wehub-resource-sync
629 lines
24 KiB
Plaintext
629 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4b2cf5f5",
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"metadata": {},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"import json\n",
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"import os\n",
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"from typing import Annotated, Any, Never\n",
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"\n",
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"from agent_framework import (\n",
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" AgentExecutor,\n",
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" AgentExecutorRequest,\n",
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" AgentExecutorResponse,\n",
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" Message,\n",
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" WorkflowBuilder,\n",
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" WorkflowContext,\n",
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" executor,\n",
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" tool,\n",
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")\n",
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"from agent_framework.foundry import FoundryChatClient\n",
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"from azure.identity import AzureCliCredential\n",
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"from dotenv import load_dotenv\n",
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"from IPython.display import HTML, display\n",
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"from pydantic import BaseModel\n",
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"\n",
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"print(\"✅ All imports successful!\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "001c224e",
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"metadata": {},
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"source": [
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"## Step 1: Define Pydantic Models for Structured Outputs\n",
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"\n",
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"These models define the **schema** that agents will return. Using `response_format` with Pydantic ensures:\n",
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"- ✅ Type-safe data extraction\n",
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"- ✅ Automatic validation\n",
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"- ✅ No parsing errors from free-text responses\n",
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"- ✅ Easy conditional routing based on fields"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6c2ef582",
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"metadata": {},
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"outputs": [],
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"source": [
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"class BookingCheckResult(BaseModel):\n",
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" \"\"\"Result from checking hotel availability at a destination.\"\"\"\n",
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"\n",
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" destination: str\n",
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" has_availability: bool\n",
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" message: str\n",
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"\n",
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"\n",
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"class AlternativeResult(BaseModel):\n",
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" \"\"\"Suggested alternative destination when no rooms available.\"\"\"\n",
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"\n",
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" alternative_destination: str\n",
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" reason: str\n",
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"\n",
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"\n",
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"class BookingConfirmation(BaseModel):\n",
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" \"\"\"Booking suggestion when rooms are available.\"\"\"\n",
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"\n",
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" destination: str\n",
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" action: str\n",
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" message: str\n",
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"\n",
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"\n",
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"print(\"✅ Pydantic models defined:\")\n",
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"print(\" - BookingCheckResult (availability check)\")\n",
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"print(\" - AlternativeResult (alternative suggestion)\")\n",
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"print(\" - BookingConfirmation (booking confirmation)\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "48423ecc",
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"metadata": {},
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"source": [
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"## Step 2: Create the Hotel Booking Tool\n",
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"\n",
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"This tool is what the **availability_agent** will call to check if rooms are available. We use the `@ai_function` decorator to:\n",
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"- Convert a Python function into an AI-callable tool\n",
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"- Automatically generate JSON schema for the LLM\n",
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"- Handle parameter validation\n",
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"- Enable automatic invocation by agents\n",
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"\n",
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"For this demo:\n",
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"- **Stockholm, Seattle, Tokyo, London, Amsterdam** → Have rooms ✅\n",
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"- **All other cities** → No rooms ❌"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aad7e7ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool(description=\"Check hotel room availability for a destination city\")\n",
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"def hotel_booking(destination: Annotated[str, \"The destination city to check for hotel rooms\"]) -> str:\n",
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" \"\"\"\n",
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" Simulates checking hotel room availability.\n",
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"\n",
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" Returns JSON string with availability status.\n",
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" \"\"\"\n",
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" display(\n",
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" HTML(f\"\"\"\n",
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" <div style='padding: 15px; background: #e3f2fd; border-left: 4px solid #2196f3; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>🔍 Tool Invoked:</strong> hotel_booking(\"{destination}\")\n",
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" </div>\n",
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" \"\"\")\n",
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" )\n",
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"\n",
