feat: 项目完成
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liangfangxing
69
_demo/function_call/_@tool.py
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69
_demo/function_call/_@tool.py
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, ToolMessage
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from langchain_core.tools import tool
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from conf import settings
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# todo: 第一步：定义工具函数
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@tool
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def add(a: int, b: int) -> int:
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"""
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将数字a与数字b相加
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a + b
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@tool
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def multiply(a: int, b: int) -> int:
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"""
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将数字a与数字b相乘
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a * b
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# 定义 JSON 格式的工具 schema
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tools = [add, multiply]
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# todo: 第二步：初始化模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# 绑定工具，允许模型自动选择工具
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llm_with_tools = llm.bind_tools(tools, tool_choice="auto")
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# todo: 第三步：调用回复
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query = "2+1等于多少？"
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messages = [HumanMessage(query)]
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try:
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# todo: 第一次调用
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ai_msg = llm_with_tools.invoke(messages)
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messages.append(ai_msg)
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print(f"\n第一轮调用后结果：\n{messages}")
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# 处理工具调用
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# 判断消息中是否有tool_calls，以判断工具是否被调用
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if hasattr(ai_msg, 'tool_calls') and ai_msg.tool_calls:
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for tool_call in ai_msg.tool_calls:
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# todo: 处理工具调用
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selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
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tool_output = selected_tool.invoke(tool_call["args"]) # 需要使用invoke进行调用
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messages.append(ToolMessage(content=tool_output, tool_call_id=tool_call["id"]))
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print(f"\n第二轮 message中增加tool_output 之后：\n{messages}")
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# todo: 第二次调用，将工具结果传回模型以生成最终回答
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final_response = llm_with_tools.invoke(messages)
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print(f"\n最终模型响应：\n{final_response.content}")
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else:
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print("模型未生成工具调用，直接返回文本:")
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print(ai_msg.content)
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except Exception as e:
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print(f"模型调用失败: {str(e)}")
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83
_demo/function_call/_pydantic.py
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83
_demo/function_call/_pydantic.py
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, ToolMessage
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"""
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Pydantic 是一个 Python 库，用于数据验证和序列化。
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它通过使用 Python 类型注解（type hints）来定义数据模型，
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并提供强大的数据验证功能。Pydantic 基于 Python 的 dataclasses 和 typing 模块，
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允许开发者定义结构化的数据模型，并自动验证输入数据是否符合指定的类型和约束。
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"""
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from pydantic.v1 import BaseModel, Field
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from conf import settings
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# todo: 第一步：定义工具函数
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class Add(BaseModel):
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"""
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将两个数字相加
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"""
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a: int = Field(..., description="第一个数字")
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b: int = Field(..., description="第二个数字")
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def invoke(self, args):
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# 验证参数
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tool_instance = self.__class__(**args) # 自动验证 a 和 b
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return tool_instance.a + tool_instance.b
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class Multiply(BaseModel):
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"""
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将两个数字相乘
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"""
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a: int = Field(..., description="第一个数字")
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b: int = Field(..., description="第二个数字")
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def invoke(self, args):
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# 验证参数
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tool_instance = self.__class__(**args) # 自动验证 a 和 b
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return tool_instance.a * tool_instance.b
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# 定义 JSON 格式的工具 schema
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tools = [Add, Multiply]
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# todo: 第二步：初始化模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# 绑定工具，允许模型自动选择工具
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llm_with_tools = llm.bind_tools(tools, tool_choice="auto")
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# todo: 第三步：调用回复
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query = "2+1等于多少？"
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messages = [HumanMessage(query)]
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try:
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# todo: 第一次调用
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ai_msg = llm_with_tools.invoke(messages)
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messages.append(ai_msg)
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print(f"\n第一轮调用后结果：\n{messages}")
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# 处理工具调用
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# 判断消息中是否有tool_calls，以判断工具是否被调用
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if hasattr(ai_msg, 'tool_calls') and ai_msg.tool_calls:
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for tool_call in ai_msg.tool_calls:
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# todo: 处理工具调用
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selected_tool = {"add": Add, "multiply": Multiply}[tool_call["name"].lower()]
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# 实例化工具类并调用 invoke
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tool_instance = selected_tool(**tool_call["args"])
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tool_output = tool_instance.invoke(tool_call["args"])
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messages.append(ToolMessage(content=tool_output, tool_call_id=tool_call["id"]))
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print(f"\n第二轮 message中增加tool_output 之后：\n{messages}")
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# todo: 第二次调用，将工具结果传回模型以生成最终回答
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final_response = llm_with_tools.invoke(messages)
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print(f"\n最终模型响应：\n{final_response.content}")
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else:
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print("模型未生成工具调用，直接返回文本:")
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print(ai_msg.content)
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except Exception as e:
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print(f"模型调用失败: {str(e)}")
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51
