Source code for langchain.chains.router.multi_prompt
"""使用单个链将输入路由到多个llm链中的一个。"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
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]class MultiPromptChain(MultiRouteChain):
"""一个多路线链,使用LLM路由器链来在提示之间进行选择。"""
@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:
"""从目标提示实例化的便利构造函数。"""
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,
)