本文特指openai使用sdk的方式调用工具链。
安装openai
pip install openai
export OPENAI_API_KEY="YOUR OPENAI KEY"
定义工具函数
from openai import OpenAI
import json
client = OpenAI()
#工具函数
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if "tokyo" in location.lower():
return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
elif "san francisco" in location.lower():
return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})
elif "paris" in location.lower():
return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
else:
return json.dumps({"location": location, "temperature": "unknown"})
#工具函数的说明,传给sdk,让大模型理解工具的功能和调用方式
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"],
},
},
}
]
大模型调用工具链
注意尽量保证调工具(传递了参数tools=tools)的prompt(messages中)不要有其它指令,否则大模型可能会出现幻觉,调用不存在的函数或者不回答其它指令(下面结果ChatCompletionMessage的content=None,就是没有回答问题,纯粹调链)。
tool_choice参数指定了调用工具的模式
- 强制模型调用特定的函数,可以通过使用特定的函数名称设置tool_choice来实现。
- 设置tool_choice:“none”来强制模型生成面向用户的消息。
- 请注意,默认行为(tool_choice:“auto”)是由模型自己决定是否调用函数
下面案例是一个指令中调用了多次工具,所以返回的结果也调用了多次。
messages = [{"role": "user", "content": "What's the weather like in San Francisco, Tokyo, and Paris?"}]
def run_conversation():
# Step 1: send the conversation and available functions to the model
response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
messages=messages,
tools=tools,
tool_choice="auto", # auto is default, but we'll be explicit
)
response_message = response.choices[0].message
print(response_message )
return response_message
run_conversation()
'''
执行结果
ChatCompletionMessage(
content=None,
role='assistant',
function_call=None,
tool_calls=[
ChatCompletionMessageToolCall(id='call_hoCM3KJJRQhkupaZR7gguZjr', function=Function(arguments='{"location": "San Francisco", "unit": "celsius"}', name='get_current_weather'), type='function'),
ChatCompletionMessageToolCall(id='call_OTiRmgF7F7AHx92RHnfa8pSl', function=Function(arguments='{"location": "Tokyo", "unit": "celsius"}', name='get_current_weather'), type='function'),
ChatCompletionMessageToolCall(id='call_zpIGNP5BuxpVVq2EUdSg8f1x', function=Function(arguments='{"location": "Paris", "unit": "celsius"}', name='get_current_weather'),
type='function')
]
)
'''
执行调用并汇总结果
注意下面汇总结果到messages时,注意这里的角色不是user也不是assistant,而是tool。文章来源:https://www.toymoban.com/news/detail-820681.html
将上下文所有的消息全都汇总到message中,即可实现,模型调用工具拿到结果后,再汇总结果输出给用户。文章来源地址https://www.toymoban.com/news/detail-820681.html
def main(response_message):
tool_calls = response_message.tool_calls
if tool_calls:
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
available_functions = {
"get_current_weather": get_current_weather,
} # only one function in this example, but you can have multiple
messages.append(response_message) # extend conversation with assistant's reply
# Step 4: send the info for each function call and function response to the model
for tool_call in tool_calls:
function_name = tool_call.function.name
function_to_call = available_functions[function_name]
function_args = json.loads(tool_call.function.arguments)
function_response = function_to_call(
location=function_args.get("location"),
unit=function_args.get("unit"),
)
messages.append(
{
"tool_call_id": tool_call.id,
"role": "tool", #注意这里的角色不是user也不是assistant,而是tool
"name": function_name,
"content": function_response,
}
) # extend conversation with function response
#汇总并输出结果
second_response = client.chat.completions.create(
model="gpt-3.5-turbo-1106",
messages=messages,
) # get a new response from the model where it can see the function response
return second_response
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