learn/function-calling/notebooks_firefunction_openai/fw_autogen_stock_chart.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "le6hjy5YGA7U"
      },
      "source": [
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1peT_mx4XCYQe1o2O6NmK0ITDJj2Ali8o?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGGLSlI9C80P"
      },
      "source": [
        "## Preable - Install Deps\n",
        "\n",
        "There are only a few dependencies for this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "S5MKzf6_tFAG"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
            "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip3 install --upgrade pip\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!pip3 install pyautogen openai fireworks-ai matplotlib opencv-python yfinance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34MJ8aTW9ys1"
      },
      "source": [
        "## Introduction -  Stock Chart Generator\n",
        "\n",
        "In this example we will use AutoGen framework to construct an agent that is capable of generating an stock price charts.\n",
        "\n",
        "For this demo, we are going to utilize [function calling](https://readme.fireworks.ai/docs/function-calling) feature launched by Fireworks. We initialize two agents - `UserProxyAgent` and `AssistantAgent`. The `AssistantAgent` is given the ability to issue a call for the provided functions but not execute them while `UserProxyAgent` is given the ability to execute the function calls issues by the `AssistantAgent`. In order to achieve this behaviour we use decorators provided by AutoGen library called `register_for_llm` and `register_for_execution`. Using these decorators allows us to easily define python functions and turn them into [JSON Spec](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) needed by function calling API.\n",
        "\n",
        "Finally, we setup system prompt for both the agents. We ask the `AssistantAgent` to be a helpful agent & focus on generating the correct function calls and we leave `UserProxyAgent` as is. For more advanced use cases we can ask `UserProxyAgent` to be a plan generator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zAuynYvM2c3w",
        "outputId": "ac058b7d-2184-4f1e-8060-9455ea601524"
      },
      "outputs": [],
      "source": [
        "import hashlib\n",
        "import json\n",
        "import os\n",
        "import re\n",
        "import uuid\n",
        "from pathlib import Path\n",
        "from typing import Any, Dict, List, Optional\n",
        "\n",
        "import autogen\n",
        "import pandas as pd\n",
        "import requests  # to perform HTTP requests\n",
        "import yfinance as yf\n",
        "from matplotlib import pyplot as plt\n",
        "from openai import OpenAI\n",
        "from typing_extensions import Annotated"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddA-KVZdMoN6"
      },
      "source": [
        "## Setup\n",
        "\n",
        "In order to use the Fireworks AI function calling model, you must first obtain Fireworks API Keys. If you don't already have one, you can one by following the instructions [here](https://readme.fireworks.ai/docs/quickstart). Replace FW_API_KEY with your obtained key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "os.environ[\"AUTOGEN_USE_DOCKER\"] = \"0\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "FD9w-Ztt3Ssm"
      },
      "outputs": [],
      "source": [
        "FW_API_KEY = \"FW_API_KEY\"\n",
        "\n",
        "config_list = [\n",
        "    {\n",
        "        'api_key': FW_API_KEY,\n",
        "        'base_url': 'https://api.fireworks.ai/inference/v1',\n",
        "        'model': 'accounts/fireworks/models/firefunction-v1'\n",
        "    }\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZX3i43aUOBUf"
      },
      "source": [
        "## Configure Tools\n",
        "\n",
        "For this notebook, we are going to use 2 sets of tools\n",
        "1. **Get Prices** - We will use the [yfinance](https://pypi.org/project/yfinance/) package to obtain stock prices for a given ticker. The stock prices can be obtained over a custom time range and would be saved as a time series in a csv file.\n",
        "2. **Show Chart** - This tool, given a valid file path, will draw a time series.\n",
        "\n",
        "\n",
        "Using the AutoGen framework we demonstrate the co-operative nature of agents working with each other to accomplish a complex task. This tutorial can be extended to perform more complicated tasks such as generating stock price charts etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "5G_iwAz_-NvY",
        "outputId": "288000ef-dc0c-4cbd-857e-7c0ac27f8f2c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
            "\n",
            "Get stock prices of NVDA stock price YTD. Then plot them on a chart.\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "[autogen.oai.client: 11-08 16:27:08] {409} WARNING - Model accounts/fireworks/models/firefunction-v1 is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
            "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
            "\n",
            "\u001b[32m***** Suggested tool call (call_Msjlx4BDdoZrOlpd4aE5wVzm): get_prices *****\u001b[0m\n",
            "Arguments: \n",
            "{\"ticker\": \"NVDA\", \"period\": \"ytd\"}\n",
