Rust

Gebruik async-openai met api14. Configureer de API-basis met OpenAIConfig::with_api_base en geef model-id's zoals openai/gpt-5-mini mee.

Installeren

cargo add async-openai futures serde_json
cargo add tokio --features full
export API14_API_KEY="sk-api14-..."

Configureren

use async_openai::{config::OpenAIConfig, Client};

let config = OpenAIConfig::new()
    .with_api_base("https://api14.com/api/v1")
    .with_api_key(std::env::var("API14_API_KEY")?);
let client = Client::with_config(config);

Lees de key zoals hierboven uit de omgeving in plaats van hem hard te coderen. Om tussen staging en productie te wisselen, lees je de base URL op dezelfde manier, bijvoorbeeld std::env::var("API14_BASE_URL").unwrap_or("https://api14.com/api/v1".into()).

Chat

use async_openai::{
    config::OpenAIConfig,
    types::{ChatCompletionRequestUserMessageArgs, CreateChatCompletionRequestArgs},
    Client,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = OpenAIConfig::new()
        .with_api_base("https://api14.com/api/v1")
        .with_api_key(std::env::var("API14_API_KEY")?);
    let client = Client::with_config(config);

    let request = CreateChatCompletionRequestArgs::default()
        .model("openai/gpt-5-mini")
        .messages([ChatCompletionRequestUserMessageArgs::default()
            .content("Explain vector databases in two sentences.")
            .build()?
            .into()])
        .build()?;

    let response = client.chat().create(request).await?;
    if let Some(content) = &response.choices[0].message.content {
        println!("{content}");
    }
    Ok(())
}

Streaming

use futures::StreamExt;

let request = CreateChatCompletionRequestArgs::default()
    .model("anthropic/claude-sonnet-4.5")
    .messages([ChatCompletionRequestUserMessageArgs::default()
        .content("Write a haiku about APIs.")
        .build()?
        .into()])
    .build()?;

let mut stream = client.chat().create_stream(request).await?;
while let Some(result) = stream.next().await {
    match result {
        Ok(chunk) => {
            for choice in chunk.choices {
                if let Some(text) = choice.delta.content {
                    print!("{text}");
                }
            }
        }
        Err(err) => eprintln!("stream error: {err}"),
    }
}

Zie Streaming.

Tool calling

use async_openai::types::{
    ChatCompletionRequestAssistantMessageArgs, ChatCompletionRequestMessage,
    ChatCompletionRequestToolMessageArgs, ChatCompletionToolArgs, ChatCompletionToolType,
    FunctionObjectArgs,
};
use serde_json::{json, Value};

let tools = vec![ChatCompletionToolArgs::default()
    .r#type(ChatCompletionToolType::Function)
    .function(FunctionObjectArgs::default()
        .name("get_weather")
        .description("Current weather for a city")
        .parameters(json!({
            "type": "object",
            "properties": { "city": { "type": "string" } },
            "required": ["city"]
        }))
        .build()?)
    .build()?];

let mut messages: Vec<ChatCompletionRequestMessage> = vec![ChatCompletionRequestUserMessageArgs::default()
    .content("What's the weather in Istanbul?")
    .build()?
    .into()];

loop {
    let request = CreateChatCompletionRequestArgs::default()
        .model("openai/gpt-5-mini")
        .messages(messages.clone())
        .tools(tools.clone())
        .build()?;
    let message = client.chat().create(request).await?.choices.remove(0).message;

    let Some(calls) = message.tool_calls else {
        println!("{}", message.content.unwrap_or_default());
        break;
    };
    messages.push(ChatCompletionRequestAssistantMessageArgs::default()
        .tool_calls(calls.clone())
        .build()?
        .into());
    for call in calls {
        let args: Value = serde_json::from_str(&call.function.arguments)?;
        let result = get_weather(args["city"].as_str().unwrap_or_default()); // je eigen functie
        messages.push(ChatCompletionRequestToolMessageArgs::default()
            .tool_call_id(call.id)
            .content(result)
            .build()?
            .into());
    }
}

Zie Tool calling.

