工具调用#
LMDeploy 支持 InternLM2、InternLM2.5、llama3.1 和 Qwen2.5 模型的工具。
单轮调用#
在运行以下示例之前，请先启动模型服务。
from openai import OpenAI
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
client = OpenAI(api_key='YOUR_API_KEY',base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False,
tools=tools)
print(response)
多轮调用#
InternLM#
一个完整的工具链调用过程可以通过以下示例进行演示。
from openai import OpenAI
def add(a: int, b: int):
return a + b
def mul(a: int, b: int):
return a * b
tools = [{
'type': 'function',
'function': {
'name': 'add',
'description': 'Compute the sum of two numbers',
'parameters': {
'type': 'object',
'properties': {
'a': {
'type': 'int',
'description': 'A number',
},
'b': {
'type': 'int',
'description': 'A number',
},
},
'required': ['a', 'b'],
},
}
}, {
'type': 'function',
'function': {
'name': 'mul',
'description': 'Calculate the product of two numbers',
'parameters': {
'type': 'object',
'properties': {
'a': {
'type': 'int',
'description': 'A number',
},
'b': {
'type': 'int',
'description': 'A number',
},
},
'required': ['a', 'b'],
},
}
}]
messages = [{'role': 'user', 'content': 'Compute (3+5)*2'}]
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False,
tools=tools)
print(response)
func1_name = response.choices[0].message.tool_calls[0].function.name
func1_args = response.choices[0].message.tool_calls[0].function.arguments
func1_out = eval(f'{func1_name}(**{func1_args})')
print(func1_out)
messages.append(response.choices[0].message)
messages.append({
'role': 'tool',
'content': f'3+5={func1_out}',
'tool_call_id': response.choices[0].message.tool_calls[0].id
})
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False,
tools=tools)
print(response)
func2_name = response.choices[0].message.tool_calls[0].function.name
func2_args = response.choices[0].message.tool_calls[0].function.arguments
func2_out = eval(f'{func2_name}(**{func2_args})')
print(func2_out)
使用InternLM2-Chat-7B模型执行上述示例，将打印以下结果。
ChatCompletion(id='1', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content='', role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='0', function=Function(arguments='{"a": 3, "b": 5}', name='add'), type='function')]))], created=1722852901, model='/nvme/shared_data/InternLM/internlm2-chat-7b', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=25, prompt_tokens=263, total_tokens=288))
8
ChatCompletion(id='2', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content='', role='assistant', function_call=None, tool_calls=[ChatCompletionMessageToolCall(id='1', function=Function(arguments='{"a": 8, "b": 2}', name='mul'), type='function')]))], created=1722852901, model='/nvme/shared_data/InternLM/internlm2-chat-7b', object='chat.completion', system_fingerprint=None, usage=CompletionUsage(completion_tokens=25, prompt_tokens=293, total_tokens=318))
16
Llama 3.1#
Meta在Llama3的官方用户指南中宣布，
注意
有三种内置工具（brave_search、wolfram_alpha 和 code interpreter）可以通过系统提示开启：
Brave 搜索：用于执行网络搜索的工具调用。
Wolfram Alpha：用于执行复杂数学计算的工具调用。
代码解释器：使模型能够输出Python代码。
此外，它警告说：“注意：我们建议使用Llama 70B-instruct或Llama 405B-instruct用于结合对话和工具调用的应用程序。Llama 8B-Instruct无法可靠地维持对话同时进行工具调用定义。它可以用于零样本工具调用，但在模型和用户之间的常规对话中应移除工具指令。”
因此，我们利用Meta-Llama-3.1-70B-Instruct来展示如何通过LMDeploy的api_server调用工具。
在A100-SXM-80G节点上，您可以按如下方式启动服务：
lmdeploy serve api_server /the/path/of/Meta-Llama-3.1-70B-Instruct/model --tp 4
要深入了解api_server，请参阅此处提供的详细文档。
以下代码片段演示了如何使用‘Wolfram Alpha’工具。假设您已经在Wolfram Alpha网站上注册并获得了API密钥。请确保您拥有有效的API密钥以访问Wolfram Alpha提供的服务。
from openai import OpenAI
import requests
def request_llama3_1_service(messages):
client = OpenAI(api_key='YOUR_API_KEY',
base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False)
return response.choices[0].message.content
# The role of "system" MUST be specified, including the required tools
messages = [
{
"role": "system",
"content": "Environment: ipython\nTools: wolfram_alpha\n\n Cutting Knowledge Date: December 2023\nToday Date: 23 Jul 2024\n\nYou are a helpful Assistant." # noqa
},
{
"role": "user",
"content": "Can you help me solve this equation: x^3 - 4x^2 + 6x - 24 = 0" # noqa
}
]
# send request to the api_server of llama3.1-70b and get the response
# the "assistant_response" is supposed to be:
# <|python_tag|>wolfram_alpha.call(query="solve x^3 - 4x^2 + 6x - 24 = 0")
assistant_response = request_llama3_1_service(messages)
print(assistant_response)
# Call the API of Wolfram Alpha with the query generated by the model
app_id = 'YOUR-Wolfram-Alpha-API-KEY'
params = {
"input": assistant_response,
"appid": app_id,
"format": "plaintext",
"output": "json",
}
wolframalpha_response = requests.get(
"https://api.wolframalpha.com/v2/query",
params=params
)
wolframalpha_response = wolframalpha_response.json()
# Append the contents obtained by the model and the wolframalpha's API
# to "messages", and send it again to the api_server
messages += [
{
"role": "assistant",
"content": assistant_response
},
{
"role": "ipython",
"content": wolframalpha_response
}
]
assistant_response = request_llama3_1_service(messages)
print(assistant_response)
Qwen2.5#
Qwen2.5 支持多工具调用，这意味着可以在一个请求中发起多个工具请求
from openai import OpenAI
import json
def get_current_temperature(location: str, unit: str = "celsius"):
"""Get current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, State, Country".
unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"])
Returns:
the temperature, the location, and the unit in a dict
"""
return {
"temperature": 26.1,
"location": location,
"unit": unit,
}
def get_temperature_date(location: str, date: str, unit: str = "celsius"):
"""Get temperature at a location and date.
Args:
location: The location to get the temperature for, in the format "City, State, Country".
date: The date to get the temperature for, in the format "Year-Month-Day".
unit: The unit to return the temperature in. Defaults to "celsius". (choices: ["celsius", "fahrenheit"])
Returns:
the temperature, the location, the date and the unit in a dict
"""
return {
"temperature": 25.9,
"location": location,
"date": date,
"unit": unit,
}
def get_function_by_name(name):
if name == "get_current_temperature":
return get_current_temperature
if name == "get_temperature_date":
return get_temperature_date
tools = [{
'type': 'function',
'function': {
'name': 'get_current_temperature',
'description': 'Get current temperature at a location.',
'parameters': {
'type': 'object',
'properties': {
'location': {
'type': 'string',
'description': 'The location to get the temperature for, in the format \'City, State, Country\'.'
},
'unit': {
'type': 'string',
'enum': [
'celsius',
'fahrenheit'
],
'description': 'The unit to return the temperature in. Defaults to \'celsius\'.'
}
},
'required': [
'location'
]
}
}
}, {
'type': 'function',
'function': {
'name': 'get_temperature_date',
'description': 'Get temperature at a location and date.',
'parameters': {
'type': 'object',
'properties': {
'location': {
'type': 'string',
'description': 'The location to get the temperature for, in the format \'City, State, Country\'.'
},
'date': {
'type': 'string',
'description': 'The date to get the temperature for, in the format \'Year-Month-Day\'.'
},
'unit': {
'type': 'string',
'enum': [
'celsius',
'fahrenheit'
],
'description': 'The unit to return the temperature in. Defaults to \'celsius\'.'
}
},
'required': [
'location',
'date'
]
}
}
}]
messages = [{'role': 'user', 'content': 'Today is 2024-11-14, What\'s the temperature in San Francisco now? How about tomorrow?'}]
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False,
tools=tools)
print(response.choices[0].message.tool_calls)
messages.append(response.choices[0].message)
for tool_call in response.choices[0].message.tool_calls:
tool_call_args = json.loads(tool_call.function.arguments)
tool_call_result = get_function_by_name(tool_call.function.name)(**tool_call_args)
messages.append({
'role': 'tool',
'name': tool_call.function.name,
'content': tool_call_result,
'tool_call_id': tool_call.id
})
response = client.chat.completions.create(
model=model_name,
messages=messages,
temperature=0.8,
top_p=0.8,
stream=False,
tools=tools)
print(response.choices[0].message.content)
使用Qwen2.5-14B-Instruct，可以获得类似的结果如下
[ChatCompletionMessageToolCall(id='0', function=Function(arguments='{"location": "San Francisco, California, USA"}', name='get_current_temperature'), type='function'),
ChatCompletionMessageToolCall(id='1', function=Function(arguments='{"location": "San Francisco, California, USA", "date": "2024-11-15"}', name='get_temperature_date'), type='function')]
The current temperature in San Francisco, California, USA is 26.1°C. For tomorrow, 2024-11-15, the temperature is expected to be 25.9°C.
需要注意的是，在涉及多个工具调用的场景中，工具调用结果的顺序可能会影响响应质量。tool_call_id 没有正确提供给LLM。