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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# 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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],
"metadata": {},
"attachments": {}
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},
{
"cell_type": "markdown",
"source": [
"## Initialization"
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],
"metadata": {
"tags": []
}
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},
{
"cell_type": "code",
"execution_count": null,
"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",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.agents import AgentExecutor\n",
"from azure.identity import DefaultAzureCredential"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
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"fast_llm = ChatOpenAI(temperature=0.5, max_tokens=1000, model_name=\"gpt-3.5-turbo\", verbose=True)\n",
"smart_llm = ChatOpenAI(temperature=0, max_tokens=100, model_name=\"gpt-4\", verbose=True)\n",
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"\n",
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"toolkit = PowerBIToolkit(\n",
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" powerbi=PowerBIDataset(dataset_id=\"<dataset_id>\", table_names=['table1', 'table2'], credential=DefaultAzureCredential()), \n",
" llm=smart_llm\n",
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")\n",
"\n",
"agent_executor = create_pbi_agent(\n",
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" llm=fast_llm,\n",
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" toolkit=toolkit,\n",
" verbose=True,\n",
")"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "markdown",
"source": [
"## Example: describing a table"
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],
"metadata": {}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"agent_executor.run(\"Describe table1\")"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "markdown",
"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."
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],
"metadata": {},
"attachments": {}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"agent_executor.run(\"How many records are in table1?\")"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "markdown",
"source": [
"## Example: running queries"
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],
"metadata": {}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"agent_executor.run(\"How many records are there by dimension1 in table2?\")"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"agent_executor.run(\"What unique values are there for dimensions2 in table2\")"
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],
"outputs": [],
"metadata": {
"tags": []
}
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},
{
"cell_type": "markdown",
"source": [
"## Example: add your own few-shot prompts"
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],
"metadata": {},
"attachments": {}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"#fictional example\n",
"few_shots = \"\"\"\n",
"Question: How many rows are in the table revenue?\n",
"DAX: EVALUATE ROW(\"Number of rows\", COUNTROWS(revenue_details))\n",
"----\n",
"Question: How many rows are in the table revenue where year is not empty?\n",
"DAX: EVALUATE ROW(\"Number of rows\", COUNTROWS(FILTER(revenue_details, revenue_details[year] <> \"\")))\n",
"----\n",
"Question: What was the average of value in revenue in dollars?\n",
"DAX: EVALUATE ROW(\"Average\", AVERAGE(revenue_details[dollar_value]))\n",
"----\n",
"\"\"\"\n",
"toolkit = PowerBIToolkit(\n",
" powerbi=PowerBIDataset(dataset_id=\"<dataset_id>\", table_names=['table1', 'table2'], credential=DefaultAzureCredential()), \n",
" llm=smart_llm,\n",
" examples=few_shots,\n",
")\n",
"agent_executor = create_pbi_agent(\n",
" llm=fast_llm,\n",
" toolkit=toolkit,\n",
" verbose=True,\n",
")"
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],
"outputs": [],
"metadata": {}
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},
{
"cell_type": "code",
"execution_count": null,
"source": [
"agent_executor.run(\"What was the maximum of value in revenue in dollars in 2022?\")"
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],
"outputs": [],
"metadata": {}
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}
],
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