zenith / src /chains /retrieval.py
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from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
DB_FAISS_PATH = 'vectorstore.faiss'
def retrieve_documents(query: str, k: int = 5):
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs={'device': 'cpu'})
db = FAISS.load_local(DB_FAISS_PATH, embeddings)
results = db.similarity_search(query, k=k)
return results
def get_relevant_documents(query: str):
documents = retrieve_documents(query)
return documents