cookbook/Parallel_function_calling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This is a tutorial on using Parallel function calling with LiteLLM"
      ],
      "metadata": {
        "id": "gHwFJ-srdnku"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RrtHuVHlZmUe"
      },
      "outputs": [],
      "source": [
        "!pip install litellm"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tutorial walks through the steps doing parallel function calling using\n",
        " - OpenAI\n",
        " - Azure OpenAI"
      ],
      "metadata": {
        "id": "sG5ANaazjU0g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set openai api key\n",
        "import os\n",
        "os.environ['OPENAI_API_KEY'] = \"\" # litellm reads OPENAI_API_KEY from .env and sends the request"
      ],
      "metadata": {
        "id": "l4GQ-M5yZ5UW"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# OpenAI gpt-3.5-turbo-1106\n",
        "## Step 1: send the conversation and available functions to the model"
      ],
      "metadata": {
        "id": "AxgR2fCgaRoW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import litellm\n",
        "import json\n",
        "# Example dummy function hard coded to return the same weather\n",
        "# In production, this could be your backend API or an external API\n",
        "def get_current_weather(location, unit=\"fahrenheit\"):\n",
        "    \"\"\"Get the current weather in a given location\"\"\"\n",
        "    if \"tokyo\" in location.lower():\n",
        "        return json.dumps({\"location\": \"Tokyo\", \"temperature\": \"10\", \"unit\": \"celsius\"})\n",
        "    elif \"san francisco\" in location.lower():\n",
        "        return json.dumps({\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"})\n",
        "    elif \"paris\" in location.lower():\n",
        "        return json.dumps({\"location\": \"Paris\", \"temperature\": \"22\", \"unit\": \"celsius\"})\n",
        "    else:\n",
        "        return json.dumps({\"location\": location, \"temperature\": \"unknown\"})\n",
        "\n",
        "messages = [{\"role\": \"user\", \"content\": \"What's the weather like in San Francisco, Tokyo, and Paris?\"}]\n",
        "tools = [\n",
        "    {\n",
        "        \"type\": \"function\",\n",
        "        \"function\": {\n",
        "            \"name\": \"get_current_weather\",\n",
        "            \"description\": \"Get the current weather in a given location\",\n",
        "            \"parameters\": {\n",
        "                \"type\": \"object\",\n",
        "                \"properties\": {\n",
        "                    \"location\": {\n",
        "                        \"type\": \"string\",\n",
        "                        \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
        "                    },\n",
        "                    \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
        "                },\n",
        "                \"required\": [\"location\"],\n",
        "            },\n",
        "        },\n",
        "    }\n",
        "]\n",
        "\n",
        "response = litellm.completion(\n",
        "    model=\"gpt-3.5-turbo-1106\",\n",
        "    messages=messages,\n",
        "    tools=tools,\n",
        "    tool_choice=\"auto\",  # auto is default, but we'll be explicit\n",
        ")\n",
        "print(\"\\nLLM Response1:\\n\", response)\n",
        "response_message = response.choices[0].message\n",
        "tool_calls = response.choices[0].message.tool_calls\n",
        "print(\"\\nTool Choice:\\n\", tool_calls)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3qteFo8ZrZP",
        "outputId": "ee6c1183-55c1-4111-cdc0-967b8fed9db3"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "LLM Response1:\n",
            " ModelResponse(id='chatcmpl-8MNdPbrhtnwiPK1x3PEoGwrH144TW', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content=None, role='assistant', tool_calls=[ChatCompletionMessageToolCall(id='call_K2Giwoq3NloGPfSv25MJVFZG', function=Function(arguments='{\"location\": \"San Francisco\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function'), ChatCompletionMessageToolCall(id='call_6K8bYCZK6qsbMY3n51FzE5Nz', function=Function(arguments='{\"location\": \"Tokyo\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function'), ChatCompletionMessageToolCall(id='call_cKSmUEJGufDwS7TaUHWzp7qx', function=Function(arguments='{\"location\": \"Paris\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')]))], created=1700344759, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint='fp_eeff13170a', usage={'completion_tokens': 77, 'prompt_tokens': 88, 'total_tokens': 165}, _response_ms=1049.913)\n",
            "\n",
            "Tool Choice:\n",
            " [ChatCompletionMessageToolCall(id='call_K2Giwoq3NloGPfSv25MJVFZG', function=Function(arguments='{\"location\": \"San Francisco\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function'), ChatCompletionMessageToolCall(id='call_6K8bYCZK6qsbMY3n51FzE5Nz', function=Function(arguments='{\"location\": \"Tokyo\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function'), ChatCompletionMessageToolCall(id='call_cKSmUEJGufDwS7TaUHWzp7qx', function=Function(arguments='{\"location\": \"Paris\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2 - Parse the Model Response and Execute Functions"
      ],
      "metadata": {
        "id": "tD4lJQ40cU44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if the model wants to call a function\n",
        "if tool_calls:\n",
        "    # Execute the functions and prepare responses\n",
        "    available_functions = {\n",
        "        \"get_current_weather\": get_current_weather,\n",
        "    }\n",
        "\n",
        "    messages.append(response_message)  # Extend conversation with assistant's reply\n",
        "\n",
        "    for tool_call in tool_calls:\n",
        "      print(f\"\\nExecuting tool call\\n{tool_call}\")\n",
        "      function_name = tool_call.function.name\n",
        "      function_to_call = available_functions[function_name]\n",
        "      function_args = json.loads(tool_call.function.arguments)\n",
        "      # calling the get_current_weather() function\n",
        "      function_response = function_to_call(\n",
        "          location=function_args.get(\"location\"),\n",
        "          unit=function_args.get(\"unit\"),\n",
        "      )\n",
        "      print(f\"Result from tool call\\n{function_response}\\n\")\n",
        "\n",
        "      # Extend conversation with function response\n",
        "      messages.append(\n",
        "          {\n",
        "              \"tool_call_id\": tool_call.id,\n",
        "              \"role\": \"tool\",\n",
        "              \"name\": function_name,\n",
        "              \"content\": function_response,\n",
        "          }\n",
        "      )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "af4oXQvicV_n",
        "outputId": "abf6ac3e-4a21-4a4f-b8d7-809b763d0632"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Executing tool call\n",
            "ChatCompletionMessageToolCall(id='call_K2Giwoq3NloGPfSv25MJVFZG', function=Function(arguments='{\"location\": \"San Francisco\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')\n",
            "Result from tool call\n",
            "{\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"}\n",
            "\n",
            "\n",
            "Executing tool call\n",
            "ChatCompletionMessageToolCall(id='call_6K8bYCZK6qsbMY3n51FzE5Nz', function=Function(arguments='{\"location\": \"Tokyo\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')\n",
            "Result from tool call\n",
            "{\"location\": \"Tokyo\", \"temperature\": \"10\", \"unit\": \"celsius\"}\n",
            "\n",
            "\n",
            "Executing tool call\n",
            "ChatCompletionMessageToolCall(id='call_cKSmUEJGufDwS7TaUHWzp7qx', function=Function(arguments='{\"location\": \"Paris\", \"unit\": \"celsius\"}', name='get_current_weather'), type='function')\n",
            "Result from tool call\n",
            "{\"location\": \"Paris\", \"temperature\": \"22\", \"unit\": \"celsius\"}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3 - Second litellm.completion() call"
      ],
      "metadata": {
        "id": "E3OL1fqUdFdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "second_response = litellm.completion(\n",
        "    model=\"gpt-3.5-turbo-1106\",\n",
        "    messages=messages,\n",
        ")\n",
        "print(\"Second Response\\n\", second_response)\n",
        "print(\"Second Response Message\\n\", second_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KYB2n-jc1_f",
        "outputId": "6c6448ae-1c09-43ae-eb90-208b118e6179"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Second Response\n",
            " ModelResponse(id='chatcmpl-8MNhat166ZqjO6egXcUh85Pd0s7KV', choices=[Choices(finish_reason='stop', index=0, message=Message(content=\"The current weather in San Francisco is 72°F, in Tokyo it's 10°C, and in Paris it's 22°C.\", role='assistant'))], created=1700345018, model='gpt-3.5-turbo-1106', object='chat.completion', system_fingerprint='fp_eeff13170a', usage={'completion_tokens': 28, 'prompt_tokens': 465, 'total_tokens': 493}, _response_ms=999.246)\n",
            "Second Response Message\n",
            " The current weather in San Francisco is 72°F, in Tokyo it's 10°C, and in Paris it's 22°C.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using Azure OpenAI"
      ],
      "metadata": {
        "id": "1cIIFEvXjofp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# set Azure env variables\n",
        "import os\n",
        "os.environ['AZURE_API_KEY'] = \"\" # litellm reads AZURE_API_KEY from .env and sends the request\n",
        "os.environ['AZURE_API_BASE'] = \"https://openai-gpt-4-test-v-1.openai.azure.com/\"\n",
        "os.environ['AZURE_API_VERSION'] = \"2023-07-01-preview\""
      ],
      "metadata": {
        "id": "lG9mUnModeeE"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 1"
      ],
      "metadata": {
        "id": "17S-Ysksj-E_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import litellm\n",
        "import json\n",
        "# Example dummy function hard coded to return the same weather\n",
        "# In production, this could be your backend API or an external API\n",
        "def get_current_weather(location, unit=\"fahrenheit\"):\n",
        "    \"\"\"Get the current weather in a given location\"\"\"\n",
        "    if \"tokyo\" in location.lower():\n",
        "        return json.dumps({\"location\": \"Tokyo\", \"temperature\": \"10\", \"unit\": \"celsius\"})\n",
        "    elif \"san francisco\" in location.lower():\n",
        "        return json.dumps({\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"})\n",
        "    elif \"paris\" in location.lower():\n",
        "        return json.dumps({\"location\": \"Paris\", \"temperature\": \"22\", \"unit\": \"celsius\"})\n",
        "    else:\n",
        "        return json.dumps({\"location\": location, \"temperature\": \"unknown\"})\n",
        "\n",
        "messages = [{\"role\": \"user\", \"content\": \"What's the weather like in San Francisco, Tokyo, and Paris?\"}]\n",
