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add responses-structured-outputs example
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‎examples/responses-structured-outputs/Cargo.toml‎

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[package]
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name = "responses-structured-outputs"
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version = "0.1.0"
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edition = "2021"
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publish = false
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[dependencies]
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async-openai = { path = "../../async-openai" }
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serde_json = "1.0"
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tokio = { version = "1", features = ["full"] }
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clap = { version = "4", features = ["derive"] }

‎examples/responses-structured-outputs/src/main.rs‎

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use std::error::Error;
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use async_openai::{
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config::OpenAIConfig,
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types::{
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chat::ResponseFormatJsonSchema,
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responses::{
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CreateResponseArgs, InputMessage, InputRole, OutputItem, OutputMessageContent,
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},
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},
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Client,
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};
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use clap::Parser;
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use serde_json::json;
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/// Chain of thought example: Guides the model through step-by-step reasoning
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async fn chain_of_thought(client: &Client<OpenAIConfig>) -> Result<(), Box<dyn Error>> {
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println!("=== Chain of Thought Example ===\n");
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let schema = json!({
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"type": "object",
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"properties": {
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"steps": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"explanation": { "type": "string" },
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"output": { "type": "string" }
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},
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"required": ["explanation", "output"],
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"additionalProperties": false
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}
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},
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"final_answer": { "type": "string" }
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},
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"required": ["steps", "final_answer"],
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"additionalProperties": false
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});
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let request = CreateResponseArgs::default()
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.model("gpt-4o-2024-08-06")
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.max_output_tokens(512u32)
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.text(ResponseFormatJsonSchema {
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description: Some(
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"A step-by-step reasoning process for solving math problems".to_string(),
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),
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name: "math_reasoning".to_string(),
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schema: Some(schema),
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strict: Some(true),
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})
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.input(vec![
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InputMessage {
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role: InputRole::System,
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content: vec![
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"You are a helpful math tutor. Guide the user through the solution step by step."
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.into(),
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],
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status: None,
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},
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InputMessage {
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role: InputRole::User,
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content: vec!["How can I solve 8x + 7 = -23?".into()],
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status: None,
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},
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])
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.build()?;
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let response = client.responses().create(request).await?;
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for output in response.output {
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if let OutputItem::Message(message) = output {
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for content in message.content {
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if let OutputMessageContent::OutputText(text) = content {
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println!("Response:\n{}\n", text.text);
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}
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}
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}
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}
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Ok(())
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}
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/// Structured data extraction example: Extracts specific fields from unstructured text
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async fn structured_data_extraction(client: &Client<OpenAIConfig>) -> Result<(), Box<dyn Error>> {
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println!("=== Structured Data Extraction Example ===\n");
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let schema = json!({
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"type": "object",
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"properties": {
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"name": { "type": "string" },
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"age": { "type": "integer" },
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"occupation": { "type": "string" },
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"location": { "type": "string" },
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"email": { "type": "string" }
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},
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"required": ["name", "age", "occupation", "email", "location"],
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"additionalProperties": false
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});
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let text = "Hi, I'm Sarah Johnson. I'm 28 years old and I work as a software engineer in San Francisco. You can reach me at [email protected].";
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let request = CreateResponseArgs::default()
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.model("gpt-4o-2024-08-06")
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.max_output_tokens(256u32)
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.text(ResponseFormatJsonSchema {
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description: Some("Extract structured information from text".to_string()),
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name: "person_info".to_string(),
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schema: Some(schema),
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strict: Some(true),
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})
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.input(vec![
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InputMessage {
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role: InputRole::System,
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content: vec!["Extract the following information from the user's text: name, age, occupation, location, and email. If any information is not present, omit that field.".into()],
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status: None,
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},
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InputMessage {
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role: InputRole::User,
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content: vec![text.into()],
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status: None,
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},
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])
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.build()?;
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let response = client.responses().create(request).await?;
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println!("Input text: {}\n", text);
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for output in response.output {
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if let OutputItem::Message(message) = output {
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for content in message.content {
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if let OutputMessageContent::OutputText(text) = content {
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println!("Extracted data:\n{}\n", text.text);
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}
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}
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}
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}
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Ok(())
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}
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/// UI generation example: Generates UI component code based on description
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async fn ui_generation(client: &Client<OpenAIConfig>) -> Result<(), Box<dyn Error>> {
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println!("=== UI Generation Example ===\n");
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let schema = json!({
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"type": "object",
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"properties": {
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"type": {
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"type": "string",
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"description": "The type of the UI component",
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"enum": ["div", "button", "header", "section", "field", "form"]
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},
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"label": {
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"type": "string",
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"description": "The label of the UI component, used for buttons or form fields"
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},
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"children": {
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"type": "array",
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"description": "Nested UI components",
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"items": {"$ref": "#"}
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},
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"attributes": {
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"type": "array",
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"description": "Arbitrary attributes for the UI component, suitable for any element",
