wehub-resource-sync
633 lines
25 KiB
Plaintext
633 lines
25 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 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": "JAPoU8Sm5E6e"
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},
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"source": [
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"# Building Search Applications with Vertex AI Search\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/search/vertexai-search-options/vertexai_search_options.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%2Fsearch%2Fvertexai-search-options%2Fvertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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/search/vertexai-search-options/vertexai_search_options.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": "84f0f73a0f76"
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},
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"source": [
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"| | |\n",
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"|-|-|\n",
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"| Author(s) | [Megha Agarwal](https://github.com/agarwal22megha/) |"
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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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"Vertex AI Search leverages decades of expertise Google has in information retrieval and brings together the power of deep information retrieval, state-of-the-art natural language processing, and the latest in large language model (LLM) processing to understand user intent and return the most relevant results for the user.\n",
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"\n",
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"Based on where Developers are in their journey, their orchestration framework of choice, they can select Vertex AI Search out-of-the-box capabilities or customize their search solutions with Vertex Retrievers or use the Vertex AI DIY APIs to build the end to end RAG application.\n",
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"\n",
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"This notebook explores how you can leverage Vertex AI Search out-of-the-box capabilities/ customize your search application with Vertex AI Search Retriever via LangChain or Grounding Service.\n",
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"\n",
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"\n",
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"We create a Vertex AI Agent Builder Search App with unstructured data store on Google Cloud Console.\n",
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"\n",
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"Once the Search App is created, this notebook walks you through how you can leverage:\n",
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"\n",
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"1. Vertex AI Search out-of-the-box capabilities by leveraging the [Vertex AI Agent Builder Python API reference documentation](https://cloud.google.com/python/docs/reference/discoveryengine/latest)\n",
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"\n",
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"2. Leverage Vertex AI Search Datastore as a [Grounding Source](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/grounding) with Gemini Models directly to ground responses in your data\n",
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"\n",
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"3. Leverage Vertex DataStore as [LangChain Retriever](https://python.langchain.com/v0.2/docs/integrations/retrievers/google_vertex_ai_search/)\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": "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 other required packages\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 --upgrade --user --quiet google-cloud-aiplatform\n",
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"%pip install google-cloud-discoveryengine\n",
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"%pip install langchain_google_community\n",
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"%pip install langchain langchain-google-vertexai\n",
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"%pip install langchain-google-community[vertexaisearch]"
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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": "R5Xep4W9lq-Z"
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},
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"source": [
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"### Restart runtime\n",
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"\n",
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"To use the newly installed packages in this Jupyter runtime, you must restart the runtime. You can do this by running the cell below, which restarts the current kernel.\n",
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"\n",
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"The restart might take a minute or longer. After it has restarted, continue to the next step."
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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": "XRvKdaPDTznN"
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},
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"outputs": [],
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"source": [
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"import IPython\n",
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"\n",
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"app = IPython.Application.instance()\n",
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"app.kernel.do_shutdown(True)"
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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": "SbmM4z7FOBpM"
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},
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"source": [
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"<div class=\"alert alert-block alert-warning\">\n",
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"<b>⚠️ The kernel is going to restart. Wait until it is finished before continuing to the next step. ⚠️</b>\n",
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"</div>\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": "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're 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": 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 and initialize Vertex AI SDK\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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"PROJECT_ID = \"your_project_id\" # @param {type:\"string\"}\n",
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"LOCATION = \"us-central1\" # @param {type:\"string\"}\n",
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"\n",
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"\n",
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"import vertexai\n",
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"\n",
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"vertexai.init(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": "EdvJRUWRNGHE"
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},
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"source": [
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"### Create a Vertex AI Search App On Google Cloud\n",
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"[Follow These Steps](https://cloud.google.com/generative-ai-app-builder/docs/try-enterprise-search#unstructured-data_1) to leverage Vertex AI Agent Builder to create a Search App with unstructured data store on Google Cloud Console.\n",
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"\n",
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"Once the search app is created successfully, make a Note of the Search App Id, Datastore ID, location from Vertex AI Agent Builder console."