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" # Simulate availability check\n",
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" cities_with_rooms = [\"stockholm\", \"seattle\", \"tokyo\", \"london\", \"amsterdam\"]\n",
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" has_rooms = destination.lower() in cities_with_rooms\n",
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"\n",
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" result = {\"has_availability\": has_rooms, \"destination\": destination}\n",
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"\n",
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" return json.dumps(result)\n",
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"\n",
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"\n",
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"print(\"✅ hotel_booking tool created with @tool decorator\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "134c54b0",
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"metadata": {},
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"source": [
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"## Step 3: Define Condition Functions for Routing\n",
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"\n",
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"These functions inspect the agent's response and determine which path to take in the workflow.\n",
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"\n",
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"**Key Pattern:**\n",
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"1. Check if the message is `AgentExecutorResponse`\n",
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"2. Parse the structured output (Pydantic model)\n",
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"3. Return `True` or `False` to control routing\n",
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"\n",
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"The workflow will evaluate these conditions on **edges** to decide which executor to invoke next."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6960edd1",
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"metadata": {},
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"outputs": [],
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"source": [
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"def has_availability_condition(message: Any) -> bool:\n",
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" \"\"\"\n",
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" Condition for routing when hotels ARE available.\n",
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" \n",
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" Returns True if the destination has hotel rooms.\n",
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" \"\"\"\n",
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" if not isinstance(message, AgentExecutorResponse):\n",
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" return True # Default to True if unexpected type\n",
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"\n",
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" try:\n",
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" result = BookingCheckResult.model_validate_json(message.agent_run_response.text)\n",
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"\n",
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" display(\n",
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" HTML(f\"\"\"\n",
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" <div style='padding: 12px; background: #c8e6c9; border-left: 4px solid #4caf50; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>✅ Condition Check:</strong> has_availability = <strong>{result.has_availability}</strong> for {result.destination}\n",
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" </div>\n",
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" \"\"\")\n",
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" )\n",
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"\n",
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" return result.has_availability\n",
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" except Exception as e:\n",
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" display(\n",
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" HTML(f\"\"\"\n",
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" <div style='padding: 12px; background: #ffcdd2; border-left: 4px solid #f44336; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>⚠️ Error:</strong> {str(e)}\n",
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" </div>\n",
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" \"\"\")\n",
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" )\n",
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" return False\n",
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"\n",
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"\n",
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"def no_availability_condition(message: Any) -> bool:\n",
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" \"\"\"\n",
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" Condition for routing when hotels are NOT available.\n",
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" \n",
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" Returns True if the destination has no hotel rooms.\n",
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" \"\"\"\n",
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" if not isinstance(message, AgentExecutorResponse):\n",
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" return False\n",
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"\n",
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" try:\n",
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" result = BookingCheckResult.model_validate_json(message.agent_run_response.text)\n",
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"\n",
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" display(\n",
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" HTML(f\"\"\"\n",
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" <div style='padding: 12px; background: #ffecb3; border-left: 4px solid #ff9800; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>❌ Condition Check:</strong> no_availability for {result.destination}\n",
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" </div>\n",
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" \"\"\")\n",
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" )\n",
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"\n",
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" return not result.has_availability\n",
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" except Exception as e:\n",
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" return False\n",
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"\n",
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"\n",
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"print(\"✅ Condition functions defined:\")\n",
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"print(\" - has_availability_condition (routes when rooms exist)\")\n",
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"print(\" - no_availability_condition (routes when no rooms)\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9dc783ba",
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"metadata": {},
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"source": [
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"## Step 4: Create Custom Display Executor\n",
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"\n",
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"Executors are workflow components that perform transformations or side effects. We use the `@executor` decorator to create a custom executor that displays the final result.\n",
