智能体¶

%%capture --no-stderr
%pip install --quiet -U langchain_openai langchain_core langgraph langgraph-prebuilt

import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

_set_env("OPENAI_API_KEY")

_set_env("LANGSMITH_API_KEY")
os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_PROJECT"] = "langchain-academy"

from langchain_openai import ChatOpenAI

def multiply(a: int, b: int) -> int:
    """Multiply a and b.

    Args:
        a: first int
        b: second int
    """
    return a * b

# This will be a tool
def add(a: int, b: int) -> int:
    """Adds a and b.

    Args:
        a: first int
        b: second int
    """
    return a + b

def divide(a: int, b: int) -> float:
    """Divide a and b.

    Args:
        a: first int
        b: second int
    """
    return a / b

tools = [add, multiply, divide]
llm = ChatOpenAI(model="gpt-4o")

# For this ipynb we set parallel tool calling to false as math generally is done sequentially, and this time we have 3 tools that can do math
# the OpenAI model specifically defaults to parallel tool calling for efficiency, see https://python.langchain.com/docs/how_to/tool_calling_parallel/
# play around with it and see how the model behaves with math equations!
llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)

from langgraph.graph import MessagesState
from langchain_core.messages import HumanMessage, SystemMessage

# System message
sys_msg = SystemMessage(content="You are a helpful assistant tasked with performing arithmetic on a set of inputs.")

# Node
def assistant(state: MessagesState):
   return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}

messages = [HumanMessage(content="Add 3 and 4. Multiply the output by 2. Divide the output by 5")]
messages = react_graph.invoke({"messages": messages})