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,
)