test_model.py

"""
This file is auto generated by the code generation script.
Do not modify this file manually.
Use the `make codegen` command to regenerate.

当前文件为自动生成的控制 API 客户端代码。请勿手动修改此文件。
使用 `make codegen` 命令重新生成。

source: tests/e2e/__test_model_async_template.py


Model 模块的 E2E 测试

测试覆盖:
- 创建 ModelService
- 获取 ModelService
- 列举 ModelService
- 更新 ModelService
- 删除 ModelService
- 创建 ModelProxy
- 获取 ModelProxy
- 列举 ModelProxy
- 更新 ModelProxy
- 删除 ModelProxy
"""

import datetime
import os
import time

import pydash
import pytest

from agentrun.model import (
    BackendType,
    ModelClient,
    ModelProxy,
    ModelProxyCreateInput,
    ModelProxyUpdateInput,
    ModelResponse,
    ModelService,
    ModelServiceCreateInput,
    ModelServiceListInput,
    ModelServiceUpdateInput,
    ModelType,
    ProviderSettings,
    ProxyConfig,
    ProxyConfigEndpoint,
)
from agentrun.utils.config import Config
from agentrun.utils.exception import (
    ResourceAlreadyExistError,
    ResourceNotExistError,
)
from agentrun.utils.model import Status

api_key = os.getenv("API_KEY", "sk-test-key")
base_url = os.getenv(
    "BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1"
)
model_names = ["qwen-flash", "qwen-max"]


class TestModelService:
    """ModelService 模块 E2E 测试"""

    @pytest.fixture
    def model_service_name(self, unique_name: str) -> str:
        """生成模型服务名称"""
        return f"{unique_name}-model-service"

    async def test_model_service_lifecycle_async(self, model_service_name: str):
        """测试 ModelService 的完整生命周期"""
        client = ModelClient()
        time1 = datetime.datetime.now(datetime.timezone.utc)

        # 创建 model service
        ms = await ModelService.create_async(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        time2 = datetime.datetime.now(datetime.timezone.utc)

        # assert ms.model_service_id
        ms2 = await client.get_async(
            name=model_service_name, backend_type=BackendType.SERVICE
        )

        # 检查返回的内容是否符合预期
        pre_created_at: datetime.datetime

        def assert_model_service(ms: ModelService):
            assert ms.status == Status.READY
            assert ms.model_service_name == model_service_name
            assert ms.model_type == ModelType.LLM
            assert ms.provider == "openai"
            assert ms.provider_settings is not None
            assert ms.provider_settings.base_url == base_url
            assert ms.provider_settings.model_names == model_names
            assert ms.description == "原始描述"

            assert ms.created_at is not None
            created_at = datetime.datetime.strptime(
                ms.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert created_at > time1
            assert ms.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                ms.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at >= created_at
            assert updated_at < time2

            nonlocal pre_created_at
            pre_created_at = created_at

        assert_model_service(ms)
        assert_model_service(ms2)
        assert ms is not ms2
        ms3 = ms

        # 更新 model service
        new_description = f"更新后的描述 - {time.time()}"
        await ms.update_async(
            ModelServiceUpdateInput(description=new_description)
        )
        ms.wait_until_ready_or_failed()

        # 检查返回的内容是否符合预期
        def assert_model_service_2(ms: ModelService):
            nonlocal pre_created_at
            assert ms.status == Status.READY
            assert ms.model_service_name == model_service_name
            assert ms.model_type == ModelType.LLM
            assert ms.provider == "openai"
            assert ms.provider_settings is not None
            assert ms.provider_settings.base_url == base_url
            assert ms.description == new_description

            assert ms.created_at is not None
            created_at = datetime.datetime.strptime(
                ms.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert pre_created_at == created_at
            assert created_at > time1
            assert ms.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                ms.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at > created_at

        assert_model_service_2(ms)
        assert_model_service_2(ms3)
        assert_model_service(ms2)
        assert ms3 is ms

        # 获取 model service
        await ms2.refresh_async()
        assert_model_service_2(ms2)

        # 列举 model services
        ms_list = await client.list_async(
            ModelServiceListInput(model_type=ModelType.LLM)
        )
        assert len(ms_list) > 0
        matched_ms = 0
        for m in ms_list:
            if m.model_service_name == model_service_name:
                matched_ms += 1
                assert_model_service_2(m)
        assert matched_ms == 1

