train/products.py

def train():    

    from langchain_community.document_loaders.csv_loader import CSVLoader
    from langchain.text_splitter import CharacterTextSplitter
    from langchain_openai import OpenAIEmbeddings
    from langchain_community.vectorstores.faiss import FAISS

    documents = CSVLoader(file_path="train/posts.csv").load()

    # Split document in chunks
    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=30)
    docs = text_splitter.split_documents(documents=documents)

    embeddings = OpenAIEmbeddings()
    # Create vectors
    vectorstore = FAISS.from_documents(docs, embeddings)
    # Persist the vectors locally on disk
    vectorstore.save_local("_rise_product_db");

    return {"trained":"success"}