            "\u001b[32m***************************************************************************\u001b[0m\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\u001b[35m\n",
            ">>>>>>>> EXECUTING FUNCTION get_prices...\u001b[0m\n",
            "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
            "\n",
            "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
            "\n",
            "\u001b[32m***** Response from calling tool (call_Msjlx4BDdoZrOlpd4aE5wVzm) *****\u001b[0m\n",
            "/tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv\n",
            "\u001b[32m**********************************************************************\u001b[0m\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "[autogen.oai.client: 11-08 16:27:11] {409} WARNING - Model accounts/fireworks/models/firefunction-v1 is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
            "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
            "\n",
            "The stock prices of NVDA stock for the year to date (YTD) have been obtained and saved to a file at /tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv. \n",
            "\n",
            "Now, I will plot the time series chart for the stock prices. \n",
            "\u001b[32m***** Suggested tool call (call_M9CvkyXQKioKJWz0icwPA5WA): draw_time_series *****\u001b[0m\n",
            "Arguments: \n",
            "{\"title\": \"NVDA Stock Prices YTD\", \"file_name\": \"/tmp/nvda_ytd.png\", \"csv_files\": {\"NVDA\": \"/tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv\"}, \"date_col\": \"Date\", \"price_col\": \"Close\"}\n",
            "\u001b[32m*********************************************************************************\u001b[0m\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "\u001b[35m\n",
            ">>>>>>>> EXECUTING FUNCTION draw_time_series...\u001b[0m\n",
            "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
            "\n",
            "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
            "\n",
            "\u001b[32m***** Response from calling tool (call_M9CvkyXQKioKJWz0icwPA5WA) *****\u001b[0m\n",
            "Error: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'\n",
            "\u001b[32m**********************************************************************\u001b[0m\n",
            "\n",
            "--------------------------------------------------------------------------------\n",
            "[autogen.oai.client: 11-08 16:27:13] {409} WARNING - Model accounts/fireworks/models/firefunction-v1 is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
            "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
            "\n",
            "The stock prices of NVDA stock for the year to date (YTD) have been obtained and saved to a file at /tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv. However, there was an error while plotting the time series chart for the stock prices. The error message is: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'. TERMINATE.\n",
            "\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "ChatResult(chat_id=None, chat_history=[{'content': 'Get stock prices of NVDA stock price YTD. Then plot them on a chart.', 'role': 'assistant', 'name': 'user_proxy'}, {'tool_calls': [{'id': 'call_Msjlx4BDdoZrOlpd4aE5wVzm', 'function': {'arguments': '{\"ticker\": \"NVDA\", \"period\": \"ytd\"}', 'name': 'get_prices'}, 'type': 'function', 'index': 0}], 'content': None, 'role': 'assistant'}, {'content': '/tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv', 'tool_responses': [{'tool_call_id': 'call_Msjlx4BDdoZrOlpd4aE5wVzm', 'role': 'tool', 'content': '/tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv'}], 'role': 'tool', 'name': 'user_proxy'}, {'content': 'The stock prices of NVDA stock for the year to date (YTD) have been obtained and saved to a file at /tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv. \\n\\nNow, I will plot the time series chart for the stock prices. ', 'tool_calls': [{'id': 'call_M9CvkyXQKioKJWz0icwPA5WA', 'function': {'arguments': '{\"title\": \"NVDA Stock Prices YTD\", \"file_name\": \"/tmp/nvda_ytd.png\", \"csv_files\": {\"NVDA\": \"/tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv\"}, \"date_col\": \"Date\", \"price_col\": \"Close\"}', 'name': 'draw_time_series'}, 'type': 'function', 'index': 0}], 'role': 'assistant'}, {'content': \"Error: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'\", 'tool_responses': [{'tool_call_id': 'call_M9CvkyXQKioKJWz0icwPA5WA', 'role': 'tool', 'content': \"Error: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'\"}], 'role': 'tool', 'name': 'user_proxy'}, {'content': \"The stock prices of NVDA stock for the year to date (YTD) have been obtained and saved to a file at /tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv. However, there was an error while plotting the time series chart for the stock prices. The error message is: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'. TERMINATE.\", 'role': 'user', 'name': 'chatbot'}], summary=\"The stock prices of NVDA stock for the year to date (YTD) have been obtained and saved to a file at /tmp/eef5738c-81d9-4cca-baef-c6a13f32186a.csv. However, there was an error while plotting the time series chart for the stock prices. The error message is: [Errno 2] No such file or directory: '/private/tmp/tmp/nvda_ytd.png'. .\", cost={'usage_including_cached_inference': {'total_cost': 0, 'accounts/fireworks/models/firefunction-v1': {'cost': 0, 'prompt_tokens': 2503, 'completion_tokens': 336, 'total_tokens': 2839}}, 'usage_excluding_cached_inference': {'total_cost': 0, 'accounts/fireworks/models/firefunction-v1': {'cost': 0, 'prompt_tokens': 2503, 'completion_tokens': 336, 'total_tokens': 2839}}}, human_input=[])"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "llm_config = {\n",
