{ "source_file": "0930", "source_platform": "Medium", "author": "John Adeojo", "title_listed": "How to Use OpenAI's Function Calling", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/how-to-use-openais-function-calling-e35bdac88ae7/", "publish_date": "2023-07-25", "corpus_stage": "", "notes": "", "id": "13e508609d7b", "collected_at": "2026-10-02T06:52:12", "title": "How to Use OpenAI's Function Calling | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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In this article, I aim to clarify function calling in the time it takes you to make a cup of coffee.\nIf you have aspirations to build LLM apps, integrate LLMs into your business, or simply expand your knowledge in this area, then this article is for you.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n## What's So Great About Function Calling?\nFunction calling allows us to develop natural language interfaces atop our existing APIs. If this sounds confusing to you, don't worry - the details will become clearer as you read on.\nSo, what does a natural language API look like? I believe it's best to demonstrate this diagrammatically. Here's an example app that uses function calling to enable users to find flights.\n![Image by Author: Diagrammatic representation of function calling](../images/001.png)\nImage by Author: Diagrammatic representation of function calling\nTo implement this type of app without function calling, you would need to prompt the user to input the flight information using menus, selection boxes, and the like.\nFunction calling also facilitates the possibility of users making requests by voice. All you would need is an additional transcription service, and voilà, you have an AI personal assistant.\nHopefully, you should now be forming a clearer picture of function calling and its purpose. Let's cement this newfound knowledge with some technical walkthroughs.\n* * *\n## Function Calling with One Function\nLet's examine a simple use case atop a weather API.\nThe most basic use case involves function calling with one function. To illustrate this, I have modelled an API that provides a temperature forecast - [see here](https://gist.github.com/john-adeojo/13ae233ecfb25aa6908ae834ef16ef53).\nPlease take a moment to study this temperature API; note that it has two arguments: location and date.\nNow, suppose we wanted to build an app based on this API. The first step we might take is to write a script that utilises a function call for our user queries. It would look something like this:\nLet's discuss the function calling. We define our functions as a list of dictionaries. The function allows us to extract arguments from a user query, which we can then use to feed our API. In our case, the arguments we wish to extract are \"location\" and \"date\".\nWe state the name of our function, in this case, \"get_temperature\", and provide a description of what it does.\nNext, we need to describe the arguments of the function, which is crucial. What we receive back from the function call is influenced by how we do this. Take a look at how I have outlined the descriptions and types for the two arguments \"location\" and \"date\".\nThe descriptions are significant because function calling is powered by OpenAI's large language models, which interpret the descriptions and respond accordingly.\nNext, we set up our OpenAI model. The setup resembles a standard text generation use case with two key differences - the \"functions\" parameter and the \"function_call\" parameter.\nWe pass the functions we defined in the dictionary to the \"functions\" parameter. For the \"function_call\" parameter, we set it to \"auto\", allowing the large language model to decide when to use a function or not use one at all.\nSo, let's see what happens when we run a query through this function call.\nHere's our query:\n> query = \"What's the temperature forecast in Berlin on the 1st of September 2023?\"\nAnd here's the response:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_temperature\",\n \"arguments\": \"{n \"location\": \"Berlin\",n \"date\": \"2023-09-01\"n}\"\n }\n}\n```\n\nThe first thing to note is that the function call has returned a structured response, a json object.\nSecondly, the function call has determined from our query that we wish to use the \"get_temperature\" function, and it has parsed the arguments for that function (location and date) accordingly.\nThis is excellent as we can now send these arguments to the weather API we constructed and obtain a response about the temperature.\npython\n\n```\n# Parse the location and date from our function call response \n\ndef parse_function(response):\n parsed_output = response[\"function_call\"][\"arguments\"]\n parsed_output = json.loads(parsed_output)\n return parsed_output\n\nlocation = parse_function(response)['location']\ndate = parse_function(response)['date']\n\n# Get our weather data\n\ndata = get_temperature(location, date)\n```\n\nAnd here's the response we get back from our weather API:\nbash\n\n```\n'{\"location\": \"Berlin\", \"date\": \"2023-09-01\", \"temerature\": 9.72, \"scale\": \"Farenheit\"}'\n```\n\n_**Note**_ _: It's important to understand that function calling does not execute the function. It merely parses out the arguments for your function from a natural language input._\n* * *\n## Function Calling with Multiple Functions\nWe can even specify multiple functions to \"call\" from. This is particularly useful when we have a natural language interface sitting atop multiple APIs. Let's illustrate this with an example.