        # 尝试重复创建
        with pytest.raises(ResourceAlreadyExistError):
            await client.create_async(
                ModelServiceCreateInput(
                    model_service_name=model_service_name,
                    description="重复创建",
                    model_type=ModelType.LLM,
                    provider="openai",
                    provider_settings=ProviderSettings(
                        api_key=api_key,
                        base_url=base_url,
                        model_names=model_names,
                    ),
                )
            )

        # 删除
        await ms.delete_async()
        ms.delete_and_wait_until_finished()

        # 尝试重复删除
        with pytest.raises(ResourceNotExistError):
            await ms.delete_async()

        # 验证删除
        with pytest.raises(ResourceNotExistError):
            await client.get_async(
                name=model_service_name, backend_type=BackendType.SERVICE
            )

    def test_model_service_lifecycle(self, model_service_name: str):
        """测试 ModelService 的完整生命周期"""
        client = ModelClient()
        time1 = datetime.datetime.now(datetime.timezone.utc)

        # 创建 model service
        ms = ModelService.create(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        time2 = datetime.datetime.now(datetime.timezone.utc)

        # assert ms.model_service_id
        ms2 = client.get(
            name=model_service_name, backend_type=BackendType.SERVICE
        )

        # 检查返回的内容是否符合预期
        pre_created_at: datetime.datetime

        def assert_model_service(ms: ModelService):
            assert ms.status == Status.READY
            assert ms.model_service_name == model_service_name
            assert ms.model_type == ModelType.LLM
            assert ms.provider == "openai"
            assert ms.provider_settings is not None
            assert ms.provider_settings.base_url == base_url
            assert ms.provider_settings.model_names == model_names
            assert ms.description == "原始描述"

            assert ms.created_at is not None
            created_at = datetime.datetime.strptime(
                ms.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert created_at > time1
            assert ms.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                ms.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at >= created_at
            assert updated_at < time2

            nonlocal pre_created_at
            pre_created_at = created_at

        assert_model_service(ms)
        assert_model_service(ms2)
        assert ms is not ms2
        ms3 = ms

        # 更新 model service
        new_description = f"更新后的描述 - {time.time()}"
        ms.update(ModelServiceUpdateInput(description=new_description))
        ms.wait_until_ready_or_failed()

        # 检查返回的内容是否符合预期
        def assert_model_service_2(ms: ModelService):
            nonlocal pre_created_at
            assert ms.status == Status.READY
            assert ms.model_service_name == model_service_name
            assert ms.model_type == ModelType.LLM
            assert ms.provider == "openai"
            assert ms.provider_settings is not None
            assert ms.provider_settings.base_url == base_url
            assert ms.description == new_description

            assert ms.created_at is not None
            created_at = datetime.datetime.strptime(
                ms.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert pre_created_at == created_at
            assert created_at > time1
            assert ms.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                ms.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at > created_at

        assert_model_service_2(ms)
        assert_model_service_2(ms3)
        assert_model_service(ms2)
        assert ms3 is ms

        # 获取 model service
        ms2.refresh()
        assert_model_service_2(ms2)

        # 列举 model services
        ms_list = client.list(ModelServiceListInput(model_type=ModelType.LLM))
        assert len(ms_list) > 0
        matched_ms = 0
        for m in ms_list:
            if m.model_service_name == model_service_name:
                matched_ms += 1
                assert_model_service_2(m)
        assert matched_ms == 1

        # 尝试重复创建
        with pytest.raises(ResourceAlreadyExistError):
            client.create(
                ModelServiceCreateInput(
                    model_service_name=model_service_name,
                    description="重复创建",
                    model_type=ModelType.LLM,
                    provider="openai",
                    provider_settings=ProviderSettings(
                        api_key=api_key,
                        base_url=base_url,
                        model_names=model_names,
                    ),
                )
            )