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"\n",
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"**Key Concepts:**\n",
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"- `@executor(id=\"...\")` - Registers a function as a workflow executor\n",
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"- `WorkflowContext[Never, str]` - Type hints for input/output\n",
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"- `ctx.yield_output(...)` - Yields the final workflow result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c67f6b55",
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"metadata": {},
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"outputs": [],
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"source": [
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"@executor(id=\"display_result\")\n",
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"async def display_result(response: AgentExecutorResponse, ctx: WorkflowContext[Never, str]) -> None:\n",
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" \"\"\"\n",
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" Display the final result as workflow output.\n",
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" \n",
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" This executor receives the final agent response and yields it as the workflow output.\n",
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" \"\"\"\n",
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" display(\n",
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" HTML(\"\"\"\n",
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" <div style='padding: 15px; background: #f3e5f5; border-left: 4px solid #9c27b0; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>📤 Display Executor:</strong> Yielding workflow output\n",
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" </div>\n",
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" \"\"\")\n",
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" )\n",
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"\n",
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" await ctx.yield_output(response.agent_run_response.text)\n",
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"\n",
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"\n",
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"print(\"✅ display_result executor created with @executor decorator\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7de6eb90",
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"metadata": {},
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"source": [
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"## Step 5: Load Environment Variables\n",
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"\n",
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"Configure the LLM client. This example works with:\n",
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"- **GitHub Models** (Free tier with GitHub token)\n",
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"- **Azure OpenAI**\n",
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"- **OpenAI**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1e8f0d88",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load environment variables\n",
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"load_dotenv()\n",
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"\n",
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"# Configure the Microsoft Foundry provider with keyless authentication\n",
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"provider = FoundryChatClient(\n",
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" project_endpoint=os.environ[\"AZURE_AI_PROJECT_ENDPOINT\"],\n",
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" model=os.environ[\"AZURE_AI_MODEL_DEPLOYMENT_NAME\"],\n",
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" credential=AzureCliCredential(),\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fc61fe7",
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"metadata": {},
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"source": [
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"## Step 6: Create AI Agents with Structured Outputs\n",
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"\n",
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"We create **three specialized agents**, each wrapped in an `AgentExecutor`:\n",
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"\n",
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"1. **availability_agent** - Checks hotel availability using the tool\n",
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"2. **alternative_agent** - Suggests alternative cities (when no rooms)\n",
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"3. **booking_agent** - Encourages booking (when rooms available)\n",
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"\n",
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"**Key Features:**\n",
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"- `tools=[hotel_booking]` - Provides the tool to the agent\n",
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"- `response_format=PydanticModel` - Forces structured JSON output\n",
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"- `AgentExecutor(..., id=\"...\")` - Wraps agent for workflow use"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "66466dda",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Agent 1: Check availability with tool\n",
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"availability_agent = AgentExecutor(\n",
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" provider.as_agent(\n",
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" name=\"availability-agent\",\n",
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" instructions=(\n",
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" \"You are a hotel booking assistant that checks room availability. \"\n",
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" \"Use the hotel_booking tool to check if rooms are available at the destination. \"\n",
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" \"Return JSON with fields: destination (string), has_availability (bool), and message (string). \"\n",
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" \"The message should summarize the availability status.\"\n",
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" ),\n",
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" tools=[hotel_booking],\n",
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" default_options={\"response_format\": BookingCheckResult},\n",
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" ),\n",
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" id=\"availability_agent\",\n",
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")\n",
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"\n",
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"# Agent 2: Suggest alternative (when no rooms)\n",
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"alternative_agent = AgentExecutor(\n",
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" provider.as_agent(\n",
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" name=\"alternative-agent\",\n",
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" instructions=(\n",
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" \"You are a helpful travel assistant. When a user cannot find hotels in their requested city, \"\n",
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" \"suggest an alternative nearby city that has availability. \"\n",
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" \"Return JSON with fields: alternative_destination (string) and reason (string). \"\n",
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" \"Make your suggestion sound appealing and helpful.\"\n",
