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This notebook showcases an agent interacting with a Power BI Dataset. The agent is answering more general questions about a dataset, as well as recover from errors. Note that, as this agent is in active development, all answers might not be correct. It runs against the executequery endpoint, which does not allow deletes.

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Notes

  • It relies on authentication with the azure.identity package, which can be installed with pip install azure-identity. Alternatively you can create the powerbi dataset with a token as a string without supplying the credentials.
  • You can also supply a username to impersonate for use with datasets that have RLS enabled.
  • The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.
  • Testing was done mostly with a gpt-3.5-turbo-instruct model, codex models did not seem to perform ver well.

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Initialization

from azure.identity import DefaultAzureCredential
from langchain_community.agent_toolkits import PowerBIToolkit, create_pbi_agent
from langchain_community.utilities.powerbi import PowerBIDataset
from langchain_openai import ChatOpenAI

fast_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,
)

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Example: describing a table

agent_executor.run("Describe table1")

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Example: simple query on a table

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?")

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Example: running queries

agent_executor.run("How many records are there by dimension1 in table2?")

agent_executor.run("What unique values are there for dimensions2 in table2")

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Example: add your own few-shot prompts

# fictional example
few_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: EVALUATE ROW("Average", AVERAGE(revenue_details[dollar_value]))
----
"""
toolkit = PowerBIToolkit(
    powerbi=PowerBIDataset(
        dataset_id="<dataset_id>",
        table_names=["table1", "table2"],
        credential=DefaultAzureCredential(),
    ),
    llm=smart_llm,
    examples=few_shots,
)
agent_executor = create_pbi_agent(
    llm=fast_llm,
    toolkit=toolkit,
    verbose=True,
)

agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")
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