wehub-resource-sync
684 lines
26 KiB
Plaintext
684 lines
26 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": "ur8xi4C7S06n"
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},
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"outputs": [],
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"source": [
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"# Copyright 2026 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": "JAPoU8Sm5E6e"
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},
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"source": [
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"# Persisting LangChain History with Vertex AI Session Service\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/langchain_vertex_ai_session_service.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%2Fagent-engine%2Flangchain_vertex_ai_session_service.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/agent-engine/langchain_vertex_ai_session_service.ipynb\">\n",
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" <img src=\"https://www.gstatic.com/images/branding/gcpiconscolors/vertexai/v1/32px.svg\" alt=\"Vertex AI logo\"><br> Open in Vertex AI 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/agent-engine/langchain_vertex_ai_session_service.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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"<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/langchain_vertex_ai_session_service.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/langchain_vertex_ai_session_service.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/langchain_vertex_ai_session_service.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/langchain_vertex_ai_session_service.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/langchain_vertex_ai_session_service.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": "tvgnzT1CKxrO"
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},
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"source": [
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"## Overview\n",
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"\n",
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"This notebook will demonstrate how to use the Vertex AI Session Service to persist the conversational history with LangChain agents.\n",
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"\n",
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"You will lean how to:\n",
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"- Create a Session with the Vertex AI Session Service\n",
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"- Store conversation turns within the sessions you created\n",
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"- Store tool calls and tool results within the sessions you created\n",
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"- Access the stored conversational history"
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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": "61RBz8LLbxCR"
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},
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"source": [
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"## Get started"
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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 Vertex AI SDK and LangChain for Google\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": 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 \"google-cloud-aiplatform[agent_engines]\" --force-reinstall --quiet\n",
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"# Install the Google Generative AI integration for LangChain\n",
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"%pip install -U \"langchain-google-genai\" --quiet"
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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-ES3MMrMlxV"
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},
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"outputs": [],
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"source": [
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"# Restart the session to ensure packages are updated\n",
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"import os\n",
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"\n",
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"os._exit(0)"
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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\n",
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"\n",
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"If you are running this notebook in **Google Colab**, run the cell below to authenticate your account."
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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": "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\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": null,
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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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"LOCATION = \"us-central1\" # @param {type: \"string\", placeholder: \"[your-project-id]\", isTemplate: true}\n",
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"# fmt: on\n",
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"\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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"if not LOCATION:\n",
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" LOCATION = os.environ.get(\"GOOGLE_CLOUD_REGION\")\n",
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"os.environ[\"GOOGLE_GENAI_USE_VERTEXAI\"] = \"1\""
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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": "5303c05f7aa6"
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},
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"source": [
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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": null,
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"metadata": {
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"id": "6fc324893334"
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},
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"outputs": [],
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"source": [
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"import datetime\n",
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"\n",
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"import requests\n",
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"from langchain_core.chat_history import BaseChatMessageHistory\n",
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"from langchain_core.messages import (\n",
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" BaseMessage,\n",
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" HumanMessage,\n",
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" message_to_dict,\n",
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" messages_from_dict,\n",
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")\n",
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
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"from langchain_core.tools import tool\n",
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"from langchain_google_genai import ChatGoogleGenerativeAI\n",
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"from vertexai import Client"
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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": "EdvJRUWRNGHE"
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},
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"source": [
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"## Create Vertex AI Chat Message History\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": "ydzH42O6O-U5"
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},
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"source": [
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"LangChain uses `BaseChatMessageHistory` to persist the chat history of a LangChain agent. It has 3 basic methods:\n",
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"- messages: a property of the history, listing previous message history\n",
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"- add_messages: add one or multiple messages to the conversation history\n",
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"- clear: delete all messages in the conversation history\n",
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"\n",
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"We will create the VertexAISessionChatMessageHistory that extends BaseChatMessageHistory, which will use the Vertex AI Session service to implement the messages and add_messages methods."
