本文导读
文本对话
功能介绍
基于文本对话功能，用户可以与模型进行自然而流畅的对话和交流。
URI
获取URI方式请参见请求URI。
NLP推理服务提供两种推理接口调用：
- 盘古推理接口（V1推理接口）
- 业界通用的OpenAI格式接口（V2推理接口）
两种接口定义如表1所示。
|
API分类 |
API访问路径（URI） |
|---|---|
|
V1推理接口 |
POST /v1/{project_id}/deployments/{deployment_id}/chat/completions |
|
V2推理接口 |
POST /api/v2/chat/completions |
V1推理接口URI需要输入额外参数，参数说明如表2 V1推理接口路径参数所示：
请求参数
V1、V2推理接口的鉴权方式不同，请求参数与响应参数也有不同，说明如下：
Header参数
- V1接口支持Token鉴权方式，也支持API Key鉴权方式。两种鉴权方式请求Header参数说明如下：
- 使用Token认证方式的请求Header参数见表3。
表3 请求Header参数（Token认证）
参数
是否必选
参数类型
描述
X-Auth-Token
是
String
用户Token。
用于获取操作API的权限。获取Token接口响应消息头中X-Subject-Token的值即为Token。
Content-Type
是
String
发送的实体的MIME类型，参数值为“application/json”。
- 使用API Key认证方式的请求Header参数见表4。
表4 请求Header参数（API Key认证）
参数
是否必选
参数类型
描述
X-Apig-AppCode
是
String
API Key值。
用于获取操作API的权限。API Key认证响应消息头中X-Apig-AppCode的值即为API Key。
Content-Type
是
String
发送的实体的MIME类型，参数值为“application/json”。
- 使用Token认证方式的请求Header参数见表3。
- V2接口仅支持API Key鉴权方式。请求Header参数见表5
请求Body参数
V1、V2推理接口请求Body参数一致，如表6 请求Body参数描述。
|
参数 |
是否必选 |
参数类型 |
描述 |
|---|---|---|---|
|
messages |
是 |
Array of message objects |
多轮对话问答对，包含两个属性：role和content。
messages参数可以帮助模型根据对话的上下文生成合适的回复。 数组长度：1 - 20 |
|
model |
V1推理接口：否 V2推理接口：是 |
String |
使用的推理服务模型名称，为推理服务部署时指定的Deployed_Model，可在推理服务详情页面查询到。V2推理接口必须指定此参数，V1推理接口不需要此参数。 |
|
stream |
否 |
boolean |
流式开关。 默认值为false，如果开启流式，请赋值true，同时n参数只能设置为1。开启流式开关后，API会在生成文本的过程中，实时地将生成的文本发送给客户端，而不是等到生成完成后一次性将所有文本发送给客户端。 |
|
temperature |
否 |
Float |
用于控制生成文本的多样性和创造力。 参数的取值范围是 (0, 1]，取值接近0表示最低的随机性，1表示最高的随机性。一般来说，temperature越低，适合完成确定性的任务。temperature越高，例如0.9，适合完成创造性的任务。 temperature参数可以影响语言模型输出的质量和多样性，但也不是唯一的因素。还有其他一些参数，如top_p参数也可以用来调整语言模型的行为和偏好。 最小值：0 最大值：1 |
|
top_p |
否 |
Float |
一种替代温度采样的方法，称为nucleus sampling，其中模型考虑具有top_p概率质量的标记的结果。 取值范围：(0, 1] |
|
max_tokens |
否 |
Integer |
生成文本的最大token数量，单位token。 输入的文本加上生成的文本总量不能超过模型所能处理的最大长度。 最小值：1 最大值：模型支持的最大token数 缺省值：模型支持的最大token数  说明：
token是指模型处理和生成文本的基本单位。token可以是词或者字符的片段。模型的输入和输出的文本都会被转换成token，然后根据模型的概率分布进行采样或者计算。 |
|
presence_penalty |
否 |
Float |
用于调整模型对新Token的处理方式。即如果一个Token已经在之前的文本中出现过，那么模型在生成这个Token时会受到一定的惩罚。当presence_penalty的值为正数时，模型会更倾向于生成新的、未出现过的Token，即模型会更倾向于谈论新的话题。 最小值：-2 最大值：2 缺省值：0 （表示该参数未生效） |
|
frequency_penalty |
否 |
Float |
用于调整模型对频繁出现的Token的处理方式。即如果一个Token在训练集中出现的频率较高，那么模型在生成这个Token时会受到一定的惩罚。当frequency_penalty的值为正数时，模型会更倾向于生成出现频率较低的Token，即模型会更倾向于使用不常见的词汇。最小值：-2，最大值：2 最小值：-2 最大值：2 缺省值：0 （表示该参数未生效） |
|
stop |
否 |
Union[String, List[String]] |
停止符，控制模型输出何时结束的参数。可以指定一个或多个字符串作为"停止符"。当模型在生成过程中产生了任何一个停止符时，它会立即停止生成更多内容。停止符本身不会出现在最终输出中。例如：["你"，"好"]，生成文本时遇到"你"或者"好"将停止文本生成。 取值范围: 非空字符串或字符串列表。 |
|
moderation_config |
否 |
moderation_config objects |
内容审核配置项。 |
|
enable_search |
否 |
boolean |
是否启动搜索增强。 默认值为false，如果开启搜索增强，请赋值为true。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
black_glossary_names |
List<String> |
黑名单词库列表。 |
|
white_glossary_names |
List<String> |
白名单词库列表。 |
|
question_moderation |
boolean |
