{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "ijGzTHJJUCPY" }, "outputs": [], "source": [ "# Copyright 2024 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "VEqbX8OhE8y9" }, "source": [ "# Working with Parallel Function Calls and Multiple Function Responses in Gemini\n", "\n", "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \"Google
Open in Colab\n", " \n", " 		\n", " \n", " \"Google
Open in Colab Enterprise\n", " \n", " 		\n", " \n", " \"Vertex
Open in Workbench\n", " \n", " 		\n", " \n", " \"GitHub
View on GitHub\n", " \n", "
\n", "\n", "
\n", "\n", "Share to:\n", "\n", "\n", " \"LinkedIn\n", "\n", "\n", "\n", " \"Bluesky\n", "\n", "\n", "\n", " \"X\n", "\n", "\n", "\n", " \"Reddit\n", "\n", "\n", "\n", " \"Facebook\n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "ZNJC1SkrsJY3" }, "source": [ "| Author(s) |\n", "| --- |\n", "| [Kristopher Overholt](https://github.com/koverholt) |" ] }, { "cell_type": "markdown", "metadata": { "id": "CkHPv2myT2cx" }, "source": [ "## Overview\n", "\n", "Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini models.\n", "\n", "[Function Calling](https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/function-calling) in Gemini lets you create a description of a function in your code, then pass that description to a language model in a request. The response from the model includes the name of a function that matches the description and the arguments to call it with.\n", "\n", "In this tutorial, you'll learn how to work with parallel function calling within Gemini Function Calling, including:\n", " \n", "- Handling parallel function calls for repeated functions\n", "- Working with parallel function calls across multiple functions\n", "- Extracting multiple function calls from a Gemini response\n", "- Calling multiple functions and returning them to Gemini\n", "\n", "### What is parallel function calling?\n", "\n", "In previous versions of the Gemini models (prior to May 2024), Gemini would return two or more chained function calls if the model determined that more than one function call was needed before returning a natural language summary. Here, a chained function call means that you get the first function call response, return the API data to Gemini, get a second function call response, return the API data to Gemini, and so on.\n", "\n", "In recent versions of specific Gemini models (from May 2024 and on), Gemini has the ability to return two or more function calls in parallel (i.e., two or more function call responses within the first function call response object). Parallel function calling allows you to fan out and parallelize your API calls or other actions that you perform in your application code, so you don't have to work through each function call response and return one-by-one! Refer to the [Gemini Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling) for more information on which Gemini model versions support parallel function calling.\n", "\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "r11Gu7qNgx1p" }, "source": [ "## Getting Started\n" ] }, { "cell_type": "markdown", "metadata": { "id": "No17Cw5hgx12" }, "source": [ "### Install Google Gen AI SDK" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "tFy3H3aPgx12" }, "outputs": [], "source": [ "%pip install --upgrade --quiet google-genai wikipedia" ] }, { "cell_type": "markdown", "metadata": { "id": "dmWOrTJ3gx13" }, "source": [ "### Authenticate your notebook environment (Colab only)\n", "\n", "If you are running this notebook on Google Colab, run the cell below to authenticate your environment." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "NyKGtVQjgx13" }, "outputs": [], "source": [ "import sys\n", "\n", "if \"google.colab\" in sys.modules:\n", " from google.colab import auth\n", "\n", " auth.authenticate_user()" ] }, { "cell_type": "markdown", "metadata": { "id": "DF4l8DTdWgPY" }, "source": [ "### Set Google Cloud project information\n", "\n", "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n", "\n", "Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "Nqwi-5ufWp_B" }, "outputs": [], "source": [ "import os\n", "\n", "# fmt: off\n", "PROJECT_ID = \"[your-project-id]\" # @param {type: \"string\", placeholder: \"[your-project-id]\", isTemplate: true}\n", "# fmt: on\n", "if not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n", " PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n", "\n", "LOCATION = \"global\"\n", "\n", "from google import genai\n", "\n", "client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)" ] }, { "cell_type": "markdown", "metadata": { "id": "jXHfaVS66_01" }, "source": [ "## Code Examples\n", "\n", "### Import libraries" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "lslYAvw37JGQ" }, "outputs": [], "source": [ "from typing import Any\n", "\n", "import wikipedia\n", "from IPython.display import Markdown, display\n", "from google.genai.types import (\n", " FunctionDeclaration,\n", " GenerateContentConfig,\n", " GenerateContentResponse,\n", " Part,\n", " Tool,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "b2acd610d52c" }, "source": [ "### Define helper function" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "32c90d8c452a" }, "outputs": [], "source": [ "# Helper function to extract one or more function calls from a Gemini Function Call response\n", "\n", "\n", "def extract_function_calls(response: GenerateContentResponse) -> list[dict]:\n", " function_calls: list[dict] = []\n", " for function_call in response.function_calls:\n", " function_call_dict: dict[str, dict[str, Any]] = {function_call.name: {}}\n", " for key, value in function_call.args.items():\n", " function_call_dict[function_call.name][key] = value\n", " function_calls.append(function_call_dict)\n", " return function_calls" ] }, { "cell_type": "markdown", "metadata": { "id": "j3KHAr6BsJY6" }, "source": [ "## Example: Parallel function calls on the same function\n", "\n", "A great use case for parallel function calling is when you have a function that only accepts one parameter per API call and you need to make repeated calls to that function.\n", "\n", "With Parallel Function Calling, rather than having to send N number of API requests to Gemini for N number function calls, instead you can send a single API request to Gemini, receive N number of Function Call Responses within a single response, make N number of external API calls in your code, then return all of the API responses to Gemini in bulk. And you can do all of this without any extra configuration in your function declarations, tools, or requests to Gemini.\n", "\n", "In this example, you'll do exactly that and use Parallel Function Calling in Gemini to ask about multiple topics on [Wikipedia](https://www.wikipedia.org/). Let's get started!\n", "\n", "### Write function declarations and wrap them in a tool\n", "\n", "First, define your function declarations and tool using the Vertex AI Python SDK:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "31a4ae78030e" }, "outputs": [], "source": [ "search_wikipedia = FunctionDeclaration(\n", " name=\"search_wikipedia\",\n", " description=\"Search for articles on Wikipedia\",\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"query\": {\n", " \"type\": \"string\",\n", " \"description\": \"Query to search for on Wikipedia\",\n", " },\n", " },\n", " },\n", ")\n", "\n", "wikipedia_tool = Tool(\n", " function_declarations=[\n", " search_wikipedia,\n", " ],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "11c5abb4bbb1" }, "source": [ "### Initialize the Gemini model\n", "\n", "Now you can initialize Gemini using a [model version that supports parallel function calling](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "521d7dbb44a7" }, "outputs": [], "source": [ "MODEL_ID = \"gemini-3.5-flash\"\n", "\n", "chat = client.chats.create(\n", " model=MODEL_ID, config=GenerateContentConfig(temperature=0, tools=[wikipedia_tool])\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "2a8ac4fd2f45" }, "source": [ "### Send prompt to Gemini\n", "\n", "Send a prompt to Gemini that includes a phrase that you expect to invoke two or more function calls.\n", "\n", "In this case we'll ask about three different article topics to search for on Wikipedia:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "aD4UJ6BcsJY6" }, "outputs": [], "source": [ "prompt = \"Search for articles related to solar panels, renewable energy, and battery storage and provide a summary of your findings\"\n", "\n", "response = chat.send_message(prompt)" ] }, { "cell_type": "markdown", "metadata": { "id": "f8e67fe6825f" }, "source": [ "### Extract function names and parameters\n", "\n", "Use the helper function that we created earlier to extract the function names and function parameters for each Function Call that Gemini responded with:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "468e7308ebb8" }, "outputs": [ { "data": { "text/plain": [ "[{'search_wikipedia': {'query': 'solar panel'}},\n", " {'search_wikipedia': {'query': 'renewable energy'}},\n", " {'search_wikipedia': {'query': 'battery storage power station'}}]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function_calls = extract_function_calls(response)\n", "function_calls" ] }, { "cell_type": "markdown", "metadata": { "id": "5d69b21870f1" }, "source": [ "Note that the helper function is just one way to extract fields from the structured Function Call response. You can modify the helper function or write your own custom code to extract and get the fields into your preferred format or data structure!