【GPT入门】第20课 langchain的function calling 初步体验

1. langchain的function calling 非常简洁

1. 在方法名说明方法用途和参数作用
2. 增加@tool 标签
3. langchain方法自动把@tool转为方法定义，后续方法调用都很简洁
   下面代码用支持单函数与多函数调用，自己体验一下

query = “3 加 4的和 的 5 倍是多少？” 这个会调用两次函数
query = " 4的 5 倍是多少？"

2. 代码

from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI


@tool
def add(a: int, b: int) -> int:
    """Add two integers together.

    Args:
        a: The first integer.
        b: The second integer.
    """
    return a + b

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers together.

    Args:
        a: The first integer.
        b: The second integer.
    """
    return a * b

import json

# model_name = 'gpt-4o-mini'
# model_name = 'gpt-4'
# llm = ChatOpenAI(model_name=model_name)
llm = ChatOpenAI()
llm_with_tools = llm.bind_tools([add, multiply])


query = "3 加 4的和 的 5 倍是多少？"
# query = " 4的 5 倍是多少？"
messages = [HumanMessage(content=query)]
output = llm_with_tools.invoke(messages)
print(output)
print("------------tool_calls:")
print(json.dumps(output.tool_calls, indent=4))

messages.append(output)





avaliable_tools = {"add":add, "multiply": multiply}
for tool_call in output.tool_calls:
    selected_tools = avaliable_tools[tool_call["name"].lower()] # tool_call就是一个字典
    tool_msg = selected_tools.invoke(tool_call)
    messages.append(tool_msg)
    print("tool_msg:, type:", type(tool_msg))  # <class 'langchain_core.messages.tool.ToolMessage'>
    print(tool_msg)

    # content='20' name='multiply' tool_call_id='call_e5EY7klNZlBD8W68y7X0BcYD'
new_output = llm_with_tools.invoke(messages)
for message in messages:
    print("message.dump:")
    print(json.dumps(message.model_dump(), indent=4, ensure_ascii=False))
print(new_output.content)

3. 执行结果：

C:\ProgramData\anaconda3\envs\gptLearning\python.exe E:\workspace\gptLearning\gptLearning\ls10\functionCalling\ls01_tools.py 
content='' additional_kwargs={'tool_calls': [{'id': 'call_0HGpTdeo2zm9G6zZPbewVi8g', 'function': {'arguments': '{"a": 3, "b": 4}', 'name': 'add'}, 'type': 'function'}, {'id': 'call_m6Uc0OfQ9ea0uTyFNbwEWIBq', 'function': {'arguments': '{"a": 7, "b": 5}', 'name': 'multiply'}, 'type': 'function'}], 'refusal': None} response_metadata={'token_usage': {'completion_tokens': 49, 'prompt_tokens': 121, 'total_tokens': 170, 'completion_tokens_details': None, 'prompt_tokens_details': None}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': 'fp_0165350fbb', 'finish_reason': 'tool_calls', 'logprobs': None} id='run-e83caecd-ca6f-4851-b35e-b5ea8f2804a2-0' tool_calls=[{'name': 'add', 'args': {'a': 3, 'b': 4}, 'id': 'call_0HGpTdeo2zm9G6zZPbewVi8g', 'type': 'tool_call'}, {'name': 'multiply', 'args': {'a': 7, 'b': 5}, 'id': 'call_m6Uc0OfQ9ea0uTyFNbwEWIBq', 'type': 'tool_call'}] usage_metadata={'input_tokens': 121, 'output_tokens': 49, 'total_tokens': 170, 'input_token_details': {}, 'output_token_details': {}}
------------tool_calls:
[
    {
        "name": "add",
        "args": {
            "a": 3,
            "b": 4
        },
        "id": "call_0HGpTdeo2zm9G6zZPbewVi8g",
        "type": "tool_call"
    },
    {
        "name": "multiply",
        "args": {
            "a": 7,
            "b": 5
        },
        "id": "call_m6Uc0OfQ9ea0uTyFNbwEWIBq",
        "type": "tool_call"
    }
]
tool_msg:, type: <class 'langchain_core.messages.tool.ToolMessage'>
content='7' name='add' tool_call_id='call_0HGpTdeo2zm9G6zZPbewVi8g'
tool_msg:, type: <class 'langchain_core.messages.tool.ToolMessage'>
content='35' name='multiply' tool_call_id='call_m6Uc0OfQ9ea0uTyFNbwEWIBq'
message.dump:
{
    "content": "3 加 4的和 的 5 倍是多少？",
    "additional_kwargs": {},
    "response_metadata": {},
    "type": "human",
    "name": null,
    "id": null,
    "example": false
}
message.dump:
{
    "content": "",
    "additional_kwargs": {
        "tool_calls": [
            {
                "id": "call_0HGpTdeo2zm9G6zZPbewVi8g",
                "function": {
                    "arguments": "{\"a\": 3, \"b\": 4}",
                    "name": "add"
                },
                "type": "function"
            },
            {
                "id": "call_m6Uc0OfQ9ea0uTyFNbwEWIBq",
                "function": {
                    "arguments": "{\"a\": 7, \"b\": 5}",
                    "name": "multiply"
                },
                "type": "function"
            }
        ],
        "refusal": null
    },
    "response_metadata": {
        "token_usage": {
            "completion_tokens": 49,
            "prompt_tokens": 121,
            "total_tokens": 170,
            "completion_tokens_details": null,
            "prompt_tokens_details": null
        },
        "model_name": "gpt-3.5-turbo-0125",
        "system_fingerprint": "fp_0165350fbb",
        "finish_reason": "tool_calls",
        "logprobs": null
    },
    "type": "ai",
    "name": null,
    "id": "run-e83caecd-ca6f-4851-b35e-b5ea8f2804a2-0",
    "example": false,
    "tool_calls": [
        {
            "name": "add",
            "args": {
                "a": 3,
                "b": 4
            },
            "id": "call_0HGpTdeo2zm9G6zZPbewVi8g",
            "type": "tool_call"
        },
        {
            "name": "multiply",
            "args": {
                "a": 7,
                "b": 5
            },
            "id": "call_m6Uc0OfQ9ea0uTyFNbwEWIBq",
            "type": "tool_call"
        }
    ],
    "invalid_tool_calls": [],
    "usage_metadata": {
        "input_tokens": 121,
        "output_tokens": 49,
        "total_tokens": 170,
        "input_token_details": {},
        "output_token_details": {}
    }
}
message.dump:
{
    "content": "7",
    "additional_kwargs": {},
    "response_metadata": {},
    "type": "tool",
    "name": "add",
    "id": null,
    "tool_call_id": "call_0HGpTdeo2zm9G6zZPbewVi8g",
    "artifact": null,
    "status": "success"
}
message.dump:
{
    "content": "35",
    "additional_kwargs": {},
    "response_metadata": {},
    "type": "tool",
    "name": "multiply",
    "id": null,
    "tool_call_id": "call_m6Uc0OfQ9ea0uTyFNbwEWIBq",
    "artifact": null,
    "status": "success"
}
3 加 4 的和是 7，7 的 5 倍是 35。

Process finished with exit code 0

``