openai/ask your Analytical DB questions in english.ipynb

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    "### DESCRIPTION:\n",
    "    Ask your Analytical DB (Azure Data Explorer aka Kusto) questions in English – generate KQL (Kusto Query language) from the user’s input using OpenAI GPT3.5 turbo model then query Azure Data Explorer (Kusto) using the generated KQL.\n",
    "    \n",
    "### REQUIREMENTS:\n",
    "* Create an .env file with your OpenAI API key and save it in the root directory of this project with the following\n",
    "    OPENAI_DEPLOYMENT_ENDPOINT =\"<your openai endpoint>\"   \n",
    "    OPENAI_API_KEY = \"<your openai api key>\"  \n",
    "    OPENAI_DEPLOYMENT_NAME = \"<your gpt35 deployment name>\"  \n",
    "    OPENAI_DEPLOYMENT_VERSION = \"<gpt35 api version>\"  \n",
    "    OPENAI_MODEL_NAME=\"<gpt35 model name>\"  \n",
    "  \n",
    "* Install an Azure Data Explorer cluster, create a DB called retail and a table called sales by ingesting the data in:\n",
    "     An ADX (Azure Data Explorer or Kusto) cluster  \n",
    "    In ADX, create a Database named \"openai\"  \n",
    "        <img src=\"images/1.png\" alt=\"Create Kusto cluster\" /> \n",
    "    Create a table called \"sales\" by ingesting data from \"./data/analytics/Warehouse_and_Retail_Sales.csv\"   \n",
    "        <img src=\"images/2.png\" alt=\"Create Kusto cluster\" /> \n",
    "    Create an AAD app registration for Authentication - see below   \n",
    "        [Create an Azure Active Directory application registration in Azure Data Explorer](https://learn.microsoft.com/en-us/azure/data-explorer/provision-azure-ad-app)\n",
    "\n",
    "    Update the environment variables in the .env file:\n",
    "    AAD_TENANT_ID = \"<your aad tenant id>\"  \n",
    "    KUSTO_CLUSTER =  \"https://<your azure data explorer name>.westeurope.kusto.windows.net\"  \n",
    "    KUSTO_DATABASE = \"<your kusto database name>\"  \n",
    "    KUSTO_TABLE = \"sales\"  \n",
    "    KUSTO_MANAGED_IDENTITY_APP_ID = \"<your aad app registration id>\"  \n",
    "    KUSTO_MANAGED_IDENTITY_SECRET = \"<your kusto managed identity secret>\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.kusto.data import KustoClient, KustoConnectionStringBuilder\n",
    "from azure.kusto.data.exceptions import KustoServiceError\n",
    "from azure.kusto.data.helpers import dataframe_from_result_table\n",
    "from dotenv import load_dotenv\n",
    "import utils\n",
    "import pandas as pd\n",
    "import os\n",
    "import openai\n",
    "import json \n",
    "\n",
    "load_dotenv()\n",
    "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\") \n",
    "OPENAI_DEPLOYMENT_ENDPOINT = os.getenv(\"OPENAI_DEPLOYMENT_ENDPOINT\")\n",
    "OPENAI_DEPLOYMENT_NAME = os.getenv(\"OPENAI_DEPLOYMENT_NAME\")\n",
    "OPENAI_MODEL_NAME = os.getenv(\"OPENAI_MODEL_NAME\")\n",
    "OPENAI_DEPLOYMENT_VERSION = os.getenv(\"OPENAI_DEPLOYMENT_VERSION\")\n",
    "# Configure OpenAI API\n",
    "openai.api_type = \"azure\"\n",
    "openai.api_version = OPENAI_DEPLOYMENT_VERSION\n",
    "openai.api_base = OPENAI_DEPLOYMENT_ENDPOINT\n",
    "openai.api_key = OPENAI_API_KEY\n",
    "\n",
    "AAD_TENANT_ID = os.getenv(\"AAD_TENANT_ID\")\n",
    "KUSTO_CLUSTER = os.getenv(\"KUSTO_CLUSTER\")\n",
    "KUSTO_DATABASE = os.getenv(\"KUSTO_DATABASE\")\n",
    "KUSTO_TABLE = os.getenv(\"KUSTO_TABLE\")\n",
    "KUSTO_MANAGED_IDENTITY_APP_ID = os.getenv(\"KUSTO_MANAGED_IDENTITY_APP_ID\")\n",
    "KUSTO_MANAGED_IDENTITY_SECRET = os.getenv(\"KUSTO_MANAGED_IDENTITY_SECRET\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect to adx using AAD app registration\n",
    "cluster = KUSTO_CLUSTER\n",
    "kcsb = KustoConnectionStringBuilder.with_aad_application_key_authentication(cluster, KUSTO_MANAGED_IDENTITY_APP_ID, KUSTO_MANAGED_IDENTITY_SECRET,  AAD_TENANT_ID)\n",
    "client = KustoClient(kcsb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item:BOOTLEG RED - 750ML\n",
      "Item:MOMENT DE PLAISIR - 750ML\n",
      "Item:S SMITH ORGANIC PEAR CIDER - 18.7OZ\n",
      "Item:SCHLINK HAUS KABINETT - 750ML\n",
      "Item:SANTORINI GAVALA WHITE - 750ML\n",
      "Item:CORTENOVA VENETO P/GRIG - 750ML\n",
      "Item:SANTA MARGHERITA P/GRIG ALTO - 375ML\n",
      "Item:JACK DANIELS COUNTRY COCKTAIL SOUTHERN PEACH - 10.OZ-NR\n",
      "Item:KNOB CREEK BOURBON 9YR - 100P - 375ML\n",
      "Item:KSARA CAB - 750ML\n"
     ]
    }
   ],
   "source": [
    "# Test the connection to kusto works - sample query to get the top 10 results from the sales table\n",
    "query = \"sales | take 10\"\n",
