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PowerBI Dataset Agent
 Contents 
Some notes
Initialization
Example: describing a table
Example: simple query on a table
Example: running queries
Example: add your own few-shot prompts
PowerBI Dataset Agent#
This notebook showcases an agent designed to interact with a Power BI Dataset. The agent is designed to answer 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.
Some 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 text-davinci-003 model, codex models did not seem to perform ver well.
Initialization#
from langchain.agents.agent_toolkits import create_pbi_agent
from langchain.agents.agent_toolkits import PowerBIToolkit
from langchain.utilities.powerbi import PowerBIDataset
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentExecutor
from azure.identity import DefaultAzureCredential
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(
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 table#
agent_executor.run("Describe table1")
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?")
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")
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(
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?")

PlayWright Browser Toolkit

Python Agent
 Contents
  
Some notes
Initialization
Example: describing a table
Example: simple query on a table
Example: running queries
Example: add your own few-shot prompts
By Harrison Chase