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一键部署

qwen-long是通义千问模型家族中,提供具备强大长文本处理能力的模型,最大可支持千万tokens的对话窗口,并通过与OpenAI兼容的模式提供API服务。参考OpenAI接口兼容,只需配置DashScope的API key以及服务的base_url,即可访问(注:Dashscope SDK调用的方式仍然兼容)。

模型概览

模型名

模型简介

qwen-long

通义千问超大规模语言模型支持长文本上下文,以及基于长文档、多文档等多个场景的对话功能。具体支持的文档格式与限制,可参见上传文件

使用说明

模型能力限制

qwen-long可支持最大10,000,000 tokens(包含您的问答历史及上传文档的总tokens)的上下文,请参照以下说明选择适合您的使用方式。

场景示例

为了帮助您选择合适的模型使用方式,我们提供以下几种较为常见的场景及模型的使用方法供您参考。

单文档对话

单文档对话

方法介绍

方法说明

方式1

通过文件服务上传文件获取fileid 放在system message中进行对话,参考单文档

推荐使用该方法。

方式2

可直接将文档内容放在system message中,参考单文档

1M tokens以下的文档可选用该方法。

多文档对话

  • 当您在本轮对话时明确有多个文档需要对话时

    多文档对话

    方法介绍

    方法说明

    方式1

    通过文件服务将所有需要对话的文档上传,并将所有的fileid输入system message,参考多文档

    推荐使用该方法。

    方式2

    直接将每个文档内容放进一个system message中,一并传给大模型,可参考多文档

    不推荐使用该方法,直接输入多文档内容进行对话。受API调用请求大小所限,大量输入文本内容(超过1M tokens)可能会受到限制。

  • 当您需要在对话中追加文档进行对话时

    追加文档对话

    方法介绍

    方法说明

    方式1

    持续通过文件服务上传文档,而后将待追加的fileid填入system message中继续对话,参考追加文档

    推荐使用该方法。

    方式2

    持续将待追加的文件内容直接放入system message中继续对话,参考追加文档

    不推荐使用该方法,直接输入多文档内容进行对话。受API调用请求大小所限,大量输入文本内容(超过1M tokens)可能会受到限制。

通过OpenAI SDK调用

前提条件

  • 请确保您的计算机上安装了Python环境。

  • 请安装最新版OpenAI SDK。

    # 如果下述命令报错,请将pip替换为pip3
    pip install -U openai
  • 已开通百炼服务并获得API-KEY:获取API-KEY

  • 我们推荐您将API-KEY配置到环境变量中以降低API-KEY的泄漏风险,配置方法可参考通过环境变量配置API-KEY。您也可以在代码中配置API-KEY,但是泄漏风险会提高

使用方式

qwen-long支持长文本(文档)对话,文档内容需放在role为system的message中,有以下两种方式可将文档信息输入给模型:

  1. 在提前上传文件获取文件ID(fileid)后,可以直接提供fileid。其中上传文件的接口和操作方法可参考文档上传接口(OpenAI)。支持在对话中使用一个或多个fileid。

  2. 直接输入需要处理的文本格式的文档内容(file content)。

重要

请避免直接将文档内容放在role为user的message中,role为user的message及用于role-play的system message限制输入最长为9K tokens。

使用qwen-long时,通过system message提供文档信息时,还必须同时提供一个正常role-play的system message,默认为"You are a helpful assistant.",您也可以根据实际需求进行自定义修改,例如"你是一个文本解读专家。"等等。请参照文档中的范例代码作为参考。

示例代码

以文档ID(fileid)方式输入文档:

单文档
  • 流式输出

    使用方法:stream设置为True

    from pathlib import Path
    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data.pdf 是一个示例文件
    file = client.files.create(file=Path("data.pdf"), purpose="file-extract")
    
    # 新文件上传后需要等待模型解析,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': f'fileid://{file.id}'
            },
            {
                'role': 'user',
                'content': '这篇文章讲了什么?'
            }
        ],
        stream=True
    )
    for chunk in completion:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
    
  • 非流式输出

    使用方法:stream设置为False

    from pathlib import Path
    from openai import OpenAI
    import os
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data.pdf 是一个示例文件
    file = client.files.create(file=Path("data.pdf"), purpose="file-extract")
    
