text: import AgentExecutorfrom azure.identity import DefaultAzureCredentialfast_llm = ChatOpenAI(    temperature=0.5, max_tokens=1000, model_name="gpt-3.5-turbo", verbose=True)smart_llm = ChatOpenAI(temperature=0, max_tokens=100, model_name="gpt-4", verbose=True)toolkit = PowerBIToolkit(    powerbi=PowerBIDataset(        dataset_id="<dataset_id>",        table_names=["table1", "table2"],        credential=DefaultAzureCredential(),    ),    llm=smart_llm,)agent_executor = create_pbi_agent(    llm=fast_llm,    toolkit=toolkit,    verbose=True,)Example: describing a tableâ€‹agent_executor.run("Describe table1")Example: simple query on a tableâ€‹In this example, the agent actually figures out the correct query to get a row count of the table.agent_executor.run("How many records are in table1?")Example: running queriesâ€‹agent_executor.run("How many records are there by dimension1 in table2?")agent_executor.run("What unique values are there for dimensions2 in table2")Example: add your own few-shot promptsâ€‹# fictional examplefew_shots = """Question: How many rows are in the table revenue?DAX: EVALUATE ROW("Number of rows", COUNTROWS(revenue_details))----Question: How many rows are in the table revenue where year is not empty?DAX: EVALUATE ROW("Number of rows", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> "")))----Question: What was the average of value in revenue in dollars?DAX: