wehub-resource-sync
630 lines
18 KiB
Plaintext
630 lines
18 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": "4KG8heslwNdJ"
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},
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"outputs": [],
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"source": [
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"# Copyright 2025 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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"source": [
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"# Vertex AI Agent Engine in Express Mode\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/agent-engine/agent_engine_express_mode.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://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/agent-engine/agent_engine_express_mode.ipynb\">\n",
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" <img width=\"32px\" src=\"https://storage.googleapis.com/github-repo/generative-ai/logos/GitHub_Invertocat_Dark.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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"<p>\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/agent-engine/agent_engine_express_mode.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/agent-engine/agent_engine_express_mode.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/agent-engine/agent_engine_express_mode.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/agent-engine/agent_engine_express_mode.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/agent-engine/agent_engine_express_mode.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>\n",
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"</p>"
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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": "oWnJG6W9s9wz"
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},
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"source": [
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"## Set up the environment\n",
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"\n",
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"https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/set-up#sdk-installation"
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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": "qn8t6OZZBR2_"
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},
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"outputs": [],
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"source": [
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"%pip install \"google-cloud-aiplatform[agent_engines]\" --force-reinstall --quiet"
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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": "UlZrGQP1kquN"
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},
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"source": [
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"# Sign up for Vertex AI Express mode"
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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": "AWXhFk3rkvHt"
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},
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"source": [
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"We can use Vertex Services like Agent Engine and access models without requiring a credit card through Vertex AI Express Mode!\n",
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"\n",
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"Sign up with your google account here: https://cloud.google.com/vertex-ai/generative-ai/docs/start/express-mode/overview to unlock access to certain services and the latest google models models!\n",
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"\n",
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"Vertex Express Mode combined with ADK allow for the creation of advanced agents with customizable tools and a context layer with sessions and memories without any billing!\n",
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"\n",
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"Once you sign up, paste your api key here:"
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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": "Zdr_R5zzkTJX"
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},
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"outputs": [],
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"source": [
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"express_mode_api_key = \"\" # @param {type:\"string\"}"
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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": "h1ey_XgwFpOx"
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},
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"source": [
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"# Develop an Adk agent locally"
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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": "KIsEeWOrfpvN"
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},
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"source": [
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"### Step 1: Define a local simple agent"
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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": "c09iKPFwhtUL"
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},
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"source": [
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"We will use AdkApp, which is a template to make Adk Agents to create a simple agent that has no tools, just a model. We will be able to converse with it, but it cannot help us with the currency conversion rates.\n",
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"\n",
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"With Vertex AI Express mode, you have access to the latest Google models. For the comprehensive list, see the [Available models and rate limits in express mode](https://docs.cloud.google.com/vertex-ai/generative-ai/docs/start/express-mode/overview#models). We will be using Gemini 2.5 Flash for this example."
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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": "J5eBt442ntq4"
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},
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"outputs": [],
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"source": [
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"# Initialize Vertex AI SDK\n",
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"import vertexai\n",
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"from vertexai import agent_engines\n",
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"\n",
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"vertexai.init(api_key=express_mode_api_key)"
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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": "u6g_sWf8fVpj"
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},
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"outputs": [],
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"source": [
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"model = \"gemini-2.5-flash\""
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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": "Xdvw9CqwfZeS"
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},
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"outputs": [],
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"source": [
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"from google.adk.agents import Agent\n",
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"from google.adk.apps import App\n",
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"\n",
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"agent = Agent(\n",
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" model=model, # Required.\n",
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" name=\"currency_exchange_agent\", # Required.\n",
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")"
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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": "r-zZiHpXfdIe"
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},
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"outputs": [],
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"source": [
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"app = agent_engines.AdkApp(\n",
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" app=App(\n",
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" name=\"currency_exchange_app\",\n",
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" root_agent=agent,\n",
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" )\n",
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")"
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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": "oClJjHMyfeBG"
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},
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"outputs": [],
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"source": [
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"async for event in app.async_stream_query(\n",
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" user_id=\"local user\", # Required\n",
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" message=\"What is the exchange rate from US dollars to Swedish krona on 2025-09-25?\",\n",
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"):\n",
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" print(event)"
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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": "rauCYC6ufmUe"
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},
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"source": [
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"### Step 2: Define a 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": "vUaZGzUmh5ls"
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},
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"source": [
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"Now that we know we can talk to the agent, lets add a tool. This tool allows the agent to check the exchange rate between two currencies ona given date using the frankfurter app."