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"items": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "The name of the attribute, for example onClick or className"
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},
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"value": {
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"type": "string",
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"description": "The value of the attribute"
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}
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},
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"required": ["name", "value"],
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"additionalProperties": false
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}
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}
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},
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"required": ["type", "label", "children", "attributes"],
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"additionalProperties": false
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});
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let request = CreateResponseArgs::default()
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.model("gpt-4o-2024-08-06")
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.max_output_tokens(1024u32)
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.text(ResponseFormatJsonSchema {
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description: Some("Generate HTML and CSS code for UI components".to_string()),
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name: "ui_component".to_string(),
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schema: Some(schema),
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strict: Some(true),
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})
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.input(vec![
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InputMessage {
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role: InputRole::System,
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content: vec!["You are a UI designer. Generate clean, modern HTML and CSS code for the requested UI component. The HTML should be semantic and accessible, and the CSS should be well-organized.".into()],
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status: None,
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},
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InputMessage {
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role: InputRole::User,
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content: vec!["Create a login form with email and password fields, a submit button, and a link to sign up. Use a modern, clean design with a light color scheme.".into()],
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status: None,
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},
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])
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.build()?;
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let response = client.responses().create(request).await?;
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for output in response.output {
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if let OutputItem::Message(message) = output {
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for content in message.content {
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if let OutputMessageContent::OutputText(text) = content {
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println!("Generated UI Component:\n{}\n", text.text);
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}
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}
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}
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}
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Ok(())
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}
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/// Moderation example: Analyzes content for policy violations with structured output
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async fn moderation(client: &Client<OpenAIConfig>) -> Result<(), Box<dyn Error>> {
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println!("=== Moderation Example ===\n");
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let schema = json!({
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"type": "object",
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"properties": {
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"is_violating": {
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"type": "boolean",
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"description": "Indicates if the content is violating guidelines"
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},
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"category": {
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"type": ["string", "null"],
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"description": "Type of violation, if the content is violating guidelines. Null otherwise.",
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"enum": ["violence", "sexual", "self_harm"]
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},
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"explanation_if_violating": {
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"type": ["string", "null"],
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"description": "Explanation of why the content is violating"
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}
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},
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"required": ["is_violating", "category", "explanation_if_violating"],
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"additionalProperties": false
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});
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let content_to_moderate = "How do I prepare for a job interview?";
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let request = CreateResponseArgs::default()
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.model("gpt-4o-2024-08-06")
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.max_output_tokens(256u32)
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.text(ResponseFormatJsonSchema {
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description: Some("Analyze content for policy violations and provide structured moderation results".to_string()),
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name: "moderation_result".to_string(),
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schema: Some(schema),
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strict: Some(true),
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})
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.input(vec![
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InputMessage {
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role: InputRole::System,
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content: vec!["Determine if the user input violates specific guidelines and explain if they do.".into()],
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status: None,
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},
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InputMessage {
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role: InputRole::User,
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content: vec![content_to_moderate.into()],
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status: None,
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},
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])
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.build()?;
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let response = client.responses().create(request).await?;
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for output in response.output {
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if let OutputItem::Message(message) = output {
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for content in message.content {
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if let OutputMessageContent::OutputText(text) = content {
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println!("Content analyzed: {}\n", content_to_moderate);
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println!("Moderation result:\n{}\n", text.text);
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}
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}
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}
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}
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Ok(())
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}
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#[derive(Parser, Debug)]
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#[command(name = "responses-structured-outputs")]
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#[command(about = "Examples of structured outputs using the Responses API", long_about = None)]
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struct Cli {
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/// Which example to run
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#[arg(value_enum)]
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example: Example,
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}
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#[derive(clap::ValueEnum, Clone, Debug)]
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enum Example {
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/// Chain of thought: Step-by-step reasoning for math problems
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ChainOfThought,
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/// Structured data extraction: Extract fields from unstructured text
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DataExtraction,
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/// UI generation: Generate HTML and CSS for UI components
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UiGeneration,
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/// Moderation: Analyze content for policy violations
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Moderation,
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/// Run all examples
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All,
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}
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#[tokio::main]
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async fn main() -> Result<(), Box<dyn Error>> {
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let cli = Cli::parse();
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let client = Client::new();
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match cli.example {
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Example::ChainOfThought => {
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chain_of_thought(&client).await?;
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}
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Example::DataExtraction => {
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structured_data_extraction(&client).await?;
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}
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Example::UiGeneration => {
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ui_generation(&client).await?;
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}
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Example::Moderation => {
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moderation(&client).await?;
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}
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Example::All => {
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chain_of_thought(&client).await?;
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structured_data_extraction(&client).await?;
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ui_generation(&client).await?;
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moderation(&client).await?;
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}
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}
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Ok(())
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}

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