是否开启对提示词进行内容审核，true：审核，false：不审核，默认为true。仅对订购用户生效。 |
|
answer_moderation |
boolean |
是否开启对推理结果进行内容审核，true：审核，false：不审核，默认为true。仅对订购用户生效。 |
|
show_result |
boolean |
是否返回内容审核不通过原因，true：审核，false：不审核，默认为false。 |
|
参数 |
是否必选 |
参数类型 |
描述 |
|---|---|---|---|
|
role |
V1推理接口：否 V2推理接口：是 |
String |
对话的角色，取值为system、user、assistant。 如果需要模型以某个人设形象回答问题，可以将role参数设置为system。不使用人设时，可设置为user。在一次会话请求中，人设只需要设置一次。多轮对话中，用户输入提示词的role设置为user，推理结果的role设置为assistant。 |
|
content |
是 |
String |
对话的内容，可以是任意文本，单位token。 设置多轮对话时，message中content个数不能超过20。 最小长度：1 最大长度：不同模型支持的token长度请参见《产品介绍》“模型能力与规格”章节。 |
响应参数
非流式
状态码： 200
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
id |
String |
用来标识每个响应的唯一字符串。 |
|
object |
String |
固定为"chat.completion"。 |
|
created |
Integer |
响应生成的时间。 |
|
model |
String |
请求模型名称标识。 |
|
choices |
Array of choices objects |
生成的文本列表，包含以下属性：
|
|
usage |
usage object |
该参数可以帮助用户了解和控制模型的使用情况，避免超出Tokens限制。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
message |
message object |
生成的文本内容。 |
|
index |
Integer |
生成的文本在列表中的索引，从0开始。 |
|
finish_reason |
String |
模型停止生成token的原因。 取值范围：[stop, length, content_filter, tool_calls, insufficient_system_resource] stop：模型自然停止生成，或遇到stop序列中列出的字符串。 length ：输出长度达到了模型上下文长度限制，或达到了max_tokens的限制。 content_filter：输出内容因触发过滤策略而被过滤。 tool_calls：模型决定调用外部工具（函数/API）来完成任务。 insufficient_system_resource：系统推理资源不足，生成被打断。 缺省值：stop |
|
stop_reason |
Union[Integer, String] |
导致生成停止的token id或者字符串。如果是遇到EOS token则返回默认值。如果是因为用户请求参数中指定的stop参数中的字符串或者token id，则返回对应的字符串或者token id。不是openai接口标准字段，但vllm接口支持。 缺省值：None |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
role |
String |
角色。 |
|
content |
String |
对话的内容。 最小长度：1 最大长度：不同模型支持的token长度请参见《产品介绍》“模型能力与规格”章节。 |
|
reasoning_content |
String |
内容为在最终答案之前的推理内容。 说明：
仅适用于Pangu-NLP-Reasoner系列模型。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
completion_tokens |
Number |
表示模型生成的答案中包含的tokens的数量。 |
|
prompt_tokens |
Number |
表示生成结果时使用的提示文本的tokens的数量。 |
|
total_tokens |
Number |
对话过程中使用的tokens总数。 |
流式（stream参数为true）
状态码： 200
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
data |
stream=true时，模型生成的消息以流式形式返回。生成的内容以增量的方式逐步发送回来，每个data字段均包含一部分生成的内容，直到所有data返回，响应结束。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
id |
String |
该对话的唯一标识符。 |
|
created |
Integer |
创建聊天完成时的Unix时间戳（以秒为单位）。流式响应的每个chunk的时间戳相同。 |
|
model |
String |
生成该completion的模型名。 |
|
object |
String |
对象的类型, 其值为chat.completion.chunk。 |
|
choices |
模型生成的completion的选择列表。 |
|
|
usage |
该对话请求的token用量信息。该参数可以帮助用户了解和控制模型的使用情况，避免超出Tokens限制。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
index |
Integer |
该completion在模型生成的completion的选择列表中的索引。 |
|
finish_reason |
String |
模型停止生成token的原因。 取值范围：[stop, length, content_filter, tool_calls, insufficient_system_resource] stop：模型自然停止生成，或遇到stop序列中列出的字符串。 length ：输出长度达到了模型上下文长度限制，或达到了max_tokens的限制。 content_filter：输出内容因触发过滤策略而被过滤。 insufficient_system_resource：系统推理资源不足，生成被打断。 |
|
delta |
V2推理接口流式返回的一个completion增量。V1推理接口返回体不包含此参数。 |
