From a1a3417d4446a75142cd326d729d02ed9a761b85 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Wed, 22 Jan 2025 15:49:08 +0900 Subject: [PATCH 1/8] Add an ipynb tutorial --- .../07-LangGraph-Multi-Agent-Supervisor.ipynb | 992 ++++++++++++++++++ .../07-langgraph-multi-agent-supervisor.png | Bin 0 -> 15137 bytes 2 files changed, 992 insertions(+) create mode 100644 17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb create mode 100644 17-LangGraph/03-Use-Cases/assets/07-langgraph-multi-agent-supervisor.png diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb new file mode 100644 index 000000000..3e7173677 --- /dev/null +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Agent Supervisor\n", + "\n", + "- Author: [Sungchul Kim](https://github.com/rlatjcj)\n", + "- Peer Review:\n", + "- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb) [![Open in GitHub](https://img.shields.io/badge/Open%20in%20GitHub-181717?style=flat-square&logo=github&logoColor=white)](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial/blob/main/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb)\n", + "\n", + "## Overview\n", + "\n", + "In the previous tutorial, we showed how to automatically route messages based on the output of the initial Researcher agent.\n", + "However, when there are multiple agents that need to be coordinated, simple branching logic has limitations.\n", + "Here, we introduce how to manage agents through [LLM-based Supervisor](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) and coordinate the entire team based on the results of each agent node.\n", + "\n", + "\n", + "In this tutorial, we'll explore how to build **a multi-agent system** using **LangGraph**, efficiently coordinate tasks between agents, and manage them through **a Supervisor**. \n", + "We'll cover handling multiple agents simultaneously, managing each agent to perform their role, and properly handling task completion.\n", + "\n", + "**Key Points**:\n", + "- The Supervisor brings together various expert agents and operates them as a single team.\n", + "- The Supervisor agent monitors the team's progress and executes logic such as calling appropriate agents for each step or terminating tasks.\n", + "\n", + "![](./assets/07-langgraph-multi-agent-supervisor.png)\n", + "\n", + "**What We'll Cover in This Tutorial**\n", + "\n", + "- **Setup**: How to install required packages and set up API keys\n", + "- **Tool Creation**: Defining tools for agents to use, such as web search and plot generation\n", + "- **Helper Utilities**: Defining utility functions needed for creating agent nodes\n", + "- **Creating the Supervisor**: Creating a Supervisor that contains logic for selecting Worker nodes and handling task completion\n", + "- **Constructing the Graph**: Constructing the complete graph by defining State and Worker nodes\n", + "- **Invoking the Team**: Calling the graph to see how the multi-agent system actually works\n", + "\n", + "In this process, we'll use LangGraph's pre-built [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) function to simplify each agent node.\n", + "\n", + "This use of \"advanced agents\" is meant to demonstrate specific design patterns in LangGraph, and can be combined with other basic patterns as needed to achieve optimal results.\n", + "\n", + "### Table of Contents\n", + "\n", + "- [Overview](#overview)\n", + "- [Environment Setup](#environment-setup)\n", + "- [Setting State](#setting-state)\n", + "- [Creating Agents](#creating-agents)\n", + "- [Constructing the Graph](#constructing-the-graph)\n", + "- [Calling the Team](#calling-the-team)\n", + "\n", + "### References\n", + "\n", + "- [LangGraph - Multi-Agent - Supervisor](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "Set up the environment. You may refer to [Environment Setup](https://wikidocs.net/257836) for more details.\n", + "\n", + "**[Note]**\n", + "- `langchain-opentutorial` is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials. \n", + "- You can checkout the [`langchain-opentutorial`](https://github.com/LangChain-OpenTutorial/langchain-opentutorial-pypi) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%%capture --no-stderr\n", + "%pip install langchain-opentutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Install required packages\n", + "from langchain_opentutorial import package\n", + "\n", + "package.install(\n", + " [\n", + " \"python-dotenv\",\n", + " \"langchain_core\",\n", + " \"langchain_community\",\n", + " \"langchain_openai\",\n", + " \"langchain_experimental\",\n", + " \"langgraph\",\n", + " \"matplotlib\",\n", + " ],\n", + " verbose=False,\n", + " upgrade=False,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Environment variables have been set successfully.\n" + ] + } + ], + "source": [ + "# Set environment variables\n", + "from langchain_opentutorial import set_env\n", + "\n", + "set_env(\n", + " {\n", + " \"OPENAI_API_KEY\": \"\",\n", + " \"LANGCHAIN_API_KEY\": \"\",\n", + " \"LANGCHAIN_TRACING_V2\": \"true\",\n", + " \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n", + " \"LANGCHAIN_PROJECT\": \"Multi-Agent-Supervisor\",\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can alternatively set API keys such as `OPENAI_API_KEY` in a `.env` file and load them.\n", + "\n", + "[Note] This is not necessary if you've already set the required API keys in previous steps." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load API keys from .env file\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv(override=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting State\n", + "\n", + "Define **state** to be used in the multi-agent system." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import operator\n", + "from typing import Annotated, Sequence\n", + "\n", + "from langchain_core.messages import BaseMessage\n", + "from typing_extensions import TypedDict\n", + "\n", + "\n", + "class AgentState(TypedDict):\n", + " \"\"\"Define state to be used in the multi-agent system.\"\"\"\n", + " messages: Annotated[Sequence[BaseMessage], operator.add] # messages\n", + " next: str # next agent to route to" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Agents\n", + "\n", + "### Creating Tools\n", + "\n", + "In this example, we'll create agents that use a search engine to perform web research and generate plots.\n", + "\n", + "Define the tools to be used below.\n", + "\n", + "- **Research**: Use `TavilySearch` tool to perform web research. To use this tool, you need to set the `TAVILY_API_KEY`. Please refer to [previous tutorial](https://langchain-opentutorial.gitbook.io/langchain-opentutorial/15-agent/01-tools#search-api-tooltavily) for more details.\n", + "- **Coder**: Use `PythonREPLTool` tool to run code." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.tools.tavily_search import TavilySearchResults\n", + "from langchain_experimental.tools import PythonREPLTool\n", + "\n", + "\n", + "# Initialize Tavily search tool that returns up to 5 search results\n", + "tavily_tool = TavilySearchResults(max_results=5)\n", + "\n", + "# Initialize Python REPL tool that runs code locally (may not be safe)\n", + "python_repl_tool = PythonREPLTool()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Utility for Creating Agents\n", + "\n", + "When building a multi-agent system using LangGraph, **helper functions** play a crucial role in creating and managing agent nodes. These functions enhance code reusability and simplify interactions between agents.\n", + "\n", + "- **Creating Agent Nodes**: Define functions to create nodes for each agent's role\n", + "- **Managing Workflow**: Provide utilities to manage the workflow between agents\n", + "- **Error Handling**: Include mechanisms to handle errors that may occur during agent execution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following is an example of defining a function called `agent_node`.\n", + "\n", + "This function creates an agent node using the given state and agent. We will call this function later using `functools.partial`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "\n", + "\n", + "# Create an agent node using the specified agent and name\n", + "def agent_node(state, agent, name):\n", + " # Call the agent\n", + " agent_response = agent.invoke(state)\n", + " # Convert the last message of the agent to a HumanMessage and return it\n", + " return {\n", + " \"messages\": [\n", + " HumanMessage(content=agent_response[\"messages\"][-1].content, name=name)\n", + " ]\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is an example of creating a `research_node` using `functools.partial`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import functools\n", + "\n", + "from langchain_openai import ChatOpenAI\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "\n", + "# Create a Research Agent\n", + "research_agent = create_react_agent(ChatOpenAI(model=\"gpt-4o\"), tools=[tavily_tool])\n", + "\n", + "# Create a Research Node\n", + "research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "> **Note**\n", + ">\n", + "> Role of `functools.partial`\n", + ">\n", + "> `functools.partial` is used to create a new function by fixing some arguments or keyword arguments of an existing function. In other words, it helps simplify commonly used function call patterns.\n", + ">\n", + "> **Roles**\n", + ">\n", + "> 1. **Create new function with predefined values**: Returns a new function with some arguments of the existing function pre-specified.\n", + "> 2. **Code simplification**: Reduces code duplication by simplifying commonly used function call patterns.\n", + "> 3. **Improved readability**: Customizes function behavior for specific tasks to make it more intuitive to use.\n", + ">\n", + "> **Example code**\n", + "> ```python\n", + "> research_node = functools.partial(agent_node, agent=research_agent, names=\"Researcher\")\n", + "> ```\n", + ">\n", + "> 1. Assume there is an existing function called `agent_node`.\n", + "> - This function can accept multiple arguments and keyword arguments.\n", + ">\n", + "> 2. `functools.partial` fixes the values `agent=research_agent` and `names=\"Researcher\"` for this function.\n", + "> - This means that `research_node` no longer needs to specify the `agent` and `names` values when calling `agent_node`.\n", + "> - For example:\n", + "> ```python\n", + "> agent_node(state, agent=research_agent, names=\"Researcher\")\n", + "> ```\n", + "> Instead, you can use:\n", + "> ```python\n", + "> research_node(state)\n", + "> ```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's run the code and check the results." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'messages': [HumanMessage(content='Here\\'s a simple \"Hello, World!\" program in several different programming languages. You can use any of these to print \"Hello, World!\" to the terminal:\\n\\n### Python\\n```python\\nprint(\"Hello, World!\")\\n```\\n\\n### JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hello, World!\");\\n```\\n\\n### Java\\n```java\\npublic class HelloWorld {\\n public static void main(String[] args) {\\n System.out.println(\"Hello, World!\");\\n }\\n}\\n```\\n\\n### C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hello, World!\\\\n\");\\n return 0;\\n}\\n```\\n\\n### C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hello, World!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n### Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n fmt.Println(\"Hello, World!\")\\n}\\n```\\n\\n### Ruby\\n```ruby\\nputs \\'Hello, World!\\'\\n```\\n\\n### Bash\\n```bash\\necho \"Hello, World!\"\\n```\\n\\nTo run any of these, save the code to a file with the appropriate extension (e.g., `.py` for Python, `.js` for JavaScript) and execute it with the corresponding interpreter or compiler. For instance, you\\'d use `python hello.py` for Python or `node hello.js` for JavaScript.', additional_kwargs={}, response_metadata={}, name='Researcher')]}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "research_node(\n", + " {\n", + " \"messages\": [\n", + " HumanMessage(content=\"Code hello world and print it to the terminal\")\n", + " ]\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating Agent Supervisor\n", + "\n", + "Create an agent that manages and supervises agents." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\n", + "\n", + "from pydantic import BaseModel\n", + "\n", + "\n", + "# Define the list of member agents\n", + "members = [\"Researcher\", \"Coder\"]\n", + "\n", + "# Define the list of options for selecting the next worker\n", + "options_for_next = [\"FINISH\"] + members\n", + "\n", + "# Define the response model for selecting the next worker: indicates selecting the next worker or completing the task\n", + "class RouteResponse(BaseModel):\n", + " next: Literal[*options_for_next]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "\n", + "# Define the system prompt: a supervisor tasked with managing a conversation between workers\n", + "system_prompt = (\n", + " \"You are a supervisor tasked with managing a conversation between the\"\n", + " \" following workers: {members}. Given the following user request,\"\n", + " \" respond with the worker to act next. Each worker will perform a\"\n", + " \" task and respond with their results and status. When finished,\"\n", + " \" respond with FINISH.\"\n", + ")\n", + "\n", + "# Create ChatPromptTemplate\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", system_prompt),\n", + " MessagesPlaceholder(variable_name=\"messages\"),\n", + " (\n", + " \"system\",\n", + " \"Given the conversation above, who should act next? \"\n", + " \"Or should we FINISH? Select one of: {options}\",\n", + " ),\n", + " ]\n", + ").partial(options=str(options_for_next), members=\", \".join(members))\n", + "\n", + "# Initialize LLM\n", + "llm = ChatOpenAI(model=\"gpt-4o-mini\", temperature=0)\n", + "\n", + "# Create Supervisor Agent\n", + "def supervisor_agent(state):\n", + " # Combine prompt and LLM to create a chain\n", + " supervisor_chain = prompt | llm.with_structured_output(RouteResponse)\n", + " # Call the agent\n", + " return supervisor_chain.invoke(state)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Graph\n", + "\n", + "Now, we're ready to build the graph. Below, we'll use the functions we just defined to define `state` and `worker` nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import functools\n", + "\n", + "from langgraph.prebuilt import create_react_agent\n", + "\n", + "\n", + "# Create Research Agent\n", + "research_agent = create_react_agent(llm, tools=[tavily_tool])\n", + "research_node = functools.partial(agent_node, agent=research_agent, name=\"Researcher\")\n", + "\n", + "code_system_prompt = \"\"\"\n", + "Be sure to use the following font in your code for visualization.\n", + "\n", + "##### Font Setting #####\n", + "import platform\n", + "\n", + "# Determine OS\n", + "current_os = platform.system()\n", + "\n", + "if current_os == \"Windows\":\n", + " # Windows environment font setting\n", + " font_path = \"C:/Windows/Fonts/malgun.ttf\" # Malgun font path\n", + " fontprop = fm.FontProperties(fname=font_path, size=12)\n", + " plt.rc(\"font\", family=fontprop.get_name())\n", + "elif current_os == \"Darwin\": # macOS\n", + " # Mac environment font setting\n", + " plt.rcParams[\"font.family\"] = \"AppleGothic\"\n", + "else: # Other OSs including Linux\n", + " # Try to set the default Korean font\n", + " try:\n", + " plt.rcParams[\"font.family\"] = \"NanumGothic\"\n", + " except:\n", + " print(\"Cannot find Korean font. Using system default font.\")\n", + "\n", + "##### Prevent minus font from being broken #####\n", + "plt.rcParams[\"axes.unicode_minus\"] = False\n", + "\"\"\"\n", + "\n", + "# Create Coder Agent\n", + "coder_agent = create_react_agent(\n", + " llm,\n", + " tools=[python_repl_tool],\n", + " state_modifier=code_system_prompt,\n", + ")\n", + "coder_node = functools.partial(agent_node, agent=coder_agent, name=\"Coder\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from langgraph.checkpoint.memory import MemorySaver\n", + "from langgraph.graph import END, START, StateGraph\n", + "\n", + "\n", + "# Create graph\n", + "workflow = StateGraph(AgentState)\n", + "\n", + "# Add nodes to the graph\n", + "workflow.add_node(\"Researcher\", research_node)\n", + "workflow.add_node(\"Coder\", coder_node)\n", + "workflow.add_node(\"Supervisor\", supervisor_agent)\n", + "\n", + "\n", + "# Add edges from member nodes to the Supervisor node\n", + "for member in members:\n", + " workflow.add_edge(member, \"Supervisor\")\n", + "\n", + "# Add conditional edges\n", + "conditional_map = {k: k for k in members}\n", + "conditional_map[\"FINISH\"] = END\n", + "\n", + "\n", + "def get_next(state):\n", + " return state[\"next\"]\n", + "\n", + "\n", + "# Add conditional edges from the Supervisor node\n", + "workflow.add_conditional_edges(\"Supervisor\", get_next, conditional_map)\n", + "\n", + "# Add starting point\n", + "workflow.add_edge(START, \"Supervisor\")\n", + "\n", + "# Compile the graph\n", + "graph = workflow.compile(checkpointer=MemorySaver())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Image, display\n", + "from langgraph.graph.state import CompiledStateGraph\n", + "\n", + "\n", + "try:\n", + " if isinstance(graph, CompiledStateGraph):\n", + " display(\n", + " Image(\n", + " graph.get_graph().draw_mermaid_png(\n", + " background_color=\"white\",\n", + " # node_colors=NodeStyles(),\n", + " )\n", + " )\n", + " )\n", + "except Exception as e:\n", + " print(f\"[ERROR] Visualize Graph Error: {e}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling the Team\n", + "\n", + "Now, we can check the performance by calling the graph." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mResearcher\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " tavily_search_results_json (call_bA10FQWyZQmC9LmrOqdDIImY)\n", + " Call ID: call_bA10FQWyZQmC9LmrOqdDIImY\n", + " Args:\n", + " query: South Korea GDP per capita 2010 to 2024\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mtools\u001b[0m in [\u001b[1;33mResearcher\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: tavily_search_results_json\n", + "\n", + "[{\"url\": \"https://statisticstimes.com/economy/country/south-korea-gdp-per-capita.php\", \"content\": \"GDP per capita of South Korea According to the IMF World Economic Outlook (October 2024), South Korea's nominal GDP per capita in 2024 is projected to be around $36,132 at current prices. Based on PPP, South Korea's GDP per capita in 2024 is forecast at 62,960 billion international dollars. South Korea ranks 33rd in the world by GDP (nominal) per capita and 29th by GDP (PPP) per capita on the 194 economies list. South Korea is ranked 8th in nominal and 10th in the PPP list among 49 European economies. GDP (Nominal) per capita of South Korea GDP (PPP) per capita of South Korea Year GDP per capita ($/Int. Year GDP (Nominal) per capita ($) GDP (PPP) per capita (Int.\"}, {\"url\": \"https://www.statista.com/statistics/939347/gross-domestic-product-gdp-per-capita-in-south-korea/\", \"content\": \"Annual car sales worldwide 2010-2023, with a forecast for 2024; ... (GDP) per capita in South Korea was forecast to continuously increase between 2024 and 2029 by in total 8,215.6 U.S. dollars\"}, {\"url\": \"https://www.macrotrends.net/global-metrics/countries/KOR/south-korea/gdp-per-capita\", \"content\": \"South Korea gdp per capita for 2023 was $33,121, a 2.24% increase from 2022. South Korea gdp per capita for 2022 was $32,395, a 7.77% decline from 2021. South Korea gdp per capita for 2021 was $35,126, a 10.73% increase from 2020. South Korea gdp per capita for 2020 was $31,721, a 0.57% decline from 2019.\"}, {\"url\": \"https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita\", \"content\": \"A country's gross domestic product (GDP) at purchasing power parity (PPP) per capita is the PPP value of all final goods and services produced within an economy in a given year, divided by the average (or mid-year) population for the same year. This is similar to nominal GDP per capita but adjusted for the cost of living in each country.. In 2023, the estimated average GDP per capita (PPP) of\"}, {\"url\": \"https://www.worldeconomics.com/GDP/Korea.aspx\", \"content\": \"GDP | 2024 | Economic Data | World Economics China, Russia, India, USA, Israel, Saudi Arabia, Japan, Malaysia, Canada, Czechia, Indonesia, Pakistan, Germany, Mexico, Singapore, Nigeria, TΓΌrkiye, Egypt, Thailand, Ethiopia, Italy, South Africa, Bangladesh, Philippines, Brazil, United Kingdom GDP Data Quality GDP Data Quality Ratings GDP Per Capita Data Quality Ratings GDP Data Quality Ratings GDP Per Capita Data Quality Ratings Outdated GDP base year data: Many countries use outdated base years for their GDP calculations, leading to an underestimation of economic growth. Omit countries with poor quality GDP data: (A-E) | | Cote d'Ivoire | $344.24 | $366.28 | C | | $73.59 | $76.32 | E | | $59.05 | $59.93 | E | GDP Data Quality\"}]\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mResearcher\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", + "\n", + "- **2010**: $24,000 (approx.)\n", + "- **2011**: $25,000 (approx.)\n", + "- **2012**: $26,000 (approx.)\n", + "- **2013**: $27,000 (approx.)\n", + "- **2014**: $28,000 (approx.)\n", + "- **2015**: $29,000 (approx.)\n", + "- **2016**: $30,000 (approx.)\n", + "- **2017**: $31,000 (approx.)\n", + "- **2018**: $32,000 (approx.)\n", + "- **2019**: $33,000 (approx.)\n", + "- **2020**: $31,721\n", + "- **2021**: $35,126\n", + "- **2022**: $32,395\n", + "- **2023**: $33,121\n", + "- **2024**: $36,132 (projected)\n", + "\n", + "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a recovery and growth following the impacts of the pandemic in 2020 and 2021. \n", + "\n", + "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mResearcher\u001b[0m πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "Name: Researcher\n", + "\n", + "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", + "\n", + "- **2010**: $24,000 (approx.)\n", + "- **2011**: $25,000 (approx.)\n", + "- **2012**: $26,000 (approx.)\n", + "- **2013**: $27,000 (approx.)\n", + "- **2014**: $28,000 (approx.)\n", + "- **2015**: $29,000 (approx.)\n", + "- **2016**: $30,000 (approx.)\n", + "- **2017**: $31,000 (approx.)\n", + "- **2018**: $32,000 (approx.)\n", + "- **2019**: $33,000 (approx.)\n", + "- **2020**: $31,721\n", + "- **2021**: $35,126\n", + "- **2022**: $32,395\n", + "- **2023**: $33,121\n", + "- **2024**: $36,132 (projected)\n", + "\n", + "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a recovery and growth following the impacts of the pandemic in 2020 and 2021. \n", + "\n", + "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mCoder\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "Tool Calls:\n", + " Python_REPL (call_jsOUzNuMuatwgX9o2JvXEqQe)\n", + " Call ID: call_jsOUzNuMuatwgX9o2JvXEqQe\n", + " Args:\n", + " query: import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import platform\n", + "\n", + "# Determine OS\n", + "current_os = platform.system()\n", + "\n", + "if current_os == \"Windows\":\n", + " # Windows environment font setting\n", + " font_path = \"C:/Windows/Fonts/malgun.ttf\" # Malgun font path\n", + " fontprop = fm.FontProperties(fname=font_path, size=12)\n", + " plt.rc(\"font\", family=fontprop.get_name())\n", + "elif current_os == \"Darwin\": # macOS\n", + " # Mac environment font setting\n", + " plt.rcParams[\"font.family\"] = \"AppleGothic\"\n", + "else: # Other OSs including Linux\n", + " # Try to set the default Korean font\n", + " try:\n", + " plt.rcParams[\"font.family\"] = \"NanumGothic\"\n", + " except:\n", + " print(\"Cannot find Korean font. Using system default font.\")\n", + "\n", + "# Prevent minus font from being broken\n", + "plt.rcParams[\"axes.unicode_minus\"] = False\n", + "\n", + "# Data for GDP per capita of South Korea from 2010 to 2024\n", + "years = np.array([2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024])\n", + "\n", + "gdp_per_capita = np.array([24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 31721, 35126, 32395, 33121, 36132])\n", + "\n", + "# Create the plot\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(years, gdp_per_capita, marker='o', linestyle='-', color='b')\n", + "plt.title('South Korea GDP per Capita (2010-2024)')\n", + "plt.xlabel('Year')\n", + "plt.ylabel('GDP per Capita (USD)')\n", + "plt.xticks(years)\n", + "plt.grid(True)\n", + "plt.tight_layout()\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "==================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mtools\u001b[0m in [\u001b[1;33mCoder\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "=================================\u001b[1m Tool Message \u001b[0m=================================\n", + "Name: Python_REPL\n", + "\n", + "\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mCoder\u001b[0m] πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================\u001b[1m Ai Message \u001b[0m==================================\n", + "\n", + "The GDP per capita of South Korea from 2010 to 2024 has been visualized in a line graph. The graph shows a general upward trend, with some fluctuations, particularly around the years impacted by the pandemic. The projected GDP per capita for 2024 indicates a recovery and growth. If you have any further questions or need additional analysis, feel free to ask!\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mCoder\u001b[0m πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "================================\u001b[1m Human Message \u001b[0m=================================\n", + "Name: Coder\n", + "\n", + "The GDP per capita of South Korea from 2010 to 2024 has been visualized in a line graph. The graph shows a general upward trend, with some fluctuations, particularly around the years impacted by the pandemic. The projected GDP per capita for 2024 indicates a recovery and growth. If you have any further questions or need additional analysis, feel free to ask!\n", + "==================================================\n", + "\n", + "==================================================\n", + "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", + "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "==================================================\n" + ] + } + ], + "source": [ + "import uuid\n", + "from typing import Callable\n", + "\n", + "from langchain_core.runnables import RunnableConfig\n", + "\n", + "\n", + "def invoke_graph(\n", + " graph: CompiledStateGraph,\n", + " inputs: dict,\n", + " config: RunnableConfig,\n", + " node_names: list[str] = [],\n", + " callback: Callable = None,\n", + "):\n", + " \"\"\"\n", + " A function that nicely streams and outputs the execution results of a LangGraph app.\n", + "\n", + " Args:\n", + " graph (CompiledStateGraph): The compiled LangGraph object to execute\n", + " inputs (dict): Dictionary of input values to pass to the graph\n", + " config (RunnableConfig): Execution configuration\n", + " node_names (list[str], optional): List of node names to output. Defaults to empty list\n", + " callback (Callable, optional): Callback function for processing each chunk. Defaults to None\n", + " The callback function takes a dictionary of the form {\"node\": str, \"content\": str} as an argument\n", + "\n", + " Returns:\n", + " None: The function only outputs streaming results and has no return value\n", + " \"\"\"\n", + "\n", + " def format_namespace(namespace):\n", + " return namespace[-1].split(\":\")[0] if len(namespace) > 0 else \"root graph\"\n", + "\n", + " # Include subgraph outputs through subgraphs=True\n", + " for namespace, chunk in graph.stream(\n", + " inputs, config, stream_mode=\"updates\", subgraphs=True\n", + " ):\n", + " for node_name, node_chunk in chunk.items():\n", + " # Filter only if node_names is not empty\n", + " if len(node_names) > 0 and node_name not in node_names:\n", + " continue\n", + "\n", + " # If callback is provided, execute it\n", + " if callback is not None:\n", + " callback({\"node\": node_name, \"content\": node_chunk})\n", + " # If no callback is provided, print the default output\n", + " else:\n", + " print(\"\\n\" + \"=\" * 50)\n", + " formatted_namespace = format_namespace(namespace)\n", + " if formatted_namespace == \"root graph\":\n", + " print(f\"πŸ”„ Node: \\033[1;36m{node_name}\\033[0m πŸ”„\")\n", + " else:\n", + " print(\n", + " f\"πŸ”„ Node: \\033[1;36m{node_name}\\033[0m in [\\033[1;33m{formatted_namespace}\\033[0m] πŸ”„\"\n", + " )\n", + " print(\"- \" * 25)\n", + "\n", + " # Print the chunk data of the node\n", + " for k, v in node_chunk.items():\n", + " if isinstance(v, BaseMessage):\n", + " v.pretty_print()\n", + " elif isinstance(v, list):\n", + " for list_item in v:\n", + " if isinstance(list_item, BaseMessage):\n", + " list_item.pretty_print()\n", + " else:\n", + " print(list_item)\n", + " elif isinstance(v, dict):\n", + " for node_chunk_key, node_chunk_value in node_chunk.items():\n", + " print(f\"{node_chunk_key}:\\n{node_chunk_value}\")\n", + " print(\"=\" * 50)\n", + "\n", + "# Set config (recursion limit, thread_id)\n", + "config = RunnableConfig(recursion_limit=10, configurable={\"thread_id\": str(uuid.uuid4())})\n", + "\n", + "# Set input (question)\n", + "inputs = {\n", + " \"messages\": [\n", + " HumanMessage(\n", + " content=\"Visualize the GDP per capita of South Korea from 2010 to 2024.\"\n", + " )\n", + " ],\n", + "}\n", + "\n", + "# Run the graph\n", + "invoke_graph(graph, inputs, config)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langchain_tutorial_1", + "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": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git 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zUKcI)Pfd=`SC>0I{>AJBVZ-J7o%>SOd4s6ro3%I(kD2C3ghjyBVE#9zBAGzt&l5x%?3}Y<>^H3inUh3RqKcC;7q~g~GpuV#brp5gKD8mzAvO!dt6W8I2nwZGutI z+8v-0&-!ui&*Ao<#lgrST$R!v^oGjEEIJhJ$m!!2LYP~CdGP0UVF>L!@gl?WmUgmvid zG_4Vvsc_t4(>&^bxYQt&$nhoFHL>%E&45-_b<7!6aG~bxHX+@AJmEx+uMx-yd{&jZ zK2+|&1HEtkN*o*V+$J)*o_qiP#bh;mbg09G=@5(~DaKN)8vG36i-5L3HR#M7b@5%H83@_ zw)xfsLA=Cc_+&c#y^luhneCpArAESsynqau?~J1ZGkjZBpy6Jdc-+E0x&6^j_xO42sx{9kjGEQd1<#(o<7aH(9b z{QBi;9&;{#buyj(ir4{#$!~{WH)E*Wr08j+_)6Zgbr9}2nRnUNpe?Lch4Iz%XUD;^ u?L|+M|8?wVJ|Ft<|JQU0*&KU7I=Xm+rDSF64u10rk$$HjUIH`t@_zwcWY;19 literal 0 HcmV?d00001 From d21a9b487a78452588660ea7502309d43bdc3644 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Wed, 22 Jan 2025 15:59:27 +0900 Subject: [PATCH 2/8] Move the image to the center --- .../03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index 3e7173677..216dc5de8 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -26,7 +26,9 @@ "- The Supervisor brings together various expert agents and operates them as a single team.\n", "- The Supervisor agent monitors the team's progress and executes logic such as calling appropriate agents for each step or terminating tasks.\n", "\n", - "![](./assets/07-langgraph-multi-agent-supervisor.png)\n", + "
\n", + " \n", + "
\n", "\n", "**What We'll Cover in This Tutorial**\n", "\n", From 21c28f4ae75ea83af7ab21126e364738dcb1dee0 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Wed, 22 Jan 2025 16:03:41 +0900 Subject: [PATCH 3/8] Update `overview` --- .../03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index 216dc5de8..d8ee32a3b 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -37,7 +37,7 @@ "- **Helper Utilities**: Defining utility functions needed for creating agent nodes\n", "- **Creating the Supervisor**: Creating a Supervisor that contains logic for selecting Worker nodes and handling task completion\n", "- **Constructing the Graph**: Constructing the complete graph by defining State and Worker nodes\n", - "- **Invoking the Team**: Calling the graph to see how the multi-agent system actually works\n", + "- **Calling the Team**: Calling the graph to see how the multi-agent system actually works\n", "\n", "In this process, we'll use LangGraph's pre-built [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) function to simplify each agent node.\n", "\n", From 0be081a1754fa3ec1ce1d7c9b58decb99074662c Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Wed, 22 Jan 2025 16:03:51 +0900 Subject: [PATCH 4/8] Update lines --- .../03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb | 3 --- 1 file changed, 3 deletions(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index d8ee32a3b..9778851c1 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -517,7 +517,6 @@ "workflow.add_node(\"Coder\", coder_node)\n", "workflow.add_node(\"Supervisor\", supervisor_agent)\n", "\n", - "\n", "# Add edges from member nodes to the Supervisor node\n", "for member in members:\n", " workflow.add_edge(member, \"Supervisor\")\n", @@ -526,11 +525,9 @@ "conditional_map = {k: k for k in members}\n", "conditional_map[\"FINISH\"] = END\n", "\n", - "\n", "def get_next(state):\n", " return state[\"next\"]\n", "\n", - "\n", "# Add conditional edges from the Supervisor node\n", "workflow.add_conditional_edges(\"Supervisor\", get_next, conditional_map)\n", "\n", From e5c05e9d96aaad446858f3f42f5524eb1caa62e4 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Wed, 22 Jan 2025 16:08:19 +0900 Subject: [PATCH 5/8] Update to only set the font for English --- .../07-LangGraph-Multi-Agent-Supervisor.ipynb | 263 +++++------------- 1 file changed, 77 insertions(+), 186 deletions(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index 9778851c1..840f1fd1b 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -152,7 +152,7 @@ "True" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -340,16 +340,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content='Here\\'s a simple \"Hello, World!\" program in several different programming languages. You can use any of these to print \"Hello, World!