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Openai Function Calling Tutorial Generate Structured Output

Emily Johnson
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openai function calling tutorial generate structured output

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. 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: When working with the OpenAI API directly, you have two main options for obtaining structured output responses from GPT models: JSON mode and Function calling.

While both are powerful tools, they also have their limitations. Understanding when to use each can enhance your workflow and give you more control over the output. After learning about these two methods, we'll dive into Instructor to gain even greater control over the output from OpenAI's models. Instructor was covered in this great "Pydantic is all you need" talk by Jason Liu. Using JSON output in your LLM applications provides more control and validation over the generated responses. It ensures that the output is always a valid JSON string, making it easier to parse and process the data in your application.

In JSON mode, the model generates outputs exclusively formatted as valid JSON strings. However, you need to explicitly specify the desired JSON structure within the system prompt to guide the model towards the expected format. It's important to note that OpenAI does not guarantee that the output text will have your specified JSON format. It only ensures that the output will be a valid string that can be parsed to JSON. Important: When using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request.

Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length. In the realm of AI-driven applications, ensuring consistent and predictable outputs is paramount. OpenAI’s introduction of Structured Outputs addresses this need by allowing developers to define the exact format of the model’s responses, ensuring they adhere to specified schemas. Structured Outputs enable developers to constrain the model’s responses to a predefined structure, typically defined using JSON Schema. This ensures that the outputs are not only valid JSON but also match the expected format, reducing the need for post-processing and error handling. 🔧 Using pydanticmodel with text_format under the method called client.responses.parse

Step 1: Define the pydantic model in models/document_extraction.py Step 2: Create a main.py and run the following code. You will have the structured output in JSON format. Learn how to use OpenAI function calling in your AI apps to enable reliable, structured outputs. LLMs are great at complex language tasks but often produce unstructured and unpredictable responses, creating challenges for developers who prefer structured data. Extracting info from unstructured text usually involves intricate methods like RegEx or prompt engineering—thus slowing development.

To simplify this, OpenAI introduced function calling to ensure more reliable structured data output from their models. After reading this tutorial, you'll understand how this feature works and how to implement various function calling techniques with OpenAI's API. With function calling you can get consistent structured data from models. Large language models are remarkably capable at reading and understanding documents. Take a photo of a receipt or a scanned invoice, and it will tell you everything on it - the vendor name, the line items, the total, the tax. The problem is that it will tell you in prose.

And prose, however accurate, is not something you can easily load into a database, compare against a ground truth label, or use to compute an F1 score. This was exactly the challenge we ran into in the FewTuRe research project, which dealt with few-shot fine-tuning for information extraction from receipts and invoices. We needed the model to extract structured information from documents, but we also needed that information to reliably arrive in a machine-readable format that matched our ground truth data schema exactly. A response that put the total under "total_amount" one time and "total" the next time was just as useless to our evaluation pipeline as a wrong answer. Consistency wasn't a nice-to-have. It was a hard requirement.

That experience made one thing very clear to us: getting the right answer is only half the problem. Getting it in the right shape is the other half. This post is about solving that second half. We'll walk through how OpenAI's structured outputs feature, combined with Pydantic and the OpenAI Python SDK, gives you a reliable and elegant way to enforce exactly the output format your application needs. To understand why structured outputs matter, it helps to think about what LLMs actually produce by default: a stream of tokens that form natural language text. Even when you prompt a model to "return a JSON object", you are essentially asking it nicely.

There is no guarantee. In practice, this means you get responses like: Get reliable JSON from any LLM using structured outputs, JSON mode, Pydantic, Instructor, and Outlines. Complete production guide with OpenAI, Claude, and Gemini code examples for consistent data extraction. Get reliable JSON from any LLM using structured outputs, JSON mode, Pydantic, Instructor, and Outlines. Complete production guide with OpenAI, Claude, and Gemini code examples for consistent data extraction.

Agenta is the open-source LLMOps platform: prompt management, evals, and LLM observability all in one place. LLMs excel at creative tasks. They write code, summarize documents, and draft emails with impressive results. But ask for structured JSON and you get inconsistent formats, malformed syntax, and unpredictable field names. The problem gets worse in production. A prompt that works perfectly in testing starts failing after a model update.

Your JSON parser breaks on unexpected field types. Your application crashes because the LLM decided to rename "status" to "current_state" without warning.

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In JSON mode, the model generates outputs exclusively formatted as valid JSON strings. However, you need to explicitly specify the desired JSON structure within the system prompt to guide the model towards the expected format. It's important to note that OpenAI does not guarantee that the output text will have your specified JSON format. It only ensures that the output will be a valid string that ...