seaart-sdk 0.1.0

Python SDK for SeaArt AI gateway APIs

seaart-sdk

SeaArt AI 平台 Python SDK，按 seaart_sdk_go 的公开接口翻译实现，当前提供三类能力：

• client.modal / client.Modal：多模态任务接口
• client.llm / client.LLM：LLM 透传接口
• client.passthrough / client.Passthrough：厂商原始 API 透传接口

特点：

• 纯标准库实现，无第三方运行时依赖
• 保留原始请求透传能力
• 支持 SSE 流式响应解析
• 支持任务轮询和通用 task builder

安装

本地开发：

pip install -e .

要求：

• Python 3.10+

初始化

import seaart_sdk as sa

client = sa.Client(
    sa.ClientConfig(
        api_key="sa-your-api-key",
    )
)

默认网关配置：

• base_url: https://gateway.example.com
• model_base_url: https://gateway.example.com/model
• llm_base_url: https://gateway.example.com/llm
• passthrough_base_url: https://gateway.example.com/model

也可以分别覆盖：

client = sa.Client(
    sa.ClientConfig(
        api_key="sa-your-api-key",
        base_url="https://gateway.example.com",
        model_base_url="https://mm-gateway.example.com",
        llm_base_url="https://llm-gateway.example.com",
        passthrough_base_url="https://mm-gateway.example.com",
        timeout=60.0,
        project="my-project",
    )
)

Modal API

原始透传请求：

task = client.modal.create(
    {
        "model": "vidu_q3_reference",
        "input": [
            {
                "type": "message",
                "role": "user",
                "content": [
                    {"type": "text", "text": "cinematic shot"},
                    {"type": "image_url", "url": "https://example.com/ref1.webp"},
                    {"type": "image_url", "url": "https://example.com/ref2.webp"},
                ],
            }
        ],
        "parameters": {"duration": 5},
    },
    sa.WithHeader("X-Trace-Id", "trace-123"),
)

print(task.id, task.status)

等待任务完成：

task = client.modal.wait(
    "task_abc123",
    sa.WithPollInterval(3.0),
    sa.WithPollTimeout(300.0),
)

print(task.status, task.progress, task.urls())

也可以在创建后继续等待：

task = client.modal.create({"model": "vidu_q3_reference"})
task = task.wait(sa.WithPollInterval(3.0))

Typed helper：

body = (
    sa.NewTask("vidu_q3_reference")
    .user(
        sa.Text("cinematic shot"),
        sa.ImageURL("https://example.com/ref1.webp"),
        sa.ImageURL("https://example.com/ref2.webp"),
    )
    .param("duration", 5)
    .metadata("trace_id", "trace-123")
    .build()
)

task = client.modal.create(body)

模型列表和参数详情：

models = client.modal.list_models(
    sa.ModelSearchParams(
        query="",
        limit=2,
    )
)
for hit in models.hits:
    print(hit["name"])

skill = client.modal.get_model_skill("alibaba_animate_anyone_detect")
print(skill)

list_models / search_models 支持的查询参数：

• query -> q
• input -> input
• output -> output
• type -> type
• provider -> provider
• limit -> limit

Passthrough API

Passthrough 层保留厂商原始 API 形态。路径需要带厂商前缀，例如 /kling/...、/vidu/...、/google/...。

resp = client.passthrough.post(
    "/kling/v1/videos/text2video",
    {
        "model_name": "kling-v1",
        "prompt": "cinematic shot",
    },
    sa.WithHeader("X-Trace-Id", "trace-123"),
)

print(resp.status_code, resp.body.decode("utf-8"))

如果要完全透传原始 JSON 字节，使用 request_raw：

resp = client.passthrough.request_raw(
    "POST",
    "/google/v1beta/models/gemini-2.5-flash-image:generateContent",
    b'{"contents":[{"parts":[{"text":"paint a cat"}]}]}',
)

当前提供：

• request
• request_raw
• get
• post
• put
• delete

LLM API

LLM 层保持“请求透传 + 原始响应返回”的形式：

raw = client.llm.chat_completions(
    {
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": "hello"}],
        "max_tokens": 64,
    },
    sa.WithHeader("X-Trace-Id", "trace-123"),
)

resp = sa.Decode(raw, sa.ChatCompletionResponse)
print(resp.choices[0].message.content)

当前支持：

• chat_completions
• chat_completions_stream
• messages
• messages_stream
• responses
• responses_stream
• rerank
• embeddings
• list_models

流式响应示例：

stream = client.llm.chat_completions_stream(
    {
        "model": "gpt-4o-mini",
        "messages": [{"role": "user", "content": "hello"}],
    }
)

for event in stream:
    if event.err:
        raise event.err
    if event.done:
        break
    chunk = sa.Decode(event.data, sa.ChatCompletionResponse)
    print(chunk.choices[0].delta.content)

测试

python3 -m unittest discover -s tests -v

发布

本地打包与检查：

make check

发布到默认 twine 仓库配置：

make release

发布到 GitLab Package Registry：

make release-gitlab \
  TWINE_REPOSITORY_URL="https://<gitlab-host>/api/v4/projects/<project_id>/packages/pypi" \
  TWINE_USERNAME="__token__" \
  TWINE_PASSWORD="<token>"

GitLab CI 已配置为在打 tag 时自动构建并发布到当前项目的 Package Registry：

git tag v0.1.0
git push origin v0.1.0