"""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}"
)
