wehub-resource-sync
678 lines
24 KiB
Plaintext
678 lines
24 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "ijGzTHJJUCPY"
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},
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"outputs": [],
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"source": [
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"# Copyright 2024 Google LLC\n",
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"#\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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"# You may obtain a copy of the License at\n",
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"#\n",
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"# https://www.apache.org/licenses/LICENSE-2.0\n",
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"#\n",
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"# Unless required by applicable law or agreed to in writing, software\n",
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"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "VEqbX8OhE8y9"
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},
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"source": [
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"# Working with Data Structures and Schemas in Gemini Function Calling\n",
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"\n",
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"<table align=\"left\">\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\">\n",
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" <img width=\"32px\" src=\"https://www.gstatic.com/pantheon/images/bigquery/welcome_page/colab-logo.svg\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
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" </a>\n",
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" </td>\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fgemini%2Ffunction-calling%2Ffunction_calling_data_structures.ipynb\">\n",
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" <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
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" </a>\n",
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" </td> \n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/gemini/function-calling/function_calling_data_structures.ipynb\">\n",
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" <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
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" </a>\n",
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" </td>\n",
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" <td style=\"text-align: center\">\n",
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" <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\">\n",
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" <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
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" </a>\n",
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" </td>\n",
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"</table>\n",
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"\n",
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"<div style=\"clear: both;\"></div>\n",
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"\n",
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"<b>Share to:</b>\n",
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"\n",
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"<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\" target=\"_blank\">\n",
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" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/8/81/LinkedIn_icon.svg\" alt=\"LinkedIn logo\">\n",
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"</a>\n",
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"\n",
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"<a href=\"https://bsky.app/intent/compose?text=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\" target=\"_blank\">\n",
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" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/7/7a/Bluesky_Logo.svg\" alt=\"Bluesky logo\">\n",
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"</a>\n",
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"\n",
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"<a href=\"https://twitter.com/intent/tweet?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\" target=\"_blank\">\n",
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" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/5a/X_icon_2.svg\" alt=\"X logo\">\n",
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"</a>\n",
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"\n",
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"<a href=\"https://reddit.com/submit?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\" target=\"_blank\">\n",
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" <img width=\"20px\" src=\"https://redditinc.com/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png\" alt=\"Reddit logo\">\n",
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"</a>\n",
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"\n",
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"<a href=\"https://www.facebook.com/sharer/sharer.php?u=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/function_calling_data_structures.ipynb\" target=\"_blank\">\n",
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" <img width=\"20px\" src=\"https://upload.wikimedia.org/wikipedia/commons/5/51/Facebook_f_logo_%282019%29.svg\" alt=\"Facebook logo\">\n",
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"</a> "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ZNJC1SkrsJY3"
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},
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"source": [
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"| Author(s) |\n",
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"| --- |\n",
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"| [Kristopher Overholt](https://github.com/koverholt) |"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CkHPv2myT2cx"
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},
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"source": [
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"## Overview\n",
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"\n",
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"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",
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"\n",
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"[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",
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"\n",
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"In this tutorial, you'll learn how to work with various data structures within Gemini Function Calling, including:\n",
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" \n",
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"- Single parameter\n",
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"- Multiple parameters\n",
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"- Lists of parameters\n",
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"- Nested parameters and data structures"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "r11Gu7qNgx1p"
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},
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"source": [
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"## Getting Started\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "No17Cw5hgx12"
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},
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"source": [
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"### Install Google Gen AI SDK"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "tFy3H3aPgx12"
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},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet google-genai"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dmWOrTJ3gx13"
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},
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"source": [
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"### Authenticate your notebook environment (Colab only)\n",
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"\n",
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"If you are running this notebook on Google Colab, run the cell below to authenticate your environment."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"id": "NyKGtVQjgx13"
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},
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"if \"google.colab\" in sys.modules:\n",
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" from google.colab import auth\n",
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"\n",
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" auth.authenticate_user()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "DF4l8DTdWgPY"
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},
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"source": [
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"### Set Google Cloud project information and create client\n",