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" ),\n",
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" default_options={\"response_format\": AlternativeResult},\n",
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" ),\n",
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" id=\"alternative_agent\",\n",
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")\n",
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"\n",
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"# Agent 3: Suggest booking (when rooms available)\n",
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"booking_agent = AgentExecutor(\n",
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" provider.as_agent(\n",
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" name=\"booking-agent\",\n",
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" instructions=(\n",
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" \"You are a booking assistant. The user has found available hotel rooms. \"\n",
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" \"Encourage them to book by highlighting the destination's appeal. \"\n",
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" \"Return JSON with fields: destination (string), action (string), and message (string). \"\n",
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" \"The action should be 'book_now' and message should be encouraging.\"\n",
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" ),\n",
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" default_options={\"response_format\": BookingConfirmation},\n",
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" ),\n",
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" id=\"booking_agent\",\n",
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")\n",
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"\n",
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"display(\n",
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" HTML(\"\"\"\n",
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" <div style='padding: 15px; background: #e3f2fd; border-left: 4px solid #2196f3; border-radius: 4px; margin: 10px 0;'>\n",
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" <strong>✅ Created 3 Agents:</strong>\n",
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" <ul style='margin: 10px 0 0 0;'>\n",
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" <li><strong>availability_agent</strong> - Checks availability with hotel_booking tool</li>\n",
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" <li><strong>alternative_agent</strong> - Suggests alternative cities</li>\n",
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" <li><strong>booking_agent</strong> - Encourages booking</li>\n",
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" </ul>\n",
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" </div>\n",
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"\"\"\")\n",
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")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7879a0cb",
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"metadata": {},
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"source": [
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"## Step 7: Build the Workflow with Conditional Edges\n",
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"\n",
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"Now we use `WorkflowBuilder` to construct the graph with conditional routing:\n",
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"\n",
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"**Workflow Structure:**\n",
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"```\n",
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"availability_agent (START)\n",
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" ↓\n",
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" Evaluate conditions\n",
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" ↙ ↘\n",
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"[no_availability] [has_availability]\n",
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" ↓ ↓\n",
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"alternative_agent booking_agent\n",
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" ↓ ↓\n",
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" display_result ←───┘\n",
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"```\n",
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"\n",
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"**Key Methods:**\n",
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"- `.set_start_executor(...)` - Sets the entry point\n",
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"- `.add_edge(from, to, condition=...)` - Adds conditional edge\n",
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"- `.build()` - Finalizes the workflow"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "90bb29dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Build the workflow with conditional routing\n",
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"workflow = (\n",
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" WorkflowBuilder(\n",
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" start_executor=availability_agent,\n",
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" output_executors=[display_result],\n",
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" )\n",
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" # NO AVAILABILITY PATH\n",
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" .add_edge(availability_agent, alternative_agent, condition=no_availability_condition)\n",
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" .add_edge(alternative_agent, display_result)\n",
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" # HAS AVAILABILITY PATH\n",
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" .add_edge(availability_agent, booking_agent, condition=has_availability_condition)\n",
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" .add_edge(booking_agent, display_result)\n",
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" .build()\n",
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")\n",
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"\n",
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"display(\n",
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" HTML(\"\"\"\n",
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" <div style='padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 8px; margin: 10px 0;'>\n",
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" <h3 style='margin: 0 0 15px 0;'>✅ Workflow Built Successfully!</h3>\n",
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" <p style='margin: 0; line-height: 1.6;'>\n",
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" <strong>Conditional Routing:</strong><br>\n",
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" • If <strong>NO availability</strong> → alternative_agent → display_result<br>\n",
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" • If <strong>availability</strong> → booking_agent → display_result\n",
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" </p>\n",
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" </div>\n",
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"\"\"\")\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0a3ad845",
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"metadata": {},
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"source": [
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"## Step 8: Run Test Case 1 - City WITHOUT Availability (Paris)\n",
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"\n",
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"Let's test the **no availability** path by requesting hotels in Paris (which has no rooms in our simulation)."
