Source code for langchain.chains.router.llm_router

"""Base classes for LLM-powered router chains."""

from __future__ import annotations

from typing import Any, Dict, List, Optional, Type, cast

from langchain_core._api import deprecated
from langchain_core.callbacks import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import root_validator
from langchain_core.utils.json import parse_and_check_json_markdown

from langchain.chains import LLMChain
from langchain.chains.router.base import RouterChain


[docs]@deprecated(
    since="0.2.12",
    removal="1.0",
    message=(
        "Use RunnableLambda to select from multiple prompt templates. See example "
        "in API reference: "
        "https://api.python.langchain.com/en/latest/chains/langchain.chains.router.llm_router.LLMRouterChain.html"  # noqa: E501
    ),
)
class LLMRouterChain(RouterChain):
    """A router chain that uses an LLM chain to perform routing.

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

    Below is an example implementation:

        .. code-block:: python

            from operator import itemgetter
            from typing import Literal
            from typing_extensions import TypedDict

            from langchain_core.output_parsers import StrOutputParser
            from langchain_core.prompts import ChatPromptTemplate
            from langchain_core.runnables import RunnableLambda, RunnablePassthrough
            from langchain_openai import ChatOpenAI

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

            prompt_1 = ChatPromptTemplate.from_messages(
                [
                    ("system", "You are an expert on animals."),
                    ("human", "{query}"),
                ]
            )
            prompt_2 = ChatPromptTemplate.from_messages(
                [
                    ("system", "You are an expert on vegetables."),
                    ("human", "{query}"),
                ]
            )

            chain_1 = prompt_1 | llm | StrOutputParser()
            chain_2 = prompt_2 | llm | StrOutputParser()

            route_system = "Route the user's query to either the animal or vegetable expert."
            route_prompt = ChatPromptTemplate.from_messages(
                [
                    ("system", route_system),
                    ("human", "{query}"),
                ]
            )


            class RouteQuery(TypedDict):
                \"\"\"Route query to destination.\"\"\"
                destination: Literal["animal", "vegetable"]


            route_chain = (
                route_prompt
                | llm.with_structured_output(RouteQuery)
                | itemgetter("destination")
            )

            chain = {
                "destination": route_chain,  # "animal" or "vegetable"
                "query": lambda x: x["query"],  # pass through input query
            } | RunnableLambda(
                # if animal, chain_1. otherwise, chain_2.
                lambda x: chain_1 if x["destination"] == "animal" else chain_2,
            )

            chain.invoke({"query": "what color are carrots"})
    """  # noqa: E501

    llm_chain: LLMChain
    """LLM chain used to perform routing"""

    @root_validator(pre=False, skip_on_failure=True)
    def validate_prompt(cls, values: dict) -> dict:
        prompt = values["llm_chain"].prompt
        if prompt.output_parser is None:
            raise ValueError(
                "LLMRouterChain requires base llm_chain prompt to have an output"
                " parser that converts LLM text output to a dictionary with keys"
                " 'destination' and 'next_inputs'. Received a prompt with no output"
                " parser."
            )
        return values

    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the LLM chain prompt expects.

        :meta private:
        """
        return self.llm_chain.input_keys

    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
        super()._validate_outputs(outputs)
        if not isinstance(outputs["next_inputs"], dict):
            raise ValueError

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()

        prediction = self.llm_chain.predict(callbacks=callbacks, **inputs)
        output = cast(
            Dict[str, Any],
            self.llm_chain.prompt.output_parser.parse(prediction),
        )
        return output

    async def _acall(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        callbacks = _run_manager.get_child()
        output = cast(
            Dict[str, Any],
            await self.llm_chain.apredict_and_parse(callbacks=callbacks, **inputs),
        )
        return output

[docs]    @classmethod
    def from_llm(
        cls, llm: BaseLanguageModel, prompt: BasePromptTemplate, **kwargs: Any
    ) -> LLMRouterChain:
        """Convenience constructor."""
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, **kwargs)


[docs]class RouterOutputParser(BaseOutputParser[Dict[str, str]]):
    """Parser for output of router chain in the multi-prompt chain."""

    default_destination: str = "DEFAULT"
    next_inputs_type: Type = str
    next_inputs_inner_key: str = "input"

[docs]    def parse(self, text: str) -> Dict[str, Any]:
        try:
            expected_keys = ["destination", "next_inputs"]
            parsed = parse_and_check_json_markdown(text, expected_keys)
            if not isinstance(parsed["destination"], str):
                raise ValueError("Expected 'destination' to be a string.")
            if not isinstance(parsed["next_inputs"], self.next_inputs_type):
                raise ValueError(
                    f"Expected 'next_inputs' to be {self.next_inputs_type}."
                )
            parsed["next_inputs"] = {self.next_inputs_inner_key: parsed["next_inputs"]}
            if (
                parsed["destination"].strip().lower()
                == self.default_destination.lower()
            ):
                parsed["destination"] = None
            else:
                parsed["destination"] = parsed["destination"].strip()
            return parsed
        except Exception as e:
            raise OutputParserException(
                f"Parsing text\n{text}\n raised following error:\n{e}"
            )