1
from langchain_core.tools import tool
2

3
@tool
4
def get_weather(city: str) -> str:
5
    """
6
    获取指定城市的天气信息
7
    参数:
8
    city: 城市名称，如"北京"、"上海"
9
    返回:
10
    天气信息字符串
11
    """
12
    # 你的实现
13
    return city + "晴天，温度 15°C"

1
# 使用 .invoke() 方法
2
result = get_weather.invoke({"city": "北京"})
3
print(result)

1
北京晴天，温度 15°C

1
from langchain.chat_models import init_chat_model
2
from dotenv import load_dotenv
3
import os
4

5
# 从.env文件中加载环境变量
6
load_dotenv(override=True)
7
CLOSEAI_API_KEY = os.getenv("CLOSEAI_API_KEY")
8
CLOSEAI_BASE_URL = os.getenv("CLOSEAI_BASE_URL")
9

10
model = init_chat_model(
11
    model="gpt-5.4-mini",
12
    model_provider="openai",
13
    api_key=CLOSEAI_API_KEY,
14
    base_url=CLOSEAI_BASE_URL
15
)

1
from langchain_core.tools import tool
2

3
# 定义工具
4
@tool
5
def get_weather(city: str) -> str:
6
    """获取指定城市的天气"""
7
    # 你的实现
8
    return "晴天，温度 15°C"
9

10
# 绑定工具
11
model_with_tools = model.bind_tools([get_weather])
12

13
# AI 可以决定是否调用工具
14
response = model_with_tools.invoke("北京天气如何？")
15
# response = model_with_tools.invoke("2 + 3 = ？")
16

17
# 检查 AI 是否要调用工具
18
if response.tool_calls:
19
    print("AI 想调用工具：", response.tool_calls)
20
else:
21
    print("AI 直接回答：", response.content)

1
AI 想调用工具： [{'name': 'get_weather', 'args': {'city': '北京'}, 'id': 'call_fR3LE8Wjqh9lnDosQ61Y892E', 'type': 'tool_call'}]

1
from langchain.messages import HumanMessage, ToolMessage
2

3
def get_weather(city: str):
4
    """获取天气的工具"""
5
    return f"{city}天气晴朗"
6

7
# 将模型和工具绑定
8
model_with_tools = model.bind_tools([get_weather])
9
messages = [
10
    HumanMessage("今天北京天气如何")
11
]
12
# 模型生成调用工具请求
13
response = model_with_tools.invoke(messages)
14
# 添加AIMessage
15
messages.append(response)
16
tool_calls = response.tool_calls
17
for tool_call in tool_calls:
18
    if tool_call["name"] == "get_weather":
19
        # 拼接出ToolMessage实例
20
        tool_response = ToolMessage(
21
            content=get_weather(**tool_call["args"]),
22
            tool_call_id=tool_call["id"],
23
            name=tool_call["name"]
24
        )
25
        messages.append(tool_response)
26

27
print("=====================> messages <=====================")
28
for msg in messages:
29
    msg.pretty_print()
30
print("=====================> messages <=====================")
31
final_response = model_with_tools.invoke(messages)
32
print(f"final_response: \n{final_response}")

1
from langchain.messages import HumanMessage, ToolMessage
2

3
@tool
4
def get_weather(city: str):
5
    """获取天气的工具"""
6
    return f"{city}天气晴朗~"
7

8
# 将模型和工具绑定
9
model_with_tools = model.bind_tools([get_weather])
10
messages = [
11
    HumanMessage("今天北京天气如何")
12
]
13
# 模型生成调用工具请求
14
response = model_with_tools.invoke(messages)
15
# 添加AIMessage
16
messages.append(response)
17
tool_calls = response.tool_calls
18
for tool_call in tool_calls:
19
    if tool_call["name"] == "get_weather":
20
        # 返回的是ToolMessage类型消息
21
        tool_response = get_weather.invoke(tool_call)
22
        print(type(tool_response))
23
        messages.append(tool_response)
24

25
print("=====================> messages <=====================")
26
for msg in messages:
27
    msg.pretty_print()
28
print("=====================> messages <=====================")
29
final_response = model_with_tools.invoke(messages)
30
print(f"final_response: \n{final_response}")

