简介
Ollama 连发两个大版本，分别是 v0.8.0和v0.9.0，0.8 版本后直接发 0.9 大版本，中间没有小功能迭代。
在 0.8 版本中，ollama 支持工具调用流式响应(stream response with tool calls)。
在 0.9 版本中，ollama 增强了对推理模型的支持，支持开启和关闭模型的推理模式。
0.8.0
支持工具调用的开源模型比较多，有：
• Qwen 3[1] • Devstral[2] • Qwen2.5[3] and Qwen2.5-coder[4] • Llama 3.1[5] • Llama 4[6] • 其它模型[7]
我们以官方仓库 ollama-python/examples[8] 中的例子修改得到获取天气的样例代码 weather.py：
from ollama import ChatResponse, chat
defget_current_weather(location: str, format: str) -> str:
return"33"
# Tools can still be manually defined and passed into chat
get_current_weather_tool = {
'type': 'function',
'function': {
'name': 'get_current_weather',
'description': 'Get weather of location',
'parameters': {
'type': 'object',
'required': ['location', 'format'],
'properties': {
'location': {'location': 'string', 'description': 'The location'},
'format': {'format': 'string', 'description': 'The result format'},
},
},
},
}
messages = [{'role': 'user', 'content': 'What is the weather today in Shanghai?'}]
print('Prompt:', messages[0]['content'])
available_functions = {
'get_current_weather': get_current_weather,
}
response: ChatResponse = chat(
'qwen3:latest',
messages=messages,
tools=[get_current_weather, get_current_weather_tool],
stream=True,
)
for resp in response:
# print('resp {}'.format(resp))
ifnot resp.message.tool_calls:
print(resp['message']['content'], end='', flush=True)
else:
# There may be multiple tool calls in the response
for tool in resp.message.tool_calls:
# Ensure the function is available, and then call it
if function_to_call := available_functions.get(tool.function.name):
print('Calling function:', tool.function.name)
print('Arguments:', tool.function.arguments)
output = function_to_call(**tool.function.arguments)
print('Function output:', output)
else:
print('Function', tool.function.name, 'not found')
# Add the function response to messages for the model to use
messages.append(resp.message)
messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name})
# Get final response from model with function outputs
for fr in chat('qwen3:latest', messages=messages, stream=True):
print(fr['message']['content'], end='', flush=True)上述代码是获取指定位置的温度，得到结果后将温度传给模型，生成最终的结果：
$ python weather.py
Prompt: What is the weather today in Shanghai?
<think>
Okay, the user is asking for the weather in Shanghai today. Let me check the tools provided. There's a function called get_current_weather. The parameters required are location and format. The description mentions getting the weather for a location. Even though the parameters' descriptions are a bit unclear, location is a string, so I should provide "Shanghai" as the location. For format, maybe the user wants a simple response, so I'll use "json" as a common format. I'll call the function with these arguments.
</think>
Calling function: get_current_weather
Arguments: {'format': 'json', 'location': 'Shanghai'}
Function output: 33
<think>
Okay, the user asked about the weather in Shanghai today. I called the get_current_weather function with the location set to Shanghai and format as JSON. The response came back as "33". Hmm, that's not a typical weather description. Maybe the API returned a temperature in Celsius? 33°C is quite hot. Let me check if there's more data. Wait, the response was just "33", so maybe the API is broken or there was an error. Alternatively, maybe it's a placeholder. I should inform the user that the information is incomplete. I'll mention the temperature if I assume it's Celsius and note that other details aren't available. Let me make sure to ask them to check again later or try another source.
</think>
The current weather in Shanghai is not fully available. However, based on the data received, the temperature is approximately **33°C**. For more detailed information (e.g., humidity, wind speed, or conditions), please check again later or use a dedicated weather service. Let me know if you'd like further assistance! ?️0.9.0
当前，只有 DeepSeek R1[9] 和 Qwen3[10] 支持推理能力，后续会有更多模型支持。
开启和关闭推理能力，可以在调用 Ollama API 时指定 "think": true
curl http://localhost:11434/api/chat -d '{
"model": "deepseek-r1",
"messages": [
{
"role": "user",
"content": "Why is the sky blue?"
},
],
"think": true
}'也可以在 ollama 终端用/set think 和 /set nothink 来开启和关闭推理模式。
总结
工具调用的流式响应，可以带来更好的用户体验。推理模式开启和关闭，ollama 支持的有点晚，印象中最先支持的是 qwen3。
这两个功能都很酷，目前看一些开源 对话应用、支持库都还没支持 开启、关闭推理模式，希望开源社区尽快支持。
