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ChatGPT connected to Supabase, Neon and Aiven databases

ChatGPT connected to multiple databases simultaneously for data analysis and visualization. Built on a fixed FastAPI connector + Custom GPT actions.

Try Live GPT LLM Context (.txt)

How this GPT Works

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Direct connection to three databases:

  • supabase_postgres -> ODI cricket ball-by-ball (~2003-2025)
  • neon_postgres -> T20 cricket ball-by-ball (~2005-2025)
  • aiven_postgres -> Tour de France riders & stages (men: 1903-2025, women: 2022-2025)

Semantic layers are pre-mapped. GPT translates your question into SQL, executes silently, and explains results in plain language. Supports charts, aggregations, rankings, and detail lookups.

What It Can Do

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  • Compute player stats: runs, strike rates, averages (ODI/T20)
  • Generate rankings: top scorers, best bowlers, most wins
  • Analyze Tour de France history: winners, distances, average speeds, jersey holders
  • Create visualizations: bar charts, line charts, comparisons
  • Always contextualizes answers by dataset coverage (e.g. ODI 2003-2025)

Important Note

ODI/T20 data = ball-by-ball but not every match globally. Tour de France = complete for men (1903-2025), women (2022-2025).

How to Use It

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  1. Click Chat Link (Custom GPT)
  2. Ask natural language questions:
    • "Top 10 ODI strikers by runs off the bat?"
    • "Tour de France winners 2015-2025 with avg speed?"
  3. Get results in tables + charts
  4. The GPT itself will guide you if the requested data is out of scope

Setup (for your own deployment)

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For full steps, see the README in the GitHub Repo link below.

Quick Overview

  1. Deploy FastAPI server (app:app)
  2. Set .env with your DB URLs + API Key
  3. Update CUSTOM_GPT_ACTION_SCHEMA.json -> server URL + API Key
  4. Upload semantic layer files:
    • CRICKET_ODI_T20_DATA_SCHEMA.yaml
    • CYCLING_TOUR_DE_FRANCE_SCHEMA.yaml
  5. Apply CUSTOM_GPT_SYSTEM_INSTRUCTIONS.md as system prompt
  6. Connect action schema + knowledge files inside Custom GPT builder

Resources

Complete Source Code

FastAPI server code with schemas and system instructions for Cricket & Tour de France data analysis
LLM Context (.txt)

Plain-text context file for feeding to AI agents about this GPT
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Built on OpenAI's GPT Platform

Custom GPT built on ChatGPT's Custom GPT framework. No custom UI builds or complex agent setups - just connect, chat, and analyze through the familiar ChatGPT interface.

Bugs,issues,questions? Drop a note: [email protected]