客户端 SDK 用法

import Phaseo from "@phaseo/sdk";

const client = new Phaseo({ apiKey: process.env.PHASEO_API_KEY! });

from phaseo import Phaseo

client = Phaseo(api_key="your-api-key")

import (
  "context"

  phaseo "github.com/phaseoteam/Phaseo/packages/sdk/sdk-go/v3"
)

client, err := phaseo.NewFromEnv() // 读取 PHASEO_API_KEY
if err != nil {
  panic(err)
}
ctx := context.Background()

using PhaseoSdk;

var client = new Phaseo(); // 读取 PHASEO_API_KEY

<?php
require 'vendor/autoload.php';

use Phaseo\Sdk\Phaseo;

$client = new Phaseo(); // 读取 PHASEO_API_KEY

require 'phaseo_sdk'

client = PhaseoSdk::Phaseo.new # 读取 PHASEO_API_KEY

import app.phaseo.sdk.Phaseo;

Phaseo client = new Phaseo(System.getenv("PHASEO_API_KEY"));

use phaseo::Phaseo;
use serde_json::json;

let client = Phaseo::from_env()?; // 或使用 Phaseo::new("phaseo_v1_sk_...")?

const completion = await client.generateText({
  model: "openai/gpt-6-astra",
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is Phaseo?" },
  ],
});

for await (const chunk of client.streamChat({
  model: "openai/gpt-6-astra",
  messages: [{ role: "user", content: "Tell me a story" }],
})) {
  if (chunk.text) process.stdout.write(chunk.text);
}

completion = client.generate_text(
    {
        "model": "openai/gpt-6-astra",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "What is Phaseo?"},
        ],
    }
)

for chunk in client.stream_chat(
    {
        "model": "openai/gpt-6-astra",
        "messages": [{"role": "user", "content": "Tell me a story"}],
    }
):
    if chunk.get("text"):
        print(chunk["text"], end="", flush=True)

chat, err := client.GenerateText(ctx, phaseo.ChatCompletionsRequest{
  Model:    "openai/gpt-6-astra",
  Messages: []map[string]interface{}{{"role": "user", "content": "Hello"}},
})

var chat = await client.GenerateText(new Dictionary<string, object>
{
    ["model"] = "openai/gpt-6-astra",
    ["messages"] = new[]
    {
        new Dictionary<string, object> { ["role"] = "user", ["content"] = "Hello" },
    },
});

$chat = $client->generateText([
    'model' => 'openai/gpt-6-astra',
    'messages' => [
        ['role' => 'user', 'content' => 'Hello'],
    ],
]);

chat = client.generate_text(
  model: 'openai/gpt-6-astra',
  messages: [{ role: 'user', content: 'Hello' }]
)

var chat = client.createChatCompletion(Map.of(
    "model", "openai/gpt-6-astra",
    "messages", List.of(Map.of("role", "user", "content", "Hello"))
));

let response = client.chat_completions(&json!({
    "model": "openai/gpt-6-astra",
    "messages": [{
        "role": "user",
        "content": "Reply with exactly: chat works"
    }]
}))?;

println!("{}", response.body);

const response = await client.generateResponse({
  model: "openai/gpt-6-astra",
  input: [{ role: "user", content: [{ type: "input_text", text: "Summarise this" }] }],
});

for await (const chunk of client.streamResponses({
  model: "openai/gpt-6-astra",
  input: [{ role: "user", content: [{ type: "input_text", text: "Stream this" }] }],
})) {
  if (chunk.text) process.stdout.write(chunk.text);
}

resp = client.generate_response(
    {
        "model": "openai/gpt-6-astra",
        "input": [{"role": "user", "content": [{"type": "input_text", "text": "Summarise this"}]}],
    }
)

for chunk in client.stream_responses(
    {
        "model": "openai/gpt-6-astra",
        "input": [{"role": "user", "content": [{"type": "input_text", "text": "Stream this"}]}],
    }
):
    if chunk.get("text"):
        print(chunk["text"], end="", flush=True)

resp, err := client.GenerateResponse(ctx, phaseo.ResponsesRequest{
  Model: "openai/gpt-6-astra",
  Input: "Reply with: go sdk works",
})

var resp = await client.GenerateResponse(new Dictionary<string, object>
{
    ["model"] = "openai/gpt-6-astra",
    ["input"] = "Reply with: csharp sdk works",
});