Gestructureerde uitvoer

use async_openai::types::{ResponseFormat, ResponseFormatJsonSchema};

let request = CreateChatCompletionRequestArgs::default()
    .model("openai/gpt-5-mini")
    .messages([ChatCompletionRequestUserMessageArgs::default()
        .content("Extract: Ada Lovelace, born 1815.")
        .build()?
        .into()])
    .response_format(ResponseFormat::JsonSchema {
        json_schema: ResponseFormatJsonSchema {
            name: "person".into(),
            description: None,
            strict: Some(true),
            schema: Some(json!({
                "type": "object",
                "properties": { "name": { "type": "string" }, "born": { "type": "integer" } },
                "required": ["name", "born"],
                "additionalProperties": false
            })),
        },
    })
    .build()?;

let response = client.chat().create(request).await?;
let person: Value = serde_json::from_str(response.choices[0].message.content.as_deref().unwrap_or("{}"))?;

Zie Structured outputs.

Afbeeldingen als invoer

use async_openai::types::{
    ChatCompletionRequestMessageContentPartImageArgs, ChatCompletionRequestMessageContentPartTextArgs,
    ImageUrlArgs,
};

let message = ChatCompletionRequestUserMessageArgs::default()
    .content(vec![
        ChatCompletionRequestMessageContentPartTextArgs::default()
            .text("What is in this picture?")
            .build()?
            .into(),
        ChatCompletionRequestMessageContentPartImageArgs::default()
            .image_url(ImageUrlArgs::default().url("https://example.com/photo.jpg").build()?)
            .build()?
            .into(),
    ])
    .build()?;

Verstuur het met google/gemini-2.5-flash of een ander model met image-invoer. Zie Beeld en bestanden.

Embeddings

use async_openai::types::CreateEmbeddingRequestArgs;

let request = CreateEmbeddingRequestArgs::default()
    .model("openai/text-embedding-3-small")
    .input(["The quick brown fox", "Lorem ipsum"])
    .build()?;
let response = client.embeddings().create(request).await?;
println!("{}", response.data[0].embedding.len());

Endpoints waarvoor de crate geen type heeft (videotaken, rerank) kun je aanroepen met reqwest, zie de HTTP-voorbeelden in het codeblok hieronder.

Fouten

Gateway-fouten worden geparset naar OpenAIError::ApiError; vertak op de machinecode code:

use async_openai::error::OpenAIError;

match client.chat().create(request).await {
    Ok(response) => println!("{:?}", response.choices[0].message.content),
    Err(OpenAIError::ApiError(err)) => match err.code.as_deref() {
        Some("insufficient_balance") => eprintln!("Balance too low, add funds"),
        Some("rate_limited") | Some("daily_limit_reached") => eprintln!("Slow down: {}", err.message),
        _ => eprintln!("API error: {}", err.message),
    },
    Err(other) => eprintln!("{other}"),
}

Zie Fouten.

Timeouts en nieuwe pogingen

async-openai doet al backoff en probeert 429 rate_limited-responses opnieuw. De standaardbackoff blijft tot 15 minuten proberen, wat voor de meeste apps te lang is en zinloos bij daily_limit_reached. Begrens dit en geef de HTTP-client een timeout (cargo add backoff reqwest, met de versies die async-openai gebruikt):

use std::time::Duration;

let http = reqwest::Client::builder()
    .timeout(Duration::from_secs(120))
    .build()?;

let client = Client::with_config(config)
    .with_http_client(http)
    .with_backoff(backoff::ExponentialBackoffBuilder::new()
        .with_max_elapsed_time(Some(Duration::from_secs(90)))
        .build());

Zie Rate limits.

Alle mogelijkheden

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.chat.completions.create({
  "model": "openai/gpt-5-mini",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);