从LLMMathChain迁移

LLMMathChain 使得由LLM生成的数学表达式的评估成为可能。生成表达式的指令被格式化为提示，并在使用numexpr库进行评估之前，从字符串响应中解析出表达式。

这更自然地通过工具调用实现。我们可以利用numexpr为聊天模型配备一个简单的计算器工具，并使用LangGraph围绕它构建一个简单的链。这种方法的一些优点包括：

利用为此目的而微调的聊天模型的工具调用能力；
减少从字符串LLM响应中提取表达式时的解析错误；
将指令委托给消息角色（例如，聊天模型可以理解ToolMessage表示的内容，而无需额外的提示）；
支持流式传输，包括单个令牌和链步骤。

%pip install --upgrade --quiet numexpr

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

遗留问题

Details

from langchain.chains import LLMMathChain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")

chain = LLMMathChain.from_llm(llm)

chain.invoke("What is 551368 divided by 82?")

API Reference:LLMMathChain | ChatPromptTemplate | ChatOpenAI

{'question': 'What is 551368 divided by 82?', 'answer': 'Answer: 6724.0'}

LangGraph

Details

import math
from typing import Annotated, Sequence

import numexpr
from langchain_core.messages import BaseMessage
from langchain_core.runnables import RunnableConfig
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt.tool_node import ToolNode
from typing_extensions import TypedDict


@tool
def calculator(expression: str) -> str:
    """Calculate expression using Python's numexpr library.

    Expression should be a single line mathematical expression
    that solves the problem.

    Examples:
        "37593 * 67" for "37593 times 67"
        "37593**(1/5)" for "37593^(1/5)"
    """
    local_dict = {"pi": math.pi, "e": math.e}
    return str(
        numexpr.evaluate(
            expression.strip(),
            global_dict={},  # restrict access to globals
            local_dict=local_dict,  # add common mathematical functions
        )
    )


llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
tools = [calculator]
llm_with_tools = llm.bind_tools(tools, tool_choice="any")


class ChainState(TypedDict):
    """LangGraph state."""

    messages: Annotated[Sequence[BaseMessage], add_messages]


async def acall_chain(state: ChainState, config: RunnableConfig):
    last_message = state["messages"][-1]
    response = await llm_with_tools.ainvoke(state["messages"], config)
    return {"messages": [response]}


async def acall_model(state: ChainState, config: RunnableConfig):
    response = await llm.ainvoke(state["messages"], config)
    return {"messages": [response]}


graph_builder = StateGraph(ChainState)
graph_builder.add_node("call_tool", acall_chain)
graph_builder.add_node("execute_tool", ToolNode(tools))
graph_builder.add_node("call_model", acall_model)
graph_builder.set_entry_point("call_tool")
graph_builder.add_edge("call_tool", "execute_tool")
graph_builder.add_edge("execute_tool", "call_model")
graph_builder.add_edge("call_model", END)
chain = graph_builder.compile()

# Visualize chain:

from IPython.display import Image

Image(chain.get_graph().draw_mermaid_png())

# Stream chain steps:

example_query = "What is 551368 divided by 82"

events = chain.astream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
async for event in events:
    event["messages"][-1].pretty_print()

================================[1m Human Message [0m=================================

What is 551368 divided by 82
==================================[1m Ai Message [0m==================================
Tool Calls:
  calculator (call_1ic3gjuII0Aq9vxlSYiwvjSb)
 Call ID: call_1ic3gjuII0Aq9vxlSYiwvjSb
  Args:
    expression: 551368 / 82
=================================[1m Tool Message [0m=================================
Name: calculator

6724.0
==================================[1m Ai Message [0m==================================

551368 divided by 82 equals 6724.

下一步

查看构建和使用工具的指南这里。

查看LangGraph文档以获取使用LangGraph构建的详细信息。

遗留问题​

LangGraph​

下一步​

这个页面有帮助吗？

遗留问题

LangGraph

下一步