1
<class 'langchain_core.messages.tool.ToolMessage'>
2
=====================> messages <=====================
3
================================ Human Message ================================
4
今天北京天气如何
5

6
================================== Ai Message ==================================
7
好的，我来帮你查一下今天北京的天气情况。
8
Tool Calls:
9
get_weather (call_00_f65kV4JKjBPK0HhURzO99449)
10
Call ID: call_00_f65kV4JKjBPK0HhURzO99449
11
Args:
12
city: 北京
13

14
================================= Tool Message =================================
15
Name: get_weather
16
北京天气晴朗~
17
=====================> messages <=====================
18
final_response:
19
content='今天北京天气晴朗，是个好天气！如果你要出门的话，可以放心出行哦～'
20
...（AIMessage 其余元数据，如 token_usage、model_name、id 等）...
21
tool_calls=[] invalid_tool_calls=[] usage_metadata={'input_tokens': 343, 'output_tokens': 24, 'total_tokens': 367, 'input_token_details': {'cache_read': 256}, 'output_token_details': {}}

1
from rich import print as rprint
2

3
# 定义工具
4
def get_weather(city: str):
5
    return f"{city}天气晴朗"
6

7
# 将模型和工具绑定
8
model_with_tools = model.bind_tools([get_weather])
9
response = model_with_tools.invoke(
10
    "今天北京天气如何"
11
)
12
rprint(response)
13
# print(response.tool_calls)

1
AIMessage(
2
    content='',
3
    ...
4
    'finish_reason': 'tool_calls',
5
    ...
6
    tool_calls=[
7
        {
8
            'name': 'get_weather',
9
            'args': {'city': '北京'},
10
            'id': 'call_ECvZNV7RLTWpKQSjhvdGzKBd',
11
            'type': 'tool_call'
12
        }
13
    ],
14
    ...
15
)

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from langchain.tools import tool
3

4
@tool
5
def get_weather(city: str):
6
    return f"{city}天气晴朗"
7

8
print(convert_to_openai_tool(get_weather))

1
Traceback...
2
ValueError: Function must have a docstring if description not provided.

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from langchain.tools import tool
3

4
@tool
5
def get_weather(city: str):
6
    """
7
    天气查询工具
8
    """
9
    return f"{city}天气晴朗"
10

11
print(convert_to_openai_tool(get_weather))

1
{
2
    'type': 'function',
3
    'function': {
4
        'name': 'get_weather',
5
        'description': '天气查询工具',
6
        'parameters': {
7
            'properties': {
8
                'city': {
9
                    'type': 'string'
10
                }
11
            },
12
            'required': [
13
                'city'
14
            ],
15
            'type': 'object'
16
        }
17
    }
18
}

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from langchain.tools import tool
3
from rich import print as rprint
4

5
@tool(description="根据城市名称查询当日天气的工具")
6
def get_weather(city: str):
7
    """
8
    天气查询工具
9
    """
10
    return f"{city}天气晴朗"
11

12
rprint(convert_to_openai_tool(get_weather))

1
{
2
    'type': 'function',
3
    'function': {
4
        'name': 'get_weather',
5
        'description': '根据城市名称查询当日天气的工具',
6
        'parameters': {
7
            'properties': {
8
                'city': {
9
                    'type': 'string'
10
                }
11
            },
12
            'required': [
13
                'city'
14
            ],
15
            'type': 'object'
16
        }
17
    }
18
}

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from rich import print as rprint
3

4
@tool
5
def get_weather(city: str, units: str = "celsius", include_forecast: bool = False) -> str:
6
    """
7
    获取当日天气，可选择是否同时查询未来五日天气预报
8
    Args:
9
        city: 城市
10
        units: 气温单位，可选：celsius-摄氏度，fahrenheit-华氏度
11
        include_forecast: 是否包含未来五日的天气预报
12
    """
13
    temp = 22 if units == "celsius" else 72
14
    result = f'{city}当天气温: {temp} {"摄氏度" if units == "celsius" else "华氏度"}'
15
    if include_forecast:
16
        result += "\n未来五天都是晴天"
17
    return result
18

19
rprint(convert_to_openai_tool(get_weather))