\n", "\n", "### Make external API calls\n", "\n", "Next, you'll loop through the Function Calls and use the `wikipedia` Python package to make an API call for each search query:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "754bfbf1864a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'search_wikipedia': {'query': 'solar panel'}}\n", "{'search_wikipedia': {'query': 'renewable energy'}}\n", "{'search_wikipedia': {'query': 'battery storage power station'}}\n" ] } ], "source": [ "api_response = []\n", "\n", "# Loop over multiple function calls\n", "for function_call in function_calls:\n", " print(function_call)\n", "\n", " # Make external API call\n", " result = wikipedia.summary(function_call[\"search_wikipedia\"][\"query\"])\n", "\n", " # Collect all API responses\n", " api_response.append(result)" ] }, { "cell_type": "markdown", "metadata": { "id": "90340a8c2949" }, "source": [ "### Get a natural language summary\n", "\n", "Now you can return all of the API responses to Gemini so that it can generate a natural language summary:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "3409d561a84b" }, "outputs": [ { "data": { "text/markdown": "Based on recent research into solar panels, renewable energy, and battery storage, here is a summary of the current state and integration of these technologies.\n\n### 1. Solar Panels (Photovoltaics)\nSolar panels are devices that convert sunlight directly into electricity using **photovoltaic (PV) cells**. \n* **Mechanism:** When exposed to light, PV cells (typically made of silicon) produce excited electrons that create a direct current (DC). An **inverter** is then used to convert this into alternating current (AC) for use in homes or the grid.\n* **Applications:** They range from small-scale residential rooftop installations to massive \"solar farms.\"\n* **Benefits & Challenges:** They provide a clean, carbon-free energy source. However, their efficiency depends on sunlight intensity and weather, making them a \"variable\" energy source that requires management when the sun isn't shining.\n\n### 2. Renewable Energy Landscape\nRenewable energy (or \"green energy\") comes from natural resources that replenish on a human timescale, such as solar, wind, hydropower, and geothermal.\n* **Growth:** As of 2024, renewables account for over **30% of global electricity generation**, with a projected increase to 45% by 2030. Solar and wind have seen the most dramatic cost reductions, often becoming the cheapest options for new power generation.\n* **Environmental Impact:** The primary driver for the shift to renewables is the reduction of greenhouse gas emissions to combat climate change. The International Energy Agency (IEA) suggests that 90% of global electricity must be renewable by 2050 to reach net-zero goals.\n* **Variability:** A key distinction in this field is between **variable** sources (solar/wind) and **controllable** sources (hydro/geothermal), which can be adjusted based on demand.\n\n### 3. Battery Storage Systems\nBattery Energy Storage Systems (BESS) are critical for modernizing the electrical grid, acting as a bridge between variable energy production and consumer demand.\n* **Grid Stabilization:** Batteries are the fastest-responding power source on the grid. They can transition from standby to full power in under a second, helping to prevent blackouts and stabilize frequency.\n* **Energy Shifting:** They store excess energy produced during peak sunlight or wind hours and discharge it during periods of high demand or low production (e.g., at night).\n* **Cost Trends:** The cost of battery storage has plummeted, with the \"levelized cost of storage\" halving roughly every four years. By 2023, prices dropped to approximately $117 per MWh.\n* **Scale:** While individual battery stations are currently smaller than traditional pumped-hydro storage, they are being deployed rapidly in urban areas and near existing power plants due to their compact size and lack of emissions.\n\n### Summary of Findings\nThe synergy between these three areas is the foundation of the energy transition. **Solar panels** provide the clean generation, **renewable energy** policies drive the large-scale adoption, and **battery storage** solves the \"intermittency problem,\" ensuring that the clean energy generated during the day can reliably power the world 24/7. Together, they are rapidly displacing fossil fuels, which shrank from 68% to 62% of the global energy mix in the last decade.", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Return the API response to Gemini\n", "response = chat.send_message(\n", " [\n", " Part.from_function_response(\n", " name=\"search_wikipedia\",\n", " response={\n", " \"content\": api_response[0],\n", " },\n", " ),\n", " Part.from_function_response(\n", " name=\"search_wikipedia\",\n", " response={\n", " \"content\": api_response[1],\n", " },\n", " ),\n", " Part.from_function_response(\n", " name=\"search_wikipedia\",\n", " response={\n", " \"content\": api_response[2],\n", " },\n", " ),\n", " ],\n", ")\n", "display(Markdown(response.text))" ] }, { "cell_type": "markdown", "metadata": { "id": "6e55e0b45931" }, "source": [ "And you're done with no extra configuration necessary!\n", "\n", "Note that Gemini will use the information in your `FunctionDeclarations` to determine if and when it should return parallel Function Call responses, or it will determine which Function Calls need to be made before others to get information that a subsequent Function Call depends on. So be sure to account for this behavior in your logic and application code!" ] }, { "cell_type": "markdown", "metadata": { "id": "YBnpF0Yl5exC" }, "source": [ "## Example: Parallel function calls across multiple functions\n", "\n", "Another good fit for parallel function calling is when you have multiple, independent functions that you want to be able to call in parallel, which reduces the number of consecutive Gemini API calls that you need to make and (ideally) reduces the overall response time to the end user who is waiting for a natural language response.\n", "\n", "In this example, you'll use Parallel Function Calling in Gemini to ask about multiple aspects of topics and articles on [Wikipedia](https://www.wikipedia.org/).\n", "\n", "### Write function declarations and wrap them in a tool\n", "\n", "First, define your function declarations and tool using the Vertex AI Python SDK:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "577bd3ad36ed" }, "outputs": [], "source": [ "search_wikipedia = FunctionDeclaration(\n", " name=\"search_wikipedia\",\n", " description=\"Search for articles on Wikipedia\",\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"query\": {\n", " \"type\": \"string\",\n", " \"description\": \"Query to search for on Wikipedia\",\n", " },\n", " },\n", " },\n", ")\n", "\n", "suggest_wikipedia = FunctionDeclaration(\n", " name=\"suggest_wikipedia\",\n", " description=\"Get suggested titles from Wikipedia for a given term\",\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"query\": {\n", " \"type\": \"string\",\n", " \"description\": \"Query to search for suggested titles on Wikipedia\",\n", " },\n", " },\n", " },\n", ")\n", "\n", "summarize_wikipedia = FunctionDeclaration(\n", " name=\"summarize_wikipedia\",\n", " description=\"Get article summaries from Wikipedia\",\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"topic\": {\n", " \"type\": \"string\",\n", " \"description\": \"Query to search for article summaries on Wikipedia\",\n", " },\n", " },\n", " },\n", ")\n", "\n", "wikipedia_tool = Tool(\n", " function_declarations=[\n", " search_wikipedia,\n", " suggest_wikipedia,\n", " summarize_wikipedia,\n", " ],\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "4884fb361482" }, "source": [ "### Initialize the Gemini model\n", "\n", "Now you can initialize Gemini using a [model version that supports parallel function calling](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling):" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "470894a34e87" }, "outputs": [], "source": [ "chat = client.chats.create(\n", " model=MODEL_ID, config=GenerateContentConfig(temperature=0, tools=[wikipedia_tool])\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "4a7026e6a6a4" }, "source": [ "### Send prompt to Gemini\n", "\n", "Send a prompt to Gemini that includes a phrase that you expect to invoke two or more function calls.\n", "\n", "In this case we'll ask about three types of details to look up for a given topic on Wikipedia:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "MVlbUOoe5exC" }, "outputs": [], "source": [ "prompt = \"Search for the solar system on Wikipedia, suggest related terms, and summarize the main article\"\n", "\n", "response = chat.send_message(prompt)" ] }, { "cell_type": "markdown", "metadata": { "id": "add3c553b773" }, "source": [ "### Extract function names and parameters\n", "\n", "Use the helper function that we created earlier to extract the function names and function parameters for each Function Call that Gemini responded with:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "f02643f2344b" }, "outputs": [ { "data": { "text/plain": [ "[{'search_wikipedia': {'query': 'Solar System'}},\n", " {'suggest_wikipedia': {'query': 'Solar System'}},\n", " {'summarize_wikipedia': {'topic': 'Solar System'}}]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function_calls = extract_function_calls(response)\n", "function_calls" ] }, { "cell_type": "markdown", "metadata": { "id": "0e86bbd26969" }, "source": [ "### Make external API calls\n", "\n", "Next, you'll loop through the Function Calls and use the `wikipedia` Python package to make APIs calls and gather information from Wikipedia:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "id": "b3ca4ba3118b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'search_wikipedia': {'query': 'Solar System'}}\n", "{'suggest_wikipedia': {'query': 'Solar System'}}\n", "{'summarize_wikipedia': {'topic': 'Solar System'}}\n" ] } ], "source": [ "api_response: dict[str, Any] = {} # type: ignore\n", "\n", "# Loop over multiple function calls\n", "for function_call in function_calls:\n", " print(function_call)\n", " for function_name, function_args in function_call.items():\n", " # Determine which external API call to make\n", " if function_name == \"search_wikipedia\":\n", " result = wikipedia.search(function_args[\"query\"])\n", " if function_name == \"suggest_wikipedia\":\n", " result = wikipedia.suggest(function_args[\"query\"])\n", " if function_name == \"summarize_wikipedia\":\n", " result = wikipedia.summary(function_args[\"topic\"], auto_suggest=False)\n", "\n", " # Collect all API responses\n", " api_response[function_name] = result" ] }, { "cell_type": "markdown", "metadata": { "id": "5edff88c747e" }, "source": [ "### Get a natural language summary\n", "\n", "Now you can return all of the API responses to Gemini so that it can generate a natural language summary:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "0c94849ed303" }, "outputs": [ { "data": { "text/markdown": "The **Solar System** is the gravitationally bound system of the Sun and the objects that orbit it. It formed approximately 4.6 billion years ago from the collapse of a giant interstellar molecular cloud.\n\n### **Summary of the Solar System**\n* **The Sun:** The central star that accounts for 99.86% of the system's total mass. It generates energy through nuclear fusion in its core.\n* **The Planets:** There are eight major planets divided into two groups:\n * **Inner Planets (Terrestrial):** Mercury, Venus, Earth, and Mars. These are rocky planets.\n * **Outer Planets (Giants):** Jupiter and Saturn (gas giants), and Uranus and Neptune (ice giants).\n* **Dwarf Planets:** Objects like Pluto, Ceres, Eris, Haumea, and Makemake that orbit the Sun but do not dominate their orbital paths.\n* **Small Bodies:** The system includes millions of asteroids (mostly in the Asteroid Belt), comets, and meteoroids.\n* **Moons:** Many planets and dwarf planets have natural satellites, such as Earth's Moon or Jupiter's Ganymede.\n* **Outer Reaches:** Beyond the planets lie the Kuiper Belt, the scattered disc, and the theorized Oort Cloud, which marks the outermost edge of the Solar System's gravitational influence.\n\n### **Related Terms and Topics**\nBased on Wikipedia's database, here are several related terms and areas of study:\n* **Formation and evolution of the Solar System:** The history of how the system began and how it will change over billions of years.\n* **Exoplanet:** Planets located outside of our Solar System orbiting other stars.\n* **List of Solar System objects by size:** A comparison of the various planets, moons, and dwarf planets.\n* **Kuiper Belt:** A region of the Solar System beyond the orbit of Neptune containing many small icy bodies.\n* **Heliopause:** The boundary where the solar wind is stopped by the interstellar medium.\n* **Celestial Mechanics:** The branch of astronomy that deals with the motions of objects in outer space.", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Return the API response to Gemini\n", "response = chat.send_message(\n", " [\n", " Part.from_function_response(\n", " name=\"search_wikipedia\",\n", " response={\n", " \"content\": api_response.get(\"search_wikipedia\", \"\"),\n", " },\n", " ),\n", " Part.from_function_response(\n", " name=\"suggest_wikipedia\",\n", " response={\n", " \"content\": api_response.get(\"suggest_wikipedia\", \"\"),\n", " },\n", " ),\n", " Part.from_function_response(\n", " name=\"summarize_wikipedia\",\n", " response={\n", " \"content\": api_response.get(\"summarize_wikipedia\", \"\"),\n", " },\n", " ),\n", " ],\n", ")\n", "\n", "display(Markdown(response.text))" ] }, { "cell_type": "markdown", "metadata": { "id": "GpDvGrmtsJY8" }, "source": [ "And you're done! You successfully made parallel function calls for a couple of different use cases. Feel free to adapt the code samples here for your own use cases and applications. Or try another notebook to continue exploring other functionality in the Gemini API.\n", "\n", "Happy parallel function calling!" ] } ], "metadata": { "colab": { "name": "parallel_function_calling.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }