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首页大模型服务平台百炼API参考（模型）更多模型意图理解

意图理解能力

更新时间：2025-04-02 06:13:03
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通义千问的意图理解模型能够在百毫秒级时间内快速、准确地解析用户意图，并选择合适的工具来解决用户的问题。

支持的模型

模型名称

上下文长度

最大输入

最大输出

输入成本

输出成本

免费额度

（注）

（Token数）

（每千Token）

模型名称

上下文长度

最大输入

最大输出

输入成本

输出成本

免费额度

（注）

（Token数）

（每千Token）

tongyi-intent-detect-v3

8,192

8,192

1,024

0.0004元

0.001元

100万Token

有效期：百炼开通后180天内

使用方法

前提条件

您需要已获取API Key并配置API Key到环境变量。如果通过OpenAI SDK或DashScope SDK进行调用，还需要安装SDK。

同时输出意图与函数调用信息

为了使意图理解模型可以同时输出意图与函数调用信息，您需要按照以下方式设置System Message：

You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{工具信息}
Response in INTENT_MODE.

您需要在System Message中说明Response in INTENT_MODE.并且放入可能使用到的工具信息。工具信息的格式为：

[{
    "name": "工具1的名称",
    "description": "工具1的描述",
    "parameters": {
        "type": "参数的类型，一般为object",
        "properties": {
            "parameter_1": {
                "description": "parameter_1的描述",
                "type": "parameter_1的类型",
                "default": "parameter_1的默认值"
            },
            ...
            "parameter_n": {
                "description": "parameter_n的描述",
                "type": "parameter_n的类型",
                "default": "parameter_n的默认值"
            }
        },
        "required": [
        "parameter_1",
        ...
        "parameter_n"
    ]
    },
},
...
{
    "name": "工具n的名称",
    "description": "工具n的描述",
    "parameters": {
        "type": "参数的类型，一般为object",
        "properties": {
            "parameter_1": {
                "description": "parameter_1的描述",
                "type": "parameter_1的类型",
                "default": "parameter_1的默认值"
            },
            ...
            "parameter_n": {
                "description": "parameter_n的描述",
                "type": "parameter_n的类型",
                "default": "parameter_n的默认值"
            }
        },
        "required": [
        "parameter_1",
        ...
        "parameter_n"
    ]
    },
}]

假设您的业务场景需要使用时间查询与天气查询两个工具，工具信息为：

[
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {}
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"]
        }
    }
]

请求示例

OpenAI兼容
DashScope
import os
import json
from openai import OpenAI

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {}
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"]
        }
    }
]

tools_string = json.dumps(tools,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in INTENT_MODE."""
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "杭州天气"}
    ]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3",
    messages=messages
)

print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {}
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"]
        }
    }
]

tools_string = json.dumps(tools,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in INTENT_MODE."""

messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "杭州天气"}
    ]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"), 
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message"
)

print(response.output.choices[0].message.content)

响应示例

<tags>
[function call, json response]
</tags><tool_call>
[{"name": "get_current_weather", "arguments": {"location": "杭州市"}}]
</tool_call><content>

</content>

在得到响应后，您需要使用parse_text函数解析出返回的工具与参数信息：

import re

def parse_text(text):
    # 定义正则表达式模式来匹配 <tags>, <tool_call>, <content> 及其内容
    tags_pattern = r'<tags>(.*?)</tags>'
    tool_call_pattern = r'<tool_call>(.*?)</tool_call>'
    content_pattern = r'<content>(.*?)</content>'
    # 使用正则表达式查找匹配的内容
    tags_match = re.search(tags_pattern, text, re.DOTALL)
    tool_call_match = re.search(tool_call_pattern, text, re.DOTALL)
    content_match = re.search(content_pattern, text, re.DOTALL)
    # 提取匹配的内容，如果没有匹配到则返回空字符串
    tags = tags_match.group(1).strip() if tags_match else ""
    tool_call = tool_call_match.group(1).strip() if tool_call_match else ""
    content = content_match.group(1).strip() if content_match else ""
    # 将提取的内容存储在字典中
    result = {
      "tags": tags,
      "tool_call": tool_call,
      "content": content
    }
    return result

response = """<tags>
[function call, json response]
</tags><tool_call>
[{"name": "get_current_weather", "arguments": {"location": "杭州市"}}]
</tool_call><content>

</content>"""
print(parse_text(response))

得到输出为：

{
    "tags": "[function call, json response]",
    "tool_call": [
        {
            "name": "get_current_weather",
            "arguments": {
                "location": "杭州市"
            }
        }
    ],
    "content": ""
}

只输出意图信息

为了使意图理解模型只输出意图信息，您需要按照以下方式设置System Message：

You are Qwen, created by Alibaba Cloud. You are a helpful assistant. \nYou should choose one tag from the tag list:\n{意图信息}\njust reply with the chosen tag.

