wehub-resource-sync
595 lines
20 KiB
Plaintext
595 lines
20 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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"# Vertex AI RAG Engine with Weaviate\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/rag-engine/rag_engine_weaviate.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%2Frag-engine%2Frag_engine_weaviate.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/rag-engine/rag_engine_weaviate.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/rag-engine/rag_engine_weaviate.ipynb\">\n",
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" <img width=\"32px\" src=\"https://raw.githubusercontent.com/primer/octicons/refs/heads/main/icons/mark-github-24.svg\" alt=\"GitHub logo\"><br> View on GitHub\n",
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" </a>\n",
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" </td>\n",
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"</table>\n",
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"\n",
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"<div style=\"clear: both;\"></div>\n",
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"\n",
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"<b>Share to:</b>\n",
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"\n",
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"<a href=\"https://www.linkedin.com/sharing/share-offsite/?url=https%3A//github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/rag-engine/rag_engine_weaviate.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/rag-engine/rag_engine_weaviate.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/rag-engine/rag_engine_weaviate.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/rag-engine/rag_engine_weaviate.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/rag-engine/rag_engine_weaviate.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) | [Ming Zhang](https://github.com/mzhang-ai) |"
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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 illustrates how to use [Vertex AI RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-overview) with [Weaviate](https://weaviate.io/) as a vector database.\n",
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"\n",
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"For more information, refer to the [official documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/use-weaviate-db).\n",
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"\n",
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"For more details on RAG corpus/file management and detailed support please visit [Vertex AI RAG Engine API](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/rag-api)"
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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"
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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's 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's 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": 3,
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"metadata": {
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"id": "Nqwi-5ufWp_B"
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},
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"outputs": [],
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"source": [
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"# Use the environment variable if the user doesn't provide Project ID.\n",
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"import os\n",
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"\n",
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"import vertexai\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\", isTemplate: true}\n",
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"# fmt: on\n",
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"if PROJECT_ID == \"[your-project-id]\":\n",
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" PROJECT_ID = str(os.environ.get(\"GOOGLE_CLOUD_PROJECT\"))\n",
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"\n",
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"LOCATION = os.environ.get(\"GOOGLE_CLOUD_REGION\", \"us-central1\")\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 RAG corpus using Weaviate as the Vector Database"
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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": 4,
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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 vertexai.preview import rag\n",
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"from vertexai.preview.generative_models import GenerativeModel, 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": "e43229f3ad4f"
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},
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"source": [
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"### Load embedding model and create RAG Config"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"id": "cf93d5f0ce00"
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},
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"outputs": [],
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"source": [
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"# Configure a Google first-party embedding model\n",
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"embedding_model_config = rag.EmbeddingModelConfig(\n",
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" publisher_model=\"publishers/google/models/text-embedding-005\"\n",
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")\n",
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"\n",
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"# Name your corpus\n",
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"DISPLAY_NAME = \"\" # @param {type:\"string\", \"placeholder\": \"your-corpus-name\"}\n",
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"\n",
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"# Configure a Weaviate Vector Database Instance for the corpus\n",
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"# More details for how to deploy a Weaviate Database Instance\n",
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"# https://cloud.google.com/vertex-ai/generative-ai/docs/use-weaviate-db\n",
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"# fmt: off\n",
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"WEAVIATE_HTTP_ENDPOINT = \"\" # @param {type:\"string\", \"placeholder\": \"your-weaviate-http-endpoint\"}\n",
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"COLLECTION_NAME = \"\" # @param {type:\"string\", \"placeholder\": \"your-weaviate-collection-name\"}\n",
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"API_KEY = \"\" # @param {type:\"string\", \"placeholder\": \"your-secret-manager-resource-name\"}\n",
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"# fmt: on\n",
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"vector_db = rag.Weaviate(\n",
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" weaviate_http_endpoint=WEAVIATE_HTTP_ENDPOINT,\n",
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" collection_name=COLLECTION_NAME,\n",
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" api_key=API_KEY,\n",
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")\n",
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"\n",
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"rag_corpus = rag.create_corpus(\n",
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" display_name=DISPLAY_NAME,\n",
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" embedding_model_config=embedding_model_config,\n",
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" vector_db=vector_db,\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": "2834a2721633"
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},
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"outputs": [],
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"source": [
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"# Check the corpus just created\n",
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"new_corpus = rag.get_corpus(name=rag_corpus.name)\n",
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"new_corpus"
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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": "f5f3a12a95ca"
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},
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"source": [
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"## Upload a file to the corpus"
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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": "e90f8ddfb7ee"
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},
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"outputs": [],
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"source": [
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"%%writefile test.txt\n",
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"\n",
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"Here's a demo for Weaviate RAG."
