autogen/notebook/autogen_agent_auto_feedback_from_code_execution.ipynb

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    "<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agent_auto_feedback_from_code_execution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "# Interactive LLM Agent with Auto Feedback from Code Execution\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 write code and execute the code. 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 the human user to execute the code written by `AssistantAgent`, or automatically execute the code. Depending on the setting of `human_input_mode` and `max_consecutive_auto_reply`, the `UserProxyAgent` either solicits feedback from the human user or uses auto-feedback based on the result of code execution. 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",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-13T23:40:52.317406Z",
     "iopub.status.busy": "2023-02-13T23:40:52.316561Z",
     "iopub.status.idle": "2023-02-13T23:40:52.321193Z",
     "shell.execute_reply": "2023-02-13T23:40:52.320628Z"
    }
   },
   "outputs": [],
   "source": [
    "# %pip install flaml[autogen]~=2.0.0rc4"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set your API Endpoint\n",
    "\n",
    "The [`config_list_from_models`](https://microsoft.github.io/FLAML/docs/reference/autogen/oai/openai_utils#config_list_from_models) function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints for the provided list of models. 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flaml import oai\n",
    "\n",
    "config_list = oai.config_list_from_models(model_list=[\"gpt-4\"])"
   ]
  },
  {
   "attachments": {},
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   "source": [
    "The config list looks like the following:\n",
    "```python\n",
    "config_list = [\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your OpenAI API key here>',\n",
    "    },  # only if OpenAI API key is found\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your first Azure OpenAI API key here>',\n",
    "        'api_base': '<your first Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-03-15-preview',\n",
    "    },  # only if the at least one Azure OpenAI API key is found\n",
    "    {\n",
    "        'model': 'gpt-4',\n",
    "        'api_key': '<your second Azure OpenAI API key here>',\n",
    "        'api_base': '<your second Azure OpenAI API base here>',\n",
    "        'api_type': 'azure',\n",
    "        'api_version': '2023-03-15-preview',\n",
    "    },  # only if the second Azure OpenAI API key is found\n",
    "]\n",
    "```\n",
    "\n",
    "You can directly override it if the above function returns an empty list, i.e., it doesn't find the keys in the specified locations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Check Stock Price Change\n",
    "\n",
    "In the example below, let's see how to use the agents in FLAML to write a python script and execute the script. This process involves constructing a `AssistantAgent` to serve as the assistant, along with a `UserProxyAgent` that acts as a proxy for the human user. In this example demonstrated below, when constructing the `UserProxyAgent`,  we select the `human_input_mode` to \"NEVER\". This means that the `UserProxyAgent` will not solicit feedback from the human user. It stops replying when the limit defined by `max_consecutive_auto_reply` is reached, or when `is_termination_msg()` returns true for the received message."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user (to assistant):\n",
      "\n",
      "What date is today? Compare the year-to-date gain for META and TESLA.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "To get the current date and compare the year-to-date gain for META and TESLA, we can use the `datetime` and `yfinance` libraries in Python. The `yfinance` library allows us to download stock market data from Yahoo Finance.\n",
      "\n",
      "Here is the Python code to do this:\n",
      "\n",
      "```python\n",
      "# filename: stock_comparison.py\n",
      "\n",
      "import datetime\n",
      "import yfinance as yf\n",
      "\n",
      "# Get today's date\n",
      "today = datetime.date.today()\n",
      "print(\"Today's date:\", today)\n",
      "\n",
      "# Get the data for the stock META and TESLA\n",
      "meta = yf.Ticker(\"META\")\n",
      "tesla = yf.Ticker(\"TSLA\")\n",
      "\n",
      "# Get the historical data for this year\n",
      "meta_data = meta.history(start=f'{today.year}-01-01', end=str(today))\n",
      "tesla_data = tesla.history(start=f'{today.year}-01-01', end=str(today))\n",
      "\n",
      "# Calculate the year-to-date gain for each stock\n",
      "meta_gain = (meta_data['Close'][-1] - meta_data['Close'][0]) / meta_data['Close'][0]\n",
      "tesla_gain = (tesla_data['Close'][-1] - tesla_data['Close'][0]) / tesla_data['Close'][0]\n",
      "\n",
      "print(f\"Year-to-date gain for META: {meta_gain * 100}%\")\n",
      "print(f\"Year-to-date gain for TESLA: {tesla_gain * 100}%\")\n",
