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from langchain.chains import ConversationalRetrievalChain
from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore
from app_modules.llm_inference import LLMInference
class QAChain(LLMInference):
vectorstore: VectorStore
def __init__(self, vectorstore, llm_loader):
super().__init__(llm_loader)
self.vectorstore = vectorstore
def create_chain(self) -> Chain:
qa = ConversationalRetrievalChain.from_llm(
self.llm_loader.llm,
self.vectorstore.as_retriever(search_kwargs=self.llm_loader.search_kwargs),
max_tokens_limit=self.llm_loader.max_tokens_limit,
return_source_documents=True,
)
return qa
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