> ## Documentation Index > Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Structured Outputs > Structured Outputs ensure that the model always follows your supplied [JSON schema](https://json-schema.org/overview/what-is-jsonschema). Portkey supports OpenAI's Structured Outputs feature out of the box with our SDKs & APIs. > [!NOTE] > > Structured Outputs is different from OpenAI's `JSON Mode` as well as `Function Calling`. [Check out this table](#difference-between-structured-outputs-json-mode-and-function-calling) for a quick comparison. Portkey SDKs for [Python](https://github.com/openai/openai-python/blob/main/helpers.md#structured-outputs-parsing-helpers) and [JavaScript](https://github.com/openai/openai-node/blob/master/helpers.md#structured-outputs-parsing-helpers) also make it easy to define object schemas using [Pydantic](https://docs.pydantic.dev/latest/) and [Zod](https://zod.dev/) respectively. Below, you can see how to extract information from unstructured text that conforms to a schema defined in code. > [tab] Pydantic with Python > > ```python theme={"system"} > from portkey_ai import Portkey > from pydantic import BaseModel > > class Step(BaseModel): > explanation: str > output: str > > class MathReasoning(BaseModel): > steps: list[Step] > final_answer: str > > portkey = Portkey( > api_key="PORTKEY_API_KEY", > provider="@OPENAI_PROVIDER" > ) > > completion = portkey.chat.completions.parse( > model="gpt-4o-2024-08-06", > messages=[ > {"role": "system", "content": "You are a helpful math tutor. Guide the user through the solution step by step."}, > {"role": "user", "content": "how can I solve 8x + 7 = -23"} > ], > response_format=MathReasoning, > ) > > print(completion.choices[0].message) > print(completion.choices[0].message.parsed) > ``` > [tab] Zod with NodeJS > > ```typescript theme={"system"} > import { Portkey } from 'portkey-ai'; > import { z } from 'zod'; > import { zodResponseFormat } from "openai/helpers/zod"; > > const MathReasoning = z.object({ > steps: z.array(z.object({ explanation: z.string(), output: z.string() })), > final_answer: z.string() > }); > > const portkey = new Portkey({ > apiKey: "YOUR_API_KEY", > provider:"@YOUR_PROVIDER" > }); > > async function runMathTutor() { > try { > const completion = await portkey.chat.completions.create({ > model: "gpt-4o-2024-08-06", > messages: [ > { role: "system", content: "You are a helpful math tutor." }, > { role: "user", content: "Solve 8x + 7 = -23" } > ], > response_format: zodResponseFormat(MathReasoning, "MathReasoning") > }); > > console.log(completion.choices[0].message.content); > } catch (error) { > console.error("Error:", error); > } > } > > runMathTutor(); > ``` The second approach, shown in the subsequent examples, uses a JSON schema directly in the API call. This method is more portable across different languages and doesn't require additional libraries, but lacks the integrated type checking of the Pydantic/Zod approach. Choose the method that best fits your project's needs and language ecosystem. > [tab] NodeJS > > ```typescript theme={"system"} > import Portkey from "portkey-ai"; > > const portkey = new Portkey({ > apiKey: "PORTKEY_API_KEY", > provider:"@OPENAI_PROVIDER", > }); > > async function main() { > const completion = await portkey.chat.completions.create({ > model: "gpt-4o-2024-08-06", > messages: [ > { role: "system", content: "Extract the event information." }, > { > role: "user", > content: "Alice and Bob are going to a science fair on Friday.", > }, > ], > response_format: { > type: "json_schema", > json_schema: { > name: "math_reasoning", > schema: { > type: "object", > properties: { > steps: { > type: "array", > items: { > type: "object", > properties: { > explanation: { type: "string" }, > output: { type: "string" }, > }, > required: ["explanation", "output"], > additionalProperties: false, > }, > }, > final_answer: { type: "string" }, > }, > required: ["steps", "final_answer"], > additionalProperties: false, > }, > strict: true, > }, > }, > }); > const event = completion.choices[0].message?.content; > console.log(event); > } > > main(); > ``` > [tab] Python > > ```python theme={"system"} > from portkey_ai import Portkey > > portkey = Portkey( > api_key="PORTKEY_API_KEY", > provider="@OPENAI_PROVIDER" > ) > > completion = portkey.chat.completions.create( > model="gpt-4o-2024-08-06", > messages=[ > {"role": "system", "content": "Extract the event information."}, > {"role": "user", "content": "A meteor the size of 1000 football stadiums will hit earth this Sunday"}, > ], > response_format={ > "type": "json_schema", > "json_schema": { > "name": "math_reasoning", > "schema": { > "type": "object", > "properties": { > "steps": { > "type": "array", > "items": { > "type": "object", > "properties": { > "explanation": { "type": "string" }, > "output": { "type": "string" } > }, > "required": ["explanation", "output"], > "additionalProperties": False > } > }, > "final_answer": { "type": "string" } > }, > "required": ["steps", "final_answer"], > "additionalProperties": False > }, > "strict": True > } > }, > ) > > print(completion.choices[0].message.content) > ``` > [tab] cURL > > ```sh theme={"system"} > curl https://api.portkey.ai/v1/chat/completions \ > -H "x-portkey-api-key: $PORTKEY_API_KEY" \ > -H "x-portkey-provider: $OPENAI_PROVIDER" \ > -H "Content-Type: application/json" \ > -d '{ > "model": "gpt-4o-2024-08-06", > "messages": [ > { > "role": "system", > "content": "You are a helpful math tutor. Guide the user through the solution step by step." > }, > { > "role": "user", > "content": "how can I solve 8x + 7 = -23" > } > ], > "response_format": { > "type": "json_schema", > "json_schema": { > "name": "math_reasoning", > "schema": { > "type": "object", > "properties": { > "steps": { > "type": "array", > "items": { > "type": "object", > "properties": { > "explanation": { "type": "string" }, > "output": { "type": "string" } > }, > "required": ["explanation", "output"], > "additionalProperties": false > } > }, > "final_answer": { "type": "string" } > }, > "required": ["steps", "final_answer"], > "additionalProperties": false > }, > "strict": true > } > } > }' > ``` ## Difference Between Structured Outputs, JSON Mode, and Function Calling * If you are connecting the model to tools, functions, data, etc. in your system, then you should use **function calling.** * And if you want to structure the model's output when it responds to the user, then you should use a structured `response_format`. * In `response_format`, you can set it as `{ "type": "json_object" }` to enable the [JSON Mode](https://platform.openai.com/docs/guides/structured-outputs/json-mode). * And you can set it as `{ "type": "json_schema" }` to use the [Structured Outputs Mode described above](https://platform.openai.com/docs/guides/structured-outputs). For more, refer to OpenAI's [detailed documentation on Structured Outputs here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas). *** > [card] [Portkey is now PRISMA AIRS AI Gateway. See it in action.](https://www.paloaltonetworks.in/ai-security/ai-gateway?utm_source=portkey\&utm_medium=referral\&utm_campaign=prisma_airs\&utm_content=docs_nav#contact) > > Contact Us ## Related topics - [Structured Outputs](https://docs.portkey.ai/docs/integrations/llms/anthropic/structured-outputs.md) - [Anthropic](https://docs.portkey.ai/docs/integrations/llms/anthropic.md) - [Open Responses](https://docs.portkey.ai/docs/product/ai-gateway/responses-api.md) - [Messages](https://docs.portkey.ai/docs/product/ai-gateway/messages-api.md) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.