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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": "m814rnX7O7D5"
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},
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"outputs": [],
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"source": [
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"class VertexAISessionChatMessageHistory(BaseChatMessageHistory):\n",
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" \"\"\"A LangChain message history backend that defers storage to\n",
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" Vertex AI Agent Engine's Session Service via the vertexai Client.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, client: Client, session_name: str, author: str = \"user\"):\n",
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" self.client = client\n",
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" self.session_name = session_name\n",
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" self.author = author\n",
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"\n",
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" @property\n",
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" def messages(self) -> list[BaseMessage]:\n",
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" # Call ListEvents from the Vertex AI Session service to return the list of messages.\n",
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" events = self.client.agent_engines.sessions.events.list(name=self.session_name)\n",
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"\n",
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" lc_messages = []\n",
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" for event in events:\n",
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" # Convert directly from the LangChain message stored in raw_event\n",
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" if event.raw_event and \"langchain_message\" in event.raw_event:\n",
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" # `messages_from_dict` expects a list of serialized messages\n",
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" extracted_message = messages_from_dict(\n",
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" [event.raw_event[\"langchain_message\"]]\n",
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" )[0]\n",
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" lc_messages.append(extracted_message)\n",
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" continue\n",
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" return lc_messages\n",
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"\n",
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" def add_message(self, message: BaseMessage) -> None:\n",
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" is_human = isinstance(message, HumanMessage)\n",
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" author = self.author if is_human else \"agent\"\n",
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"\n",
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" # Dump the entire LangChain message structure (id, tool_calls, kwargs, response_metadata)\n",
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" # into a JSON serializable dict\n",
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" serialized_lc_message = message_to_dict(message)\n",
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"\n",
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" self.client.agent_engines.sessions.events.append(\n",
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" name=self.session_name,\n",
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" author=author,\n",
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" invocation_id=\"langchain-invocation\",\n",
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" timestamp=datetime.datetime.now(datetime.timezone.utc),\n",
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" config={\n",
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" # Store the full LangChain payload in the raw_event field\n",
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" \"raw_event\": {\"langchain_message\": serialized_lc_message}\n",
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" },\n",
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" )\n",
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"\n",
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" def clear(self) -> None:\n",
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" \"\"\"Clears the session. Vertex AI Session Service currently doesn't support\n",
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" easily clearing individual events; typical workarounds include\n",
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" deleting the session natively via `self.client.agent_engines.sessions.delete(...)`.\n",
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" \"\"\"\n",
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" raise NotImplementedError(\n",
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" \"Clear is not natively supported without deleting the entire session via SDK.\"\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": "FRl1IPZPSXZi"
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},
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"source": [
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"## Initialize Vertex AI\n",
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"\n",
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"We will use the Vertex AI SDK to create a session instance.\n",
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"\n",
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"Vertex AI Sessions are sub resources of Agent Engines, which is the managed solution for deploying agents to Google Cloud. For more details, see [link]. This means all sessions must be associated with an Agent Engine.\n",
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"\n",
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"For this demo, we don't need to deploy the agent. We will just use agent_engines.create to create an empty Agent Engine to store our sessions."
