colab-enterprise/rideshare_llm_ai_lakehouse

{ "cells": [ { "cell_type": "markdown", "id": "gZ1vYZyi9od5", "metadata": { "id": "gZ1vYZyi9od5" }, "source": [ "# <img src=\"https://lh3.googleusercontent.com/mUTbNK32c_DTSNrhqETT5aQJYFKok2HB1G2nk2MZHvG5bSs0v_lmDm_ArW7rgd6SDGHXo0Ak2uFFU96X6Xd0GQ=w160-h128\" width=\"45\" valign=\"top\" alt=\"BigQuery\"> Welcome to Rideshare AI Lakehouse powered by BigQuery Studio" ] }, { "cell_type": "markdown", "id": "tsSPC9jg9t2t", "metadata": { "id": "tsSPC9jg9t2t" }, "source": [ "## **Step 01: Overview**" ] }, { "cell_type": "markdown", "id": "DFMjT7JU9yLR", "metadata": { "id": "DFMjT7JU9yLR" }, "source": [ "This portion of the demo will showcase how you can build an AI Lakehouse using Google Cloud's Data and Analytics stack. In this notebook we will match prefered drivers to customers based on their preferences.\n", "\n", "All code is located on GitHub: https://goo.gle/dagd\n", "\n", "Please check out the other part of the demo which uses AI, image object analysis and data at scale." ] }, { "cell_type": "markdown", "id": "DnqIMCA6-D8u", "metadata": { "id": "DnqIMCA6-D8u" }, "source": [ "This the steps we will perform:\n", "- Generate a semantic embedding using the `gecko` model for the column `llm_customer_quantitative_analysis`on the table `customer`, this column contains a LLM based summarization of the customer preferences. This is done using the BQML function `ML.GENERATE_TEXT_EMBEDDING`\n", "- Generate a semantic embedding using the `gecko` model for the column `llm_driver_quantitative_analysis`on the table `driver`, this column contains a LLM based summarization of the driver preferences. This is done using the BQML function `ML.GENERATE_TEXT_EMBEDDING`\n", "- For each customer, find the most relevant drivers, three in thos case, this is done searching for the \"closests\" embeddings between the two tables. This is done using the BQML function `ML.DISTANCE`\n" ] }, { "cell_type": "markdown", "id": "Z_kyGm-zDJcD", "metadata": { "id": "Z_kyGm-zDJcD" }, "source": [ "## **Step 02: Generate semantic embeddings**" ] }, { "cell_type": "markdown", "id": "ggUd3saPL6dn", "metadata": { "id": "ggUd3saPL6dn" }, "source": [ "Next, we will use the Vertex AI `text-embedding-005` foundation model with the ML.GENERATE_TEXT_EMBEDDING function to embed the customer and driver preferences" ] }, { "cell_type": "markdown", "id": "2XPcicsoHO9T", "metadata": { "id": "2XPcicsoHO9T" }, "source": [ "Create embeddings for customer preferences" ] }, { "cell_type": "code", "execution_count": 10, "id": "LC1z-7anXh9c", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "a52e3a9a6df54614aa6a6af0d24519b0", "aa513d49a95443f1a296311f21e43b4c", "73d83e4cc28b4b229a7d6bdb67b45287", "57a6ca7532a4401f80c683f40100e7fa", "e7d16cefa7c24d18abc32eef7fb2f7bc", "fcca7d53d8d04799a68b5c74db24cac2", "c4bd5a085f2a4cfab6a3bc85b485875e", "b83606098d9b48bf8be55504baa021c6", "e719cd19e33e442698a527f6a2b8312b", "12f2f552049648509306efe8bc1848f6", "219b3332cb724680a630a2fc1bb18bdb" ] }, "executionInfo": { "elapsed": 4430, "status": "ok", "timestamp": 1696862390920, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "LC1z-7anXh9c", "outputId": "913304a5-4ca8-45ef-9dba-62f3bb34314f" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a52e3a9a6df54614aa6a6af0d24519b0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Query is running: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <div id=\"df-b676b55b-bc9c-4eb6-9f31-80790bb71cc0\" class=\"colab-df-container\">\n", " <div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div class=\"colab-df-buttons\">\n", " \n", " <div class=\"colab-df-container\">\n", " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b676b55b-bc9c-4eb6-9f31-80790bb71cc0')\"\n", " title=\"Convert this dataframe to an interactive table.