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AI-Based Table Retriever Using Natural Language and Sentence Transformers

This project uses AI (via Sentence Transformers) and a vector database (Qdrant) to retrieve the most relevant database tables based on a user's natural language input.

Objective

To build a system that dynamically analyzes natural language queries and returns the most relevant subset of database tables using semantic similarity.

Tech Stack Used

  • Backend: FastAPI (Python)
  • AI Model: Sentence Transformers (all-MiniLM-L6-v2)
  • Vector Database: Qdrant
  • Frontend: React.js (with plain CSS)
  • Containerization: Docker & Docker Compose
  • Data Format: JSON schema

Features

  • Accepts a natural language description from the user
  • Converts both the schema and user query to vector embeddings
  • Uses Qdrant to store and retrieve table embeddings based on similarity
  • Returns relevant table names and columns in JSON format
  • Frontend UI built using React for easy interaction

Folder Structure

project-root/
├── backend/
│   ├── app.py
│   ├── schema.json
│   ├── requirements.txt
├── frontend/
│   ├── public/
│   ├── src/
│   │   └── App.js
│   ├── package.json
│   ├── Dockerfile
├── docker-compose.yml
├── README.md

Installation & Setup

1. Clone the repository:

git clone https://github.com/your-username/ai-table-retriever.git
cd ai-table-retriever

2. Start Docker (Required for Qdrant and FastAPI):

Make sure Docker Desktop is running and Linux containers are enabled.

3. Build and start the containers:

docker-compose build
docker-compose up

4. Backend will be available at:

http://localhost:8000

Frontend (optional) will be available at:

http://localhost:3000

Sample Input (JSON)

POST to http://localhost:8000/retrieve-tables/ with:

{
  "description": "I want to see customer names and their purchases"
}

Sample Output (JSON)

{
  "tables": [
    {
      "table_name": "customers",
      "columns": ["customer_id", "name", "age", "location"]
    },
    {
      "table_name": "purchases",
      "columns": ["purchase_id", "customer_id", "product", "amount"]
    }
  ]
}

schema.json (Sample)

[
  {
    "table_name": "customers",
    "columns": ["customer_id", "name", "age", "location"]
  },
  {
    "table_name": "purchases",
    "columns": ["purchase_id", "customer_id", "product", "amount"]
  },
  {
    "table_name": "products",
    "columns": ["product_id", "product_name", "category"]
  }
]

✅ Requirements

Inside backend/requirements.txt:

fastapi
uvicorn
sentence-transformers
qdrant-client

AI Model Used

  • all-MiniLM-L6-v2 by Sentence Transformers (open-source, no API key needed)

Troubleshooting

  • Make sure Docker is running
  • Ensure schema.json is located inside the backend folder
  • If Qdrant is not connecting, check port 6333 is open
  • Use docker logs backend to debug FastAPI container

License

This project is open-source and free to use for educational and demo purposes.