    # 新文件上传后需要等待模型解析,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': f'fileid://{file.id}'
            },
            {
                'role': 'user',
                'content': '这篇文章讲了什么?'
            }
        ],
        stream=False
    )
    
    print(completion.choices[0].message.model_dump())
    
多文档
  • 流式输出

    使用方法:stream设置为True

    from openai import OpenAI
    from pathlib import Path
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data_1.pdf与data_2.pdf为两个示例pdf文件
    file_1 = client.files.create(file=Path("data_1.pdf"), purpose="file-extract")
    file_2 = client.files.create(file=Path("data_2.pdf"), purpose="file-extract")
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': f"fileid://{file_1.id},fileid://{file_2.id}"
            },
            {
                'role': 'user',
                'content': '这几篇文章讲了什么?'
            }
        ],
        stream=True
    )
    for chunk in completion:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
    
  • 非流式输出

    使用方法:stream设置为False

    from openai import OpenAI
    from pathlib import Path
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data_1.pdf与data_2.pdf为两个示例pdf文件
    file_1 = client.files.create(file=Path("data_1.pdf"), purpose="file-extract")
    file_2 = client.files.create(file=Path("data_2.pdf"), purpose="file-extract")
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': f"fileid://{file_1.id},fileid://{file_2.id}"
            },
            {
                'role': 'user',
                'content': '这几篇文章讲了什么?'
            }
        ],
        stream=False
    )
    
    print(completion.choices[0].message.model_dump())
    
追加文档
说明

可在file system message中提供文档内容进行对话。包括直接提供文本以及通过文档服务上传文档后提供文档ID来进行对话的两种方式。这两种文档输入方式在messages中暂不支持混合使用。

  • 流式输出

    使用方法:stream设置为True

    from pathlib import Path
    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data_1.pdf为初始文档
    file_1 = client.files.create(file=Path("data_1.pdf"), purpose="file-extract")
    
    # 初始化messages列表
    messages = [
        {
            'role': 'system',
            'content': 'You are a helpful assistant.'
        },
        {
            'role': 'system',
            'content': f'fileid://{file_1.id}'
        },
        {
            'role': 'user',
            'content': '这篇文章讲了什么?'
        },
    ]
    # 第一轮响应
    completion_1 = client.chat.completions.create(
        model="qwen-long",
        messages=messages,
        stream=False
    )
    
    # 打印出第一轮响应
    # 如果需要流式输出第一轮的响应,需要将stream设置为True,并拼接每一段输出内容,在构造assistant_message的content时传入拼接后的字符
    print(f"第一轮响应:{completion_1.choices[0].message.model_dump()}")
    
    # 构造assistant_message
    assistant_message = {
        "role": "assistant",
        "content": completion_1.choices[0].message.content}
    
    # 将assistant_message添加到messages中
    messages.append(assistant_message)
    
    # data_2.pdf为追加文档
    file_2 = client.files.create(file=Path("data_2.pdf"), purpose="file-extract")
    
    # 将追加文档的fileid添加到messages中
    system_message = {
        'role': 'system',
        'content': f'fileid://{file_2.id}'
    }
    messages.append(system_message)
    
    # 添加用户问题
    messages.append({
        'role': 'user',
        'content': '这两篇文章讨论的方法有什么异同点?'
    })
    
    # 追加文档后的响应
    completion_2 = client.chat.completions.create(
        model="qwen-long",
        messages=messages,
        stream=True
    )
    
    # 流式打印出追加文档后的响应
    print("追加文档后的响应:")
    for chunk in completion_2:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
    
  • 非流式输出

    使用方法:stream设置为False

    from pathlib import Path
    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务base_url
    )
    
    # data_1.pdf为原文档,data_2.pdf为追加文档
    file_1 = client.files.create(file=Path("data_1.pdf"), purpose="file-extract")
    
    # 初始化messages列表
    messages = [
        {
            'role': 'system',
            'content': 'You are a helpful assistant.'
        },
        {
            'role': 'system',
            'content': f'fileid://{file_1.id}'
        },
        {
            'role': 'user',
            'content': '这篇文章讲了什么?'
        },
    ]
    # 第一轮响应
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=messages,
        stream=False
    )
    
    # 打印出第一轮响应
    print(f"第一轮响应:{completion.choices[0].message.model_dump()}")
    
    # 构造assistant_message
    assistant_message = {
        "role": "assistant",
        "content": completion.choices[0].message.content}
    
