global_handler.start_trace_params(user_id=request.email_id, tags=[env.ENVIRONMENT, "support-bot"])
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'start_trace_params'

python 
import os
from dotenv import load_dotenv
import nest_asyncio

load_dotenv()
nest_asyncio.apply()

from llama_index.core.evaluation import RetrieverEvaluator

Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002", embed_batch_size=10)
llm = OpenAI(model="gpt-4o")

client = qdrant_client.QdrantClient(
    url=os.getenv("QDRANT_URI"), api_key=os.getenv("QDRANT_API_KEY")
)
vector_store = QdrantVectorStore(client=client, collection_name="mlofo-loan-officer-july")
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

qa_dataset = EmbeddingQAFinetuneDataset.from_json("pg_eval_dataset.json")


metrics = ["mrr", "hit_rate"]

retriever_evaluator = RetrieverEvaluator.from_metric_names(
    metrics, retriever=index.as_retriever(similarity_top_k=2)
)

sample_id, sample_query = list(qa_dataset.queries.items())[0]
sample_expected = qa_dataset.relevant_docs[sample_id]

eval_result = retriever_evaluator.evaluate(sample_query, sample_expected)
print(eval_result)

nodes = vector_store.get_nodes()

qa_dataset = generate_question_context_pairs(
    nodes, llm=llm, num_questions_per_chunk=2
)

def get_chat_engine() -> "CondensePlusContextChatEngine":

    Settings.llm = OpenAI(model="gpt-4o", temperature=0.1)
    
    index =  VectorStoreIndex.from_vector_store(vector_store=vector_store)
    retriever = index.as_retriever(similarity_top_k=3)

    retriever = QueryFusionRetriever(
        [retriever],
        similarity_top_k=4,
        num_queries=4,
        mode="reciprocal_rerank",
        use_async=True,
        verbose=True,
        query_gen_prompt=BOT_QUERY_GEN_PROMPT
    )
    chat_engine = CondensePlusContextChatEngine.from_defaults(retriever=retriever, system_prompt=SUPPORT_BOT_SYSTEM_PROMPT, streaming=True)
    return chat_engine

async def chat(request: ChatRequestBody):
    try:
       
        engine = get_chat_engine()
        response_stream = engine.stream_chat(message, chat_history=history)
        return StreamingResponse(
            stream_generator(response_stream, request.history, request.timezone),
            media_type="application/x-ndjson",
        )

    except Exception as e:
        traceback.print_exc()
        raise HTTPException(
            status_code=500, detail=f"An error occurred while processing the request. {str(e)}"
        ) from e

llm = AzureOpenAI(
    model=os.getenv("AOAI_COMPLETION_MODEL"),
    deployment_name=os.getenv("AOAI_DEPLOYMENT_NAME_COMPLETION"),
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    azure_endpoint=os.getenv("AOAI_ENDPOINT"),
    api_version=os.getenv("AOAI_API_VERSION"),
)

query_engine = index.as_query_engine(
    similarity_top_k=2, sparse_top_k=12, vector_store_query_mode="hybrid"
)

def get_chat_engine() -> "CondensePlusContextChatEngine":
    Settings.llm = OpenAI(model="gpt-4o", temperature=0.1)
    index = VectorStoreIndex.from_vector_store(vector_store=vector_store)

    retriever = QueryFusionRetriever(
        [
            index.as_retriever(similarity_top_k=3),
            BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=2, verbose=True),
        ],
        similarity_top_k=2,
        num_queries=2,
        mode="reciprocal_rerank",
        use_async=False,
        verbose=True,
        query_gen_prompt=BOT_QUERY_GEN_PROMPT,
    )
    chat_engine = CondensePlusContextChatEngine.from_defaults(
        retriever=retriever, system_prompt=SUPPORT_BOT_SYSTEM_PROMPT, streaming=True
    )
    return chat_engine