DeepSeek官方文档阅读笔记

DeepSeek今天发布了V3.2，深度思考模式首次支持工具调用，并且将最大输出token长度扩充至32k-64k。同时，发布了一个测试版的v3.2-speciale，该模型可以看做一个超级加强版长思考DeepSeek，具有出色的数学推理能力和高复杂任务解决能力。但既不支持工具调用，也没有针对日常应用场景进行专项优化，仅仅供探索模型能力边界研究使用。（消耗token量也显著更多）

 关注完了前沿，那么，如何在api调用中开启深度思考？

一. 开启深度思考

1. 设置model为'deepseek-reasoner'

2. model仍然设置成'deepseek-chat'，同时设置thinking参数：

'thinking': {'type': 'enabled'}

如果使用openai sdk，需要这样传：

response = client.chat.completions.create(
    model='deepseek-chat',
    # ...
     extra_body={'thinking': {'type': 'enabled'}}
)

当开启深度思考后，返回字段中会比普通模型调用多一个reasoning_content参数（即深度思考的思维链内容），与content同级，在多轮对话时，官方推荐下一轮对话的输入仅包含上一轮的普通输出content，不会包含reasoning_content，来节省带宽。

二. 工具调用

目前DeepSeek仅支持function类型的工具调用。（有模型支持其它方式吗？待学习...）

首先我们要明白一个概念，大模型本身是没有函数执行能力的。所有的工具调用概念，指的是开发者预先定义好可以使用的函数，同时在请求时将可以调用的函数信息以标准json的形式编码，形成函数列表，用特定的tools参数传递给大模型，大模型经过思考，不输出文本，而是输出标准的json，内容为提示调用的函数名，和json编码过的函数参数。开发者拿到大模型输出后，解析输出对大模型要调用的方法+参数手动执行，并将结果跟随消息记录一同返回给大模型（以ToolMessage的形式），大模型再进行下一步思考。

重点是：函数是开发者自己定义的，执行也是开发者执行的。（当然现在很多agent框架替我们封装好了这些，比如langchain，google ADK）

 整个过程示意图，可以看出在最终答复用户之前，实际上调用了2次大模型（根据思考情况可以重复调用更多次）

 多次的示意图。

示例代码：

import os
import json
from openai import OpenAI

# The definition of the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_date",
            "description": "Get the current date",
            "parameters": { "type": "object", "properties": {} },
        }
    },
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather of a location, the user should supply the location and date.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": { "type": "string", "description": "The city name" },
                    "date": { "type": "string", "description": "The date in format YYYY-mm-dd" },
                },
                "required": ["location", "date"]
            },
        }
    },
]

# The mocked version of the tool calls
def get_date_mock():
    return "2025-12-01"

def get_weather_mock(location, date):
    return "Cloudy 7~13°C"

TOOL_CALL_MAP = {
    "get_date": get_date_mock,
    "get_weather": get_weather_mock
}

def clear_reasoning_content(messages):
    for message in messages:
        if hasattr(message, 'reasoning_content'):
            message.reasoning_content = None

def run_turn(turn, messages):
    sub_turn = 1
    while True:
        response = client.chat.completions.create(
            model='deepseek-chat',
            messages=messages,
            tools=tools,
            extra_body={ "thinking": { "type": "enabled" } }
        )
        messages.append(response.choices[0].message)
        reasoning_content = response.choices[0].message.reasoning_content
        content = response.choices[0].message.content
        tool_calls = response.choices[0].message.tool_calls
        print(f"Turn {turn}.{sub_turn}\n{reasoning_content=}\n{content=}\n{tool_calls=}")
        # If there is no tool calls, then the model should get a final answer and we need to stop the loop
        if tool_calls is None:
            break
        for tool in tool_calls:
            tool_function = TOOL_CALL_MAP[tool.function.name]
            tool_result = tool_function(**json.loads(tool.function.arguments))
            print(f"tool result for {tool.function.name}: {tool_result}\n")
            messages.append({
                "role": "tool",
                "tool_call_id": tool.id,
                "content": tool_result,
            })
        sub_turn += 1

client = OpenAI(
    api_key=os.environ.get('DEEPSEEK_API_KEY'),
    base_url=os.environ.get('DEEPSEEK_BASE_URL'),
)