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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": "SSUldKSkflJW"
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},
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"outputs": [],
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"source": [
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"def get_exchange_rate(\n",
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" currency_from: str = \"USD\",\n",
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" currency_to: str = \"EUR\",\n",
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" currency_date: str = \"latest\",\n",
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"):\n",
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" \"\"\"Retrieves the exchange rate between two currencies on a specified date.\"\"\"\n",
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" import requests\n",
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"\n",
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" response = requests.get(\n",
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" f\"https://api.frankfurter.app/{currency_date}\",\n",
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" params={\"from\": currency_from, \"to\": currency_to},\n",
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" )\n",
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" return response.json()"
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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": "YwjPSaVof4LZ"
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},
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"source": [
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"To test the function before you use it in your agent, run the following:"
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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": "AsR76YB2f2Od"
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},
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"outputs": [],
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"source": [
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"get_exchange_rate(currency_from=\"USD\", currency_to=\"SEK\")"
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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": "Fsk4g4fYf8ns"
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},
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"source": [
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"### Step 3: Add the tool to your agent and test locally"
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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": "0FveK16ZgA6z"
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},
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"source": [
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"You have to associate the tools with the Agent when it is initialized:"
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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": "FYhxaPnGf_oA"
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},
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"outputs": [],
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"source": [
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"from google.adk.agents import Agent\n",
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"\n",
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"agent = Agent(\n",
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" model=model, # Required.\n",
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" name=\"currency_exchange_agent\", # Required.\n",
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" tools=[get_exchange_rate], # [new] Optional.\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": "H3HTcZFmgHE2"
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},
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"source": [
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"Then, we test locally it like we did before."
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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": "cQXwtqm_gI_1"
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},
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"outputs": [],
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"source": [
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"app = agent_engines.AdkApp(\n",
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" app=App(\n",
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" name=\"currency_exchange_app\",\n",
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" root_agent=agent,\n",
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" )\n",
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")\n",
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"async for event in app.async_stream_query(\n",
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" user_id=\"local user\",\n",
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" message=\"What is the exchange rate from US dollars to Swedish krona on 2025-09-25?\",\n",
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"):\n",
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" print(event)"
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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": "nmyRh5qckedk"
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},
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"source": [
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"# Deploy your Agent with Agent Engine"
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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": "6PbMnOLE25NA"
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},
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"source": [
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"### Step 4: Write agent files"
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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": "ONXGYYIWgZdm"
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},
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"source": [
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"Now that we know our agent will work locally, we can write our code to the local files and deploy it to Google Cloud without cost to Agent Engine with source based deployment! More info can be found at [Deploy an Agent Engine](https://docs.cloud.google.com/agent-builder/agent-engine/deploy#from-source-files)."
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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": "bqDemcnqHUG2"
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},
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"outputs": [],
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"source": [
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"!mkdir test"
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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": "uJS6VpvKBZAN"
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},
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"outputs": [],
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"source": [
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"%%writefile test/requirements.txt\n",
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"google-cloud-aiplatform[agent_engines,adk]"
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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": "l1G9gtSDw8yt"
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},
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"outputs": [],
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"source": [
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"%%writefile test/my_agent.py\n",
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"from google.adk.agents import Agent\n",
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"from vertexai import agent_engines\n",
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"\n",
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"def get_exchange_rate(\n",
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" currency_from: str = \"USD\",\n",
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" currency_to: str = \"EUR\",\n",
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" currency_date: str = \"latest\",\n",
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"):\n",
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" \"\"\"Retrieves the exchange rate between two currencies on a specified date.\"\"\"\n",
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" import requests\n",
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"\n",
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" response = requests.get(\n",
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" f\"https://api.frankfurter.app/{currency_date}\",\n",
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" params={\"from\": currency_from, \"to\": currency_to},\n",
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" )\n",
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" return response.json()\n",
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"\n",
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"agent = Agent(\n",
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" model=\"gemini-2.5-flash\", # Required.\n",
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" name='currency_exchange_agent', # Required.\n",
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" tools=[get_exchange_rate], # [new] Optional.\n",
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")\n",
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"\n",
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"adk_app=agent_engines.AdkApp(agent=agent, enable_tracing=True)"
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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": "GnvSbk0-CY5V"
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},
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"outputs": [],
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"source": [
|
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"# Define Class Methods\n",
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"class_methods = [\n",
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" {\n",
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" \"parameters\": {},\n",
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" \"api_mode\": \"async_stream\",\n",
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" \"description\": None,\n",
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" \"name\": \"async_stream_query\",\n",
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" },\n",
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" {\n",
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" \"parameters\": {},\n",
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" \"api_mode\": \"async\",\n",
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" \"description\": None,\n",
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" \"name\": \"async_create_session\",\n",
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" },\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": "GeXDv0UIFrC4"
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},
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"source": [
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"### Step 5: Deploy your agent to Agent Engine"
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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": "yFcWb-zEicMr"
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},
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"source": [
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"Deploying your agent to Agent Engine unlocks many Google Cloud services, including the Vertex AI Memory Bank and Session services and a suite of observability, logging, and tracing. All of the conversations with the agent are stored in Vertex AI and can be used to give the agent context for future conversations through long term memories. Visit the Vertex AI documentation to learn more about the [Vertex AI Session service](https://docs.cloud.google.com/agent-builder/agent-engine/sessions/overview) and the [Vertex AI Memory Bank service](https://docs.cloud.google.com/agent-builder/agent-engine/memory-bank/overview)."
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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": {
|
|
"id": "Yed9Q_OY3RI8"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"client = vertexai.Client(api_key=express_mode_api_key)\n",
|
|
"\n",
|
|
"remote_agent = client.agent_engines.create(\n",
|
|
" config={\n",
|
|
" \"display_name\": \"test agent\",\n",
|
|
" \"description\": \"test agent\",\n",
|
|
" \"source_packages\": [\"test\"],\n",
|
|
" \"entrypoint_module\": \"test.my_agent\",\n",
|
|
" \"entrypoint_object\": \"adk_app\",\n",
|
|
" \"requirements_file\": \"test/requirements.txt\",\n",
|
|
" \"class_methods\": class_methods,\n",
|
|
" \"agent_framework\": \"google-adk\",\n",
|
|
" },\n",
|
|
")\n",
|
|
"remote_agent"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "XsJsdx2XFjRN"
|
|
},
|
|
"source": [
|
|
"### Step 6: Talk to your deployed Agent Engine"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "ZlFJb6KP3g3G"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"async for item in remote_agent.async_stream_query(\n",
|
|
" message=\"What is the exchange rate between USD and Korean Won on 2025-09-25?\",\n",
|
|
" user_id=\"user1\",\n",
|
|
"):\n",
|
|
" print(item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "MydwzEUoEXDY"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"async for item in remote_agent.async_stream_query(\n",
|
|
" message=\"What can you help me with?\",\n",
|
|
" user_id=\"user1\",\n",
|
|
"):\n",
|
|
" print(item)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "vFXN8L1PnmT2"
|
|
},
|
|
"source": [
|
|
"# Manage your Agent Engine deployment"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "McPSMcn4mMv1"
|
|
},
|
|
"source": [
|
|
"### Step 7: Talk with your agent through the Agent Engine UI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "19m_DYgalznc"
|
|
},
|
|
"source": [
|
|
"You can also converse with your deployed agent though the Agent Engine UI in your express mode console. Visit the [Express mode UI](https://console.cloud.google.com/expressmode) and navigate to the Agent Engine menu.\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Once you select your agent, use the Playground to converse with it!\n",
|
|
"\n",
|
|
""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "PDn4ZY03jf1E"
|
|
},
|
|
"source": [
|
|
"### Step 8: View conversation history with Vertex AI Session Service"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "SpqgjdNtitaG"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"session = list(client.agent_engines.sessions.list(name=remote_agent.api_resource.name))[\n",
|
|
" 0\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "WK1xKnfsj9Wj"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"list(client.agent_engines.sessions.events.list(name=session.name))"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "express-mode-agent-engine-deployment.ipynb",
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|