Embedding.py

import os
from dotenv import load_dotenv
from llama_index.llms.gemini import Gemini
from llama_index.core.memory import ChatMemoryBuffer, ChatSummaryMemoryBuffer
from llama_index.core import Settings, Document,StorageContext , ServiceContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, PromptTemplate, Prompt
from llama_index.embeddings.gemini import GeminiEmbedding
# from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings


def DataSaver() -> bool:
    load_dotenv() 
    API_KEY = os.getenv("GOOGLE_API_KEY")
    os.environ["GOOGLE_API_KEY"] = API_KEY
    
    llm = Gemini()
    documents = SimpleDirectoryReader("./Data").load_data()

    model_name = "models/embedding-001"
    embed_model = GeminiEmbedding(model_name=model_name, api_key=API_KEY)

    service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model, chunk_size=512)
    index = VectorStoreIndex.from_documents(documents,service_context=service_context)

    index.storage_context.persist(persist_dir="Storage")
    return True