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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": "GhiU7PzERpOE"
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},
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"outputs": [],
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"source": [
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"import vertexai\n",
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"\n",
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"# Initialize the Vertex AI SDK Client with your project and location\n",
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"client = vertexai.Client(project=PROJECT_ID, location=LOCATION)\n",
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"\n",
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"# Create an agent engine to hold the session\n",
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"agent_engine = client.agent_engines.create(config={\"display_name\": \"langchain_test\"})\n",
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"agent_engine_path = agent_engine.api_resource.name"
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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": "mvZ4-TjoRrAh"
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},
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"outputs": [],
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"source": [
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"# Create a Vertex AI session resource\n",
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"session_operation = client.agent_engines.sessions.create(\n",
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" name=agent_engine_path, user_id=\"langchain_user\"\n",
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")\n",
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"\n",
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"# Extract the full session resource name from the finished operation response\n",
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"session_resource_name = session_operation.response.name"
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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": "jfT0Zt0pRsce"
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},
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"outputs": [],
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"source": [
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"# Define a factory function that LangChain will call per session ID\n",
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"def get_vertex_session_history(session_id: str) -> BaseChatMessageHistory:\n",
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" return VertexAISessionChatMessageHistory(client=client, session_name=session_id)"
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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": "JIhMzITHTNia"
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},
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"source": [
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"## Start a conversation\n",
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"Since you already signed into your Google Cloud project, we will use Gemini as the model for our agent.\n",
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"\n",
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"Here, we will just create a simple conversational agent. When we send messages to the LangChain agent, the messages will be stored in our Session using the `add_messages` method we defined in `VertexAISessionChatMessageHistory`"
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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": "ydyerW-3RuXt"
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},
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"outputs": [],
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"source": [
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"# Create a standard LangChain Model and Prompt\n",
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"model = ChatGoogleGenerativeAI(\n",
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" model=\"gemini-2.5-flash\", project=PROJECT_ID, location=LOCATION\n",
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")\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\"system\", \"You are a helpful assistant.\"),\n",
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" MessagesPlaceholder(variable_name=\"history\"),\n",
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" (\"human\", \"{input}\"),\n",
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" ]\n",
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")\n",
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"\n",
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"chain = prompt | model\n",
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"\n",
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"# Wrap the chain with RunnableWithMessageHistory\n",
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"chain_with_history = RunnableWithMessageHistory(\n",
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" chain,\n",
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" get_vertex_session_history,\n",
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" input_messages_key=\"input\",\n",
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" history_messages_key=\"history\",\n",
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")"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "JWVTDPUXV9Pq"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Invoke the chain using the session resource name we generated\n",
|
|
"input = \"Hi! My name is John Doe.\"\n",
|
|
"print(input)\n",
|
|
"response = chain_with_history.invoke(\n",
|
|
" {\"input\": input}, config={\"configurable\": {\"session_id\": session_resource_name}}\n",
|
|
")\n",
|
|
"print(\"Gemini: \", response.content)\n",
|
|
"\n",
|
|
"input = \"What is my name?\"\n",
|
|
"print(input)\n",
|
|
"response = chain_with_history.invoke(\n",
|
|
" {\"input\": input}, config={\"configurable\": {\"session_id\": session_resource_name}}\n",