\"\n", " style=\"display:none;\">\n", " \n", " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", " </svg>\n", " </button>\n", " \n", " <style>\n", " .colab-df-container {\n", " display:flex;\n", " gap: 12px;\n", " }\n", "\n", " .colab-df-convert {\n", " background-color: #E8F0FE;\n", " border: none;\n", " border-radius: 50%;\n", " cursor: pointer;\n", " display: none;\n", " fill: #1967D2;\n", " height: 32px;\n", " padding: 0 0 0 0;\n", " width: 32px;\n", " }\n", "\n", " .colab-df-convert:hover {\n", " background-color: #E2EBFA;\n", " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", " fill: #174EA6;\n", " }\n", "\n", " .colab-df-buttons div {\n", " margin-bottom: 4px;\n", " }\n", "\n", " [theme=dark] .colab-df-convert {\n", " background-color: #3B4455;\n", " fill: #D2E3FC;\n", " }\n", "\n", " [theme=dark] .colab-df-convert:hover {\n", " background-color: #434B5C;\n", " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", " fill: #FFFFFF;\n", " }\n", " </style>\n", "\n", " <script>\n", " const buttonEl =\n", " document.querySelector('#df-b676b55b-bc9c-4eb6-9f31-80790bb71cc0 button.colab-df-convert');\n", " buttonEl.style.display =\n", " google.colab.kernel.accessAllowed ? 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rideshare_llm_curated.llm_embedding_model,\n", " (SELECT customer_id,customer_quantitative_analysis as content FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.customer` WHERE include_in_llm_processing = TRUE),\n", " STRUCT(TRUE AS flatten_json_output)\n", " )) AS child\n", "WHERE customer.customer_id = child.customer_id" ] }, { "cell_type": "code", "execution_count": 12, "id": "2SapIgGHrRGc", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 270, "referenced_widgets": [ "516b284003b643c78891323c08931616", "50c8bc09a647414e99ee39d7f26dfcf9", "5918c59cfc96460eba1c77d2f8fc9303", "72dfc1f3c333425dacccc91e45e0075c", "3a2a2c858a1546ab95182a5112b8b3fa", "64814a77f6f7423ea8c6bdf53d50d62f", "f030668a9a964ebd8035df9f26e41586", "cc6be2db7c1d4f249856abfb0e6934d0", "175563ab2ab94f24a23596ffe1c90569", "8c02c750198645bc93f313008653da6e", "3742ca53d2bd4f16859d18a12bc0672a", "0baa5d3c3cfe4fd9b2d7537892173ca2", "b7a65839c8d34458bb19911f2a027684", 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"Lets inspect just one customer record" ] }, { "cell_type": "code", "execution_count": 13, "id": "ooBuVbAwM_Pf", "metadata": { "executionInfo": { "elapsed": 373, "status": "ok", "timestamp": 1696862421787, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "ooBuVbAwM_Pf" }, "outputs": [], "source": [ "customer_id = 4027 #@param{type:\"number\"}\n", "params = { \"customer_id\": customer_id}" ] }, { "cell_type": "code", "execution_count": 14, "id": "UlY0G4zhNKK0", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "915bb23628f74d8b95d900329ee54578", "51326a5b7c8149f7b5a1a1f4acfcf03b", "2378978bd2aa483bbd4623ae50490e16", "8a7c44a86ef9430aad258f62dc54000c", "b08579e1dae94b67b280809bd70106d9", "11533d325fcd4e0eb48c3a11661da059", "8b4efd9e490f48bdabc64a3ff322eb1f", "da9b8391518f4219a9adf4519db279f2", "ab73a94a36f64836a2511eaed9e45fb5", "c4a145acb89f4b2a8a495a05ab701a82", "85b5ecaa475247fd835e18399077877c", 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" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --params $params\n", "SELECT customer_id,customer_quantitative_analysis,llm_customer_quantitative_analysis_embedding\n", "FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.customer`\n", "WHERE customer_id = @customer_id;" ] }, { "cell_type": "markdown", "id": "ADPjZcUAbPv2", "metadata": { "id": "ADPjZcUAbPv2" }, "source": [ "Create embeddings for driver preferences" ] }, { "cell_type": "code", "execution_count": 15, "id": "9h4w2NLJbRyA", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 170, "referenced_widgets": [ "cbe7b448d93c44b685c44fdf69c49123", "0774a903acf341b984604779733b828d", "1c59b124701541cfb3b86162b461d6ec", "4dcd87000c27426c8a35e16f0e26a2e5", "bdf68e0f0e2a495c9d7831d4a4b8ea87", "83ed9c25731f45d09f512f63e748cea3", "8e1d8155564c41fdbf7b941de44d9f6b", "a2c80ff8e9b94c0ca350c547b7292ada", "1a731d0826104603af651bf4c1bd42f1", "04f15c8f59fe499599cbb7f4b28e7d1e", "4265ccf0a09a4312a3adbbdb3d64d35a" ] }, "executionInfo": { "elapsed": 4493, "status": "ok", "timestamp": 1696862446031, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "9h4w2NLJbRyA", "outputId": "d5583eea-4d8a-484e-8ad0-327c9149b496" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cbe7b448d93c44b685c44fdf69c49123", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Query is running: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <div id=\"df-94963191-3939-4115-a3f2-a5d4aab8018a\" class=\"colab-df-container\">\n", " <div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div class=\"colab-df-buttons\">\n", " \n", " <div class=\"colab-df-container\">\n", " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94963191-3939-4115-a3f2-a5d4aab8018a')\"\n", " title=\"Convert this dataframe to an interactive table.\"\n", " style=\"display:none;\">\n", " \n", " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", " </svg>\n", " </button>\n", " \n", " <style>\n", " .colab-df-container {\n", " display:flex;\n", " gap: 12px;\n", " }\n", "\n", " .colab-df-convert {\n", " background-color: #E8F0FE;\n", " border: none;\n", " border-radius: 50%;\n", " cursor: pointer;\n", " display: none;\n", " fill: #1967D2;\n", " height: 32px;\n", " padding: 0 0 0 0;\n", " width: 32px;\n", " }\n", "\n", " .colab-df-convert:hover {\n", " background-color: #E2EBFA;\n", " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", " fill: #174EA6;\n", " }\n", "\n", " .colab-df-buttons div {\n", " margin-bottom: 4px;\n", " }\n", "\n", " [theme=dark] .colab-df-convert {\n", " background-color: #3B4455;\n", " fill: #D2E3FC;\n", " }\n", "\n", " [theme=dark] .colab-df-convert:hover {\n", " background-color: #434B5C;\n", " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", " fill: #FFFFFF;\n", " }\n", " </style>\n", "\n", " <script>\n", " const buttonEl =\n", " document.querySelector('#df-94963191-3939-4115-a3f2-a5d4aab8018a button.colab-df-convert');\n", " buttonEl.style.display =\n", " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", "\n", " async function convertToInteractive(key) {\n", " const element = document.querySelector('#df-94963191-3939-4115-a3f2-a5d4aab8018a');\n", " const dataTable =\n", " await google.colab.kernel.invokeFunction('convertToInteractive',\n", " [key], {});\n", " if (!dataTable) return;\n", "\n", " const docLinkHtml = 'Like what you see? 