    # 将assistant_message添加到messages中
    messages.append(assistant_message)
    
    # 获取追加文档的fileid
    file_2 = client.files.create(file=Path("data_2.pdf"), purpose="file-extract")
    
    # 将追加文档的fileid添加到messages中
    system_message = {
        'role': 'system',
        'content': f'fileid://{file_2.id}'
    }
    messages.append(system_message)
    
    # 添加用户问题
    messages.append({
        'role': 'user',
        'content': '这两篇文章讨论的方法有什么异同点?'
    })
    
    # 追加文档后的响应
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=messages,
        stream=False
    )
    
    print(f"追加文档后的响应:{completion.choices[0].message.model_dump()}")
    

以文本方式直接输入文档内容:

单文档
  • 流式输出

    stream设置为True

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                # 将文档内容粘贴在此处
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'user',
                'content': '文章讲了什么?'
            }
        ],
        stream=True
    )
    for chunk in completion:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
    
  • 非流式输出

    stream设置为False

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                # 将文档内容粘贴在此处
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'user',
                'content': '文章讲了什么?'
            }
        ],
        stream=False
    )
    
    print(completion.choices[0].message.model_dump())
多文档
说明

您可直接输入多文档内容进行对话。受API调用请求大小所限,大量输入文本内容可能会受到限制(当前的报文长度可支持约1M tokens的content长度,超过限制时无法保证)。为保证您的对话体验,我们建议您在需要基于大量文本内容进行对话时,尤其是使用了多文档场景时,尽量单独上传文档,再根据文档ID进行对话。

  • 流式输出

    stream设置为True

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                # 将第一篇文档内容放在此处
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'system',
                # 将第二篇文档内容放在此处
                'content': '大型语言模型的训练分为两个阶段:...'
            },
            {
                'role': 'user',
                'content': '这两篇文章讨论的方法有什么异同点?'
            },
        ],
        stream=True
    )
    for chunk in completion:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
    
  • 非流式输出

    stream设置为False

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    # 首次对话会等待文档解析完成,首轮响应时间可能较长
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                # 将第一篇文档内容放在此处
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'system',
                # 将第二篇文档内容放在此处
                'content': '大型语言模型的训练分为两个阶段:...'
            },
            {
                'role': 'user',
                'content': '这两篇文章讨论的方法有什么异同点?'
            },
        ],
        stream=False
    )
    
    print(completion.choices[0].message.model_dump())
    
追加文档
说明

您可直接输入多文档内容进行对话。受API调用请求大小所限,大量输入文本内容可能会受到限制(当前的报文长度可支持约1M tokens的content长度,超过限制时无法保证)。为保证您的对话体验,我们建议您在需要基于大量文本内容进行对话时,尤其是使用了多文档场景时,尽量单独上传文档,再根据文档ID进行对话。

  • 流式输出

    stream设置为True

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'user',
                'content': '文章讲了什么?'
            },
            {
                'role': 'assistant',
                # 此处为模拟响应内容,使用时请传入真实响应内容
                'content': '这篇文章主题为大模型预训练方法,主要内容是...'
            },
            {
                'role': 'system',
                # 追加文档的内容放在此处
                'content': '大型语言模型的训练分为两个阶段:...'
            },
            {
                'role': 'user',
                'content': '这两篇文章讨论的方法有什么异同点?'
            },
        ],
        stream=True
    )
    for chunk in completion:
        if chunk.choices[0].delta.content is not None:
            print(chunk.choices[0].model_dump())
  • 非流式输出

    stream设置为False

    from openai import OpenAI
    import os
    
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),  # 如果您没有配置环境变量,请在此处替换您的API-KEY
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",  # 填写DashScope服务endpoint
    )
    
    completion = client.chat.completions.create(
        model="qwen-long",
        messages=[
            {
                'role': 'system',
                'content': 'You are a helpful assistant.'
            },
            {
                'role': 'system',
                'content': '大型语言模型(llm)已经彻底改变了人工智能领域,使以前被认为是人类独有的自然语言处理任务成为可能...'
            },
            {
                'role': 'user',
                'content': '文章讲了什么?'
            },
            {
                'role': 'assistant',
                # 此处为模拟响应内容,使用时请传入真实响应内容
                'content': '这篇文章主题为大模型预训练方法,主要内容是...'
            },
            {
                'role': 'system',
                # 追加文档的内容放在此处
                'content': '大型语言模型的训练分为两个阶段:...'
            },
            {
                'role': 'user',
                'content': '这两篇文章讨论的方法有什么异同点?'
            },
        ],
        stream=False
    )
    
    print(completion.choices[0].message.model_dump())