|
|
message |
V1推理接口流式返回的一个completion增量。V2推理接口返回体不包含此参数。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
role |
String |
产生这条消息的角色。固定为：assistant。 |
|
content |
String |
completion增量的内容。 |
|
reasoning_content |
String |
内容为最终答案之前的推理内容（模型的思考过程）。 说明：
仅适用于Pangu-NLP-Reasoner系列模型。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
prompt_tokens |
Integer |
用户输入的提示词及默认人设的Token数。 |
|
completion_tokens |
Integer |
推理服务返回结果的Token数。 |
|
total_tokens |
Integer |
总消耗token数。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
suggestion |
String |
审核结果：block表示未通过。 |
|
reply |
String |
兜底回复：审核未通过时兜底回复为有效回复，兜底策略。 |
状态码： 400
接口报错的场景下，V1推理接口返回的报错信息符合华为云规范；V2推理接口则会对外透传推理服务返回的错误信息，通常符合OpenAi接口格式。
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
error_msg |
String |
错误信息。 |
|
error_code |
String |
错误码。 |
|
details |
List<Object> |
推理服务返回的报错信息，具体的格式、内容取决于推理服务。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
error |
ErrorResp |
错误信息。 |
|
id |
String |
请求ID。 |
|
参数 |
参数类型 |
描述 |
|---|---|---|
|
code |
String |
错误码。 |
|
type |
String |
错误类型。 |
|
message |
String |
错误详情。 |
请求示例
- 单轮问答
V1推理接口： POST https://{endpoint}/v1/{project_id}/deployments/{deployment_id}/chat/completions Request Header: Content-Type: application/json X-Auth-Token: MIINRwYJKoZIhvcNAQcCoIINODCCDTQCAQExDTALBglghkgBZQMEAgEwgguVBgkqhkiG... Request Body: { "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role":"user", "content": "介绍下长江，以及长江中典型的鱼类" } ], "temperature": 0.9, "max_tokens": 600 }V2推理接口： POST https://{endpoint}/api/v2/chat/completions Request Header: Content-Type: application/json Authorization: Bearer 201ca68f-45f9-4e19-8fa4-831e... Request Body: { "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role":"user", "content": "介绍下长江，以及长江中典型的鱼类" } ], "temperature": 0.9, "max_tokens": 600 } - 单轮流式问答（stream参数值为true）
{ "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role":"user", "content": "五岳分别是哪些山" } ], "temperature": 0.9, "max_tokens": 600, "stream": "true" } - 带有人设的单轮问答（role参数值为system）
{ "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role": "system", "content": "你的名字叫智子，是一名幼儿园老师，请用幼儿园老师的口吻回答问题，注意语气温和亲切，通过提问、引导、赞美等方式，激发学生的思维和想象力。" }, { "role": "user", "content": "介绍下长江，以及长江中典型的鱼类" } ], "temperature": 0.9, "max_tokens": 600 } - 带有人设的单轮流式问答（role参数值为system，stream参数值为true）
{ "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role": "system", "content": "你的名字叫智子，是一名幼儿园老师，请用幼儿园老师的口吻回答问题，注意语气温和亲切，通过提问、引导、赞美等方式，激发学生的思维和想象力。" }, { "role": "user", "content": "写一首诗" } ], "temperature": 0.9, "max_tokens": 600, "stream": "true" } - 多轮问答