意图信息的格式为：

{
    "意图1": "意图1的描述",
    "意图2": "意图2的描述",
    "意图3": "意图3的描述",
    ...
}

请求示例

OpenAI兼容
DashScope
import os
import json
from openai import OpenAI


intent_dict = {
    "play_game": "玩游戏",
    "email_querycontact": "电子邮件查询联系人",
    "general_quirky": "quirky",
    "email_addcontact": "电子邮件添加联系人",
    "takeaway_query": "外卖查询",
    "recommendation_locations": "地点推荐",
    "transport_traffic": "交通运输",
    "iot_cleaning": "物联网-吸尘器, 清洁器",
    "general_joke": "笑话",
    "lists_query": "查询列表/清单",
    "calendar_remove": "日历删除事件",
    "transport_taxi": "打车, 出租车预约",
    "qa_factoid": "事实性问答",
    "transport_ticket": "交通票据",
    "play_radio": "播放广播",
    "alarm_set": "设置闹钟",
}

intent_string = json.dumps(intent_dict,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. 
You should choose one tag from the tag list:
{intent_string}
Just reply with the chosen tag."""


client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "星期五早上九点叫醒我"}
    ]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3",
    messages=messages
)

print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

intent_dict = {
    "play_game": "玩游戏",
    "email_querycontact": "电子邮件查询联系人",
    "general_quirky": "quirky",
    "email_addcontact": "电子邮件添加联系人",
    "takeaway_query": "外卖查询",
    "recommendation_locations": "地点推荐",
    "transport_traffic": "交通运输",
    "iot_cleaning": "物联网-吸尘器, 清洁器",
    "general_joke": "笑话",
    "lists_query": "查询列表/清单",
    "calendar_remove": "日历删除事件",
    "transport_taxi": "打车, 出租车预约",
    "qa_factoid": "事实性问答",
    "transport_ticket": "交通票据",
    "play_radio": "播放广播",
    "alarm_set": "设置闹钟",
}

intent_string = json.dumps(intent_dict,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. 
You should choose one tag from the tag list:
{intent_string}
Just reply with the chosen tag."""

messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "周五早上九点叫醒我"}
    ]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"), 
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message"
)

print(response.output.choices[0].message.content)

响应示例

alarm_set

提升意图识别的响应速度

为了提升意图识别的响应速度，您可以将意图的分类种类用一个简单的大写字母进行指代，意图识别响应结果将始终为一个 Token，这可以优化模型调用的响应时间。

OpenAI兼容
DashScope
import os
import json
from openai import OpenAI


intent_dict = {
    "A": "玩游戏",
    "B": "电子邮件查询联系人",
    "C": "quirky",
    "D": "电子邮件添加联系人",
    "E": "外卖查询",
    "F": "地点推荐",
    "G": "交通运输",
    "H": "物联网-吸尘器, 清洁器",
    "I": "笑话",
    "J": "查询列表/清单",
    "K": "日历删除事件",
    "L": "打车, 出租车预约",
    "M": "事实性问答",
    "N": "交通票据",
    "O": "播放广播",
    "P": "设置闹钟",
}

intent_string = json.dumps(intent_dict, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. 
You should choose one tag from the tag list:
{intent_string}
Just reply with the chosen tag."""


client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "从北京去杭州最早的飞机是？"},
]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3", messages=messages
)

print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

intent_dict = {
    "A": "玩游戏",
    "B": "电子邮件查询联系人",
    "C": "quirky",
    "D": "电子邮件添加联系人",
    "E": "外卖查询",
    "F": "地点推荐",
    "G": "交通运输",
    "H": "物联网-吸尘器, 清洁器",
    "I": "笑话",
    "J": "查询列表/清单",
    "K": "日历删除事件",
    "L": "打车, 出租车预约",
    "M": "事实性问答",
    "N": "交通票据",
    "O": "播放广播",
    "P": "设置闹钟",
}

intent_string = json.dumps(intent_dict, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. 
You should choose one tag from the tag list:
{intent_string}
Just reply with the chosen tag."""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "从北京去杭州最早的飞机是？"},
]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message",
)

print(response.output.choices[0].message.content)

运行代码后可以得到一个 Token 的意图分类结果。

M

只输出函数调用信息

为了使意图理解模型只输出函数调用信息，您需要按照以下方式设置System Message：

You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:\n{工具信息}\nResponse in NORMAL_MODE.

其中工具信息与同时输出意图与函数调用信息中的工具信息格式相同。

请求示例

OpenAI兼容
DashScope
import os
import json
from openai import OpenAI

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {}
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"]
        }
    }
]

tools_string = json.dumps(tools,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in NORMAL_MODE."""
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "杭州天气"}
    ]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3",
    messages=messages
)

print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {}
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"]
        }
    }
]

tools_string = json.dumps(tools,ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in NORMAL_MODE."""

messages = [
    {'role': 'system', 'content': system_prompt},
    {'role': 'user', 'content': "杭州天气"}
    ]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"), 
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message"
)

print(response.output.choices[0].message.content)

响应示例

<tool_call>
{"name": "get_current_weather", "arguments": {"location": "杭州市"}}
</tool_call>

在得到响应后，您需要使用parse_text函数解析出返回的工具与参数信息：

import re


def parse_text(text):
    tool_call_pattern = r'<tool_call>(.*?)</tool_call>'
    # 使用正则表达式查找匹配的内容
    tool_call_match = re.search(tool_call_pattern, text, re.DOTALL)
    # 提取匹配的内容，如果没有匹配到则返回空字符串
    tool_call = tool_call_match.group(1).strip() if tool_call_match else ""
    return tool_call

response = """<tool_call>
{"name": "get_current_weather", "arguments": {"location": "杭州市"}}
</tool_call>"""
print(parse_text(response))

得到输出为：

{"name": "get_current_weather", "arguments": {"location": "杭州市"}}

多轮对话

如果用户在提问时未提供充足的信息，意图理解模型会进行反问，通过多轮对话采集到必要的参数后，再输出函数调用的信息。

同时输出意图与函数调用信息
只输出函数调用信息
请求示例
OpenAI 兼容
DashScope
import os
import json
from openai import OpenAI

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {},
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"],
        },
    },
]

tools_string = json.dumps(tools, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in INTENT_MODE."""
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {"role": "system", "content": system_prompt},
    # 第一轮对话提出的问题
    {"role": "user", "content": "我想查天气"},
]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3", messages=messages
)

print("查询问题：我想查天气")
print("第一轮输出：\n")
print(response.choices[0].message.content)
messages.append(response.choices[0].message)
# 第二轮对话提出的问题
messages.append({"role": "user", "content": "杭州的"})
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3", messages=messages
)
print("\n查询问题：杭州的")
print("第二轮输出：\n")
print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {},
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"],
        },
    },
]

tools_string = json.dumps(tools, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in INTENT_MODE."""

messages = [
    {"role": "system", "content": system_prompt},
    # 第一轮对话提出的问题
    {"role": "user", "content": "我想查天气"},
]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message",
)
print("查询问题：我想查天气")
print("第一轮输出：\n")
print(response.output.choices[0].message.content)

messages.append(
    {"role": "assistant", "content": response.output.choices[0].message.content}
)
# 第二轮对话提出的问题
messages.append({"role": "user", "content": "杭州"})

response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message",
)
print("\n查询问题：杭州")
print("第二轮输出：\n")
print(response.output.choices[0].message.content)
响应示例
查询问题：我想查天气
第一轮输出：

<tags>
[weather inquiry]
</tags><tool_call>
[]
</tool_call><content>
好的，请问您想查询哪个城市的天气呢？
</content>

查询问题：杭州
第二轮输出：

<tags>
[function call, json response]
</tags><tool_call>
[{"name": "get_current_weather", "arguments": {"location": "杭州"}}]
</tool_call><content>

</content>
请求示例
OpenAI兼容
DashScope
import os
import json
from openai import OpenAI

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {},
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"],
        },
    },
]

tools_string = json.dumps(tools, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in NORMAL_MODE."""
client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
)
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "我想查天气"},
]
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3", messages=messages
)

messages.append(response.choices[0].message)
print("查询问题：我想查天气")
print("第一轮输出：\n")
print(response.choices[0].message.content)
messages.append({"role": "user", "content": "杭州"})
response = client.chat.completions.create(
    model="tongyi-intent-detect-v3", messages=messages
)
print("\n查询问题：杭州")
print("第二轮输出：\n")
print(response.choices[0].message.content)
import os
import json
from dashscope import Generation

# 定义工具
tools = [
    {
        "name": "get_current_time",
        "description": "当你想知道现在的时间时非常有用。",
        "parameters": {},
    },
    {
        "name": "get_current_weather",
        "description": "当你想查询指定城市的天气时非常有用。",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "城市或县区，比如北京市、杭州市、余杭区等。",
                }
            },
            "required": ["location"],
        },
    },
]

tools_string = json.dumps(tools, ensure_ascii=False)

system_prompt = f"""You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You may call one or more tools to assist with the user query. The tools you can use are as follows:
{tools_string}
Response in NORMAL_MODE."""

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "我想查天气"},
]
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message",
)
print("查询问题：我想查天气")
print("第一轮输出：\n")
print(response.output.choices[0].message.content)
messages.append(
    {"role": "assistant", "content": response.output.choices[0].message.content}
)
messages.append({"role": "user", "content": "杭州"})
response = Generation.call(
    # 若没有配置环境变量，请用百炼API Key将下行替换为：api_key = "sk-xxx",
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    model="tongyi-intent-detect-v3",
    messages=messages,
    result_format="message",
)
print("\n查询问题：杭州")
print("第二轮输出：\n")
print(response.output.choices[0].message.content)
响应示例
查询问题：我想查天气
第一轮输出：

请问您想查询哪个城市的天气呢？

查询问题：杭州
第二轮输出：

<tool_call>
{"name": "get_current_weather", "arguments": {"location": "杭州"}}
</tool_call>

常见问题

Q：最多传入几个工具？

A：我们建议您传入不超过10个的工具，否则模型调用工具的准确率可能会降低。

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  • 支持的模型
  • 使用方法
  • 前提条件
  • 同时输出意图与函数调用信息
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  • 只输出函数调用信息
  • 多轮对话
  • 常见问题
  • Q：最多传入几个工具？
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