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"\n",
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"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",
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"\n",
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"Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "Nqwi-5ufWp_B"
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},
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"outputs": [],
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"source": [
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"import os\n",
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"\n",
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"# fmt: off\n",
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"PROJECT_ID = \"[your-project-id]\" # @param {type: \"string\", placeholder: \"[your-project-id]\", isTemplate: true}\n",
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"# fmt: on\n",
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"if not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n",
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" PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n",
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"\n",
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"LOCATION = \"global\"\n",
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"\n",
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"from google import genai\n",
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"\n",
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"client = genai.Client(vertexai=True, project=PROJECT_ID, location=LOCATION)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "jXHfaVS66_01"
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},
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"source": [
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"## Code Examples\n",
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"\n",
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"### Import libraries"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "lslYAvw37JGQ"
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},
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"outputs": [],
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"source": [
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"from google.genai.types import FunctionDeclaration, GenerateContentConfig, Tool"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Bfy0nopcsJY5"
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},
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"source": [
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"### Initialize model\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "hvQy2EObsJY5"
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},
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"outputs": [],
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"source": [
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"MODEL_ID = \"gemini-3.5-flash\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "j3KHAr6BsJY6"
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},
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"source": [
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"### Example: Single parameter\n",
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"\n",
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"Let's say that you want to extract a location from a prompt to help a user navigate to their desired destination.\n",
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"\n",
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"You can build out a simple schema for a function that takes a single parameter as an input:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"id": "ElbXcrGWsJY6"
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},
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"outputs": [],
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"source": [
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"get_destination = FunctionDeclaration(\n",
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" name=\"get_destination\",\n",
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" description=\"Get directions to a destination\",\n",
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" parameters={\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"destination\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Destination that the user wants to go to\",\n",
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" },\n",
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" },\n",
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" },\n",
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")\n",
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"\n",
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"destination_tool = Tool(\n",
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" function_declarations=[get_destination],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "T_u_cPW7sJY6"
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},
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"source": [
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"Now you can send a prompt with a destination, and the model will return structured data with a single key/value pair:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"id": "aD4UJ6BcsJY6"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[FunctionCall(\n",
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" args={\n",
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" 'destination': 'Paris'\n",
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" },\n",
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" name='get_destination'\n",
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" )]"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"prompt = \"I'd like to travel to Paris\"\n",
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"\n",
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"response = client.models.generate_content(\n",
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" model=MODEL_ID,\n",
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" contents=prompt,\n",
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" config=GenerateContentConfig(temperature=0, tools=[destination_tool]),\n",
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")\n",
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"\n",
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"response.function_calls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Q7jimk65sJY7"
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},
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"source": [
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"### Example: Multiple parameters\n",
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"\n",
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"What if you want the function call to return more than one parameter?\n",
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"\n",
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"You can build out a simple schema for a function that takes multiple parameters as an input:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"id": "OTaA258isJY7"
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},
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"outputs": [],
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"source": [
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"get_destination_params = FunctionDeclaration(\n",
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" name=\"get_destination_params\",\n",
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" description=\"Get directions to a destination\",\n",
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" parameters={\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"destination\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Destination that the user wants to go to\",\n",
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" },\n",
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" \"mode_of_transportation\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Mode of transportation to use\",\n",
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" },\n",
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" \"departure_time\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Time that the user will leave for the destination\",\n",
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" },\n",
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" },\n",
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" },\n",
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")\n",
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"\n",
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"destination_tool = Tool(\n",
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" function_declarations=[get_destination_params],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {
|
|
"id": "xS-qg3udsJY7"
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},
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"source": [
|
