Generation

These Rust examples are real runnable files. Edit the source file first; this page is rebuilt from the checked-in example and its metadata header.

Rust Typed Generation

Runs a small typed generation program against OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: beginner
Run: npm run example -- rust src/examples/rust/generation/basic_generation.rs
Source: src/examples/rust/generation/basic_generation.rs

Rust

use axllm::{ax, AxResult, OpenAICompatibleClient};
use serde_json::json;
use std::env;


fn openai_client() -> AxResult<OpenAICompatibleClient> {
    let api_key = env::var("OPENAI_API_KEY").or_else(|_| env::var("OPENAI_APIKEY")).map_err(|_| axllm::AxError::runtime("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example."))?;
    let model = env::var("AX_OPENAI_MODEL").unwrap_or_else(|_| "gpt-5.4-mini".to_string());
    Ok(OpenAICompatibleClient::new(api_key, model).with_model_config(json!({"temperature": 0})))
}

fn main() -> AxResult<()> {
    let mut client = openai_client()?;
    let mut program = ax("question:string -> answer:string")?;
    let output = program.forward(&mut client, json!({"question": "In one sentence, explain Ax as a language-agnostic LLM programming library."}))?;
    println!("{}", serde_json::to_string_pretty(&output)?);
    Ok(())
}

Rust Structured Extraction

Extracts structured fields and labels from support text with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: intermediate
Run: npm run example -- rust src/examples/rust/generation/structured_generation.rs
Source: src/examples/rust/generation/structured_generation.rs

Rust

use axllm::{ax, AxResult, OpenAICompatibleClient};
use serde_json::json;
use std::env;


fn openai_client() -> AxResult<OpenAICompatibleClient> {
    let api_key = env::var("OPENAI_API_KEY").or_else(|_| env::var("OPENAI_APIKEY")).map_err(|_| axllm::AxError::runtime("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example."))?;
    let model = env::var("AX_OPENAI_MODEL").unwrap_or_else(|_| "gpt-5.4-mini".to_string());
    Ok(OpenAICompatibleClient::new(api_key, model).with_model_config(json!({"temperature": 0})))
}

fn main() -> AxResult<()> {
    let mut client = openai_client()?;
    let mut program = ax("ticket:string -> priority:class \"high, normal, low\", summary:string, labels:string[]")?;
    let output = program.forward(&mut client, json!({"ticket": "Checkout has failed for enterprise customers since 09:00. Support wants a concise summary and tags."}))?;
    println!("{}", serde_json::to_string_pretty(&output)?);
    Ok(())
}

Rust Contextual Generation

Answers from supplied context and returns compact citations with OpenAI.

Provider: openai
Env: OPENAI_API_KEY, OPENAI_APIKEY
Level: advanced
Run: npm run example -- rust src/examples/rust/generation/context_generation.rs
Source: src/examples/rust/generation/context_generation.rs

Rust

use axllm::{ax, AxResult, OpenAICompatibleClient};
use serde_json::json;
use std::env;


fn openai_client() -> AxResult<OpenAICompatibleClient> {
    let api_key = env::var("OPENAI_API_KEY").or_else(|_| env::var("OPENAI_APIKEY")).map_err(|_| axllm::AxError::runtime("Set OPENAI_API_KEY or OPENAI_APIKEY to run this example."))?;
    let model = env::var("AX_OPENAI_MODEL").unwrap_or_else(|_| "gpt-5.4-mini".to_string());
    Ok(OpenAICompatibleClient::new(api_key, model).with_model_config(json!({"temperature": 0})))
}

fn main() -> AxResult<()> {
    let mut client = openai_client()?;
    let mut program = ax("context:string, question:string -> answer:string, citations:string[]")?;
    let output = program.forward(&mut client, json!({"context": "Ax uses signatures, ai(), ax(), agent(), flow(), and optimize().", "question": "How should a new developer think about Ax?"}))?;
    println!("{}", serde_json::to_string_pretty(&output)?);
    Ok(())
}