{ "cells": [ { "cell_type": "markdown", "id": "cdc74b23", "metadata": {}, "source": [ "# Build a Data Analyst AI Agent from Scratch" ] }, { "cell_type": "markdown", "id": "db160825", "metadata": {}, "source": [ "## Project Setup \n", "\n", "This section outlines the steps required to set up the project: \n", "- Import necessary libraries \n", "- Load the required API keys \n", "- Establish the database connection \n", "- Load the sample data into the database \n" ] }, { "cell_type": "code", "execution_count": 1, "id": "a3eda0fc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Requirement already satisfied: openai in /home/jovyan/.local/lib/python3.11/site-packages (2.6.1)\n", "Requirement already satisfied: anyio<5,>=3.5.0 in /home/jovyan/.local/lib/python3.11/site-packages (from openai) (4.11.0)\n", "Requirement already satisfied: distro<2,>=1.7.0 in /home/jovyan/.local/lib/python3.11/site-packages (from openai) (1.9.0)\n", "Requirement already satisfied: httpx<1,>=0.23.0 in /home/jovyan/.local/lib/python3.11/site-packages (from openai) (0.28.1)\n", "Requirement already satisfied: jiter<1,>=0.10.0 in /home/jovyan/.local/lib/python3.11/site-packages (from openai) (0.11.1)\n", "Requirement already satisfied: pydantic<3,>=1.9.0 in /opt/conda/envs/py311/lib/python3.11/site-packages (from openai) (2.12.2)\n", "Requirement already satisfied: sniffio in /home/jovyan/.local/lib/python3.11/site-packages (from openai) (1.3.1)\n", "Requirement already satisfied: tqdm>4 in /opt/conda/envs/py311/lib/python3.11/site-packages (from openai) (4.67.1)\n", "Requirement already satisfied: typing-extensions<5,>=4.11 in /opt/conda/envs/py311/lib/python3.11/site-packages (from openai) (4.15.0)\n", "Requirement already satisfied: idna>=2.8 in /opt/conda/envs/py311/lib/python3.11/site-packages (from anyio<5,>=3.5.0->openai) (3.11)\n", "Requirement already satisfied: certifi in /opt/conda/envs/py311/lib/python3.11/site-packages (from httpx<1,>=0.23.0->openai) (2025.10.5)\n", "Requirement already satisfied: httpcore==1.* in /home/jovyan/.local/lib/python3.11/site-packages (from httpx<1,>=0.23.0->openai) (1.0.9)\n", "Requirement already satisfied: h11>=0.16 in /home/jovyan/.local/lib/python3.11/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->openai) (0.16.0)\n", "Requirement already satisfied: annotated-types>=0.6.0 in /opt/conda/envs/py311/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (0.7.0)\n", "Requirement already satisfied: pydantic-core==2.41.4 in /opt/conda/envs/py311/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (2.41.4)\n", "Requirement already satisfied: typing-inspection>=0.4.2 in /opt/conda/envs/py311/lib/python3.11/site-packages (from pydantic<3,>=1.9.0->openai) (0.4.2)\n" ] } ], "source": [ "!pip install openai" ] }, { "cell_type": "code", "execution_count": 2, "id": "d6717e73-fcac-4d57-96c0-d87c05f06c2f", "metadata": {}, "outputs": [], "source": [ "from openai import OpenAI\n", "import json\n", "import inspect\n", "import requests\n", "import pandas as pd\n", "from teradataml import *\n", "from datetime import date, datetime\n", "from decimal import Decimal\n", "from typing import Any, Optional" ] }, { "cell_type": "code", "execution_count": 3, "id": "00de9d73-5f8b-4be9-9cea-ba7d20bd4e13", "metadata": {}, "outputs": [], "source": [ "with open('configs.json', 'r') as file:\n", " configs=json.load(file)" ] }, { "cell_type": "code", "execution_count": 4, "id": "515a6b17-6d23-4787-81d3-e1d3cd165208", "metadata": {}, "outputs": [], "source": [ "client = OpenAI(api_key = configs['llm-api-key'])" ] }, { "cell_type": "code", "execution_count": 5, "id": "e66e2aef-96e0-4461-878f-1461721af3eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performing setup ...\n", "Setup complete\n" ] }, { "name": "stdin", "output_type": "stream", "text": [ "\n", "Enter password: ············\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "... Logon successful\n", "Connected as: teradatasql://demo_user:xxxxx@host.docker.internal/dbc\n", "Engine(teradatasql://demo_user:***@host.docker.internal)\n" ] } ], "source": [ "%run -i ../startup.ipynb\n", "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n", "print(eng)" ] }, { "cell_type": "code", "execution_count": null, "id": "2b82e827-b6b6-4637-846b-044562de45c1", "metadata": {}, "outputs": [], "source": [ "data_loading_queries = [\n", "'''\n", "CREATE DATABASE teddy_retailers\n", "AS PERMANENT = 50000000;\n", "''',\n", "'''\n", "CREATE TABLE teddy_retailers.fct_order_details AS\n", "(\n", " SELECT order_id,product_id,customer_id,unit_price,quantity,amount\n", " FROM (\n", "\t\tLOCATION='/s3/dev-rel-demos.s3.amazonaws.com/ai_agent_dbt/fct_order_details.csv') as order_details\n", ") WITH DATA;''',\n", "\n", "'''\n", "CREATE TABLE teddy_retailers.dim_customers AS\n", "(\n", " SELECT customer_id,first_name,last_name,email\n", " FROM (\n", "\t\tLOCATION='/s3/dev-rel-demos.s3.amazonaws.com/ai_agent_dbt/dim_customers.csv') as customers\n", ") WITH DATA;\n", "''',\n", "'''\n", "CREATE TABLE teddy_retailers.dim_orders AS\n", "(\n", " SELECT order_id,order_date,order_status\n", " FROM (\n", "\t\tLOCATION='/s3/dev-rel-demos.s3.amazonaws.com/ai_agent_dbt/dim_orders.csv') as orders\n", ") WITH DATA;\n", "''',\n", "'''\n", "CREATE TABLE teddy_retailers.dim_products AS\n", "(\n", " SELECT product_id,product_name,product_category,price_dollars\n", " FROM (\n", "\t\tLOCATION='/s3/dev-rel-demos.s3.amazonaws.com/ai_agent_dbt/dim_products.csv') as products\n", ") WITH DATA;\n", "'''\n", "]\n", "for query in data_loading_queries:\n", " execute_sql(query)" ] }, { "cell_type": "markdown", "id": "8d1d5a34", "metadata": {}, "source": [ "## Agent Configuration \n", "\n", "This section covers the configuration of the agent, including: \n", "* Defining the data context that the agent will interact with \n", "* Setting up the routine the agent will follow as a system prompt (embedding the data context) \n", "* Establishing the list of tools available for the agent to complete its tasks \n" ] }, { "cell_type": "code", "execution_count": 6, "id": "cfe478df-bbc9-4579-b0d4-77052bc4a5b1", "metadata": {}, "outputs": [], "source": [ "def query_dbt_catalog(path):\n", " response = requests.get(path)\n", " catalog = response.json()\n", " return catalog" ] }, { "cell_type": "code", "execution_count": 7, "id": "194f583a-4535-49a1-bbff-e9dca8181eac", "metadata": {}, "outputs": [], "source": [ "dbt_catalog_path = 'https://raw.githubusercontent.com/Teradata/simple_data_ai_agent/refs/heads/main/catalogs/catalog.json'" ] }, { "cell_type": "code", "execution_count": 8, "id": "b8df6707-d96b-4560-a3dd-37f9a98261f2", "metadata": {}, "outputs": [], "source": [ "system_prompt_full = f\"\"\"\n", "You are an advanced data analyst for a retail company, specializing in analyzing data from a Teradata system. Your primary responsibility is to assist users by answering business-related questions using SQL queries on the Teradata database. Follow these steps:\n", "\n", "1. Understanding User Requests\n", " - Users provide business questions in plain English.\n", " - Extract relevant data points needed to construct a meaningful response.\n", "\n", "2. Generating SQL Queries\n", " - Construct an optimized Teradata SQL query to retrieve the necessary data.\n", " - The query must be a **single-line string** without carriage returns or line breaks.\n", " - The catalog of databases, tables, and columns to query is in the following JSON structure, which is a dbt catalog. Use only the dimensional and fact tables, which are prefixed with `dim_` and `fct_`, respectively, to build the query. \n", " {query_dbt_catalog(dbt_catalog_path)}\n", " - Ensure that the SQL query adheres to **Teradata SQL syntax** and avoids unsupported keywords such as `LIMIT`, `FETCH`, use Teradata statements such as `SAMPLE`.\n", " - Apply necessary joins between tables if the user's question requires it.\n", " - Apply appropriate filtering, grouping, and ordering to enhance performance and accuracy.\n", "\n", "3. Executing the Query\n", " - Run the query in the Teradata database.\n", "\n", "4. Responding to the User\n", " - Respond to the user's question based on the result of running the query.\n", " - Show the user the query you ran and your reasoning for running it.\n", " \n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 9, "id": "c39da44e-c3e2-4fcd-bac1-51ad6e7fd2e6", "metadata": {}, "outputs": [], "source": [ "def function_to_schema(func) -> dict:\n", " type_map = {\n", " str: \"string\",\n", " int: \"integer\",\n", " float: \"number\",\n", " bool: \"boolean\",\n", " list: \"array\",\n", " dict: \"object\",\n", " type(None): \"null\",\n", " }\n", "\n", " try:\n", " signature = inspect.signature(func)\n", " except ValueError as e:\n", " raise ValueError(\n", " f\"Failed to get signature for function {func.__name__}: {str(e)}\"\n", " )\n", "\n", " parameters = {}\n", " for param in signature.parameters.values():\n", " try:\n", " param_type = type_map.get(param.annotation, \"string\")\n", " except KeyError as e:\n", " raise KeyError(\n", " f\"Unknown type annotation {param.annotation} for parameter {param.name}: {str(e)}\"\n", " )\n", " parameters[param.name] = {\"type\": param_type}\n", "\n", " required = [\n", " param.name\n", " for param in signature.parameters.values()\n", " if param.default == inspect._empty\n", " ]\n", "\n", " return {\n", " \"type\": \"function\",\n", " \"function\": {\n", " \"name\": func.__name__,\n", " \"description\": (func.__doc__ or \"\").strip(),\n", " \"parameters\": {\n", " \"type\": \"object\",\n", " \"properties\": parameters,\n", " \"required\": required,\n", " },\n", " },\n", " }" ] }, { "cell_type": "code", "execution_count": 10, "id": "4bdf19a9-2164-499b-a74b-b5cbee3b11eb", "metadata": {}, "outputs": [], "source": [ "def serialize_teradata_types(obj: Any) -> Any:\n", " \"\"\"Convert Teradata-specific types to JSON serializable formats.\"\"\"\n", " if isinstance(obj, (date, datetime)):\n", " return obj.isoformat()\n", " if isinstance(obj, Decimal):\n", " return float(obj)\n", " return str(obj)\n", "\n", "def rows_to_json(cursor_description: Any, rows: list[Any]) -> list[dict[str, Any]]:\n", " \"\"\"Convert DB rows into JSON objects using column names as keys.