        # 删除
        ms.delete()
        ms.delete_and_wait_until_finished()

        # 尝试重复删除
        with pytest.raises(ResourceNotExistError):
            ms.delete()

        # 验证删除
        with pytest.raises(ResourceNotExistError):
            client.get(
                name=model_service_name, backend_type=BackendType.SERVICE
            )

    async def test_model_service_invoke_async(self, model_service_name: str):
        # 创建 model service
        ms = await ModelService.create_async(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=True,
        )
        content = ""
        chunk_size = 0
        for chunk in result:
            from agentrun.utils.log import logger

            logger.error(chunk)
            delta = pydash.get(chunk, "choices[0].delta.content")
            if delta:
                content += delta
                chunk_size += 1
        assert chunk_size > 0
        assert content == "今天天气怎么样？"

        ms.delete()

    def test_model_service_invoke(self, model_service_name: str):
        # 创建 model service
        ms = ModelService.create(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=True,
        )
        content = ""
        chunk_size = 0
        for chunk in result:
            from agentrun.utils.log import logger

            logger.error(chunk)
            delta = pydash.get(chunk, "choices[0].delta.content")
            if delta:
                content += delta
                chunk_size += 1
        assert chunk_size > 0
        assert content == "今天天气怎么样？"

        ms.delete()

    async def test_model_service_with_credential_async(
        self, model_service_name: str
    ):
        # 创建 Credential
        from agentrun.credential import (
            Credential,
            CredentialConfig,
            CredentialCreateInput,
        )

        cr = await Credential.create_async(
            CredentialCreateInput(
                credential_name=f"{model_service_name}-credential",
                enabled=True,
                credential_config=CredentialConfig.outbound_llm_api_key(
                    api_key=api_key,
                    provider="openai",
                ),
            )
        )

        # 创建 model service
        ms = await ModelService.create_async(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                credential_name=cr.credential_name,
                provider_settings=ProviderSettings(
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        await ms.delete_async()
        await cr.delete_async()

    def test_model_service_with_credential(self, model_service_name: str):
        # 创建 Credential
        from agentrun.credential import (
            Credential,
            CredentialConfig,
            CredentialCreateInput,
        )

        cr = Credential.create(
            CredentialCreateInput(
                credential_name=f"{model_service_name}-credential",
                enabled=True,
                credential_config=CredentialConfig.outbound_llm_api_key(
                    api_key=api_key,
                    provider="openai",
                ),
            )
        )

        # 创建 model service
        ms = ModelService.create(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                credential_name=cr.credential_name,
                provider_settings=ProviderSettings(
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        result = ms.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        ms.delete()
        cr.delete()


class TestModelProxy:
    """ModelProxy 模块 E2E 测试"""

    @pytest.fixture
    def model_proxy_name(self, unique_name: str) -> str:
        """生成模型代理名称"""
        return f"{unique_name}-model-proxy"

    async def prepare_model_proxy_async(self, model_proxy_name: str):
        model_service_name = f"{model_proxy_name}-service"
        ms = await ModelService.create_async(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        mp = await ModelProxy.create_async(
            ModelProxyCreateInput(
                model_proxy_name=model_proxy_name,
                description="原始描述",
                model_type=ModelType.LLM,
                proxy_config=ProxyConfig(
                    endpoints=[
                        ProxyConfigEndpoint(
                            model_names=[model_name],
                            model_service_name=model_service_name,
                        )
                        for model_name in model_names[:1]
                    ],
                    policies={},
                ),
            )
        )
        mp.wait_until_ready_or_failed()

        async def defer():
            await mp.delete_and_wait_until_finished_async()
            await ms.delete_and_wait_until_finished_async()

        return ms, mp, defer

    def prepare_model_proxy(self, model_proxy_name: str):
        model_service_name = f"{model_proxy_name}-service"
        ms = ModelService.create(
            ModelServiceCreateInput(
                model_service_name=model_service_name,
                description="原始描述",
                model_type=ModelType.LLM,
                provider="openai",
                provider_settings=ProviderSettings(
                    api_key=api_key,
                    base_url=base_url,
                    model_names=model_names,
                ),
            )
        )
        ms.wait_until_ready_or_failed()

        mp = ModelProxy.create(
            ModelProxyCreateInput(
                model_proxy_name=model_proxy_name,
                description="原始描述",
                model_type=ModelType.LLM,
                proxy_config=ProxyConfig(
                    endpoints=[
                        ProxyConfigEndpoint(
                            model_names=[model_name],
                            model_service_name=model_service_name,
                        )
                        for model_name in model_names[:1]
                    ],
                    policies={},
                ),
            )
        )
        mp.wait_until_ready_or_failed()

        def defer():
            mp.delete_and_wait_until_finished()
            ms.delete_and_wait_until_finished()

        return ms, mp, defer

    async def test_model_proxy_lifecycle_async(self, model_proxy_name: str):
        """测试 ModelProxy 的完整生命周期"""
        client = ModelClient()
        time1 = datetime.datetime.now(datetime.timezone.utc)

