diff --git a/docutranslate/app.py b/docutranslate/app.py index a17d6e5..f1cbe00 100644 --- a/docutranslate/app.py +++ b/docutranslate/app.py @@ -339,7 +339,7 @@ class TranslateServiceRequest(BaseModel): description="是否在翻译前，使用AI对原始解析出的Markdown进行一次优化，目前不推荐常规使用。" ) convert_engin: str = Field( - ..., + "mineru", description="文档解析和转换引擎。`mineru` 是默认的在线服务，`docling` 是可选的本地引擎（如果已安装）。", examples=["mineru", "docling"] ) @@ -349,17 +349,17 @@ class TranslateServiceRequest(BaseModel): examples=["your-secret-mineru-token"] ) chunk_size: int = Field( - ..., + default_params["chunk_size"], description="将文本分割的块大小（以字符为单位）。", examples=[3000] ) concurrent: int = Field( - ..., + default_params["concurrent"], description="同时向LLM API发送的并发请求数量。增加此值可以加快翻译速度，但需注意不要超过API的速率限制。", examples=[10] ) temperature: float = Field( - ..., + default_params["temperature"], description="LLM的温度参数，介于0和2之间。较高的值（如0.8）会使输出更随机，而较低的值（如0.2）会使其更具确定性。对于翻译任务，建议使用较低的值。", examples=[0.1] ) @@ -485,7 +485,7 @@ async def service_translate(request: TranslateServiceRequest = Body(..., descrip } ) async def service_cancel_translate( - task_id: str = FastApiPath(..., description="要取消的任务的ID", example="b2865b93")): + task_id: str = FastApiPath(..., description="要取消的任务的ID", examples=["b2865b93"])): """根据任务ID取消一个正在进行的翻译任务。""" try: response_data = _cancel_translation_logic(task_id) @@ -519,7 +519,7 @@ async def service_cancel_translate( } ) async def service_release_task( - task_id: str = FastApiPath(..., description="要释放资源的任务的ID", example="b2865b93") + task_id: str = FastApiPath(..., description="要释放资源的任务的ID", examples=["b2865b93"]) ): """根据任务ID释放其占用的所有服务器资源。""" if task_id not in tasks_state: @@ -637,7 +637,7 @@ async def service_release_task( } ) async def service_get_status( - task_id: str = FastApiPath(..., description="要查询状态的任务的ID", example="b2865b93")): + task_id: str = FastApiPath(..., description="要查询状态的任务的ID", examples=["b2865b93"])): """根据任务ID获取任务的当前状态和结果下载链接。""" task_state = tasks_state.get(task_id) if not task_state: @@ -689,7 +689,7 @@ async def service_get_status( } ) async def service_get_logs( - task_id: str = FastApiPath(..., description="要获取日志的任务的ID", example="b2865b93")): + task_id: str = FastApiPath(..., description="要获取日志的任务的ID", examples=["b2865b93"])): """获取指定任务ID自上次查询以来的新日志。""" if task_id not in tasks_log_queues: raise HTTPException(status_code=404, detail=f"找不到任务ID '{task_id}' 的日志队列。") @@ -727,8 +727,8 @@ FileType = Literal["markdown", "markdown_zip", "html"] } ) async def service_download_file( - task_id: str = FastApiPath(..., description="已完成任务的ID", example="b2865b93"), - file_type: FileType = FastApiPath(..., description="要下载的文件类型。", example="html") + task_id: str = FastApiPath(..., description="已完成任务的ID", examples=["b2865b93"]), + file_type: FileType = FastApiPath(..., description="要下载的文件类型。", examples=["html"]) ): """根据任务ID和文件类型下载翻译结果。""" task_state = tasks_state.get(task_id) @@ -806,8 +806,8 @@ async def service_download_file( } ) async def service_content( - task_id: str = FastApiPath(..., description="已完成任务的ID", example="b2865b93"), - file_type: FileType = FastApiPath(..., description="要获取内容的文件类型。", example="html") + task_id: str = FastApiPath(..., description="已完成任务的ID", examples=["b2865b93"]), + file_type: FileType = FastApiPath(..., description="要获取内容的文件类型。", examples=["html"]) ): """根据任务ID和文件类型，以JSON格式返回内容。zip文件会进行Base64编码。""" task_state = tasks_state.get(task_id) @@ -819,7 +819,7 @@ async def service_content( content_map = { "markdown": (task_state.get("markdown_content"), task_state['original_filename']), - "markdown_zip": (task_state.get("markdown_zip_content"),task_state['original_filename']), + "markdown_zip": (task_state.get("markdown_zip_content"), task_state['original_filename']), "html": (task_state.get("html_content"), task_state['original_filename']), } @@ -956,9 +956,9 @@ async def main_page_admin(): } }) async def temp_translate( - base_url: str = Body(..., description="LLM API的基础URL。", example="https://api.openai.com/v1"), - api_key: str = Body(..., description="LLM API的密钥。", example="sk-xxxxxxxxxx"), - model_id: str = Body(..., description="使用的模型ID。", example="gpt-4-turbo"), + base_url: str = Body(..., description="LLM API的基础URL。", examples=["https://api.openai.com/v1"]), + api_key: str = Body(..., description="LLM API的密钥。", examples=["sk-xxxxxxxxxx"]), + model_id: str = Body(..., description="使用的模型ID。", examples=["gpt-4-turbo"]), mineru_token: str = Body(..., description="Mineru引擎的Token。"), file_name: str = Body(..., description="文件名，用以判断文件类型。当后缀为txt时该接口返回普通文本，为其他后缀时返回翻译后的markdown文本", @@ -966,9 +966,10 @@ async def temp_translate( file_content: str = Body(..., description="文件内容，可以是纯文本或Base64编码的字符串。"), to_lang: str = Body("中文", description="目标语言。", examples=["中文", "英文", "English"]), concurrent: int = Body(default_params["concurrent"], description="ai翻译请求并发数"), - temperature:float|None = Body(default_params["temperature"], description="ai翻译请求温度"), - chunk_size:int =Body(default_params["chunk_size"],description="文本分块大小（bytes）"), - custom_prompt_translate: str | None = Body(None, description="翻译自定义提示词",example="人名保持原文不翻译"), + temperature: float | None = Body(default_params["temperature"], description="ai翻译请求温度"), + chunk_size: int = Body(default_params["chunk_size"], description="文本分块大小（bytes）"), + custom_prompt_translate: str | None = Body(None, description="翻译自定义提示词", + examples=["人名保持原文不翻译"]), ): """一个用于快速测试的同步翻译接口。""" @@ -1016,12 +1017,12 @@ def run_app(port: int | None = None): port_to_use = find_free_port(initial_port) if port_to_use != initial_port: print(f"端口 {initial_port} 被占用，将使用端口 {port_to_use} 代替") print(f"正在启动 DocuTranslate WebUI 版本号：{__version__}") - print(f"请用浏览器访问 http://127.0.0.1:{port_to_use}") - print(f"服务接口文档: http://127.0.0.1:{port_to_use}/docs") + print(f"请用浏览器访问 http://127.0.0.1:{port_to_use}\n") + print(f"服务接口文档: http://127.0.0.1:{port_to_use}/docs\n") uvicorn.run(app, host=None, port=port_to_use, workers=1) except Exception as e: print(f"启动失败: {e}") if __name__ == "__main__": - run_app() \ No newline at end of file + run_app()