autobuild_agents.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOrUQSTDQU+5PpqXoBJ0lZk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shah-zeb-naveed/autogen-udemy-course/blob/main/autobuild_agents.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OFb4TgpvQioM"
      },
      "outputs": [],
      "source": [
        "pip install pyautogen[autobuild] --upgrade"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from datetime import datetime\n",
        "from typing import Callable, Dict, Literal, Optional, Union\n",
        "\n",
        "from typing_extensions import Annotated\n",
        "\n",
        "import autogen\n",
        "from autogen import (\n",
        "    Agent,\n",
        "    AssistantAgent,\n",
        "    ConversableAgent,\n",
        "    GroupChat,\n",
        "    GroupChatManager,\n",
        "    UserProxyAgent\n",
        ")\n",
        "from autogen.agentchat.contrib import agent_builder\n",
        "from autogen.cache import Cache\n",
        "from autogen.coding import DockerCommandLineCodeExecutor, LocalCommandLineCodeExecutor\n",
        "\n",
        "\n",
        "config_file_or_env = \"OAI_CONFIG_LIST\"\n",
        "config_list = autogen.config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
        "default_llm_config = {'temperature': 0}"
      ],
      "metadata": {
        "id": "kQeQ2wgcVge-"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from autogen.agentchat.contrib.agent_builder import AgentBuilder\n",
        "\n",
        "builder = AgentBuilder(\n",
        "    config_file_or_env=\"OAI_CONFIG_LIST\",\n",
        "    builder_model=config_list[0]['model'],\n",
        "    agent_model=config_list[0]['model']\n",
        ")"
      ],
      "metadata": {
        "id": "kQWyY2hCRF3a"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "building_task = \"\"\"Find a paper on arxiv by programming, and\n",
        "analyze its application in some domain. For example, find a latest\n",
        "paper about gpt-4 on arxiv and find its potential applications in software.\"\"\"\n",
        "\n",
        "building_task"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "mk7xwZDrRGbW",
        "outputId": "28aa2c58-012a-48a9-e15e-89502189e6df"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Find a paper on arxiv by programming, and \\nanalyze its application in some domain. For example, find a latest \\npaper about gpt-4 on arxiv and find its potential applications in software.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent_list, agent_configs = builder.build(building_task, default_llm_config, coding=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5K8uPeYRl3q",
        "outputId": "e2ea8cf3-7c19-4450-eaa1-1c40ead46625"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==> Generating agents...\n",
            "['1._research_scientist\\n2._software'] are generated.\n",
            "==> Generating system message...\n",
            "Preparing system message for 1._research_scientist\n",
            "2._software\n",
            "==> Generating description...\n",
            "Preparing description for 1._research_scientist\n",
            "2._software\n",
            "==> Creating agents...\n",
            "Creating agent 1._research_scientist\n",
            "2._software with backbone gpt-3.5-turbo...\n",
            "Adding user console proxy...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(agent_list)\n",
        "\n",
        "import pprint\n",
        "\n",
        "pprint.pp(agent_configs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdN66W3-YcQ2",
        "outputId": "581bf674-a190-4bb3-b1d7-309040ab194d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[<autogen.agentchat.user_proxy_agent.UserProxyAgent object at 0x7dba11f92bc0>, <autogen.agentchat.assistant_agent.AssistantAgent object at 0x7dba004f6da0>]\n",
            "{'building_task': 'Find a paper on arxiv by programming, and \\n'\n",
            "                  'analyze its application in some domain. For example, find a '\n",
            "                  'latest \\n'\n",
            "                  'paper about gpt-4 on arxiv and find its potential '\n",
            "                  'applications in software.',\n",
            " 'agent_configs': [{'name': '1._research_scientist\\n2._software',\n",
            "                    'model': 'gpt-3.5-turbo',\n",
            "                    'system_message': 'You are now in a group chat. You need',\n",
            "                    'description': \"[POSITION's name] is a research scientist \"\n",
            "                                   'with'}],\n",
            " 'coding': True,\n",
            " 'default_llm_config': {'temperature': 0},\n",
            " 'code_execution_config': {'last_n_messages': 2,\n",
            "                           'work_dir': 'groupchat',\n",
            "                           'use_docker': False,\n",
            "                           'timeout': 60}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "build_path = builder.save()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NH0mGdf7aG2F",
        "outputId": "8ae70466-15df-4c92-f275-156e87960837"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Building config saved to ./save_config_8338de6e0faeb1146dbda0d8019df755.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "builder_clone = AgentBuilder(config_file_or_env=config_file_or_env)\n",
        "agent_list, agent_config = builder_clone.load(build_path)\n",
        "pprint.pp(agent_config)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR68gTewaks8",
        "outputId": "deca56a3-1945-480b-e5cf-457efa04bce0"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading config from ./save_config_8338de6e0faeb1146dbda0d8019df755.json\n",
            "==> Creating agents...\n",
            "Creating agent 1._research_scientist\n",
            "2._software with backbone gpt-3.5-turbo...\n",
            "Adding user console proxy...\n",
            "{'building_task': 'Find a paper on arxiv by programming, and \\n'\n",
            "                  'analyze its application in some domain. For example, find a '\n",
            "                  'latest \\n'\n",
            "                  'paper about gpt-4 on arxiv and find its potential '\n",
            "                  'applications in software.',\n",
            " 'agent_configs': [{'name': '1._research_scientist\\n2._software',\n",
