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datagenx

A scalable engine that generates realistic and structured data from database schemas, enabling automated seeding, testing, and environment setup.

DataGen Local (Streamlit)

• Local CSV output at schemas/<schema_name>/Tables_generated/<table_name>.csv
• Schema metadata JSON at schemas/<schema_name>/instructions/

Setup

1. Create venv:
   • python -m venv .venv
2. Install:
   • python -m pip install -r requirements.txt
3. Fill .env:
   • OPENAI_API_KEY=<your_key>
   • OPENAI_MODEL=gpt-4o-mini (or any supported model)
4. Run Streamlit UI:
   • python -m streamlit run app.py

Headless FastAPI

The project includes a headless API that accepts one JSON payload with the schema prompt and table definitions, then runs the same backend generation flow used by the UI.

How it works

1. Start the uvicorn server to host the FastAPI app and expose local HTTP endpoints.
2. Send a JSON payload to POST /generate.
3. FastAPI validates the request body against the request model.
4. FastAPI passes the validated payload to the existing backend.
5. The backend creates or updates the schema, writes the schema metadata files, generates CSV data, runs validations, and returns output paths and status.

Start the API

Open a terminal in the repo root:

• C:\Users\aakas\Desktop\DLS\Datagenx\datagenx

Run:

• .\.venv\Scripts\python.exe -m uvicorn headless_api:app --host 127.0.0.1 --port 8000 --reload

After startup:

• Swagger UI: http://127.0.0.1:8000/docs
• Health check: http://127.0.0.1:8000/health

Main endpoint

• POST /generate

Payload shape:

{
  "schema_name": "Banking",
  "schema_prompt": "Generate realistic synthetic US banking data with referential integrity.",
  "replace_existing": true,
  "tables": [
    {
      "table_name": "CUSTOMERS",
      "num_entries": 1000,
      "ddl": "CREATE TABLE CUSTOMERS (\n  CUSTOMER_ID BIGINT PRIMARY KEY,\n  FULL_NAME VARCHAR(100) NOT NULL,\n  EMAIL VARCHAR(120) UNIQUE\n);",
      "instructions": "Generate realistic customer names and unique emails."
    },
    {
      "table_name": "ACCOUNTS",
      "num_entries": 1200,
      "ddl": "CREATE TABLE ACCOUNTS (\n  ACCOUNT_ID BIGINT PRIMARY KEY,\n  CUSTOMER_ID BIGINT NOT NULL,\n  ACCOUNT_NUMBER VARCHAR(20) UNIQUE,\n  FOREIGN KEY (CUSTOMER_ID) REFERENCES CUSTOMERS(CUSTOMER_ID)\n);",
      "instructions": "Every CUSTOMER_ID must exist in CUSTOMERS."
    }
  ]
}

Example request

Example payload files are available in the examples/ folder.

• Retail two-table example: examples/retail_two_table_payload.json

PowerShell:

Invoke-RestMethod -Method Post `
  -Uri "http://127.0.0.1:8000/generate" `
  -ContentType "application/json" `
  -InFile "examples\retail_two_table_payload.json"

cURL:

curl -X POST "http://127.0.0.1:8000/generate" \
  -H "Content-Type: application/json" \
  --data-binary "@examples/retail_two_table_payload.json"

Other endpoints

• GET /schemas to list saved schemas
• GET /schemas/{org_id} to inspect one saved schema and its statuses

Response

The POST /generate response includes:

• org_id
• schema_name
• output_dir
• generated_files
• validation_report_path
• log_path
• status

Notes

• Generated files are saved in project schemas/.
• Per schema, instruction folder contains:
  • ddl.json
  • instructions.json
  • schema_prompt.json
• UI keeps the same core generate/schema-management flow as your existing app.
• Query module and login are intentionally removed per requirement.