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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": "CaXr_Fz9OXkQ"
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},
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"outputs": [],
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"source": [
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"# Set to your data store location\n",
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"VERTEX_AI_SEARCH_LOCATION = \"global\" # @param {type:\"string\"}\n",
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"# Set to your search app ID\n",
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"VERTEX_AI_SEARCH_APP_ID = \"your_search_app_id\" # @param {type:\"string\"}\n",
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"# Set to your data store ID\n",
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"VERTEX_AI_SEARCH_DATASTORE_ID = \"your_datastore_id\" # @param {type:\"string\"}\n",
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"\n",
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"MODEL = \"gemini-2.0-flash\" # @param {type:\"string\"}\n",
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"\n",
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"SEARCH_QUERY = \"When does Alphabet plan to get to net zero?\" # @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": "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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"from google.api_core.client_options import ClientOptions\n",
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"from google.cloud import discoveryengine_v1 as discoveryengine\n",
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"from langchain.chains import RetrievalQAWithSourcesChain\n",
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"from langchain_google_community import VertexAISearchRetriever\n",
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"from langchain_google_vertexai import VertexAI\n",
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"from vertexai.generative_models import (\n",
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" GenerationConfig,\n",
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" GenerativeModel,\n",
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" SafetySetting,\n",
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" Tool,\n",
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")\n",
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"import vertexai.preview.generative_models as generative_models"
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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": "QhOI5q1LIzmU"
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},
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"source": [
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"## Part 1: Vertex AI Search Out-of-the-box capabilities\n",
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"Leverage the [Vertex AI Agent Builder Python API reference documentation](https://cloud.google.com/python/docs/reference/discoveryengine/latest) to get the search results"
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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": "Chkv32AgF9Sx"
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},
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"outputs": [],
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"source": [
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"CUSTOM_PROMPT = \"\"\"\n",
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" <PERSONA_AND_GOAL>\n",
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" You are a helpful assistant knowledgeable about Alphabet quarterly earning reports.\n",
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" Be mindful of the time frame user inputs\n",
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" Help user with their queries related with Alphabet\n",
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" Only respond with relevant information available in Grounding Knowledge snippet.\n",
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" If no relevant snippet is available, respond with you dont know\n",
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" Do not make up information\n",
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" </PERSONA_AND_GOAL>\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": "0eOlSO08IK2Z"
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},
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"outputs": [],
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"source": [
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"def search_spec():\n",
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" content_search_spec = discoveryengine.SearchRequest.ContentSearchSpec(\n",
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" snippet_spec=discoveryengine.SearchRequest.ContentSearchSpec.SnippetSpec(\n",
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" return_snippet=True\n",
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" ),\n",
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" summary_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec(\n",
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" summary_result_count=10,\n",
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" include_citations=True,\n",
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" ignore_adversarial_query=True,\n",
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" ignore_non_summary_seeking_query=True,\n",
