mirror of
https://github.com/meta-llama/llama-stack.git
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698 lines
24 KiB
Text
698 lines
24 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# AutoGen + Llama Stack Integration\n",
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"\n",
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"## Overview\n",
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"\n",
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"This notebook demonstrates how to use **AutoGen (AG2)** with **Llama Stack** as the backend.\n",
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"\n",
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"### What is AutoGen?\n",
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"- Microsoft's framework for **conversational multi-agent** systems\n",
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"- Emphasizes **chat-based** interactions between agents\n",
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"- Built-in **code execution** and **human-in-the-loop**\n",
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"- Great for **interactive problem-solving**\n",
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"\n",
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"### Why Llama Stack?\n",
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"- **Unified backend** for any LLM (Ollama, Together, vLLM, etc.)\n",
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"- **One integration point** instead of many\n",
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"- **Production-ready** infrastructure\n",
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"- **Open-source** and flexible\n",
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"\n",
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"### Use Cases Covered:\n",
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"1. **Two-Agent Conversation** - UserProxy + Assistant solving a problem\n",
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"2. **Code Generation & Execution** - AutoGen generates and runs code\n",
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"3. **Group Chat** - Multiple specialists collaborating\n",
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"4. **Human-in-the-Loop** - Interactive problem-solving\n",
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"5. **Sequential Task Solving** - Math problem → Code → Execute → Verify\n",
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"\n",
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"---\n",
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"\n",
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"## Prerequisites\n",
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"\n",
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"```bash\n",
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"# Install AutoGen (AG2)\n",
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"pip install pyautogen\n",
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"\n",
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"# Llama Stack should already be running\n",
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"# Default: http://localhost:8321\n",
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"```"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"import os\n",
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"from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager\n",
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"from autogen.oai import OpenAIWrapper\n",
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"\n",
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"# Check Llama Stack connectivity\n",
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"import httpx\n",
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"\n",
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"LLAMA_STACK_URL = \"http://localhost:8321\"\n",
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"\n",
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"try:\n",
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" response = httpx.get(f\"{LLAMA_STACK_URL}/health\")\n",
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" print(f\"✅ Llama Stack is running at {LLAMA_STACK_URL}\")\n",
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" print(f\"Status: {response.status_code}\")\n",
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"except Exception as e:\n",
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" print(f\"❌ Llama Stack not accessible: {e}\")\n",
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" print(\"Make sure Llama Stack is running on port 8321\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Configuration: AutoGen with Llama Stack\n",
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"\n",
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"### How It Works\n",
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"\n",
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"AutoGen uses the **OpenAI API format**, which Llama Stack is compatible with!\n",
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"\n",
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"```python\n",
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"config_list = [\n",
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" {\n",
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" \"model\": \"ollama/llama3.3:70b\", # Your Llama Stack model\n",
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" \"base_url\": \"http://localhost:8321/v1\", # Llama Stack endpoint\n",
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" \"api_key\": \"not-needed\", # Llama Stack doesn't need auth\n",
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" }\n",
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"]\n",
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"```\n",
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"\n",
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"**Key Points:**\n",
