Hi everyone, I am playing around with

class QA():
    def __init__(self):
        self.CHUNK_SIZE = 256
        self.MODEL_NAME = "gpt-3.5-turbo"
        self.NUM_OF_PRODUCT_REQUIRED_PAGES = 5
        self.SIMILARITY_TOP_K = 2

    def get_model(self):
        token_counter = TokenCountingHandler(
            tokenizer=tiktoken.encoding_for_model(self.MODEL_NAME).encode
        )
        callback_manager = CallbackManager([token_counter])
        llm_predictor =  Bedrock(model="anthropic.claude-v2", profile_name='ABC'
)

        # llm_predictor = OpenAI(temperature=0, model=self.MODEL_NAME)
        # service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, chunk_size=self.CHUNK_SIZE)
        service_context = ServiceContext.from_defaults(llm=llm_predictor, chunk_size=self.CHUNK_SIZE, callback_manager=callback_manager)
        set_global_service_context(service_context)
        return service_context, token_counter

from llama_index.callbacks import TokenCountingHandler, CallbackManager
from llama_index import ServiceContext, SimpleDirectoryReader, VectorStoreIndex

documents = SimpleDirectoryReader("./docs/examples/data/paul_graham").load_data()

service_context = ServiceContext.from_defaults(
    callback_manager=CallbackManager([TokenCountingHandler()]), chunk_size=256
)

index = VectorStoreIndex.from_documents(documents, service_context=service_context)

query_engine = index.as_query_engine(similarity_top_k=2)

response = query_engine.query("What is the best way to raise money for a startup?")

token_counter = TokenCountingHandler(
    tokenizer=tiktoken.encoding_for_model( "gpt-3.5-turbo").encode
)
callback_manager = CallbackManager([token_counter])
llm_predictor = Bedrock(model="anthropic.claude-v2")

service_context = ServiceContext.from_defaults(llm=llm_predictor, chunk_size=256, callback_manager=callback_manager)
set_global_service_context(service_context)

pages_text = ['Investors write checks when the idea they hear is compelling, when they are persuaded that the team of founders can realize its vision, and that the opportunity described is real and sufficiently large. When founders are ready to tell this story, they can raise money. And usually when you can raise money, you should.']
documents = [Document(text=t) for t in pages_text]

custom_llm_index = VectorStoreIndex.from_documents(documents, service_context=service_context)
custom_llm_query_engine = custom_llm_index.as_query_engine(similarity_top_k=2)
question = "how to raise money for a startup"
response = custom_llm_query_engine.query(question)
token_counter.llm_token_counts