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"Now you can send a prompt with a destination, and the model will return structured data with multiple key/value pairs:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"id": "2gm8YuoesJY7"
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},
|
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"outputs": [
|
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{
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"data": {
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"text/plain": [
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"[FunctionCall(\n",
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" args={\n",
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" 'departure_time': '9:00 am',\n",
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" 'destination': 'Paris',\n",
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" 'mode_of_transportation': 'train'\n",
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" },\n",
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" name='get_destination_params'\n",
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" )]"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"prompt = \"I'd like to travel to Paris by train and leave at 9:00 am\"\n",
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"\n",
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"response = client.models.generate_content(\n",
|
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" model=MODEL_ID,\n",
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" contents=prompt,\n",
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" config=GenerateContentConfig(temperature=0, tools=[destination_tool]),\n",
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")\n",
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"\n",
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"response.function_calls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "y-ZP3TKrsJY7"
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},
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"source": [
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"### Example: Lists of parameters\n",
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"\n",
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"What if you want the function call to return an array or list of parameters within an object?\n",
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"\n",
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"For example, you might want to call an API to get the geocoded coordinates of several different locations within a single prompt.\n",
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"\n",
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"In that case, you can build out a schema for a function that takes an array as an input:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"id": "jGMeCeftsJY7"
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},
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"outputs": [],
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"source": [
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"get_multiple_location_coordinates = FunctionDeclaration(\n",
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" name=\"get_location_coordinates\",\n",
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" description=\"Get coordinates of multiple locations\",\n",
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" parameters={\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"locations\": {\n",
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" \"type\": \"array\",\n",
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" \"description\": \"A list of locations\",\n",
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" \"items\": {\n",
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" \"description\": \"Components of the location\",\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"point_of_interest\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"Name or type of point of interest\",\n",
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" },\n",
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" \"city\": {\"type\": \"string\", \"description\": \"City\"},\n",
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" \"country\": {\"type\": \"string\", \"description\": \"Country\"},\n",
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" },\n",
|
|
" \"required\": [\n",
|
|
" \"point_of_interest\",\n",
|
|
" \"city\",\n",
|
|
" \"country\",\n",
|
|
" ],\n",
|
|
" },\n",
|
|
" }\n",
|
|
" },\n",
|
|
" },\n",
|
|
")\n",
|
|
"\n",
|
|
"geocoding_tool = Tool(\n",
|
|
" function_declarations=[get_multiple_location_coordinates],\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "p2X7KXuYsJY8"
|
|
},
|
|
"source": [
|
|
"Now we'll send a prompt with a few different locations and points of interest:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {
|
|
"id": "8YtBm7-XsJY8"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[FunctionCall(\n",
|
|
" args={\n",
|
|
" 'locations': [\n",
|
|
" {\n",
|
|
" 'city': 'Paris',\n",
|
|
" 'country': 'France',\n",
|
|
" 'point_of_interest': 'Eiffel Tower'\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'city': 'New York',\n",
|
|
" 'country': 'USA',\n",
|
|
" 'point_of_interest': 'Statue of Liberty'\n",
|
|
" },\n",
|
|
" {\n",
|
|
" 'city': 'Port Douglas',\n",
|
|
" 'country': 'Australia',\n",
|
|
" 'point_of_interest': 'Port Douglas'\n",
|
|
" },\n",
|
|
" ]\n",
|
|
" },\n",
|
|
" name='get_location_coordinates'\n",
|
|
" )]"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"prompt = \"\"\"\n",
|
|
" I'd like to get the coordinates for\n",
|
|
" the Eiffel tower in Paris,\n",
|
|
" the statue of liberty in New York,\n",
|
|
" and Port Douglas near the Great Barrier Reef.\n",
|
|
"\"\"\"\n",
|
|
"\n",
|
|
"response = client.models.generate_content(\n",
|
|
" model=MODEL_ID,\n",
|
|
" contents=prompt,\n",
|
|
" config=GenerateContentConfig(temperature=0, tools=[geocoding_tool]),\n",
|
|
")\n",
|
|
"\n",
|
|
"response.function_calls"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "D8giW_8OsJY8"
|
|
},
|
|
"source": [
|
|
"Note that the generative model populated values for all of the parameters for a given location since all three parameters are required."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "jEHIc8x6sJY8"
|
|
},
|
|
"source": [
|
|
"### Example: Nested parameters and data structures\n",
|
|
"\n",
|
|
"What if you want the function call to include nested parameters or other complex data structures?\n",
|
|
"\n",
|
|
"You might want to send a command to create a product listing based on a few sentences that include the product details.\n",
|
|
"\n",
|
|
"In that case, you can build out a schema for a function that takes nested data structures as an input:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"id": "27PuX0-fsJY8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"create_product_listing = FunctionDeclaration(\n",
|
|
" name=\"create_product_listing\",\n",
|
|
" description=\"Create a product listing using the details provided by the user.\",\n",
|
|
" parameters={\n",
|
|
" \"type\": \"object\",\n",
|
|
" \"properties\": {\n",
|
|
" \"product\": {\n",
|
|
" \"type\": \"object\",\n",
|
|
" \"properties\": {\n",
|
|
" \"name\": {\"type\": \"string\"},\n",
|
|
" \"price\": {\"type\": \"number\"},\n",
|
|
" \"category\": {\"type\": \"string\"},\n",
|
|
" \"description\": {\"type\": \"string\"},\n",
|
|
" },\n",
|
|
" }\n",
|
|
" },\n",
|
|
" },\n",
|
|
")\n",
|
|
"\n",
|
|
"product_listing_tool = Tool(\n",
|
|
" function_declarations=[create_product_listing],\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "k8sKF69fsJY8"
|
|
},
|
|
"source": [
|
|
"Now we'll send a prompt with product details and the model will extract the nested parameters:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"id": "Pt-i-srosJY8"
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[FunctionCall(\n",
|
|
" args={\n",
|
|
" 'product': {\n",
|
|
" 'category': 'Electronics',\n",
|
|
" 'description': 'These headphones create a distraction-free environment.',\n",
|
|
" 'name': 'Noise-canceling headphones',\n",
|
|
" 'price': 149.99\n",
|
|
" }\n",
|
|
" },\n",
|
|
" name='create_product_listing'\n",
|
|
" )]"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"prompt = \"\"\"Create a listing for noise-canceling headphones for $149.99.\n",
|
|
"These headphones create a distraction-free environment.\"\"\"\n",
|
|
"\n",
|
|
"response = client.models.generate_content(\n",
|
|
" model=MODEL_ID,\n",
|
|
" contents=prompt,\n",
|
|
" config=GenerateContentConfig(temperature=0, tools=[product_listing_tool]),\n",
|
|
")\n",
|
|
"\n",
|
|
"response.function_calls"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "GpDvGrmtsJY8"
|
|
},
|
|
"source": [
|
|
"And you're done! You successfully generated various types of data structures, including a single parameter, multiple parameters, a list of parameters, and nested parameters. Try another notebook to continue exploring other functionality in the Gemini API!"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "function_calling_data_structures.ipynb",
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|