In this guide, I show how to connect a Custom GPT to live data warehouses .. AWS RDS MySQL and Azure MySQL ...simultaneously.
Just change database connections to connect to any other warehouse.
Run queries, transform, analyze and create charts with this Custom GPT. With text prompt, voice mode ...including in Hindi / Hinglish. A powerful analytics assistant.
Detailed implementation guide available at links below:
𝗠𝗲𝗱𝗶𝘂𝗺.𝗰𝗼𝗺
𝙁𝙧𝙞𝙚𝙣𝙙𝙨 𝙡𝙞𝙣𝙠 𝙛𝙤𝙧 𝙛𝙧𝙚𝙚 𝙖𝙘𝙘𝙚𝙨𝙨:
𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼 𝗟𝗶𝗻𝗸
✡ 𝗢𝗽𝗲𝗻𝗔𝗜'𝘀 𝗖𝘂𝘀𝘁𝗼𝗺 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝗮 𝘃𝗲𝗿𝘆 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗳𝗲𝗮𝘁𝘂𝗿𝗲, allowing a GPT to connect to any data warehouse or web resource outside with just a simple API call. With two way transfer of data. I show an easy way to setup and generate the OpenAPI JSON schema required for Custom Actions.
𝘊𝘶𝘴𝘵𝘰𝘮 𝘈𝘤𝘵𝘪𝘰𝘯𝘴 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮 𝘢 𝘎𝘗𝘛 𝘪𝘯𝘵𝘰 𝘢𝘯 𝘓𝘓𝘔 𝘱𝘰𝘸𝘦𝘳𝘦𝘥 𝘶𝘴𝘦𝘳 𝘪𝘯𝘵𝘦𝘳𝘧𝘢𝘤𝘦 𝘧𝘰𝘳 𝘺𝘰𝘶𝘳 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘢𝘴 𝘸𝘦𝘭𝘭 𝘢𝘴 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘦𝘴 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘦𝘥 𝘵𝘰 𝘢𝘭𝘭 𝘺𝘰𝘶𝘳 𝘣𝘢𝘤𝘬𝘦𝘯𝘥, 𝘸𝘦𝘣 𝘢𝘯𝘥 𝘥𝘪𝘨𝘪𝘵𝘢𝘭 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴
✡ 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗟𝗟𝗠 𝗔𝗽𝗽 - 𝗗𝗲𝗽𝗹𝗼𝘆𝗲𝗱 𝗼𝗻 𝗣𝘂𝗯𝗹𝗶𝗰 𝗪𝗲𝗯𝘀𝗶𝘁𝗲 - 𝗢𝘂𝘁𝘀𝗶𝗱𝗲 𝗚𝗣𝗧 𝗦𝘁𝗼𝗿𝗲
In my last post I demoed Analytics Assistant LLM Aapp deployed on a public website outside of GPT store. Connected to a FastAPI server. Along with a detailed step-by-step guide on how to build and deploy one
I use a modular flexible FastAPI architecture. All I did here was just plug in Custom GPT to the same FastAPI Server. And I was all set (shared in the guide)
I have successfully implemented both frameworks for clients... different clients / different use cases for both.
✡ 𝗪𝗛𝗔𝗧 𝗜𝗦 𝗖𝗢𝗩𝗘𝗥𝗘𝗗 𝗜𝗡 𝗧𝗛𝗘 𝗜𝗠𝗣𝗟𝗘𝗠𝗘𝗡𝗧𝗔𝗧𝗜𝗢𝗡 𝗚𝗨𝗜𝗗𝗘:
♦ Analytics Assistant CustomGPT: How it works — Architecture and Process Flow
♦ Implementation: Step 1 — FastAPI Server — Code
♦ Implementation: Step 2 — FastAPI Server — Deploy
♦ Implementation: Step 3 — Custom GPT: Setup Custom Function
♦ Data Warehouse — Azure — AWS — Others
♦ Additional Consideration: Security, Monitoring, Tracing, Feedback
♦ Cost Considerations
♦ Resources