Simplifying Openai Function Calling With Structured Output A 2024 Guid

Emily Johnson

-Mar 12, 2026, 12:04 PM

simplifying openai function calling with structured output a 2024 guid

OpenAI’s function calling feature, introduced earlier, empowers developers to create interactive applications by enabling language models to call functions. This feature allows models to return structured data that can trigger APIs, extract data, or perform tasks. Recently, OpenAI has enhanced this capability with Structured Outputs, simplifying the way developers interact with models. Now, instead of defining complex JSON schemas for each function, the new feature allows developers to define output structures using simpler SDK tools, making function calling more accessible and efficient. In this blog, we’ll compare our previous blog “OpenAI Function Calling With External API Examples” with the new structured output feature, demonstrating how this new approach significantly reduces complexity while improving functionality. Before starting with this blog, please generate API keys for Finnhub and Alpha Vantage from our previous blog.

Once, you have generated the APIs, you can start with the following steps: Install the required libraries using the below code: Next, define the necessary variables that hold the values for API keys and the OpenAI model name you want to use. After that, initialize the OpenAI and Finnhub clients for further processing. JSON is one of the most widely used formats in the world for applications to exchange data. Structured Outputs is a feature that ensures the model will always generate responses that adhere to your supplied JSON Schema, so you don’t need to worry about the model omitting a required key, or...

Some benefits of Structured Outputs include: In addition to supporting JSON Schema in the REST API, the OpenAI SDKs for Python and JavaScript also make it easy to define object schemas using Pydantic and Zod respectively. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. Structured Outputs is available in our latest large language models, starting with GPT-4o. Older models like gpt-4-turbo and earlier may use JSON mode instead. OpenAI’s function calling feature, introduced earlier, empowers developers to create interactive applications by enabling language models to call functions.

This feature allows models to return structured data that can trigger APIs, extract data, or perform tasks. Recently, OpenAI has enhanced this capability with Structured Outputs, simplifying the way developers interact with models. Now, instead of defining complex JSON schemas for each function, the new feature allows developers to define output structures using simpler SDK tools, making function calling more accessible and efficient. In this blog, we’ll compare our previous blog “OpenAI Function Calling With External API Examples” with the new structured output feature, demonstrating how this new approach significantly reduces complexity while improving functionality. Before starting with this blog, please generate API keys for Finnhub and Alpha Vantage from our previous blog. Once, you have generated the APIs, you can start with the following steps:

Install the required libraries using the below code: Next, define the necessary variables that hold the values for API keys and the OpenAI model name you want to use. After that, initialize the OpenAI and Finnhub clients for further processing. In January 2024, OpenAI introduced Function Calling, a powerful feature designed to allow developers to generate structured JSON output with GPT models. This feature addresses one of the biggest pain points developers have faced when using large language models: dealing with unstructured text. With OpenAI Function Calling, developers no longer need to rely on prompt engineering or regular expressions (RegEx) to parse responses into usable data formats.

In this guide, we’ll walk through how to use Function Calling, its applications, and how it can help you build more reliable AI applications. Before diving into OpenAI Function Calling, here’s a detailed guide on how to learn AI that you may refer to. Before Function Calling, developers using the OpenAI API often faced challenges in working with unstructured data. When trying to extract structured information like names, grades, or other specific details from text, it was difficult to ensure consistent results. Developers had to depend on prompt engineering or RegEx to parse the text into structured formats like JSON. This approach worked but was inefficient and prone to errors.

OpenAI Function Calling allows GPT models like GPT-3.5 and GPT-4 to take user-defined functions and output structured JSON directly, which makes data extraction more reliable and consistent. With this capability, developers can integrate AI seamlessly into applications that require structured outputs, such as forms, databases, or other systems that require regular, predictable data. Before diving into function calling, let’s explore how to extract data without this feature. Function calling and structured outputs let you go from chatbots that just talk to agents that interact with the world. They’re two of the most important techniques for building LLM applications. Function calling let LLMs access external tools and services.

