类似于聊天完成 API，助手 API 支持函数调用。函数调用允许您描述函数给助手 API，并让它智能地返回需要调用的函数及其参数。
快速入门
在这个示例中，我们将创建一个天气助手，并定义两个函数，get_current_temperature 和 get_rain_probability，作为助手可以调用的工具。根据用户的查询，如果使用我们在 2023 年 11 月 6 日或之后发布的最新模型，模型将调用并行函数调用。在我们使用并行函数调用的示例中，我们将询问助手今天旧金山的天气如何以及下雨的几率。我们还展示了如何通过流式输出助手的响应。
步骤 1：定义函数
在创建您的助手时，您首先会在助手的 tools 参数下定义这些函数。
from openai import OpenAI
client = OpenAI()
assistant = client.beta.assistants.create(
instructions="You are a weather bot. Use the provided functions to answer questions.",
model="gpt-4-turbo",
tools=[
{
"type": "function",
"function": {
"name": "get_current_temperature",
"description": "Get the current temperature for a specific location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g., San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["Celsius", "Fahrenheit"],
"description": "The temperature unit to use. Infer this from the user's location."
}
},
"required": ["location", "unit"]
}
}
},
{
"type": "function",
"function": {
"name": "get_rain_probability",
"description": "Get the probability of rain for a specific location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g., San Francisco, CA"
}
},
"required": ["location"]
}
}
}
]
)
步骤 2：创建线程并添加消息
当用户开始对话时，创建一个线程，并随着用户提问，向线程添加消息。
thread = client.beta.threads.create()
message = client.beta.threads.messages.create(
thread_id=thread.id,
role="user",
content="What's the weather in San Francisco today and the likelihood it'll rain?",
)
第三步：发起运行
当您在包含触发一个或多个函数的用户消息的线程上发起运行时，该运行将进入待处理状态。在处理完成后，运行将进入需要行动的状态，您可以通过检查运行的状态来验证。这表明您需要运行工具并提交它们的输出到助手，以继续运行执行。在我们的案例中，我们将看到两个工具调用，这表明用户查询导致了并行函数调用。
请注意，运行会在创建后的十分钟后过期。请确保在 10 分钟标记之前提交您的工具输出。
您将在 required_action 中看到两个工具调用，这表明用户查询触发了并行函数调用。
{
"id": "run_qJL1kI9xxWlfE0z1yfL0fGg9",
...
"status": "requires_action",
"required_action": {
"submit_tool_outputs": {
"tool_calls": [
{
"id": "call_FthC9qRpsL5kBpwwyw6c7j4k",
"function": {
"arguments": "{
"location": "San Francisco, CA"}",
"name": "get_rain_probability"
},
"type": "function"
},
{
"id": "call_RpEDoB8O0FTL9JoKTuCVFOyR",
"function": {
"arguments": "{
"location": "San Francisco, CA", "unit": "Fahrenheit"}",
"name": "get_current_temperature"
},
"type": "function"
}
]
},
...
"type": "submit_tool_outputs"
}
}
如何发起运行并提交工具调用将取决于您是否使用了流式处理，虽然在这两种情况下，所有工具调用都需要同时提交。然后，您可以通过提交您调用的函数的工具输出来完成运行。将 required_action 对象中引用的每个 tool_call_id 传递，以将输出与每个函数调用匹配。
使用流式处理
对于流式处理的情况，我们创建一个 EventHandler 类来处理响应流中的事件，并使用 Python 和 Node SDK 中的 "submit tool outputs stream" 助手一次性提交所有工具输出。
from typing_extensions import override
from openai import AssistantEventHandler
class EventHandler(AssistantEventHandler):
@override
def on_event(self, event):
# Retrieve events that are denoted with 'requires_action'
# since these will have our tool_calls
if event.event == 'thread.run.requires_action':
run_id = event.data.id # Retrieve the run ID from the event data
self.handle_requires_action(event.data, run_id)
def handle_requires_action(self, data, run_id):
tool_outputs = []
for tool in data.required_action.submit_tool_outputs.tool_calls:
if tool.function.name == "get_current_temperature":
tool_outputs.append({
"tool_call_id": tool.id, "output": "57"})
elif tool.function.name == "get_rain_probability":
tool_outputs.append({
"tool_call_id": tool.id, "output": "0.06"})
# Submit all tool_outputs at the same time
self.submit_tool_outputs(tool_outputs, run_id)
def submit_tool_outputs(self, tool_outputs, run_id):
# Use the submit_tool_outputs_stream helper
with client.beta.threads.runs.submit_tool_outputs_stream(
thread_id=self.current_run.thread_id,
run_id=self.current_run.id,
tool_outputs=tool_outputs,
event_handler=EventHandler(),
) as stream:
for text in stream.text_deltas:
print(text, end="", flush=True)
print()
with client.beta.threads.runs.stream(
thread_id=thread.id,
assistant_id=assistant.id,
event_handler=EventHandler()
) as stream:
stream.until_done()
不使用流式处理
运行是异步的，这意味着您会希望通过轮询运行对象的状态，直到达到终端状态为止。为方便起见，“创建和轮询”SDK 助手在创建运行后会自动轮询其完成。一旦运行完成，您可以列出助手添加到线程中的所有消息。最后，您将从 required_action 中检索所有的 tool_outputs，并一次性将它们提交给“提交工具输出并轮询”助手。
run = client.beta.threads.runs.create_and_poll(
thread_id=thread.id,
assistant_id=assistant.id,
)
if run.status == 'completed':
messages = client.beta.threads.messages.list(
thread_id=thread.id
)
print(messages)
else:
print(run.status)
# Define the list to store tool outputs
tool_outputs = []
# Loop through each tool in the required action section
for tool in run.required_action.submit_tool_outputs.tool_calls:
if tool.function.name == "get_current_temperature":
tool_outputs.append({
"tool_call_id": tool.id,
"output": "57"
})
elif tool.function.name == "get_rain_probability":
tool_outputs.append({
"tool_call_id": tool.id,
"output": "0.06"
})
# Submit all tool outputs at once after collecting them in a list
if tool_outputs:
try:
run = client.beta.threads.runs.submit_tool_outputs_and_poll(
thread_id=thread.id,
run_id=run.id,
tool_outputs=tool_outputs
)
print("Tool outputs submitted successfully.")
except Exception as e:
print("Failed to submit tool outputs:", e)
else:
print("No tool outputs to submit.")
if run.status == 'completed':
messages = client.beta.threads.messages.list(
thread_id=thread.id
)
print(messages)
else:
print(run.status)
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