        "tools = [\n",
        "    {\n",
        "        \"type\": \"function\",\n",
        "        \"function\": {\n",
        "            \"name\": \"get_current_weather\",\n",
        "            \"description\": \"Get the current weather in a given location\",\n",
        "            \"parameters\": {\n",
        "                \"type\": \"object\",\n",
        "                \"properties\": {\n",
        "                    \"location\": {\n",
        "                        \"type\": \"string\",\n",
        "                        \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
        "                    },\n",
        "                    \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
        "                },\n",
        "                \"required\": [\"location\"],\n",
        "            },\n",
        "        },\n",
        "    }\n",
        "]\n",
        "\n",
        "response = litellm.completion(\n",
        "    model=\"azure/chatgpt-functioncalling\", # model = azure/<your-azure-deployment-name>\n",
        "    messages=messages,\n",
        "    tools=tools,\n",
        "    tool_choice=\"auto\",  # auto is default, but we'll be explicit\n",
        ")\n",
        "print(\"\\nLLM Response1:\\n\", response)\n",
        "response_message = response.choices[0].message\n",
        "tool_calls = response.choices[0].message.tool_calls\n",
        "print(\"\\nTool Choice:\\n\", tool_calls)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boAIHLEXj80m",
        "outputId": "00afcf09-5b6b-4805-c374-ba089cc6eb43"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "LLM Response1:\n",
            " ModelResponse(id='chatcmpl-8MOBPvEnqG7qitkmVqZmCrzSGEmDj', choices=[Choices(finish_reason='tool_calls', index=0, message=Message(content=None, role='assistant', tool_calls=[ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n  \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')]))], created=1700346867, model='gpt-35-turbo', object='chat.completion', system_fingerprint=None, usage={'completion_tokens': 19, 'prompt_tokens': 88, 'total_tokens': 107}, _response_ms=833.4319999999999)\n",
            "\n",
            "Tool Choice:\n",
            " [ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n  \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 2"
      ],
      "metadata": {
        "id": "hqh1y1IMkmGO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check if the model wants to call a function\n",
        "if tool_calls:\n",
        "    # Execute the functions and prepare responses\n",
        "    available_functions = {\n",
        "        \"get_current_weather\": get_current_weather,\n",
        "    }\n",
        "\n",
        "    messages.append(response_message)  # Extend conversation with assistant's reply\n",
        "\n",
        "    for tool_call in tool_calls:\n",
        "      print(f\"\\nExecuting tool call\\n{tool_call}\")\n",
        "      function_name = tool_call.function.name\n",
        "      function_to_call = available_functions[function_name]\n",
        "      function_args = json.loads(tool_call.function.arguments)\n",
        "      # calling the get_current_weather() function\n",
        "      function_response = function_to_call(\n",
        "          location=function_args.get(\"location\"),\n",
        "          unit=function_args.get(\"unit\"),\n",
        "      )\n",
        "      print(f\"Result from tool call\\n{function_response}\\n\")\n",
        "\n",
        "      # Extend conversation with function response\n",
        "      messages.append(\n",
        "          {\n",
        "              \"tool_call_id\": tool_call.id,\n",
        "              \"role\": \"tool\",\n",
        "              \"name\": function_name,\n",
        "              \"content\": function_response,\n",
        "          }\n",
        "      )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGu7DY7PkOiG",
        "outputId": "96d39ae7-7fc8-4dd8-c82f-5ee9a486724c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Executing tool call\n",
            "ChatCompletionMessageToolCall(id='call_7gZ0PkmmmgzTOxfF01ATp0U5', function=Function(arguments='{\\n  \"location\": \"San Francisco, CA\"\\n}', name='get_current_weather'), type='function')\n",
            "Result from tool call\n",
            "{\"location\": \"San Francisco\", \"temperature\": \"72\", \"unit\": \"fahrenheit\"}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 3"
      ],
      "metadata": {
        "id": "4MjYyeajkpBl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "second_response = litellm.completion(\n",
        "    model=\"azure/chatgpt-functioncalling\",\n",
        "    messages=messages,\n",
        ")\n",
        "print(\"Second Response\\n\", second_response)\n",
        "print(\"Second Response Message\\n\", second_response.choices[0].message.content)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHgXyZq1kqGn",
        "outputId": "61a30470-d7f5-484d-c42b-681c9b60b34a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Second Response\n",
            " ModelResponse(id='chatcmpl-8MOC90vwZ2LHX0DE796XYtsOxdGcc', choices=[Choices(finish_reason='stop', index=0, message=Message(content='The current weather in San Francisco is 72°F.', role='assistant'))], created=1700346913, model='gpt-35-turbo', object='chat.completion', system_fingerprint=None, usage={'completion_tokens': 11, 'prompt_tokens': 69, 'total_tokens': 80}, _response_ms=824.882)\n",
            "Second Response Message\n",
            " The current weather in San Francisco is 72°F.\n"
          ]
        }
      ]
    }
  ]
}