1
{
2
    'type': 'function',
3
    'function': {
4
        'name': 'get_weather',
5
        'description': '获取当日天气，可选择是否同时查询未来五日天气预报\n\nArgs:\n    city: 城市\n    units: 气温单位，可选：celsius-摄氏度，fahrenheit-华氏度\n    include_forecast: 是否包含未来五日的天气预报',
6
        'parameters': {
7
            'properties': {
8
                'city': {
9
                    'type': 'string'
10
                },
11
                'units': {
12
                    'default': 'celsius',
13
                    'type': 'string'
14
                },
15
                'include_forecast': {
16
                    'default': False,
17
                    'type': 'boolean'
18
                }
19
            },
20
            'required': [
21
                'city'
22
            ],
23
            'type': 'object'
24
        }
25
    }
26
}

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from rich import print as rprint
3

4
@tool(parse_docstring=True)
5
def get_weather(city: str, units: str = "celsius", include_forecast: bool = False) -> str:
6
    """
7
    获取当日天气，可选择是否同时查询未来五日天气预报
8
    Args:
9
        city: 城市
10
        units: 气温单位，可选：celsius-摄氏度，fahrenheit-华氏度
11
        include_forecast: 是否包含未来五日的天气预报
12
    """
13
    temp = 22 if units == "celsius" else 72
14
    result = f'{city}当天气温: {temp} {"摄氏度" if units == "celsius" else "华氏度"}'
15
    if include_forecast:
16
        result += "\n未来五天都是晴天"
17
    return result
18

19
rprint(convert_to_openai_tool(get_weather))

1
{
2
    'type': 'function',
3
    'function': {
4
        'name': 'get_weather',
5
        'description': '获取当日天气，可选择是否同时查询未来五日天气预报',
6
        'parameters': {
7
            'properties': {
8
                'city': {
9
                    'description': '城市',
10
                    'type': 'string'
11
                },
12
                'units': {
13
                    'default': 'celsius',
14
                    'description': '气温单位，可选：celsius-摄氏度，fahrenheit-华氏度',
15
                    'type': 'string'
16
                },
17
                'include_forecast': {
18
                    'default': False,
19
                    'description': '是否包含未来五日的天气预报',
20
                    'type': 'boolean'
21
                }
22
            },
23
            'required': [
24
                'city'
25
            ],
26
            'type': 'object'
27
        }
28
    }
29
}

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2

3
@tool(parse_docstring=True)
4
def get_weather(city: str, units: str = "celsius", include_forecast: bool = False) -> str:
5
    """
6
    获取当日天气，可选择是否同时查询未来五日天气预报
7
    Args:
8
        city: 城市
9
        units: 气温单位，可选：celsius-摄氏度，fahrenheit-华氏度
10
        include_forecast: 是否包含未来五日的天气预报
11
    """
12
    temp = 22 if units == "celsius" else 72
13
    result = f'{city}当天气温: {temp} {"摄氏度" if units == "celsius" else "华氏度"}'
14
    if include_forecast:
15
        result += "\n未来五天都是晴天"
16
    return result
17

18
convert_to_openai_tool(get_weather)

1
Traceback...
2
ValueError: Found invalid Google-Style docstring.

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from langchain.tools import tool
3

4
@tool(name_or_callable="getWeather")
5
def get_weather(city: str):
6
    """
7
    天气查询工具
8
    """
9
    return f"{city}天气晴朗"
10

11
print(convert_to_openai_tool(get_weather))

1
{
2
    'type': 'function',
3
    'function': {
4
        'name': 'getWeather',
5
        'description': '天气查询工具',
6
        'parameters': {
7
            'properties': {
8
                'city': {
9
                    'type': 'string'
10
                }
11
            },
12
            'required': [
13
                'city'
14
            ],
15
            'type': 'object'
16
        }
17
    }
18
}

1
from langchain_core.utils.function_calling import convert_to_openai_tool
2
from langchain.tools import tool
3

4
@tool("getWeather")
5
def get_weather(city: str):
6
    """
7
    天气查询工具
8
    """
9
    print("天气晴朗")
10

11
print(convert_to_openai_tool(get_weather))

1
from pydantic import BaseModel, Field
2
from langchain.tools import tool
3
from langchain.messages import HumanMessage
4
from langchain_core.utils.function_calling import convert_to_openai_tool
5