    "\n",
    "response = client.execute(\"retail\", query)\n",
    "for row in response.primary_results[0]:\n",
    "    print(\"Item:{}\".format(row[\"item_description\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a prompt for the model to generate the KQL query\n",
    "kusto_query_template_prefix = \"\"\"\n",
    "<|im_start|>system\n",
    "I have an Azure Data Explorer (Kusto) table containing the following columns: \n",
    "year, month, supplier, item_code, item_description,item_type,retail_sales,retail_transfers,warehouse_sales\n",
    "\n",
    "Write an KQL query based on the user input below. Answer in a concise manner. Answer only with the KQL query where the table name is T, no extra text.\n",
    "\n",
    "user input: \n",
    "\"\"\"\n",
    "template_sufix = \"<|im_end|>\\n<|im_start|>assistant\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functions to call the OpenAI API and run KQL query\n",
    "def call_openai(template_prefix, text):\n",
    "    prompt = template_prefix + text + template_sufix\n",
    "    response = openai.Completion.create(\n",
    "        engine=utils.OPENAI_DEPLOYMENT_NAME,\n",
    "        prompt=prompt,\n",
    "        temperature=0,\n",
    "        max_tokens=4096,\n",
    "        top_p=0.95,\n",
    "        frequency_penalty=0,\n",
    "        presence_penalty=0,\n",
    "        stop=[\"<|im_end|>\"])\n",
    "    response = response['choices'][0]['text']\n",
    "    response = utils.remove_chars(\"\\n\", response)\n",
    "    response=utils.start_after_string(\"Answer:\", response)\n",
    "    response=utils.remove_tail_tags(\"<|im_end|>\", response)\n",
    "    return response\n",
    "\n",
    "def call_openai_kql_response(text):\n",
    "    response = call_openai(kusto_query_template_prefix, text)\n",
    "    query = response.replace(\"T\", \"sales\")\n",
    "    query = query.replace(\"```\", \"\")\n",
    "    response = client.execute(\"retail\", query)\n",
    "    df = dataframe_from_result_table(response.primary_results[0])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   month  total_retail_sales\n",
      "0      1            74318.77\n",
      "1      7            94538.96\n",
      "2      3           109411.29\n",
      "3      9            82108.68\n"
     ]
    }
   ],
   "source": [
    "# Test the model with a sample question - aggregate the retail sales by month for the year 2020\n",
    "df = call_openai_kql_response(\"I would like to get the total retail sales for 2020 by months\")\n",
    "print(df)"
   ]
  },
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    "# Use plotly to visualize the results\n",
    "import plotly.express as px\n",
    "\n",
    "fig = px.bar(df, x = 'month', y = 'total_retail_sales')\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   supplier  total_retail_sales\n",
      "0        E & J GALLO WINERY           166170.53\n",
      "1  DIAGEO NORTH AMERICA INC            145343.2\n",
      "2      CONSTELLATION BRANDS           131664.79\n",
      "3        ANHEUSER BUSCH INC           109960.82\n",
      "4        JIM BEAM BRANDS CO            96164.04\n",
      "5    MILLER BREWING COMPANY            87156.44\n",
      "6             CROWN IMPORTS            84437.66\n",
      "7                SAZERAC CO            70414.62\n",
      "8           BACARDI USA INC            66610.23\n",
      "9            THE WINE GROUP            64270.04\n"
     ]
    }
   ],
   "source": [
    "# Generate KQL and run - aggregate the retail sales by supplier - get the top 10 \n",
    "df = call_openai_kql_response(\"I would like to get the top 10 suppliers by total retail sales\")\n",
    "print(df)"
   ]
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use plotly to visualize the results\n",
    "import plotly.express as px\n",
    "\n",
    "fig = px.pie(df, names = 'supplier', values = 'total_retail_sales')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   year  month  total_retail_sales\n",
      "0  2017     12            28119.88\n",
      "1  2020      3            15822.92\n",
      "2  2019     11             7959.42\n",
      "3  2019      5             7935.54\n",
      "4  2017      6             7863.66\n",
      "5  2019      3             7529.03\n",
      "6  2017      7             7438.81\n"
     ]
    }
   ],
   "source": [
    "# Generate KQL and run \n",
    "df = call_openai_kql_response(\"I would like to get the total retail sales by year and month for the top 10 suppliers\")\n",
    "print(df)"
   ]
  }
 ],
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