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]
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},
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{
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"cell_type": "code",
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|
"execution_count": null,
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"id": "af1538cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"display(\n",
|
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" HTML(\"\"\"\n",
|
|
" <div style='padding: 20px; background: #fff3e0; border-left: 4px solid #ff9800; border-radius: 8px; margin: 20px 0;'>\n",
|
|
" <h3 style='margin: 0 0 10px 0; color: #e65100;'>🧪 TEST CASE 1: Paris (No Availability)</h3>\n",
|
|
" <p style='margin: 0;'>Expected workflow path: availability_agent → alternative_agent → display_result</p>\n",
|
|
" </div>\n",
|
|
"\"\"\")\n",
|
|
")\n",
|
|
"\n",
|
|
"# Create request for Paris\n",
|
|
"request_paris = AgentExecutorRequest(\n",
|
|
" messages=[Message(role=\"user\", text=\"I want to book a hotel in Paris\")], should_respond=True\n",
|
|
")\n",
|
|
"\n",
|
|
"# Run the workflow\n",
|
|
"events_paris = await workflow.run(request_paris)\n",
|
|
"outputs_paris = events_paris.get_outputs()\n",
|
|
"\n",
|
|
"# Display results\n",
|
|
"if outputs_paris:\n",
|
|
" result_paris = AlternativeResult.model_validate_json(outputs_paris[0])\n",
|
|
"\n",
|
|
" display(\n",
|
|
" HTML(f\"\"\"\n",
|
|
" <div style='padding: 25px; background: linear-gradient(135deg, #FFD700 0%, #FFA500 100%); border-radius: 12px; box-shadow: 0 4px 12px rgba(255,165,0,0.3); margin: 20px 0;'>\n",
|
|
" <h3 style='margin: 0 0 15px 0; color: #333;'>🏆 WORKFLOW RESULT (Paris)</h3>\n",
|
|
" <div style='background: white; padding: 20px; border-radius: 8px;'>\n",
|
|
" <p style='margin: 0 0 10px 0; font-size: 16px;'><strong>Status:</strong> ❌ No rooms in Paris</p>\n",
|
|
" <p style='margin: 0 0 10px 0; font-size: 16px;'><strong>Alternative Suggestion:</strong> 🏨 {result_paris.alternative_destination}</p>\n",
|
|
" <p style='margin: 0; font-size: 14px; color: #666;'><strong>Reason:</strong> {result_paris.reason}</p>\n",
|
|
" </div>\n",
|
|
" </div>\n",
|
|
" \"\"\")\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "408a3f60",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step 9: Run Test Case 2 - City WITH Availability (Stockholm)\n",
|
|
"\n",
|
|
"Now let's test the **availability** path by requesting hotels in Stockholm (which has rooms in our simulation)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e1471000",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"display(\n",
|
|
" HTML(\"\"\"\n",
|
|
" <div style='padding: 20px; background: #e8f5e9; border-left: 4px solid #4caf50; border-radius: 8px; margin: 20px 0;'>\n",
|
|
" <h3 style='margin: 0 0 10px 0; color: #1b5e20;'>🧪 TEST CASE 2: Stockholm (Has Availability)</h3>\n",
|
|
" <p style='margin: 0;'>Expected workflow path: availability_agent → booking_agent → display_result</p>\n",
|
|
" </div>\n",
|
|
"\"\"\")\n",
|
|
")\n",
|
|
"\n",
|
|
"# Create request for Stockholm\n",
|
|
"request_stockholm = AgentExecutorRequest(\n",
|
|
" messages=[Message(role=\"user\", text=\"I want to book a hotel in Stockholm\")], should_respond=True\n",
|
|
")\n",
|
|
"\n",
|
|
"# Run the workflow\n",
|
|
"events_stockholm = await workflow.run(request_stockholm)\n",
|
|
"outputs_stockholm = events_stockholm.get_outputs()\n",
|
|
"\n",
|
|
"# Display results\n",
|
|
"if outputs_stockholm:\n",
|
|
" result_stockholm = BookingConfirmation.model_validate_json(outputs_stockholm[0])\n",