6
class WeatherSchema(BaseModel):
7
    city: str = Field(default="北京", description="城市名称")
8
    if_forecast: bool = Field(default=False, description="是否包含明日天气预报")
9

10
@tool("get_weather_and_forecast", description="查询当日天气，可以包含明日天气预报", args_schema=WeatherSchema)
11
def get_weather(city: str, if_forecast: bool):
12
    res = f"{city} 今天天气不错"
13
    if if_forecast:
14
        res += "\n明天也不错"
15
    return res
16

17
print(convert_to_openai_tool(get_weather))
18
model_with_tools = model.bind_tools([get_weather])
19
messages = [HumanMessage("今天杭州天气如何？明天呢？")]
20
response = model_with_tools.invoke(messages)
21
messages.append(response)
22
tool_calls = response.tool_calls
23
for tool_call in tool_calls:
24
    if tool_call["name"] == "get_weather_and_forecast":
25
        tool_msg = get_weather.invoke(tool_call)
26
        messages.append(tool_msg)
27
final_response = model_with_tools.invoke(messages)
28
messages.append(final_response)
29
for msg in messages:
30
    msg.pretty_print()

1
{'type': 'function', 'function': {'name': 'get_weather_and_forecast', 'description': '查询当日天气，可以包含明日天气预报', 'parameters': {'properties': {'city': {'default': '北京', 'description': '城市名称', 'type': 'string'}, 'if_forecast': {'default': False, 'description': '是否包含明日天气预报', 'type': 'boolean'}}, 'type': 'object'}}}
2

3
================================ Human Message ================================
4
今天杭州天气如何？明天呢？
5

6
================================== Ai Message ==================================
7
Tool Calls:
8
get_weather_and_forecast (call_c82UrCpAdVAqiHfl4OkZocbg)
9
Call ID: call_c82UrCpAdVAqiHfl4OkZocbg
10
Args:
11
city: 杭州
12
if_forecast: True
13

14
================================= Tool Message =================================
15
Name: get_weather_and_forecast
16
杭州 今天天气不错
17
明天也不错
18

19
================================== Ai Message ==================================
20
杭州今天：天气不错。
21
杭州明天：也不错。

1
from langchain.tools import tool
2
from langchain.messages import HumanMessage
3
from langchain_core.utils.function_calling import convert_to_openai_tool
4

5
@tool("get_weather_and_forecast", parse_docstring=True)
6
def get_weather(city: str="北京", if_forecast: bool=False):
7
    """
8
    查询当日天气，可以包含明日天气预报
9
    Args:
10
        city: 城市名称
11
        if_forecast: 是否包含明日天气预报
12
    """
13
    res = f"{city} 今天天气不错"
14
    if if_forecast:
15
        res += "\n明天要下雨"
16
    return res
17

18
print(convert_to_openai_tool(get_weather))
19
model_with_tools = model.bind_tools([get_weather])
20
messages = [HumanMessage("今天杭州天气如何？明天呢？")]
21
response = model_with_tools.invoke(messages)
22
messages.append(response)
23
tool_calls = response.tool_calls
24
# 将工具调用的结果添加到消息列表中
25
for tool_call in tool_calls:
26
    if tool_call["name"] == "get_weather_and_forecast":
27
        # 返回值tool_msg类型是ToolMessage
28
        tool_msg = get_weather.invoke(tool_call)
29
        messages.append(tool_msg)
30
final_response = model_with_tools.invoke(messages)
31
messages.append(final_response)
32
for msg in messages:
33
    msg.pretty_print()

1
{'type': 'function', 'function': {'name': 'get_weather_and_forecast', 'description': '查询当日天气，可以包含明日天气预报', 'parameters': {'properties': {'city': {'default': '北京', 'description': '城市名称', 'type': 'string'}, 'if_forecast': {'default': False, 'description': '是否包含明日天气预报', 'type': 'boolean'}}, 'type': 'object'}}}
2

3
================================ Human Message ================================
4
今天杭州天气如何？明天呢？
5