# The user starts a question
turn = 1
messages = [{
    "role": "user",
    "content": "How's the weather in Hangzhou Tomorrow"
}]
run_turn(turn, messages)

# The user starts a new question
turn = 2
messages.append({
    "role": "user",
    "content": "How's the weather in Hangzhou Tomorrow"
})
# We recommended to clear the reasoning_content in history messages so as to save network bandwidth
clear_reasoning_content(messages)
run_turn(turn, messages)

该代码的输出：

Turn 1.1
reasoning_content="The user is asking about the weather in Hangzhou tomorrow. I need to get the current date first, then calculate tomorrow's date, and then call the weather API. Let me start by getting the current date."
content=''
tool_calls=[ChatCompletionMessageToolCall(id='call_00_Tcek83ZQ4fFb1RfPQnsPEE5w', function=Function(arguments='{}', name='get_date'), type='function', index=0)]
tool_result(get_date): 2025-12-01

Turn 1.2
reasoning_content='Today is December 1, 2025. Tomorrow is December 2, 2025. I need to format the date as YYYY-mm-dd: "2025-12-02". Now I can call get_weather with location Hangzhou and date 2025-12-02.'
content=''
tool_calls=[ChatCompletionMessageToolCall(id='call_00_V0Uwt4i63m5QnWRS1q1AO1tP', function=Function(arguments='{"location": "Hangzhou", "date": "2025-12-02"}', name='get_weather'), type='function', index=0)]
tool_result(get_weather): Cloudy 7~13°C

Turn 1.3
reasoning_content="I have the weather information: Cloudy with temperatures between 7 and 13°C. I should respond in a friendly, helpful manner. I'll mention that it's for tomorrow (December 2, 2025) and give the details. I can also ask if they need any other information. Let's craft the response."
content="Tomorrow (Tuesday, December 2, 2025) in Hangzhou will be **cloudy** with temperatures ranging from **7°C to 13°C**.  \n\nIt might be a good idea to bring a light jacket if you're heading out. Is there anything else you'd like to know about the weather?"
tool_calls=None

Turn 2.1
reasoning_content="The user wants clothing advice for tomorrow based on the weather in Hangzhou. I know tomorrow's weather: cloudy, 7-13°C. That's cool but not freezing. I should suggest layered clothing, maybe a jacket, long pants, etc. I can also mention that since it's cloudy, an umbrella might not be needed unless there's rain chance, but the forecast didn't mention rain. I should be helpful and give specific suggestions. I can also ask if they have any specific activities planned to tailor the advice. Let me respond."
content="Based on tomorrow's forecast of **cloudy weather with temperatures between 7°C and 13°C** in Hangzhou, here are some clothing suggestions:\n\n**Recommended outfit:**\n- **Upper body:** A long-sleeve shirt or sweater, plus a light to medium jacket (like a fleece, windbreaker, or light coat)\n- **Lower body:** Long pants or jeans\n- **Footwear:** Closed-toe shoes or sneakers\n- **Optional:** A scarf or light hat for extra warmth, especially in the morning and evening\n\n**Why this works:**\n- The temperature range is cool but not freezing, so layering is key\n- Since it's cloudy but no rain mentioned, you likely won't need an umbrella\n- The jacket will help with the morning chill (7°C) and can be removed if you warm up during the day\n\n**If you have specific plans:**\n- For outdoor activities: Consider adding an extra layer\n- For indoor/office settings: The layered approach allows you to adjust comfortably\n\nWould you like more specific advice based on your planned activities?"
tool_calls=None