> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-lsapi-1779323176-451247e.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Powerbi toolkit integration

> Integrate with the Powerbi toolkit using LangChain Python.

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](https://learn.microsoft.com/en-us/rest/api/power-bi/datasets/execute-queries), which does not allow deletes.

### 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.

## Initialization

```python theme={null}
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
```

```python theme={null}
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,
)
```

## Example: describing a table

```python theme={null}
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.

```python theme={null}
agent_executor.run("How many records are in table1?")
```

## Example: running queries

```python theme={null}
agent_executor.run("How many records are there by dimension1 in table2?")
```

```python theme={null}
agent_executor.run("What unique values are there for dimensions2 in table2")
```

## Example: add your own few-shot prompts

```python theme={null}
# 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,
)
```

```python theme={null}
agent_executor.run("What was the maximum of value in revenue in dollars in 2022?")
```

***

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