{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Dario Radečić",
  "title_listed": "How to Use OpenAI ChatGPT API... In SQL",
  "keywords": "SQL,ChatGPT,API",
  "domain": "大模型算法",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/how-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e/",
  "publish_date": "2024-04",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "d73f097412d4",
  "collected_at": "2026-10-01T21:19:43",
  "title": "How to Use OpenAI ChatGPT API... In SQL | Towards Data Science",
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In SQL\nCould SQL be your next choice for building AI applications?\n[Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nApril 4, 20245 min read\n![Photo by Resource Database on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/0LL1eWTDaHqaCIJCJ.jpg)\nPhoto by [Resource Database](https://unsplash.com/@resourcedatabase?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nWhen it comes to ChatGPT and using the OpenAI API, SQL isn't the first language that comes to mind. But it should - it's the **language of data** , and the fact you can send HTTP requests from SQL opens up a world of possibilities.\nToday's article will show you how to write a custom Oracle SQL function using PL/SQL. It will take in a question string and return formatted JSON. Oracle's `dbms_cloud` package will do most of the heavy lifting, as it's responsible for making API calls. If you're using a different database vendor, I'm sure you can find an alternative package and function set to do the job.\nLet's begin by covering the prerequisites you'll need to follow along.\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Prompt Engineering](https://roadmap.sh/prompt-engineering) and [SQL](https://roadmap.sh/sql) roadmaps.\n## ChatGPT in SQL - The Prerequisites\nAs mentioned in the introduction, I'm using Oracle SQL running on an always free instance of **Autonomous Database 21c** provisioned on [Oracle Cloud](https://www.oracle.com/cloud/). If you want to follow along, register for a free account, provision a database instance, and download a connection wallet.\nThe other thing you'll need is the [OpenAI API key](https://www.maisieai.com/help/how-to-get-an-openai-api-key-for-chatgpt). The linked article shows you how to get one in minutes.\nAnd that's pretty much it! Let's get down to business.\n## OpenAI API - Testing the Chat Completion Endpoint\nThe chat completion example we'll implement in SQL is quite straightforward. OpenAI's [official documentation](https://platform.openai.com/docs/api-reference/making-requests) shows you what goes into making a request to their API:\n![Image 1 - OpenAI requests documentation \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1LReeodcgCLmUBFIJrc8dcg.png)\nImage 1 - OpenAI requests documentation (image by author)\nSince there's no third-party library for OpenAI in SQL (like in Python), you'll have to opt for a more manual approach. **The theory is** - if you can run the above curl command and get a response back, you can do the same in SQL.\nThe easiest way to demonstrate this is through [Postman](https://www.postman.com/). Populate the headers and JSON body of a new POST request to `https://api.openai.com/v1/chat/completions` and you should get a response similar to mine:\n![Image 2 - Making a request to the OpenAI API through Postman \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1_mh7bzhFweCfmHfGPLcBNA.png)\nImage 2 - Making a request to the OpenAI API through Postman (image by author)\nNot the most impressive GPT response, but it's working. Let's bring it to SQL next.\n## ChatGPT in SQL - How to Use OpenAI API in a Custom PL/SQL Function\nPL/SQL allows you to define custom functions, among other things. The `get_gpt_response()` function takes in a string question and returns CLOB, which is a special data type for storing strings that might be too large for the default VARCHAR2 type.\nInside the function, the `v_api_key` constant holds value to your OpenAI API key, so don't forget to change it.\nThe function uses Oracle's `dbms_cloud` package to send an HTTP request to OpenAI's chat completion endpoint. The `send_request()` procedure needs the following parameters:\n  * `uri` - The URL of the endpoint.\n  * `method` - HTTP method used for the request. You need to set yours to POST.\n  * `headers` - JSON object that specifies request headers. As you've seen earlier in the article, you need to specify `Content-Type` and `Authorization`.\n  * `body` - JSON object converted to BLOB. It contains the data you're sending with your request, such as the model you want to use, its temperature parameter (randomness), and the question you want GPT to answer.\n\n\nThe result of `send_request()` is stored into `v_response` variable which is then returned as a text to the user:\nsql\n\n```\ncreate or replace function get_gpt_response(\n    p_question varchar2\n) return clob\nis\n    v_api_key constant varchar2(100) := '<your-api-key>';\n    v_response dbms_cloud_types.resp;\nbegin\n    v_response := dbms_cloud.send_request(\n        uri     => 'https://api.openai.com/v1/chat/completions',\n        method  => dbms_cloud.method_post,\n        -- Headers must be of type JSON_OBJECT\n        headers => json_object(\n            'Content-Type'  value 'application/json',\n            'Authorization' value 'Bearer ' || v_api_key\n        ),\n        -- Request body must be a JSON structure converted to BLOB\n        body    => utl_raw.cast_to_raw(\n            json_object(\n                'model'    value 'gpt-3.5-turbo',\n                'messages' value json_array(\n                    json_object(\n                        'role'    value 'user',\n                        'content' value p_question\n                    )\n                ),\n                'temperature' value 0.5\n            )\n        )\n    );\n    return dbms_cloud.get_response_text(v_response);\nend get_gpt_response;\n/\n```\n\nOnce you run the above snippet, the function will be available to use.\nLet's see if it works as expected:\nsql\n\n```\nselect \n    json_query(get_gpt_response('What is the capital of United States?'), '$' returning clob pretty) as response \nfrom dual;\n```\n\nHere's the response I got:\n![Image 3 - Making a request to the OpenAI API through SQL \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/183omDT-PxXzXhn0-YyXSQ.png)\nImage 3 - Making a request to the OpenAI API through SQL (image by author)\n**Works like a charm!** The only problem is - the response is shown as JSON, which isn't something you typically want when working with relational databases.\nLuckily, Oracle has superb JSON support, which means you can extract relevant fields and format the response as a plain database table:\nsql\n\n```\nwith response as (\n    select get_gpt_response('What is the capital of United States?') as data\n    from dual\n)\nselect\n    jt.*\nfrom response r,\n    json_table(\n        r.data,\n        '$'\n        columns(\n            id                varchar2(50)   path '$.id',\n            answer            varchar2(1000) path '$.choices[0].message.content',\n            prompt_tokens     number         path '$.usage.prompt_tokens',\n            completion_tokens number         path '$.usage.completion_tokens',\n            total_tokens      number         path '$.usage.total_tokens'\n        )\n    ) jt;\n```\n\nHere's the output:\n![Image 4 - OpenAI response formatted as a table \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/100MM3Dr9tAOZQ7MeuDk11w.png)\nImage 4 - OpenAI response formatted as a table (image by author)\nAnd from here, **the world is your oyster**. You can use the results as-is, or you can save them into a table. The token usage information should be a good indicator of resources spent, which is a big plus if you plan to run this function frequently.\n## What You Can (And Should) Improve\nThe solution you implemented today works but is rather basic and could benefit from a couple of tweaks:\n  * **Exception handling** - Not implemented at all currently. Make sure you catch potential `dbms_cloud` errors, as well as other, more generic ones.\n  * **Hardcoded endpoint** - The function sends requests only to the chat completion endpoint in the current state. Making the endpoint dynamic would be preferred.\n  * **The response is not persisted** - It would be a good idea to save the response to a database table before returning it to the user.\n\n\nAll of these areas are easy enough to address if you have some basic SQL skills, so I'll leave it up to you.\n* * *\n## Summing up OpenAI API in SQL\nNot many people expect SQL to be a viable option for making HTTP calls to OpenAI API. Heck, many newcomers aren't aware SQL can do more than basic data manipulation. It's the language of data, and as such can do almost anything Python can, but **without data movement**.\nToday's example was limited to Oracle SQL and PL/SQL, but I'm sure you can find a way to implement my solution in SQL Server, MySQL, and Postgres.\nStay tuned for more interesting SQL use cases, such as machine learning and spatial data analysis.\nThanks for reading.\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657095692_93hujy.png)\nWritten By\n## [Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\nhttps://writingfordevs.substack.com\n[See all from Dario Radečić](https://towardsdatascience.com/author/radecicdario/)\n[Ai App Development](https://towardsdatascience.com/tag/ai-app-development/)[ChatGPT](https://towardsdatascience.com/tag/chatgpt/)[Data Science](https://towardsdatascience.com/tag/data-science/)[OpenAI](https://towardsdatascience.com/tag/openai/)[Sql](https://towardsdatascience.com/tag/sql/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e%2F&title=How%20to%20Use%20OpenAI%20ChatGPT%20API...%20In%20SQL)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e%2F&title=How%20to%20Use%20OpenAI%20ChatGPT%20API...%20In%20SQL)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openai-chatgpt-api-in-sql-9b60d2526a9e%2F&text=How%20to%20Use%20OpenAI%20ChatGPT%20API...%20In%20SQL)\n\n\nTowards Data Science is a community publication. 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