\"\"\"\n", " if not cursor_description or not rows:\n", " return []\n", " columns = [col[0] for col in cursor_description]\n", " out: list[dict[str, Any]] = []\n", " for row in rows:\n", " out.append({col: serialize_teradata_types(val) for col, val in zip(columns, row)})\n", " return out" ] }, { "cell_type": "code", "execution_count": 11, "id": "55b50f35-6e6e-4d71-890a-eef2826e3381", "metadata": {}, "outputs": [], "source": [ "def query_teradata_database(sql_statement):\n", " result = execute_sql(sql_statement)\n", " data = rows_to_json(result.description, result.fetchall())\n", " return json.dumps(data)" ] }, { "cell_type": "markdown", "id": "a796bc2b", "metadata": {}, "source": [ "## Agent Runtime\n", "This section covers the code executed while the agent is in action, including:\n", "* Preparing the tools for use by the agent\n", "* The agent's runtime function" ] }, { "cell_type": "code", "execution_count": 12, "id": "88fd833b-57aa-4b11-b86c-89d5a7b41705", "metadata": {}, "outputs": [], "source": [ "tools = [query_teradata_database]\n", "tool_schemas = [function_to_schema(tool) for tool in tools]\n", "tools_map = {tool.__name__: tool for tool in tools}" ] }, { "cell_type": "code", "execution_count": 13, "id": "f533f016-86f3-46fa-a111-f6931e72145b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'type': 'function',\n", " 'function': {'name': 'query_teradata_database',\n", " 'description': '',\n", " 'parameters': {'type': 'object',\n", " 'properties': {'sql_statement': {'type': 'string'}},\n", " 'required': ['sql_statement']}}}]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tool_schemas" ] }, { "cell_type": "code", "execution_count": 14, "id": "c020fb27-819b-4645-a950-aff57370226c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'query_teradata_database': }" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tools_map" ] }, { "cell_type": "code", "execution_count": 15, "id": "d7a03fa4", "metadata": {}, "outputs": [], "source": [ "def execute_tool_call(tool_call, tools_map):\n", " name = tool_call.function.name\n", " args = json.loads(tool_call.function.arguments)\n", " print(f\"Assistant: {name}({args})\")\n", " # call corresponding function with provided arguments\n", " return tools_map[name](**args)" ] }, { "cell_type": "code", "execution_count": 16, "id": "ceda72dd-8e44-4a83-b9ea-29f40b5929d4", "metadata": {}, "outputs": [], "source": [ "def run_full_turn(system_message, messages):\n", "\n", " while True:\n", " print(f\"just logging messages {messages}\")\n", " response = client.chat.completions.create(\n", " model=\"gpt-4.1\",\n", " messages=[{\"role\": \"system\", \"content\": system_message}] + messages,\n", " tools=tool_schemas or None,\n", " seed = 2\n", " )\n", " print(f\"logging response {response}\")\n", " message = response.choices[0].message\n", " messages.append(message)\n", "\n", " if message.content: # print assistant response\n", " print(\"Assistant Response:\", message.content)\n", "\n", " if message.tool_calls: # if finished handling tool calls, break\n", " # === handle tool calls ===\n", " for tool_call in message.tool_calls:\n", " result = execute_tool_call(tool_call, tools_map)\n", "\n", " result_message = {\n", " \"role\": \"tool\",\n", " \"tool_call_id\": tool_call.id,\n", " \"content\": result,\n", " }\n", " messages.append(result_message)\n", " else:\n", " break" ] }, { "cell_type": "markdown", "id": "1673e7da", "metadata": {}, "source": [ "## Running the Agent \n", "Ask a business question and receive a response. Since this is a simple agent, it can only handle basic questions. While its capabilities can be enhanced, such improvements are currently out of scope." ] }, { "cell_type": "code", "execution_count": 17, "id": "ff9c8d62-b4ab-45ba-a98a-f09a7fdc16ed", "metadata": {}, "outputs": [ { "name": "stdin", "output_type": "stream", "text": [ "User: Who are our most profitable customers\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "just logging messages [{'role': 'user', 'content': 'Who are our most profitable customers'}]\n", "logging response ChatCompletion(id='chatcmpl-CYW5sm4GkSbC8BfnDRNdpHVfLAyDN', choices=[Choice(finish_reason='tool_calls', index=0, logprobs=None, message=ChatCompletionMessage(content='To answer this question, I need to identify the customers who generated the highest total sales (profitability) for the company. This requires:\\n\\n- Calculating the total amount spent by each customer, which can be derived from the fct_order_details table (amount column).\\n- Retrieving customer information from the dim_customers table.\\n\\nI will join fct_order_details with dim_customers using customer_id, sum the amount for each customer, and order by total amount in descending order.