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rideshare_llm_curated.llm_embedding_model,\n", " (SELECT driver_id,driver_quantitative_analysis as content FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.driver` WHERE include_in_llm_processing = TRUE),\n", " STRUCT(TRUE AS flatten_json_output)\n", " )) AS child\n", "WHERE driver.driver_id = child.driver_id" ] }, { "cell_type": "code", "execution_count": 17, "id": "dZagUjQJrksC", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 270, "referenced_widgets": [ "6cb4f85d78af492688872a091698ee40", "02358e8e4c0440b5bd177f811511b6c5", "ae85d92830204657a33f5c21d8b03a1f", "3e3e784323e44b7f9e01ed02efabbff3", "3267db37416e4b3b80b1d4cff9cf864a", "42e333bf38704883889e7292d0266ce4", "062375a43ca146a4ac2406ca8cc2c6a8", "be7efe5a4a7e48a2b605675d86dea71a", "bec40a770ee243789c0dba8c10e0ffd7", "3728f04cdd114a88a90d0f7c2420830f", "80b97547259343789d0d36ab9319e946", "bafa59d6645e4841b99b55b594a420b4", "2e709214e27e4df49b0a53246762bdb1", "90e667c757114446bd0581ee01b094f6", "a0685bc9802d4d049f868278eb211602", "3083a944e8934fa28814cebb2b42ae4c", "e3cdc8856a234cff81b33821e6f33c54", "29f00a301671475fad14d5a31911648d", "82fbd37df2d0419cb2f6973bcde59e10", "db29f10ae9fe4c39b9ad7476e2aeea60", "3eebb80fa82a421595b08f0ccbeca23d", "2c19bbf50ca144ac921b3fad6c4db8f4" ] }, "executionInfo": { "elapsed": 2472, "status": "ok", "timestamp": 1696862468505, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "dZagUjQJrksC", "outputId": "eb4a74ef-7d17-426c-931a-b1649a1ea240" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6cb4f85d78af492688872a091698ee40", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Query is running: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bafa59d6645e4841b99b55b594a420b4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <div id=\"df-bd291f1a-dd77-4ee0-8064-ef646a7fa30d\" class=\"colab-df-container\">\n", " <div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>driver_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>17</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>55</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>29</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>79</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>80</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div class=\"colab-df-buttons\">\n", " \n", " 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'block' : 'none';\n", "\n", " async function convertToInteractive(key) {\n", " const element = document.querySelector('#df-bd291f1a-dd77-4ee0-8064-ef646a7fa30d');\n", " const dataTable =\n", " await google.colab.kernel.invokeFunction('convertToInteractive',\n", " [key], {});\n", " if (!dataTable) return;\n", "\n", " const docLinkHtml = 'Like what you see? Visit the ' +\n", " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", " + ' to learn more about interactive tables.';\n", " element.innerHTML = '';\n", " dataTable['output_type'] = 'display_data';\n", " await google.colab.output.renderOutput(dataTable, element);\n", " const docLink = document.createElement('div');\n", " docLink.innerHTML = docLinkHtml;\n", " element.appendChild(docLink);\n", " }\n", " </script>\n", " </div>\n", " \n", " </div>\n", " </div>\n", " " ], "text/plain": [ " driver_id\n", "0 17\n", "1 55\n", "2 29\n", "3 79\n", "4 80" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT driver_id\n", "FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.driver`\n", "WHERE include_in_llm_processing = TRUE\n", "LIMIT 5;" ] }, { "cell_type": "markdown", "id": "qr_oPfyPOPFO", "metadata": { "id": "qr_oPfyPOPFO" }, "source": [ "Lets inspect just one driver record" ] }, { "cell_type": "code", "execution_count": 18, "id": "_gJext61OSsw", "metadata": { "executionInfo": { "elapsed": 454, "status": "ok", "timestamp": 1696862474913, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "_gJext61OSsw" }, "outputs": [], "source": [ "driver_id = 29 #@param{type:\"number\"}\n", "params = { \"driver_id\": driver_id}" ] }, { "cell_type": "code", "execution_count": 