How can I check cache existence before retrieval when using RetrievalQA of langchain?

[SleepingSkipper]

The existence of cache is checked after document retrieval is done (after the _get_docs method of RetrievalQA class is called).
https://github.com/langchain-ai/langchain/blob/b379c5f9c89199370d9175ce6ee34d469567e533/libs/langchain/langchain/chains/retrieval_qa/base.py#L139

To be more efficient, I would like to check the existence of cache before retrieval. Any idea how to implement this?

Here is my code. I referenced this page: https://gptcache.readthedocs.io/en/latest/bootcamp/langchain/question_answering.html#init-for-similar-match-cache

from gptcache.manager import manager_factory
from gptcache.embedding import Huggingface
from gptcache import cache
from gptcache.adapter.langchain_models import LangChainLLMs
from gptcache.similarity_evaluation.distance import SearchDistanceEvaluation
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA
import hashlib

os.environ['OPENAI_API_KEY'] ="openaikey"

# prepare database and retriever from the pdf file
loader = PyMuPDFLoader(filename)
docs = loader.load()
text_splitter = CharacterTextSplitter()
documents= text_splitter.split_documents(docs)

db_embeddings =  OpenAIEmbeddings()
db = Chroma.from_documents(documents, 
                           db_embeddings, 
                           persist_directory=None)
chroma_retriever = db.as_retriever()


# prepare RetrievalQA object from the retriever
prompt_template = """Use the following pieces of context and source information to answer the question at the end.
If you cannot find the answer, do not try to make up an answer, just say that you don't know.
{context}

Question: {question}
answer in Japanese:"""

PROMPT = PromptTemplate(
    template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
llm = LangChainLLMs(llm=OpenAI(temperature=0.0, model_name="gpt-3.5-turbo",request_timeout=60,
                max_retries=3))
qa = RetrievalQA.from_chain_type(
    llm = llm,
    chain_type_kwargs = chain_type_kwargs,
    retriever=chroma_retrievers,
    return_source_documents=True,
    )

# prepare cache for the pdf file
def get_content_func(data, **_):
    return data.get("prompt").split("Question")[-1]

cache_embedding = Huggingface(model_name)
data_manager =manager_factory(
    "sqlite,faiss",
    data_dir = f"./data/similarity_cache/similar_cache_{filehash}",    # Separate directories for each file
    vector_params = {"dimension": cache_embedding.dimension}
)
cache.init(
    pre_embedding_func=get_content_func,
    embedding_func = cache_embedding.to_embeddings,
    data_manager=data_manager,
    similarity_evaluation=SearchDistanceEvaluation(),
    )