\nTo complement our weather API, I have modelled a news [headline API](https://gist.github.com/john-adeojo/d90431d2d6847c01af3fbe497b1267ad), which generates a mock news headline based on a topic.\nThe topic itself will be requested from a natural language query, which we can parse using function calling.\nWe can add this to our existing function call. Here's how it appears:\nBecause we have set the \"function_call\" parameter to \"auto\", the large language model can deduce the most suitable function to call based solely on the query. Let's see how this works for two different queries.\n> query_1 = \"What's the temperature forecast in NYC at the end of September 2023?\"\nResponse from query 1:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_temperature\",\n \"arguments\": \"{n \"location\": \"NYC\",n \"date\": \"2023-09-30\"n}\"\n }\n}\n```\n\nNow let's run the multi-function function call on a different query.\n> query_2 = \"Give me the latest headline news on Berlin\"\nResponse from query 2:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_current_news\",\n \"arguments\": \"{n \"topic\": \"Berlin\"n}\"\n }\n}\n```\n\nIn both cases, the function call has correctly selected which function to get the arguments for based on the queries.\n* * *\n## What If We Don't Use It?\nAt this point, you might be asking yourself: \"This is great, but can't I achieve the same result with the chat completion API and a prompt? Why do we need function calling at all?\"\nWell, the short answer is structure. While the chat completion API is trained to respond to a user query, you can prompt it to perform a \"function call\", but the output you get may not be structured, making it difficult to parse the arguments.\nConsider this for example:\nHere we have skipped function calling altogether and simply asked the chat completion API to respond with our arguments with some prompting.\nAnd the response:\nbash\n\n```\n'The news topic required is \"Berlin\".'\n```\n\nAlthough the answer is correct, it's more difficult to parse as an argument because it is unstructured.\nThere is also the issue of consistency. You can attempt to enforce some structure through prompting, but the responses may not always be structured as you desire.\nFunction calling guarantees a structured response every time, making it a more reliable method of parsing arguments to feed a downstream function.\n* * *\n## Conclusion\nFunction calling opens the door to the development of a host of new LLM-based applications. We can now consistently generate structured outputs from unstructured data.\nIf you wish to explore how you can build your first LLM app, read this:\n> [**How to Build an LLM Application**](https://towardsdatascience.com/how-to-build-an-llm-application-360848c957db)\nThanks for reading.\nThe full notebook, is available via my [GitHub](https://github.com/john-adeojo/function_calling_tutorial) repo. There is also a live video tutorial on [YouTube](https://youtu.be/VswwV9RJN7s).\n* * *\nIf you're keen to enhance your skills in artificial intelligence, join the waiting list for [my course](https://www.data-centric-solutions.com/course), where I will guide you through the process of developing large language model powered applications.\nIf you're seeking AI-transformation for your business, book a discovery call today.\n> [**Brainqub3 | AI software development**](https://www.brainqub3.com/)\nFor more insights on artificial intelligence, data science, and large language models you can subscribe to the [YouTube](https://www.youtube.com/channel/UCkXe-exqi25V4GnZendgEaA) channel.\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![](../images/body-ead068aa953e7b688a96.png)\nWritten By\n## [John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[See all from John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Large Language Models](https://towardsdatascience.com/tag/large-language-models/)[OpenAI](https://towardsdatascience.com/tag/openai/)[Programming](https://towardsdatascience.com/tag/programming/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&title=How%20to%20Use%20OpenAI's%20Function%20Calling)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&title=How%20to%20Use%20OpenAI's%20Function%20Calling)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&text=How%20to%20Use%20OpenAI's%20Function%20Calling)\n\n\nTowards Data Science is a community publication. