@h,SrSSKJr SSKrSSKrSSKrSSKJrJr SSK J r SSK J r J r SSKJr SSKJr SS KJrJr SS KJr SS KJr SS KJr \ "S SSS9"SS\55rg)zCChain that interprets a prompt and executes python code to do math.) annotationsN)AnyOptional) deprecated)AsyncCallbackManagerForChainRunCallbackManagerForChainRun)BaseLanguageModel)BasePromptTemplate) ConfigDictmodel_validator)ChainLLMChain)PROMPTz0.2.13zThis class is deprecated and will be removed in langchain 1.0. See API reference for replacement: https://api.python.langchain.com/en/latest/chains/langchain.chains.llm_math.base.LLMMathChain.htmlz1.0)sincemessageremovalcb\rSrSr%SrS\S'SrS\S'\rS\S 'S r S \S 'S r S \S'\ "SSS9r \ "SS9\SSj55r\S Sj5r\S Sj5rS!SjrS"SjrS#SjrS$S%SjjrS$S&Sjjr\S'Sj5r\\4S(Sjj5rSrg)) LLMMathChaina]Chain that interprets a prompt and executes python code to do math. Note: this class is deprecated. See below for a replacement implementation using LangGraph. The benefits of this implementation are: - Uses LLM tool calling features; - Support for both token-by-token and step-by-step streaming; - Support for checkpointing and memory of chat history; - Easier to modify or extend (e.g., with additional tools, structured responses, etc.) Install LangGraph with: .. code-block:: bash pip install -U langgraph .. code-block:: python import math from typing import Annotated, Sequence 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 import numexpr 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() .. code-block:: python 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() .. code-block:: none ================================ Human Message ================================= What is 551368 divided by 82 ================================== Ai Message ================================== Tool Calls: calculator (call_MEiGXuJjJ7wGU4aOT86QuGJS) Call ID: call_MEiGXuJjJ7wGU4aOT86QuGJS Args: expression: 551368 / 82 ================================= Tool Message ================================= Name: calculator 6724.0 ================================== Ai Message ================================== 551368 divided by 82 equals 6724. Example: .. code-block:: python from langchain.chains import LLMMathChain from langchain_community.llms import OpenAI llm_math = LLMMathChain.from_llm(OpenAI()) r llm_chainNzOptional[BaseLanguageModel]llmr promptquestionstr input_keyanswer output_keyTforbid)arbitrary_types_allowedextrabefore)modecSSKnSU;aG[R"SSS9 SU;a,USb&UR S[ 5n[ USUS 9US'U$![anSn[U5UeSnAff=f) NrzXLLMMathChain requires the numexpr package. Please install it with `pip install numexpr`.rzDirectly instantiating an LLMMathChain with an llm is deprecated. Please instantiate with llm_chain argument or using the from_llm class method.) stacklevelrrrr)numexpr ImportErrorwarningswarngetrr)clsvaluesr(emsgrs V/var/www/html/shao/venv/lib/python3.13/site-packages/langchain/chains/llm_math/base.pyraise_deprecationLLMMathChain.raise_deprecations *  F? MM    &(VE]-FHf5&.6%=&P{# ! *@ c" )  *sA A2A--A2cUR/$)z"Expect input key. :meta private: )rselfs r1 input_keysLLMMathChain.input_keyss cUR/$)z#Expect output key. :meta private: )rr5s r1 output_keysLLMMathChain.output_keyss   r9cSSKn[R[RS.n[ UR UR 50US95n[R"SSU5$![anSUSUS3n[U5UeSnAff=f) Nr)pir/) global_dict local_dictzLLMMathChain._evaluate("z") raised error: z4. Please try again with a valid numerical expressionz^\[|\]$) r(mathr>r/revaluatestrip Exception ValueErrorresub)r6 expressionr(r@outputr/r0s r1_evaluate_expression!LLMMathChain._evaluate_expressions ) $dff5J  $$& ")!Fvvj"f-- )*:,6GsKFF S/q (  )sA A'' B 1BB cURUSURS9 UR5n[R"SU[R 5nU(a]UR S5nURU5nURSURS9 URUSURS9 SU-nOGURS 5(aUnO.S U;aSURS 5S -nOS U3n[U5eURU0$ Ngreen)colorverbosez^```text(.*?)```z Answer: )rQyellowzAnswer: zAnswer:zunknown format from LLM: on_textrQrDrGsearchDOTALLgrouprK startswithsplitrFrr6 llm_output run_manager text_matchrIrJrr0s r1_process_llm_result LLMMathChain._process_llm_results Jgt||L%%' YY2J J #))!,J..z:F    dll  C   h  M&(F  " "9 - -F * $*"2"29"=b"AAF-j\:CS/ !((r9c\# URUSURS9IShvN UR5n[R"SU[R 5nU(amUR S5nURU5nURSURS9IShvN URUSURS9IShvN SU-nOGURS 5(aUnO.S U;aSURS 5S -nOS U3n[U5eURU0$GNNNd7frNrUr\s r1_aprocess_llm_result!LLMMathChain._aprocess_llm_results  !!*GT\\!RRR%%' YY2J J #))!,J..z:F%%lDLL%I I I%%fHdll%S S S&(F  " "9 - -F * $*"2"29"=b"AAF-j\:CS/ !((! S J Ss5D,D%BD,"D(#"D,D*A D,(D,*D,cU=(d [R"5nURXR5 URR XRS/UR 5S9nURXC5$Nz ```output)rstop callbacks)rget_noop_managerrVrrpredict get_childr`r6inputsr^ _run_managerr]s r1_callLLMMathChain._callst #S&@&Q&Q&S VNN34^^++NN+",,., '' AAr9cJ# U=(d [R"5nURXR5IShvN URR XRS/UR 5S9IShvN nURXC5IShvN $N[NN7frf)rrirVrrapredictrkrcrls r1_acallLLMMathChain._acalls #X&E&V&V&X ""6..#9:::>>22NN+",,.3  ..zHHH ; Is5?B#B=B#?BB#B!B#B#!B#cg)Nllm_math_chainr5s r1 _chain_typeLLMMathChain._chain_type+sr9c ([XS9nU"SSU0UD6$)Nr'rrwr)r-rrkwargsrs r1from_llmLLMMathChain.from_llm/s!4 1Y1&11r9rw)r.dictreturnr)rz list[str])rIrrr)r]rr^rrdict[str, str])r]rr^rrr)N)rmrr^z$Optional[CallbackManagerForChainRun]rr)rmrr^z)Optional[AsyncCallbackManagerForChainRun]rr)rr)rr rr r{rrr)__name__ __module__ __qualname____firstlineno____doc____annotations__rrrrrr model_configr classmethodr2propertyr7r;rKr`rcrorsrxr|__static_attributes__rwr9r1rrssj'+C $+*!'F 'IIsJ $L (#$*  !!.,))0)  ).))5)  )4=A B B: B  B"BF I I? I  I  &,2 2#2 2  22r9r)r __future__rrBrGr*typingrrlangchain_core._apirlangchain_core.callbacksrrlangchain_core.language_modelsr langchain_core.promptsr pydanticr r langchain.chains.baser langchain.chains.llmr langchain.chains.llm_math.promptrrrwr9r1rsdI"  *=50')3  m V25V2V2r9