# 多轮问答传参方法：在完成第一轮问答，进行第二轮问答时，需要将第一轮的问题和答案、第二轮问题作为参数传入……依次类推，完成多轮对话。 { "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role":"user", "content": "介绍下长江，以及长江中典型的鱼类" //第一轮问题 }, { "role":"assistant", "content": "长江是中国第一大河,全长6300多公里。它发源于青藏高原的唐古拉山脉南麓,流经中国11个省市自治区和两个特别行政区,最终注入东海。\n长江中典型的鱼类有:鲢鱼、鳙鱼、草鱼、鲤鱼等。" //第一轮答案 }, { "role":"user", "content": "途径的省份列2个，并说明有哪些人文风景" //第二轮问题 }, { "role":"assistant", "content": "1. 湖南 - 长沙岳麓山、张家界天门山等\n2. 四川 - 九寨沟、峨眉山等" //第二轮答案 }, { "role":"user", "content": "提到的湖南景点，详细说明下" //第三轮问题 } ], "temperature": 0.9, "max_tokens": 600 } - 多轮流式问答（stream参数值为true）
# 多轮问答传参方法：在完成第一轮问答，进行第二轮问答时，需要将第一轮的问题和答案、第二轮问题作为参数传入……依次类推，完成多轮对话。 { "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "role":"user", "content": "介绍下长江，以及长江中典型的鱼类" //第一轮问题 }, { "role":"assistant", "content": "长江是中国第一大河,全长6300多公里。它发源于青藏高原的唐古拉山脉南麓,流经中国11个省市自治区和两个特别行政区,最终注入东海。\n长江中典型的鱼类有:鲢鱼、鳙鱼、草鱼、鲤鱼等。" //第一轮答案 }, { "role":"user", "content": "途径的省份列2个，并说明有哪些人文风景" //第二轮问题 }, { "role":"assistant", "content": "1. 湖南 - 长沙岳麓山、张家界天门山等\n2. 四川 - 九寨沟、峨眉山等" //第二轮答案 }, { "role":"user", "content": "张家界具体在哪里" //第三轮问题 } ], "temperature": 0.9, "max_tokens": 600, "stream": "true" }
- 带有人设的多轮流式问答（role参数值为system，stream参数值为true）
{ "model": "pangu-nlp-n1-32k", // 仅V2接口需要此参数 "messages": [ { "content": "你是一个热心的导游", //人设 "role": "system" }, { "role":"user", "content": "介绍下长江，以及长江中典型的鱼类" //第一轮问题 }, { "role":"assistant", "content": "长江，亚洲最长河流，世界第三长河流，发源于青藏高原，流经我国多个省份，最终注入东海。长江流域资源丰富，特别是鱼类资源，例如：鲥鱼、白鲟、白鳍豚等，都是长江特有的珍稀物种。" //第一轮答案 }, { "role":"user", "content": "途径的省份列2个，并说明有哪些人文风景" //第二轮问题 }, { "role":"assistant", "content": "长江流经的省份众多，这里为您介绍两个：湖北和四川。湖北，长江流经此省9年，有武汉、宜昌等知名城市，人文风景丰富，如黄鹤楼、古琴台等。四川，长江流经此省14年，有成都、重庆等大城市，人文风景包括都江堰、乐山大佛等世界文化遗产。" //第二轮答案 }, { "role":"user", "content": "张家界具体在哪里" //第三轮问题 } ], "temperature": 0.5, "max_tokens": 800, "stream": true }
响应示例
状态码： 200
OK
- 非流式问答响应
{ "id": "chat-3f7d8e038f244ae5b7cb0cecf352e154", "object": "chat.completion", "created": 1745725506, "model": "pangu-nlp-n1-32k_kpyq10", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "你好！有什么我可以帮助你的吗？", "tool_calls": [] }, "logprobs": null, "finish_reason": "stop", "stop_reason": null } ], "usage": { "prompt_tokens": 64, "total_tokens": 73, "completion_tokens": 9 }, "prompt_logprobs": null } - 带有思维链的非流式问答响应
{ "id": "4c0dcef2-7f8f-4c57-93c7-484c03b0a216", "object": "chat.completion", "created": 1741937420, "model": "pangu-nlp-n1-32k_kpyq10", "choices": [ { "index": 0, "message": { "role": "assistant", "content": "\n\n你好！很高兴见到你，有什么我可以帮忙的吗？", "reasoning_content": "嗯，用户发来“你好”，这是一个常见的中文问候。我要用中文回应用户，保持友好和亲切。首先要分析用户的需求，可能只是想打个招呼，或者有具体的问题想问。\n\n接下来要考虑用户可能的背景，可能来自不同的地方，但使用简体中文，所以可能是中国大陆的用户。需要保持回答的通用性，适合所有使用中文的人。\n\n还要判断是否有更深层次的需求，用户可能在测试聊天机器人，或者真的需要帮助。这时候需要给出一个开放式的回应，鼓励用户进一步说明需求。例如询问有什么可以帮忙的，这样既回应了问候，又引导用户提出具体问题。\n\n另外要注意语言的流畅和自然，避免过于机械化的回答。保持礼貌和专业，同时适当使用表情符号增加亲和力，比如用[微笑]或者之类的，不过可能根据平台的不同需要调整。\n\n最后要确保回答符合规范，不涉及敏感话题，内容积极健康。检查是否有拼写或语法错误，确保回复准确无误。\n", "tool_calls": [] }, "logprobs": null, "finish_reason": "stop", "stop_reason": null } ], "usage": { "prompt_tokens": 22, "total_tokens": 377, "completion_tokens": 355 }, "prompt_logprobs": null } - 流式问答响应
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状态码
请参见状态码。
错误码
请参见错误码。
父主题： NLP大模型