            "                    'model': 'gpt-3.5-turbo',\n",
            "                    'system_message': 'You are now in a group chat. You need',\n",
            "                    'description': \"[POSITION's name] is a research scientist \"\n",
            "                                   'with'}],\n",
            " 'coding': True,\n",
            " 'default_llm_config': {'temperature': 0},\n",
            " 'code_execution_config': {'last_n_messages': 2,\n",
            "                           'work_dir': 'groupchat',\n",
            "                           'use_docker': False,\n",
            "                           'timeout': 60}}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user_proxy = UserProxyAgent(\n",
        "    name=\"user_proxy\",\n",
        "    human_input_mode=\"NEVER\",\n",
        "    code_execution_config=False\n",
        ")\n",
        "\n",
        "autobuild_assistant = AssistantAgent(\n",
        "    name=\"Autobuild Assistant\",\n",
        "    llm_config={\"config_list\": config_list},\n",
        ")\n",
        "\n",
        "def autobuild_reply(recipient, messages, sender, config):\n",
        "    building_task = messages[-1][\"content\"]\n",
        "    builder = AgentBuilder(\n",
        "        config_file_or_env=\"OAI_CONFIG_LIST\",\n",
        "        builder_model=config_list[0]['model'],\n",
        "        agent_model=config_list[0]['model']\n",
        "    )\n",
        "\n",
        "    agent_list, agent_configs = builder.build(building_task, default_llm_config, coding=True)\n",
        "\n",
        "    # create group chat\n",
        "    nested_group_chat = GroupChat(\n",
        "        agents=agent_list,\n",
        "        messages=[],\n",
        "    )\n",
        "\n",
        "    # create group chat manager\n",
        "    manager = GroupChatManager(groupchat=nested_group_chat, llm_config={\"config_list\": config_list, \"cache_seed\": None})\n",
        "\n",
        "    # solve task\n",
        "    chat_res = agent_list[0].initiate_chat(\n",
        "        manager, message=building_task, summary_method=\"reflection_with_llm\"\n",
        "    )\n",
        "\n",
        "    # clear state of agents if current chat not needed for next user interaction\n",
        "    #builder.clear_all_agents()\n",
        "\n",
        "    return True, chat_res.summary\n",
        "\n",
        "\n",
        "autobuild_assistant.register_reply([Agent, None], autobuild_reply)\n",
        "\n",
        "with Cache.disk(cache_seed=41) as cache:\n",
        "    user_proxy.initiate_chat(autobuild_assistant, message=building_task, max_turns=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "id": "UvvSmMpmZwMu",
        "outputId": "c9caf2ed-54f4-4ce7-8025-25cc70821a1c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user_proxy (to Autobuild Assistant):\n",
            "\n",
            "Find a paper on arxiv by programming, and \n",
            "analyze its application in some domain. For example, find a latest \n",
            "paper about gpt-4 on arxiv and find its potential applications in software.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "==> Generating agents...\n",
            "['1._research_scientist\\n2._software'] are generated.\n",
            "==> Generating system message...\n",
            "Preparing system message for 1._research_scientist\n",
            "2._software\n",
            "==> Generating description...\n",
            "Preparing description for 1._research_scientist\n",
            "2._software\n",
            "==> Creating agents...\n",
            "Creating agent 1._research_scientist\n",
            "2._software with backbone gpt-3.5-turbo...\n",
            "Adding user console proxy...\n",
            "User_console_and_code_interpreter (to chat_manager):\n",
            "\n",
            "Find a paper on arxiv by programming, and \n",
            "analyze its application in some domain. For example, find a latest \n",
            "paper about gpt-4 on arxiv and find its potential applications in software.\n",
            "\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:autogen.agentchat.groupchat:GroupChat is underpopulated with 2 agents. Consider setting speaker_selection_method to 'round_robin' or allow_repeat_speaker to False, or use direct communication, unless repeated speaker is desired.\n",
            "WARNING:autogen.agentchat.groupchat:GroupChat select_speaker failed to resolve the next speaker's name. This is because the speaker selection OAI call returned:\n",
            "1._research_scientist\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1._research_scientist\n",
            "2._software (to chat_manager):\n",
            "\n",
            "Hey there! That sounds like an interesting task.\n",
            "\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:autogen.agentchat.groupchat:GroupChat is underpopulated with 2 agents. Consider setting speaker_selection_method to 'round_robin' or allow_repeat_speaker to False, or use direct communication, unless repeated speaker is desired.\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "BadRequestError",
          "evalue": "Error code: 400 - {'error': {'message': \"'1._research_scientist\\\\n2._software' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'messages.2.name'\", 'type': 'invalid_request_error', 'param': None, 'code': None}}",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-e04e00c776ed>\u001b[0m in \u001b[0;36m<cell line: 41>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mCache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m41\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m     \u001b[0muser_proxy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitiate_chat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mautobuild_assistant\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuilding_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_turns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36minitiate_chat\u001b[0;34m(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs)\u001b[0m\n\u001b[1;32m    982\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmsg2send\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 984\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg2send\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecipient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    