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"- Use `/v1` suffix for OpenAI-compatible endpoint\n",
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"- `api_key` can be any string (Llama Stack ignores it)\n",
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"- `model` must match what's available in Llama Stack"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# AutoGen configuration for Llama Stack\n",
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"config_list = [\n",
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" {\n",
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" \"model\": \"ollama/llama3.3:70b\", # Your Llama Stack model\n",
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" \"base_url\": \"http://localhost:8321/v1\", # OpenAI-compatible endpoint\n",
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" \"api_key\": \"not-needed\", # Llama Stack doesn't require auth\n",
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" }\n",
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"]\n",
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"\n",
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"llm_config = {\n",
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" \"config_list\": config_list,\n",
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" \"temperature\": 0.7,\n",
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" \"timeout\": 120,\n",
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"}\n",
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"\n",
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"print(\"✅ AutoGen configuration ready for Llama Stack\")\n",
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"print(f\"Model: {config_list[0]['model']}\")\n",
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"print(f\"Base URL: {config_list[0]['base_url']}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example 1: Two-Agent Conversation\n",
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"\n",
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"### Pattern: User Proxy + Assistant\n",
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"\n",
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"**UserProxyAgent:**\n",
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"- Represents the human user\n",
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"- Can execute code\n",
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"- Provides feedback to assistant\n",
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"\n",
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"**AssistantAgent:**\n",
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"- AI assistant powered by Llama Stack\n",
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"- Generates responses and code\n",
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"- Solves problems conversationally\n",
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"\n",
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"### Use Case: Solve a Math Problem"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create AssistantAgent (AI assistant)\n",
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"assistant = AssistantAgent(\n",
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" name=\"MathAssistant\",\n",
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" system_message=\"You are a helpful AI assistant that solves math problems. Provide clear explanations.\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"# Create UserProxyAgent (represents human)\n",
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"user_proxy = UserProxyAgent(\n",
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" name=\"User\",\n",
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" human_input_mode=\"NEVER\", # Fully automated (no human input)\n",
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" max_consecutive_auto_reply=5,\n",
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" code_execution_config={\"use_docker\": False}, # Allow local code execution\n",
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")\n",
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"\n",
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"print(\"✅ Agents created\")\n",
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"print(f\"Assistant: {assistant.name}\")\n",
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"print(f\"User Proxy: {user_proxy.name}\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Start conversation\n",
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"user_proxy.initiate_chat(\n",
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" assistant,\n",
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" message=\"What is the sum of the first 100 prime numbers? Please write Python code to calculate it.\"\n",
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")\n",
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"\n",
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"print(\"\\n\" + \"=\"*50)\n",
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"print(\"Conversation complete!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example 2: Code Generation & Execution\n",
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"\n",
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"### Pattern: Assistant generates code → UserProxy executes it\n",
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"\n",
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"This is AutoGen's killer feature: **automatic code execution**!\n",
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"\n",
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"### Use Case: Data Analysis Task"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a coding assistant\n",
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"coding_assistant = AssistantAgent(\n",
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" name=\"DataScientist\",\n",
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" system_message=\"\"\"You are an expert data scientist.\n",
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" Write Python code to solve data analysis problems.\n",
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" Always include visualizations when appropriate.\"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"# User proxy with code execution enabled\n",
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"user_proxy_code = UserProxyAgent(\n",
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" name=\"UserProxy\",\n",
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" human_input_mode=\"NEVER\",\n",
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" max_consecutive_auto_reply=3,\n",
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" code_execution_config={\n",
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" \"work_dir\": \"coding\",\n",
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" \"use_docker\": False,\n",
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" },\n",
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")\n",
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"\n",
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"# Start data analysis task\n",
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"user_proxy_code.initiate_chat(\n",
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" coding_assistant,\n",
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" message=\"\"\"Generate 100 random numbers from a normal distribution (mean=50, std=10).\n",
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" Calculate the mean, median, and standard deviation.\n",
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" Create a histogram to visualize the distribution.\"\"\"\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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"metadata": {},
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"source": [
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"## Example 3: Group Chat (Multi-Agent Collaboration)\n",
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"\n",
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"### Pattern: Multiple Specialists Collaborating\n",
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"\n",
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"**Scenario:** Write a technical blog post about AI\n",
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"\n",
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"**Agents:**\n",
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"1. **Researcher** - Finds information\n",
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"2. **Writer** - Writes content\n",
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"3. **Critic** - Reviews and suggests improvements\n",
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"4. **UserProxy** - Orchestrates and provides final approval\n",
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"\n",
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"This is similar to llamacrew's workflow but **conversational** instead of DAG-based!"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create specialist agents\n",
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"researcher = AssistantAgent(\n",
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" name=\"Researcher\",\n",
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" system_message=\"\"\"You are a researcher. Your job is to find accurate information\n",
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" about topics and provide facts, statistics, and recent developments.\"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"writer = AssistantAgent(\n",
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" name=\"Writer\",\n",
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" system_message=\"\"\"You are a technical writer. Your job is to write clear,\n",
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" engaging content based on research provided. Use simple language and examples.\"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"critic = AssistantAgent(\n",
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" name=\"Critic\",\n",
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" system_message=\"\"\"You are an editor. Review content for clarity, accuracy,\n",
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" and engagement. Suggest specific improvements.\"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"# User proxy to orchestrate\n",
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"user_proxy_group = UserProxyAgent(\n",
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" name=\"UserProxy\",\n",
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" human_input_mode=\"NEVER\",\n",
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" max_consecutive_auto_reply=10,\n",
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" code_execution_config=False,\n",
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")\n",
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"\n",
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"print(\"✅ Group chat agents created\")"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create group chat\n",
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"groupchat = GroupChat(\n",
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" agents=[user_proxy_group, researcher, writer, critic],\n",
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" messages=[],\n",
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" max_round=12, # Maximum conversation rounds\n",
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")\n",
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"\n",
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"# Create manager to orchestrate\n",
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"manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)\n",
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"\n",
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"# Start group chat\n",
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"user_proxy_group.initiate_chat(\n",
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" manager,\n",
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" message=\"\"\"Write a 300-word blog post about the benefits of using\n",
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" Llama Stack for LLM applications. Include:\n",
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" 1. What Llama Stack is\n",
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" 2. Key benefits\n",
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" 3. A simple use case example\n",
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"\n",
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" Researcher: gather information\n",
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" Writer: create the blog post\n",
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" Critic: review and suggest improvements\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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"metadata": {},
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"source": [
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"## Example 4: Human-in-the-Loop\n",
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"\n",
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"### Pattern: Interactive Problem Solving\n",
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"\n",
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"Autogen excels at **human-in-the-loop** workflows where you can:\n",
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"- Provide feedback mid-conversation\n",
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"- Approve/reject suggestions\n",
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"- Guide the agent's direction\n",
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"\n",
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"**Note:** In notebooks, this requires `human_input_mode=\"ALWAYS\"` or `\"TERMINATE\"` and manual input."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Interactive assistant (uncomment to try)\n",
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"# WARNING: This will prompt for user input!\n",
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"\n",
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"# assistant_interactive = AssistantAgent(\n",
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"# name=\"InteractiveAssistant\",\n",
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"# system_message=\"You are a helpful assistant. Ask clarifying questions when needed.\",\n",
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"# llm_config=llm_config,\n",
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"# )\n",
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"\n",
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"# user_proxy_interactive = UserProxyAgent(\n",
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"# name=\"Human\",\n",
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"# human_input_mode=\"TERMINATE\", # Ask for human input when TERMINATE is mentioned\n",
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"# max_consecutive_auto_reply=5,\n",
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"# )\n",
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"\n",
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"# user_proxy_interactive.initiate_chat(\n",
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"# assistant_interactive,\n",
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"# message=\"Help me plan a machine learning project for customer churn prediction.\"\n",
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"# )\n",
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"\n",
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"print(\"💡 Human-in-the-loop example (commented out to avoid blocking notebook execution)\")\n",
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"print(\"Uncomment the code above to try interactive mode!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example 5: Sequential Task Solving\n",
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"\n",
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"### Pattern: Chain of Thought Problem Solving\n",
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"\n",
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"**Scenario:** Solve a complex problem requiring multiple steps\n",
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"\n",
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"1. **Understand** the problem\n",
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"2. **Plan** the solution approach\n",
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"3. **Implement** the solution (code)\n",
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"4. **Execute** and verify\n",
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"5. **Explain** the results\n",
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"\n",
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"### Use Case: Fibonacci Sequence Analysis"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a reasoning assistant\n",
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"reasoning_assistant = AssistantAgent(\n",
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" name=\"ReasoningAssistant\",\n",
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" system_message=\"\"\"You are a problem-solving assistant.\n",
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" For complex problems:\n",
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" 1. Break down the problem\n",
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" 2. Plan the solution step-by-step\n",
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" 3. Write clean, well-commented code\n",
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" 4. Explain results clearly\n",