_demo/function_call/agent.py
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51
_demo/function_call/agent.py
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@@ -0,0 +1,51 @@
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from langchain.agents import initialize_agent, AgentType
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from langchain_openai import ChatOpenAI
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from langchain_core.tools import tool
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from conf import settings
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# todo: 第一步：定义工具函数
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@tool
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def add(a: int, b: int) -> int:
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"""
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将数字a与数字b相加
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a + b
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@tool
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def multiply(a: int, b: int) -> int:
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"""
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将数字a与数字b相乘
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a * b
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# 加载工具
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tools = [add, multiply]
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# todo: 第二步：初始化模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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# todo: 第三步：创建Agent
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agent = initialize_agent(
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tools,
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llm,
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AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
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verbose=True
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)
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# todo: 第四步：调用Agent
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query = "2+1等于多少？"
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result = agent.invoke(query)
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print(f'result: {result["output"]}')
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115
_demo/function_call/json_schema.py
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115
_demo/function_call/json_schema.py
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@@ -0,0 +1,115 @@
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage, ToolMessage
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from conf import settings
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# todo: 第一步：定义工具函数
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def add(a: int, b: int) -> int:
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"""
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将数字a与数字b相加
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a + b
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def multiply(a: int, b: int) -> int:
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"""
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将数字a与数字b相乘
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Args:
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a: 第一个数字
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b: 第二个数字
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"""
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return a * b
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tools = [
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{
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"type": "function",
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"function": {
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"name": "add",
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"description": "将数字a与数字b相加",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"type": "integer",
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"description": "第一个数字"
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},
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"b": {
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"type": "integer",
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"description": "第二个数字"
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}
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},
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"required": ["a", "b"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "multiply",
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"description": "将数字a与数字b相乘",
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"parameters": {
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"type": "object",
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"properties": {
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"a": {
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"type": "integer",
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"description": "第一个数字"
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},
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"b": {
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"type": "integer",
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"description": "第二个数字"
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}
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},
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"required": ["a", "b"]
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}
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}
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}
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]
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# todo: 第二步：初始化模型
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llm = ChatOpenAI(
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base_url=settings.base_url,
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api_key=settings.api_key,
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model=settings.model_name,
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temperature=0.1
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)
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llm_with_tools = llm.bind(tools=tools, tool_choice="auto")
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# todo: 第三步：调用回复
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query = "2*10+1？"
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messages = [HumanMessage(query)]
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try:
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# todo: 第一次调用
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ai_msg = llm_with_tools.invoke(messages)
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messages.append(ai_msg)
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print(f"\n第一轮调用后结果：\n{messages}")
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# 处理工具调用
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# 判断消息中是否有tool_calls，以判断工具是否被调用
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if hasattr(ai_msg, "tool_calls") and ai_msg.tool_calls:
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for tool_call in ai_msg.tool_calls:
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# todo: 处理工具调用
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selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
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tool_output = selected_tool(**tool_call["args"])
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messages.append(ToolMessage(content=tool_output, tool_call_id=tool_call["id"]))
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print(f"\n第二轮 message中增加tool_output 之后：\n{messages}")
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# todo: 第二次调用，将工具结果传回模型以生成最终回答
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final_response = llm_with_tools.invoke(messages)
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print(f"\n最终模型响应：\n{final_response.content}")
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else:
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print("模型未生成工具调用，直接返回文本:")
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print(ai_msg.content)
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except Exception as e:
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print(f"模型调用失败: {str(e)}")
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# llm.invoke(messages, tools=tools, ...):
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# 绑定方式: 直接在 .invoke() 调用中传入 tools 参数。这是一种临时、一次性的绑定方式，仅对本次调用有效。
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# 调用方式: 如果你想再次调用模型并使用工具，你必须在下一次 .invoke() 调用中再次传递 tools 参数。
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# 适用场景: 适用于简单、单次的工具调用需求，
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