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" model_prompt_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec.ModelPromptSpec(\n",
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" preamble=CUSTOM_PROMPT\n",
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" ),\n",
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" model_spec=discoveryengine.SearchRequest.ContentSearchSpec.SummarySpec.ModelSpec(\n",
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" version=\"stable\",\n",
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" ),\n",
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" ),\n",
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" )\n",
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" return content_search_spec\n",
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"\n",
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"\n",
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"def search_sample(project_id: str, location: str, engine_id: str, search_query: str):\n",
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" client_options = (\n",
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" ClientOptions(api_endpoint=f\"{location}-discoveryengine.googleapis.com\")\n",
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" if location != \"global\"\n",
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" else None\n",
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" )\n",
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"\n",
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" client = discoveryengine.SearchServiceClient(client_options=client_options)\n",
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"\n",
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" serving_config = f\"projects/{project_id}/locations/{location}/collections/default_collection/engines/{engine_id}/servingConfigs/default_config\"\n",
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"\n",
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" content_search_spec = search_spec()\n",
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"\n",
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" request = discoveryengine.SearchRequest(\n",
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" serving_config=serving_config,\n",
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" query=search_query,\n",
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" page_size=10,\n",
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" content_search_spec=content_search_spec,\n",
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" query_expansion_spec=discoveryengine.SearchRequest.QueryExpansionSpec(\n",
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" condition=discoveryengine.SearchRequest.QueryExpansionSpec.Condition.AUTO,\n",
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" ),\n",
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" spell_correction_spec=discoveryengine.SearchRequest.SpellCorrectionSpec(\n",
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" mode=discoveryengine.SearchRequest.SpellCorrectionSpec.Mode.AUTO\n",
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" ),\n",
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" )\n",
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" response = client.search(request)\n",
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"\n",
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" return response"
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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": "IV52oaTJIK-i"
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},
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"outputs": [],
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"source": [
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"search_response = search_sample(\n",
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" PROJECT_ID, VERTEX_AI_SEARCH_LOCATION, VERTEX_AI_SEARCH_APP_ID, SEARCH_QUERY\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": "JsbqEIgyILBJ"
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},
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"outputs": [],
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"source": [
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"search_response.summary.summary_with_metadata"
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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": "S_OnZDEfJCzi"
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},
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"source": [
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"## Part 2: Grounding with Gemini on Vertex AI Search Datastore\n",
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"\n",
|
|
"Leverage Vertex AI Search Datastore as a [Grounding Source](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/grounding) with Gemini Models directly to ground responses in your data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "uGV_iw5YJMNK"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"system_instructions = \"\"\"\n",
|
|
"<PERSONA_AND_GOAL>\n",
|
|
" You are a helpful assistant knowledgeable about Alphabet quarterly earning reports.\n",
|
|
" Help user with their queries related with Alphabet by following given <INSTRUCTIONS> and <CONTEXT>\n",
|
|
" only respond with information available in Grounding Knowledge store.\n",
|
|
"</PERSONA_AND_GOAL>\n",
|
|
"\n",
|
|
"<INSTRUCTIONS>\n",
|
|
"- Always refer to the tool and Ground your answers in it\n",
|
|
"- Understand the retrieved snippet by the tool and only use that information to help users\n",