输入参数

参数配置与OpenAI的接口参数对齐,当前已支持的参数如下,更多参数支持添加中,详情可参考输入参数配置

参数

类型

默认值

说明

model

string

-

当前模型为qwen-long

messages

list

-

  • 用户与模型的对话历史。list中的每个元素形式为{"role":角色, "content": 内容}。

  • 角色当前可选值:system、user、assistant。其中user和assistant需要交替出现。

  • messages如出现多个role为system的message,第一个会作为系统设置,第二个及以后的system message会作为file system输入。

top_p(可选)

float

-

生成过程中的核采样方法概率阈值,例如,取值为0.8时,仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为(0,1.0),取值越大,生成的随机性越高;取值越低,生成的确定性越高。

temperature(可选)

float

-

用于控制模型回复的随机性和多样性。具体来说,temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值,使得更多的低概率词被选择,生成结果更加多样化;而较低的temperature值则会增强概率分布的峰值,使得高概率词更容易被选择,生成结果更加确定。

取值范围: [0, 2),不建议取值为0,无意义。

max_tokens(可选)

integer

2000

指定模型可生成的最大token个数。

最大值和默认值均为2000 tokens。

stream(可选)

boolean

False

用于控制是否使用流式输出。当以stream模式输出结果时,接口返回结果为generator,需要通过迭代获取结果,默认每次输出为当前生成的整个序列,最后一次输出为最终全部生成结果

stop(可选)

string or array

None

stop参数用于实现内容生成过程的精确控制,在模型生成的内容即将包含指定的字符串或token_id时自动停止。stop可以为string类型或array类型。

  • string类型

    当模型将要生成指定的stop词语时停止。

    例如将stop指定为"你好",则模型将要生成“你好”时停止。

  • array类型

    array中的元素可以为token_id或者字符串,或者元素为token_id的array。当模型将要生成的token或其对应的token_id在stop中时,模型生成将会停止。以下为stop为array时的示例(tokenizer对应模型为qwen-turbo):

    1.元素为token_id:

    token_id为108386和104307分别对应token为“你好”和“天气”,设定stop为[108386,104307],则模型将要生成“你好”或者“天气”时停止。

    2.元素为字符串:

    设定stop为["你好","天气"],则模型将要生成“你好”或者“天气”时停止。

    3.元素为array:

    token_id为108386和103924分别对应token为“你好”和“啊”,token_id为35946和101243分别对应token为“我”和“很好”。设定stop为[[108386, 103924],[35946, 101243]],则模型将要生成“你好啊”或者“我很好”时停止。

    说明

    stop为array类型时,不可以将token_id和字符串同时作为元素输入,比如不可以指定stop为["你好",104307]

返回结果

  • 非stream返回结果示例

{
  'content': '文章探讨了大型语言模型训练的两个阶段:无监督预训练和大规模指令微调与强化学习,并提出了一种名为LIMA的语言模型,它是一个基于LLaMa的650亿参数模型,仅通过1000个精心挑选的提示和响应进行标准监督损失微调,未涉及强化学习或人类偏好建模。LIMA展示了强大的性能,能从少数示例中学习到特定的响应格式,处理包括规划旅行行程到推测替代历史等复杂查询,并能较好地泛化到未见过的任务上。\n\n通过对比实验,发现LIMA在43%的情况下,其生成的回复与GPT-4相比要么等同要么更受欢迎;与Bard相比这一比例为58%,与经过人类反馈训练的DaVinci003相比更是高达65%。这些结果强烈表明,大型语言模型中的几乎所有知识都是在预训练阶段学到的,而只需要有限的指令微调数据即可教会模型产生高质量的输出。\n\n此外,文章还涵盖了关于时间旅行的虚构故事创作、对古埃及文明的兴趣描述、以及如何帮助聪明孩子交友的建议等内容,展示了语言模型在多样任务上的应用能力。同时,提到了一个关于营销策略的计划概要,以及美国总统拜登面临的经济挑战与就业市场分析。最后,还包含了有关回答质量影响的测试、如何以乔治·卡林风格编写单口相声段子的示例,以及如何制作shakshuka食谱的指导。',
  'role': 'assistant',
  'function_call': None,
  'tool_calls': None
}
  • stream返回结果