985\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_chat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecipient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclear_history\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, message, recipient, request_reply, silent)\u001b[0m\n\u001b[1;32m    630\u001b[0m         \u001b[0mvalid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_append_oai_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"assistant\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecipient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m             \u001b[0mrecipient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreceive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_reply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    633\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m             raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36mreceive\u001b[0;34m(self, message, sender, request_reply, silent)\u001b[0m\n\u001b[1;32m    790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrequest_reply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrequest_reply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreply_at_receive\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 792\u001b[0;31m         \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_reply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat_messages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    793\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36mgenerate_reply\u001b[0;34m(self, messages, sender, **kwargs)\u001b[0m\n\u001b[1;32m   1932\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_match_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply_func_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"trigger\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1934\u001b[0;31m                 \u001b[0mfinal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreply_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreply_func_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"config\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1935\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1936\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-32-e04e00c776ed>\u001b[0m in \u001b[0;36mautobuild_reply\u001b[0;34m(recipient, messages, sender, config)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;31m# solve task\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     chat_res = agent_list[0].initiate_chat(\n\u001b[0m\u001b[1;32m     33\u001b[0m         \u001b[0mmanager\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuilding_task\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msummary_method\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"reflection_with_llm\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36minitiate_chat\u001b[0;34m(self, recipient, clear_history, silent, cache, max_turns, summary_method, summary_args, message, **kwargs)\u001b[0m\n\u001b[1;32m    989\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m                 \u001b[0mmsg2send\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_init_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg2send\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecipient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m         summary = self._summarize_chat(\n\u001b[1;32m    993\u001b[0m             \u001b[0msummary_method\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, message, recipient, request_reply, silent)\u001b[0m\n\u001b[1;32m    630\u001b[0m         \u001b[0mvalid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_append_oai_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"assistant\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecipient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 632\u001b[0;31m             \u001b[0mrecipient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreceive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_reply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    633\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m             raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36mreceive\u001b[0;34m(self, message, sender, request_reply, silent)\u001b[0m\n\u001b[1;32m    790\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mrequest_reply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrequest_reply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreply_at_receive\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 792\u001b[0;31m         \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_reply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchat_messages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    793\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msilent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36mgenerate_reply\u001b[0;34m(self, messages, sender, **kwargs)\u001b[0m\n\u001b[1;32m   1932\u001b[0m                 \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_match_trigger\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply_func_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"trigger\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1934\u001b[0;31m                 \u001b[0mfinal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreply_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreply_func_tuple\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"config\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1935\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1936\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/groupchat.py\u001b[0m in \u001b[0;36mrun_chat\u001b[0;34m(self, messages, sender, config)\u001b[0m\n\u001b[1;32m    662\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    663\u001b[0m                 \u001b[0;31m# select the next speaker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 664\u001b[0;31m                 \u001b[0mspeaker\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroupchat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_speaker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeaker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    665\u001b[0m                 \u001b[0;31m# let the speaker speak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    666\u001b[0m                 \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspeaker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_reply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/groupchat.py\u001b[0m in \u001b[0;36mselect_speaker\u001b[0;34m(self, last_speaker, selector)\u001b[0m\n\u001b[1;32m    466\u001b[0m         \u001b[0;31m# auto speaker selection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m         \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_system_message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_speaker_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m         \u001b[0mfinal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate_oai_reply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize_speaker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_speaker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0magents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36mgenerate_oai_reply\u001b[0;34m(self, messages, sender, config)\u001b[0m\n\u001b[1;32m   1298\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmessages\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1299\u001b[0m             \u001b[0mmessages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_oai_messages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msender\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1300\u001b[0;31m         extracted_response = self._generate_oai_reply_from_client(\n\u001b[0m\u001b[1;32m   1301\u001b[0m             \u001b[0mclient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_oai_system_message\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient_cache\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1302\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/agentchat/conversable_agent.py\u001b[0m in \u001b[0;36m_generate_oai_reply_from_client\u001b[0;34m(self, llm_client, messages, cache)\u001b[0m\n\u001b[1;32m   1317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m         \u001b[0;31m# TODO: #1143 handle token limit exceeded error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m         response = llm_client.create(\n\u001b[0m\u001b[1;32m   1320\u001b[0m             \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"context\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m             \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_messages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/oai/client.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, **config)\u001b[0m\n\u001b[1;32m    636\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m                 \u001b[0mrequest_ts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_current_ts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 638\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    639\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mAPITimeoutError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"config {i} timed out\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/autogen/oai/client.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m    283\u001b[0m             \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m             \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"stream\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 285\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompletions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    287\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_utils/_utils.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    273\u001b[0m                         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Missing required argument: {quote(missing[0])}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/resources/chat/completions.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, messages, model, frequency_penalty, function_call, functions, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream, temperature, tool_choice, tools, top_logprobs, top_p, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    579\u001b[0m         \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mhttpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimeout\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mNotGiven\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNOT_GIVEN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     ) -> ChatCompletion | Stream[ChatCompletionChunk]:\n\u001b[0;32m--> 581\u001b[0;31m         return self._post(\n\u001b[0m\u001b[1;32m    582\u001b[0m             \u001b[0;34m\"/chat/completions\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             body=maybe_transform(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mpost\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1231\u001b[0m             \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"post\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mto_httpx_files\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1232\u001b[0m         )\n\u001b[0;32m-> 1233\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mResponseT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream_cls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstream_cls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1235\u001b[0m     def patch(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    920\u001b[0m         \u001b[0mstream_cls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_StreamT\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m     ) -> ResponseT | _StreamT:\n\u001b[0;32m--> 922\u001b[0;31m         return self._request(\n\u001b[0m\u001b[1;32m    923\u001b[0m             \u001b[0mcast_to\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcast_to\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0moptions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/openai/_base_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1011\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1012\u001b[0m             \u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Re-raising status error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_status_error_from_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1015\u001b[0m         return self._process_response(\n",
            "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"'1._research_scientist\\\\n2._software' does not match '^[a-zA-Z0-9_-]{1,64}$' - 'messages.2.name'\", 'type': 'invalid_request_error', 'param': None, 'code': None}}"
          ]
        }
      ]
    }
  ]
}