|
|
")\n",
|
|
"print(\"Gemini: \", response.content)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "3XQbxqL-T4-4"
|
|
},
|
|
"source": [
|
|
"We can view the entire chat history using the `messages` property we defined in `VertexAISessionChatMessageHistory`"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "iLK18ollRv-r"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# View the conversation history\n",
|
|
"history = get_vertex_session_history(session_resource_name)\n",
|
|
"for message in history.messages:\n",
|
|
" print(f\"{message.type.capitalize()}: {message.content}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "tZKASjWbSTaJ"
|
|
},
|
|
"source": [
|
|
"## Tool Call\n",
|
|
"\n",
|
|
"Now that we have used a simple agent, we will show how the Vertex AI Session service can store more advanced conversations from agents with Tools.\n",
|
|
"\n",
|
|
"In this example, we will use a tool called `get_weather` to allow the agent to check the weather in any city we ask."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "gBB4xZoORx14"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Create a new Vertex AI session resource\n",
|
|
"session_operation = client.agent_engines.sessions.create(\n",
|
|
" name=agent_engine_path, user_id=\"langchain_user\"\n",
|
|
")\n",
|
|
"\n",
|
|
"# Extract the full session resource name from the finished operation response\n",
|
|
"session_resource_name = session_operation.response.name"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "xjeed8KhVcuz"
|
|
},
|
|
"source": [
|
|
"## Define the get_weather tool\n",
|
|
"We will use a basic weather API for our tool. This allows the agent to check the weather for a given city."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "EdSStEwTU1Tq"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Define the tool\n",
|
|
"@tool\n",
|
|
"def get_weather(location: str) -> str:\n",
|
|
" \"\"\"Returns the current weather for a given city name.\"\"\"\n",
|
|
" # format=3 gives a simple string like: \"London: ⛅️ +15°C\"\n",
|
|
" try:\n",
|
|
" response = requests.get(f\"https://wttr.in/{location}?format=3\")\n",
|
|
" return response.text.strip()\n",
|
|
" except Exception:\n",
|
|
" return \"Sorry, I couldn't fetch the weather right now.\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Ro3tWwbARzLC"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Bind the tool to the model\n",
|
|
"# ChatVertexAI natively supports LangChain tool integration\n",
|
|
"model_with_tools = ChatGoogleGenerativeAI(\n",
|
|
" model=\"gemini-2.5-flash\", project=PROJECT_ID, location=LOCATION\n",
|
|
").bind_tools([get_weather])\n",
|
|
"\n",
|
|
"# Create a Prompt Template with STRICT system instructions\n",
|
|
"prompt = ChatPromptTemplate.from_messages(\n",
|
|
" [\n",
|
|
" (\n",
|
|
" \"system\",\n",
|
|
" \"You are a specialized weather assistant. You MUST use the `get_weather` tool to answer any questions about the weather. Do not use your internal knowledge.\",\n",
|
|
" ),\n",
|
|
" MessagesPlaceholder(variable_name=\"history\"),\n",
|
|
" (\"human\", \"{input}\"),\n",
|
|
" ]\n",
|
|
")\n",
|
|
"\n",
|
|
"# Combine the prompt and the model into a standard runnable chain\n",
|
|
"chain = prompt | model_with_tools\n",
|
|
"\n",
|
|
"# Wrap the chain with RunnableWithMessageHistory\n",
|
|
"chain_with_history = RunnableWithMessageHistory(\n",
|
|
" chain,\n",
|
|
" get_vertex_session_history,\n",
|
|
" input_messages_key=\"input\",\n",
|
|
" history_messages_key=\"history\",\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "7VkQ6H_VXe5I"
|
|
},
|
|
"source": [
|
|
"Now that we registered the tool with the model, lets try a basic conversation.\n",
|
|
"\n",
|
|
"In LangChain, the application logic is responsible for calling the tool. Here, if the model tells us to call get_weather, we call the function then provide the response to the model."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "fkMq12VgWGuJ"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# --- Turn 1: AI Uses the Tool ---\n",
|
|
"input = \"What is the weather in Paris?\"\n",
|
|
"print(input)\n",
|
|
"response = chain_with_history.invoke(\n",
|
|
" {\"input\": input}, config={\"configurable\": {\"session_id\": session_resource_name}}\n",
|
|
")\n",
|
|
"\n",
|
|
"# If the system instruction worked, the model will output a tool call instead of text\n",
|
|
"print(\"(Gemini Tool Request):\", response.tool_calls)\n",
|
|
"\n",
|
|
"# --- Turn 2: Providing the tool result ---\n",
|
|
"if response.tool_calls:\n",
|
|
" tool_call = response.tool_calls[0]\n",
|
|
" # Execute the python function manually\n",
|
|
" tool_result = get_weather.invoke(tool_call)\n",
|
|
" print(\"(Tool Response):\", tool_result)\n",
|
|
" request_payload = {\n",
|
|
" # Pass the ToolMessage containing the tool_call_id directly into the input\n",
|
|
" \"input\": [tool_result]\n",
|
|
" }\n",
|
|
" final_response = chain_with_history.invoke(\n",
|
|
" request_payload, config={\"configurable\": {\"session_id\": session_resource_name}}\n",
|
|
" )\n",
|
|
" print(\"(Gemini Final Answer):\", final_response.content)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "WH6c62lvXymu"
|
|
},
|
|
"source": [
|
|
"We can view the entire chat history using the `messages` property we defined in `VertexAISessionChatMessageHistory`"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "bRp0xp88R1AS"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"history = get_vertex_session_history(session_resource_name)\n",
|
|
"for message in history.messages:\n",
|
|
" print(f\"{message.type.capitalize()}: {message.content}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "2a4e033321ad"
|
|
},
|
|
"source": [
|
|
"## Cleaning up\n",
|
|
"\n",
|
|
"Now that the demo is done, we can delete the Agent Engine instance to delete the sessions we created."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "3ADJzvTWX9hB"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"client.agent_engines.delete(name=agent_engine_path, force=True)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "langchain_vertex_ai_session_service.ipynb",
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|