autogen/notebook/autogen_agent_web_info.ipynb

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   "source": [
    "<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agent_web_info.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
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   "metadata": {
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   "source": [
    "# Interactive LLM Agent Dealing with Web Info\n",
    "\n",
    "FLAML offers an experimental feature of interactive LLM agents, which can be used to solve various tasks with human or automatic feedback, including tasks that require using tools via code.\n",
    "\n",
    "In this notebook, we demonstrate how to use `AssistantAgent` and `UserProxyAgent` to discuss a paper based on its URL. Here `AssistantAgent` is an LLM-based agent that can write Python code (in a Python coding block) for a user to execute for a given task. `UserProxyAgent` is an agent which serves as a proxy for a user to execute the code written by `AssistantAgent`. By setting `human_input_mode` properly, the `UserProxyAgent` can also prompt the user for feedback to `AssistantAgent`. For example, when `human_input_mode` is set to \"ALWAYS\", the `UserProxyAgent` will always prompt the user for feedback. When user feedback is provided, the `UserProxyAgent` will directly pass the feedback to `AssistantAgent` without doing any additional steps. When no user feedback is provided, the `UserProxyAgent` will execute the code written by `AssistantAgent` directly and return the execution results (success or failure and corresponding outputs) to `AssistantAgent`.\n",
    "\n",
    "## Requirements\n",
    "\n",
    "FLAML requires `Python>=3.8`. To run this notebook example, please install flaml with the [autogen] option:\n",
    "```bash\n",
    "pip install flaml[autogen]\n",
    "```"
   ]
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     "shell.execute_reply": "2023-02-13T23:40:52.320628Z"
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   "outputs": [],
   "source": [
    "# %pip install flaml[autogen]==2.0.0rc3"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_openai_aoai`](https://microsoft.github.io/FLAML/docs/reference/autogen/oai/openai_utils#config_list_openai_aoai) function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints. It assumes the api keys and api bases are stored in the corresponding environment variables or local txt files:\n",
    "\n",
    "- OpenAI API key: os.environ[\"OPENAI_API_KEY\"] or `openai_api_key_file=\"key_openai.txt\"`.\n",
    "- Azure OpenAI API key: os.environ[\"AZURE_OPENAI_API_KEY\"] or `aoai_api_key_file=\"key_aoai.txt\"`. Multiple keys can be stored, one per line.\n",
    "- Azure OpenAI API base: os.environ[\"AZURE_OPENAI_API_BASE\"] or `aoai_api_base_file=\"base_aoai.txt\"`. Multiple bases can be stored, one per line.\n",
    "\n",
    "It's OK to have only the OpenAI API key, or only the Azure OpenAI API key + base.\n",
    "\n",
    "The following code excludes openai endpoints from the config list.\n",
    "Change to `exclude=\"aoai\"` to exclude Azure OpenAI, or remove the `exclude` argument to include both.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml import oai\n",
    "\n",
    "config_list = oai.config_list_openai_aoai(exclude=\"openai\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "## Construct Agents\n",
    "\n",
    "We construct the assistant agent and the user proxy agent. We specify `human_input_mode` as \"TERMINATE\" in the user proxy agent, which will ask for feedback when it receives a \"TERMINATE\" signal from the assistant agent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml.autogen.agent import AssistantAgent, UserProxyAgent\n",
    "\n",
    "# create an AssistantAgent instance named \"assistant\"\n",
    "assistant = AssistantAgent(\n",
    "    name=\"assistant\",\n",
    "    request_timeout=600,\n",
    "    seed=42,\n",
    "    config_list=config_list,\n",
    "    model=\"gpt-4-32k\",  # make sure the endpoint you use supports the model\n",
    ")\n",
    "# create a UserProxyAgent instance named \"user\"\n",
    "user = UserProxyAgent(\n",
    "    name=\"user\",\n",
    "    human_input_mode=\"TERMINATE\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "    work_dir='web',\n",
    ")"
   ]
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   "source": [
    "## Perform a task\n",
    "\n",
    "We invoke the `receive()` method of the coding agent to start the conversation. When you run the cell below, you will be prompted to provide feedback after the coding agent sends a \"TERMINATE\" signal in the end of the message. If you don't provide any feedback (by pressing Enter directly), the conversation will finish. Before the \"TERMINATE\" signal, the user proxy agent will try to execute the code suggested by the coding agent on behalf of the user."
   ]
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     "text": [
      "user (to assistant):\n",
      "\n",
      "\n",
      "Who should read this paper: https://arxiv.org/abs/2306.01337\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "I will need to check the abstract of the paper to determine who should read it. Let me do that for you.\n",
      "\n",
      "```python\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://arxiv.org/abs/2306.01337\"\n",
      "response = requests.get(url)\n",
      "soup = BeautifulSoup(response.text, \"html.parser\")\n",
      "abstract = soup.find(\"blockquote\", {\"class\": \"abstract\"}).text.strip()\n",
      "\n",
      "print(\"Abstract:\", abstract)\n",
      "```\n",
      "\n",
      "Please run the above Python code to fetch the abstract of the paper. Based on the abstract, we can determine who should read it.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\n",
      ">>>>>>>> NO HUMAN INPUT RECEIVED. USING AUTO REPLY FOR THE USER...\n",
      "user (to assistant):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "Abstract: Abstract:  Employing Large Language Models (LLMs) to address mathematical problems is an\n",
      "intriguing research endeavor, considering the abundance of math problems\n",
      "expressed in natural language across numerous science and engineering fields.\n",
      "While several prior works have investigated solving elementary mathematics\n",
      "using LLMs, this work explores the frontier of using GPT-4 for solving more\n",
      "complex and challenging math problems. We evaluate various ways of using GPT-4.\n",
      "Some of them are adapted from existing work, and one is MathChat, a\n",
      "conversational problem-solving framework newly proposed in this work. We\n",
      "perform the evaluation on difficult high school competition problems from the\n",
      "MATH dataset, which shows the advantage of the proposed conversational\n",
      "approach.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Based on the abstract, the paper is about using Large Language Models, specifically GPT-4, to solve complex mathematical problems. The paper introduces a conversational problem-solving framework called MathChat and evaluates its performance on challenging high school competition problems from the MATH dataset.\n",
      "\n",
      "The paper should be of interest to the following groups:\n",
      "\n",
      "1. Researchers and practitioners working in the field of artificial intelligence, specifically natural language processing and language models.\n",
      "2. Professionals interested in the application of AI for solving mathematical problems.\n",
      "3. Educators and instructors focused on teaching mathematics and incorporating AI-based solutions into teaching methodologies.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# the assistant receives a message from the user, which contains the task description\n",
    "assistant.receive(\n",
    "    \"\"\"\n",
    "Who should read this paper: https://arxiv.org/abs/2306.01337\n",
    "\"\"\",\n",
    "    user\n",
    ")"
   ]
  }
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