"""加载具有源链的问题回答。"""
from __future__ import annotations
from typing import Any, Mapping, Optional, Protocol
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_documents.map_rerank import MapRerankDocumentsChain
from langchain.chains.combine_documents.reduce import ReduceDocumentsChain
from langchain.chains.combine_documents.refine import RefineDocumentsChain
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain
from langchain.chains.qa_with_sources import (
map_reduce_prompt,
refine_prompts,
stuff_prompt,
)
from langchain.chains.question_answering.map_rerank_prompt import (
PROMPT as MAP_RERANK_PROMPT,
)
[docs]class LoadingCallable(Protocol):
"""用于加载合并文档链的接口。"""
def __call__(
self, llm: BaseLanguageModel, **kwargs: Any
) -> BaseCombineDocumentsChain:
"""加载合并文档链的可调用函数。"""
def _load_map_rerank_chain(
llm: BaseLanguageModel,
prompt: BasePromptTemplate = MAP_RERANK_PROMPT,
verbose: bool = False,
document_variable_name: str = "context",
rank_key: str = "score",
answer_key: str = "answer",
**kwargs: Any,
) -> MapRerankDocumentsChain:
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
return MapRerankDocumentsChain(
llm_chain=llm_chain,
rank_key=rank_key,
answer_key=answer_key,
document_variable_name=document_variable_name,
**kwargs,
)
def _load_stuff_chain(
llm: BaseLanguageModel,
prompt: BasePromptTemplate = stuff_prompt.PROMPT,
document_prompt: BasePromptTemplate = stuff_prompt.EXAMPLE_PROMPT,
document_variable_name: str = "summaries",
verbose: Optional[bool] = None,
**kwargs: Any,
) -> StuffDocumentsChain:
llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose) # type: ignore[arg-type]
return StuffDocumentsChain(
llm_chain=llm_chain,
document_variable_name=document_variable_name,
document_prompt=document_prompt,
verbose=verbose, # type: ignore[arg-type]
**kwargs,
)
def _load_map_reduce_chain(
llm: BaseLanguageModel,
question_prompt: BasePromptTemplate = map_reduce_prompt.QUESTION_PROMPT,
combine_prompt: BasePromptTemplate = map_reduce_prompt.COMBINE_PROMPT,
document_prompt: BasePromptTemplate = map_reduce_prompt.EXAMPLE_PROMPT,
combine_document_variable_name: str = "summaries",
map_reduce_document_variable_name: str = "context",
collapse_prompt: Optional[BasePromptTemplate] = None,
reduce_llm: Optional[BaseLanguageModel] = None,
collapse_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
token_max: int = 3000,
**kwargs: Any,
) -> MapReduceDocumentsChain:
map_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose) # type: ignore[arg-type]
_reduce_llm = reduce_llm or llm
reduce_chain = LLMChain(llm=_reduce_llm, prompt=combine_prompt, verbose=verbose) # type: ignore[arg-type]
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_chain,
document_variable_name=combine_document_variable_name,
document_prompt=document_prompt,
verbose=verbose, # type: ignore[arg-type]
)
if collapse_prompt is None:
collapse_chain = None
if collapse_llm is not None:
raise ValueError(
"collapse_llm provided, but collapse_prompt was not: please "
"provide one or stop providing collapse_llm."
)
else:
_collapse_llm = collapse_llm or llm
collapse_chain = StuffDocumentsChain(
llm_chain=LLMChain(
llm=_collapse_llm,
prompt=collapse_prompt,
verbose=verbose, # type: ignore[arg-type]
),
document_variable_name=combine_document_variable_name,
document_prompt=document_prompt,
)
reduce_documents_chain = ReduceDocumentsChain(
combine_documents_chain=combine_documents_chain,
collapse_documents_chain=collapse_chain,
token_max=token_max,
verbose=verbose, # type: ignore[arg-type]
)
return MapReduceDocumentsChain(
llm_chain=map_chain,
reduce_documents_chain=reduce_documents_chain,
document_variable_name=map_reduce_document_variable_name,
verbose=verbose, # type: ignore[arg-type]
**kwargs,
)
def _load_refine_chain(
llm: BaseLanguageModel,
question_prompt: BasePromptTemplate = refine_prompts.DEFAULT_TEXT_QA_PROMPT,
refine_prompt: BasePromptTemplate = refine_prompts.DEFAULT_REFINE_PROMPT,
document_prompt: BasePromptTemplate = refine_prompts.EXAMPLE_PROMPT,
document_variable_name: str = "context_str",
initial_response_name: str = "existing_answer",
refine_llm: Optional[BaseLanguageModel] = None,
verbose: Optional[bool] = None,
**kwargs: Any,
) -> RefineDocumentsChain:
initial_chain = LLMChain(llm=llm, prompt=question_prompt, verbose=verbose) # type: ignore[arg-type]
_refine_llm = refine_llm or llm
refine_chain = LLMChain(llm=_refine_llm, prompt=refine_prompt, verbose=verbose) # type: ignore[arg-type]
return RefineDocumentsChain(
initial_llm_chain=initial_chain,
refine_llm_chain=refine_chain,
document_variable_name=document_variable_name,
initial_response_name=initial_response_name,
document_prompt=document_prompt,
verbose=verbose, # type: ignore[arg-type]
**kwargs,
)
[docs]def load_qa_with_sources_chain(
llm: BaseLanguageModel,
chain_type: str = "stuff",
verbose: Optional[bool] = None,
**kwargs: Any,
) -> BaseCombineDocumentsChain:
"""加载一个带有来源链的问答系统。
参数:
llm: 在链中使用的语言模型。
chain_type: 要使用的文档组合链的类型。应该是"stuff"、"map_reduce"、"refine"和"map_rerank"中的一个。
verbose: 链是否应以详细模式运行。请注意，这适用于构成最终链的所有链。
返回:
用于带有来源的问答系统的链。
"""
loader_mapping: Mapping[str, LoadingCallable] = {
"stuff": _load_stuff_chain,
"map_reduce": _load_map_reduce_chain,
"refine": _load_refine_chain,
"map_rerank": _load_map_rerank_chain,
}
if chain_type not in loader_mapping:
raise ValueError(
f"Got unsupported chain type: {chain_type}. "
f"Should be one of {loader_mapping.keys()}"
)
_func: LoadingCallable = loader_mapping[chain_type]
return _func(llm, verbose=verbose, **kwargs)