        "    \"config_list\": config_list,\n",
        "    \"timeout\": 120,\n",
        "    \"temperature\": 0\n",
        "}\n",
        "\n",
        "chatbot = autogen.AssistantAgent(\n",
        "    name=\"chatbot\",\n",
        "    system_message=\" If the user request HAS been addressed, respond with a summary of the result. The summary MUST end with the word TERMINATE. You are a helpful AI assistant with access to functions. Use them if required.\",\n",
        "    llm_config=llm_config,\n",
        ")\n",
        "\n",
        "# create a UserProxyAgent instance named \"user_proxy\"\n",
        "user_proxy = autogen.UserProxyAgent(\n",
        "    name=\"user_proxy\",\n",
        "    is_termination_msg=lambda x: x.get(\"content\", \"\") and (\n",
        "        x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\") \n",
        "        or x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE.\")\n",
        "    ),\n",
        "    human_input_mode=\"NEVER\",\n",
        "    max_consecutive_auto_reply=10,\n",
        "    code_execution_config={\n",
        "        \"work_dir\": \"coding\",\n",
        "        \"use_docker\": False  # Add this line\n",
        "    },\n",
        ")\n",
        "\n",
        "\n",
        "@user_proxy.register_for_execution()\n",
        "@chatbot.register_for_llm(\n",
        "    name=\"get_prices\",\n",
        "    description=\"Helper function to obtain stock price history of a company over specified period. The price information is written to a file and the path of the file is returned. The file is csv and contains following columns - Date,Open,High,Low,Close,Volume,Dividends,Stock Splits\",\n",
        ")\n",
        "def get_prices(\n",
        "    ticker: Annotated[str, \"Stock ticker for a company\"],\n",
        "    period: Annotated[\n",
        "        str,\n",
        "        \"data period to download (Either Use period parameter or use start and end) Valid periods are: 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max\",\n",
        "    ],\n",
        ") -> Annotated[\n",
        "    str, \"File which contains the price of the a ticker, each price in a new line\"\n",
        "]:\n",
        "    # Generate a random UUID as the file name\n",
        "    file_name: str = \"/tmp/\" + str(uuid.uuid4()) + \".csv\"\n",
        "    file_path = Path(file_name)\n",
        "\n",
        "    tk = yf.Ticker(ticker=ticker)\n",
        "    prices = tk.history(period=period)\n",
        "\n",
        "    with open(file_path, \"w\") as f:\n",
        "        prices.to_csv(f)\n",
        "    return file_name\n",
        "\n",
        "@user_proxy.register_for_execution()\n",
        "@chatbot.register_for_llm(\n",
        "    name=\"draw_time_series\",\n",
        "    description=\"Plot time series chart given stock prices of multiple stock tickers in a csv file.\",\n",
        ")\n",
        "def plot_time_series(\n",
        "    title: Annotated[str, \"Title of the plot that is generated\"],\n",
        "    file_name: Annotated[str, \"File to which to save the chart\"],\n",
        "    csv_files: Annotated[\n",
        "        Dict[str, str],\n",
        "        \"Dictionary from stock ticker to CSV file that contain stock prices. The csv files should have schema - Date,Open,High,Low,Close,Volume,Dividends,Stock Splits\",\n",
        "    ],\n",
        "    date_col: Annotated[str, \"Column name in csv that contains the date\"] = \"Date\",\n",
        "    price_col: Annotated[str, \"Column name that contains the price\"] = \"Close\",\n",
        ") -> str:\n",
        "    file_name = \"/tmp/\" + file_name\n",
        "    for label, csv_file in csv_files.items():\n",
        "        # Load the data into pandas dataframes\n",
        "        price_data = pd.read_csv(csv_file, index_col=date_col, parse_dates=True)\n",
        "\n",
        "        # Plot NVDA stock price data\n",
        "        plt.plot(price_data.index, price_data[\"Close\"], label=label)\n",
        "\n",
        "    plt.xlabel(date_col)\n",
        "    plt.ylabel(price_col)\n",
        "    plt.title(title)\n",
        "    plt.legend()\n",
        "\n",
        "    # Save the plot to a file\n",
        "    plt.savefig(file_name)\n",
        "\n",
        "    print(f\"Plot saved to {file_name}\")\n",
        "\n",
        "    return file_name\n",
        "\n",
        "\n",
        "user_proxy.initiate_chat(\n",
        "    chatbot,\n",
        "    message=\"Get stock prices of NVDA stock price YTD. Then plot them on a chart.\",\n",
        "    clear_history=True\n",
        ")"
      ]
    }
  ],
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      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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