源码 examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
# SPDX-License-Identifier: Apache-2.0
"""
An example demonstrates how to use tool calling with reasoning models
like QwQ-32B. The reasoning_content will not be parsed by the tool
calling process; only the final output will be parsed.
To run this example, you need to start the vLLM server with both
the reasoning parser and tool calling enabled.
本示例演示如何在像 QwQ-32B 这样的推理模型中使用工具调用。
reasoning_content 将不会被工具调用过程解析，只有最终输出会被解析。
要运行此示例，您需要启动 vLLM 服务器，并启用推理解析器和工具调用。
```bash
vllm serve Qwen/QwQ-32B \
--enable-reasoning --reasoning-parser deepseek_r1 \
--enable-auto-tool-choice --tool-call-parser hermes
"""
from openai import OpenAI
Now, simulate a tool call
现在，模拟一个工具调用
def get_current_weather(city: str, state: str, unit: 'str'): return ("The weather in Dallas, Texas is 85 degrees fahrenheit. It is " "partly cloudly, with highs in the 90's.")
available_tools = {"get_current_weather": get_current_weather}
Modify OpenAI's API key and API base to use vLLM's API server.
修改 OpenAI 的 API key 和 API base, 使用 vLLM 的 API 服务器。
openai_api_key = "EMPTY" openai_api_base = "http://localhost:8000/v1"
client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, )
models = client.models.list() model = models.data[0].id
tools = [{ "type": "function", "function": { "name": "get_current_weather", "description": "Get the current weather in a given location", "parameters": { "type": "object", "properties": { "city": { "type": "string", "description": "The city to find the weather for, e.g. 'San Francisco'" }, "state": { "type": "string", "description": "the two-letter abbreviation for the state that the city is" " in, e.g. 'CA' which would mean 'California'" }, "unit": { "type": "string", "description": "The unit to fetch the temperature in", "enum": ["celsius", "fahrenheit"] } }, "required": ["city", "state", "unit"] } } }] messages = [{ "role": "user", "content": "Hi! How are you doing today?" }, { "role": "assistant", "content": "I'm doing well! How can I help you?" }, { "role": "user", "content": "Can you tell me what the temperate will be in Dallas, in fahrenheit?" }]
def extract_reasoning_and_calls(chunks: list): reasoning_content = "" tool_call_idx = -1 arguments = [] function_names = [] for chunk in chunks: if chunk.choices[0].delta.tool_calls: tool_call = chunk.choices[0].delta.tool_calls[0] if tool_call.index != tool_call_idx: tool_call_idx = chunk.choices[0].delta.tool_calls[0].index arguments.append("") function_names.append("")
if tool_call.function:
if tool_call.function.name:
function_names[tool_call_idx] = tool_call.function.name
if tool_call.function.arguments:
arguments[tool_call_idx] += tool_call.function.arguments
else:
if hasattr(chunk.choices[0].delta, "reasoning_content"):
reasoning_content += chunk.choices[0].delta.reasoning_content
return reasoning_content, arguments, function_names
print("---------Full Generate With Automatic Function Calling-------------") tool_calls = client.chat.completions.create(messages=messages, model=model, tools=tools) print(f"reasoning_content: {tool_calls.choices[0].message.reasoning_content}") print(f"function name: " f"{tool_calls.choices[0].message.tool_calls[0].function.name}") print(f"function arguments: " f"{tool_calls.choices[0].message.tool_calls[0].function.arguments}")
print("----------Stream Generate With Automatic Function Calling-----------") tool_calls_stream = client.chat.completions.create(messages=messages, model=model, tools=tools, stream=True) chunks = [] for chunk in tool_calls_stream: chunks.append(chunk)
reasoning_content, arguments, function_names = extract_reasoning_and_calls( chunks)
print(f"reasoning_content: {reasoning_content}") print(f"function name: {function_names[0]}") print(f"function arguments: {arguments[0]}")
print("----------Full Generate With Named Function Calling-----------------") tool_calls = client.chat.completions.create(messages=messages, model=model, tools=tools, tool_choice={ "type": "function", "function": { "name": "get_current_weather" } })
tool_call = tool_calls.choices[0].message.tool_calls[0].function print(f"reasoning_content: {tool_calls.choices[0].message.reasoning_content}") print(f"function name: {tool_call.name}") print(f"function arguments: {tool_call.arguments}") print("----------Stream Generate With Named Function Calling--------------")
tool_calls_stream = client.chat.completions.create( messages=messages, model=model, tools=tools, tool_choice={ "type": "function", "function": { "name": "get_current_weather" } }, stream=True)
chunks = [] for chunk in tool_calls_stream: chunks.append(chunk)
reasoning_content, arguments, function_names = extract_reasoning_and_calls( chunks) print(f"reasoning_content: {reasoning_content}") print(f"function name: {function_names[0]}") print(f"function arguments: {arguments[0]}") print("\n\n")