import os
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings

def update_vector_db(data_folder="new_data", db_path="chroma_store"):
    embed = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    db = Chroma(persist_directory=db_path, embedding_function=embed)

    docs = []
    for file in os.listdir(data_folder):
        if file.endswith(".txt"):
            loader = TextLoader(os.path.join(data_folder, file), encoding="utf-8")
            docs.extend(loader.load())

    splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    chunks = splitter.split_documents(docs)
    db.add_documents(chunks)
    db.persist()