code: import asyncio
  
  import chromadb
  import os
  
  from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
  from llama_index.vector_stores.chroma import ChromaVectorStore
  from llama_index.embeddings.huggingface import HuggingFaceEmbedding
  from traceloop.sdk import Traceloop
  
  os.environ["TOKENIZERS_PARALLELISM"] = "false"
  
  Traceloop.init(app_name="llama_index_example")
  
  chroma_client = chromadb.EphemeralClient()
  chroma_collection = chroma_client.create_collection("quickstart")
  
  # define embedding function
  embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
  
  # load documents
  documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
  
  # set up ChromaVectorStore and load in data
  vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
  storage_context = StorageContext.from_defaults(vector_store=vector_store)
  index = VectorStoreIndex.from_documents(
      documents, storage_context=storage_context, embed_model=embed_model
  )
  
  
  async def main():
      # Query Data
      query_engine = index.as_query_engine()
      response = await query_engine.aquery("What did the author do growing up?")
  
      print(response)
  
  
  if __name__ == "__main__":
      asyncio.run(main())
  
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