Spaces:
Sleeping
Sleeping
File size: 6,346 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "0e499e90-7a6d-4fab-8aab-31a4df417601",
"metadata": {},
"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."
]
},
{
"cell_type": "markdown",
"id": "ec927ac6-9b2a-4e8a-9a6e-3e429191875c",
"metadata": {
"tags": []
},
"source": [
"## Initialization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53422913-967b-4f2a-8022-00269c1be1b1",
"metadata": {
"tags": []
},
"outputs": [],
"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",
"execution_count": null,
"id": "090f3699-79c6-4ce1-ab96-a94f0121fd64",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"fast_llm = AzureOpenAI(temperature=0.5, max_tokens=1000, deployment_name=\"gpt-35-turbo\", verbose=True)\n",
"smart_llm = AzureOpenAI(temperature=0, max_tokens=100, deployment_name=\"gpt-4\", verbose=True)\n",
"\n",
"toolkit = PowerBIToolkit(\n",
" powerbi=PowerBIDataset(dataset_id=\"<dataset_id>\", table_names=['table1', 'table2'], credential=DefaultAzureCredential()), \n",
" llm=smart_llm\n",
")\n",
"\n",
"agent_executor = create_pbi_agent(\n",
" llm=fast_llm,\n",
" toolkit=toolkit,\n",
" verbose=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "36ae48c7-cb08-4fef-977e-c7d4b96a464b",
"metadata": {},
"source": [
"## Example: describing a table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff70e83d-5ad0-4fc7-bb96-27d82ac166d7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"agent_executor.run(\"Describe table1\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "9abcfe8e-1868-42a4-8345-ad2d9b44c681",
"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",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"agent_executor.run(\"How many records are in table1?\")"
]
},
{
"cell_type": "markdown",
"id": "6fbc26af-97e4-4a21-82aa-48bdc992da26",
"metadata": {},
"source": [
"## Example: running queries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17bea710-4a23-4de0-b48e-21d57be48293",
"metadata": {
"tags": []
},
"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\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "6fd950e4",
"metadata": {},
"source": [
"## Example: add your own few-shot prompts"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87d677f9",
"metadata": {},
"outputs": [],
"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",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "33f4bb43",
"metadata": {},
"outputs": [],
"source": [
"agent_executor.run(\"What was the maximum of value in revenue in dollars in 2022?\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|