\\n\\nHere is the Teradata SQL query I will use:\\nSELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\\n\\nI will now run this query to find your most profitable customers.', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_lCvRkVUCw363qs4PVNdYDvFR', function=Function(arguments='{\"sql_statement\":\"SELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\"}', name='query_teradata_database'), type='function')]))], created=1762342556, model='gpt-4.1-2025-04-14', object='chat.completion', service_tier='default', system_fingerprint='fp_d38c7f4fa7', usage=CompletionUsage(completion_tokens=287, prompt_tokens=3952, total_tokens=4239, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))\n", "Assistant Response: To answer this question, I need to identify the customers who generated the highest total sales (profitability) for the company. This requires:\n", "\n", "- Calculating the total amount spent by each customer, which can be derived from the fct_order_details table (amount column).\n", "- Retrieving customer information from the dim_customers table.\n", "\n", "I will join fct_order_details with dim_customers using customer_id, sum the amount for each customer, and order by total amount in descending order.\n", "\n", "Here is the Teradata SQL query I will use:\n", "SELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\n", "\n", "I will now run this query to find your most profitable customers.\n", "Assistant: query_teradata_database({'sql_statement': 'SELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;'})\n", "just logging messages [{'role': 'user', 'content': 'Who are our most profitable customers'}, ChatCompletionMessage(content='To answer this question, I need to identify the customers who generated the highest total sales (profitability) for the company. This requires:\\n\\n- Calculating the total amount spent by each customer, which can be derived from the fct_order_details table (amount column).\\n- Retrieving customer information from the dim_customers table.\\n\\nI will join fct_order_details with dim_customers using customer_id, sum the amount for each customer, and order by total amount in descending order.\\n\\nHere is the Teradata SQL query I will use:\\nSELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\\n\\nI will now run this query to find your most profitable customers.', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=[ChatCompletionMessageFunctionToolCall(id='call_lCvRkVUCw363qs4PVNdYDvFR', function=Function(arguments='{\"sql_statement\":\"SELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\"}', name='query_teradata_database'), type='function')]), {'role': 'tool', 'tool_call_id': 'call_lCvRkVUCw363qs4PVNdYDvFR', 'content': '[{\"customer_id\": \"752\", \"first_name\": \"John\", \"last_name\": \"Dorsey\", \"email\": \"peter68@example.net\", \"total_spent\": \"2020\"}, {\"customer_id\": \"163\", \"first_name\": \"Brandon\", \"last_name\": \"Ward\", \"email\": \"brandon95@example.net\", \"total_spent\": \"2000\"}, {\"customer_id\": \"680\", \"first_name\": \"Justin\", \"last_name\": \"Thomas\", \"email\": \"veronica41@example.net\", \"total_spent\": \"1901\"}, {\"customer_id\": \"381\", \"first_name\": \"Jessica\", \"last_name\": \"Hill\", \"email\": \"vlopez@example.net\", \"total_spent\": \"1770\"}, {\"customer_id\": \"319\", \"first_name\": \"Jonathan\", \"last_name\": \"Moreno\", \"email\": \"robert16@example.org\", \"total_spent\": \"1765\"}, {\"customer_id\": \"540\", \"first_name\": \"Alison\", \"last_name\": \"Jones\", \"email\": \"ahawkins@example.net\", \"total_spent\": \"1717\"}, {\"customer_id\": \"460\", \"first_name\": \"Pam\", \"last_name\": \"Tucker\", \"email\": \"ijacobs@example.com\", \"total_spent\": \"1695\"}, {\"customer_id\": \"660\", \"first_name\": \"Kaitlyn\", \"last_name\": \"Smith\", \"email\": \"xolson@example.com\", \"total_spent\": \"1690\"}, {\"customer_id\": \"188\", \"first_name\": \"Chelsey\", \"last_name\": \"Bradley\", \"email\": \"william18@example.com\", \"total_spent\": \"1622\"}, {\"customer_id\": \"637\", \"first_name\": \"Steven\", \"last_name\": \"Chan\", \"email\": \"kharrell@example.com\", \"total_spent\": \"1591\"}, {\"customer_id\": \"809\", \"first_name\": \"Richard\", \"last_name\": \"Mcdaniel\", \"email\": \"johnschneider@example.net\", \"total_spent\": \"1526\"}, {\"customer_id\": \"624\", \"first_name\": \"Elizabeth\", \"last_name\": \"Beck\", \"email\": \"mollymyers@example.org\", \"total_spent\": \"1518\"}, {\"customer_id\": \"853\", \"first_name\": \"Michael\", \"last_name\": \"Robles\", \"email\": \"simmonsmichelle@example.net\", \"total_spent\": \"1501\"}, {\"customer_id\": \"166\", \"first_name\": \"Clinton\", \"last_name\": \"Wright\", \"email\": \"holly42@example.org\", \"total_spent\": \"1490\"}, {\"customer_id\": \"863\", \"first_name\": \"Samantha\", \"last_name\": \"Richards\", \"email\": \"christensenlynn@example.org\", \"total_spent\": \"1478\"}, {\"customer_id\": \"617\", \"first_name\": \"Jeremy\", \"last_name\": \"Vega\", \"email\": \"deborahwest@example.net\", \"total_spent\": \"1392\"}, {\"customer_id\": \"15\", \"first_name\": \"Scott\", \"last_name\": \"Benson\", \"email\": \"pcoffey@example.net\", \"total_spent\": \"1390\"}, {\"customer_id\": \"629\", \"first_name\": \"Gloria\", \"last_name\": \"Chang\", \"email\": \"wthompson@example.com\", \"total_spent\": \"1384\"}, {\"customer_id\": \"583\", \"first_name\": \"Carolyn\", \"last_name\": \"Bell\", \"email\": \"qgrant@example.com\", \"total_spent\": \"1383\"}, {\"customer_id\": \"5\", \"first_name\": \"Lisa\", \"last_name\": \"Owens\", \"email\": \"kimberlyscott@example.net\", \"total_spent\": \"1355\"}, {\"customer_id\": \"378\", \"first_name\": \"Stacey\", \"last_name\": \"Pham\", \"email\": \"vmyers@example.org\", \"total_spent\": \"1326\"}, {\"customer_id\": \"122\", \"first_name\": \"Francisco\", \"last_name\": \"Bell\", \"email\": \"ycurtis@example.com\", \"total_spent\": \"1318\"}, {\"customer_id\": \"315\", \"first_name\": \"Linda\", \"last_name\": \"Ramos\", \"email\": \"djones@example.com\", \"total_spent\": \"1289\"}, {\"customer_id\": \"923\", \"first_name\": \"Victor\", \"last_name\": \"Joseph\", \"email\": \"gsmith@example.com\", \"total_spent\": \"1278\"}, 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{\"customer_id\": \"569\", \"first_name\": \"Anthony\", \"last_name\": \"Lewis\", \"email\": \"ericaphillips@example.net\", \"total_spent\": \"210\"}, {\"customer_id\": \"114\", \"first_name\": \"Elijah\", \"last_name\": \"Monroe\", \"email\": \"flynnmelissa@example.com\", \"total_spent\": \"209\"}, {\"customer_id\": \"176\", \"first_name\": \"Isaac\", \"last_name\": \"King\", \"email\": \"linda52@example.com\", \"total_spent\": \"208\"}, {\"customer_id\": \"714\", \"first_name\": \"John\", \"last_name\": \"Houston\", \"email\": \"boltonarthur@example.org\", \"total_spent\": \"204\"}, {\"customer_id\": \"483\", \"first_name\": \"Jasmine\", \"last_name\": \"Wallace\", \"email\": \"dana09@example.com\", \"total_spent\": \"204\"}, {\"customer_id\": \"320\", \"first_name\": \"Jennifer\", \"last_name\": \"Friedman\", \"email\": \"kathryncarter@example.com\", \"total_spent\": \"202\"}, {\"customer_id\": \"516\", \"first_name\": \"Jeremy\", \"last_name\": \"Smith\", \"email\": 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\"last_name\": \"Hernandez\", \"email\": \"jdalton@example.com\", \"total_spent\": \"188\"}, {\"customer_id\": \"970\", \"first_name\": \"Holly\", \"last_name\": \"Lopez\", \"email\": \"gary98@example.com\", \"total_spent\": \"186\"}, {\"customer_id\": \"144\", \"first_name\": \"Brittney\", \"last_name\": \"Smith\", \"email\": \"gloria54@example.net\", \"total_spent\": \"186\"}, {\"customer_id\": \"242\", \"first_name\": \"Andrew\", \"last_name\": \"Howard\", \"email\": \"qperez@example.net\", \"total_spent\": \"186\"}, {\"customer_id\": \"666\", \"first_name\": \"Adrian\", \"last_name\": \"James\", \"email\": \"cindyweaver@example.net\", \"total_spent\": \"186\"}, {\"customer_id\": \"909\", \"first_name\": \"Margaret\", \"last_name\": \"Buck\", \"email\": \"jeremythompson@example.com\", \"total_spent\": \"184\"}, {\"customer_id\": \"268\", \"first_name\": \"Ryan\", \"last_name\": \"White\", \"email\": \"lawrencewhite@example.com\", \"total_spent\": \"184\"}, {\"customer_id\": \"339\", \"first_name\": \"Stephen\", \"last_name\": \"Bates\", \"email\": \"bchang@example.org\", \"total_spent\": \"184\"}, {\"customer_id\": \"797\", \"first_name\": \"Melanie\", \"last_name\": \"Coleman\", \"email\": \"jacqueline99@example.org\", \"total_spent\": \"183\"}, {\"customer_id\": \"57\", \"first_name\": \"Savannah\", \"last_name\": \"Velazquez\", \"email\": \"johnnyjordan@example.com\", \"total_spent\": \"181\"}, {\"customer_id\": \"44\", \"first_name\": \"Margaret\", \"last_name\": \"Powell\", \"email\": \"sharper@example.com\", \"total_spent\": \"181\"}, {\"customer_id\": \"141\", \"first_name\": \"Diana\", \"last_name\": \"Sawyer\", \"email\": \"bowens@example.org\", \"total_spent\": \"176\"}, {\"customer_id\": \"370\", \"first_name\": \"Debra\", \"last_name\": \"Williams\", \"email\": \"derrickstrickland@example.org\", \"total_spent\": \"174\"}, {\"customer_id\": \"282\", \"first_name\": \"Christopher\", \"last_name\": \"Thomas\", \"email\": \"ywallace@example.org\", \"total_spent\": \"172\"}, {\"customer_id\": \"162\", \"first_name\": \"Denise\", \"last_name\": \"Winters\", \"email\": \"karen07@example.org\", \"total_spent\": \"170\"}, {\"customer_id\": \"555\", \"first_name\": \"Michael\", \"last_name\": \"Scott\", \"email\": \"justingonzales@example.org\", \"total_spent\": \"170\"}, {\"customer_id\": \"607\", \"first_name\": \"Brianna\", \"last_name\": \"Wallace\", \"email\": \"corey82@example.net\", \"total_spent\": \"169\"}, {\"customer_id\": \"875\", \"first_name\": \"Ronald\", \"last_name\": \"Morris\", \"email\": \"tami83@example.net\", \"total_spent\": \"169\"}, {\"customer_id\": \"683\", \"first_name\": \"Eric\", \"last_name\": \"Estrada\", \"email\": \"ywebb@example.com\", \"total_spent\": \"168\"}, {\"customer_id\": \"953\", \"first_name\": \"Robin\", \"last_name\": \"Smith\", \"email\": \"gdavis@example.org\", \"total_spent\": \"168\"}, {\"customer_id\": \"997\", \"first_name\": \"Adam\", \"last_name\": \"Anderson\", \"email\": \"jenny87@example.net\", \"total_spent\": \"165\"}, {\"customer_id\": \"47\", \"first_name\": \"Edward\", \"last_name\": \"Choi\", \"email\": \"zwhite@example.com\", \"total_spent\": \"164\"}, {\"customer_id\": \"664