6
================================== Ai Message ==================================
7
Tool Calls:
8
get_weather_and_forecast (call_y5vpFccj2DfWSAF6ng5dZOYH)
9
Call ID: call_y5vpFccj2DfWSAF6ng5dZOYH
10
Args:
11
city: 杭州
12
if_forecast: True
13

14
================================= Tool Message =================================
15
Name: get_weather_and_forecast
16
杭州 今天天气不错
17
明天要下雨
18

19
================================== Ai Message ==================================
20
杭州今天“天气不错”，明天“要下雨”。
21
如果你愿意，我也可以帮你继续看一下适合出行/穿衣的建议。

1
from langchain_core.tools import tool
2
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
3

4
# 1.定义工具
5
# 定义股票查询工具
6
@tool(parse_docstring=True)
7
def get_stock_price(company: str, timeframe: str = "today") -> str:
8
    """获取指定公司的股票价格信息
9
    Args:
10
        company: 公司名称（如：苹果公司, 微软公司, 谷歌公司）
11
        timeframe: 时间范围（today-今日, week-本周, month-本月）
12
    """
13
    # 模拟股票数据
14
    mock_data = {
15
        "苹果公司": {"today": 185.20, "week": 183.50, "month": 180.75},
16
        "微软公司": {"today": 415.86, "week": 412.30, "month": 405.42},
17
        "谷歌公司": {"today": 15.42, "week": 15.20, "month": 14.85}
18
    }
19
    if company in mock_data:
20
        price = mock_data[company].get(timeframe, "未知时间范围")
21
        return f"{company} {timeframe}价格: {price}美元"
22
    else:
23
        return f"未找到股票代码 {company} 的数据"
24

25
# 定义新闻搜索工具
26
@tool(parse_docstring=True)
27
def search_news(company: str) -> str:
28
    """搜索指定公司的财经新闻
29
    Args:
30
        company: 公司名称
31
    Returns:
32
        公司的财经新闻，每个新闻占一行
33
    """
34
    # 模拟新闻数据
35
    mock_news = {
36
        "苹果公司": [
37
            "苹果发布新款iPhone，股价上涨3%",
38
            "苹果与欧盟达成反垄断和解协议",
39
            "苹果将在印度扩大生产规模"
40
        ],
41
        "微软公司": [
42
            "微软Azure云业务季度增长超预期",
43
            "微软完成对Nuance的收购",
44
            "微软推出新一代AI助手Copilot"
45
        ],
46
        "谷歌公司": [
47
            "谷歌发布新AI模型，性能提升20%",
48
            "谷歌与OpenAI合作，开发新的AI助手",
49
            "谷歌在欧洲展开AI研究项目"
50
        ]
51
    }
52
    news_list = mock_news.get(company, [f"未找到{company}的相关新闻"])
53
    return "\n".join(news_list)
54

55
# rprint(convert_to_openai_tool(search_news))
56

57
# 2.初始化模型并绑定工具
58
tools = [get_stock_price, search_news]
59
model_with_tools = model.bind_tools(tools)
60
message_list = []
61
human_message = HumanMessage(content="苹果公司今天的股价是多少？最近有什么新闻？")
62
# human_message = HumanMessage(content="比较一下微软和苹果的股价")
63
# human_message = HumanMessage(content="腾讯最近有什么重大新闻？")
64
# human_message = HumanMessage(content="海水为什么是咸的？")
65
message_list.append(human_message)
66

67
# 3.工具调用
68
while True:
69
    response = model_with_tools.invoke(message_list)
70
    message_list.append(response)
71
    # 如果模型不需要调用工具，直接退出循环
72
    if not response.tool_calls:
73
        print("没有工具调用，直接返回答案")
74
        break
75
    # 如果有调用工具，处理工具调用响应
76
    # 4.开发者根据模型的响应，调用工具并获取结果
77
    for tool_call in response.tool_calls:
78
        if tool_call["name"] == "get_stock_price":
79
            stock_result = get_stock_price.invoke(tool_call)
80
            print("stock_result", stock_result)
81
            message_list.append(stock_result)
82
        if tool_call["name"] == "search_news":
83
            news_result = search_news.invoke(tool_call)
84
            print("news_result", news_result)
85
            message_list.append(news_result)
86

87
for msg in message_list:
88
    msg.pretty_print()