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# retrievers.py
import pickle
from langchain_cohere import CohereRerank
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_community.vectorstores import FAISS
from langchain.retrievers import EnsembleRetriever
from .config import FAISS_DB_INDEX, BM25_INDEX
def load_bm25_retriever():
with open(BM25_INDEX, "rb") as f:
bm25_retriever = pickle.load(f)
return bm25_retriever.with_config(run_name="BM25Retriever")
def load_faiss_retriever(embeddings):
faiss_db = FAISS.load_local(
FAISS_DB_INDEX, embeddings, allow_dangerous_deserialization=True
)
faiss_retriever = faiss_db.as_retriever(search_type="mmr", search_kwargs={"k": 10})
return faiss_retriever.with_config(run_name="FaissRetriever")
def load_retrievers(embeddings):
faiss_retriever = load_faiss_retriever(embeddings)
bm25_retriever = load_bm25_retriever()
ensemble_retriever = EnsembleRetriever(
retrievers=[bm25_retriever, faiss_retriever],
weights=[0.7, 0.3],
search_type="mmr",
)
compressor = CohereRerank(model="rerank-multilingual-v3.0", top_n=5)
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor,
base_retriever=ensemble_retriever,
).with_config(run_name="ContextualCompressionRetriever")
return compression_retriever
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