vectorstore code:
        vector_store = CognitiveSearchVectorStore(
            search_or_index_client=index_client,
            index_name=client_slug,
            filterable_metadata_field_keys=metadata_fields,
                index_management=IndexManagement.CREATE_IF_NOT_EXISTS,
            id_field_key="id",
            chunk_field_key="content",
            embedding_field_key="embedding",
            metadata_string_field_key="li_jsonMetadata",
            doc_id_field_key="li_doc_id",
        )

index create:
        index = VectorStoreIndex.from_documents(
            [], storage_context=self.storage_context,   service_context=self.service_context
        )

from llama_index.schema import Document
from datetime import datetime
from pydantic import BaseModel, Field
from typing import Optional
import uuid

class DocumentObject(BaseModel):
    id: str = Field(default_factory=lambda: str(uuid.uuid4()), alias="_id")
    knowledgeBaseId: str = Field(...)
    document : Document
    createdAt: Optional[datetime] = None 
    updatedAt: Optional[datetime] = None
    deletedAt: Optional[datetime] = None