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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": "94d84155accc"
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},
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"outputs": [],
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"source": [
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"rag_file = rag.upload_file(\n",
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" corpus_name=rag_corpus.name,\n",
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" path=\"test.txt\",\n",
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" display_name=\"test.txt\",\n",
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" description=\"my test\",\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": "2627b2ac8caf"
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},
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"source": [
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"## Import files from Google Cloud Storage\n",
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"\n",
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"Remember to grant \"Viewer\" access to the \"Vertex RAG Data Service Agent\" (with the format of `service-{project_number}@gcp-sa-vertex-rag.iam.gserviceaccount.com`) for your Google Cloud Storage bucket"
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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": "1c7d989d75bb"
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},
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"outputs": [],
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"source": [
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"# fmt: off\n",
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"GCS_BUCKET = \"\" # @param {type:\"string\", \"placeholder\": \"your-gs-bucket\"}\n",
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"# fmt: on\n",
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"\n",
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"response = rag.import_files(\n",
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" corpus_name=rag_corpus.name,\n",
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" paths=[GCS_BUCKET],\n",
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" chunk_size=512,\n",
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" chunk_overlap=50,\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": "66c8b9325082"
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},
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"outputs": [],
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"source": [
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"# Check the files just imported. It may take a few seconds to process the imported files.\n",
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"rag.list_files(corpus_name=rag_corpus.name)"
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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": "43d6712c4bd2"
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},
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"source": [
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"## Import files from Google Drive\n",
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"\n",
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"Eligible paths can be:\n",
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"\n",
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"- `https://drive.google.com/drive/folders/{folder_id}`\n",
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"- `https://drive.google.com/file/d/{file_id}`\n",
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"\n",
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"Remember to grant \"Viewer\" access to the \"Vertex RAG Data Service Agent\" (with the format of `service-{project_number}@gcp-sa-vertex-rag.iam.gserviceaccount.com`) for your Drive folder/files.\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": "d3df465a3eb2"
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},
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"outputs": [],
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"source": [
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"# fmt: off\n",
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"FILE_ID = \"\" # @param {type:\"string\", \"placeholder\": \"your-file-id\"}\n",
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"# fmt: on\n",
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"FILE_PATH = f\"https://drive.google.com/file/d/{FILE_ID}\"\n",
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"\n",
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"rag.import_files(\n",
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" corpus_name=rag_corpus.name,\n",
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" paths=[FILE_PATH],\n",
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" chunk_size=1024,\n",
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" chunk_overlap=100,\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": "bcfca523ca38"
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},
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"outputs": [],
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"source": [
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"# Check the files just imported. It may take a few seconds to process the imported files.\n",
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"rag.list_files(corpus_name=rag_corpus.name)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "e1c1c7944dc8"
|
|
},
|
|
"source": [
|
|
"## Using Gemini GenerateContent API with Rag Retrieval Tool\n",
|
|
"\n",
|
|
"When retrieved contexts similarity distance < `vector_distance_threshold`, the contexts (from `RagStore`) will be used for content generation."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "d3afdf13dbbb"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"rag_resource = rag.RagResource(\n",
|
|
" rag_corpus=rag_corpus.name,\n",
|
|
")\n",
|
|
"\n",
|
|
"rag_retrieval_tool = Tool.from_retrieval(\n",
|
|
" retrieval=rag.Retrieval(\n",
|
|
" source=rag.VertexRagStore(\n",
|
|
" rag_resources=[rag_resource], # Currently only 1 corpus is allowed.\n",
|
|
" similarity_top_k=10,\n",
|
|
" vector_distance_threshold=0.4,\n",
|
|
" ),\n",
|
|
" )\n",
|
|
")\n",
|
|
"\n",
|
|
"rag_model = GenerativeModel(\"gemini-2.0-flash\", tools=[rag_retrieval_tool])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "484f5242dcae"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fmt: off\n",
|
|
"GENERATE_CONTENT_PROMPT = \"What is RAG and why it is helpful?\" # @param {type:\"string\"}\n",
|
|
"# fmt: on\n",
|
|
"\n",
|
|
"response = rag_model.generate_content(GENERATE_CONTENT_PROMPT)\n",
|
|
"\n",
|
|
"response"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "287a90fed14f"
|
|
},
|
|
"source": [
|
|
"## Using other generation API with Rag Retrieval Tool\n",
|
|
"\n",
|
|
"The retrieved contexts can be passed to any SDK or model generation API to generate final results."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "428921dea97d"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fmt: off\n",
|
|
"RETRIEVAL_QUERY = \"What is RAG and why it is helpful?\" # @param {type:\"string\"}\n",
|
|
"# fmt: on\n",
|
|
"\n",
|
|
"rag_resource = rag.RagResource(\n",
|
|
" rag_corpus=rag_corpus.name,\n",
|
|
" # Need to manually get the ids from rag.list_files.\n",
|
|
" # rag_file_ids=[],\n",
|
|
")\n",
|
|
"\n",
|
|
"response = rag.retrieval_query(\n",
|
|
" rag_resources=[rag_resource], # Currently only 1 corpus is allowed.\n",
|
|
" text=RETRIEVAL_QUERY,\n",
|
|
" similarity_top_k=10,\n",
|
|
")\n",
|
|
"\n",
|
|
"# The retrieved context can be passed to any SDK or model generation API to generate final results.\n",
|
|
"retrieved_context = \" \".join(\n",
|
|
" [context.text for context in response.contexts.contexts]\n",
|
|
").replace(\"\\n\", \"\")\n",
|
|
"\n",
|
|
"retrieved_context"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "2a4e033321ad"
|
|
},
|
|
"source": [
|
|
"## Cleaning up\n",
|
|
"\n",
|
|
"Clean up resources created in this notebook."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"id": "a105caffd9e7"
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# fmt: off\n",
|
|
"delete_rag_corpus = False # @param {type:\"boolean\"}\n",
|
|
"# fmt: on\n",
|
|
"\n",
|
|
"if delete_rag_corpus:\n",
|
|
" rag.delete_corpus(name=rag_corpus.name)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"colab": {
|
|
"name": "rag_engine_weaviate.ipynb",
|
|
"toc_visible": true
|
|
},
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"name": "python3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|