o {q i,@sdZddlmZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z ddlmZddlmZdd lmZmZdd lmZdd lmZdd lmZe d dddGdddeZdS)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)ZsincemessageZremovalc@seZdZUdZded<dZded< eZded< d Zd ed <d Z d ed <e dddZ e dde d7ddZed8ddZed8ddZd9dd Zd:d%d&Zd;d(d)Z dd/d0Zed?d1d2Ze efd@d5d6ZdS)A LLMMathChainaChain 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_keyTZforbid)Zarbitrary_types_allowedextrabefore)modevaluesdictreturnrc Cszddl}Wnty}zd}t||d}~wwd|vr?tjdddd|vr?|ddur?|dt}t|d|d |d<|S) 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)clsrr#emsgrr+e/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/langchain/chains/llm_math/base.pyraise_deprecations"   zLLMMathChain.raise_deprecation list[str]cC|jgS)z2Expect input key. :meta private: )rselfr+r+r, input_keyszLLMMathChain.input_keyscCr/)z3Expect output key. :meta private: )rr0r+r+r, output_keysr3zLLMMathChain.output_keys expressionc Csvddl}ztjtjd}t|j|i|d}Wnty3}zd|d|d}t||d}~wwt dd|S) Nr)pir))Z global_dict local_dictzLLMMathChain._evaluate("z") raised error: z4. Please try again with a valid numerical expressionz^\[|\]$) r#mathr6r)revaluatestrip Exception ValueErrorresub)r1r5r#r7outputr)r*r+r+r,_evaluate_expressions" z!LLMMathChain._evaluate_expression llm_output run_managerrdict[str, str]cCs|j|d|jd|}td|tj}|r7|d}||}|jd|jd|j|d|jdd|}n|d r?|}nd |vrMd| d d }n d |}t ||j |iS Ngreen)colorverbosez^```text(.*?)```z Answer: )rHyellowzAnswer: zAnswer:zunknown format from LLM: on_textrHr;r>searchDOTALLgrouprA startswithsplitr=rr1rBrCZ text_matchr5r@rr*r+r+r,_process_llm_results       z LLMMathChain._process_llm_resultrcs|j|d|jdIdH|}td|tj}|rA|d}||}|jd|jdIdH|j|d|jdIdHd|}n|d rI|}nd |vrWd| d d }n d |}t ||j |iSrErLrSr+r+r,_aprocess_llm_results"      z!LLMMathChain._aprocess_llm_resultinputs$Optional[CallbackManagerForChainRun]cCsF|pt}|||j|jj||jdg|d}|||SNz ```output)rstop callbacks)rget_noop_managerrMrrZpredict get_childrTr1rVrCZ _run_managerrBr+r+r,_calls  zLLMMathChain._call)Optional[AsyncCallbackManagerForChainRun]csZ|pt}|||jIdH|jj||jdg|dIdH}|||IdHSrX)rr[rMrrZapredictr\rUr]r+r+r,_acalls  zLLMMathChain._acallcCsdS)NZllm_math_chainr+r0r+r+r, _chain_type+szLLMMathChain._chain_typerkwargscKst||d}|dd|i|S)Nr"rr+r )r(rrrbrr+r+r,from_llm/s zLLMMathChain.from_llm)rrrr)rr.)r5rrr)rBrrCrrrD)rBrrCrrrD)N)rVrDrCrWrrD)rVrDrCr_rrD)rr)rrrr rbrrr)__name__ __module__ __qualname____doc____annotations__rrrrrr Z model_configr classmethodr-propertyr2r4rArTrUr^r`rarcr+r+r+r,rs@  u            r)rg __future__rr9r>r%typingrrZlangchain_core._apirZlangchain_core.callbacksrrZlangchain_core.language_modelsrZlangchain_core.promptsr Zpydanticr r Zlangchain.chains.baser Zlangchain.chains.llmrZ langchain.chains.llm_math.promptrrr+r+r+r,s(