FINE_TUNED_MODEL=your_model_name

Run the Application

streamlit run app.py

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🚀 How It Works

1. Upload a CSV file containing business information.
2. The system validates each business entry using Fine-Tuned GPT-4 Turbo.
3. Valid or Invalid status is assigned to each entry based on custom validation rules.
4. The data is stored in Neon PostgreSQL Database.
5. Users can query the verified data through the chatbot.

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📌 Folder Structure

├── app.py             # Streamlit App
├── llm.py            # LLM Validation Logic (Fine-Tuned Model)
├── neon.py           # PostgreSQL DB Connection
├── .env              # Environment Variables
├── requirements.txt   # Dependencies
└── README.md         # Documentation

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🎯 API Endpoints

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📄 Example Dataset Format

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🔗 Dependencies

• OpenAI API (Fine-Tuned LLM)
• Streamlit
• pandas
• psycopg2
• dotenv

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🎯 Future Scope

• Improve Business Data Validation Accuracy
• Add More Validation Rules
• Multi-Language Support
• Graph-Based Data Insights
• Custom Fine-Tuning for Industry-Specific Data

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📌 License

This project is licensed under the MIT License.

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🤝 Contributing

Contributions are welcome! Please create an issue or submit a pull request.

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📧 Contact

Kumar Mayank
Email: kumarmayank@gmail.com
LinkedIn: Kumar Mayank