|
|
"- For supporting references, you can provide the Grounding tool snippets verbatim, and any other info like page number\n",
|
|
"- For Information not available in the tool, mention you dont have access to the information.\n",
|
|
"- Output \"answer\" should be I dont know when the user question is irrelevant or outside the <CONTEXT>\n",
|
|
"- Leave \"reference_snippet\" as null if you are not sure about the page and text snippet\n",
|
|
"<INSTRUCTIONS>\n",
|
|
"\n",
|
|
"<CONTEXT>\n",
|
|
" Grounding tool finds most relevant snippets from the Alphabet earning reports data store.\n",
|
|
" Use the information provided by the tool as your knowledge base.\n",
|
|
"</CONTEXT>\n",
|
|
"\n",
|
|
"<CONSTRAINTS>\n",
|
|
"- ONLY use information available from the Grounding tool\n",
|
|
"</CONSTRAINTS>\n",
|
|
"\n",
|
|
"<OUTPUT_FORMAT>\n",
|
|
"- Response should ALWAYS be in following JSON Output with answer and reference_snippet as keys, e.g. {\"answer\": , \"reference_snippet\": }\n",
|
|
"</OUTPUT_FORMAT>\n",
|
|
"\"\"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "wSo25gr9MBSd"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"tools = [\n",
|
|
" Tool.from_retrieval(\n",
|
|
" retrieval=generative_models.grounding.Retrieval(\n",
|
|
" source=generative_models.grounding.VertexAISearch(\n",
|
|
" datastore=VERTEX_AI_SEARCH_DATASTORE_ID,\n",
|
|
" project=PROJECT_ID,\n",
|
|
" location=VERTEX_AI_SEARCH_LOCATION,\n",
|
|
" ),\n",
|
|
" disable_attribution=False,\n",
|
|
" )\n",
|
|
" ),\n",
|
|
"]\n",
|
|
"\n",
|
|
"generation_config = GenerationConfig(max_output_tokens=8192, temperature=1, top_p=0.95)\n",
|
|
"\n",
|
|
"safety_settings = [\n",
|
|
" SafetySetting(\n",
|
|
" category=SafetySetting.HarmCategory.HARM_CATEGORY_HATE_SPEECH,\n",
|
|
" threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
|
|
" ),\n",
|
|
" SafetySetting(\n",
|
|
" category=SafetySetting.HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT,\n",
|
|
" threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
|
|
" ),\n",
|
|
" SafetySetting(\n",
|
|
" category=SafetySetting.HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT,\n",
|
|
" threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
|
|
" ),\n",
|
|
" SafetySetting(\n",
|
|
" category=SafetySetting.HarmCategory.HARM_CATEGORY_HARASSMENT,\n",
|
|
" threshold=SafetySetting.HarmBlockThreshold.BLOCK_ONLY_HIGH,\n",
|
|
" ),\n",
|
|
"]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "jCJAOvHdLrGI"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"model = GenerativeModel(MODEL, tools=tools, system_instruction=[system_instructions])\n",
|
|
"\n",
|
|
"response = model.generate_content(\n",
|
|
" [SEARCH_QUERY],\n",
|
|
" generation_config=generation_config,\n",
|
|
" safety_settings=safety_settings,\n",
|
|
" stream=True,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "BNrZ-w3gRoLx"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"for candidate in response:\n",
|
|
" print(candidate)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "lQv61qA5Q71g"
|
|
},
|
|
"source": [
|
|
"##### [Here](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/grounding/intro-grounding-gemini.ipynb) is another Grounding Gemini with Vertex AI Search Example which you might find useful"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "V2X81IQUOImp"
|
|
},
|
|
"source": [
|
|
"## Part 3: Vertex AI Search with LangChain\n",
|
|
"\n",
|
|
"Developers have the flexibility to incorporate Vertex AI Search as a [LangChain Retriever](https://python.langchain.com/v0.2/docs/integrations/retrievers/google_vertex_ai_search/) in their existing LangChain applications.\n",
|
|
"\n",
|
|
"This means you can continue leveraging your preferred orchestrator while seamlessly integrating Vertex AI Search data stores into existing RAG pipelines. Vertex AI Search enables Google-quality search capabilities applied directly to your custom data, elevating the search result quality and relevance of your retrieval-augmented generation workflows.\n",
|
|
"\n",
|
|
"Find more notebook examples of leveraging VertexAISearchRetriever with LangChain [here](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/search/retrieval-augmented-generation/examples/question_answering.ipynb).\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Kt09YTDmJMYW"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"llm = VertexAI(model_name=MODEL)\n",
|
|
"\n",
|
|
"retriever = VertexAISearchRetriever(\n",
|
|
" project_id=PROJECT_ID,\n",
|
|
" location_id=VERTEX_AI_SEARCH_LOCATION,\n",
|
|
" data_store_id=VERTEX_AI_SEARCH_DATASTORE_ID,\n",
|
|
" get_extractive_answers=True,\n",
|
|
" max_documents=10,\n",
|
|
" max_extractive_segment_count=1,\n",
|
|
" max_extractive_answer_count=5,\n",
|
|
")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "Dkt_l9tmPeYH"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"retrieval_qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(\n",
|
|
" llm=llm, chain_type=\"stuff\", retriever=retriever\n",
|
|
")\n",
|
|
"\n",
|
|
"retrieval_qa_with_sources.invoke(SEARCH_QUERY, return_only_outputs=True)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "2a4e033321ad"
|
|
},
|
|
"source": [
|
|
"## Cleaning up\n",
|
|
"\n",
|
|
"[Delete the Vertex AI Search App](https://cloud.google.com/generative-ai-app-builder/docs/delete-engine) and\n",
|
|
"\n",
|
|
"[Delete the datastore](https://cloud.google.com/generative-ai-app-builder/docs/delete-a-data-store) your created"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "vertexai_search_options.ipynb",
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|