Source code for langchain.chains.qa_with_sources.retrieval

"""在索引上使用来源进行问答。"""

from typing import Any, Dict, List

from langchain_core.callbacks import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain_core.documents import Document
from langchain_core.pydantic_v1 import Field
from langchain_core.retrievers import BaseRetriever

from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.qa_with_sources.base import BaseQAWithSourcesChain


[docs]class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain): """在索引上使用来源进行问答。""" retriever: BaseRetriever = Field(exclude=True) """连接的索引。""" reduce_k_below_max_tokens: bool = False """根据令牌限制减少从存储中返回的结果数量""" max_tokens_limit: int = 3375 """根据令牌限制从存储返回的文档, 仅对StuffDocumentChain强制执行,如果reduce_k_below_max_tokens设置为true。""" def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]: num_docs = len(docs) if self.reduce_k_below_max_tokens and isinstance( self.combine_documents_chain, StuffDocumentsChain ): tokens = [ self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) for doc in docs ] token_count = sum(tokens[:num_docs]) while token_count > self.max_tokens_limit: num_docs -= 1 token_count -= tokens[num_docs] return docs[:num_docs] def _get_docs( self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun ) -> List[Document]: question = inputs[self.question_key] docs = self.retriever.invoke( question, config={"callbacks": run_manager.get_child()} ) return self._reduce_tokens_below_limit(docs) async def _aget_docs( self, inputs: Dict[str, Any], *, run_manager: AsyncCallbackManagerForChainRun ) -> List[Document]: question = inputs[self.question_key] docs = await self.retriever.ainvoke( question, config={"callbacks": run_manager.get_child()} ) return self._reduce_tokens_below_limit(docs) @property def _chain_type(self) -> str: """返回链的类型。""" return "retrieval_qa_with_sources_chain"