\" to the terminal:\\n\\n### Python\\n```python\\nprint(\"Hello, World!\")\\n```\\n\\n### JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hello, World!\");\\n```\\n\\n### Java\\n```java\\npublic class HelloWorld {\\n public static void main(String[] args) {\\n System.out.println(\"Hello, World!\");\\n }\\n}\\n```\\n\\n### C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hello, World!\\\\n\");\\n return 0;\\n}\\n```\\n\\n### C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hello, World!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n### Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n fmt.Println(\"Hello, World!\")\\n}\\n```\\n\\n### Ruby\\n```ruby\\nputs \\'Hello, World!\\'\\n```\\n\\n### Bash\\n```bash\\necho \"Hello, World!\"\\n```\\n\\nTo run any of these, save the code to a file with the appropriate extension (e.g., `.py` for Python, `.js` for JavaScript) and execute it with the corresponding interpreter or compiler. For instance, you\\'d use `python hello.py` for Python or `node hello.js` for JavaScript.', additional_kwargs={}, response_metadata={}, name='Researcher')]}" + "{'messages': [HumanMessage(content='Here\\'s a simple program to print \"Hello, World!\" to the terminal in a few different programming languages.\\n\\n### Python\\n```python\\nprint(\"Hello, World!\")\\n```\\n\\n### JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hello, World!\");\\n```\\n\\n### Java\\n```java\\npublic class HelloWorld {\\n public static void main(String[] args) {\\n System.out.println(\"Hello, World!\");\\n }\\n}\\n```\\n\\n### C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hello, World!\\\\n\");\\n return 0;\\n}\\n```\\n\\n### C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hello, World!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n### Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n fmt.Println(\"Hello, World!\")\\n}\\n```\\n\\n### Ruby\\n```ruby\\nputs \"Hello, World!\"\\n```\\n\\nDepending on your interest or need, you can choose any of the languages above to run the \"Hello, World!\" program.', additional_kwargs={}, response_metadata={}, name='Researcher')]}" ] }, - "execution_count": 23, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -397,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -465,29 +465,16 @@ "code_system_prompt = \"\"\"\n", "Be sure to use the following font in your code for visualization.\n", "\n", - "##### Font Setting #####\n", - "import platform\n", - "\n", - "# Determine OS\n", - "current_os = platform.system()\n", - "\n", - "if current_os == \"Windows\":\n", - " # Windows environment font setting\n", - " font_path = \"C:/Windows/Fonts/malgun.ttf\" # Malgun font path\n", - " fontprop = fm.FontProperties(fname=font_path, size=12)\n", - " plt.rc(\"font\", family=fontprop.get_name())\n", - "elif current_os == \"Darwin\": # macOS\n", - " # Mac environment font setting\n", - " plt.rcParams[\"font.family\"] = \"AppleGothic\"\n", - "else: # Other OSs including Linux\n", - " # Try to set the default Korean font\n", - " try:\n", - " plt.rcParams[\"font.family\"] = \"NanumGothic\"\n", - " except:\n", - " print(\"Cannot find Korean font. Using system default font.\")\n", - "\n", - "##### Prevent minus font from being broken #####\n", - "plt.rcParams[\"axes.unicode_minus\"] = False\n", + "##### Font Settings #####\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set universal font settings\n", + "plt.rcParams[\"font.family\"] = \"DejaVu Sans\"\n", + "plt.rcParams[\"axes.unicode_minus\"] = False # Prevent minus sign from breaking\n", + "\n", + "# Set English locale\n", + "import locale\n", + "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n", "\"\"\"\n", "\n", "# Create Coder Agent\n", @@ -501,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -547,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -591,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -609,8 +596,8 @@ "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " tavily_search_results_json (call_bA10FQWyZQmC9LmrOqdDIImY)\n", - " Call ID: call_bA10FQWyZQmC9LmrOqdDIImY\n", + " tavily_search_results_json (call_EN0Ikq06zuQ4fpn39fRAaYlE)\n", + " Call ID: call_EN0Ikq06zuQ4fpn39fRAaYlE\n", " Args:\n", " query: South Korea GDP per capita 2010 to 2024\n", "==================================================\n", @@ -621,7 +608,7 @@ "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: tavily_search_results_json\n", "\n", - "[{\"url\": \"https://statisticstimes.com/economy/country/south-korea-gdp-per-capita.php\", \"content\": \"GDP per capita of South Korea According to the IMF World Economic Outlook (October 2024), South Korea's nominal GDP per capita in 2024 is projected to be around $36,132 at current prices. Based on PPP, South Korea's GDP per capita in 2024 is forecast at 62,960 billion international dollars. South Korea ranks 33rd in the world by GDP (nominal) per capita and 29th by GDP (PPP) per capita on the 194 economies list. South Korea is ranked 8th in nominal and 10th in the PPP list among 49 European economies. GDP (Nominal) per capita of South Korea GDP (PPP) per capita of South Korea Year GDP per capita ($/Int. Year GDP (Nominal) per capita ($) GDP (PPP) per capita (Int.\"}, {\"url\": \"https://www.statista.com/statistics/939347/gross-domestic-product-gdp-per-capita-in-south-korea/\", \"content\": \"Annual car sales worldwide 2010-2023, with a forecast for 2024; ... (GDP) per capita in South Korea was forecast to continuously increase between 2024 and 2029 by in total 8,215.6 U.S. dollars\"}, {\"url\": \"https://www.macrotrends.net/global-metrics/countries/KOR/south-korea/gdp-per-capita\", \"content\": \"South Korea gdp per capita for 2023 was $33,121, a 2.24% increase from 2022. South Korea gdp per capita for 2022 was $32,395, a 7.77% decline from 2021. South Korea gdp per capita for 2021 was $35,126, a 10.73% increase from 2020. South Korea gdp per capita for 2020 was $31,721, a 0.57% decline from 2019.\"}, {\"url\": \"https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita\", \"content\": \"A country's gross domestic product (GDP) at purchasing power parity (PPP) per capita is the PPP value of all final goods and services produced within an economy in a given year, divided by the average (or mid-year) population for the same year. This is similar to nominal GDP per capita but adjusted for the cost of living in each country.. In 2023, the estimated average GDP per capita (PPP) of\"}, {\"url\": \"https://www.worldeconomics.com/GDP/Korea.aspx\", \"content\": \"GDP | 2024 | Economic Data | World Economics China, Russia, India, USA, Israel, Saudi Arabia, Japan, Malaysia, Canada, Czechia, Indonesia, Pakistan, Germany, Mexico, Singapore, Nigeria, TΓΌrkiye, Egypt, Thailand, Ethiopia, Italy, South Africa, Bangladesh, Philippines, Brazil, United Kingdom GDP Data Quality GDP Data Quality Ratings GDP Per Capita Data Quality Ratings GDP Data Quality Ratings GDP Per Capita Data Quality Ratings Outdated GDP base year data: Many countries use outdated base years for their GDP calculations, leading to an underestimation of economic growth. Omit countries with poor quality GDP data: (A-E) | | Cote d'Ivoire | $344.24 | $366.28 | C | | $73.59 | $76.32 | E | | $59.05 | $59.93 | E | GDP Data Quality\"}]\n", + "[{\"url\": \"https://statisticstimes.com/economy/country/south-korea-gdp-per-capita.php\", \"content\": \"GDP per capita of South Korea According to the IMF World Economic Outlook (October 2024), South Korea's nominal GDP per capita in 2024 is projected to be around $36,132 at current prices. Based on PPP, South Korea's GDP per capita in 2024 is forecast at 62,960 billion international dollars. South Korea ranks 33rd in the world by GDP (nominal) per capita and 29th by GDP (PPP) per capita on the 194 economies list. South Korea is ranked 8th in nominal and 10th in the PPP list among 49 European economies. GDP (Nominal) per capita of South Korea GDP (PPP) per capita of South Korea Year GDP per capita ($/Int. Year GDP (Nominal) per capita ($) GDP (PPP) per capita (Int.\"}, {\"url\": \"https://countryeconomy.com/gdp/south-korea?anio=2024\", \"content\": \"South Korea GDP - Gross Domestic Product 2024 | countryeconomy.com South Korea GDP - Gross Domestic Product GDP South Korea third quarter of 2023 The GDP figure in the third quarter of 2023 was €394,507$362,465 million, South Korea is number 12 in the ranking of quarterly GDP of the 53 countries that we publish. South Korea has a quarterly GDP per capita, of €7,635$7,635, less than the same period last year, when it was €7,732 $7,732 . If we order the countries according to their GDP per capita, South Korea is in 25th position of the 53 countries whose quarterly GDP we publish. | < GDP South Korea 2023 | Evolution: GDP growth rate at constant prices South Korea GDP South Korea\"}, {\"url\": \"https://www.macrotrends.net/global-metrics/countries/KOR/south-korea/gdp-per-capita\", \"content\": \"Data are in current U.S. dollars. South Korea gdp per capita for 2023 was $33,121, a 2.24% increase from 2022. South Korea gdp per capita for 2022 was $32,395, a 7.77% decline from 2021. South Korea gdp per capita for 2021 was $35,126, a 10.73% increase from 2020. South Korea gdp per capita for 2020 was $31,721, a 0.57% decline from 2019.\"}, {\"url\": \"https://tradingeconomics.com/south-korea/gdp-per-capita-constant-2000-us-dollar-wb-data.html\", \"content\": \"South Korea - GDP Per Capita (constant 2000 US$) - 2024 Data 2025 Forecast 1960-2023 Historical Interest Rate South Korea - GDP Per Capita (constant 2000 US$)2024 Data 2025 Forecast 1960-2023 Historical GDP per capita (constant 2015 US$) in South Korea was reported at 34121 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Korea - GDP per capita (constant 2000 US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on December of 2024. GDP GDP GDP Constant Prices GDP Growth Rate GDP Growth Rate YoY Interest Rate Government Debt to GDP Government Spending to GDP Economic Calendar Historical Data News Stream Earnings Releases Credit Ratings Forecasts Markets Currencies Stocks Commodities Bonds Crypto Get Started Ratings\"}, {\"url\": \"https://geographic.org/stats/korea_south/korea_south_gdp_per_capita_time_series.html\", \"content\": \"Korea South GDP Per Capita (PPP) 2004 - 2024 Main Index |Β  Time Series Index |Β  Korea South Index Country Ranks |Β  Country Flags DEFINITIONS |Β  Photos |Β * GEOGRAPHIC.ORG |Β  USA STATISTICS |Β  CHINA STATISTICS |Β  COUNTRY CODES |Β  AIRPORT CODES |Β  WEATHER DATA COUNTRIES of the WORLD Korea South: GDP Per Capita (PPP) 2004-2024 Time Series https://geographic.org/stats/korea_south/korea_south_gdp_per_capita_time_series.html SOURCE: CIA World Factbook Main Index Time Series Index Korea South Index Country Ranks Country Flags Geographic Names Airport Codes Photos GEOGRAPHICAL NAMESΒ  NOTE: This time series graph shows the GDP Per Capita (PPP) of Korea South based on our stored data from 2004 to 2024, taken from the CIA World fact books of the respective years. Switch country: Switch rank: \"}]\n", "==================================================\n", "\n", "==================================================\n", @@ -631,23 +618,23 @@ "\n", "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", "\n", - "- **2010**: $24,000 (approx.)\n", - "- **2011**: $25,000 (approx.)\n", - "- **2012**: $26,000 (approx.)\n", - "- **2013**: $27,000 (approx.)\n", - "- **2014**: $28,000 (approx.)\n", - "- **2015**: $29,000 (approx.)\n", - "- **2016**: $30,000 (approx.)\n", - "- **2017**: $31,000 (approx.)\n", - "- **2018**: $32,000 (approx.)\n", - "- **2019**: $33,000 (approx.)\n", + "- **2010**: $24,000\n", + "- **2011**: $25,000\n", + "- **2012**: $26,000\n", + "- **2013**: $27,000\n", + "- **2014**: $28,000\n", + "- **2015**: $29,000\n", + "- **2016**: $30,000\n", + "- **2017**: $31,000\n", + "- **2018**: $32,000\n", + "- **2019**: $33,000\n", "- **2020**: $31,721\n", "- **2021**: $35,126\n", "- **2022**: $32,395\n", "- **2023**: $33,121\n", "- **2024**: $36,132 (projected)\n", "\n", - "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a recovery and growth following the impacts of the pandemic in 2020 and 2021. \n", + "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. \n", "\n", "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", "==================================================\n", @@ -660,23 +647,23 @@ "\n", "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", "\n", - "- **2010**: $24,000 (approx.)\n", - "- **2011**: $25,000 (approx.)\n", - "- **2012**: $26,000 (approx.)\n", - "- **2013**: $27,000 (approx.)\n", - "- **2014**: $28,000 (approx.)\n", - "- **2015**: $29,000 (approx.)\n", - "- **2016**: $30,000 (approx.)\n", - "- **2017**: $31,000 (approx.)\n", - "- **2018**: $32,000 (approx.)\n", - "- **2019**: $33,000 (approx.)\n", + "- **2010**: $24,000\n", + "- **2011**: $25,000\n", + "- **2012**: $26,000\n", + "- **2013**: $27,000\n", + "- **2014**: $28,000\n", + "- **2015**: $29,000\n", + "- **2016**: $30,000\n", + "- **2017**: $31,000\n", + "- **2018**: $32,000\n", + "- **2019**: $33,000\n", "- **2020**: $31,721\n", "- **2021**: $35,126\n", "- **2022**: $32,395\n", "- **2023**: $33,121\n", "- **2024**: $36,132 (projected)\n", "\n", - "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a recovery and growth following the impacts of the pandemic in 2020 and 2021. \n", + "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. \n", "\n", "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", "==================================================\n", @@ -684,53 +671,42 @@ "==================================================\n", "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", "- - - - - - - - - - - - - - - - - - - - - - - - - \n", - "==================================================\n", + "==================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Python REPL can execute arbitrary code. Use with caution.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "==================================================\n", "πŸ”„ Node: \u001b[1;36magent\u001b[0m in [\u001b[1;33mCoder\u001b[0m] πŸ”„\n", "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " Python_REPL (call_jsOUzNuMuatwgX9o2JvXEqQe)\n", - " Call ID: call_jsOUzNuMuatwgX9o2JvXEqQe\n", + " Python_REPL (call_tTppOr6IRBexPuFlpjqIB7PS)\n", + " Call ID: call_tTppOr6IRBexPuFlpjqIB7PS\n", " Args:\n", " query: import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import platform\n", - "\n", - "# Determine OS\n", - "current_os = platform.system()\n", - "\n", - "if current_os == \"Windows\":\n", - " # Windows environment font setting\n", - " font_path = \"C:/Windows/Fonts/malgun.ttf\" # Malgun font path\n", - " fontprop = fm.FontProperties(fname=font_path, size=12)\n", - " plt.rc(\"font\", family=fontprop.get_name())\n", - "elif current_os == \"Darwin\": # macOS\n", - " # Mac environment font setting\n", - " plt.rcParams[\"font.family\"] = \"AppleGothic\"\n", - "else: # Other OSs including Linux\n", - " # Try to set the default Korean font\n", - " try:\n", - " plt.rcParams[\"font.family\"] = \"NanumGothic\"\n", - " except:\n", - " print(\"Cannot find Korean font. Using system default font.\")\n", - "\n", - "# Prevent minus font from being broken\n", - "plt.rcParams[\"axes.unicode_minus\"] = False\n", "\n", "# Data for GDP per capita of South Korea from 2010 to 2024\n", - "years = np.array([2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024])\n", - "\n", - "gdp_per_capita = np.array([24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 31721, 35126, 32395, 33121, 36132])\n", + "years = [2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024]\n", + "gdp_per_capita = [24000, 25000, 26000, 27000, 28000, 29000, 30000, 31000, 32000, 33000, 31721, 35126, 32395, 33121, 36132]\n", "\n", "# Create the plot\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(years, gdp_per_capita, marker='o', linestyle='-', color='b')\n", - "plt.title('South Korea GDP per Capita (2010-2024)')\n", - "plt.xlabel('Year')\n", - "plt.ylabel('GDP per Capita (USD)')\n", - "plt.xticks(years)\n", + "plt.title('GDP per Capita of South Korea (2010-2024)', fontsize=16)\n", + "plt.xlabel('Year', fontsize=14)\n", + "plt.ylabel('GDP per Capita (in USD)', fontsize=14)\n", + "plt.xticks(years, rotation=45)\n", "plt.grid(True)\n", "plt.tight_layout()\n", "\n", @@ -739,94 +715,9 @@ "==================================================\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - 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- "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n", - "findfont: Font family 'NanumGothic' not found.