{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-10T04:38:14.228948Z",
     "start_time": "2024-04-10T04:38:14.224972Z"
    }
   },
   "source": [
    "import nest_asyncio\n",
    "from dbgpt.agent import (\n",
    "    AgentContext,\n",
    "    GptsMemory,\n",
    "    LLMConfig,\n",
    "    ResourceLoader,\n",
    "    UserProxyAgent,\n",
    ")\n",
    "from dbgpt.agent.expand.code_assistant_agent import CodeAssistantAgent\n",
    "from dbgpt.agent.plan import AutoPlanChatManager\n",
    "from dbgpt.model.proxy import OpenAILLMClient\n",
    "\n",
    "nest_asyncio.apply()"
   ],
   "execution_count": 7,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "is_executing": true
   },
   "source": [
    "# Set your api key and api base url\n",
    "# os.environ[\"OPENAI_API_KEY\"] = \"Your API\"\n",
    "# os.environ[\"OPENAI_API_BASE\"] = \"https://api.openai.com/v1\""
   ],
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-10T04:19:47.838081Z",
     "start_time": "2024-04-10T04:17:54.465616Z"
    }
   },
   "source": [
    "llm_client = OpenAILLMClient(model_alias=\"gpt-4\")\n",
    "context: AgentContext = AgentContext(conv_id=\"test456\", gpts_app_name=\"代码分析助手\")\n",
    "\n",
    "default_memory = GptsMemory()\n",
    "\n",
    "resource_loader = ResourceLoader()\n",
    "\n",
    "coder = (\n",
    "    await CodeAssistantAgent()\n",
    "    .bind(context)\n",
    "    .bind(LLMConfig(llm_client=llm_client))\n",
    "    .bind(default_memory)\n",
    "    .bind(resource_loader)\n",
    "    .build()\n",
    ")\n",
    "\n",
    "manager = (\n",
    "    await AutoPlanChatManager()\n",
    "    .bind(context)\n",
    "    .bind(default_memory)\n",
    "    .bind(LLMConfig(llm_client=llm_client))\n",
    "    .build()\n",
    ")\n",
    "manager.hire([coder])\n",
    "\n",
    "user_proxy = await UserProxyAgent().bind(context).bind(default_memory).build()\n",
    "\n",
    "\n",
    "await user_proxy.initiate_chat(\n",
    "    recipient=manager,\n",
    "    reviewer=user_proxy,\n",
    "    message=\"Obtain simple information about issues in the repository 'eosphoros-ai/DB-GPT' in the past three days and analyze the data. Create a Markdown table grouped by day and status.\",\n",
    "    # message=\"Find papers on gpt-4 in the past three weeks on arxiv, and organize their titles, authors, and links into a markdown table\",\n",
    "    # message=\"find papers on LLM applications from arxiv in the last month, create a markdown table of different domains.\",\n",
    ")"
   ],
   "execution_count": 4,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dbgpt_env",
   "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": "3.10.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f8b6b0e04f284afd2fbb5e4163e7d03bbdc845eaeb6e8c78fae04fce6b51dae6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}