MultiPromptChain Class — langchain Architecture

Architecture documentation for the MultiPromptChain class in multi_prompt.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  3ae7eef8_cc33_69bc_5027_f4aeaf44c219["MultiPromptChain"]
  741a7f73_f662_9d17_6b91_e7e8709f4338["MultiRouteChain"]
  3ae7eef8_cc33_69bc_5027_f4aeaf44c219 -->|extends| 741a7f73_f662_9d17_6b91_e7e8709f4338
  cb6f3b1f_efd0_9cbd_ea23_b31bf637a289["multi_prompt.py"]
  3ae7eef8_cc33_69bc_5027_f4aeaf44c219 -->|defined in| cb6f3b1f_efd0_9cbd_ea23_b31bf637a289
  79ae7ae4_18fa_79aa_3fdb_3aef4f5c8dc6["output_keys()"]
  3ae7eef8_cc33_69bc_5027_f4aeaf44c219 -->|method| 79ae7ae4_18fa_79aa_3fdb_3aef4f5c8dc6
  d887c8ae_ab3e_431f_44fe_a37a7abaf796["from_prompts()"]
  3ae7eef8_cc33_69bc_5027_f4aeaf44c219 -->|method| d887c8ae_ab3e_431f_44fe_a37a7abaf796

Relationship Graph

Source Code

libs/langchain/langchain_classic/chains/router/multi_prompt.py lines 33–190

class MultiPromptChain(MultiRouteChain):
    """A multi-route chain that uses an LLM router chain to choose amongst prompts.

    This class is deprecated. See below for a replacement, which offers several
    benefits, including streaming and batch support.

    Below is an example implementation:

        ```python
        from operator import itemgetter
        from typing import Literal

        from langchain_core.output_parsers import StrOutputParser
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_core.runnables import RunnableConfig
        from langchain_openai import ChatOpenAI
        from langgraph.graph import END, START, StateGraph
        from typing_extensions import TypedDict

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

        # Define the prompts we will route to
        prompt_1 = ChatPromptTemplate.from_messages(
            [
                ("system", "You are an expert on animals."),
                ("human", "{input}"),
            ]
        )
        prompt_2 = ChatPromptTemplate.from_messages(
            [
                ("system", "You are an expert on vegetables."),
                ("human", "{input}"),
            ]
        )

        # Construct the chains we will route to. These format the input query
        # into the respective prompt, run it through a chat model, and cast
        # the result to a string.
        chain_1 = prompt_1 | model | StrOutputParser()
        chain_2 = prompt_2 | model | StrOutputParser()


        # Next: define the chain that selects which branch to route to.
        # Here we will take advantage of tool-calling features to force
        # the output to select one of two desired branches.
        route_system = "Route the user's query to either the animal "
        "or vegetable expert."
        route_prompt = ChatPromptTemplate.from_messages(
            [
                ("system", route_system),
                ("human", "{input}"),
            ]
        )


        # Define schema for output:
        class RouteQuery(TypedDict):
            \"\"\"Route query to destination expert.\"\"\"

            destination: Literal["animal", "vegetable"]


        route_chain = route_prompt | model.with_structured_output(RouteQuery)


        # For LangGraph, we will define the state of the graph to hold the query,
        # destination, and final answer.
        class State(TypedDict):
            query: str
            destination: RouteQuery
            answer: str


        # We define functions for each node, including routing the query:
        async def route_query(state: State, config: RunnableConfig):
            destination = await route_chain.ainvoke(state["query"], config)
            return {"destination": destination}


        # And one node for each prompt
        async def prompt_1(state: State, config: RunnableConfig):