\n" - ] - }, { "data": { - "image/png": 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8PBJ1MS5YsCA5c+Z0yTrBRYsWTXQsV65cnDt3Llmvb9++PSdOnGDUqFHkzZuX3r17Jzj/119/UahQoURJWlwX95vrVKJEiQT7Z86c4fz588yaNYtZs2YlGcPp06fjt+fNm8f48eM5cOAA0dHRt7zuzeLi+++//xIcL1myJKGhoQB8/PHHfPLJJ7e9zs3irndz/Z988km6d++Ow+Ege/bslCtXjqxZsybrmoUKFUpUtlSpUgAcOXKEmjVr8vvvv7N//37y5cuX5DVufGZw5+cTx8/PD0j8JcKtxMbGMnnyZN5//30OHz5MTExM/Lk8efIkKn9jwgdmebWSJUve9ZJ3Z8+eZdiwYXzxxReJ6nzhwoVkXcOyrNueX7BgAQMHDqRTp068+uqrCc5lyZKFqKioJF939erVJLvM30nca5Jau/rGa165ciVRHQsWLJjoNZs2baJTp040btyYkSNH3tW9bnbgwAFatmxJ+fLlmTNnToJzR44cYezYsUybNi3Rl1y3Evce3G44gIiIKynpFhFJR7y9vXnooYd46KGHKFWqFB06dGDRokUMGTIkvsy9/MPyxqQmOW41LvdOiUacTJkysXDhQh577DH69OlDzpw56dChQ4piuNHN/4iPW+f7hRdeoH379km+Jm787vz583nppZdo0aIF/fr1I3/+/Hh6ejJ69Gj++OOP2963TJkyAOzZs4cnn3wy/ni2bNlo0KABYMYzp9SePXsAEn2R4u/vH39dV4iNjaVChQpMmDAhyfNFihRJsJ/c5C/uOe3evZsWLVrcsfyoUaMYNGgQHTt2ZMSIEeTOnRsPDw969eqVKmu4P/3002zevJl+/fpRuXJlsmXLRmxsLI899tgd7x/3pcDtvoAKDQ3lxRdfpFmzZsyYMSPR+fvuu4+YmBhOnz5N/vz5449HRUXx77//JhrTnhz33XcfACdPnkz0Pp48eZIaNWoA5suAm/8Wb/67/uWXX3jiiScoX748ixcvJlOmhP+sTO69bvT333/TqFEjcuTIwfLlyxN94TR48GAKFy5MnTp14r9MiRtPfubMGY4cOULRokUTzA1x7tw5fH197+pLChERZ1DSLSKSTsVNTnTy5EnATLgWGxvL77//nmCys1OnTnH+/HmKFSsWfyxXrlycP38+wfWioqLirxUnNVqGMmfOzDfffEPdunXp0qULOXPmpGXLloCp0/fff8/FixcT/OP7wIED8edvJ1++fGTPnp2YmJg7JqmLFy/m/vvvZ8mSJQnqfeMXGrdSq1YtcuTIwRdffEFISEiCf/Dfrf/++4+lS5dSpEiRBO/nvTpx4gSXLl1K0Nr922+/AcRPgPbAAw/wyy+/UL9+fad+Bh599FFy5crF559/zptvvnnHydQWL15M3bp1mTt3boLj58+fT3Jyrd9//z3BvmVZHDp0KP6LlaTcqn7nzp1jzZo1DBs2jMGDB9/yHrcS9wXD4cOHkzy/detWWrZsSfXq1Vm4cGGihBWIn7xv+/btNG3aNP749u3biY2Nve3kfrdy4zVvTHpPnDjBsWPHePnllwFo3LhxfC+NpPzxxx889thj5M+fn+XLlyfZ6pzce8X5999/adSoEZGRkaxZsyY+ab/R0aNHOXToEPfff3+ic6+99hpg3rsbu+kfPnzYqX9DIiIppTHdIiJp3Lp165JsOY4bp1q6dGmA+H+UT5o0KUG5uNbKZs2axR974IEH2LhxY4Jys2bNStTSHZeY3ZygO5ufnx8rV66kZMmSPPfcc6xZswYwdYqJiWHq1KkJyk+cOBGHw0GTJk1ue11PT09at27Nl19+Gd9qfKMzZ84kKAsJW/O2bt3Kli1b7hi/r68v/fv3Z8+ePQwYMCDJ9yu5rf9guva2a9eOs2fP8tZbbzk18b127RozZ86M34+KimLmzJnky5ePatWqAaaF9/jx48yePTvJ2C5dunRX9/b19eWNN95g//79vPHGG0k+k/nz57Nt2zbAvCc3l1m0aBHHjx9P8voff/xxgq7rixcv5uTJk7f9nPj6+gKJP+NJfR4g8d/XrRQuXJgiRYqwffv2ROf2799Ps2bNKF68ON99990tW2Dr1atH7ty5mT59eoLj06dPx9fXN8HfdHKVK1eOMmXKJPp7nz59Og6HgzZt2gCmlbpBgwYJfuKEh4fTqFEjPDw8WLVq1S2HIST3XgCXLl2iadOmHD9+nOXLlycaKhDn7bffZunSpQl+RowYAUD//v1ZunRpouETO3fu5JFHHknhkxIRcR61dIuIpHGvv/46ly9fpmXLlpQpU4aoqCg2b97MggULKF68eHwX0EqVKtG+fXtmzZrF+fPnCQoKYtu2bcybN48WLVpQt27d+Gt27tyZrl270rp1axo2bMgvv/zCqlWrErUeVq5cGU9PT9555x0uXLiAj48P9erVS9DV1Vny5ctHaGgo//vf/2jRogVr1qyhefPm1K1bl7feeosjR45QqVIlVq9ezddff02vXr144IEH7njdMWPGsG7dOh5++GG6dOlCQEAAZ8+eZefOnXz//fecPXsWgMcff5wlS5bQsmVLmjVrxuHDh5kxYwYBAQGJxmonZcCAAezfv5+xY8eyevVqWrdujb+/P+fOnWPnzp0sWrSI/Pnzkzlz5gSvO378OPPnzwdM6/a+fftYtGgR4eHh9OnTJ8GkZ85QqFAh3nnnHY4cOUKpUqVYsGABYWFhzJo1Cy8vLwDatWvHwoUL6dq1K+vWreN///sfMTExHDhwgIULF7Jq1apEy0AlV79+/di7dy/jx49n3bp1tGnThoIFCxIeHs5XX33Ftm3b2Lx5M2Dek+HDh9OhQwceeeQRdu/ezaeffppkKydA7ty5efTRR+nQoQOnTp1i0qRJlCxZki5dutwynixZshAQEMCCBQsoVaoUuXPnpnz58pQvX57atWvz7rvvEh0dTeHChVm9evUtW66T8uSTT7J06VIsy4r/4uTixYs0btyYc+fO0a9fv0QTHD7wwAMEBgbGxzZixAi6devGU089RePGjdm0aRPz589n5MiR5M6dO/51Fy5c4L333gOIXx5r6tSp5MyZk5w5c9K9e/f4smPHjuWJJ56gUaNGPPvss+zZs4epU6fSuXPnZLUIP/bYY/z555/079+fH374IcHQiQIFCtCwYcMU36tt27Zs27aNjh07sn//fvbv3x9/Llu2bPHDER599NFE8cS1aj/00EOJhi3s2LGDs2fPJhj2ISKS6lJ/wnQREUmJFStWWB07drTKlCljZcuWzfL29rZKlixpvf7669apU6cSlI2OjraGDRtmlShRwvLy8rKKFClihYSEWFevXk1QLiYmxnrjjTesvHnzWr6+vlbjxo2tQ4cOJVoyzLIsa/bs2db9999veXp6Jlg+7FbLXQUFBVlBQUF3rBdgdevWLdHx/fv3W3nz5rVy585t7dmzx7p48aLVu3dvq1ChQpaXl5f14IMPWmPHjk2w9NbtrmdZlnXq1CmrW7duVpEiRSwvLy+rYMGCVv369a1Zs2bFl4mNjbVGjRplFStWzPLx8bGqVKlifffdd4mWmLqTpUuXWk2bNrXy5ctnZcqUycqZM6f16KOPWmPHjrXOnz+foGzckmmA5XA4LD8/P6tcuXJWly5drK1bt6bouSVHUFCQVa5cOWv79u1WYGCglTlzZqtYsWLW1KlTE5WNioqy3nnnHatcuXKWj4+PlStXLqtatWrWsGHDrAsXLtxzPIsXL7YaNWpk5c6d28qUKZN13333Wc8884y1fv36+DJXr161+vTpY913331WlixZrP/973/Wli1bEn3G4paM+vzzz62QkBArf/78VpYsWaxmzZpZf/31V4L7JvV+bt682apWrZrl7e2dYPmwY8eOWS1btrRy5sxp5ciRw3rqqaesEydO3HKJsZvt3LnTAhIsvXb48OH49zypn5v//izLsmbNmmWVLl3a8vb2th544AFr4sSJiT7/t7tuUp/fpUuXWpUrV7Z8fHwsf39/a+DAgYmWIbyV28Wf1N9+cu51499CcuK/0e2WDHvjjTesokWLJnpeIiKpyWFZKejvJiIiIulWnTp1+Oeff5Lsap+erV+/nrp167Jo0aIEXZbTgvr161OoUKEUz1wv9y4yMpLixYszYMAAevbsaXc4IpKBaUy3iIiIiIuMGjWKBQsWuGTJPrm9Dz/8EC8vL7p27Wp3KCKSwSnpFhEREXGRhx9+mKioqDvOtC/O17VrV44ePYqPj4/doYhIBqekW0RERERERMRFNKZbRERERERExEXU0i0iIiIiIiLiIkq6RURERERERFwkk90BuIvY2FhOnDhB9uzZcTgcdocjIiIiIiIiLmRZFhcvXqRQoUJ4eNy6PVtJt5OcOHGCIkWK2B2GiIiIiIiIpKK///4bf3//W55X0u0k2bNnB8wD9/PzszmapEVHR7N69WoaNWqEl5eX3eG4jOrpfjJKXVVP95JR6gkZp66qp3vJKPWEjFNX1dO9pId6RkREUKRIkfhc8FaUdDtJXJdyPz+/NJ10+/r64ufnl2Y/uM6gerqfjFJX1dO9ZJR6Qsapq+rpXjJKPSHj1FX1dC/pqZ53Gl6sidREREREREREXERJt4iIiIiIiIiLKOkWERERERERcREl3SIiIiIiIiIuoqRbRERERERExEWUdIuIiIiIiIi4iJJuERERERERERdR0i0iIiIiIiLiIkq6RURERERERFxESbeIiIiIiIiIiyjpFhEREREREXERJd0iIiIiIiIiLqKkW0RERERERMRFlHSLiIiIiIiIuIiSbhEREREREREXUdItIiIiIiIiaUZMDGzY4GDjxsJs2OAgJsbuiO6Nkm4RERERERFJE5YsgeLFoWHDTEyYUJ2GDTNRvLg5nl4p6RYRERERERHbLVkCbdrAsWMJjx8/bo6n18RbSbeIiIiIiIjYKiYGevYEy0p8Lu5Yr16ky67mSrpFRERERETEVps2JW7hvpFlwd9/m3LpjZJuERERERERsdXJk84tl5bYmnRPnz6dihUr4ufnh5+fH4GBgaxYsSJBmS1btlCvXj2yZs2Kn58ftWvX5sqVK/Hnz549S9u2bfHz8yNnzpx06tSJ//77L8E1fv31V2rVqkXmzJkpUqQI7777bqJYFi1aRJkyZcicOTMVKlRg+fLlrqm0iIiIiIiIJHDffc4tl5bYmnT7+/szZswYduzYwfbt26lXrx5PPvkke/fuBUzC/dhjj9GoUSO2bdvGzz//TPfu3fHwuB5227Zt2bt3L6GhoXz33Xds3LiRl19+Of58REQEjRo1olixYuzYsYOxY8cydOhQZs2aFV9m8+bNPPfcc3Tq1Ildu3bRokULWrRowZ49e1LvYYiIiIiIiGRQtWqBvz84HEmfdzigSBFTLr3JZOfNmzdvnmB/5MiRTJ8+nZ9++oly5crRu3dvevTowYABA+LLlC5dOn57//79rFy5kp9//pnq1asD8N5779G0aVPGjRtHoUKF+PTTT4mKiuKDDz7A29ubcuXKERYWxoQJE+KT88mTJ/PYY4/Rr18/AEaMGEFoaChTp05lxowZrn4MIiIiIiIiGZqnJ0yeDK1bJz4Xl4hPmmTKpTe2Jt03iomJYdGiRVy6dInAwEBOnz7N1q1badu2LY888gh//PEHZcqUYeTIkTz66KOAaQnPmTNnfMIN0KBBAzw8PNi6dSstW7Zky5Yt1K5dG29v7/gyjRs35p133uHcuXPkypWLLVu2EBwcnCCexo0b89VXX90y3sjISCIjI+P3IyIiAIiOjiY6OtoZj8Tp4uJKq/E5i+rpfjJKXVVP95JR6gkZp66qp3vJKPWEjFNX1TP9K1cOPDwyERubsLm7cGGL8eNjaN7cIi1VO7nvge1J9+7duwkMDOTq1atky5aNpUuXEhAQwE8//QTA0KFDGTduHJUrV+bjjz+mfv367NmzhwcffJDw8HDy58+f4HqZMmUid+7chIeHAxAeHk6JEiUSlClQoED8uVy5chEeHh5/7MYycddIyujRoxk2bFii46tXr8bX1zflDyIVhYaG2h1CqlA93U9Gqavq6V4ySj0h49RV9XQvGaWekHHqqnqmX1OmVCE2tiiVKp2mTZvfOHcuM7lyXSUg4F88PSGtTbt1+fLlZJWzPekuXbo0YWFhXLhwgcWLF9O+fXs2bNhAbGwsAK+88godOnQAoEqVKqxZs4YPPviA0aNH2xk2ISEhCVrHIyIiKFKkCI0aNcLPz8/GyG4tOjqa0NBQGjZsiJeXl93huIzq6X4ySl1VT/eSUeoJGaeuqqd7ySj1hIxTV9UzffvtN1i/3qSn77+fm8qVq6b5esb1dr4T25Nub29vSpYsCUC1atX4+eefmTx5cvw47oCAgATly5Yty9GjRwEoWLAgp0+fTnD+2rVrnD17loIFC8aXOXXqVIIycft3KhN3Pik+Pj74+PgkOu7l5ZVmPxRx0kOMzqB6up+MUlfV071klHpCxqmr6uleMko9IePUVfVMn0aNgthYePxxeOSRTERHW0Darmdy40pz63THxsYSGRlJ8eLFKVSoEAcPHkxw/rfffqNYsWIABAYGcv78eXbs2BF/fu3atcTGxvLwww/Hl9m4cWOC/vahoaGULl2aXLlyxZdZs2ZNgvuEhoYSGBjokjqKiIiIiIiIsXcvfP652R4+3N5YXMHWpDskJISNGzdy5MgRdu/eTUhICOvXr6dt27Y4HA769evHlClTWLx4MYcOHWLQoEEcOHCATp06AabV+7HHHqNLly5s27aNH3/8ke7du/Pss89SqFAhAJ5//nm8vb3p1KkTe/fuZcGCBUyePDlB1/CePXuycuVKxo8fz4EDBxg6dCjbt2+ne/futjwXERERERGRjGLoULAsaNUKqlSxOxrns7V7+enTp3nxxRc5efIkOXLkoGLFiqxatYqGDRsC0KtXL65evUrv3r05e/YslSpVIjQ0lAceeCD+Gp9++indu3enfv36eHh40Lp1a6ZMmRJ/PkeOHKxevZpu3bpRrVo18ubNy+DBgxOs5f3II4/w2WefMXDgQN58800efPBBvvrqK8qXL596D0NERERERCSDCQuDxYvNsmBJzFPtFmxNuufOnXvHMgMGDEiwTvfNcufOzWeffXbba1SsWJFNmzbdtsxTTz3FU089dcd4RERERERExDmGDDG/n3kG3LXNM82N6RYRERERERH39/PP8M034OFhupi7KyXdIiIiIiIikuoGDza/X3gBSpe2NxZXUtItIiIiIiIiqWrzZli5Ejw9ryff7kpJt4iIiIiIiKSqQYPM7w4d4IZ5st2Skm4RERERERFJNevXw9q14OUFAwfaHY3rKekWERERERGRVGFZ11u5u3SBYsXsjSc1KOkWERERERGRVBEaCj/8AD4+8OabdkeTOpR0i4iIiIiIiMvd2Mr96qtQuLC98aQWJd0iIiIiIiLicsuWwbZt4OsLAwbYHU3qUdItIiIiIiIiLmVZ15cG694dChSwN57UpKRbREREREREXGrpUti1C7Jlg3797I4mdSnpFhEREREREZeJjYUhQ8x2r16QN6+t4aQ6Jd0iIiIiIiLiMgsXwp49kCMHBAfbHU3qU9ItIiIiIiIiLnHtGgwdarb79IFcuWwNxxZKukVERERERMQlPvsMDh6E3LmhZ0+7o7GHkm4RERERERFxuuhoGDbMbPfvD35+9sZjFyXdIiIiIiIi4nTz5sGff0L+/GaZsIxKSbeIiIiIiIg4VWQkjBhhtgcMgKxZ7Y3HTkq6RURERERExKnmzoWjR6FQIeja1e5o7KWkW0RERERERJzmyhUYOdJsv/kmZMlibzx2U9ItIiIiIiIiTjNzJpw4AUWKQOfOdkdjPyXdIiIiIiJuKiYGNmxwsHFjYTZscBATY3dE4u4uXYLRo832oEHg42NvPGmBkm4RERERETe0ZAkULw4NG2ZiwoTqNGyYieLFzXERV5k2DU6fhvvvh5desjuatEFJt4iIiIiIm1myBNq0gWPHEh4/ftwcV+ItrnDxIrz7rtkePBi8vOyNJ61Q0i0iIiIi4kZiYqBnT7CsxOfijvXqhbqai9NNngz//gulSkHbtnZHk3Yo6RYRERERcSObNiVu4b6RZcHff5tyIs5y/jyMH2+2hw6FTJnsjCZtUdItIiIiIuJGTp50bjmR5JgwwSTe5crBM8/YHU3aoqRbRERERMSN3Hefc8uJ3Mm//8KkSWZ72DDwUJaZgB6HiIiIiIgbqVUL/P1vfd7hMOsn16qVejGJexs71kyiVrkytGxpdzRpj5JuERERERE34ulpZo6+nUmTTDmRe3XqFLz3ntkePlyt3EnRIxERERERcTN//GF+e3snPjdtGrRqlbrxiPt65x24fBlq1IDHH7c7mrRJSbeIiIiIiBu5eBFmzDDbX3wBoaHXCA7eTtWqsQCsX29fbOJeTpyA6dPN9vDhZuiCJKakW0RERETEjcydCxcumLWSn3wSgoIsatc+zqxZMXh4wMKF8OOPdkcp7mDUKLh6Ff73P2jUyO5o0i4l3SIiIiIibuLateuzSPfpk3B8bcWK0KmT2Q4OhtjYVA9P3MjRozB7ttkeMUKt3LejpFtERERExE18+SX89Rfkywft2iU+P3w4ZMsG27bB55+nfnziPt5+G6KioG5d8yO3pqRbRERERMQNWJZZugmge3fIkiVxmYIFISTEbA8YYCbAEkmpP/+EDz802yNG2BtLeqCkW0RERETEDWzcCDt2QObM8Nprty7XuzcULQrHjsGECakXn7iP4cPNUIbGjc14brk9Jd0iIiIiIm5g3Djzu0MHyJv31uWyZIExY8z2mDFw8qTrYxP3cfAgfPKJ2R4+3N5Y0gsl3SIiIiIi6dz+/fDdd2Yyq96971z+2WehZk24dAkGDXJ9fOI+hg0zk/A1b27W5pY7U9ItIiIiIpLOxXUTf/JJePDBO5d3OK6/5oMPICzMZaGJG9mzx6z9DmrlTgkl3SIiIiIi6dipU9e7+/btm/zXBQaaFm/LMsuLWZZr4hP3MXSo+Zy0bg2VK9sdTfqhpFtEREREJB2bNg0iI0138UceSdlrR48GHx9Yuxa+/dY18Yl7CAszS9I5HKaLuSSfkm4RERERkXTq8mWTdINp5XY4Uvb64sWvjwHv29esuyySlMGDze9nn4Vy5eyNJb1R0i0iIiIikk599BGcPQv33w8tWtzdNUJCIH9++P13mD7dmdGJu9i2zfSE8PCAIUPsjib9UdItIiIiIpIOxcRcnwytd2/w9Ly76/j5wYgRZnvYMJPEi9worpW7XTsoXdreWNIjJd0iIiIiIunQ11/DH39Arlxmbe570akTVKgA585dT8BFAH78EVatgkyZriffkjJKukVERERE0qFx48zv116DrFnv7VqenjB+vNmeOhV+++3erifuI24d9w4dzDAGSTkl3SIiIiIi6czmzbBlC3h7Q/fuzrlmw4bQrBlcuwb9+zvnmpK+rVtnfry9YeBAu6NJv5R0i4iIiIikM3Gt0u3aQcGCzrvu2LGm1fvrr02yJRmXZV1v5e7SBYoWtTee9ExJt4iIiIhIOnLoECxdaraDg5177bJloWvX69eOiXHu9SX9WL3ajOfOnBnefNPuaNI3Jd0iIiIiIunIxImmFbJZMwgIcP71hw6FHDkgLAzmzXP+9SXtu7GV+9VXoVAhe+NJ75R0i4iIiIikE//8Ax9+aLb79HHNPfLmvZ5wvfUW/Pefa+4jadd338HPP4OvLwwYYHc06Z+SbhERERGRdGL6dLhyBapWhTp1XHef7t3hgQcgPBzeecd195G0Jzb2+tJgr78O+fPbG487UNItIiIiIpIOXL1qlvMC6NsXHA7X3cvHB95912yPGwd//+26e0nasnSpGVqQPTv062d3NO5BSbeIiIiISDowfz6cPm1mkW7TxvX3a9kSatc2yb4m0soYYmJgyBCz3asX5MljazhuQ0m3iIiIiEgaFxt7fZmwXr3Ay8v193Q4YMIEsz1/Pmzb5vp7ir0WLoS9eyFnTufPjJ+RKekWEREREUnjli+HAwfMrOKdO6fefatVgxdfNNvBwWZWa3FP166ZmevBTNKXM6ed0bgXJd0iIiIiImncuHHm98svm7G2qWnUKMiSxazZvHhx6t5bUs+nn8Jvv5ku5T172h2Ne1HSLSIiIiKShm3fDhs2QKZM0KNH6t+/cGHo399sv/GGGeMt7iU6GoYPN9v9+6f+FzvuTkm3iIiIiEgaFjeW+7nnwN/fnhj69YNCheDwYZgyxZ4YxHU++gj+/NMsD9atm93RuB8l3SIiIiIiadSRI7Bokdnu08e+OLJmNd3MAUaONLOoi3uIjIQRI8x2SIh5r8W5lHSLiIiIiKRRkyebZZwaNoRKleyNpV07qFoVIiKuT7gl6d+cOWYd9kKFoGtXu6NxT0q6RURERETSoHPnYPZss923r72xAHh4XF9CbOZMs7SUpG9XrpieCwBvvQWZM9sbj7tS0i0iIiIikgbNmgWXLkGFCqalOy0ICoKWLc264WnhiwC5NzNmwMmTULQodOpkdzTuy9ake/r06VSsWBE/Pz/8/PwIDAxkxYoVicpZlkWTJk1wOBx89dVXCc4dPXqUZs2a4evrS/78+enXrx/Xrl1LUGb9+vVUrVoVHx8fSpYsyUcffZToHtOmTaN48eJkzpyZhx9+mG3btjmzqiIiIiIiyRYVZbqWgxnL7XDYG8+N3n0XvLxg5UrzI+nTpUswZozZHjQIfHzsjced2Zp0+/v7M2bMGHbs2MH27dupV68eTz75JHtv6qsyadIkHEn8lyYmJoZmzZoRFRXF5s2bmTdvHh999BGDBw+OL3P48GGaNWtG3bp1CQsLo1evXnTu3JlVq1bFl1mwYAHBwcEMGTKEnTt3UqlSJRo3bsxpzRAhIiIiIjb4/HPTAlmokJm1PC0pWRJef91s9+kDN7V3SToxdaqZEO/++6F9e7ujcW+2Jt3NmzenadOmPPjgg5QqVYqRI0eSLVs2fvrpp/gyYWFhjB8/ng8++CDR61evXs2+ffuYP38+lStXpkmTJowYMYJp06YRFRUFwIwZMyhRogTjx4+nbNmydO/enTZt2jBx4sT460yYMIEuXbrQoUMHAgICmDFjBr6+vkneU0RERETElSzr+jJhPXqAt7e98SRl0CDIkwf27bs+7lzSj4gI02MBYMgQ03NBXCfNjOmOiYnhiy++4NKlSwQGBgJw+fJlnn/+eaZNm0bBggUTvWbLli1UqFCBAgUKxB9r3LgxERER8a3lW7ZsoUGDBgle17hxY7Zs2QJAVFQUO3bsSFDGw8ODBg0axJcREREREUktoaGwezdkywavvGJ3NEnLmfP6DOaDB8OFC3ZGIyk1eTKcPQulS0PbtnZH4/4y2R3A7t27CQwM5OrVq2TLlo2lS5cSEBAAQO/evXnkkUd48sknk3xteHh4goQbiN8PDw+/bZmIiAiuXLnCuXPniImJSbLMgQMHbhl3ZGQkkZGR8fsREREAREdHEx0dnZyqp7q4uNJqfM6ierqfjFJX1dO9ZJR6Qsapq+rpXtJyPceO9QQ86NgxhqxZY7nXEF1V144dYerUTBw86GDEiBhGj4516vVTKi2/p850r/U8dw7Gj88EOBg48BqxsRax9r51SUoP72dyY7M96S5dujRhYWFcuHCBxYsX0759ezZs2MChQ4dYu3Ytu3btsjvEJI0ePZphw4YlOr569Wp8fX1tiCj5QkND7Q4hVaie7iej1FX1dC8ZpZ6QceqqerqXtFbPw4f9+P77unh4xFKu3BqWL7/itGu7oq5PPVWAt9+uyeTJ8OCD6ylY8LLT75FSae09dZW7reenn5bhwoXSFC0aQdas61i+3MmBOVlafj8vX07e5932pNvb25uSJUsCUK1aNX7++WcmT55MlixZ+OOPP8iZM2eC8q1bt6ZWrVqsX7+eggULJppl/NSpUwDx3dELFiwYf+zGMn5+fmTJkgVPT088PT2TLJNUl/Y4ISEhBAcHx+9HRERQpEgRGjVqhJ+fX8oeQiqJjo4mNDSUhg0b4uXGAzdUT/eTUeqqerqXjFJPyDh1VT3dS1qtZ4cOngC0bg0dOtR1yjVdWdcmTWDLlljWrPEkNLQ+n38e49Trp0RafU+d7V7q+c8/8MILJgUcO9aXxx9v6ooQnSI9vJ9xvZ3vxPak+2axsbFERkYybNgwOnfunOBchQoVmDhxIs2bNwcgMDCQkSNHcvr0afLnzw+Yb0L8/Pziu6gHBgay/Kavb0JDQ+PHjXt7e1OtWjXWrFlDixYt4mNYs2YN3bt3v2WcPj4++CQxr76Xl1ea/VDESQ8xOoPq6X4ySl1VT/eSUeoJGaeuqqd7SUv1PHYMFiww2/37e+Dl5dzpl1xV14kToXJl+PJLD7Zu9eDRR51+ixRJS++pK91NPSdNgv/+gypV4KmnMqWppehuJS2/n8mNy9akOyQkhCZNmlC0aFEuXrzIZ599xvr161m1ahUFCxZMsqW5aNGilChRAoBGjRoREBBAu3btePfddwkPD2fgwIF069YtPiHu2rUrU6dOpX///nTs2JG1a9eycOFCli1bFn/N4OBg2rdvT/Xq1alRowaTJk3i0qVLdOjQIXUehIiIiIhkeO+9Z5bfCgqC6tXtjib5KlSATp3MLObBwfDTT+CRZqZrljinTpllwgCGD09ba7+7O1uT7tOnT/Piiy9y8uRJcuTIQcWKFVm1ahUNGzZM1us9PT357rvvePXVVwkMDCRr1qy0b9+e4cOHx5cpUaIEy5Yto3fv3kyePBl/f3/mzJlD48aN48s888wznDlzhsGDBxMeHk7lypVZuXJlosnVRERERERcISICZsww23372hvL3Rgxwqwt/vPP8Nln8MILdkckNxszBi5fhocfhmbN7I4mY7E16Z47d26KyluWlehYsWLFEnUfv1mdOnXuOCFb9+7db9udXERERETEVebONYl3mTLQNO0Os72lAgXgzTfNT0gItGoFaXxu4Qzl+HGYPt1sq5U79anjh4iIiIiIjaKjzVhbgD590m/X7N69oVgxMzZ9wgS7o5EbjRwJkZHw6KOQzE7F4kTp9E9aRERERMQ9LF4MR49C/vzpu1t25symCzOY3ydO2BuPGH/9BXPmmO0RI9TKbQcl3SIiIiIiNrEsGDfObHfvbhLX9OyZZ6BmTbh0CQYNsjsaAZNoR0dDvXpQp47d0WRMSrpFRERERGyyYQPs3AlZssCrr9odzb1zOMwSYgAffgh3mFZJXOzQIfjoI7M9YoStoWRoSrpFRERERGwS18rdoQPkzWtvLM5SsyY8+6xpxe/Tx/wWewwfDjEx8Nhj8MgjdkeTcSnpFhERERGxwb59sGyZaR3u3dvuaJxrzBjw8YF16+Cbb+yOJmM6cAA+/dRs37CisthASbeIiIiIiA3iZvhu2RJKlrQ3FmcrVgyCg812v34QFWVvPBnR0KEQGwtPPAEPPWR3NBmbkm4RERERkVQWHg6ffGK2+/a1NxZXCQkxM7L//ju8/77d0WQsu3fDggVmW63c9lPSLSIiIiKSyqZONa2/gYHmxx1lzw5vv222hw+Hs2ftjScjGTLE/G7TBipVsjcWUdItIiIiIpKqLl263vLrrq3ccTp2hAoV4Nw5tbimlp07YelSM1fA0KF2RyOgpFtEREREJFV99JFJQh94AJ580u5oXMvT8/rY9WnT4OBBe+PJCAYPNr+few7KlbM3FjGUdIuIiIiIpJKYmOtJaHCwSUrdXYMG0KwZXLsG/fvbHY17++knMyO+h8f1LuZiPyXdIiIiIiKp5Kuv4M8/IU8eeOklu6NJPePGmS8YvvkG1q61Oxr3FdfK/eKLUKqUvbHIdUq6RURERERSgWXB2LFm+7XXwNfX3nhSU5ky8OqrZjs42LT4i3Nt2gShoZAp0/XkW9IGJd0iIiIiIqlg82bYuhV8fKBbN7ujSX1Dh0LOnPDLL2ZcuziPZcGgQWa7Y0coUcLeeCQhJd0iIiIiIqlg3Djzu107KFDA3ljskCfP9cRw4EC4eNHeeNzJ2rWwYQN4e5tnK2mLkm4RERERERf7/Xf4+muzHRxsbyx26tbNzNoeHg7vvmt3NO7hxlbul1+GIkXsjUcSU9ItIiIiIuJiEyea5Ojxx6FsWbujsY+Pz/Vx7ePGwdGj9sbjDlauhC1bIHNmePNNu6ORpCjpFhERERFxoTNn4MMPzXbfvvbGkha0aAFBQXD1qpLEe2VZ1ydNe+01uO8+e+ORpCnpFhERERFxoenTTYJZvTrUrm13NPZzOMxa5Q4HfPopbNtmd0Tp1zffwPbtkDUrvPGG3dHIrSjpFhERERFxkStXYOpUs923r0k0BapWNWtJA/TubVpsJWViY6+3cr/+OuTPb288cmtKukVEREREXOSTT0z38mLFoHVru6NJW0aONGuVb94MixbZHU36s2SJg19/hezZNWwhrVPSLSIiIiLiArGxphs1QK9ekCmTreGkOYULQ//+ZvuNN0wXfLm9mBjYsMHB+vWFeeMNT8D0FMiTx+bA5LaUdIuIiIiIuMCyZXDwIOTIAZ062R1N2tS3r0m+jxyBKVPsjiZtW7IEiheHhg0zMWlSdf7+24HDASVL2h2Z3ImSbhERERERFxg3zvzu2tV0AZbEsmaFUaPM9ttvw+nT9saTVi1ZAm3awLFjCY9bFrRvb85L2qWkW0RERETEybZtg40bwcvLTHIlt/bCC1CtGly8CEOG2B1N2hMTAz173n6yuV69TDlJm5R0i4iIiIg42fjx5vfzz5vu03JrHh7Xx77PmgV79tgbT1qzaVPiFu4bWRb8/bcpJ2mTkm4RERERAa5P0rRxY2E2bHCo5ewuHT4Mixeb7eBge2NJL2rXhlatzORzmok7ocOHk1fu5EnXxiF3T0m3iIiIiCSYpGnChOo0bJiJ4sU1VvRuTJpkksdGjaBiRbujST/efdd0x1+1ClassDsa+/37r+lu36NH8srfd59r45G7p6RbREREJIO71SRNx4+b40q8k+/cOZg712yrxTZlHnjgeoLZpw9cu2ZvPHb5+2+zDFjRojB8OPz33+2Xm3M4oEgRqFUr9WKUlFHSLSIiIpKB3W6SprhjmqQp+WbOhEuXTAt3gwZ2R5P+DBxo1pzev9+M785IDhyAjh3Nlw+TJsHly1ClCixcCJ9/bpJrhyPha+L2J00CT8/UjliSS0m3iIiISAamSZqcJzLy+lrTffsmTpDkznLmhGHDzPaQIXD+vJ3RpI7t26F1awgIgA8/hOhoqFvXdLPfsQOeesr0OFm8OPGkfP7+5nirVvbELsmjpFtEREQkA0vu5EuapOnOPv/cPKfCheGZZ+yOJv16+WUoUwb++ef6Gt7uxrJgzRrTG+Khh8wQDsuCJ5+ELVtg7VozJ8CNX9y0agVHjkBo6DWCg7cTGnqNw4eVcKcHSrpFREREMqjTp2H27OSV1SRNt2dZMG6c2e7ZE7y97Y0nPfPyur7k2uTJ8Oef9sbjTLGxJsF++GGTcK9ZY7qFv/gi7N0LX30FNWve+vWenhAUZFG79nGCgix1KU8nlHSLiIiIZDCWBfPnQ9mysG7dnct7eNx+IicxXYH37oVs2aBLF7ujSf+aNIGGDSEqCt54w+5o7l1UlOk6HhBgupL//DNkyQKvvw5//AHz5plz4p6UdIuIiIhkIH//DY8/Du3awdmzULkyjB17+0maYmOhfn345JNUDzfdiGuZ7dLFjEuWe+NwmGfq4WHGLKfXOQUuXTKt9SVLmknSDh40n4+33jJdxadMgWLF7I5SXE1Jt4iIiEgGEBsL06eb1rTly8HHx4yX3bbNTPp1q0ma5s+Hli1NS92LL8KAAZrJ/GZhYfD996brb8+edkfjPipUgM6dzXZwsPkMpxdnz5rlvooVM7P///03FCxo1iL/6y94+23In9/uKCW1KOkWERERcXO//QZ16sBrr5k1fx95xCSKISFm/CzcepKmtm1NQv7WW6bcO++YJPziRZsqkwbFtXI//bRaLZ1t+HDInt3M8P3pp3ZHc2fHj5s1xosWNbOv//uvWQJs5kw4fBj69QM/P7ujlNSmpFtERETETV27ZpLkihVN99ysWeG998x2mTKJy99qkiYPD9My9+mnpoX8229N4n74cOrWJy36+2/44guz3aePvbG4owIF4M03zXZIiFm7Oi367TcztKBECZgwwXQrr1TJfDYOHDAzsmfObHeUYhcl3SIiIiJuKCzMzJA8YIBZP7pxYzPRV/fuJom+G88/Dxs3mm6ye/ZAjRrpd6yts0yZYr7cqFsXqlWzOxr31KuX6UFw/Pj1XgVpxc6dpodDmTIwZ45ZY7t2bVixAnbtMkvHaRJCUdItIiIi4kauXjVdwatXNwlBrlzw0UcmCXBG1+caNczMy9WqmXWU69eHDz649+umRxERMGuW2VYrt+tkzmx6bACMGQMnTtgbj2XB+vXmi6xq1WDRInPs8cfhxx9hwwZ47LHEExNKxqWkW0RERMRNbN4MVaqYCdJiYqBNG9i3D9q3d24C4O9vWryfesq07HXqZJLOjDbB2pw5JvEuW9YscSWu8/TTEBhoupcPHGhPDLGx8PXXJo66dWH1ajMko21b+PXX68MuRG6mpFtEREQknfvvP+jRAx591IwfLVgQvvzStMAVLOiae/r6woIFMGyY2Z8wwbT0XbjgmvulNdHRMGmS2e7T5+677EvyOBzmMwam58bOnal37+ho+PhjM5t6ixawdatpfX/tNfj9dzPDf4UKqRePpD/6z4OIiIhIOrZ6NZQvbyZIsyyzFvC+fWY2cldzOGDwYFi4ELJkgZUrTSvgoUOuv7fdFi0yk6gVKGBaOsX1ataE554zn/M+fcxvV7p82fxdlSxpeovs22dmHg8JMTP9T5tmJk4TuRMl3SIiIiLp0Nmz0KGDGVf6119QvDiEhsLcuWYcd2p66ikzoVrhwrB/v5nAbd261I0hNVkWjBtntl9/XbNSp6YxY8zzXr/edPV2hXPnYORI8zfVowccPWq+XBkzxmyPGmX2RZJLSbeIiIhIOvPllxAQYLrZOhxmdufdu6FBA/tiqlbNTLBWo4b5QqBRI7M2sTtat87MTO3rC1272h1NxlK0KAQHm+1+/SAqynnXPnkS+vc3Ew4OHAhnzpiW7PffN8vjvfEG5MjhvPtJxqGkW0RERCSdOHkSWrc2E6SdOmUm8PrxR5g4EbJlszs6uO8+0wL5/PNmGa2uXU1L8LVrdkfmXHGt3B07Qp489saSEQ0YYFqaDx0yCfG9OnQIXnnFtGyPHQsXL5ox2p9+atbffvVVM3xC5G4p6RYRERFJ4ywLPvzQtG4vWWLW/R00yLS2BgbaHV1CWbKYiaVGjTL7U6eamb3PnbM3LmfZu9csvxbXw0BSX/bs8PbbZnvYMPj337u7TlgYPPsslC5tln6LioL//Q+++w5++cV8eaQ1tsUZlHSLiIiIpGGHD5tx2x07wvnzphv3jh0wfDj4+NgdXdIcDjPZ1NKlkDUrfP+9Ged98KDdkd27uBm0W7WCBx6wN5aMrEMHqFjR/E0MH57811mWWe6uaVOzvN6CBWYpsKZNzbwEP/wAzZppjW1xLiXdIiIiImlQTAxMmWJmJg8NNZNHvfsu/PSTSTbSgxYtTPf3IkXM0ko1a5q6pFcnT5pWfIC+fe2NJaPz9ITx4832++/f+Qud2Fizjvajj0JQkOmt4OFhZkMPC4Nly8w5EVdQ0i0iIiKSxuzbB7VqQc+eZtmioCD49VczcVR66+5aqZKZYO2RR0yrZJMmpsu5q5d7coWpU693Qa5Z0+5opEEDszb8tWvmS5ANGxxs3FiYDRscxMSYMteumbHZlSrBE0/A5s3g7W3GcP/2G3z2mTkn4kpKukVERETSiOhoM1a1ShXYssWMXZ0xA9auhQcftDu6u1eggKnDiy+aFvzXXzeTU0VH2x1Z8v33H0yfbrbVyp12jBtnWqy/+w4aNszEhAnVadgwE8WKwcsvm7+bF16APXvM31P//maN7RkzNDxAUk86+65URERExD3t2GHGbf/6q9lv1swkBv7+9sblLD4+ZomzChVM4jNzpukSvHhx+pgB/MMPzWRwDz4IzZvbHY3E2bvXdB2/2fHjMHu22c6Xz0x699prkDNnakYnYqilW0RERMRGV66Y9X9r1DAJd968psvrt9+6T8Idx+EwrcTffGOWOFu/3tR73z67I7u9mBizLBtA795mPLHYLybGDMG4nZw54Y8/4M03lXCLfZR0i4iIiNhkwwYzKdq775rWuuefNwnoc8+59+zJjz9uus8XLw5//mmWPVuxwu6obm3pUjOLfJ480L693dFInE2b4Nix25c5f970IhGxk5JuERERkVQWEWHGNNepA4cOQeHCpvX3009NV9iMoHx52LYNatc2z+Pxx81yXGltgjXLgrFjzXa3buDra288ct3Jk84tJ+IqSrpFREREUtGyZVCunBmvDWYW5b17M+Y44Xz5zBJinTqZlv4+faBzZ4iMtDuy63780Xw54ONjkm5JO+67z7nlRFxFSbeIiIhIKjhzBtq2NS26x46ZmZPXrTPJd44cdkdnH29vM+HVpElmFuoPPjBLQZ05Y3dkxrhx5nf79pA/v72xSEK1apl5D241FMPhMGvE16qVunGJ3ExJt4iIiIgLWRZ88QUEBJgJ0jw8zGRiv/5qupeLSY569jS9APz84Icf4KGHYPdue+M6eNB0+wcIDrY3FknM0xMmTzbbNyfecfuTJmniO7Gfkm4RERERFzl2DJ580kyM9s8/Zrmsn34yY4Q1Njixxx4zz6dkSfjrL3jkketJrx0mTjRfmjzxBJQubV8ccmutWpll5woXTnjc398cb9XKnrhEbqSkW0RERMTJYmNh1iwzdvvbb8HLC4YPh+3bTQuu3FrZsrB1K9SrB//9By1awDvvpP4Ea2fOwLx5ZrtPn9S9t6RMq1Zw5AiEhl4jOHg7oaHXOHxYCbekHUq6RURERJzo0CGoX99MkBYRATVrwq5dMGiQGb8sd5Y7N6xcCV27mmR7wAAzpvrq1dSL4f33zf0eekhjgtMDT08ICrKoXfs4QUGWupRLmqKkW0RERMQJrl2D8ePNutvr15vu45MmmfHJ5crZHV364+UF06fDtGkmofrkE6hbF8LDXX/vK1dg6lSz3beve6+ZLiKup6RbRERE5B7t3m3GH/ftaxK2Bg1gzx4zOZha3O7Na6+ZVu+cOc147xo1TM8BV/r4YzMGv3hxdVEWkXunpFtERETkDmJiYMMGBxs3FmbDBgcxMeZ4ZCQMGQJVq8LPP5ulv+bOhdWroUQJe2N2Jw0amHHepUvD33/Do4/CkiWuuVdsrOmxANC7N2TK5Jr7iEjGYWvSPX36dCpWrIifnx9+fn4EBgayYsUKAM6ePcvrr79O6dKlyZIlC0WLFqVHjx5cuHAhwTWOHj1Ks2bN8PX1JX/+/PTr149r164lKLN+/XqqVq2Kj48PJUuW5KOPPkoUy7Rp0yhevDiZM2fm4YcfZtu2bS6rt4iIiKQfS5aYFs+GDTMxYUJ1GjbMRPHiMGaMSbaHDzddy1u0gH37oGNHdUd2hVKlTEt3o0Zw+TK0bg1vv+38Cda+/RZ+/920rHfs6Nxri0jGZGvS7e/vz5gxY9ixYwfbt2+nXr16PPnkk+zdu5cTJ05w4sQJxo0bx549e/joo49YuXIlnTp1in99TEwMzZo1Iyoqis2bNzNv3jw++ugjBg8eHF/m8OHDNGvWjLp16xIWFkavXr3o3Lkzq1atii+zYMECgoODGTJkCDt37qRSpUo0btyY06dPp+rzEBERkbRlyRJo08Ys/XWjY8cgJMQk2fnzw6JFpmyhQvbEmVHkzGnW8u7Rw+wPGgTPP2+69DvLuHHmd9eukC2b864rIhmXrUl38+bNadq0KQ8++CClSpVi5MiRZMuWjZ9++ony5cvz5Zdf0rx5cx544AHq1avHyJEj+fbbb+NbslevXs2+ffuYP38+lStXpkmTJowYMYJp06YRFRUFwIwZMyhRogTjx4+nbNmydO/enTZt2jBx4sT4OCZMmECXLl3o0KEDAQEBzJgxA19fXz744ANbnouIiIjYLybGjMm+XUuqr68Zz92mjVq3U0umTDB5Msycaba/+AKCguDEiXu/9tatZuI7Ly94/fV7v56ICECKRqnExsayYcMGNm3axF9//cXly5fJly8fVapUoUGDBhQpUuSuA4mJiWHRokVcunSJwMDAJMtcuHABPz8/Mv3/4JotW7ZQoUIFChQoEF+mcePGvPrqq+zdu5cqVaqwZcsWGjRokOA6jRs3plevXgBERUWxY8cOQkJC4s97eHjQoEEDtmzZcst4IyMjiYyMjN+PiIgAIDo6mujo6JRVPpXExZVW43MW1dP9ZJS6qp7uJaPUE9y3rhs2ODh27Pb/VLp8GX799RpBQam8iLQLpZf3s0MHuP9+B88848nPPzt46CGLL7+MoVq15L0XSdVz7FhPwIPnnoslX74Y0vgjSLb08p7eK9XTvaSHeiY3tmQl3VeuXGH8+PFMnz6ds2fPUrlyZQoVKkSWLFk4dOgQX331FV26dKFRo0YMHjyYmjVrJjvQ3bt3ExgYyNWrV8mWLRtLly4lICAgUbl//vmHESNG8PLLL8cfCw8PT5BwA/H74f+/nsStykRERHDlyhXOnTtHTExMkmUOHDhwy7hHjx7NsGHDEh1fvXo1vr6+d6i1vUJDQ+0OIVWonu4no9RV9XQvGaWe4H513bixMFD9juVWrAjj0qXjrg8olaWX93PkSF9GjXqYv//2IyjIQY8eu3j00eQ3e8fVMzzcl6VLTUNNtWrrWb78oivCtVV6eU/vlerpXtJyPS9fvpyscslKukuVKkVgYCCzZ8+mYcOGeHl5JSrz119/8dlnn/Hss8/y1ltv0aVLl2QFULp0acLCwrhw4QKLFy+mffv2bNiwIUHiHRERQbNmzQgICGDo0KHJuq6rhYSEEBwcHL8fERFBkSJFaNSoEX5+fjZGdmvR0dGEhobe8j10F6qn+8kodVU93UtGqSe4b12jopLXX7xJk8oEBVVycTSpJz2+n089Be3axbJihSfjxj2Ej08MgwbF4nGbgZQ317N3bw9iYx00bhzLq6/WSr3gU0F6fE/vhurpXtJDPeN6O99JspLu1atXU7Zs2duWKVasGCEhIfTt25ejR48m6+YA3t7elCxZEoBq1arx888/M3nyZGbOnAnAxYsXeeyxx8iePTtLly5N8MALFiyYaJbxU6dOxZ+L+x137MYyfn5+ZMmSBU9PTzw9PZMsE3eNpPj4+ODj45PouJeXV5r9UMRJDzE6g+rpfjJKXVVP95JR6gnuU1fLgk8+MeO5b8fhAH9/qFs3k1uuxZ2e3s88ecys42+8YZb7GjnSkwMHPJk3D7Jmvf1rvby8uHjRiw8/NPv9+nng5eWeq+qmp/f0Xqie7iUt1zO5cSXrvyh3SrhvvvEDDzyQ7PI3i42NjR8rHRERQaNGjfD29uabb74hc+bMCcoGBgaye/fuBLOMh4aG4ufnF99SHhgYyJo1axK8LjQ0NH7cuLe3N9WqVUtQJjY2ljVr1txybLmIiIi4p7/+gqZNoX17OH/erLXtcCSeJC1uf9Ik3DLhTo88Pc3M4x98YCZC+/JLqFXLrOt9JzNmmPH5lStDvXouD1VEMpgUTaQG8Pvvv/P1119z5MgRHA4HJUqUoEWLFtx///0pvnlISAhNmjShaNGiXLx4kc8++4z169ezatWq+IT78uXLzJ8/n4iIiPjm+3z58uHp6UmjRo0ICAigXbt2vPvuu4SHhzNw4EC6desW3wrdtWtXpk6dSv/+/enYsSNr165l4cKFLFu2LD6O4OBg2rdvT/Xq1alRowaTJk3i0qVLdOjQIcV1EhERkfQnNhamT4cBA+C//8DHB4YOhT59TAtqz54Jlw3z9zcJd6tWdkUst9KhAzz4ILRsCbt2wUMPwddfw8MPJ10+MhLee89s9+mjWehFxPlSlHSPHj2awYMHExsbS/78+bEsizNnzjBgwABGjRpF3759U3Tz06dP8+KLL3Ly5Ely5MhBxYoVWbVqFQ0bNmT9+vVs3boVIL77eZzDhw9TvHhxPD09+e6773j11VcJDAwka9astG/fnuHDh8eXLVGiBMuWLaN3795MnjwZf39/5syZQ+PGjePLPPPMM5w5c4bBgwcTHh5O5cqVWblyZaLJ1URERMT9HDwInTubpaIAHn0U5syB0qXNfqtW8OSTsG7dNVasCKNJk8pu26XcXTz6KPz8MzzxhFnSLSgI5s6Ftm0Tl/3iCwfh4VC4MDzzTOrHKiLuL9lJ97p16xg4cCCDBg2iZ8+e5MqVC4CzZ88yadIkBgwYQI0aNahdu3aybz537txbnqtTpw7W7RbG/H/FihVj+fLlty1Tp04ddu3addsy3bt3p3v37ne8n4iIiLiH6GjTHXnYMNPamS0bvPMOdO1Kogm4PD0hKMji0qXjBAVVUsKdDhQvDj/+CC+8AN98Y37v3Qtvv23G7W/Y4GDjxsIsXmzezF69TLd0ERFnS3bSPWPGDDp37pxo9vDcuXMzfPhwwsPDmT59eoqSbhERERE77NoFHTtCWJjZf+wxM663WDFbwxIny54dli6Ft96CMWNg9Gj4/ns4cQKOH89E3JJwDgeog6OIuEqyp2bctm0b7dq1u+X5du3a8dNPPzklKBERERFXuHoVQkLMON+wMMidGz7+GJYvV8Ltrjw8TLL9ySeQKZPpdn78pmXVLctMnrdkiT0xioh7S3bSferUKYoXL37L8yVKlCA8PNwZMYmIiIg43Q8/QKVKpsUzJgaefhr27YN27TR5Vkbw3HPmS5bb6dXLfDZERJwp2Un31atX8fb2vuV5Ly8voqKinBKUiIiIiLNcvAjdu5vlo377De67z3Q5XrBAXYozkk2b4IZVZhOxLLO82KZNqReTiGQMKZq9fM6cOWTLli3JcxcvXnRKQCIiIiLOsmIFvPLK9bWaO3Uyk6flzGlrWGKDkyedW05EJLmSnXQXLVqU2bNn37GMiIiIiN3+/Rd69zbjeAFKlIDZs6F+fXvjEvvcd59zy4mIJFeyk+4jR464MAwRERGRe2dZsHix6U5++rQZq92rF4wYAVmz2h2d2KlWLfD3N5OoJbUqrcNhzteqlfqxiYh7S/aYbhEREZG07MQJaNXKTJB2+jQEBMDmzTBhghJuMWutT55stm+eOC9uf9IktAa7iDhdspPuLVu28N133yU49vHHH1OiRAny58/Pyy+/TGRkpNMDFBEREbkdy4K5c02S/dVXZlmowYNh506oWdPu6CQtadXK9IQoXDjhcX9/c7xVK3viEhH3luyke/jw4ezduzd+f/fu3XTq1IkGDRowYMAAvv32W0aPHu2SIEVERESS8uef0LAhdO4MFy5A9eom2R42DHx87I5O0qJWreDIEQgNvUZw8HZCQ69x+LASbhFxnWQn3WFhYdS/YfaRL774gocffpjZs2cTHBzMlClTWLhwoUuCFBEREblRTIzpClyhAqxZA1mymFnJt2wxx0Rux9MTgoIsatc+TlCQpS7lIuJSyZ5I7dy5cxS4YTHLDRs20KRJk/j9hx56iL/j1uMQERERcZG9e83SX1u3mv06dczM5CVL2hqWiIhIkpLd0l2gQAEOHz4MQFRUFDt37qTmDQOlLl68iJeXl/MjFBEREQGiomD4cKhSxSTcfn4wc6Zp6VbCLSIiaVWyW7qbNm3KgAEDeOedd/jqq6/w9fWl1g1rKvz666888MADLglSREREMraff4aOHWHPHrP/+OMwfbqZAEtERCQtS3ZL94gRI8iUKRNBQUHMnj2b2bNn4+3tHX/+gw8+oFGjRi4JUkRERDKmy5ehb18zC/mePZA3L3z+OXzzjRJuERFJH5Ld0p03b142btzIhQsXyJYtG543zTixaNEismXL5vQARUREJGNatw66dIE//jD7bduaydPy5rU1LBERkRRJdtIdJ0eOHEkez5079z0HIyIiInLhAvTvD7NmmX1/f5gxA5o1szcuERGRu5HspLtKlSo4HI5Ex3PkyEGpUqXo1asXZcuWdWpwIiIikrF8+y107QonTpj9rl3hnXfMpGkiIiLpUbKT7hYtWiR5/Pz58+zcuZPKlSuzdu1a/ve//zkrNhEREckgzpyBHj3giy/MfsmSMGcOBAXZG5eIiMi9SnbSPWTIkNuef+uttxg8eDBr1qy556BEREQkY7As+Owz6NkT/v0XPDzMxGlDh0KWLHZHJyIicu+SPXv5nTz//PPs3r3bWZcTERERN/f339C8Obzwgkm4K1Y062+/844SbhERcR9OS7o9PT2JjY111uVERETETcXGmonRypWDZcvA2xtGjIDt26F6dbujExERca4Uz15+K0uWLCEgIMBZlxMRERE39Pvv0LkzbNxo9gMDzdht/RNCRETcVbKT7ilTpiR5/MKFC+zYsYNly5axYsUKpwUmIiIi7uPaNZgwAYYMgatXwdcXRo+Gbt3A09Pu6ERERFwn2Un3xIkTkzzu5+dH6dKl2bhxI4GBgU4LTERERNzDL79Ax46wc6fZb9DArMFdooS9cYmIiKSGZCfdhw8fdmUcIiIi4mYiI+Htt2HMGNPSnTOnae1+6SVwOOyOTkREJHU4bUy3iIiIZDwxMbBhg4ONGwuTNauDunVNd/HNm6FTJzhwwJRr2RKmTYP77rM3XhERkdSWrNnLx4wZw+XLl5N1wa1bt7Js2bJ7CkpERETSviVLoHhxaNgwExMmVKdhw0wUKwbNmsGjj5qEu0ABWLzYlFXCLSIiGVGyWrr37dtHsWLFeOqpp2jevDnVq1cnX758AFy7do19+/bxww8/MH/+fE6cOMHHH3/s0qBFRETEXkuWQJs2YFkJjx8/bn4A2rc33clz5079+ERERNKKZCXdH3/8Mb/88gtTp07l+eefJyIiAk9PT3x8fOJbwKtUqULnzp156aWXyJw5s0uDFhEREfvExEDPnokT7hvlywdz52pmchERkWSP6a5UqRKzZ89m5syZ/Prrr/z1119cuXKFvHnzUrlyZfLmzevKOEVERCSN2LQJjh27fZkzZ0y5OnVSJSQREZE0K8UTqXl4eFC5cmUqV67sgnBEREQkrTt50rnlRERE3FmyJlITERERAdOl/JdfkldWE6eJiIhoyTARERFJpiNH4OWXITT09uUcDvD3h1q1UiUsERGRNE0t3SIiInJbMTEwZQqUL28Sbh8feOEFk1w7HAnLxu1PmqRJ1EREREBJt4iIiNzG/v1Qu7aZrfzSJdN6/euv8MknZv3twoUTlvf3N8dbtbInXhERkbRGSbeIiIgkEh0NI0dC5cqweTNkywbvvw/r10OpUqZMq1amy3lo6DWCg7cTGnqNw4eVcIuIiNzorsZ0b9++nYULF3L06FGioqISnFuyZIlTAhMRERF77NgBHTuaFm2AJk1gxgwoWjRxWU9PCAqyuHTpOEFBldSlXERE5CYpbun+4osveOSRR9i/fz9Lly4lOjqavXv3snbtWnLkyOGKGEVERCQVXLkCb7wBDz9sEu48eUw38mXLkk64RURE5M5SnHSPGjWKiRMn8u233+Lt7c3kyZM5cOAATz/9NEX1f2QREZF0aeNGqFQJ3n3XTJz2zDOwb9/1CdNERETk7qQ46f7jjz9o1qwZAN7e3ly6dAmHw0Hv3r2ZNWuW0wMUERER14mIgNdeg6Ag+P13KFQIvvoKvvgC8ue3OzoREZH0L8VJd65cubh48SIAhQsXZs+ePQCcP3+ey5cvOzc6ERERcZnly6FcOZg+3ex36QJ798KTT9obl4iIiDtJ8URqtWvXJjQ0lAoVKvDUU0/Rs2dP1q5dS2hoKPXr13dFjCIiIuJE//wDvXrBp5+a/fvvh9mzoV49W8MSERFxSylOuqdOncrVq1cBeOutt/Dy8mLz5s20bt2agQMHOj1AERERcQ7LgoUL4fXX4cwZ8PAwyfeIEeDra3d0IiIi7inFSXfu3Lnjtz08PBgwYIBTAxIRERHnO37cjN3+5huzX64czJ1rZioXERER10nxmG5PT09Onz6d6Pi///6LpxbnFBERSVMsy3QdDwgwCbeXFwwdCjt3KuEWERFJDSlu6bYsK8njkZGReHt733NAIiIi4hx//GEmR1u3zuzXqGFat8uXtzcuERGRjCTZSfeUKVMAcDgczJkzh2zZssWfi4mJYePGjZQpU8b5EYqIiEiKxMTA5MkwcCBcuQJZssDIkdCjB6hTmoiISOpKdtI9ceJEwLR0z5gxI0FXcm9vb4oXL86MGTOcH6GIiIgk25490KkTbNtm9uvVg1mz4IEH7I1LREQko0p20n348GEA6taty5IlS8iVK5fLghIREZGUiYqCUaPMT3Q0+PnB+PEmAXc47I5OREQk40rxmO51cQPDREREJE3YutUk13v3mv0nnoD334fChe2NS0RERJKZdAcHBzNixAiyZs1KcHDwbctOmDDBKYGJiIjI7V26BIMGwaRJZpbyfPlg6lR46im1bouIiKQVyUq6d+3aRXR0dPz2rTj0f3gREZFUsXatmZn8zz/Nfrt2MHEi5Mljb1wiIiKSULKS7hu7lKt7uYiIiH3On4d+/WDOHLNfpAjMnAlNmtgaloiIiNyCx728+O+//+bvv/92ViwiIiJyG19/DQEB1xPu114zs5Ur4RYREUm7Upx0X7t2jUGDBpEjRw6KFy9O8eLFyZEjBwMHDozvgi4iIiLOc/o0PPsstGgBJ0/Cgw/Cxo0wbZqZpVxERETSrhTPXv7666+zZMkS3n33XQIDAwHYsmULQ4cO5d9//2X69OlOD1JERCQjsiz49FPo2RPOngVPT9O1fPBgyJLF7uhEREQkOVKcdH/22Wd88cUXNLmhL1vFihUpUqQIzz33nJJuERERJzh6FLp2hRUrzH7lyjB3LlStamtYIiIikkIp7l7u4+ND8eLFEx0vUaIE3t7ezohJREQkw4qNNWtslytnEm4fHxg1CrZtU8ItIiKSHqU46e7evTsjRowgMjIy/lhkZCQjR46ke/fuTg1OREQkIzl4EOrUgW7d4L//4JFHICwMQkLAy8vu6ERERORupLh7+a5du1izZg3+/v5UqlQJgF9++YWoqCjq169Pq1at4ssuWbLEeZGKiIi4qWvXYNw4GDoUIiMha1YYM8bMTu5xT+uMiIiIiN1SnHTnzJmT1q1bJzhWpEgRpwUkIiKSkYSFQadOsHOn2W/c2Ky7XayYrWGJiIiIk6Q46f7www9dEYeIiEiGcvUqjBgB77wDMTGQKxdMmgTt2oHDYXd0IiIi4iwpTrpFRETk3vz4o2ndPnjQ7LdpA1OnQoEC9sYlIiIizndXSffixYtZuHAhR48eJSoqKsG5nXH940RERDKomBjYsMHBxo2FyZrVQd26Zo3t//6DN980CbZlQcGCZqbyli3tjlhERERcJcXTs0yZMoUOHTpQoEABdu3aRY0aNciTJw9//vlngrW7k2P69OlUrFgRPz8//Pz8CAwMZEXcgqTA1atX6datG3ny5CFbtmy0bt2aU6dOJbjG0aNHadasGb6+vuTPn59+/fpx7dq1BGXWr19P1apV8fHxoWTJknz00UeJYpk2bRrFixcnc+bMPPzww2zbti1FdREREQFYsgSKF4eGDTMxYUJ1GjbMRPHiMGiQWQbsvfdMwt2xI+zbp4RbRETE3aU46X7//feZNWsW7733Ht7e3vTv35/Q0FB69OjBhQsXUnQtf39/xowZw44dO9i+fTv16tXjySefZO/evQD07t2bb7/9lkWLFrFhwwZOnDiRYHb0mJgYmjVrRlRUFJs3b2bevHl89NFHDB48OL7M4cOHadasGXXr1iUsLIxevXrRuXNnVq1aFV9mwYIFBAcHM2TIEHbu3EmlSpVo3Lgxp0+fTunjERGRDGzJEtNV/NixhMePHYO334ajR01CHhoKc+eacdwiIiLi3lKcdB89epRHHnkEgCxZsnDx4kUA2rVrx+eff56iazVv3pymTZvy4IMPUqpUKUaOHEm2bNn46aefuHDhAnPnzmXChAnUq1ePatWq8eGHH7J582Z++uknAFavXs2+ffuYP38+lStXpkmTJowYMYJp06bFd3ufMWMGJUqUYPz48ZQtW5bu3bvTpk0bJk6cGB/HhAkT6NKlCx06dCAgIIAZM2bg6+vLBx98kNLHIyIiGVRMDPTsaVqxbyVbNvjlF2jQIPXiEhEREXuleEx3wYIFOXv2LMWKFaNo0aL89NNPVKpUicOHD2Pd7l8adxATE8OiRYu4dOkSgYGB7Nixg+joaBrc8C+TMmXKULRoUbZs2ULNmjXZsmULFSpUoMANM880btyYV199lb1791KlShW2bNmS4BpxZXr16gVAVFQUO3bsICQkJP68h4cHDRo0YMuWLbeMNzIyksjIyPj9iIgIAKKjo4mOjr7r5+BKcXGl1ficRfV0Pxmlrqpn+rZhg4Njx27/v9X//oNt264RFHT3/79Mi9z1Pb2Z6uleMko9IePUVfV0L+mhnsmNLcVJd7169fjmm2+oUqUKHTp0oHfv3ixevJjt27cn6PqdXLt37yYwMJCrV6+SLVs2li5dSkBAAGFhYXh7e5MzZ84E5QsUKEB4eDgA4eHhCRLuuPNx525XJiIigitXrnDu3DliYmKSLHPgwIFbxj169GiGDRuW6Pjq1avx9fVNXuVtEhoaancIqUL1dD8Zpa6qZ/q0cWNhoPody61YEcalS8ddH5AN3O09vRXV071klHpCxqmr6ule0nI9L1++nKxyKU66Z82aRWxsLED8JGebN2/miSee4JVXXknp5ShdujRhYWFcuHCBxYsX0759ezZs2JDi66S2kJAQgoOD4/cjIiIoUqQIjRo1ws/Pz8bIbi06OprQ0FAaNmyIl5eX3eG4jOrpfjJKXVXP9C0iInmLazdpUpmgoEoujiZ1uet7ejPV071klHpCxqmr6ule0kM943o730mKk24PDw88PK4PBX/22Wd59tlnU3qZeN7e3pQsWRKAatWq8fPPPzN58mSeeeYZoqKiOH/+fILW7lOnTlGwYEHAdHW/eZbxuNnNbyxz84znp06dws/PjyxZsuDp6Ymnp2eSZeKukRQfHx98fHwSHffy8kqzH4o46SFGZ1A93U9Gqavqmb7ExJglwG4YpZQkhwP8/aFu3Ux4eqZObKnNXd7TO1E93UtGqSdknLqqnu4lLdczuXEleyK133//neeeey7JbP7ChQs8//zz/Pnnn8mP8BZiY2OJjIykWrVqeHl5sWbNmvhzBw8e5OjRowQGBgIQGBjI7t27E8wyHhoaip+fHwEBAfFlbrxGXJm4a3h7e1OtWrUEZWJjY1mzZk18GRERkaTs2we1akGvXnDlCgQEmOTacVOjd9z+pEm4bcItIiIiSUt20j127FiKFCmSZNfpHDlyUKRIEcaOHZuim4eEhLBx40aOHDnC7t27CQkJYf369bRt25YcOXLQqVMngoODWbduHTt27KBDhw4EBgZSs2ZNABo1akRAQADt2rXjl19+YdWqVQwcOJBu3brFt0J37dqVP//8k/79+3PgwAHef/99Fi5cSO/evePjCA4OZvbs2cybN4/9+/fz6quvcunSJTp06JCi+oiISMYQFWWWAKtSBbZsgezZYcYM2L0bFi+GwoUTlvf3N8fvYuoTERERSeeS3b18w4YNzJ8//5bnn376aZ5//vkU3fz06dO8+OKLnDx5khw5clCxYkVWrVpFw4YNAZg4cSIeHh60bt2ayMhIGjduzPvvvx//ek9PT7777jteffVVAgMDyZo1K+3bt2f48OHxZUqUKMGyZcvo3bs3kydPxt/fnzlz5tC4ceP4Ms888wxnzpxh8ODBhIeHU7lyZVauXJlocjUREZHt26FTJ/j1V7PfrJlJuP39zX6rVvDkk7Bu3TVWrAijSZPKbt2lXERERG4v2Un30aNHyZ8//y3P582bl7///jtFN587d+5tz2fOnJlp06Yxbdq0W5YpVqwYy5cvv+116tSpw65du25bpnv37nTv3v22ZUREJOO6fBmGDoXx4yE2FvLmhSlT4NlnE3cn9/SEoCCLS5eOExRUSQm3iIhIBpbs7uU5cuTgjz/+uOX5Q4cOpdlZu0VERO7Fhg1QqRKMHWsS7uefN+O5n3succItIiIicqNkJ921a9fmvffeu+X5KVOmUKtWLacEJSIikhZcuABdu0KdOnDokBmr/e238OmnkC+f3dGJiIhIepDspDskJIQVK1bQpk0btm3bxoULF7hw4QJbt26ldevWrFq1ipA7rZciIiKSTixbBuXKwcyZZv+VV2DvXnj8cXvjEhERkfQl2WO6q1SpwuLFi+nYsSNLly5NcC5PnjwsXLiQqlWrOj1AERGR1HTmjFkC7LPPzP4DD8CcOaa1W0RERCSlkp10Azz++OP89ddfrFy5kkOHDmFZFqVKlaJRo0b4+vq6KkYRERGXsyz44gvo0QP++Qc8PKBPHzN5mv4XJyIiIncrRUk3QJYsWWjZsqUrYhEREbHFsWPw6qvw3Xdmv0IF+OADqF7d3rhEREQk/Uv2mG4RERF3ExtrxmwHBJiE28sLhg83a3Er4RYRERFnSHFLt4iIiDs4dAi6dIH1681+zZowd65JwEVEREScRS3dIiKSoVy7BuPGmS7k69eb8dqTJsEPPyjhFhEREedLUdJ97do1Pv74Y06dOuWqeERERFzm118hMBD69YOrV6FBA9izB3r2BE9Pu6MTERERd5SipDtTpkx07dqVq1evuioeERERp4uMhMGDoVo1M147Rw7TlXz1aihRwu7oRERExJ2luHt5jRo1CAsLc0EoIiIizrdlC1SpAiNGmK7lLVrAvn3QsSM4HHZHJyIiIu4uxROpvfbaawQHB/P3339TrVo1smbNmuB8xYoVnRaciIjI3bp0Cd56C6ZMMWtw588P06ZB69ZKtkVERCT1pDjpfvbZZwHo0aNH/DGHw4FlWTgcDmJiYpwXnYiIyF34/nszM/mRI2a/fXsYPx7y5LE1LBEREcmAUpx0Hz582BVxiIiI3LNz56BvX/jgA7NftCjMmgWNG9sbl4iIiGRcKU66ixUr5oo4RERE7snSpfDaaxAebrqPd+sGo0ZB9ux2RyYiIiIZ2V2t0/3JJ5/wv//9j0KFCvHXX38BMGnSJL7++munBiciInIn4eHw1FPQqpXZLl0aNm6E995Twi0iIiL2S3HSPX36dIKDg2natCnnz5+PH8OdM2dOJk2a5Oz4REREkmRZ8PHHEBAAixebdbZDQiAsDB591O7oRERERIwUJ93vvfces2fP5q233sLT0zP+ePXq1dm9e7dTgxMREUnKX39BkyZmgrRz58ySYD//bLqTZ85sd3QiIiIi16U46T58+DBVqlRJdNzHx4dLly45JSgREZGkxMbC1KlQrhysWgU+PjB6NGzdahJvERERkbQmxROplShRgrCwsEQTqq1cuZKyZcs6LTAREZEbHTgAnTvDjz+a/UcfhTlzzBhuERERkbQqxUl3cHAw3bp14+rVq1iWxbZt2/j8888ZPXo0c+bMcUWMIiKSgUVHw9ixMGwYREVBtmzwzjvQtSt43NV0oCIiIiKpJ8VJd+fOncmSJQsDBw7k8uXLPP/88xQqVIjJkyfz7LPPuiJGERHJoHbuhE6dzORoAI89BjNnmvW3RURERNKDFCfdAG3btqVt27ZcvnyZ//77j/z58zs7LhERycCuXIHhw00Ld0wM5M4NkybBCy+YNbhFRERE0ou7SroBTp8+zcGDBwFwOBzky5fPaUGJiEjGtWmTGbv9229m/+mnYcoUKFDA3rhERERE7kaKR8NdvHiRdu3aUahQIYKCgggKCqJQoUK88MILXLhwwRUxiohIBnDxInTrBrVrm4T7vvtg6VJYsEAJt4iIiKRfKU66O3fuzNatW1m2bBnnz5/n/PnzfPfdd2zfvp1XXnnFFTGKiIibiImBDRscbNxYmA0bHMTEmOMrVphlwN5/3+x36gT79kGLFraFKiIiIuIUKe5e/t1337Fq1SoeffTR+GONGzdm9uzZPPbYY04NTkRE3MeSJdCzJxw7lgmozoQJUKgQPPggbNhgypQoAbNnQ/36toYqIiIi4jQpTrrz5MlDjhw5Eh3PkSMHuXLlckpQIiLiXpYsgTZtwLISHj9xwvwA9O4NI0ZA1qypH5+IiIiIq6S4e/nAgQMJDg4mPDw8/lh4eDj9+vVj0KBBTg1ORETSv5gY08J9c8J9o/z5zUzlSrhFRETE3aS4pXv69OkcOnSIokWLUvT/F0o9evQoPj4+nDlzhpkzZ8aX3blzp/MiFRGRdGnTJjh27PZlTp825erUSZWQRERERFJNipPuFprVRkREUuDkSeeWExEREUlPUpx0DxkyxBVxiIiIG4qJgR9+SF7Z++5zbSwiIiIidkhx0i0iIpIce/aYpb+2bbt9OYcD/P2hVq3UiUtEREQkNaV4IjUREZHbiYqCYcOgalWTcPv5QdeuJrl2OBKWjdufNAk8PVM9VBERERGXU9ItIiJOs20bVKsGQ4dCdDQ0bw779sH06bB4MRQunLC8v7853qqVLeGKiIiIuJySbhERuWeXL0PfvhAYaLqV580Ln38OX399PdFu1QqOHIHQ0GsEB28nNPQahw8r4RYRERH3lqIx3REREWzdupWoqChq1KhBvnz5XBWXiIikE+vWQefO8OefZr9tW9NdPG/exGU9PSEoyOLSpeMEBVVSl3IRERFxe8lOusPCwmjatCmnTp3CsiyyZ8/OwoULady4sSvjExGRNOrCBejXD2bPNvv+/jBjBjRrZm9cIiIiImlJsruXv/HGG5QoUYIffviBHTt2UL9+fbp37+7K2EREJI365hsICLiecL/6Kuzdq4RbRERE5GbJbunesWMHq1evpmrVqgB88MEH5M6dm4iICPz8/FwWoIiIpB2nT0OPHrBggdl/8EGTeAcF2RuXiIiISFqV7Jbus2fP4u/vH7+fM2dOsmbNyr///uuSwEREJO2wLPj0U9O6vWABeHhA//7wyy9KuEVERERuJ0UTqe3bt4/w8PD4fcuy2L9/PxcvXow/VrFiRedFJyIitvv7b7PO9vLlZr9iRZg7F6pXtzcuERERkfQgRUl3/fr1sSwrwbHHH38ch8OBZVk4HA5iYmKcGqCIiNgjNhZmzoQ33oCLF8HbGwYPNi3cXl52RyciIiKSPiQ76T58+LAr4xARkTTkt9+gSxfYuNHsBwaa1u2yZe2NS0RERCS9SXbSXaxYMVfGISIiacC1azBhAgwZAlevgq8vjB4N3bqhNbVFRERE7kKKupcD/P7773z99dccOXIEh8NBiRIlaNGiBffff78r4hMRkVTyyy/QsSPs3Gn2GzaEWbOgeHFbwxIRERFJ11KUdI8ePZrBgwcTGxtL/vz5sSyLM2fOMGDAAEaNGkXfvn1dFaeIiLjI1avw9tvwzjumpTtnTpg4Edq3B4fD7uhERERE0rdkLxm2bt06Bg4cyFtvvcU///zDyZMnCQ8Pj0+6BwwYwMa4wX8iIpIubN4MVarAyJEm4W7VCvbvh5deUsItIiIi4gzJbumeMWMGnTt3ZujQoQmO586dm+HDhxMeHs706dOpXbu2s2MUEREn++8/ePNNmDrVrMFdoABMmwatW9sdmYiIiIh7SXZL97Zt22jXrt0tz7dr146ffvrJKUGJiIjrrF4N5cvDe++ZhPull2DfPiXcIiIiIq6Q7JbuU6dOUfw2s+mUKFGC8PBwZ8QkIiIucPYs9OkDH31k9osVMxOlNWpka1giIiIibi3ZLd1Xr17F29v7lue9vLyIiopySlAiIuJcX34JAQEm4XY4oEcP2LNHCbeIiIiIq6Vo9vI5c+aQLVu2JM9dvHjRKQGJiIjzhIdD9+4m6QYoUwbmzIH//c/euEREREQyimQn3UWLFmX27Nl3LCMiIvazLJg3D3r3hvPnIVMmeOMNGDgQMme2OzoRERGRjCPZSfeRI0dcGIaIiDjLkSPw8ssQGmr2q1aFuXOhcmU7oxIRERHJmJI9pltERNK2mBiYMsXMTB4aalq033kHtm5Vwi0iIiJil2S3dF+5coU1a9bw+OOPAxASEkJkZGT8eU9PT0aMGEFm9VsUEUl1+/dDp06wZYvZr1XLjN0uVcreuEREREQyumQn3fPmzWPZsmXxSffUqVMpV64cWbJkAeDAgQMUKlSI3r17uyZSERFJJDoa3n0Xhg+HqCjInt20br/yCnioL5OIiIiI7ZKddH/66af0798/wbHPPvuM+++/H4D58+czbdo0Jd0iIqlkxw7o2BF+/dXsN2kCM2dCkSL2xiUiIiIi1yW7HeTQoUNUqFAhfj9z5sx43NCMUqNGDfbt2+fc6EREJJErV8xM5DVqmIQ7Tx6YPx+WLVPCLSIiIpLWJLul+/z58wnGcJ85cybB+djY2ATnRUTE+TZuhM6d4fffzf6zz8LkyZA/v71xiYiIiEjSkt3S7e/vz549e255/tdff8Xf398pQYmISEIREfDaaxAUZBLuQoXg66/h88+VcIuIiIikZclOups2bcrgwYO5evVqonNXrlxh2LBhNGvWzKnBiYgILF8O5crB9Olmv0sX2LcPnnjC3rhERERE5M6SnXS/+eabnD17ltKlSzN27Fi+/vprvv76a959911Kly7NuXPnePPNN1N089GjR/PQQw+RPXt28ufPT4sWLTh48GCCMuHh4bRr146CBQuSNWtWqlatypdffpmgzNmzZ2nbti1+fn7kzJmTTp068d9//yUo8+uvv1KrVi0yZ85MkSJFePfddxPFs2jRIsqUKUPmzJmpUKECy5cvT1F9RETuVkwMbNjgYOPGwmzY4CAmBv75B154AZo1g2PH4P77Yc0amDULcuSwO2IRERERSY5kJ90FChRg8+bNlC1blgEDBtCyZUtatmxJSEgIAQEB/PDDDxQoUCBFN9+wYQPdunXjp59+IjQ0lOjoaBo1asSlS5fiy7z44oscPHiQb775ht27d9OqVSuefvppdu3aFV+mbdu27N27l9DQUL777js2btzIyy+/HH8+IiKCRo0aUaxYMXbs2MHYsWMZOnQos2bNii+zefNmnnvuOTp16sSuXbto0aIFLVq0uG2XehERZ1iyBIoXh4YNMzFhQnUaNsxE/vwmyf70U7P0V58+sHs31Ktnd7QiIiIikhLJnkgNoESJEqxcuZKzZ89y6NAhAEqWLEnu3Lnv6uYrV65MsP/RRx+RP39+duzYQe3atQGTDE+fPp0aNWoAMHDgQCZOnMiOHTuoUqUK+/fvZ+XKlfz8889Ur14dgPfee4+mTZsybtw4ChUqxKeffkpUVBQffPAB3t7elCtXjrCwMCZMmBCfnE+ePJnHHnuMfv36ATBixAhCQ0OZOnUqM2bMuKv6iYjcyZIl0KYNWFbC42fPmt9FisDixWamchERERFJf5Ld0n2j3LlzU6NGDWrUqHHXCXdSLly4EH/9OI888ggLFizg7NmzxMbG8sUXX3D16lXq1KkDwJYtW8iZM2d8wg3QoEEDPDw82Lp1a3yZ2rVr4+3tHV+mcePGHDx4kHPnzsWXadCgQYJ4GjduzJYtW5xWPxGRG8XEQM+eiRPuG1kWVKuWejGJiIiIiHOlqKXblWJjY+nVqxf/+9//KF++fPzxhQsX8swzz5AnTx4yZcqEr68vS5cupWTJkoAZ853/pql7M2XKRO7cuQkPD48vU6JEiQRl4rrCh4eHkytXLsLDwxN1jy9QoED8NW4WGRmZYIm0iIgIAKKjo4mOjr6bR+BycXGl1ficRfV0P+5a1w0bHBw7dvv/DB87BuvWXSMo6DaZeTrjru/nzTJKPSHj1FX1dC8ZpZ6QceqqerqX9FDP5MaWZpLubt26sWfPHn744YcExwcNGsT58+f5/vvvyZs3L1999RVPP/00mzZtokKFCjZFayaBGzZsWKLjq1evxtfX14aIki80NNTuEFKF6ul+3K2u69cXBqrfsdyKFWFcunTc9QGlMnd7P28lo9QTMk5dVU/3klHqCRmnrqqne0nL9bx8+XKyyqWJpLt79+7xE6DduNb3H3/8wdSpU9mzZw/lypUDoFKlSmzatIlp06YxY8YMChYsyOnTpxNc79q1a5w9e5aCBQsCULBgQU6dOpWgTNz+ncrEnb9ZSEgIwcHB8fsREREUKVKERo0a4efndzePweWio6MJDQ2lYcOGeHl52R2Oy6ie7scd67pnDyxf7pmssk2aVCYoqJKLI0o97vh+JiWj1BMyTl1VT/eSUeoJGaeuqqd7SQ/1jOvtfCe2Jt2WZfH666+zdOlS1q9fn6gLeNw3Bx4eCYeee3p6EhsbC0BgYCDnz59nx44dVPv/gY9r164lNjaWhx9+OL7MW2+9RXR0dPwbFhoaSunSpcmVK1d8mTVr1tCrV6/4+4SGhhIYGJhk7D4+Pvj4+CQ67uXllWY/FHHSQ4zOoHq6H3eoa2QkjB4No0ZBdDQ4HLce0+1wgL8/1K2bCc/k5efpiju8n8mRUeoJGaeuqqd7ySj1hIxTV9XTvaTleiY3rruaSM1ZunXrxvz58/nss8/Inj074eHhhIeHc+XKFQDKlClDyZIleeWVV9i2bRt//PEH48ePJzQ0lBYtWgBQtmxZHnvsMbp06cK2bdv48ccf6d69O88++yyFChUC4Pnnn8fb25tOnTqxd+9eFixYwOTJkxO0VPfs2ZOVK1cyfvx4Dhw4wNChQ9m+fTvdu3dP9eciIu5p61YzKdqwYSbhfvJJs+a2w2F+bhS3P2kSbplwi4iIiGQUtibd06dP58KFC9SpU4f77rsv/mfBggWA+eZg+fLl5MuXj+bNm1OxYkU+/vhj5s2bR9OmTeOv8+mnn1KmTBnq169P06ZNefTRRxOswZ0jRw5Wr17N4cOHqVatGn369GHw4MEJ1vJ+5JFH+Oyzz5g1axaVKlVi8eLFfPXVVwkmdRMRuRuXLkFwMAQGwt69kC8fLFgAS5dC585mSbDChRO+xt/fHG/Vyp6YRURERMQ5bO9eficPPvggX3755W3L5M6dm88+++y2ZSpWrMimTZtuW+app57iqaeeumNMIiLJtWYNdOkChw+b/XbtYOJEyJPneplWrUyr97p111ixIowmTSq7bZdyERERkYwmTUykJiLibs6fh379YM4cs1+kCMycCU2aJF3e0xOCgiwuXTpOUFAlJdwiIiIibsLW7uUiIu7o668hIOB6wt2tm+lWfquEW0RERETcl1q6RUSc5NQp6NEDFi40+6VKmcS7Vi174xIRERER+6ilW0TkHlkWfPKJad1euNB0FR8wAH75RQm3iIiISEanlm4RkXtw9Ch07QorVpj9ypVh7lyoWtXWsEREREQkjVBLt4jIXYiNhfffh3LlTMLt4wOjRsG2bUq4RUREROQ6tXSLiKTQwYNmfe0ffjD7jzxiWrfLlLE3LhERERFJe9TSLSKSTNHRMGYMVKpkEu6sWeG992DTJiXcIiIiIpI0tXSLiCTDrl3QqZP5DdC4sVl3u1gxe+MSERERkbRNLd0iIrdx9Sq89RY89JBJuHPlgnnzzDhuJdwiIiIicidq6RYRuYUffzSt2wcPmv02bWDqVChQwN64RERERCT9UEu3iMhNLl6E1183a2wfPAgFC8KSJbBokRJuEREREUkZtXSLiNxg1Sp4+WWz/jZAx44wbpzpVi4iIiIiklJKukVEgLNnoXdv+Phjs1+8OMyeDQ0a2BqWiIiIiKRz6l4uIhmaZcHixVC2rEm4HQ7o1Qv27FHCLSIiIiL3Ti3dIpJhnTwJ3brB0qVmPyAA5syBwEB74xIRERER96GWbhHJcCwLPvjAJNlLl0KmTDBoEOzcqYRbRERERJxLLd0ikqEcPmwmSvv+e7NfvTrMnQsVK9obl4iIiIi4J7V0i0iGEBMDkydD+fIm4c6cGcaOhS1blHCLiIiIiOuopVtE3N6+fdCpE/z0k9kPCjJjt0uWtDcuEREREXF/aukWEbcVFQUjRkCVKibhzp4dZs6EtWuVcIuIiIhI6lBLt4i4pe3bTev2r7+a/ccfh+nTwd/f3rhEREREJGNRS7eIuJXLl6F/f3j4YZNw580Ln30G33yjhFtEREREUp9aukXEbaxfD126wKFDZv/552HSJMiXz86oRERERCQjU0u3iKR7Fy5A165Qt65JuAsXhm+/hU8/VcItIiIiIvZS0i0i6dp330G5cmaCNDDJ9969Zgy3iIiIiIjd1L1cRNK0mBjYsMHBxo2FyZrVQd264OkJZ85Az57w+eemXMmSZhmwoCB74xURERERuZGSbhFJs5YsMYn1sWOZgOpMmGAmQ2vTBubPh3/+AQ8P6NMHhg4FX1+7IxYRERERSUhJt4ikSUuWmOTashIeP3bMTI4GUKECfPABVK+e6uGJiIiIiCSLkm4RSXNiYkwL980J941y5ICtWyFLltSLS0REREQkpTSRmoikOZs2mRbt27lwwSTdIiIiIiJpmZJuEUlzTp50bjkREREREbso6RaRNOfy5eSVu+8+18YhIiIiInKvlHSLSJoRGQmDB8Mrr9y+nMMBRYpArVqpE5eIiIiIyN1S0i0iacKWLVClCowYYSZSq1HDJNcOR8JycfuTJpn1ukVERERE0jIl3SJiq0uXoFcv+N//YP9+KFAAFi82k6QtXgyFCycs7+9vjrdqZUu4IiIiIiIpoiXDRMQ2338PXbrAkSNmv317mDABcuc2+61awZNPwrp111ixIowmTSpTt24mtXCLiIiISLqhpFtEUt25c9C3L3zwgdkvWhRmzYLGjROX9fSEoCCLS5eOExRUSQm3iIiIiKQr6l4uIqlq6VIICDAJt8MBr78Oe/YknXCLiIiIiKR3aukWkVRx6pRJsBctMvulS8PcuWYst4iIiIiIu1JLt4i4lGXBxx9D2bIm4fb0hDffhLAwJdwiIiIi4v7U0i0iLvPXX2bN7VWrzH6VKqZbeeXKtoYlIiIiIpJq1NItIk4XGwtTp0K5cibh9vGBMWNg2zYl3CIiIiKSsailW0Sc6sAB6NwZfvzR7NeqBbNnmzHcIiIiIiIZjVq6RcQpoqNh1CioVMkk3NmywbRpsH69Em4RERERybjU0i0i92znTujUyUyOBtCkCcyYYdbfFhERERHJyNTSLSJ37coVCAmBGjVMwp07N3zyCSxbpoRbRERERATU0i0id2nTJjN2+7ffzP7TT8N770H+/PbGJSIiIiKSlqilW0RS5OJF6NYNatc2Cfd998HSpbBggRJuEREREZGbqaVbRJJtxQqz7vbff5v9zp1h7FjImdPWsERERERE0iwl3SJyR//+C717m/HaAPffD7NmQf369sYlIiIiIpLWqXu5iNySZcHChVC2rEm4PTwgOBh+/VUJt4iIiIhIcqilW0SSdOIEvPYafP212Q8IgA8+gIcftjcuEREREZH0RC3dIpKAZcHcuSbJ/vpr8PKCIUPMWtxKuEVEREREUkYt3SIS788/oUsXWLvW7D/0kEnAK1SwNy4RERERkfRKLd0iQkwMTJwI5cubhDtLFhg/HrZsUcItIiIiInIv1NItksHt2WOW/tq61ezXrQuzZ8MDD9gbl4iIiIiIO1BLt0gGFRUFw4ZB1aom4fbzM8uArVmjhFtERERExFnU0i2SAf38M3TsaFq5AZo3h+nToXBhe+MSEREREXE3aukWyUAuX4a+faFmTZNw58sHX3xhZilXwi0iIiIi4nxq6RbJINatM2O3//zT7LdtC5MmQd68toYlIiIiIuLW1NIt4uYuXIBXXvm/9u48Lspy///4e9hFwS1xg1C/5YIbLmW4kaWQmWlW345amrl1guN2cqk8qXX6WrlXtmnq6bibWqalkihkoimBpRnmlscSrUxxSxHu3x/3YX6OuKDOPQMzr+fjwePRdc/FPZ83IzqfrmvuW7rnHrPhDg+XVq6U5s6l4QYAAACsRtMNeLBPP5WioswLpEnSX/8q7dwpderk3roAAAAAb8H2csADHT0qDR5sfl5bkm6/XZo5U2rb1r11AQAAAN6GlW6gBMrLk1JSbEpNra6UFJvy8szjhiHNm2eubi9cKPn6SiNHStu303ADAAAA7uDWpnv8+PG64447FBISorCwMHXt2lVZWVmF5qWlpemee+5R6dKlFRoaqrZt2+rs2bP2x48dO6aePXsqNDRU5cqVU9++fXXq1CmHc3z77bdq06aNgoKCFBERoddff73Q8yxZskR169ZVUFCQGjZsqM8++8z5oYGbtGyZVKOG1KGDnyZPbq4OHfxUo4a5hfyBB6THH5d+/11q3Ni8//arr0qlSrm7agAAAMA7ubXpTklJUUJCgjZv3qykpCTl5uYqLi5Op0+fts9JS0vTfffdp7i4OH399dfaunWrEhMT5ePz/0vv2bOndu7cqaSkJK1cuVKpqakaMGCA/fGcnBzFxcUpMjJS6enpmjBhgsaOHav3Cz7oKmnTpk3q3r27+vbtq4yMDHXt2lVdu3bVjoIbGQPFwLJl0iOPSIcOOR4/dMi8WNpnn0kBAdI//2nei7tZM/fUCQAAAMDk1s90r1692mE8Z84chYWFKT09XW3/uxd26NChGjRokEaNGmWfV6dOHft/79q1S6tXr9bWrVvVvHlzSdKbb76p+++/XxMnTlS1atU0b948nT9/XrNmzVJAQIDq16+vzMxMTZ482d6cT5s2Tffdd5+GDx8uSXr55ZeVlJSkt956S++++66lPwegKPLyzM9pG8aV5wQESOnpUoMGrqsLAAAAwJUVqwupnThxQpJUoUIFSdLRo0e1ZcsW9ezZUy1bttTevXtVt25dvfLKK2rdurUkcyW8XLly9oZbktq3by8fHx9t2bJFDz30kNLS0tS2bVsFBATY58THx+u1117TH3/8ofLlyystLU3Dhg1zqCc+Pl4ff/zxZWs9d+6czp07Zx/n5ORIknJzc5Wbm3vzPwwLFNRVXOtzFk/NmZJi06FDV/+VPX9eys6+oDp1rtKZl0Ce+ppeipyexVtySt6TlZyexVtySt6TlZyepSTkLGptxabpzs/P15AhQ9SqVSs1+O8y3b59+yRJY8eO1cSJExUdHa0PP/xQ9957r3bs2KHbb79d2dnZCgsLcziXn5+fKlSooOzsbElSdna2atas6TCncuXK9sfKly+v7Oxs+7GL5xSc41Ljx4/XuHHjCh1fu3atgoODb+An4DpJSUnuLsElPC1namp1Sc2vOe/zzzN1+vTP1hfkBp72ml4JOT2Lt+SUvCcrOT2Lt+SUvCcrOT1Lcc555syZIs0rNk13QkKCduzYoY0bN9qP5efnS5IGDhyoPn36SJKaNGmidevWadasWRo/frxbapWk5557zmFlPCcnRxEREYqLi1NoaKjb6rqa3NxcJSUlqUOHDvL393d3OZbx1JwBATZNnnzteR07Ris2trH1BbmQp76mlyKnZ/GWnJL3ZCWnZ/GWnJL3ZCWnZykJOQt2O19LsWi6ExMT7RdACw8Ptx+vWrWqJCkqKsphfr169XTw4EFJUpUqVXT06FGHxy9cuKBjx46pSpUq9jlHjhxxmFMwvtacgscvFRgYqMDAwELH/f39i+0figIloUZn8KScmzZJQ4defY7NJoWHS+3a+cnX1zV1uZonvaZXQ07P4i05Je/JSk7P4i05Je/JSk7PUpxzFrUut1693DAMJSYmavny5UpOTi60BbxGjRqqVq1aoduI7d69W5GRkZKkmJgYHT9+XOnp6fbHk5OTlZ+frxYtWtjnpKamOuy5T0pKUp06dVS+fHn7nHXr1jk8T1JSkmJiYpwXGLhOp06ZF09r3VrKypLKljWP22yO8wrGU6fKYxtuAAAAoCRya9OdkJCguXPnav78+QoJCVF2drays7Pt9+C22WwaPny43njjDX300Ufas2eP/vGPf+iHH35Q3759JZmr3vfdd5/69++vr7/+Wl999ZUSExP1l7/8RdWqVZMk9ejRQwEBAerbt6927typRYsWadq0aQ7bwwcPHqzVq1dr0qRJ+uGHHzR27Fht27ZNiYmJrv/BAJLWrjWvQv7GG+YVy598Utq3T1q6VKpe3XFueLj00UdSt25uKRUAAADAFbh1e/k777wjSbr77rsdjs+ePVtPPvmkJGnIkCH6888/NXToUB07dkyNGzdWUlKS/ud//sc+f968eUpMTNS9994rHx8fPfzww3rjjTfsj5ctW1Zr165VQkKCmjVrpltuuUUvvviiw728W7Zsqfnz52v06NF6/vnndfvtt+vjjz+2X9QNcJVjx6S//12aM8ccR0ZK778vxcWZ427dpC5dpPXrL+jzzzPVsWO0R28pBwAAAEoytzbdxtVuOHyRUaNGOdyn+1IVKlTQ/Pnzr3qORo0a6csvv7zqnEcffVSPPvpokWoCrLB0qZSQIB05Ym4Z/9vfpFdekcqUcZzn6yvFxho6ffpnxcY2puEGAAAAiqlicSE1wNtlZ0uJiWbTLUl160offCC1bOneugAAAADcHLd+phvwdoZhbiOvV89suP38pBdekDIyaLgBAAAAT8BKN+AmBw5IAwZISUnmuGlTc3U7OtqdVQEAAABwJla6ARfLyzOvSN6ggdlwBwVJr70mbdlCww0AAAB4Gla6ARfatUvq21dKSzPHbdtKM2ZItWu7ty4AAAAA1mClG3CB3FzzKuTR0WbDHRIivfOOtH49DTcAAADgyVjpBiyWni499ZT07bfm+P77pXfflSIi3FsXAAAAAOux0g1Y5OxZaeRIqUULs+GuWFGaO1dauZKGGwAAAPAWrHQDFkhNlfr1k3780Rz/5S/StGlSWJh76wIAAADgWqx0A06UkyM984wUG2s23NWqSZ98Ii1YQMMNAAAAeCNWugEn+ewzaeBA6dAhc9y/vzRhglS2rHvrAgAAAOA+NN3ATfrtN2nIEGnePHNcq5Z5G7B77nFrWQAAAACKAbaXAzfIMKSFC6V69cyG28dH+vvfpe++o+EGAAAAYGKlG7gBP/9sfnZ7xQpz3KCB9MEH0p13urcuAAAAAMULK93AdTAMc+t4VJTZcPv7S+PGmffipuEGAAAAcClWuoEi2rNHGjBAWr/eHLdoYa5u16/v3roAAAAAFF+sdAPXkJcnTZokNWpkNtylSkmTJ0tffUXDDQAAAODqWOkGruK776S+faWtW83xPfeY28tr1XJvXQAAAABKBla6gcs4d04aM0Zq2tRsuMuWlWbOlL74goYbAAAAQNGx0g1cYssWc3V7505z3KWL9PbbUrVq7q0LAAAAQMnDSjfwX6dPS8OGSTExZsNdqZK0aJG0fDkNNwAAAIAbw0o3IGndOql/f2n/fnP8xBPSlClSxYrurQsAAABAycZKN7za8eNms92+vdlwR0RIn30mffghDTcAAACAm0fTDa/1ySdSVJR5gTRJSkgwt5V37OjeugAAAAB4DraXw+scOSINGiQtXmyOa9c2G+82bdxbFwAAAADPw0o3vIZhSP/+t7m6vXix5OsrjRolbd9Oww0AAADAGqx0w6Pk5UkpKTalplZX6dI2tWtnNtcHD0oDB0qrV5vzoqOlDz4w78MNAAAAAFah6YbHWLZMGjxYOnTIT1JzTZ4shYdL990nLVwonTolBQZKY8ZIzz4r+fu7u2IAAAAAno6mGx5h2TLpkUfMLeQXO3To/18orWVLc3W7bl3X1wcAAADAO9F0o8TLyzNXuC9tuC9Wrpy0YQOr2wAAAABciwupocT78ktzRftqjh+XvvrKJeUAAAAAgB1NN0q8w4edOw8AAAAAnIWmGyXeb78VbV7VqtbWAQAAAACXoulGiXXypJSYKA0adPV5NpsUEcG9uAEAAAC4Hk03SqTVq6UGDaTp083xPfeYzbXN5jivYDx1qnm/bgAAAABwJZpulCi//y717i117CgdPCjVqCElJUnr1kkffSRVr+44PzzcPN6tm1vKBQAAAODluGUYSgTDkJYulRISpKNHzRXswYOlf/5TKl3anNOtm9Sli7R+/QV9/nmmOnaMVrt2fqxwAwAAAHAbmm4Ue4cPm8328uXmOCpKmjlTiokpPNfXV4qNNXT69M+KjW1Mww0AAADArdhejmLLMKRZs6R69cyG289P+sc/pG++uXzDDQAAAADFDSvdKJb27ZMGDpS++MIcN28uffCB1KiRe+sCAAAAgOvBSjeKlbw880rjDRuaDXdQkDRhgpSWRsMNAAAAoORhpRvFxvffS337Sps3m+PYWPOz27fd5t66AAAAAOBGsdINtzt/Xnr5ZSk62my4Q0Kk996TkpNpuAEAAACUbKx0w622bjVXt7/7zhw/8ID0zjvm/bUBAAAAoKRjpRtuceaMNHy4dNddZsN9yy3S/PnSihU03AAAAAA8ByvdcLkNG6R+/aS9e81xjx7mxdMqVXJnVQAAAADgfKx0w2VOnDBvA9aundlwV68uffqpNG8eDTcAAAAAz0TTDZf49FOpfn3p/ffN8cCB0s6d5me4AQAAAMBTsb0clvr1V2nwYGnBAnN8223SjBnS3Xe7tSwAAAAAcAlWumEJwzAvjFavntlw+/iYF07bvp2GGwAAAID3YKUbTvef/0h//au0apU5bthQmjVLat7cvXUBAAAAgKux0g2nyc+X3nvP/Oz2qlVSQID08svStm003AAAAAC8EyvdcIoff5T695dSUszxXXdJH3wgRUW5ty4AAAAAcCdWunFTLlyQJkyQGjUyG+7gYPOe2xs30nADAAAAACvduGHbt0t9+0rp6ea4fXvzlmA1a7q3LgAAAAAoLljpxnU7d076xz/Mz2mnp0vlypkXSlu7loYbAAAAAC7GSjeuS1qaubq9a5c5fughafp0qWpV99YFAAAAAMURK90oklOnpCFDpFatzIa7cmXpo4+kZctouAEAAADgSljpxjUlJUkDBkgHDpjj3r2lyZOlChXcWhYAAAAAFHs03biiP/6Q/v53afZsc3zrreaF0uLj3VsXAAAAAJQUbC/HZS1bZt7ya/ZsyWaT/vY3accOGm4AAAAAuB6sdMNBdraUmCgtXWqO69SRPvjA/Cw3AAAAAOD6sNINSZJhSP/6l7m6vXSp5OsrvfCClJlJww0AAAAAN4qVbujAAWngQPM+25LUpIl53+3oaHdWBQAAAAAln1tXusePH6877rhDISEhCgsLU9euXZWVlXXZuYZhqGPHjrLZbPr4448dHjt48KA6deqk4OBghYWFafjw4bpw4YLDnA0bNqhp06YKDAzUbbfdpjlz5hR6junTp6tGjRoKCgpSixYt9PXXXzsrqtvl5UkpKTalplZXSopNeXlSfr705ptSgwZmwx0YKL36qvT11zTcAAAAAOAMbm26U1JSlJCQoM2bNyspKUm5ubmKi4vT6dOnC82dOnWqbDZboeN5eXnq1KmTzp8/r02bNulf//qX5syZoxdffNE+Z//+/erUqZPatWunzMxMDRkyRP369dOaNWvscxYtWqRhw4ZpzJgx+uabb9S4cWPFx8fr6NGj1oR3oWXLpBo1pA4d/DR5cnN16OCn8HCpfn1p0CDp9GmpTRvp22+lkSMlP/Y/AAAAAIBTuLW9Wr16tcN4zpw5CgsLU3p6utq2bWs/npmZqUmTJmnbtm2qWrWqw/esXbtW33//vb744gtVrlxZ0dHRevnllzVy5EiNHTtWAQEBevfdd1WzZk1NmjRJklSvXj1t3LhRU6ZMUfx/L8c9efJk9e/fX3369JEkvfvuu1q1apVmzZqlUaNGWfljsNSyZdIjj5if2b5Ydrb5FRRk3nN74EDJh0/4AwAAAIBTFas268SJE5KkChUq2I+dOXNGPXr00PTp01WlSpVC35OWlqaGDRuqcuXK9mPx8fHKycnRzp077XPat2/v8H3x8fFKS0uTJJ0/f17p6ekOc3x8fNS+fXv7nJIoL08aPLhww32x8uWlAQNouAEAAADACsVmI3F+fr6GDBmiVq1aqUGDBvbjQ4cOVcuWLdWlS5fLfl92drZDwy3JPs7Ozr7qnJycHJ09e1Z//PGH8vLyLjvnhx9+uOzznjt3TufOnbOPc3JyJEm5ubnKzc0tSmTLpaTYdOjQ1V/iw4el9esvKDb2Kp15CVPw8y8ur4NVvCWn5D1ZyelZvCWn5D1ZyelZvCWn5D1ZyelZSkLOotZWbJruhIQE7dixQxs3brQfW7FihZKTk5WRkeHGyi5v/PjxGjduXKHja9euVXBwsBsqKiw1tbqk5tec9/nnmTp9+mfrC3KxpKQkd5fgEt6SU/KerOT0LN6SU/KerOT0LN6SU/KerOT0LMU555kzZ4o0r1g03YmJiVq5cqVSU1MVHh5uP56cnKy9e/eqXLlyDvMffvhhtWnTRhs2bFCVKlUKXWX8yJEjkmTfjl6lShX7sYvnhIaGqlSpUvL19ZWvr+9l51xuS7skPffccxo2bJh9nJOTo4iICMXFxSk0NPT6fgAWKV3apsmTrz2vY8doxcY2tr4gF8nNzVVSUpI6dOggf39/d5djGW/JKXlPVnJ6Fm/JKXlPVnJ6Fm/JKXlPVnJ6lpKQs2C387W4tek2DEN/+9vftHz5cm3YsEE1a9Z0eHzUqFHq16+fw7GGDRtqypQp6ty5syQpJiZGr7zyio4ePaqwsDBJ5v8NCQ0NVVRUlH3OZ5995nCepKQkxcTESJICAgLUrFkzrVu3Tl27dpVkbndft26dEhMTL1t7YGCgAgMDCx339/cvNn8o2rWTwsOln3++/Oe6bTbz8Xbt/OTr6/r6rFacXgsreUtOyXuyktOzeEtOyXuyktOzeEtOyXuyktOzFOecRa3LrU13QkKC5s+fr08++UQhISH2z2CXLVtWpUqVUpUqVS670nzrrbfaG/S4uDhFRUXpiSee0Ouvv67s7GyNHj1aCQkJ9qb46aef1ltvvaURI0boqaeeUnJyshYvXqxVq1bZzzls2DD17t1bzZs315133qmpU6fq9OnT9quZl0S+vtK0aebVy202x8a74O5rU6fKIxtuAAAAACgO3HrN6nfeeUcnTpzQ3XffrapVq9q/Fi1aVORz+Pr6auXKlfL19VVMTIwef/xx9erVSy+99JJ9Ts2aNbVq1SolJSWpcePGmjRpkmbOnGm/XZgkPfbYY5o4caJefPFFRUdHKzMzU6tXry50cbWSpls36aOPpOrVHY+Hh5vHu3VzT10AAAAA4A3cvr3cGd8TGRlZaPv4pe6+++5rXpAtMTHxitvJS7Ju3aQuXcyrlH/+eaY6doz22C3lAAAAAFCcFIsLqcF6vr5SbKyh06d/VmxsYxpuAAAAAHABt24vBwAAAADAk9F0AwAAAABgEZpuAAAAAAAsQtMNAAAAAIBFaLoBAAAAALAITTcAAAAAABah6QYAAAAAwCI03QAAAAAAWISmGwAAAAAAi9B0AwAAAABgEZpuAAAAAAAsQtMNAAAAAIBFaLoBAAAAALAITTcAAAAAABah6QYAAAAAwCJ+7i7AUxiGIUnKyclxcyVXlpubqzNnzignJ0f+/v7uLscy5PQ83pKVnJ7FW3JK3pOVnJ7FW3JK3pOVnJ6lJOQs6P0KesEroel2kpMnT0qSIiIi3FwJAAAAAMBVTp48qbJly17xcZtxrbYcRZKfn69ffvlFISEhstls7i7nsnJychQREaH//Oc/Cg0NdXc5liGn5/GWrOT0LN6SU/KerOT0LN6SU/KerOT0LCUhp2EYOnnypKpVqyYfnyt/cpuVbifx8fFReHi4u8soktDQ0GL7B9eZyOl5vCUrOT2Lt+SUvCcrOT2Lt+SUvCcrOT1Lcc95tRXuAlxIDQAAAAAAi9B0AwAAAABgEZpuLxIYGKgxY8YoMDDQ3aVYipyex1uyktOzeEtOyXuyktOzeEtOyXuyktOzeFJOLqQGAAAAAIBFWOkGAAAAAMAiNN0AAAAAAFiEphsAAAAAAIvQdJcg48eP1x133KGQkBCFhYWpa9euysrKcpjz559/KiEhQRUrVlSZMmX08MMP68iRIw5zBg0apGbNmikwMFDR0dGXfa5vv/1Wbdq0UVBQkCIiIvT6669bFeuyXJX1zz//1JNPPqmGDRvKz89PXbt2tTBVYa7KuWHDBnXp0kVVq1ZV6dKlFR0drXnz5lkZzYGrcmZlZaldu3aqXLmygoKCVKtWLY0ePVq5ublWxrNz5e9ogT179igkJETlypVzcpqrc1XWAwcOyGazFfravHmzlfHsXPmaGoahiRMnqnbt2goMDFT16tX1yiuvWBXNgatyjh079rKvZ+nSpa2MZ+fK13PNmjW66667FBISokqVKunhhx/WgQMHLErmyJU5Fy9erOjoaAUHBysyMlITJkywKtZlOSPr9u3b1b17d0VERKhUqVKqV6+epk2bVui5NmzYoKZNmyowMFC33Xab5syZY3U8O1flPHz4sHr06KHatWvLx8dHQ4YMcUU8O1flXLZsmTp06KBKlSopNDRUMTExWrNmjUsySq7LuXHjRrVq1UoVK1ZUqVKlVLduXU2ZMsUlGQu48ne0wFdffSU/P79rvodyJZruEiQlJUUJCQnavHmzkpKSlJubq7i4OJ0+fdo+Z+jQofr000+1ZMkSpaSk6JdfflG3bt0Kneupp57SY489dtnnycnJUVxcnCIjI5Wenq4JEyZo7Nixev/99y3LdilXZc3Ly1OpUqU0aNAgtW/f3rI8V+KqnJs2bVKjRo20dOlSffvtt+rTp4969eqllStXWpbtYq7K6e/vr169emnt2rXKysrS1KlTNWPGDI0ZM8aybBdzVc4Cubm56t69u9q0aeP0LNfi6qxffPGFDh8+bP9q1qyZ0zNdjitzDh48WDNnztTEiRP1ww8/aMWKFbrzzjstyXUpV+V89tlnHV7Hw4cPKyoqSo8++qhl2S7mqpz79+9Xly5ddM899ygzM1Nr1qzRb7/9dtnzWMFVOT///HP17NlTTz/9tHbs2KG3335bU6ZM0VtvvWVZtks5I2t6errCwsI0d+5c7dy5Uy+88IKee+45hxz79+9Xp06d1K5dO2VmZmrIkCHq16+fyxo1V+U8d+6cKlWqpNGjR6tx48YuyXYxV+VMTU1Vhw4d9Nlnnyk9PV3t2rVT586dlZGR4VE5S5curcTERKWmpmrXrl0aPXq0Ro8eXeLe0xcla4Hjx4+rV69euvfee12Sr8gMlFhHjx41JBkpKSmGYRjG8ePHDX9/f2PJkiX2Obt27TIkGWlpaYW+f8yYMUbjxo0LHX/77beN8uXLG+fOnbMfGzlypFGnTh3nhygiq7JerHfv3kaXLl2cWfZ1c0XOAvfff7/Rp08fp9R9vVyZc+jQoUbr1q2dUvf1sjrniBEjjMcff9yYPXu2UbZsWWeXf12syrp//35DkpGRkWFV6dfFqpzff/+94efnZ/zwww+W1X49XPU7mpmZaUgyUlNTnVb79bAq55IlSww/Pz8jLy/PfmzFihWGzWYzzp8/7/wg12BVzu7duxuPPPKIw7E33njDCA8PN/Lz850boohuNmuBZ555xmjXrp19PGLECKN+/foOcx577DEjPj7eyQmKxqqcF4uNjTUGDx7s1LqvlytyFoiKijLGjRvnnMKvkytzPvTQQ8bjjz/unMJvgNVZH3vsMWP06NHX9V7RFVjpLsFOnDghSapQoYIk8/8C5ebmOqzY1q1bV7feeqvS0tKKfN60tDS1bdtWAQEB9mPx8fHKysrSH3/84aTqr49VWYsbV+Y8ceKE/XlczVU59+zZo9WrVys2NvbmCr5BVuZMTk7WkiVLNH36dOcVfBOsfk0ffPBBhYWFqXXr1lqxYoVzir4BVuX89NNPVatWLa1cuVI1a9ZUjRo11K9fPx07dsy5AYrIVb+jM2fOVO3atd2yW0OyLmezZs3k4+Oj2bNnKy8vTydOnNC///1vtW/fXv7+/s4NUQRW5Tx37pyCgoIcjpUqVUqHDh3STz/95ITKr5+zsl76b2RaWlqhHXHx8fFue89hVc7ixlU58/PzdfLkyRL/vuhaOTMyMrRp0ya3vS+SrM06e/Zs7du3z2U7HK8HTXcJlZ+fryFDhqhVq1Zq0KCBJCk7O1sBAQGFPttZuXJlZWdnF/nc2dnZqly5cqFzFDzmalZmLU5cmXPx4sXaunWr+vTpczMl3xBX5GzZsqWCgoJ0++23q02bNnrppZecUfp1sTLn77//rieffFJz5sxRaGioM8u+IVZmLVOmjCZNmqQlS5Zo1apVat26tbp27eqWxtvKnPv27dNPP/2kJUuW6MMPP9ScOXOUnp6uRx55xJkRisRVfxf9+eefmjdvnvr27XuzJd8QK3PWrFlTa9eu1fPPP6/AwECVK1dOhw4d0uLFi50ZoUiszBkfH69ly5Zp3bp1ys/P1+7duzVp0iRJ5meDXc1ZWTdt2qRFixZpwIAB9mNXem+Uk5Ojs2fPOjfINViZszhxZc6JEyfq1KlT+t///V+n1V9UrsgZHh6uwMBANW/eXAkJCerXr5/TcxSFlVl//PFHjRo1SnPnzpWfn59lGW5U8asIRZKQkKAdO3Zo48aN7i7Fct6S1VU5169frz59+mjGjBmqX7++pc91Oa7IuWjRIp08eVLbt2/X8OHDNXHiRI0YMcKy57scK3P2799fPXr0UNu2bZ1+7hthZdZbbrlFw4YNs4/vuOMO/fLLL5owYYIefPBBpz/f1ViZMz8/X+fOndOHH36o2rVrS5I++OADNWvWTFlZWapTp47Tn/NKXPV30fLly3Xy5En17t3b0ue5EitzZmdnq3///urdu7e6d++ukydP6sUXX9QjjzyipKQk2Ww2pz/nlVj9d9HevXv1wAMPKDc3V6GhoRo8eLDGjh0rHx/Xr+s4I+uOHTvUpUsXjRkzRnFxcU6sznnIWXRFyTl//nyNGzdOn3zyicLCwm74uW6UK3J++eWXOnXqlDZv3qxRo0bptttuU/fu3W+m7BtiVda8vDz16NFD48aNs/8bWtyw0l0CJSYmauXKlVq/fr3Cw8Ptx6tUqaLz58/r+PHjDvOPHDmiKlWqFPn8VapUKXQF04Lx9ZzHGazOWly4KmdKSoo6d+6sKVOmqFevXjdb9nVzVc6IiAhFRUWpe/fuevXVVzV27Fjl5eXdbPlFZnXO5ORkTZw4UX5+fvLz81Pfvn114sQJ+fn5adasWc6KUSTu+B1t0aKF9uzZc1PnuF5W56xatar8/Pwc3izUq1dPknTw4MGbK/46uPL1nDlzph544IFCq4euYHXO6dOnq2zZsnr99dfVpEkTtW3bVnPnztW6deu0ZcsWZ8W4Jqtz2mw2vfbaazp16pR++uknZWdn2y/+V6tWLadkKCpnZP3+++917733asCAARo9erTDY1d6bxQaGqpSpUo5N8xVWJ2zuHBVzoULF6pfv35avHixWy6o66qcNWvWVMOGDdW/f38NHTpUY8eOdXaUa7Iy68mTJ7Vt2zYlJiba3xu99NJL2r59u/z8/JScnGxptqKg6S5BDMNQYmKili9fruTkZNWsWdPh8WbNmsnf31/r1q2zH8vKytLBgwcVExNT5OeJiYlRamqqw22WkpKSVKdOHZUvX/7mgxSBq7K6mytzbtiwQZ06ddJrr73m8q1k7nw98/PzlZubq/z8/Js6T1G4KmdaWpoyMzPtXy+99JJCQkKUmZmphx56yGl5rsadr2lmZqaqVq16U+coKlflbNWqlS5cuKC9e/faj+3evVuSFBkZeZMprs3Vr+f+/fu1fv16l28td1XOM2fOFFrp9fX1lSSP+ruogK+vr6pXr66AgAAtWLBAMTExqlSp0k3nKApnZd25c6fatWun3r17X/ZWfTExMQ7nkMz3Rq56z+GqnO7mypwLFixQnz59tGDBAnXq1MmaQFfgztezYGeVq7gia2hoqL777juH90ZPP/206tSpo8zMTLVo0cLakEXhhou34Qb99a9/NcqWLWts2LDBOHz4sP3rzJkz9jlPP/20ceuttxrJycnGtm3bjJiYGCMmJsbhPD/++KORkZFhDBw40Khdu7aRkZFhZGRk2K9Wfvz4caNy5crGE088YezYscNYuHChERwcbLz33nsel9UwDGPnzp1GRkaG0blzZ+Puu++2z/GknMnJyUZwcLDx3HPPOTzP77//7lE5586dayxatMj4/vvvjb179xqLFi0yqlWrZvTs2dOjcl7KHVcvd1XWOXPmGPPnzzd27dpl7Nq1y3jllVcMHx8fY9asWR6VMy8vz2jatKnRtm1b45tvvjG2bdtmtGjRwujQoYNH5SwwevRoo1q1asaFCxdckq+Aq3KuW7fOsNlsxrhx44zdu3cb6enpRnx8vBEZGenwXCU956+//mq88847xq5du4yMjAxj0KBBRlBQkLFlyxbLMzoz63fffWdUqlTJePzxxx3OcfToUfucffv2GcHBwcbw4cONXbt2GdOnTzd8fX2N1atXe1ROwzDsr3OzZs2MHj16GBkZGcbOnTs9Kue8efMMPz8/Y/r06Q5zjh8/7lE533rrLWPFihXG7t27jd27dxszZ840QkJCjBdeeMElOV2Z9VLF7erlNN0liKTLfs2ePds+5+zZs8YzzzxjlC9f3ggODjYeeugh4/Dhww7niY2Nvex59u/fb5+zfft2o3Xr1kZgYKBRvXp149VXX3VRSpMrs0ZGRl52jifl7N2792Ufj42N9aicCxcuNJo2bWqUKVPGKF26tBEVFWX83//9n3H27FmPynkpdzTdrso6Z84co169ekZwcLARGhpq3HnnnQ63FfGUnIZhGD///LPRrVs3o0yZMkblypWNJ5980mX/Y8yVOfPy8ozw8HDj+eefd0m2i7ky54IFC4wmTZoYpUuXNipVqmQ8+OCDxq5duzwq56+//mrcddddRunSpY3g4GDj3nvvNTZv3uySjAWckXXMmDGXPUdkZKTDc61fv96Ijo42AgICjFq1ajk8h9VcmbMoc0p6ziv92e7du7dH5XzjjTeM+vXr2/8NbdKkifH222873M7QU7Jeqrg13TbDMAwBAAAAAACn4zPdAAAAAABYhKYbAAAAAACL0HQDAAAAAGARmm4AAAAAACxC0w0AAAAAgEVougEAAAAAsAhNNwAAAAAAFqHpBgAAAADAIjTdAAAAAABYhKYbAABIkgzDUPv27RUfH1/osbffflvlypXToUOH3FAZAAAlF003AACQJNlsNs2ePVtbtmzRe++9Zz++f/9+jRgxQm+++abCw8Od+py5ublOPR8AAMUNTTcAALCLiIjQtGnT9Oyzz2r//v0yDEN9+/ZVXFycmjRpoo4dO6pMmTKqXLmynnjiCf3222/27129erVat26tcuXKqWLFinrggQe0d+9e++MHDhyQzWbTokWLFBsbq6CgIM2bN88dMQEAcBmbYRiGu4sAAADFS9euXXXixAl169ZNL7/8snbu3Kn69eurX79+6tWrl86ePauRI0fqwoULSk5OliQtXbpUNptNjRo10qlTp/Tiiy/qwIEDyszMlI+Pjw4cOKCaNWuqRo0amjRpkpo0aaKgoCBVrVrVzWkBALAOTTcAACjk6NGjql+/vo4dO6alS5dqx44d+vLLL7VmzRr7nEOHDikiIkJZWVmqXbt2oXP89ttvqlSpkr777js1aNDA3nRPnTpVgwcPdmUcAADchu3lAACgkLCwMA0cOFD16tVT165dtX37dq1fv15lypSxf9WtW1eS7FvIf/zxR3Xv3l21atVSaGioatSoIUk6ePCgw7mbN2/u0iwAALiTn7sLAAAAxZOfn5/8/My3CqdOnVLnzp312muvFZpXsD28c+fOioyM1IwZM1StWjXl5+erQYMGOn/+vMP80qVLW188AADFBE03AAC4pqZNm2rp0qWqUaOGvRG/2O+//66srCzNmDFDbdq0kSRt3LjR1WUCAFDssL0cAABcU0JCgo4dO6bu3btr69at2rt3r9asWaM+ffooLy9P5cuXV8WKFfX+++9rz549Sk5O1rBhw9xdNgAAbkfTDQAArqlatWr66quvlJeXp7i4ODVs2FBDhgxRuXLl5OPjIx8fHy1cuFDp6elq0KCBhg4dqgkTJri7bAAA3I6rlwMAAAAAYBFWugEAAAAAsAhNNwAAAAAAFqHpBgAAAADAIjTdAAAAAABYhKYbAAAAAACL0HQDAAAAAGARmm4AAAAAACxC0w0AAAAAgEVougEAAAAAsAhNNwAAAAAAFqHpBgAAAADAIjTdAAAAAABY5P8BjuP2kQYMz4YAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -853,7 +744,7 @@ "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", - "The GDP per capita of South Korea from 2010 to 2024 has been visualized in a line graph. The graph shows a general upward trend, with some fluctuations, particularly around the years impacted by the pandemic. The projected GDP per capita for 2024 indicates a recovery and growth. If you have any further questions or need additional analysis, feel free to ask!\n", + "The line graph visualizing the GDP per capita of South Korea from 2010 to 2024 has been created. It shows a general upward trend with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. If you have any further questions or need additional analysis, feel free to ask!\n", "==================================================\n", "\n", "==================================================\n", @@ -862,7 +753,7 @@ "================================\u001b[1m Human Message \u001b[0m=================================\n", "Name: Coder\n", "\n", - "The GDP per capita of South Korea from 2010 to 2024 has been visualized in a line graph. The graph shows a general upward trend, with some fluctuations, particularly around the years impacted by the pandemic. The projected GDP per capita for 2024 indicates a recovery and growth. If you have any further questions or need additional analysis, feel free to ask!\n", + "The line graph visualizing the GDP per capita of South Korea from 2010 to 2024 has been created. It shows a general upward trend with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. If you have any further questions or need additional analysis, feel free to ask!\n", "==================================================\n", "\n", "==================================================\n", From 3da2a327f6d5a19e005ede995cfee7a35fc30075 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Thu, 23 Jan 2025 10:47:28 +0900 Subject: [PATCH 6/8] Reflect a comment - use `langchain_opentutorial` tools - add `TAVILY_API_KEY` in Environment Setup - add `Design` in the top --- .../07-LangGraph-Multi-Agent-Supervisor.ipynb | 192 ++++++------------ 1 file changed, 61 insertions(+), 131 deletions(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index 840f1fd1b..729830ae9 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -7,6 +7,7 @@ "# Multi-Agent Supervisor\n", "\n", "- Author: [Sungchul Kim](https://github.com/rlatjcj)\n", + "- Design:\n", "- Peer Review:\n", "- This is a part of [LangChain Open Tutorial](https://github.com/LangChain-OpenTutorial/LangChain-OpenTutorial)\n", "\n", @@ -82,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -125,6 +126,7 @@ " {\n", " \"OPENAI_API_KEY\": \"\",\n", " \"LANGCHAIN_API_KEY\": \"\",\n", + " \"TAVILY_API_KEY\": \"\",\n", " \"LANGCHAIN_TRACING_V2\": \"true\",\n", " \"LANGCHAIN_ENDPOINT\": \"https://api.smith.langchain.com\",\n", " \"LANGCHAIN_PROJECT\": \"Multi-Agent-Supervisor\",\n", @@ -143,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -152,7 +154,7 @@ "True" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -249,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -340,16 +342,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'messages': [HumanMessage(content='Here\\'s a simple program to print \"Hello, World!\" to the terminal in a few different programming languages.\\n\\n### Python\\n```python\\nprint(\"Hello, World!\")\\n```\\n\\n### JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hello, World!\");\\n```\\n\\n### Java\\n```java\\npublic class HelloWorld {\\n public static void main(String[] args) {\\n System.out.println(\"Hello, World!\");\\n }\\n}\\n```\\n\\n### C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hello, World!\\\\n\");\\n return 0;\\n}\\n```\\n\\n### C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hello, World!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n### Go\\n```go\\npackage main\\n\\nimport \"fmt\"\\n\\nfunc main() {\\n fmt.Println(\"Hello, World!\")\\n}\\n```\\n\\n### Ruby\\n```ruby\\nputs \"Hello, World!\"\\n```\\n\\nDepending on your interest or need, you can choose any of the languages above to run the \"Hello, World!\" program.', additional_kwargs={}, response_metadata={}, name='Researcher')]}" + "{'messages': [HumanMessage(content='To code a \"Hello, World!\" program and print it to the terminal, you can use various programming languages. Here\\'s how you can do it in a few popular languages:\\n\\n### Python\\n```python\\nprint(\"Hello, World!\")\\n```\\n\\n### JavaScript (Node.js)\\n```javascript\\nconsole.log(\"Hello, World!\");\\n```\\n\\n### Java\\n```java\\npublic class HelloWorld {\\n public static void main(String[] args) {\\n System.out.println(\"Hello, World!\");\\n }\\n}\\n```\\n\\n### C\\n```c\\n#include \\n\\nint main() {\\n printf(\"Hello, World!\\\\n\");\\n return 0;\\n}\\n```\\n\\n### C++\\n```cpp\\n#include \\n\\nint main() {\\n std::cout << \"Hello, World!\" << std::endl;\\n return 0;\\n}\\n```\\n\\n### Ruby\\n```ruby\\nputs \"Hello, World!\"\\n```\\n\\n### Bash\\n```bash\\necho \"Hello, World!\"\\n```\\n\\nChoose the language you\\'re most comfortable with or the one you want to learn and execute the code in the respective environment for that language. For example, use a Python interpreter for Python, Node.js for JavaScript, etc.', additional_kwargs={}, response_metadata={}, name='Researcher')]}" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -375,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -397,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -488,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -534,12 +536,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "" ] @@ -549,22 +551,9 @@ } ], "source": [ - "from IPython.display import Image, display\n", - "from langgraph.graph.state import CompiledStateGraph\n", - "\n", - "\n", - "try:\n", - " if isinstance(graph, CompiledStateGraph):\n", - " display(\n", - " Image(\n", - " graph.get_graph().draw_mermaid_png(\n", - " background_color=\"white\",\n", - " # node_colors=NodeStyles(),\n", - " )\n", - " )\n", - " )\n", - "except Exception as e:\n", - " print(f\"[ERROR] Visualize Graph Error: {e}\")" + "from langchain_opentutorial.graphs import visualize_graph\n", + "\n", + "visualize_graph(graph)" ] }, { @@ -578,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -589,6 +578,8 @@ "==================================================\n", "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "\u001b[1;32mnext\u001b[0m:\n", + "Researcher\n", "==================================================\n", "\n", "==================================================\n", @@ -596,8 +587,8 @@ "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " tavily_search_results_json (call_EN0Ikq06zuQ4fpn39fRAaYlE)\n", - " Call ID: call_EN0Ikq06zuQ4fpn39fRAaYlE\n", + " tavily_search_results_json (call_Agr9v67vqXFatamF7EXxEUjP)\n", + " Call ID: call_Agr9v67vqXFatamF7EXxEUjP\n", " Args:\n", " query: South Korea GDP per capita 2010 to 2024\n", "==================================================\n", @@ -608,7 +599,7 @@ "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: tavily_search_results_json\n", "\n", - "[{\"url\": \"https://statisticstimes.com/economy/country/south-korea-gdp-per-capita.php\", \"content\": \"GDP per capita of South Korea According to the IMF World Economic Outlook (October 2024), South Korea's nominal GDP per capita in 2024 is projected to be around $36,132 at current prices. Based on PPP, South Korea's GDP per capita in 2024 is forecast at 62,960 billion international dollars. South Korea ranks 33rd in the world by GDP (nominal) per capita and 29th by GDP (PPP) per capita on the 194 economies list. South Korea is ranked 8th in nominal and 10th in the PPP list among 49 European economies. GDP (Nominal) per capita of South Korea GDP (PPP) per capita of South Korea Year GDP per capita ($/Int. Year GDP (Nominal) per capita ($) GDP (PPP) per capita (Int.\"}, {\"url\": \"https://countryeconomy.com/gdp/south-korea?anio=2024\", \"content\": \"South Korea GDP - Gross Domestic Product 2024 | countryeconomy.com South Korea GDP - Gross Domestic Product GDP South Korea third quarter of 2023 The GDP figure in the third quarter of 2023 was €394,507$362,465 million, South Korea is number 12 in the ranking of quarterly GDP of the 53 countries that we publish. South Korea has a quarterly GDP per capita, of €7,635$7,635, less than the same period last year, when it was €7,732 $7,732 . If we order the countries according to their GDP per capita, South Korea is in 25th position of the 53 countries whose quarterly GDP we publish. | < GDP South Korea 2023 | Evolution: GDP growth rate at constant prices South Korea GDP South Korea\"}, {\"url\": \"https://www.macrotrends.net/global-metrics/countries/KOR/south-korea/gdp-per-capita\", \"content\": \"Data are in current U.S. dollars. South Korea gdp per capita for 2023 was $33,121, a 2.24% increase from 2022. South Korea gdp per capita for 2022 was $32,395, a 7.77% decline from 2021. South Korea gdp per capita for 2021 was $35,126, a 10.73% increase from 2020. South Korea gdp per capita for 2020 was $31,721, a 0.57% decline from 2019.\"}, {\"url\": \"https://tradingeconomics.com/south-korea/gdp-per-capita-constant-2000-us-dollar-wb-data.html\", \"content\": \"South Korea - GDP Per Capita (constant 2000 US$) - 2024 Data 2025 Forecast 1960-2023 Historical Interest Rate South Korea - GDP Per Capita (constant 2000 US$)2024 Data 2025 Forecast 1960-2023 Historical GDP per capita (constant 2015 US$) in South Korea was reported at 34121 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Korea - GDP per capita (constant 2000 US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on December of 2024. GDP GDP GDP Constant Prices GDP Growth Rate GDP Growth Rate YoY Interest Rate Government Debt to GDP Government Spending to GDP Economic Calendar Historical Data News Stream Earnings Releases Credit Ratings Forecasts Markets Currencies Stocks Commodities Bonds Crypto Get Started Ratings\"}, {\"url\": \"https://geographic.org/stats/korea_south/korea_south_gdp_per_capita_time_series.html\", \"content\": \"Korea South GDP Per Capita (PPP) 2004 - 2024 Main Index |Β  Time Series Index |Β  Korea South Index Country Ranks |Β  Country Flags DEFINITIONS |Β  Photos |Β * GEOGRAPHIC.ORG |Β  USA STATISTICS |Β  CHINA STATISTICS |Β  COUNTRY CODES |Β  AIRPORT CODES |Β  WEATHER DATA COUNTRIES of the WORLD Korea South: GDP Per Capita (PPP) 2004-2024 Time Series https://geographic.org/stats/korea_south/korea_south_gdp_per_capita_time_series.html SOURCE: CIA World Factbook Main Index Time Series Index Korea South Index Country Ranks Country Flags Geographic Names Airport Codes Photos GEOGRAPHICAL NAMESΒ  NOTE: This time series graph shows the GDP Per Capita (PPP) of Korea South based on our stored data from 2004 to 2024, taken from the CIA World fact books of the respective years. Switch country: Switch rank: \"}]\n", + "[{\"url\": \"https://statisticstimes.com/economy/country/south-korea-gdp-per-capita.php\", \"content\": \"GDP per capita of South Korea According to the IMF World Economic Outlook (October 2024), South Korea's nominal GDP per capita in 2024 is projected to be around $36,132 at current prices. Based on PPP, South Korea's GDP per capita in 2024 is forecast at 62,960 billion international dollars. South Korea ranks 33rd in the world by GDP (nominal) per capita and 29th by GDP (PPP) per capita on the 194 economies list. South Korea is ranked 8th in nominal and 10th in the PPP list among 49 European economies. GDP (Nominal) per capita of South Korea GDP (PPP) per capita of South Korea Year GDP per capita ($/Int. Year GDP (Nominal) per capita ($) GDP (PPP) per capita (Int.\"}, {\"url\": \"https://www.statista.com/statistics/939347/gross-domestic-product-gdp-per-capita-in-south-korea/\", \"content\": \"Annual car sales worldwide 2010-2023, with a forecast for 2024; ... (GDP) per capita in South Korea was forecast to continuously increase between 2024 and 2029 by in total 8,215.6 U.S. dollars\"}, {\"url\": \"https://www.macrotrends.net/global-metrics/countries/KOR/south-korea/gdp-per-capita\", \"content\": \"South Korea gdp per capita for 2023 was $33,121, a 2.24% increase from 2022. South Korea gdp per capita for 2022 was $32,395, a 7.77% decline from 2021. South Korea gdp per capita for 2021 was $35,126, a 10.73% increase from 2020. South Korea gdp per capita for 2020 was $31,721, a 0.57% decline from 2019.\"}, {\"url\": \"https://tradingeconomics.com/south-korea/gdp-per-capita-constant-2000-us-dollar-wb-data.html\", \"content\": \"South Korea - GDP Per Capita (constant 2000 US$) - 2024 Data 2025 Forecast 1960-2023 Historical Interest Rate South Korea - GDP Per Capita (constant 2000 US$)2024 Data 2025 Forecast 1960-2023 Historical GDP per capita (constant 2015 US$) in South Korea was reported at 34121 USD in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. South Korea - GDP per capita (constant 2000 US$) - actual values, historical data, forecasts and projections were sourced from the World Bank on December of 2024. GDP GDP GDP Constant Prices GDP Growth Rate GDP Growth Rate YoY Interest Rate Government Debt to GDP Government Spending to GDP Economic Calendar Historical Data News Stream Earnings Releases Credit Ratings Forecasts Markets Currencies Stocks Commodities Bonds Crypto Get Started Ratings\"}, {\"url\": \"https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita\", \"content\": \"A country's gross domestic product (GDP) at purchasing power parity (PPP) per capita is the PPP value of all final goods and services produced within an economy in a given year, divided by the average (or mid-year) population for the same year. This is similar to nominal GDP per capita but adjusted for the cost of living in each country.. In 2023, the estimated average GDP per capita (PPP) of\"}]\n", "==================================================\n", "\n", "==================================================\n", @@ -618,16 +609,16 @@ "\n", "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", "\n", - "- **2010**: $24,000\n", - "- **2011**: $25,000\n", - "- **2012**: $26,000\n", - "- **2013**: $27,000\n", - "- **2014**: $28,000\n", - "- **2015**: $29,000\n", - "- **2016**: $30,000\n", - "- **2017**: $31,000\n", - "- **2018**: $32,000\n", - "- **2019**: $33,000\n", + "- **2010**: $24,000 (approx.)\n", + "- **2011**: $25,000 (approx.)\n", + "- **2012**: $26,000 (approx.)\n", + "- **2013**: $27,000 (approx.)\n", + "- **2014**: $28,000 (approx.)\n", + "- **2015**: $29,000 (approx.)\n", + "- **2016**: $30,000 (approx.)\n", + "- **2017**: $31,000 (approx.)\n", + "- **2018**: $32,000 (approx.)\n", + "- **2019**: $33,000 (approx.)\n", "- **2020**: $31,721\n", "- **2021**: $35,126\n", "- **2022**: $32,395\n", @@ -636,7 +627,7 @@ "\n", "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. \n", "\n", - "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", + "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and the GDP per capita on the y-axis.\n", "==================================================\n", "\n", "==================================================\n", @@ -647,16 +638,16 @@ "\n", "Here is the GDP per capita of South Korea from 2010 to 2024 based on the available data:\n", "\n", - "- **2010**: $24,000\n", - "- **2011**: $25,000\n", - "- **2012**: $26,000\n", - "- **2013**: $27,000\n", - "- **2014**: $28,000\n", - "- **2015**: $29,000\n", - "- **2016**: $30,000\n", - "- **2017**: $31,000\n", - "- **2018**: $32,000\n", - "- **2019**: $33,000\n", + "- **2010**: $24,000 (approx.)\n", + "- **2011**: $25,000 (approx.)\n", + "- **2012**: $26,000 (approx.)\n", + "- **2013**: $27,000 (approx.)\n", + "- **2014**: $28,000 (approx.)\n", + "- **2015**: $29,000 (approx.)\n", + "- **2016**: $30,000 (approx.)\n", + "- **2017**: $31,000 (approx.)\n", + "- **2018**: $32,000 (approx.)\n", + "- **2019**: $33,000 (approx.)\n", "- **2020**: $31,721\n", "- **2021**: $35,126\n", "- **2022**: $32,395\n", @@ -665,12 +656,14 @@ "\n", "This data shows a general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. \n", "\n", - "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and GDP per capita on the y-axis.\n", + "For a visual representation, you can create a line graph using this data, plotting the years on the x-axis and the GDP per capita on the y-axis.\n", "==================================================\n", "\n", "==================================================\n", "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "\u001b[1;32mnext\u001b[0m:\n", + "Coder\n", "==================================================\n" ] }, @@ -691,8 +684,8 @@ "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", - " Python_REPL (call_tTppOr6IRBexPuFlpjqIB7PS)\n", - " Call ID: call_tTppOr6IRBexPuFlpjqIB7PS\n", + " Python_REPL (call_9Lc3Q5Vp2vrJwIOy7L9uDwJP)\n", + " Call ID: call_9Lc3Q5Vp2vrJwIOy7L9uDwJP\n", " Args:\n", " query: import matplotlib.pyplot as plt\n", "\n", @@ -705,7 +698,7 @@ "plt.plot(years, gdp_per_capita, marker='o', linestyle='-', color='b')\n", "plt.title('GDP per Capita of South Korea (2010-2024)', fontsize=16)\n", "plt.xlabel('Year', fontsize=14)\n", - "plt.ylabel('GDP per Capita (in USD)', fontsize=14)\n", + "plt.ylabel('GDP per Capita (USD)', fontsize=14)\n", "plt.xticks(years, rotation=45)\n", "plt.grid(True)\n", "plt.tight_layout()\n", @@ -717,7 +710,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -744,7 +737,7 @@ "- - - - - - - - - - - - - - - - - - - - - - - - - \n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", - "The line graph visualizing the GDP per capita of South Korea from 2010 to 2024 has been created. It shows a general upward trend with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. If you have any further questions or need additional analysis, feel free to ask!\n", + "Here is the visualization of the GDP per capita of South Korea from 2010 to 2024. The line graph illustrates the general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase.\n", "==================================================\n", "\n", "==================================================\n", @@ -753,86 +746,23 @@ "================================\u001b[1m Human Message \u001b[0m=================================\n", "Name: Coder\n", "\n", - "The line graph visualizing the GDP per capita of South Korea from 2010 to 2024 has been created. It shows a general upward trend with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase. If you have any further questions or need additional analysis, feel free to ask!\n", + "Here is the visualization of the GDP per capita of South Korea from 2010 to 2024. The line graph illustrates the general upward trend in GDP per capita over the years, with some fluctuations in specific years. The projected GDP per capita for 2024 indicates a continued increase.\n", "==================================================\n", "\n", "==================================================\n", "πŸ”„ Node: \u001b[1;36mSupervisor\u001b[0m πŸ”„\n", "- - - - - - - - - - - - - - - - - - - - - - - - - \n", + "\u001b[1;32mnext\u001b[0m:\n", + "FINISH\n", "==================================================\n" ] } ], "source": [ "import uuid\n", - "from typing import Callable\n", "\n", "from langchain_core.runnables import RunnableConfig\n", - "\n", - "\n", - "def invoke_graph(\n", - " graph: CompiledStateGraph,\n", - " inputs: dict,\n", - " config: RunnableConfig,\n", - " node_names: list[str] = [],\n", - " callback: Callable = None,\n", - "):\n", - " \"\"\"\n", - " A function that nicely streams and outputs the execution results of a LangGraph app.\n", - "\n", - " Args:\n", - " graph (CompiledStateGraph): The compiled LangGraph object to execute\n", - " inputs (dict): Dictionary of input values to pass to the graph\n", - " config (RunnableConfig): Execution configuration\n", - " node_names (list[str], optional): List of node names to output. Defaults to empty list\n", - " callback (Callable, optional): Callback function for processing each chunk. Defaults to None\n", - " The callback function takes a dictionary of the form {\"node\": str, \"content\": str} as an argument\n", - "\n", - " Returns:\n", - " None: The function only outputs streaming results and has no return value\n", - " \"\"\"\n", - "\n", - " def format_namespace(namespace):\n", - " return namespace[-1].split(\":\")[0] if len(namespace) > 0 else \"root graph\"\n", - "\n", - " # Include subgraph outputs through subgraphs=True\n", - " for namespace, chunk in graph.stream(\n", - " inputs, config, stream_mode=\"updates\", subgraphs=True\n", - " ):\n", - " for node_name, node_chunk in chunk.items():\n", - " # Filter only if node_names is not empty\n", - " if len(node_names) > 0 and node_name not in node_names:\n", - " continue\n", - "\n", - " # If callback is provided, execute it\n", - " if callback is not None:\n", - " callback({\"node\": node_name, \"content\": node_chunk})\n", - " # If no callback is provided, print the default output\n", - " else:\n", - " print(\"\\n\" + \"=\" * 50)\n", - " formatted_namespace = format_namespace(namespace)\n", - " if formatted_namespace == \"root graph\":\n", - " print(f\"πŸ”„ Node: \\033[1;36m{node_name}\\033[0m πŸ”„\")\n", - " else:\n", - " print(\n", - " f\"πŸ”„ Node: \\033[1;36m{node_name}\\033[0m in [\\033[1;33m{formatted_namespace}\\033[0m] πŸ”„\"\n", - " )\n", - " print(\"- \" * 25)\n", - "\n", - " # Print the chunk data of the node\n", - " for k, v in node_chunk.items():\n", - " if isinstance(v, BaseMessage):\n", - " v.pretty_print()\n", - " elif isinstance(v, list):\n", - " for list_item in v:\n", - " if isinstance(list_item, BaseMessage):\n", - " list_item.pretty_print()\n", - " else:\n", - " print(list_item)\n", - " elif isinstance(v, dict):\n", - " for node_chunk_key, node_chunk_value in node_chunk.items():\n", - " print(f\"{node_chunk_key}:\\n{node_chunk_value}\")\n", - " print(\"=\" * 50)\n", + "from langchain_opentutorial.messages import invoke_graph\n", "\n", "# Set config (recursion limit, thread_id)\n", "config = RunnableConfig(recursion_limit=10, configurable={\"thread_id\": str(uuid.uuid4())})\n", From 693153b8d791cc66651f2852060185771ba4b799 Mon Sep 17 00:00:00 2001 From: Sungchul Kim Date: Thu, 23 Jan 2025 10:51:23 +0900 Subject: [PATCH 7/8] Update image --- .../07-langgraph-multi-agent-supervisor.png | Bin 15137 -> 15758 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/17-LangGraph/03-Use-Cases/assets/07-langgraph-multi-agent-supervisor.png b/17-LangGraph/03-Use-Cases/assets/07-langgraph-multi-agent-supervisor.png index a203c30531f72c6b4248356b8e63e1537fabb5a6..2dcbf8b8f1be38dec0d6e59f5729cc9deb9ca29e 100644 GIT binary patch literal 15758 zcmaKT1z1%-fuDH#9Odec$35Uk@=< zk>pG0d{;Mpq7#64sEek?niA$J8IeX6Rb-8CC~86zETg40_u+0#p^-m9xEK|r) zEqM2^kqP$x3;>{bFZ^KmOz80f@aynS1WSZ#V%wqhp6}<}+<`aJ zaJhyh)D~83p>ehr~8< zzG@l`gp5HdP8q3c&H+)Z8Q?%|)A!3>NTJfXV*(iWv3=Qu!(_GVnrsP-RVM2@-GxOe zd9T3Ed3~EszuK);V{wojJ#r!|qpK?3Z-^t+`yD}U66R=oc65T(V1L3IBfZb|0U7c! 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`References` --- .../07-LangGraph-Multi-Agent-Supervisor.ipynb | 53 +++++++++---------- 1 file changed, 24 insertions(+), 29 deletions(-) diff --git a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb index 729830ae9..31077ef9e 100644 --- a/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb +++ b/17-LangGraph/03-Use-Cases/07-LangGraph-Multi-Agent-Supervisor.ipynb @@ -20,10 +20,10 @@ "Here, we introduce how to manage agents through [LLM-based Supervisor](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor) and coordinate the entire team based on the results of each agent node.\n", "\n", "\n", - "In this tutorial, we'll explore how to build **a multi-agent system** using **LangGraph**, efficiently coordinate tasks between agents, and manage them through **a Supervisor**. \n", + "In this tutorial, we'll explore how to build **a multi-agent system** using **LangGraph** , efficiently coordinate tasks between agents, and manage them through **a Supervisor** . \n", "We'll cover handling multiple agents simultaneously, managing each agent to perform their role, and properly handling task completion.\n", "\n", - "**Key Points**:\n", + "**Key Points** :\n", "- The Supervisor brings together various expert agents and operates them as a single team.\n", "- The Supervisor agent monitors the team's progress and executes logic such as calling appropriate agents for each step or terminating tasks.\n", "\n", @@ -33,12 +33,12 @@ "\n", "**What We'll Cover in This Tutorial**\n", "\n", - "- **Setup**: How to install required packages and set up API keys\n", - "- **Tool Creation**: Defining tools for agents to use, such as web search and plot generation\n", - "- **Helper Utilities**: Defining utility functions needed for creating agent nodes\n", - "- **Creating the Supervisor**: Creating a Supervisor that contains logic for selecting Worker nodes and handling task completion\n", - "- **Constructing the Graph**: Constructing the complete graph by defining State and Worker nodes\n", - "- **Calling the Team**: Calling the graph to see how the multi-agent system actually works\n", + "- **Setup** : How to install required packages and set up API keys\n", + "- **Tool Creation** : Defining tools for agents to use, such as web search and plot generation\n", + "- **Helper Utilities** : Defining utility functions needed for creating agent nodes\n", + "- **Creating the Supervisor** : Creating a Supervisor that contains logic for selecting Worker nodes and handling task completion\n", + "- **Constructing the Graph** : Constructing the complete graph by defining State and Worker nodes\n", + "- **Calling the Team** : Calling the graph to see how the multi-agent system actually works\n", "\n", "In this process, we'll use LangGraph's pre-built [create_react_agent](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.chat_agent_executor.create_react_agent) function to simplify each agent node.\n", "\n", @@ -55,7 +55,9 @@ "\n", "### References\n", "\n", - "- [LangGraph - Multi-Agent - Supervisor](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor)" + "- [LangGraph - Multi-Agent - Supervisor](https://langchain-ai.github.io/langgraph/concepts/multi_agent/#supervisor)\n", + "\n", + "----" ] }, { @@ -206,8 +208,8 @@ "\n", "Define the tools to be used below.\n", "\n", - "- **Research**: Use `TavilySearch` tool to perform web research. To use this tool, you need to set the `TAVILY_API_KEY`. Please refer to [previous tutorial](https://langchain-opentutorial.gitbook.io/langchain-opentutorial/15-agent/01-tools#search-api-tooltavily) for more details.\n", - "- **Coder**: Use `PythonREPLTool` tool to run code." + "- **Research** : Use `TavilySearch` tool to perform web research. To use this tool, you need to set the `TAVILY_API_KEY` . Please refer to [previous tutorial](https://langchain-opentutorial.gitbook.io/langchain-opentutorial/15-agent/01-tools#search-api-tooltavily) for more details.\n", + "- **Coder** : Use `PythonREPLTool` tool to run code." ] }, { @@ -235,18 +237,18 @@ "\n", "When building a multi-agent system using LangGraph, **helper functions** play a crucial role in creating and managing agent nodes. These functions enhance code reusability and simplify interactions between agents.\n", "\n", - "- **Creating Agent Nodes**: Define functions to create nodes for each agent's role\n", - "- **Managing Workflow**: Provide utilities to manage the workflow between agents\n", - "- **Error Handling**: Include mechanisms to handle errors that may occur during agent execution" + "- **Creating Agent Nodes** : Define functions to create nodes for each agent's role\n", + "- **Managing Workflow** : Provide utilities to manage the workflow between agents\n", + "- **Error Handling** : Include mechanisms to handle errors that may occur during agent execution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The following is an example of defining a function called `agent_node`.\n", + "The following is an example of defining a function called `agent_node` .\n", "\n", - "This function creates an agent node using the given state and agent. We will call this function later using `functools.partial`." + "This function creates an agent node using the given state and agent. We will call this function later using `functools.partial` ." ] }, { @@ -274,7 +276,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Below is an example of creating a `research_node` using `functools.partial`." + "Below is an example of creating a `research_node` using `functools.partial` ." ] }, { @@ -309,20 +311,20 @@ ">\n", "> **Roles**\n", ">\n", - "> 1. **Create new function with predefined values**: Returns a new function with some arguments of the existing function pre-specified.\n", - "> 2. **Code simplification**: Reduces code duplication by simplifying commonly used function call patterns.\n", - "> 3. **Improved readability**: Customizes function behavior for specific tasks to make it more intuitive to use.\n", + "> 1. **Create new function with predefined values** : Returns a new function with some arguments of the existing function pre-specified.\n", + "> 2. **Code simplification** : Reduces code duplication by simplifying commonly used function call patterns.\n", + "> 3. **Improved readability** : Customizes function behavior for specific tasks to make it more intuitive to use.\n", ">\n", "> **Example code**\n", "> ```python\n", "> research_node = functools.partial(agent_node, agent=research_agent, names=\"Researcher\")\n", "> ```\n", ">\n", - "> 1. Assume there is an existing function called `agent_node`.\n", + "> 1. Assume there is an existing function called `agent_node` .\n", "> - This function can accept multiple arguments and keyword arguments.\n", ">\n", "> 2. `functools.partial` fixes the values `agent=research_agent` and `names=\"Researcher\"` for this function.\n", - "> - This means that `research_node` no longer needs to specify the `agent` and `names` values when calling `agent_node`.\n", + "> - This means that `research_node` no longer needs to specify the `agent` and `names` values when calling `agent_node` .\n", "> - For example:\n", "> ```python\n", "> agent_node(state, agent=research_agent, names=\"Researcher\")\n", @@ -779,13 +781,6 @@ "# Run the graph\n", "invoke_graph(graph, inputs, config)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {