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
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结果
为了使意图理解模型可以同时输出意图与函数调用结果,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
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结果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"}}
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- 本页导读 (1)
- 支持的模型
- 使用方法
- 前提条件
- 同时输出意图与函数调用结果
- 只输出意图信息
- 只输出函数调用信息