{'delta': {'content': '文章', 'function_call': None, 'role': 'assistant', 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '主要', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '探讨', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '了一种名为LIMA', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '的语言模型的训练方法及其对齐', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '能力的评估。LIMA是一个拥有', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '650亿参数的大型语言', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '模型,它仅通过在10', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '00个精心挑选的提示和', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '响应上进行标准监督微调来', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '完成训练,过程中并未采用强化学习', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '或人类偏好建模。研究结果显示', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': ',尽管训练数据有限,LIMA', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '仍能展现出强大的性能,能够从', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '少数示例中学习到特定的', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '响应格式,并泛化到未见过', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '的任务上。\n\n对比实验表明,在控制', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '条件下,相对于GPT-4、', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': 'Bard和DaVinci00', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '3等其他模型,人们更倾向于', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': 'LIMA生成的回复,分别有', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '43%、58%和', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '65%的情况下认为LIMA的表现', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '更好或至少相当。这表明大型', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '语言模型在预训练阶段已经学', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '到了大量的知识,而只需少量的', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '指令微调数据即可让模型产生', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '高质量的输出,强调了“少', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '即是多”(Less is More)', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '的理念在模型对齐上的有效性。', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '\n\n此外,文章还提及了关于模型', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '输出质量的测试,以及使用不同', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '数量的示例进行微调时', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '模型稳定性的观察,进一步证明了', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '即使是小规模的、经过筛选的数据', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '也能显著提升模型性能。同时,', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '文中还包含了一些示例输出,', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '如有关如何帮助聪明的孩子交友的', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '建议、模仿乔治·卡林风格', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '的单口相声段子,以及', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '如何制作北非风味的shak', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': 'shuka菜谱等,展示了模型', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '根据特定要求生成多样化内容的能力。', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'null', 'index': 0, 'logprobs': None}
{'delta': {'content': '', 'function_call': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'stop', 'index': 0, 'logprobs': None}

通过HTTP接口调用

您可以通过HTTP接口来调用服务,获得与通过HTTP接口调用OpenAI服务相同结构的返回结果。

前提条件

  • 已开通百炼服务并获得API-KEY:获取API-KEY

  • 我们推荐您将API-KEY配置到环境变量中以降低API-KEY的泄漏风险,配置方法可参考通过环境变量配置API-KEY。您也可以在代码中配置API-KEY,但是泄漏风险会提高

提交接口调用

POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions

请求示例

以下示例展示通过CURL命令来调用API的脚本。

非流式输出

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen-long",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "system",
            "content": "fileid://file-fe-xxx"
        },
        {
            "role": "user",
            "content": "文章讲了什么?"
        }
    ]
}'

运行命令可得到以下结果:

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "文章主要探讨了大型语言模型的训练方法及其对齐(alignment)问题,特别是关于如何使这些模型更好地服务于最终任务和用户偏好。研究通过一个名为LIMA的项目进行,该项目基于一个650亿参数的LLaMa语言模型,仅使用精心挑选的1000个提示及其响应进行微调,未采用强化学习或人类反馈直接指导。结果显示,即使在如此有限的指令调整数据下,LIMA仍能展现出强劲的性能,能够从少数示例中学习到特定的响应格式,并泛化到未见过的任务上。\n\n研究强调了预训练阶段对于模型获取广泛知识的重要性,表明几乎所有知识都是在这一无监督阶段习得的,而后续的指令调优仅需少量数据即可引导模型产生高质量输出。此外,文中还提到了一些实验细节,比如使用过滤与未过滤数据源训练模型产生的质量差异,以及模型在不同场景下的应用示例,如提供建议、编写故事、讽刺喜剧等,进一步证明了模型的有效性和灵活性。"
      },
      "finish_reason": "stop",
      "index": 0,
      "logprobs": null
    }
  ],
  "object": "chat.completion",
  "usage": {
    "prompt_tokens": 0,
    "completion_tokens": 0,
    "total_tokens": 0
  },
  "created": 1715324557,
  "system_fingerprint": "",
  "model": "qwen-long",
  "id": "chatcmpl-07b1b68992a091a08d7e239bd5a4a566"
}

