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from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.schema import Document
from typing import List
class Retrieval:
def __init__(self, model_name, max_model_tokens=384):
self.model_name = model_name
self.embeddings = HuggingFaceEmbeddings(
model_name=model_name,
encode_kwargs={"max_length": max_model_tokens, "truncation": True},
)
def create_vector_store(self, chunks: List[Document]):
self.chunks = chunks
# Create FAISS vector store
self.vectorstore = FAISS.from_documents(self.chunks, self.embeddings)
def search(self, query, k=10) -> List[Document]:
# Retrieve top 10 similar chunks
similar_docs = self.vectorstore.similarity_search(query, k)
return similar_docs
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