        # 创建 model service
        ms, mp, defer = await self.prepare_model_proxy_async(model_proxy_name)

        time2 = datetime.datetime.now(datetime.timezone.utc)

        assert mp.model_proxy_id
        mp2 = await client.get_async(
            name=model_proxy_name, backend_type=BackendType.PROXY
        )

        # 检查返回的内容是否符合预期
        pre_created_at: datetime.datetime

        def assert_model_proxy(mp: ModelProxy):
            assert mp.status == Status.READY
            assert mp.model_proxy_name == model_proxy_name
            assert mp.model_type == ModelType.LLM
            assert mp.proxy_mode == "single"
            assert mp.proxy_config is not None
            assert mp.description == "原始描述"
            assert mp.cpu == 2
            assert mp.memory == 4096

            assert mp.created_at is not None
            created_at = datetime.datetime.strptime(
                mp.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert created_at > time1
            assert mp.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                mp.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at >= created_at
            assert updated_at < time2

            nonlocal pre_created_at
            pre_created_at = created_at

        assert_model_proxy(mp)
        assert_model_proxy(mp2)
        assert mp is not mp2
        mp3 = mp

        # 更新 model proxy
        new_description = f"更新后的描述 - {time.time()}"
        await mp.update_async(
            ModelProxyUpdateInput(description=new_description)
        )
        mp.wait_until_ready_or_failed()

        # 检查返回的内容是否符合预期
        def assert_model_proxy_2(mp: ModelProxy):
            nonlocal pre_created_at
            assert mp.status == Status.READY
            assert mp.model_proxy_name == model_proxy_name
            assert mp.model_type == ModelType.LLM
            assert mp.proxy_mode == "single"
            assert mp.description == new_description

            assert mp.created_at is not None
            created_at = datetime.datetime.strptime(
                mp.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert pre_created_at == created_at
            assert created_at > time1
            assert mp.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                mp.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at > created_at

        assert_model_proxy_2(mp)
        assert_model_proxy_2(mp3)
        assert_model_proxy(mp2)
        assert mp3 is mp

        # 获取 model proxy
        await mp2.refresh_async()
        assert_model_proxy_2(mp2)

        # 尝试重复创建
        with pytest.raises(ResourceAlreadyExistError):
            await client.create_async(
                ModelProxyCreateInput(
                    model_proxy_name=model_proxy_name,
                    description="重复创建",
                    model_type=ModelType.LLM,
                    proxy_config=ProxyConfig(
                        endpoints=[
                            ProxyConfigEndpoint(
                                model_names=[model_name],
                                model_service_name=ms.model_service_name,
                            )
                            for model_name in model_names[:1]
                        ],
                        policies={},
                    ),
                )
            )

        # 删除
        await mp.delete_async()
        mp.delete_and_wait_until_finished()

        # 尝试重复删除
        with pytest.raises(ResourceNotExistError):
            await mp.delete_async()

        # 验证删除
        with pytest.raises(ResourceNotExistError):
            await client.get_async(
                name=model_proxy_name, backend_type=BackendType.PROXY
            )

        await defer()

    def test_model_proxy_lifecycle(self, model_proxy_name: str):
        """测试 ModelProxy 的完整生命周期"""
        client = ModelClient()
        time1 = datetime.datetime.now(datetime.timezone.utc)

        # 创建 model service
        ms, mp, defer = self.prepare_model_proxy(model_proxy_name)

        time2 = datetime.datetime.now(datetime.timezone.utc)

        assert mp.model_proxy_id
        mp2 = client.get(name=model_proxy_name, backend_type=BackendType.PROXY)

        # 检查返回的内容是否符合预期
        pre_created_at: datetime.datetime

        def assert_model_proxy(mp: ModelProxy):
            assert mp.status == Status.READY
            assert mp.model_proxy_name == model_proxy_name
            assert mp.model_type == ModelType.LLM
            assert mp.proxy_mode == "single"
            assert mp.proxy_config is not None
            assert mp.description == "原始描述"
            assert mp.cpu == 2
            assert mp.memory == 4096

            assert mp.created_at is not None
            created_at = datetime.datetime.strptime(
                mp.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert created_at > time1
            assert mp.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                mp.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at >= created_at
            assert updated_at < time2

            nonlocal pre_created_at
            pre_created_at = created_at

        assert_model_proxy(mp)
        assert_model_proxy(mp2)
        assert mp is not mp2
        mp3 = mp

        # 更新 model proxy
        new_description = f"更新后的描述 - {time.time()}"
        mp.update(ModelProxyUpdateInput(description=new_description))
        mp.wait_until_ready_or_failed()

        # 检查返回的内容是否符合预期
        def assert_model_proxy_2(mp: ModelProxy):
            nonlocal pre_created_at
            assert mp.status == Status.READY
            assert mp.model_proxy_name == model_proxy_name
            assert mp.model_type == ModelType.LLM
            assert mp.proxy_mode == "single"
            assert mp.description == new_description

            assert mp.created_at is not None
            created_at = datetime.datetime.strptime(
                mp.created_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert pre_created_at == created_at
            assert created_at > time1
            assert mp.last_updated_at is not None
            updated_at = datetime.datetime.strptime(
                mp.last_updated_at, "%Y-%m-%dT%H:%M:%S.%f%z"
            )
            assert updated_at > created_at

        assert_model_proxy_2(mp)
        assert_model_proxy_2(mp3)
        assert_model_proxy(mp2)
        assert mp3 is mp

        # 获取 model proxy
        mp2.refresh()
        assert_model_proxy_2(mp2)

        # 尝试重复创建
        with pytest.raises(ResourceAlreadyExistError):
            client.create(
                ModelProxyCreateInput(
                    model_proxy_name=model_proxy_name,
                    description="重复创建",
                    model_type=ModelType.LLM,
                    proxy_config=ProxyConfig(
                        endpoints=[
                            ProxyConfigEndpoint(
                                model_names=[model_name],
                                model_service_name=ms.model_service_name,
                            )
                            for model_name in model_names[:1]
                        ],
                        policies={},
                    ),
                )
            )

        # 删除
        mp.delete()
        mp.delete_and_wait_until_finished()

        # 尝试重复删除
        with pytest.raises(ResourceNotExistError):
            mp.delete()

        # 验证删除
        with pytest.raises(ResourceNotExistError):
            client.get(name=model_proxy_name, backend_type=BackendType.PROXY)

        defer()

    async def test_model_proxy_invoke_single_async(self, model_proxy_name: str):
        # 创建 model service
        _, mp, defer = await self.prepare_model_proxy_async(model_proxy_name)

        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=True,
        )
        content = ""
        chunk_size = 0
        for chunk in result:
            from agentrun.utils.log import logger

            logger.error(chunk)
            delta = pydash.get(chunk, "choices[0].delta.content")
            if delta:
                content += delta
                chunk_size += 1
        assert chunk_size > 0
        assert content == "今天天气怎么样？"

        await defer()

    def test_model_proxy_invoke_single(self, model_proxy_name: str):
        # 创建 model service
        _, mp, defer = self.prepare_model_proxy(model_proxy_name)

        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=True,
        )
        content = ""
        chunk_size = 0
        for chunk in result:
            from agentrun.utils.log import logger

            logger.error(chunk)
            delta = pydash.get(chunk, "choices[0].delta.content")
            if delta:
                content += delta
                chunk_size += 1
        assert chunk_size > 0
        assert content == "今天天气怎么样？"

        defer()

    async def test_model_proxy_invoke_multi_async(self, model_proxy_name: str):
        ms, mp, defer = await self.prepare_model_proxy_async(model_proxy_name)

        await mp.update_async(
            ModelProxyUpdateInput(
                proxy_config=ProxyConfig(
                    endpoints=[
                        ProxyConfigEndpoint(
                            model_names=[model_name],
                            model_service_name=ms.model_service_name,
                        )
                        for model_name in model_names[:1]
                    ]
                )
            )
        )
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )
        await defer()

    def test_model_proxy_invoke_multi(self, model_proxy_name: str):
        ms, mp, defer = self.prepare_model_proxy(model_proxy_name)