$response = $client->generateResponse([
    'model' => 'openai/gpt-6-astra',
    'input' => 'Reply with: php sdk works',
]);

response = client.generate_response(
  model: 'openai/gpt-6-astra',
  input: 'Reply with: ruby sdk works'
)

var response = client.createResponse(Map.of(
    "model", "openai/gpt-6-astra",
    "input", "Reply with: Java SDK works"
));

try (var lines = client.streamResponse(Map.of(
    "model", "openai/gpt-6-astra", "input", "Hello"
))) {
    lines.forEach(System.out::println);
}

let response = client.responses(&json!({
    "model": "openai/gpt-6-astra",
    "input": "Summarise why provider fallbacks improve reliability."
}))?;

println!("{}", response.body);

const message = await client.generateMessage({
  model: "anthropic/claude-haiku-4.5",
  messages: [{ role: "user", content: "Hello from the Messages API" }],
  max_tokens: 128,
});

for await (const chunk of client.streamMessages({
  model: "anthropic/claude-haiku-4.5",
  messages: [{ role: "user", content: "Stream a short hello" }],
  max_tokens: 128,
})) {
  if (chunk.text) process.stdout.write(chunk.text);
}

message = client.generate_message(
    {
        "model": "anthropic/claude-haiku-4.5",
        "messages": [{"role": "user", "content": "Hello from the Messages API"}],
        "max_tokens": 128,
    }
)

for chunk in client.stream_message(
    {
        "model": "anthropic/claude-haiku-4.5",
        "messages": [{"role": "user", "content": "Stream a short hello"}],
        "max_tokens": 128,
    }
):
    if chunk.get("text"):
        print(chunk["text"], end="", flush=True)

await client.generateImage({
  model: "openai/gpt-image-1",
  prompt: "A lighthouse at golden hour",
});

await client.generateImageEdit({
  model: "openai/gpt-image-1",
  prompt: "Make it sunset",
  image: "data:image/png;base64,...",
});

image = client.generate_image(
    {"model": "openai/gpt-image-1", "prompt": "A lighthouse at golden hour"}
)

edited = client.generate_image_edit(
    {
        "model": "openai/gpt-image-1",
        "prompt": "Make it sunset",
        "image": "data:image/png;base64,...",
    }
)

image, err := client.CreateImage(ctx, map[string]interface{}{
  "model":  "openai/gpt-image-1",
  "prompt": "A lighthouse at golden hour",
})

var image = await client.GenerateImage(new Dictionary<string, object>
{
    ["model"] = "openai/gpt-image-1",
    ["prompt"] = "A lighthouse at golden hour",
});

$image = $client->generateImage([
    'model' => 'openai/gpt-image-1',
    'prompt' => 'A lighthouse at golden hour',
]);

$edit = $client->generateImageEdit([
    'model' => 'openai/gpt-image-1',
    'prompt' => 'Make it sunset',
    'image' => 'data:image/png;base64,...',
]);

image = client.generate_image(
  model: 'openai/gpt-image-1',
  prompt: 'A lighthouse at golden hour'
)

edit = client.generate_image_edit(
  model: 'openai/gpt-image-1',
  prompt: 'Make it sunset',
  image: 'data:image/png;base64,...'
)

var image = client.createImage(Map.of(
    "model", "openai/gpt-image-1",
    "prompt", "A lighthouse at golden hour"
));

import { readFile } from "node:fs/promises";

const speech = await client.generateSpeech({
  model: "openai/gpt-4o-mini-tts",
  input: "Hello world",
});

