#!/usr/bin/env python
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS


def create_vector_db():
    """
    This function creates a vector database
    """
    # Load CSV data
    loader = CSVLoader(file_path="mylib/combined.csv")
    data = loader.load()
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
    texts = text_splitter.split_documents(data)
    embeddings = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )
    db = FAISS.from_documents(texts, embeddings)
    return db.save_local("mylib/vector_db")


if __name__ == "__main__":
    vector_db = create_vector_db()
    print("Vector database created and indexed.")