流式输出

如果您需要使用流式输出,请在请求体中指定stream参数为true。

curl --location 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "qwen-long",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "system",
            "content": "fileid://file-fe-xxx"
        },
        {
            "role": "user",
            "content": "文章讲了什么?"
        }
    ],
    "stream":true
}'

运行命令可得到以下结果:

data:{"choices":[{"delta":{"content":"文章","role":"assistant"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"探讨"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"了"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"大型语言模型的训练"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"方法及其对齐(alignment)的重要性"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":",主要分为两个阶段:无监督"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"预训练和大规模指令微调及"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"强化学习。研究通过一个名为L"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"IMA的650亿参数语言"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"模型实验,该模型仅使用精心"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"挑选的1000个提示"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"及其响应进行标准监督损失微调"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":",未采用任何强化学习或人类"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"偏好建模。LIMA展现出强大的"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"性能,能从少数示例中"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"学习到特定的回答格式,处理复杂"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"查询,并且在未见过的任务上"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"表现出了良好的泛化能力。\n\n对比"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"人类评估显示,相比GPT-"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"4、Bard和DaVinci"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"003(后者经过人类反馈"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"训练),参与者更偏好或认为L"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"IMA的回答等同的比例分别达到了4"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"3%、58%和6"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"5%。这表明大型语言模型"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"在预训练阶段几乎学到了所有"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"知识,只需有限的指令微调"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"数据即可产生高质量输出。此外,"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"文中还提到了质量对模型输出"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"的影响,以及不同类型的生成任务示"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"例,如关于育儿建议、模仿"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"乔治·卡林风格的单口"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"相声、处理职场情感问题的建议"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"和制作北非风味菜肴shak"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"shuka的食谱。这些示"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"例进一步证明了模型在多样任务"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":"中的应用潜力与灵活性。"},"finish_reason":"null","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data:{"choices":[{"delta":{"content":""},"finish_reason":"stop","index":0,"logprobs":null}],"object":"chat.completion.chunk","usage":null,"created":1715334042,"system_fingerprint":"","model":"qwen-long","id":"chatcmpl-35e3c387759692f98041ed0a5dd9b72a"}

data: [DONE]

异常响应示例

在访问请求出错的情况下,输出的结果中会通过 code 和 message 指明出错原因。

{
    "error": {
        "message": "Incorrect API key provided. ",
        "type": "invalid_request_error",
        "param": null,
        "code": "invalid_api_key"
    }
}

状态码说明

错误码

说明

400 - Invalid file [id:xxx].

提供的文件id存在问题

400 - Too many files provided.

提供的对话文档数量大于等于100

400 - File [id:xxx] cannot be found.

输入的文件已经被删除

400 - File [id:xxx] exceeds size limit.

文档大小超限

400 - File [id:xxx] exceeds page limits (15000 pages).

文档页数超限

400 - Multiple types of file system prompt detected, please do not mix file-id and text content in one request.

输入的文件中包含了file id 和文件内容两种方式,当前暂不支持两种方式混用

400 - File [id:xxx] format is not supported.

文档格式不支持

400 - File [id:xxx] content blank.

文档内容为空

400 - Total message token length exceed model limit (10000000 tokens).

输入的messages 总token数超过了10M

400 - Single round file-content exceeds token limit, please use fileid to supply lengthy input.

输入的单条message token数超过了9K

400 - Role specification invalid, please refer to API documentation for usage.

messages组装格式存在问题,请参考上述参数描述与示例代码进行参考

400 - File parsing in progress, please try again later.

文档解析中,请稍后再试

500 - File parsing error [id:xxx].

文档解析失败

500 - File prasing timeout, [id:xxx].

文档解析超时

500 - Preprocessor error.

大模型前处理错误

500 - Postprocessor error.

大模型后处理错误

500 - File content conversion error.

文档message处理错误

500 - An unspecified internal error has occured.

调用大模型出现异常

500 - Response timeout.

处理超时,可尝试重试

503 - The engine is currently overloaded, please try again later.

服务端负载过高,可重试

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