// 转写和翻译需要 multipart 上传。
const form = new FormData();
form.append("model", "openai/gpt-4o-transcribe");
form.append("file", new Blob([await readFile("call.mp3")], { type: "audio/mpeg" }), "call.mp3");
const transcript = await client.generateTranscription(form as unknown as Record<string, unknown>);

speech = client.generate_speech(
    {"model": "openai/gpt-4o-mini-tts", "input": "Hello world"}
)

# generate_transcription 发送 JSON，所以要把 URL 传给接受 file_url 的模型。
transcription = client.generate_transcription(
    {"model": "mistral/voxtral-mini-transcribe-2", "file_url": "https://example.com/call.mp3"}
)

speech, err := client.CreateSpeech(ctx, map[string]interface{}{
  "model": "openai/gpt-4o-mini-tts",
  "input": "Hello from Go",
})

var speech = await client.CreateSpeech(new Dictionary<string, object>
{
    ["model"] = "openai/gpt-4o-mini-tts",
    ["input"] = "Hello from .NET",
});

$speech = $client->createSpeech([
    'model' => 'openai/gpt-4o-mini-tts',
    'input' => 'Hello from PHP',
]);

$transcript = $client->generateTranscription([
    'model' => 'mistral/voxtral-mini-transcribe-2',
    'file_url' => 'https://example.com/meeting.mp3',
]);

speech = client.generate_speech(
  model: 'openai/gpt-4o-mini-tts',
  input: 'Hello world'
)

transcription = client.generate_transcription(
  model: 'mistral/voxtral-mini-transcribe-2',
  file_url: 'https://example.com/meeting.mp3'
)

const video = await client.videos.generateAndWait({
  model: "openai/sora-2",
  prompt: "A serene mountain lake at sunrise",
});

video = client.videos.generate_and_wait(
    {"model": "openai/sora-2", "prompt": "A serene mountain lake at sunrise"}
)

const embedding = await client.generateEmbedding({
  model: "openai/text-embedding-3-large",
  input: "Sample text",
});

const ranked = await client.rerank.create({
  model: "voyage/rerank-2",
  query: "How do I list models?",
  documents: ["Call GET /v1/models.", "Create an image with POST /v1/images/generations."],
});

await client.generateModeration({
  model: "openai/omni-moderation",
  input: "Some text to check",
});

embedding = client.generate_embedding(
    {"model": "openai/text-embedding-3-large", "input": "Sample text"}
)

ranked = client.rerank.create(
    {
        "model": "voyage/rerank-2",
        "query": "How do I list models?",
        "documents": ["Call GET /v1/models.", "Create an image with POST /v1/images/generations."],
    }
)

moderation = client.generate_moderation(
    {"model": "openai/omni-moderation", "input": "Text to check"}
)

embedding, err := client.GenerateEmbedding(ctx, map[string]interface{}{
  "model": "openai/text-embedding-3-large",
  "input": "Sample text",
})

moderation, err := client.GenerateModeration(ctx, map[string]interface{}{
  "model": "openai/omni-moderation",
  "input": "Some text to check",
})

var embedding = await client.CreateEmbedding(new Dictionary<string, object>
{
    ["model"] = "openai/text-embedding-3-large",
    ["input"] = "Sample text",
});

var moderation = await client.GenerateModeration(new Dictionary<string, object>
{
    ["model"] = "openai/omni-moderation",
    ["input"] = "Some text to check",
});

$embedding = $client->generateEmbedding([
    'model' => 'openai/text-embedding-3-large',
    'input' => 'Sample text',
]);

$moderation = $client->generateModeration([
    'model' => 'openai/omni-moderation',
    'input' => 'Some text to check',
]);

embedding = client.generate_embedding(
  model: 'openai/text-embedding-3-large',
  input: 'Sample text'
)

moderation = client.generate_moderation(
  model: 'openai/omni-moderation',
  input: 'Some text to check'
)

var embedding = client.createEmbedding(Map.of(
    "model", "openai/text-embedding-3-large",
    "input", "Sample text"
));