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# How to Use OpenAI's Function Calling\nAn Overview of Function Calling and Its Implications for Building LLM Apps\n[John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\nJuly 25, 20236 min read\n![Image by Author: Generated with Midjourney](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1Wz5WwZbhs-W92GJR97913A.png)\nImage by Author: Generated with Midjourney\n## Structuring the Unstructured\nFunction calling is an innovation from [OpenAI](https://openai.com/) that has expanded the possibilities when it comes to app development around large language models.\nHowever, I have found that it remains misunderstood by some. In this article, I aim to clarify function calling in the time it takes you to make a cup of coffee.\nIf you have aspirations to build LLM apps, integrate LLMs into your business, or simply expand your knowledge in this area, then this article is for you.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Computer Science](https://roadmap.sh/computer-science) roadmaps.\n## What's So Great About Function Calling?\nFunction calling allows us to develop natural language interfaces atop our existing APIs. If this sounds confusing to you, don't worry - the details will become clearer as you read on.\nSo, what does a natural language API look like? I believe it's best to demonstrate this diagrammatically. Here's an example app that uses function calling to enable users to find flights.\n![Image by Author: Diagrammatic representation of function calling](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1gWh9ICOtpjb0sJxoqb0eUQ.png)\nImage by Author: Diagrammatic representation of function calling\nTo implement this type of app without function calling, you would need to prompt the user to input the flight information using menus, selection boxes, and the like.\nFunction calling also facilitates the possibility of users making requests by voice. All you would need is an additional transcription service, and voilà, you have an AI personal assistant.\nHopefully, you should now be forming a clearer picture of function calling and its purpose. Let's cement this newfound knowledge with some technical walkthroughs.\n* * *\n## Function Calling with One Function\nLet's examine a simple use case atop a weather API.\nThe most basic use case involves function calling with one function. To illustrate this, I have modelled an API that provides a temperature forecast - [see here](https://gist.github.com/john-adeojo/13ae233ecfb25aa6908ae834ef16ef53).\nPlease take a moment to study this temperature API; note that it has two arguments: location and date.\nNow, suppose we wanted to build an app based on this API. The first step we might take is to write a script that utilises a function call for our user queries. It would look something like this:\nLet's discuss the function calling. We define our functions as a list of dictionaries. The function allows us to extract arguments from a user query, which we can then use to feed our API. In our case, the arguments we wish to extract are \"location\" and \"date\".\nWe state the name of our function, in this case, \"get_temperature\", and provide a description of what it does.\nNext, we need to describe the arguments of the function, which is crucial. What we receive back from the function call is influenced by how we do this. Take a look at how I have outlined the descriptions and types for the two arguments \"location\" and \"date\".\nThe descriptions are significant because function calling is powered by OpenAI's large language models, which interpret the descriptions and respond accordingly.\nNext, we set up our OpenAI model. The setup resembles a standard text generation use case with two key differences - the \"functions\" parameter and the \"function_call\" parameter.\nWe pass the functions we defined in the dictionary to the \"functions\" parameter. For the \"function_call\" parameter, we set it to \"auto\", allowing the large language model to decide when to use a function or not use one at all.\nSo, let's see what happens when we run a query through this function call.\nHere's our query:\n> query = \"What's the temperature forecast in Berlin on the 1st of September 2023?\"\nAnd here's the response:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_temperature\",\n \"arguments\": \"{n \"location\": \"Berlin\",n \"date\": \"2023-09-01\"n}\"\n }\n}\n```\n\nThe first thing to note is that the function call has returned a structured response, a json object.\nSecondly, the function call has determined from our query that we wish to use the \"get_temperature\" function, and it has parsed the arguments for that function (location and date) accordingly.\nThis is excellent as we can now send these arguments to the weather API we constructed and obtain a response about the temperature.\npython\n\n```\n# Parse the location and date from our function call response \n\ndef parse_function(response):\n parsed_output = response[\"function_call\"][\"arguments\"]\n parsed_output = json.loads(parsed_output)\n return parsed_output\n\nlocation = parse_function(response)['location']\ndate = parse_function(response)['date']\n\n# Get our weather data\n\ndata = get_temperature(location, date)\n```\n\nAnd here's the response we get back from our weather API:\nbash\n\n```\n'{\"location\": \"Berlin\", \"date\": \"2023-09-01\", \"temerature\": 9.72, \"scale\": \"Farenheit\"}'\n```\n\n_**Note**_ _: It's important to understand that function calling does not execute the function. It merely parses out the arguments for your function from a natural language input._\n* * *\n## Function Calling with Multiple Functions\nWe can even specify multiple functions to \"call\" from. This is particularly useful when we have a natural language interface sitting atop multiple APIs. Let's illustrate this with an example.