        mp.update(
            ModelProxyUpdateInput(
                proxy_config=ProxyConfig(
                    endpoints=[
                        ProxyConfigEndpoint(
                            model_names=[model_name],
                            model_service_name=ms.model_service_name,
                        )
                        for model_name in model_names[:1]
                    ]
                )
            )
        )
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )
        defer()

    async def prepare_model_proxy_with_credential_async(
        self, model_proxy_name: str
    ):

        from agentrun.credential import (
            Credential,
            CredentialConfig,
            CredentialCreateInput,
        )

        credential_name = f"{model_proxy_name}-credential"
        cred = await Credential.create_async(
            CredentialCreateInput(
                credential_name=credential_name,
                description="测试凭证",
                credential_config=CredentialConfig.inbound_api_key("sk-123456"),
            )
        )

        ms, mp, defer = await self.prepare_model_proxy_async(model_proxy_name)
        mp.update(ModelProxyUpdateInput(credential_name=credential_name))
        mp.wait_until_ready_or_failed()

        async def defer2():
            await defer()
            await cred.delete_async()

        return ms, mp, defer2

    def prepare_model_proxy_with_credential(self, model_proxy_name: str):

        from agentrun.credential import (
            Credential,
            CredentialConfig,
            CredentialCreateInput,
        )

        credential_name = f"{model_proxy_name}-credential"
        cred = Credential.create(
            CredentialCreateInput(
                credential_name=credential_name,
                description="测试凭证",
                credential_config=CredentialConfig.inbound_api_key("sk-123456"),
            )
        )

        ms, mp, defer = self.prepare_model_proxy(model_proxy_name)
        mp.update(ModelProxyUpdateInput(credential_name=credential_name))
        mp.wait_until_ready_or_failed()

        def defer2():
            defer()
            cred.delete()

        return ms, mp, defer2

    async def test_model_proxy_invoke_credential_async(
        self, model_proxy_name: str
    ):
        _, mp, defer = await self.prepare_model_proxy_with_credential_async(
            model_proxy_name
        )

        # 没有权限的情况下 invoke
        with pytest.raises(Exception) as e:
            mp.completions(
                messages=[
                    {
                        "role": "system",
                        "content": "你是一个回音壁，会原封不动返回用户的输入",
                    },
                    {"role": "user", "content": "你好！"},
                    {"role": "assistant", "content": "你好！"},
                    {"role": "user", "content": "今天天气怎么样？"},
                ],
                stream=False,
                config=Config(access_key_id="000"),
            )
        assert "API key mismatc" in f"{e.value!s}"

        # 直接使用 key 进行 invoke
        info = mp.model_info()
        api_key = pydash.get(info, "headers.Agentrun-Access-Token")
        assert api_key
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
            config=Config(access_key_id="000", token=api_key),
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        # 使用 ak/sk 自动签名
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        await defer()

    def test_model_proxy_invoke_credential(self, model_proxy_name: str):
        _, mp, defer = self.prepare_model_proxy_with_credential(
            model_proxy_name
        )

        # 没有权限的情况下 invoke
        with pytest.raises(Exception) as e:
            mp.completions(
                messages=[
                    {
                        "role": "system",
                        "content": "你是一个回音壁，会原封不动返回用户的输入",
                    },
                    {"role": "user", "content": "你好！"},
                    {"role": "assistant", "content": "你好！"},
                    {"role": "user", "content": "今天天气怎么样？"},
                ],
                stream=False,
                config=Config(access_key_id="000"),
            )
        assert "API key mismatc" in f"{e.value!s}"

        # 直接使用 key 进行 invoke
        info = mp.model_info()
        api_key = pydash.get(info, "headers.Agentrun-Access-Token")
        assert api_key
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
            config=Config(access_key_id="000", token=api_key),
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        # 使用 ak/sk 自动签名
        result = mp.completions(
            messages=[
                {
                    "role": "system",
                    "content": "你是一个回音壁，会原封不动返回用户的输入",
                },
                {"role": "user", "content": "你好！"},
                {"role": "assistant", "content": "你好！"},
                {"role": "user", "content": "今天天气怎么样？"},
            ],
            stream=False,
        )
        assert isinstance(result, ModelResponse)
        assert (
            pydash.get(result, "choices[0].message.content")
            == "今天天气怎么样？"
        )

        defer()