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" \"\"\",\n",
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" llm_config=llm_config,\n",
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")\n",
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"\n",
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"user_proxy_reasoning = UserProxyAgent(\n",
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" name=\"User\",\n",
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" human_input_mode=\"NEVER\",\n",
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" max_consecutive_auto_reply=5,\n",
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" code_execution_config={\"work_dir\": \"reasoning\", \"use_docker\": False},\n",
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")\n",
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"\n",
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"# Complex problem requiring sequential reasoning\n",
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"user_proxy_reasoning.initiate_chat(\n",
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" reasoning_assistant,\n",
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" message=\"\"\"Find the first 20 Fibonacci numbers where the number is also a prime number.\n",
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"\n",
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" Requirements:\n",
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" 1. Explain the approach\n",
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" 2. Write efficient Python code\n",
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" 3. Display the results in a table\n",
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" 4. Calculate what percentage of the first 100 Fibonacci numbers are prime\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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"metadata": {},
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"source": [
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"## Comparison: AutoGen vs llamacrew\n",
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"\n",
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"### When to Use AutoGen + Llama Stack\n",
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"\n",
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"✅ **Use AutoGen when you need:**\n",
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"- **Conversational** interactions between agents\n",
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"- **Human-in-the-loop** workflows (interactive approval, feedback)\n",
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"- **Code generation & execution** (data analysis, scripting)\n",
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"- **Group discussions** (multiple agents debating, collaborating)\n",
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"- **Dynamic problem-solving** (unknown number of back-and-forth exchanges)\n",
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"- **Research/prototyping** (exploratory work)\n",
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"\n",
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"**Example Use Cases:**\n",
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"- Interactive coding assistant\n",
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"- Research assistant with human feedback\n",
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"- Multi-agent debate/discussion\n",
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"- Tutoring/educational applications\n",
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"- Dynamic customer support\n",
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"\n",
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"---\n",
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"\n",
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"### When to Use llamacrew\n",
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"\n",
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"✅ **Use llamacrew when you need:**\n",
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"- **Production workflows** (blog writing, data pipelines)\n",
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"- **Declarative DAGs** (predefined task dependencies)\n",
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"- **Automatic parallelization** (framework optimizes)\n",
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"- **Non-interactive automation** (scheduled jobs)\n",
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"- **Minimal dependencies** (lightweight deployment)\n",
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"- **Predictable workflows** (known steps)\n",
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"\n",
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"**Example Use Cases:**\n",
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"- Automated blog post generation\n",
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"- Data ETL pipelines\n",
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"- Report generation\n",
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"- Batch processing\n",
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"- Production automation\n",
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"\n",
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"---\n",
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"\n",
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"### They're Complementary!\n",
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"\n",
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"- **AutoGen**: Conversational, interactive, exploratory\n",
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"- **llamacrew**: Workflow, automated, production\n",
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"\n",
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"You might use **AutoGen for prototyping** then move to **llamacrew for production**!"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
|
"## Advanced: Custom Agent Behaviors\n",
|
|
"\n",
|
|
"### Pattern: Specialized Agent with Custom Logic\n",
|
|
"\n",
|
|
"You can create agents with custom behavior beyond just prompts."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from typing import Dict, List, Union\n",
|
|
"import re\n",
|
|
"\n",
|
|
"class CodeReviewAgent(AssistantAgent):\n",
|
|
" \"\"\"Custom agent that reviews code for specific patterns.\"\"\"\n",
|
|
"\n",
|
|
" def __init__(self, *args, **kwargs):\n",
|
|
" super().__init__(*args, **kwargs)\n",
|
|
" self.issues_found = []\n",
|
|
"\n",
|
|
" def review_code(self, code: str) -> Dict[str, List[str]]:\n",
|
|
" \"\"\"Custom method to review code for common issues.\"\"\"\n",
|
|
" issues = []\n",
|
|
"\n",
|
|
" # Check for common issues\n",
|
|
" if \"TODO\" in code or \"FIXME\" in code:\n",
|
|
" issues.append(\"Code contains TODO/FIXME comments\")\n",
|
|
"\n",
|
|
" if not re.search(r'def \\w+\\(.*\\):', code):\n",
|
|
" issues.append(\"No function definitions found\")\n",
|
|
"\n",
|
|
" if \"print(\" in code:\n",
|
|
" issues.append(\"Contains print statements (consider logging)\")\n",
|
|
"\n",
|
|
" self.issues_found.extend(issues)\n",
|
|
" return {\"issues\": issues, \"total\": len(issues)}\n",
|
|
"\n",
|
|
"# Create custom reviewer\n",
|
|
"code_reviewer = CodeReviewAgent(\n",
|
|
" name=\"CodeReviewer\",\n",
|
|
" system_message=\"\"\"You are a code reviewer. Analyze code for:\n",
|
|
" - Code quality\n",
|
|
" - Best practices\n",
|
|
" - Potential bugs\n",
|
|
" - Performance issues\n",
|
|
" Provide specific, actionable feedback.\n",
|
|
" \"\"\",\n",
|
|
" llm_config=llm_config,\n",
|
|
")\n",
|
|
"\n",
|
|
"print(\"✅ Custom CodeReviewAgent created with specialized review logic\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Performance Tips\n",
|
|
"\n",
|
|
"### 1. Model Selection\n",
|
|
"\n",
|
|
"```python\n",
|
|
"# Fast models for simple tasks\n",
|
|
"config_fast = {\n",
|
|
" \"model\": \"ollama/llama3.2:3b\", # Smaller, faster\n",
|
|
" \"temperature\": 0.5,\n",
|
|
"}\n",
|
|
"\n",
|
|
"# Powerful models for complex reasoning\n",
|
|
"config_powerful = {\n",
|
|
" \"model\": \"ollama/llama3.3:70b\", # Larger, better quality\n",
|
|
" \"temperature\": 0.7,\n",
|
|
"}\n",
|
|
"```\n",
|
|
"\n",
|
|
"### 2. Limit Conversation Rounds\n",
|
|
"\n",
|
|
"```python\n",
|
|
"user_proxy = UserProxyAgent(\n",
|
|
" name=\"User\",\n",
|
|
" max_consecutive_auto_reply=3, # Prevent infinite loops\n",
|
|
")\n",
|
|
"```\n",
|
|
"\n",
|
|
"### 3. Set Timeouts\n",
|
|
"\n",
|
|
"```python\n",
|
|
"llm_config = {\n",
|
|
" \"timeout\": 60, # 60 second timeout per request\n",
|
|
" \"config_list\": config_list,\n",
|
|
"}\n",
|
|
"```\n",
|
|
"\n",
|
|
"### 4. Use Work Directories\n",
|
|
"\n",
|
|
"```python\n",
|
|
"code_execution_config = {\n",
|
|
" \"work_dir\": \"autogen_workspace\", # Isolate generated files\n",
|
|
" \"use_docker\": False,\n",
|
|
"}\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Troubleshooting\n",
|
|
"\n",
|
|
"### Common Issues\n",
|
|
"\n",
|
|
"#### 1. \"Could not connect to Llama Stack\"\n",
|
|
"\n",
|
|
"```bash\n",
|
|
"# Check if Llama Stack is running\n",
|
|
"curl http://localhost:8321/health\n",
|
|
"\n",
|
|
"# Start Llama Stack if needed\n",
|
|
"llama stack run\n",
|
|
"```\n",
|
|
"\n",
|
|
"#### 2. \"Model not found\"\n",
|
|
"\n",
|
|
"```bash\n",
|
|
"# List available models\n",
|
|
"curl http://localhost:8321/models\n",
|
|
"\n",
|
|
"# Make sure model name matches exactly:\n",
|
|
"# ✅ \"ollama/llama3.3:70b\"\n",
|
|
"# ❌ \"llama3.3:70b\"\n",
|
|
"```\n",
|
|
"\n",
|
|
"#### 3. \"Agent not responding\"\n",
|
|
"\n",
|
|
"- Check `max_consecutive_auto_reply` isn't set too low\n",
|
|
"- Increase `timeout` in `llm_config`\n",
|
|
"- Verify Llama Stack model is loaded and warm\n",
|
|
"\n",
|
|
"#### 4. \"Code execution failed\"\n",
|
|
"\n",
|
|
"- Make sure `code_execution_config` is set correctly\n",
|
|
"- Check file permissions on `work_dir`\n",
|
|
"- Install required Python packages\n",
|
|
"\n",
|
|
"---\n",
|
|
"\n",
|
|
"### Debug Mode\n",
|
|
"\n",
|
|
"```python\n",
|
|
"import logging\n",
|
|
"\n",
|
|
"# Enable AutoGen debug logging\n",
|
|
"logging.basicConfig(level=logging.DEBUG)\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Summary\n",
|
|
"\n",
|
|
"### What We Covered\n",
|
|
"\n",
|
|
"1. ✅ **Two-Agent Conversations** - UserProxy + Assistant pattern\n",
|
|
"2. ✅ **Code Generation & Execution** - AutoGen's killer feature\n",
|
|
"3. ✅ **Group Chat** - Multiple agents collaborating\n",
|
|
"4. ✅ **Human-in-the-Loop** - Interactive workflows\n",
|
|
"5. ✅ **Sequential Reasoning** - Complex problem solving\n",
|
|
"6. ✅ **Custom Agents** - Specialized behaviors\n",
|
|
"\n",
|
|
"### Key Takeaways\n",
|
|
"\n",
|
|
"**AutoGen + Llama Stack is powerful for:**\n",
|
|
"- 🗣️ **Conversational** multi-agent systems\n",
|
|
"- 👤 **Interactive** problem-solving with humans\n",
|
|
"- 💻 **Code generation** and execution\n",
|
|
"- 🤝 **Collaborative** agent discussions\n",
|
|
"\n",
|
|
"**vs llamacrew which is better for:**\n",
|
|
"- 🔄 **Production workflows** and pipelines\n",
|
|
"- 📊 **Declarative** task orchestration\n",
|
|
"- ⚡ **Automatic parallelization**\n",
|
|
"- 🤖 **Non-interactive** automation\n",
|
|
"\n",
|
|
"---\n",
|
|
"\n",
|
|
"### Next Steps\n",
|
|
"\n",
|
|
"1. Experiment with different agent combinations\n",
|
|
"2. Try human-in-the-loop workflows\n",
|
|
"3. Build custom agents for your use case\n",
|
|
"4. Compare AutoGen vs llamacrew for your specific needs\n",
|
|
"\n",
|
|
"### Resources\n",
|
|
"\n",
|
|
"- **AutoGen Docs**: https://microsoft.github.io/autogen/\n",
|
|
"- **Llama Stack Docs**: https://llama-stack.readthedocs.io/\n",
|
|
"- **llamacrew Docs**: `/home/omara/Desktop/llamacrew/README.md`\n",
|
|
"\n",
|
|
"---\n",
|
|
"\n",
|
|
"**Happy multi-agent building! 🚀**"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|