vectorize_documents.py

from langchain_community.document_loaders import UnstructuredFileLoader
from langchain_community.document_loaders import DirectoryLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma


# # Define a function to perform vectorization
def vectorize_documents():
    embeddings = HuggingFaceEmbeddings()

    loader = DirectoryLoader(
        path="Data_1",
        glob="./*.pdf",
        loader_cls=UnstructuredFileLoader
    )

    documents = loader.load()

    # Splitting the text and creating chunks of these documents.
    text_splitter = CharacterTextSplitter(
        chunk_size=2000,
        chunk_overlap=500
    )

    text_chunks = text_splitter.split_documents(documents)

        # Store in Chroma vector DB
    vectordb = Chroma.from_documents(
        documents=text_chunks,
        embedding=embeddings,
        persist_directory="vector_db_dir_notes_ai"
    )

    print("Documents Vectorized and saved in VectorDB")



# Expose embeddings if needed
embeddings = HuggingFaceEmbeddings()


# Main guard to prevent execution on import
if __name__ == "__main__":
    vectorize_documents()



# # Define a function to perform vectorization
# def vectorize_documents():
#     # Loading the embedding model
#     embeddings = HuggingFaceEmbeddings()

#     loader = DirectoryLoader(
#         path="Data",
#         glob="./*.pdf",
#         loader_cls=UnstructuredFileLoader
#     )

#     documents = loader.load()

#     # Splitting the text and creating chunks of these documents.
#     text_splitter = CharacterTextSplitter(
#         chunk_size=2000,
#         chunk_overlap=500
#     )

#     text_chunks = text_splitter.split_documents(documents)

#     # Store in Chroma vector DB
#     vectordb = Chroma.from_documents(
#         documents=text_chunks,
#         embedding=embeddings,
#         persist_directory="vector_db_dir"
#     )

#     print("Documents Vectorized and saved in VectorDB")


# # Expose embeddings if needed
# embeddings = HuggingFaceEmbeddings()


# # Main guard to prevent execution on import
# if __name__ == "__main__":
#     vectorize_documents()