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langchain/docs/modules/agents/toolkits/examples/powerbi.ipynb

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"# PowerBI Dataset Agent\n",
"\n",
"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.\n",
"\n",
"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.\n",
"\n",
"### Some notes\n",
"- 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.\n",
"- You can also supply a username to impersonate for use with datasets that have RLS enabled. \n",
"- The toolkit uses a LLM to create the query from the question, the agent uses the LLM for the overall execution.\n",
"- Testing was done mostly with a `text-davinci-003` model, codex models did not seem to perform ver well."
]
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"source": [
"## Initialization"
]
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"source": [
"from langchain.agents.agent_toolkits import create_pbi_agent\n",
"from langchain.agents.agent_toolkits import PowerBIToolkit\n",
"from langchain.utilities.powerbi import PowerBIDataset\n",
"from langchain.llms.openai import AzureOpenAI\n",
"from langchain.agents import AgentExecutor\n",
"from azure.identity import DefaultAzureCredential"
]
},
{
"cell_type": "code",
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"llm = AzureOpenAI(temperature=0, deployment_name=\"text-davinci-003\", verbose=True)\n",
"toolkit = PowerBIToolkit(\n",
" powerbi=PowerBIDataset(None, \"<dataset_id>\", ['table1', 'table2'], DefaultAzureCredential()), \n",
" llm=llm\n",
")\n",
"\n",
"agent_executor = create_pbi_agent(\n",
" llm=llm,\n",
" toolkit=toolkit,\n",
" verbose=True,\n",
")"
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"source": [
"## Example: describing a table"
]
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"source": [
"agent_executor.run(\"Describe table1\")"
]
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"metadata": {},
"source": [
"## Example: simple query on a table\n",
"In this example, the agent actually figures out the correct query to get a row count of the table."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bea76658-a65b-47e2-b294-6d52c5556246",
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"source": [
"agent_executor.run(\"How many records are in table1?\")"
]
},
{
"cell_type": "markdown",
"id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
"metadata": {},
"source": [
"## Example: running queries"
]
},
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"id": "17bea710-4a23-4de0-b48e-21d57be48293",
"metadata": {
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"outputs": [],
"source": [
"agent_executor.run(\"How many records are there by dimension1 in table2?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "474dddda-c067-4eeb-98b1-e763ee78b18c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"agent_executor.run(\"What unique values are there for dimensions2 in table2\")"
]
}
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