      "```\n",
      "\n",
      "You can save this code in a file named `stock_comparison.py` and run it using Python.\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",
      "Today's date: 2023-07-12\n",
      "Year-to-date gain for META: 139.12940007477712%\n",
      "Year-to-date gain for TESLA: 149.5744795126052%\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Great! The code executed successfully. Today's date is July 12, 2023. The year-to-date gain for META is approximately 139.13%, and the year-to-date gain for TESLA is approximately 149.57%. \n",
      "\n",
      "If you have any other questions or need further assistance, feel free to ask.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from flaml.autogen.agent import AssistantAgent, UserProxyAgent\n",
    "\n",
    "# create an AssistantAgent named \"assistant\"\n",
    "assistant = AssistantAgent(\n",
    "    \"assistant\",\n",
    "    seed=42,\n",
    "    config_list=config_list,\n",
    "    temperature=0,\n",
    ")\n",
    "# create a UserProxyAgent instance named \"user\"\n",
    "user = UserProxyAgent(\n",
    "    \"user\",\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\") or x.get(\"content\", \"\").rstrip().endswith('\"TERMINATE\".'),\n",
    "    work_dir=\"coding\",\n",
    "    use_docker=False,  # set to True if you are using docker\n",
    ")\n",
    "# the assistant receives a message from the user, which contains the task description\n",
    "user.initiate_chat(\n",
    "    assistant,\n",
    "    \"\"\"What date is today? Compare the year-to-date gain for META and TESLA.\"\"\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example above involves code execution. In FLAML, code execution is triggered automatically by the `UserProxyAgent` when it detects an executable code block in a received message and no human user input is provided. This process occurs in a designated working directory, using a Docker container by default. Unless a specific directory is specified, FLAML defaults to the `flaml/autogen/extensions` directory. Users have the option to specify a different working directory by setting the `work_dir` argument when constructing a new instance of the `UserProxyAgent`.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the feedback is auto generated."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Task: Plot Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user (to assistant):\n",
      "\n",
      "Plot a chart of their stock price change YTD and save to stock_price_ytd.png.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "To plot a chart of the stock price change year-to-date (YTD) for META and TESLA and save it to a file, we can use the `matplotlib` library in Python. Here is the Python code to do this:\n",
      "\n",
      "```python\n",
      "# filename: stock_price_plot.py\n",
      "\n",
      "import datetime\n",
      "import yfinance as yf\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# Get today's date\n",
      "today = datetime.date.today()\n",
      "\n",
      "# Get the data for the stock META and TESLA\n",
      "meta = yf.Ticker(\"META\")\n",
      "tesla = yf.Ticker(\"TSLA\")\n",
      "\n",
      "# Get the historical data for this year\n",
      "meta_data = meta.history(start=f'{today.year}-01-01', end=str(today))\n",
      "tesla_data = tesla.history(start=f'{today.year}-01-01', end=str(today))\n",
      "\n",
      "# Plot the closing price of META and TESLA\n",
      "plt.figure(figsize=(14, 7))\n",
      "plt.plot(meta_data['Close'], label='META')\n",
      "plt.plot(tesla_data['Close'], label='TESLA')\n",
      "plt.title('META vs TESLA Stock Price YTD')\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Closing Price')\n",
      "plt.legend()\n",
      "\n",
      "# Save the plot to a file\n",
      "plt.savefig('stock_price_ytd.png')\n",
      "```\n",
      "\n",
      "You can save this code in a file named `stock_price_plot.py` and run it using Python. This will create a file named `stock_price_ytd.png` in the same directory.\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",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "assistant (to user):\n",
      "\n",
      "Great! The code executed successfully and should have created a file named `stock_price_ytd.png` in the same directory. This file contains a chart of the stock price change year-to-date (YTD) for META and TESLA.\n",
      "\n",
      "If you have any other questions or need further assistance, feel free to ask.\n",
      "\n",
      "TERMINATE\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# followup of the previous question\n",
    "user.send(\n",
    "    recipient=assistant,\n",
    "    message=\"\"\"Plot a chart of their stock price change YTD and save to stock_price_ytd.png.\"\"\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's display the generated figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename='coding/stock_price_ytd.png')"
   ]
  }
 ],
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