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