def get_query_engine_supabase(llm, filename):
    # use Huggingface embeddings

    print("-----LOGGING----- start query_engine - SUPABASE")
    embed_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-mpnet-base-v2"
    )
    # create a service context
    service_context = ServiceContext.from_defaults(
        llm=llm,
        embed_model=embed_model,
    )

    # set_global_service_context(service_context)

    # # load documents
    documents = SimpleDirectoryReader(
        input_files = [f"./docs/{filename}"]
    ).load_data()

    DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>" # I HAVE THESE SET

    print("-----LOGGING----- initializing vector_store")

    vector_store = SupabaseVectorStore(
        postgres_connection_string=DB_CONNECTION, 
        collection_name='reviewIndexes',
        dimension='768',
    )
    # TRIED dimension=768 ABOVE, NO LUCK

    print("-----LOGGING----- initialized vector_store")

    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    print("-----LOGGING----- initialized storage_context") # IT DOES EXECUTE TILL HERE

    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context, service_context=service_context)

    print("-----LOGGING----- generated index:",index)

    # set up query engine
    query_engine = index.as_query_engine(
        streaming=True,
        similarity_top_k=1
    )
    return query_engine

 raise ValueError('expected %d dimensions, not %d' % (dim, len(value)))
sqlalchemy.exc.StatementError: (builtins.ValueError) expected 1536 dimensions, not 768
[SQL: INSERT INTO vecs."reviewIndexes" (id, vec, metadata) VALUES (%(id_m0)s, %(vec_m0)s,

def get_query_engine(llm, filename):
    # use Huggingface embeddings

    embed_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-mpnet-base-v2"
    )
    # create a service context
    service_context = ServiceContext.from_defaults(
        llm=llm,
        embed_model=embed_model,
    )

    # set_global_service_context(service_context)

    # # load documents
    documents = SimpleDirectoryReader(
        input_files = [f"./docs/{filename}"]
    ).load_data()

    # documents = SimpleDirectoryReader("./test_docs").load_data()
    
    # # create vector store index
    index = VectorStoreIndex.from_documents(documents, service_context=service_context)

    index.storage_context.persist("./test_indexcache_one/")

    # set up query engine
    query_engine = index.as_query_engine(
        streaming=True,
        similarity_top_k=1
    )
    return query_engine