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AI-Powered-Inventory-Search-Using-LLMs-for-SQL

AI-Powered Inventory Search Using LLMs for SQL allows users to query product databases using natural language. By leveraging large language models (LLMs), the system converts questions into SQL queries, providing real-time insights into inventory data without requiring technical expertise.

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System Overview

What the project does

An AI‑driven web app that translates natural‑language inventory questions into accurate MySQL queries using the Google Gemini LLM, returning results instantly without users writing SQL.

Key features

  • Natural‑language query interface built with Streamlit
  • LLM‑generated SQL queries, refined via Few‑Shot Learning for complex requests
  • Direct integration with MySQL/MariaDB to fetch and compute inventory data
  • Example‑driven prompt engineering to reduce incorrect query generation
  • Organized codebase (helpers, few‑shot prompts, Chroma vector store) and easy setup.

    • Tech stack

    Python 3.8+, Streamlit, LangChain, Google Gemini (google‑generativeai), PyMySQL, python‑dotenv, ChromaDB, HuggingFace Hub, MySQL/MariaDB.

    Use case

    Enables data analysts or business users to quickly retrieve and analyze inventory metrics (stock levels, pricing, revenue forecasts, etc.) without needing SQL expertise, ideal for fast decision‑making on large product datasets.

    Architecture Details

    This system integrates multiple components for a seamless automation flow. Structural interpretation based on project focus:

    Backend Infrastructure

    Core execution layer for robust data processing and API handling.

    AI / Logic Core

    Intelligent decisioning via models or logical workflow rules.

    Tech Stack

    PythonIntegrationAutomationAPIs

    Key Capabilities

    • ▹ Custom workflow execution
    • ▹ Data transformation and routing
    • ▹ Extensible architecture