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 interactively query the validated data using a Fine-Tuned GPT-4 Turbo chatbot. The system is designed to ensure accurate business information through automated validation and conversational insights.

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🎯 Features

• Fine-Tuned LLM-Based Business Data Validation
• Interactive Business Chatbot
• CSV File Upload for Bulk Data
• Automatic Data Storage in Neon PostgreSQL
• Real-time Validation Status Display
• Verified Data History View
• Fine-tuned for business context and data cleaning tasks

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🔍 LLM Fine-Tuning

We fine-tuned GPT-4 Turbo using a custom dataset containing business information like name, address, website, phone numbers, and reviews. The model was fine-tuned to:

• Validate Business Information
• Detect Missing or Incorrect Fields
• Provide Contextual Insights during Chat
• Suggest Improvements to Business Data

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🛠️ Tech Stack

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🔑 Installation & Setup

Prerequisites

• Python 3.9+
• Neon PostgreSQL Account
• OpenAI API Key
• 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

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. Assigns "Valid" or "Invalid" status to each entry.
4. Stores the verified data in Neon PostgreSQL Database.
5. Users can query the data via an interactive chatbot.

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📌 Folder Structure

├── app.py             # Streamlit App
├── llm.py            # LLM Validation Logic
├── 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
• Streamlit
• pandas
• psycopg2
• dotenv

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🎯 Future Scope

• Enhance Fine-Tuning for More Business Contexts
• Add Multi-Language Support
• Graph-Based Data Insights
• Business Review Sentiment Analysis

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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: krmayank2002@gmail.com
LinkedIn: Kumar Mayank