Structured outputs ensure that the data coming back from your models is ready to integrate These are two of the most important techniques for building LLM applications. I can tell you that mastering them will make your applications better and easier to maintain. Function calling refers to the ability to get LLMs to use external tools or functions. It matters because it gives LLMs more capabilities, allows them to talk to external systems, and enables complex task automation. This is one of the key features that unlocked agents.

Here’s a diagram that illustrates how function calling works: In August 2024, OpenAI introduced "Structured Outputs", a remarkable new capability in the Chat Completions API and Assistants API, and successor to the earlier "JSON mode." As the name suggests, Structured Outputs enables developers to obtain consistent, predictable JSON outputs from OpenAI chatbots, according to a predefined schema. Essentially, it's just a reliable way to get ChatGPT to output JSON. In this guide, we'll explore how to leverage Structured Outputs by building a simple chatbot capable of providing detailed flight information in response to a simple user query. We'll then use Helicone to monitor our chatbot's performance.

Structured Outputs allow you to generate structured data from unstructured inputs to your chatbots. Unlike unstructured text, structured outputs are organized, making them easier to parse, manipulate, and integrate into various applications. The key difference between the new Structured Outputs and the likely soon-to-be-deprecated JSON mode lies in its enhanced reliability. OpenAI strongly recommends transitioning to Structured Outputs for any functionality previously handled by JSON mode. OpenAI has introduced a new feature in their API that allows developers to enforce specific JSON schemas for model responses.This includes a 'strict' mode for function calling and a new 'response_format' parameter. Generating structured data from unstructured input is one of the a key use of AI in today's application.

Developers often use the OpenAI API to build powerful tools that can fetch data, answer questions through function calls, organize data for entry, and build complex multi-step agentic workflows that allow LLMs to take... Previously, developers faced challenges due to the basic limitations of language models. They would use various open source tools, craft careful prompts, and frequently resend requests to make sure the outputs from the models matched the needed formats for their systems to work well together. Structured Outputs tackles these issues by forcing OpenAI models to stick to the formats developers provide and by training our models to better understand and handle complex formats. The way they have achieved this is very interesting, read on for the implementation and in-depth explanation of their approach. Developers can now easily integrate schemas by using Pydantic or Zod objects.

These SDKs automatically convert the specified data types into compatible JSON schemas, deserialize JSON responses into structured data formats, and handle any refusals seamlessly. OpenAI used a two part approach for this. They initially trained their latest model, gpt-4o-2024-08-06, to accurately understand and generate outputs based on complex schemas. Despite reaching a 93% benchmark score, the model wasn’t reliable enough for robust applications. Thus, they adopted a deterministic engineering approach called Constraint Decoding to tighten output restrictions, achieving 100% reliability. The new model, gpt-4o-2024–08–06, with Structured Outputs scores a perfect 100% on OpenAI's structured extraction evaluation.

In comparison, gpt-4–0613 scores less than 40%. Source: OpenAI's blog post This new feature ensures that the model's output will exactly match the JSON Schemas provided by developers, making it easier to build powerful assistants and extract structured data. ⚠️ If you want to use this technique with GPT-4o or other LLMs to extract clean structured data from any PDF, Word doc, or website, check out this open source extractor tool Under the hood, OpenAI uses a technique called constrained sampling or constrained decoding. Instead of allowing the model to select any token from the vocabulary, it constrains the output to only tokens that are valid according to the supplied schema.

This is done dynamically, so the model can still generate flexible and diverse responses while adhering to the specified structure. The constrained decoding approach used by OpenAI involves dynamically determining which tokens are valid after each token is generated, based on the previously generated tokens and the rules within the context-free grammar (CFG) that... This ensures that the model's output always adheres to the specified schema. I'm more of a "learn-by-example" man, so I'll give the simplest possible example I could come up with. In this example, we define a Person class using Pydantic, which has two fields: name and age

Simplifying Openai Function Calling With Structured Output A 2024 Guid

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