// post(...) 向没有高层辅助方法的端点发送 JSON。
// 路径可以带开头的斜杠，也可以不带。
let response = client.post("/embeddings", &json!({
    "model": "openai/text-embedding-3-small",
    "input": "Phaseo routes across AI providers"
}))?;

const file = await client.uploadFile({
  purpose: "batch",
  file: new Blob(["{}"], { type: "application/json" }),
  filename: "batch.jsonl",
});

const batch = await client.createBatch({
  input_file_id: file.id,
  endpoint: "/v1/responses",
  completion_window: "24h",
});

const batchStatus = await client.getBatch(batch.id);

uploaded = client.upload_file(
    purpose="batch",
    file=b'{"test": true}',
    filename="batch.jsonl",
    content_type="application/json",
)

batch = client.create_batch(
    {
        "endpoint": "/v1/responses",
        "input_file_id": uploaded["id"],
        "completion_window": "24h",
        "session_id": "agent-run-42",
    }
)

status = client.get_batch(batch["id"])

const batchSocketUrl = client.batches.websocketUrl(batch.id, {
  intervalMs: 1500,
  closeOnTerminal: false,
});

const videoSocketUrl = client.videos.websocketUrl(video.id);

batch_socket_url = client.batches.websocket_url(
    batch["id"], interval_ms=1500, close_on_terminal=False
)

video_socket_url = client.videos.websocket_url(video["id"])

String batchSocketUrl = client.getBatchWebSocketUrl(batchId, 1500, false);
String videoSocketUrl = client.getVideoWebSocketUrl(videoId);

const models = await client.getModels();
const health = await client.getHealth();
const providers = await client.listProviders();

// 额度和活动需要管理 API 密钥。
const credits = await client.getCredits();
const activity = await client.getActivity({ days: 30 });

models = client.get_models()
health = client.get_health()
providers = client.list_providers()
generation = client.get_generation("G-01ABC...")

# 额度和活动需要管理 API 密钥。
credits = client.get_credits()
activity = client.get_activity({"days": 30})

models, err := client.GetModels(ctx, map[string]string{"limit": "10"})
health, err := client.Health(ctx)

var models = await client.ListModels(new Dictionary<string, string> { ["limit"] = "10" });
var health = await client.Health();

$models = $client->listModels(['limit' => 10]);
$health = $client->health();

models = client.list_models(limit: 10)
health = client.health

var models = client.listModels(Map.of("limit", "10"));

import { PhaseoHttpError } from "@phaseo/sdk";

try {
  await client.generateText({ model: "invalid", messages: [{ role: "user", content: "Hi" }] });
} catch (error) {
  if (error instanceof PhaseoHttpError) {
    console.error(error.status, error.code, error.requestId, error.traceUrl);
  } else {
    throw error;
  }
}

from phaseo import PhaseoHTTPError

try:
    client.generate_text({"model": "invalid", "messages": [{"role": "user", "content": "Hi"}]})
except PhaseoHTTPError as error:
    print(error.status, error.code, error.request_id, error.trace_url)

import app.phaseo.gen.Client;
import app.phaseo.sdk.Phaseo;
import java.time.Duration;
import java.util.Map;

Phaseo client = new Phaseo(System.getenv("PHASEO_API_KEY"))
    .setTimeout(Duration.ofSeconds(30))
    .setMaxRetries(2);

try {
    var result = client.requestWithResponse("GET", "/models",
        Map.of("limit", "5"), null, null, null);
    System.out.println(result.requestId());
    System.out.println(result.data);
} catch (Client.ApiException error) {
    System.err.println(error.getStatusCode());
    System.err.println(error.getRequestId());
    System.err.println(error.getResponseBody());
}

let request = serde_json::json!({
    "model": "openai/gpt-6-astra",
    "input": "Handle this request"
});

match client.responses(&request) {
    Ok(response) => println!("{}", response.body),
    Err(error) => {
        eprintln!("message: {}", error.message);
        eprintln!("status: {:?}", error.status);
        eprintln!("body: {:?}", error.body);
    }
}