super().__init__(
            callback_manager=callback_manager or Settings.callback_manager,
            object_map=object_map,
            verbose=verbose,
        )

documents = [
    Document(
        text="LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models.",
        metadata={
            "tenant": "llama_index",
        },
    ),
    Document(
        text="Qdrant is a vector database & vector similarity search engine.",
        metadata={
            "tenant": "qdrant",
        },
    ),
]

llm = OpenAI(model="gpt-3.5-turbo", temperature=0)

aclient = AsyncQdrantClient(
    url="localhost",
    port=6333,
    prefer_grpc=True,
)

vector_store = QdrantVectorStore(
    collection_name="multitenant",
    aclient=aclient,
    enable_hybrid=True, 
    batch_size=20,
    prefer_grpc=True
)

qdrant_index = VectorStoreIndex.from_vector_store(
    llm=llm,
    vector_store=vector_store,
    embed_model=FastEmbedEmbedding(
        model_name="BAAI/bge-base-en-v1.5"
    ),
    transformations=[splitter],
    use_async=True,
    show_progress=True,
)

for document in documents:
    # qdrant_index.insert(document)
    await qdrant_index.insert(document)

from llama_index.core.evaluation import DatasetGenerator, FaithfulnessEvaluator, RelevancyEvaluator, BatchEvalRunner

evaluator_llm = OpenAI(model="gpt-4", temperature=0)
faithfulness_evaluator = FaithfulnessEvaluator(llm=evaluator_llm)
relevancy_evaluator = RelevancyEvaluator(llm=evaluator_llm)

eval_runner = BatchEvalRunner(
    {"faithfulness": faithfulness_evaluator, "relevancy": relevancy_evaluator},
    workers=6,
    show_progress=True,
)

eval_results = await eval_runner.aevaluate_queries(
    query_engine=hybrid_query_engine, queries=subsapmple_synth_questions
)