code: from pathlib import Path
  from llama_index import download_loader, LLMPredictor, ServiceContext, VectorStoreIndex
  from llama_index.vector_stores import MilvusVectorStore
  from llama_index.readers import PDFReader
  from llama_index import StorageContext
  from pymilvus import MilvusClient
  import os
  
  # Define constants for Milvus configuration
  MILVUS_HOST = os.environ.get("MILVUS_HOST", "10.97.151.193")
  MILVUS_PORT = os.environ.get("MILVUS_PORT", "19530")
  MILVUS_URI = f"http://{MILVUS_HOST}:{MILVUS_PORT}"
  
  # Initialize PDFReader
  pdf_reader = PDFReader()
  
  # Load documents from a PDF file
  document_path = Path('ingestion/keiichi_tsuchiya.pdf')  #ToDo: load from S3 instead of local
  documents = pdf_reader.load_data(file=document_path)
  
  # Create an LLMPredictor with default parameters
  llm_predictor = LLMPredictor(llm=None)
  
  # Create a ServiceContext with LLMPredictor
  service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
  
  # Initialize a MilvusVectorStore with Milvus server configuration
  vector_store = MilvusVectorStore(
      uri=MILVUS_URI,
      dim=384,
      use_secure=False
  )
  
  # Create a StorageContext with the MilvusVectorStore
  storage_context = StorageContext.from_defaults(vector_store=vector_store)
  
  # Create a VectorStoreIndex from the loaded documents
  index = VectorStoreIndex.from_documents(
      documents=documents,
      overwrite=True,  # Set to False if you don't want to overwrite the index
      service_context=service_context,
      storage_context=storage_context
  )
  
  # You can now perform queries with the index
  # For example:
  # result = index.query("What communication protocol is used in Pymilvus for communicating with Milvus?")
  # print(result)
  
apis: llama_index.VectorStoreIndex.from_documents llama_index.LLMPredictor llama_index.ServiceContext.from_defaults llama_index.StorageContext.from_defaults llama_index.readers.PDFReader llama_index.vector_stores.MilvusVectorStore
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