Source code for langchain.chains.router.multi_prompt

"""Use a single chain to route an input to one of multiple llm chains."""

from __future__ import annotations

from typing import Any, Dict, List, Optional

from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate

from langchain.chains import ConversationChain
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.router.base import MultiRouteChain
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE


[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.multi_prompt.MultiPromptChain.html" # noqa: E501 ), ) 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: .. 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 @property def output_keys(self) -> List[str]: return ["text"]
[docs] @classmethod def from_prompts( cls, llm: BaseLanguageModel, prompt_infos: List[Dict[str, str]], default_chain: Optional[Chain] = None, **kwargs: Any, ) -> MultiPromptChain: """Convenience constructor for instantiating from destination prompts.""" destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos] destinations_str = "\n".join(destinations) router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format( destinations=destinations_str ) router_prompt = PromptTemplate( template=router_template, input_variables=["input"], output_parser=RouterOutputParser(), ) router_chain = LLMRouterChain.from_llm(llm, router_prompt) destination_chains = {} for p_info in prompt_infos: name = p_info["name"] prompt_template = p_info["prompt_template"] prompt = PromptTemplate(template=prompt_template, input_variables=["input"]) chain = LLMChain(llm=llm, prompt=prompt) destination_chains[name] = chain _default_chain = default_chain or ConversationChain(llm=llm, output_key="text") return cls( router_chain=router_chain, destination_chains=destination_chains, default_chain=_default_chain, **kwargs, )