|
|
"\n",
|
|
" display(\n",
|
|
" HTML(f\"\"\"\n",
|
|
" <div style='padding: 25px; background: linear-gradient(135deg, #4caf50 0%, #8bc34a 100%); color: white; border-radius: 12px; box-shadow: 0 4px 12px rgba(76,175,80,0.3); margin: 20px 0;'>\n",
|
|
" <h3 style='margin: 0 0 15px 0;'>🏆 WORKFLOW RESULT (Stockholm)</h3>\n",
|
|
" <div style='background: white; color: #333; padding: 20px; border-radius: 8px;'>\n",
|
|
" <p style='margin: 0 0 10px 0; font-size: 16px;'><strong>Status:</strong> ✅ Rooms Available!</p>\n",
|
|
" <p style='margin: 0 0 10px 0; font-size: 16px;'><strong>Destination:</strong> 🏨 {result_stockholm.destination}</p>\n",
|
|
" <p style='margin: 0 0 10px 0; font-size: 16px;'><strong>Action:</strong> {result_stockholm.action}</p>\n",
|
|
" <p style='margin: 0; font-size: 14px; color: #666;'><strong>Message:</strong> {result_stockholm.message}</p>\n",
|
|
" </div>\n",
|
|
" </div>\n",
|
|
" \"\"\")\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a415537c",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Key Takeaways and Next Steps\n",
|
|
"\n",
|
|
"### ✅ What You've Learned:\n",
|
|
"\n",
|
|
"1. **WorkflowBuilder Pattern**\n",
|
|
" - Use `.set_start_executor()` to define entry point\n",
|
|
" - Use `.add_edge(from, to, condition=...)` for conditional routing\n",
|
|
" - Call `.build()` to finalize the workflow\n",
|
|
"\n",
|
|
"2. **Conditional Routing**\n",
|
|
" - Condition functions inspect `AgentExecutorResponse`\n",
|
|
" - Parse structured outputs to make routing decisions\n",
|
|
" - Return `True` to activate an edge, `False` to skip it\n",
|
|
"\n",
|
|
"3. **Tool Integration**\n",
|
|
" - Use `@ai_function` to convert Python functions into AI tools\n",
|
|
" - Agents call tools automatically when needed\n",
|
|
" - Tools return JSON that agents can parse\n",
|
|
"\n",
|
|
"4. **Structured Outputs**\n",
|
|
" - Use Pydantic models for type-safe data extraction\n",
|
|
" - Set `response_format=MyModel` when creating agents\n",
|
|
" - Parse responses with `Model.model_validate_json()`\n",
|
|
"\n",
|
|
"5. **Custom Executors**\n",
|
|
" - Use `@executor(id=\"...\")` to create workflow components\n",
|
|
" - Executors can transform data or perform side effects\n",
|
|
" - Use `ctx.yield_output()` to produce workflow results\n",
|
|
"\n",
|
|
"### 🚀 Real-World Applications:\n",
|
|
"\n",
|
|
"- **Travel Booking**: Check availability, suggest alternatives, compare options\n",
|
|
"- **Customer Service**: Route based on issue type, sentiment, priority\n",
|
|
"- **E-commerce**: Check inventory, suggest alternatives, process orders\n",
|
|
"- **Content Moderation**: Route based on toxicity scores, user flags\n",
|
|
"- **Approval Workflows**: Route based on amount, user role, risk level\n",
|
|
"- **Multi-stage Processing**: Route based on data quality, completeness\n",
|
|
"\n",
|
|
"### 📚 Next Steps:\n",
|
|
"\n",
|
|
"- Add more complex conditions (multiple criteria)\n",
|
|
"- Implement loops with workflow state management\n",
|
|
"- Add sub-workflows for reusable components\n",
|
|
"- Integrate with real APIs (hotel booking, inventory systems)\n",
|
|
"- Add error handling and fallback paths\n",
|
|
"- Visualize workflows with the built-in visualization tools"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv (3.12.12)",
|
|
"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": "3.12.12"
|
|
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|
|
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|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
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|