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from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader, DirectoryLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
DATA_PATH="data/"
DB_FAISS_PATH="vectorstores/db_faiss"
def create_vector_db():
loader = DirectoryLoader(DATA_PATH, glob='*.pdf', loader_cls=PyPDFLoader)
documents =loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
model_kwargs = {'device': 'cpu'})
db = FAISS.from_documents(texts, embeddings)
db.save_local(DB_FAISS_PATH)
if __name__ == "__main__":
create_vector_db() |