\nTo complement our weather API, I have modelled a news [headline API](https://gist.github.com/john-adeojo/d90431d2d6847c01af3fbe497b1267ad), which generates a mock news headline based on a topic.\nThe topic itself will be requested from a natural language query, which we can parse using function calling.\nWe can add this to our existing function call. Here's how it appears:\nBecause we have set the \"function_call\" parameter to \"auto\", the large language model can deduce the most suitable function to call based solely on the query. Let's see how this works for two different queries.\n> query_1 = \"What's the temperature forecast in NYC at the end of September 2023?\"\nResponse from query 1:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_temperature\",\n \"arguments\": \"{n \"location\": \"NYC\",n \"date\": \"2023-09-30\"n}\"\n }\n}\n```\n\nNow let's run the multi-function function call on a different query.\n> query_2 = \"Give me the latest headline news on Berlin\"\nResponse from query 2:\njson\n\n```\n JSON: {\n \"role\": \"assistant\",\n \"content\": null,\n \"function_call\": {\n \"name\": \"get_current_news\",\n \"arguments\": \"{n \"topic\": \"Berlin\"n}\"\n }\n}\n```\n\nIn both cases, the function call has correctly selected which function to get the arguments for based on the queries.\n* * *\n## What If We Don't Use It?\nAt this point, you might be asking yourself: \"This is great, but can't I achieve the same result with the chat completion API and a prompt? Why do we need function calling at all?\"\nWell, the short answer is structure. While the chat completion API is trained to respond to a user query, you can prompt it to perform a \"function call\", but the output you get may not be structured, making it difficult to parse the arguments.\nConsider this for example:\nHere we have skipped function calling altogether and simply asked the chat completion API to respond with our arguments with some prompting.\nAnd the response:\nbash\n\n```\n'The news topic required is \"Berlin\".'\n```\n\nAlthough the answer is correct, it's more difficult to parse as an argument because it is unstructured.\nThere is also the issue of consistency. You can attempt to enforce some structure through prompting, but the responses may not always be structured as you desire.\nFunction calling guarantees a structured response every time, making it a more reliable method of parsing arguments to feed a downstream function.\n* * *\n## Conclusion\nFunction calling opens the door to the development of a host of new LLM-based applications. We can now consistently generate structured outputs from unstructured data.\nIf you wish to explore how you can build your first LLM app, read this:\n> [**How to Build an LLM Application**](https://towardsdatascience.com/how-to-build-an-llm-application-360848c957db)\nThanks for reading.\nThe full notebook, is available via my [GitHub](https://github.com/john-adeojo/function_calling_tutorial) repo. There is also a live video tutorial on [YouTube](https://youtu.be/VswwV9RJN7s).\n* * *\nIf you're keen to enhance your skills in artificial intelligence, join the waiting list for [my course](https://www.data-centric-solutions.com/course), where I will guide you through the process of developing large language model powered applications.\nIf you're seeking AI-transformation for your business, book a discovery call today.\n> [**Brainqub3 | AI software development**](https://www.brainqub3.com/)\nFor more insights on artificial intelligence, data science, and large language models you can subscribe to the [YouTube](https://www.youtube.com/channel/UCkXe-exqi25V4GnZendgEaA) channel.\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/1787657091747_phu3q1.png)\nWritten By\n## [John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[See all from John Adeojo](https://towardsdatascience.com/author/johnadeojo/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Data Science](https://towardsdatascience.com/tag/data-science/)[Large Language Models](https://towardsdatascience.com/tag/large-language-models/)[OpenAI](https://towardsdatascience.com/tag/openai/)[Programming](https://towardsdatascience.com/tag/programming/)\nShare This Article\n * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&title=How%20to%20Use%20OpenAI's%20Function%20Calling)\n * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&title=How%20to%20Use%20OpenAI's%20Function%20Calling)\n * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-use-openais-function-calling-e35bdac88ae7%2F&text=How%20to%20Use%20OpenAI's%20Function%20Calling)\n\n\nTowards Data Science is a community publication. 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