LangChain Tools工具使用

from langchain.tools import tool
from pydantic import BaseModel
from pydantic import Field

class FiledInfo(BaseModel):
    """
    定义参数信息
    """
    city: str = Field(description='城市')


@tool(args_schema=FiledInfo, description='根据城市名称获取温度')
def tp_tool(city: str) -> int:
    print('=======tp_tool=======')
    if city == '北京':
        return 12
    elif city == '武汉':
        return 23
    elif city == '沈阳':
        return -10
    elif city == '泉州':
        return 27
    else:
        return None


if __name__ == '__main__':
    print( tp_tool.invoke({'city': '沈阳'}) )

pip install langchain-classic

import os

from langchain_classic.agents import create_tool_calling_agent
from langchain_classic.agents import AgentExecutor
from langchain.chat_models import init_chat_model
from langchain_core.prompts import ChatPromptTemplate
from my_tool import tp_tool

llm = init_chat_model(
    model = 'deepseek-chat',
    model_provider = 'openai',
    api_key = os.getenv('DSKEY'),
    base_url = 'https://api.deepseek.com'
)

# 定义 Prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", "你是一个天气查询助手"),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

# 创建 Agent
agent = create_tool_calling_agent(llm, [tp_tool], prompt)

agent_executor = AgentExecutor(agent=agent, tools=[tp_tool], verbose=True)

# 执行
result = agent_executor.invoke({"input": "泉州温度多少？"})

print('*****result = ', result)

D:\PycharmProjects\langchain-test\.venv\Scripts\python.exe D:\PycharmProjects\langchain-test\test_tool2.py 


> Entering new AgentExecutor chain...

Invoking: `k` with `{'city': '泉州'}`
responded: 我来帮您查询泉州的温度。

=======tp_tool=======
27根据查询结果，泉州当前的温度是 **27°C**。

> Finished chain.
*****result =  {'input': '泉州温度多少？', 'output': '根据查询结果，泉州当前的温度是 **27°C**。'}

Process finished with exit code 0