Openai Openai Structured Outputs Samples Deepwiki

Emily Johnson

-Mar 12, 2026, 1:20 PM

openai openai structured outputs samples deepwiki

openai/openai-structured-outputs-samples This document provides an introduction to the OpenAI Structured Outputs Samples repository, which demonstrates practical implementations of OpenAI's structured outputs capabilities. The repository contains demonstrations that showcase how to create deterministic, type-safe interfaces between the OpenAI API and applications. For detailed information about the resume extraction demo specifically, see Resume Extraction Demo. The OpenAI Structured Outputs Samples repository serves as a practical guide and reference implementation for developers looking to integrate OpenAI's structured output capabilities into their applications. The samples demonstrate how to:

The primary demonstration is a resume extraction application that processes PDF resumes and extracts structured information in a consistent format. Structured Outputs is an OpenAI API feature that ensures responses and tool calls adhere to a defined JSON schema. This makes building with our models more reliable, bridging the gap between unpredictable model outputs and deterministic workflows. This repository contains a collection of sample apps showcasing the use of Structured Outputs. Each app demonstrates practical ways to leverage this feature to build applications with NextJS. Explore the code, update it, and use it in your own projects or as a starting point!

There are three sample apps in this repository: 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. This section provides examples of common use cases for working with OpenAI Structured Outputs using the openai-structured library. Extract structured movie reviews using OpenAI Structured Outputs with streaming: Analyze code using OpenAI Structured Outputs with custom rules and streaming: Configure buffer settings for different OpenAI Structured Outputs use cases:

Use different models with version validation: OpenAI’s Structured Outputs fundamentally change how developers build reliable applications on top of large language models. Instead of coaxing models with elaborate prompts to “return valid JSON,” you can now guarantee that responses conform to a precise JSON Schema or typed model, drastically reducing parsing errors, retries, and brittle post-processing.[1][2][7] This article explains very detailed structured outputs with OpenAI: what they are, how they differ from older patterns (like plain JSON mode), how to design robust schemas, integration patterns (Node, Python, Azure OpenAI, LangChain,... Structured Outputs are an OpenAI API feature that ensures model responses always match a supplied JSON Schema, or equivalent type definition, when strict: true is enabled.[1][2][7] This is unlike earlier approaches where you had to parse free-form text or rely only on “valid JSON” promises.

OpenAI’s evolution of output control can be summarized as: 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. 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: openai/openai-structured-outputs-samples This document describes the user interface components and interactions for the Resume Extraction Demo application in the openai-structured-outputs-samples repository. The UI provides a web-based interface that allows users to upload PDF resumes, view the structured data extracted by the OpenAI API, and toggle between different views of the extracted information. For information about how the extracted data is validated against schema definitions, see Zod Schema Implementation.

For details on how PDF processing works, see PDF Processing. The Resume Extraction Demo features a clean, functional interface that enables users to upload PDF resumes or use a provided example, then view the structured information extracted by the OpenAI API as it streams... The upload area provides a way for users to upload their own PDF resumes for processing. It leverages react-dropzone to enable a drag-and-drop interface with the following features:

Openai Openai Structured Outputs Samples Deepwiki

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