LLMMathChain Class — langchain Architecture

Architecture documentation for the LLMMathChain class in base.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  54db45e3_8f5a_e755_e4fb_23f335cffa98["LLMMathChain"]
  097a4781_5519_0b5d_6244_98c64eadc0d6["Chain"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|extends| 097a4781_5519_0b5d_6244_98c64eadc0d6
  0851c588_52de_6693_758a_4d949d667f63["base.py"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|defined in| 0851c588_52de_6693_758a_4d949d667f63
  066c6306_0b98_eeac_29fc_e587271af30a["_raise_deprecation()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 066c6306_0b98_eeac_29fc_e587271af30a
  1d7c53f9_9b81_0cdb_79ec_174c53e06d20["input_keys()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 1d7c53f9_9b81_0cdb_79ec_174c53e06d20
  05477227_00e7_14f2_066a_d0f1c05bd9a8["output_keys()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 05477227_00e7_14f2_066a_d0f1c05bd9a8
  26fc76f2_e675_bea5_1a32_d9d29918e38f["_evaluate_expression()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 26fc76f2_e675_bea5_1a32_d9d29918e38f
  58d0dc9a_f0f4_cf10_3a10_96782b144ce0["_process_llm_result()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 58d0dc9a_f0f4_cf10_3a10_96782b144ce0
  bce039c8_3a25_83da_5cea_51b3a420d375["_aprocess_llm_result()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| bce039c8_3a25_83da_5cea_51b3a420d375
  9d0652c9_5cca_c2f8_b9e7_3ca7791915bc["_call()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 9d0652c9_5cca_c2f8_b9e7_3ca7791915bc
  23316940_b9c3_0c81_ee2c_e6fe045917f5["_acall()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 23316940_b9c3_0c81_ee2c_e6fe045917f5
  53e2e6a8_9ba8_358f_dd83_9a084ca9366c["_chain_type()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 53e2e6a8_9ba8_358f_dd83_9a084ca9366c
  037ab4ca_903d_9719_9377_0e54151da77e["from_llm()"]
  54db45e3_8f5a_e755_e4fb_23f335cffa98 -->|method| 037ab4ca_903d_9719_9377_0e54151da77e

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/llm_math/base.py lines 33–315

class LLMMathChain(Chain):
    """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:

        ```bash
        pip install -U langgraph
        ```

        ```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
                    )
                )

            model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
            tools = [calculator]
            model_with_tools = model.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 model_with_tools.ainvoke(state["messages"], config)
                return {"messages": [response]}

            async def acall_model(state: ChainState, config: RunnableConfig):
                response = await model.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()