\", \"first_name\": \"Barbara\", \"last_name\": \"Gray\", \"email\": \"hcohen@example.net\", \"total_spent\": \"164\"}, {\"customer_id\": \"678\", \"first_name\": \"Charlotte\", \"last_name\": \"Miller\", \"email\": \"qjones@example.net\", \"total_spent\": \"160\"}, {\"customer_id\": \"541\", \"first_name\": \"Beth\", \"last_name\": \"Alvarez\", \"email\": \"marcusward@example.org\", \"total_spent\": \"159\"}, {\"customer_id\": \"466\", \"first_name\": \"Paula\", \"last_name\": \"Rogers\", \"email\": \"xclark@example.org\", \"total_spent\": \"157\"}, {\"customer_id\": \"587\", \"first_name\": \"Crystal\", \"last_name\": \"Burgess\", \"email\": \"mcain@example.net\", \"total_spent\": \"156\"}, {\"customer_id\": \"469\", \"first_name\": \"Jacqueline\", \"last_name\": \"Brock\", \"email\": \"arthur80@example.com\", \"total_spent\": \"152\"}, {\"customer_id\": \"128\", \"first_name\": \"Christian\", \"last_name\": \"Skinner\", \"email\": \"jonathan60@example.com\", \"total_spent\": \"151\"}, {\"customer_id\": \"790\", \"first_name\": \"John\", \"last_name\": \"Rios\", \"email\": \"matthewroberts@example.org\", \"total_spent\": \"150\"}, {\"customer_id\": \"453\", \"first_name\": \"William\", \"last_name\": \"Bailey\", \"email\": \"mary47@example.com\", \"total_spent\": \"148\"}, {\"customer_id\": \"703\", \"first_name\": \"John\", \"last_name\": \"Jones\", \"email\": \"heather89@example.com\", \"total_spent\": \"142\"}, {\"customer_id\": \"778\", \"first_name\": \"James\", \"last_name\": \"Kirby\", \"email\": \"victoriaflores@example.net\", \"total_spent\": \"136\"}, {\"customer_id\": \"495\", \"first_name\": \"Darren\", \"last_name\": \"Newman\", \"email\": \"rachel12@example.net\", \"total_spent\": \"135\"}, {\"customer_id\": \"507\", \"first_name\": \"Ashley\", \"last_name\": \"Powell\", \"email\": \"dmcgee@example.org\", \"total_spent\": \"134\"}, {\"customer_id\": \"335\", \"first_name\": \"Jillian\", \"last_name\": \"Rivera\", \"email\": \"janet11@example.net\", \"total_spent\": \"133\"}, {\"customer_id\": \"554\", \"first_name\": \"Kevin\", \"last_name\": \"Sullivan\", \"email\": \"ysmith@example.com\", \"total_spent\": \"126\"}, {\"customer_id\": \"928\", \"first_name\": \"Melissa\", \"last_name\": \"Salazar\", \"email\": \"brownsarah@example.org\", \"total_spent\": \"125\"}, {\"customer_id\": \"861\", \"first_name\": \"Jeremy\", \"last_name\": \"Sandoval\", \"email\": \"anorman@example.net\", \"total_spent\": \"124\"}, {\"customer_id\": \"852\", \"first_name\": \"Jose\", \"last_name\": \"Anderson\", \"email\": \"donaldcarpenter@example.com\", \"total_spent\": \"123\"}, {\"customer_id\": \"568\", \"first_name\": \"David\", \"last_name\": \"Smith\", \"email\": \"yharper@example.net\", \"total_spent\": \"122\"}, {\"customer_id\": \"592\", \"first_name\": \"Olivia\", \"last_name\": \"Carey\", \"email\": \"jonessamantha@example.net\", \"total_spent\": \"120\"}, {\"customer_id\": \"182\", \"first_name\": \"Daniel\", \"last_name\": \"Harding\", \"email\": \"ugarcia@example.com\", \"total_spent\": \"117\"}, {\"customer_id\": \"883\", \"first_name\": \"James\", \"last_name\": \"Robinson\", \"email\": \"hesterdebbie@example.net\", \"total_spent\": \"116\"}, {\"customer_id\": \"789\", \"first_name\": \"Manuel\", \"last_name\": \"Simpson\", \"email\": \"chelsea87@example.net\", \"total_spent\": \"114\"}, {\"customer_id\": \"233\", \"first_name\": \"Darlene\", \"last_name\": \"Hernandez\", \"email\": \"fieldslynn@example.org\", \"total_spent\": \"112\"}, {\"customer_id\": \"688\", \"first_name\": \"Karen\", \"last_name\": \"Lyons\", \"email\": \"fosborn@example.net\", \"total_spent\": \"111\"}, {\"customer_id\": \"281\", \"first_name\": \"Jody\", \"last_name\": \"Rojas\", \"email\": \"wangrobert@example.com\", \"total_spent\": \"110\"}, {\"customer_id\": \"992\", \"first_name\": \"Jason\", \"last_name\": \"Smith\", \"email\": \"burgessjessica@example.org\", \"total_spent\": \"110\"}, {\"customer_id\": \"931\", \"first_name\": \"Karen\", \"last_name\": \"Deleon\", \"email\": \"robert35@example.com\", \"total_spent\": \"110\"}, {\"customer_id\": \"99\", \"first_name\": \"Adam\", \"last_name\": \"Smith\", \"email\": \"lopezkevin@example.net\", \"total_spent\": \"108\"}, {\"customer_id\": \"762\", \"first_name\": \"Samuel\", \"last_name\": \"Jackson\", \"email\": \"maria10@example.net\", \"total_spent\": \"108\"}, {\"customer_id\": \"72\", \"first_name\": \"Sandra\", \"last_name\": \"Williams\", \"email\": \"hthomas@example.org\", \"total_spent\": \"103\"}, {\"customer_id\": \"406\", \"first_name\": \"Katrina\", \"last_name\": \"Phillips\", \"email\": \"lopezgarrett@example.net\", \"total_spent\": \"100\"}, {\"customer_id\": \"876\", \"first_name\": \"Heather\", \"last_name\": \"Travis\", \"email\": \"beckeramber@example.com\", \"total_spent\": \"99\"}, {\"customer_id\": \"214\", \"first_name\": \"Stephen\", \"last_name\": \"Mclean\", \"email\": \"umiller@example.net\", \"total_spent\": \"98\"}, {\"customer_id\": \"354\", \"first_name\": \"Marcus\", \"last_name\": \"Wright\", \"email\": \"david00@example.com\", \"total_spent\": \"98\"}, {\"customer_id\": \"972\", \"first_name\": \"Miguel\", \"last_name\": \"Flynn\", \"email\": \"kristenarnold@example.com\", \"total_spent\": \"97\"}, {\"customer_id\": \"24\", \"first_name\": \"Brandy\", \"last_name\": \"Wright\", \"email\": \"karen11@example.com\", \"total_spent\": \"93\"}, {\"customer_id\": \"371\", \"first_name\": \"Jennifer\", \"last_name\": \"Collins\", \"email\": \"jilljohnson@example.com\", \"total_spent\": \"90\"}, {\"customer_id\": \"523\", \"first_name\": \"Matthew\", \"last_name\": \"Mills\", \"email\": \"maryjohnson@example.com\", \"total_spent\": \"87\"}, {\"customer_id\": \"715\", \"first_name\": \"James\", \"last_name\": \"Drake\", \"email\": \"mitchellsteven@example.org\", \"total_spent\": \"86\"}, {\"customer_id\": \"936\", \"first_name\": \"Kerri\", \"last_name\": \"Solomon\", \"email\": \"zachary25@example.com\", \"total_spent\": \"82\"}, {\"customer_id\": \"772\", \"first_name\": \"Hannah\", \"last_name\": \"Chapman\", \"email\": \"guywilkins@example.org\", \"total_spent\": \"80\"}, {\"customer_id\": \"918\", \"first_name\": \"Joseph\", \"last_name\": \"Daugherty\", \"email\": \"tiffanyfletcher@example.org\", \"total_spent\": \"80\"}, {\"customer_id\": \"635\", \"first_name\": \"Alicia\", \"last_name\": \"Johnson\", \"email\": \"kevin57@example.net\", \"total_spent\": \"75\"}, {\"customer_id\": \"628\", \"first_name\": \"John\", \"last_name\": \"Harris\", \"email\": \"aclements@example.com\", \"total_spent\": \"68\"}, {\"customer_id\": \"754\", \"first_name\": \"Samuel\", \"last_name\": \"Hinton\", \"email\": \"pharrison@example.com\", \"total_spent\": \"62\"}, {\"customer_id\": \"570\", \"first_name\": \"Savannah\", \"last_name\": \"Sweeney\", \"email\": \"gonzaleztaylor@example.org\", \"total_spent\": \"61\"}, {\"customer_id\": \"649\", \"first_name\": \"Stephen\", \"last_name\": \"Hicks\", \"email\": \"cchan@example.net\", \"total_spent\": \"61\"}, {\"customer_id\": \"671\", \"first_name\": \"Mark\", \"last_name\": \"Mills\", \"email\": \"russelljackson@example.com\", \"total_spent\": \"60\"}, {\"customer_id\": \"360\", \"first_name\": \"Kevin\", \"last_name\": \"Smith\", \"email\": \"tvazquez@example.net\", \"total_spent\": \"60\"}, {\"customer_id\": \"385\", \"first_name\": \"Ricky\", \"last_name\": \"Copeland\", \"email\": \"mwilliams@example.net\", \"total_spent\": \"60\"}, {\"customer_id\": \"725\", \"first_name\": \"Paul\", \"last_name\": \"Anderson\", \"email\": \"sanderstimothy@example.net\", \"total_spent\": \"60\"}, {\"customer_id\": \"862\", \"first_name\": \"Aaron\", \"last_name\": \"Sullivan\", \"email\": \"benjaminbanks@example.org\", \"total_spent\": \"60\"}, {\"customer_id\": \"184\", \"first_name\": \"Nancy\", \"last_name\": \"Taylor\", \"email\": \"jennifer35@example.org\", \"total_spent\": \"56\"}, {\"customer_id\": \"846\", \"first_name\": \"William\", \"last_name\": \"Shah\", \"email\": \"charleshowell@example.org\", \"total_spent\": \"54\"}, {\"customer_id\": \"292\", \"first_name\": \"Steven\", \"last_name\": \"Cox\", \"email\": \"michael95@example.com\", \"total_spent\": \"52\"}, {\"customer_id\": \"756\", \"first_name\": \"Matthew\", \"last_name\": \"Gross\", \"email\": \"laurietorres@example.com\", \"total_spent\": \"50\"}, {\"customer_id\": \"117\", \"first_name\": \"Megan\", \"last_name\": \"Martinez\", \"email\": \"brittneywise@example.com\", \"total_spent\": \"50\"}, {\"customer_id\": \"700\", \"first_name\": \"David\", \"last_name\": \"Sandoval\", \"email\": \"markjohnson@example.net\", \"total_spent\": \"48\"}, {\"customer_id\": \"112\", \"first_name\": \"Michael\", \"last_name\": \"Dean\", \"email\": \"esparzapamela@example.net\", \"total_spent\": \"48\"}, {\"customer_id\": \"169\", \"first_name\": \"Steven\", \"last_name\": \"Watkins\", \"email\": \"clarkperry@example.com\", \"total_spent\": \"45\"}, {\"customer_id\": \"590\", \"first_name\": \"Andrew\", \"last_name\": \"Baker\", \"email\": \"lemichael@example.com\", \"total_spent\": \"45\"}, {\"customer_id\": \"662\", \"first_name\": \"William\", \"last_name\": \"Hernandez\", \"email\": \"olsonamber@example.org\", \"total_spent\": \"45\"}, {\"customer_id\": \"490\", \"first_name\": \"Patrick\", \"last_name\": \"Morrow\", \"email\": \"taraholloway@example.com\", \"total_spent\": \"45\"}, {\"customer_id\": \"670\", \"first_name\": \"John\", \"last_name\": \"Winters\", \"email\": \"gardneremma@example.org\", \"total_spent\": \"38\"}, {\"customer_id\": \"651\", \"first_name\": \"Tanya\", \"last_name\": \"Cordova\", \"email\": \"ryan48@example.com\", \"total_spent\": \"36\"}, {\"customer_id\": \"608\", \"first_name\": \"Ryan\", \"last_name\": \"Figueroa\", \"email\": \"lisapadilla@example.org\", \"total_spent\": \"32\"}, {\"customer_id\": \"7\", \"first_name\": \"Randy\", \"last_name\": \"Sanchez\", \"email\": \"calebpeters@example.com\", \"total_spent\": \"30\"}, {\"customer_id\": \"691\", \"first_name\": \"Robert\", \"last_name\": \"Boyd\", \"email\": \"nicole10@example.com\", \"total_spent\": \"30\"}, {\"customer_id\": \"503\", \"first_name\": \"William\", \"last_name\": \"Baker\", \"email\": \"strongaaron@example.com\", \"total_spent\": \"23\"}, {\"customer_id\": \"323\", \"first_name\": \"Brittany\", \"last_name\": \"Perry\", \"email\": \"perkinsjesse@example.com\", \"total_spent\": \"20\"}, {\"customer_id\": \"508\", \"first_name\": \"Jessica\", \"last_name\": \"Ortiz\", \"email\": \"blairkimberly@example.org\", \"total_spent\": \"18\"}, {\"customer_id\": \"971\", \"first_name\": \"Jessica\", \"last_name\": \"Carey\", \"email\": \"longconnie@example.net\", \"total_spent\": \"16\"}, {\"customer_id\": \"728\", \"first_name\": \"Kathleen\", \"last_name\": \"Wright\", \"email\": \"mlam@example.com\", \"total_spent\": \"15\"}, {\"customer_id\": \"186\", \"first_name\": \"Jessica\", \"last_name\": \"Parker\", \"email\": \"ugarcia@example.com\", \"total_spent\": \"15\"}, {\"customer_id\": \"230\", \"first_name\": \"James\", \"last_name\": \"Evans\", \"email\": \"vmckay@example.org\", \"total_spent\": \"12\"}, {\"customer_id\": \"247\", \"first_name\": \"Kathleen\", \"last_name\": \"Thomas\", \"email\": \"hoffmanjeffery@example.net\", \"total_spent\": \"12\"}, {\"customer_id\": \"656\", \"first_name\": \"Leslie\", \"last_name\": \"Byrd\", \"email\": \"adam72@example.com\", \"total_spent\": \"12\"}, {\"customer_id\": \"733\", \"first_name\": \"Tiffany\", \"last_name\": \"White\", \"email\": \"smiththomas@example.com\", \"total_spent\": \"10\"}, {\"customer_id\": \"750\", \"first_name\": \"Michelle\", \"last_name\": \"Peters\", \"email\": \"kblack@example.com\", \"total_spent\": \"10\"}, {\"customer_id\": \"20\", \"first_name\": \"Sharon\", \"last_name\": \"Gardner\", \"email\": \"kevinmatthews@example.org\", \"total_spent\": \"8\"}, {\"customer_id\": \"489\", \"first_name\": \"Kathryn\", \"last_name\": \"Smith\", \"email\": \"ypittman@example.org\", \"total_spent\": \"8\"}]'}]\n", "logging response ChatCompletion(id='chatcmpl-CYW5zi2Kc585L3xEB52x8mkG1Do4V', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content='Your most profitable customers are those who have spent the most over the history of their transactions. To determine this, I summed up all purchases (amount) for each customer using the fct_order_details table and joined that to the dim_customers table to get the customer names and emails. The results are ordered by total spent, highest first.\\n\\nHere is the query I ran:\\nSELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\\n\\nThis identifies your top customers by sales value. For example, the top 3 most profitable customers are:\\n1. John Dorsey (peter68@example.net) – $2020\\n2. Brandon Ward (brandon95@example.net) – $2000\\n3. Justin Thomas (veronica41@example.net) – $1901\\n\\nLet me know if you want a deeper breakdown or just the top 10, 20, etc.!', refusal=None, role='assistant', annotations=[], audio=None, function_call=None, tool_calls=None))], created=1762342563, model='gpt-4.1-2025-04-14', object='chat.completion', service_tier='default', system_fingerprint='fp_d38c7f4fa7', usage=CompletionUsage(completion_tokens=242, prompt_tokens=27505, total_tokens=27747, completion_tokens_details=CompletionTokensDetails(accepted_prediction_tokens=0, audio_tokens=0, reasoning_tokens=0, rejected_prediction_tokens=0), prompt_tokens_details=PromptTokensDetails(audio_tokens=0, cached_tokens=0)))\n", "Assistant Response: Your most profitable customers are those who have spent the most over the history of their transactions. To determine this, I summed up all purchases (amount) for each customer using the fct_order_details table and joined that to the dim_customers table to get the customer names and emails. The results are ordered by total spent, highest first.\n", "\n", "Here is the query I ran:\n", "SELECT c.customer_id, c.first_name, c.last_name, c.email, SUM(f.amount) AS total_spent FROM teddy_retailers.fct_order_details f JOIN teddy_retailers.dim_customers c ON f.customer_id = c.customer_id GROUP BY c.customer_id, c.first_name, c.last_name, c.email ORDER BY total_spent DESC;\n", "\n", "This identifies your top customers by sales value. For example, the top 3 most profitable customers are:\n", "1. John Dorsey (peter68@example.net) – $2020\n", "2. Brandon Ward (brandon95@example.net) – $2000\n", "3. Justin Thomas (veronica41@example.net) – $1901\n", "\n", "Let me know if you want a deeper breakdown or just the top 10, 20, etc.!\n" ] } ], "source": [ "messages =[]\n", "user_input = input(\"User: \")\n", "messages.append({\"role\": \"user\", \"content\": user_input})\n", "new_messages = run_full_turn(system_prompt_full, messages)" ] }, { "cell_type": "markdown", "id": "e8b7ef0f", "metadata": {}, "source": [ "## Cleaning testing data" ] }, { "cell_type": "code", "execution_count": null, "id": "1302f8cf", "metadata": {}, "outputs": [], "source": [ "data_cleaning_queries = [\n", "'''\n", "DELETE DATABASE teddy_retailers ALL;\n", "''',\n", "'''\n", "DROP DATABASE teddy_retailers\n", "'''\n", "]\n", "for query in data_cleaning_queries:\n", " execute_sql(query)" ] } ], "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": "3.11.14" } }, "nbformat": 4, "nbformat_minor": 5 }