> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Function Calling Structured Outputs > This article explains how to use structured outputs with the OpenAI API for consistent, machine-readable responses. Structured outputs ensure your application receives well-defined, machine-readable responses every time. By enforcing formats such as JSON, CSV, or custom Pydantic models, you gain: | Benefit | Description | | ----------------- | ------------------------------------------------------------- | | Type Safety | Guarantees fields are present and correctly typed | | Explicit Refusals | Model clearly indicates when it can’t comply | | Simpler Prompting | Remove guesswork—just specify the format | | Exact Formats | Enforce JSON, CSV, or any structure your application requires | *** ## 1. Structured Outputs with Pydantic Define a Pydantic model for your desired schema and instruct the API to parse directly into that model. ```python theme={null} from pydantic import BaseModel from openai import OpenAI client = OpenAI() class CalendarEvent(BaseModel): name: str date: str participants: list[str] completion = client.beta.chat.completions.parse( model="gpt-4-2024-08-06", messages=[ {"role": "system", "content": "Extract the event information."}, {"role": "user", "content": "Alice and Bob are going to a science fair."} ], response_format=CalendarEvent, ) event = completion.choices[0].message.parsed print(event) # CalendarEvent(name='Science Fair Visit', date='2024-08-10', participants=['Alice', 'Bob']) ``` Make sure you’re using the `openai` Python package version that supports `.parse()` (e.g., v0.27+).\ See [Pydantic documentation](https://docs.pydantic.dev/latest/) for detailed model usage. *** ## 2. Step-by-Step Reasoning as Structured Output You can capture a chain of thought or intermediate steps by defining a Pydantic model that holds your reasoning. ```python theme={null} from pydantic import BaseModel from openai import OpenAI client = OpenAI() class MathReasoning(BaseModel): steps: list[str] final_answer: str completion = client.beta.chat.completions.parse( model="gpt-4-2024-08-06", messages=[ {"role": "system", "content": "You are a helpful math tutor. Guide the user."}, {"role": "user", "content": "How can I solve 8x + 7 = -23?"} ], response_format=MathReasoning, ) math = completion.choices[0].message.parsed print("Steps:", math.steps) print("Answer:", math.final_answer) ``` *** ## 3. Building a Recipe Generator with JSON Output Prompt the model to output strict JSON so downstream services can ingest it without additional parsing. ```python theme={null} from openai import OpenAI client = OpenAI(api_key="sk-REPLACE_WITH_YOUR_KEY") def recipe_gen(ingredients: list[str]) -> str: messages = [{"role": "user", "content": ing} for ing in ingredients] messages.extend([ {"role": "system", "content": "JSON Format Required"}, {"role": "assistant", "content": "You are a high-end chef. Generate a recipe in JSON!"} ]) response = client.chat.completions.create( model="gpt-4", messages=messages, max_tokens=300, temperature=0.9 ) return response.choices[0].message.content if __name__ == "__main__": ingredients = [] while True: ing = input("Enter an ingredient (or type done): ") if ing.strip().lower() == "done": break ingredients.append(ing) print(recipe_gen(ingredients)) ``` Run the script: ```bash theme={null} $ python recipe_generator.py Enter an ingredient (or type done): chocolate Enter an ingredient (or type done): grapes Enter an ingredient (or type done): pizza dough Enter an ingredient (or type done): done ``` Example JSON response: ```json theme={null} { "recipe": { "title": "Chocolate-Infused Grape Pizza Tacos", "servings": 4, "ingredients": [ { "name": "Corn tortillas", "quantity": 8, "unit": "pieces" }, { "name": "Pizza dough", "quantity": 1, "unit": "pound" }, { "name": "Dark chocolate", "quantity": 100, "unit": "grams", "type": "finely chopped" }, { "name": "Red grapes", "quantity": 200, "unit": "grams", "type": "halved" }, { "name": "Mozzarella cheese","quantity": 200, "unit": "grams", "type": "shredded" }, { "name": "Ricotta cheese", "quantity": 100, "unit": "grams" }, { "name": "Fresh basil leaves","quantity": 12, "unit": "leaves" } ] } } ``` Never commit your API keys to public repositories. Use environment variables or a secure vault. *** ## 4. Switching to CSV Output Simply update the system prompt to request CSV, then process the comma-separated response. ```python theme={null} from openai import OpenAI client = OpenAI(api_key="sk-REPLACE_WITH_YOUR_KEY") def recipe_gen_csv(ingredients: list[str]) -> str: messages = [{"role": "user", "content": ing} for ing in ingredients] messages.extend([ {"role": "system", "content": "CSV Format Required"}, {"role": "assistant", "content": "You are a high-end chef. Generate a CSV recipe!"} ]) response = client.chat.completions.create( model="gpt-4", messages=messages, max_tokens=300, temperature=0.9 ) return response.choices[0].message.content if __name__ == "__main__": ingredients = [] while True: ing = input("Enter an ingredient (or type done): ") if ing.strip().lower() == "done": break ingredients.append(ing) print(recipe_gen_csv(ingredients)) ``` Sample CSV output: ```csv theme={null} Ingredient,Quantity,Preparation Chicken Breast,2 pieces,Cut into cubes Potatoes,2 medium,Peeled and diced Eggs,4,Hard-boiled and sliced Olive Oil,2 Tbsp,For cooking Garlic,2 cloves,Minced Onion,1 medium,Finely chopped Salt,1 tsp,To taste Black Pepper,1/2 tsp,To taste Paprika,1/2 tsp,To taste Fresh Parsley,2 tbsp,Chopped for garnish ``` You can even mix formats—return cooking steps as CSV: ```text theme={null} Step 1,Heat olive oil in a large pan over medium heat. Step 2,Add chicken and onion; sauté until translucent. Step 3,Add garlic, potatoes, and paprika; cook until tender. Step 4,Stir in eggs until heated through. Step 5,Garnish with parsley and serve. ``` *** By specifying your desired output format in the `system` and `assistant` messages, you guarantee consistency and simplify downstream processing. For more advanced examples, see the [OpenAI Structured Outputs Guide](https://platform.openai.com/docs/guides/structured-outputs). ## Links and References * [OpenAI API Documentation](https://platform.openai.com/docs/) * [Pydantic Documentation](https://docs.pydantic.dev/latest/)