19, "id": "83qrrH20OXXa", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "cddbb4b7f0f142c9b31376e703f6e02f", "36acf8fd1bfd4846ae53cf57462af8c3", "814279179e6049669422a15ea06dfb71", "cbc2ea3b77a4414cbdaa8a62731e153f", "728e10c00aaa41f48dd0ce55b6e9e25c", "f4c9c0a76ee64fab9fbab206e7b50054", "f3eea5a8657240f0ae0abd34645dc1cf", "4f4e78d89817466eb2159ac6b32370d1", "bff2cecfc40e484a9989ac22ad0d151c", "259d7bdbb8a1453c8656ae94fda1bf2b", "69c4732e08f149b3a1fbb087265c76d5", "e327c6af502f460bbef8f318d2ff0722", "df3bb76cc4a54fd68fcacf45a30e6a9b", "e432f37d004348d98405091bb0109087", "f02a5735082444c8883d9191d67960c5", "71bb20dc4c794f52a2096db057c2c5f2", "2a01d55a76e04b77975970f24df932b0", "9db127e58dc441a6be6544c29754d1f4", "cb15402815a04ee2952f426b714ffa9d", "8866756e4b3b43cda7906e010c3d868a", "73b0113772b54dd3a0d9c65d6eaf4874", "b58bd546f6ad4261ae336d1a68244eef" ] }, "executionInfo": { "elapsed": 2332, "status": "ok", "timestamp": 1696862483181, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "83qrrH20OXXa", "outputId": "3aae136d-7ea6-4adb-d312-cbcad40d3124" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cddbb4b7f0f142c9b31376e703f6e02f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Query is running: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e327c6af502f460bbef8f318d2ff0722", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <div id=\"df-d542785c-7a0c-4878-9e86-e74d724dca5c\" class=\"colab-df-container\">\n", " <div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>driver_id</th>\n", " <th>driver_quantitative_analysis</th>\n", " <th>llm_driver_quantitative_analysis_embedding</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>29</td>\n", " <td>The driver picks up customers from many pickup...</td>\n", " <td>[-0.01263249572366476, -0.03208749368786812, -...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div 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.colab-df-convert:hover {\n", " background-color: #E2EBFA;\n", " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", " fill: #174EA6;\n", " }\n", "\n", " .colab-df-buttons div {\n", " margin-bottom: 4px;\n", " }\n", "\n", " [theme=dark] .colab-df-convert {\n", " background-color: #3B4455;\n", " fill: #D2E3FC;\n", " }\n", "\n", " [theme=dark] .colab-df-convert:hover {\n", " background-color: #434B5C;\n", " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", " fill: #FFFFFF;\n", " }\n", " </style>\n", "\n", " <script>\n", " const buttonEl =\n", " document.querySelector('#df-d542785c-7a0c-4878-9e86-e74d724dca5c button.colab-df-convert');\n", " buttonEl.style.display =\n", " google.colab.kernel.accessAllowed ? 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Visit the ' +\n", " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n", " + ' to learn more about interactive tables.';\n", " element.innerHTML = '';\n", " dataTable['output_type'] = 'display_data';\n", " await google.colab.output.renderOutput(dataTable, element);\n", " const docLink = document.createElement('div');\n", " docLink.innerHTML = docLinkHtml;\n", " element.appendChild(docLink);\n", " }\n", " </script>\n", " </div>\n", " \n", " </div>\n", " </div>\n", " " ], "text/plain": [ " driver_id driver_quantitative_analysis \\\n", "0 29 The driver picks up customers from many pickup... \n", "\n", " llm_driver_quantitative_analysis_embedding \n", "0 [-0.01263249572366476, -0.03208749368786812, -... " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery --params $params\n", "SELECT driver_id,driver_quantitative_analysis,llm_driver_quantitative_analysis_embedding\n", "FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.driver`\n", "WHERE driver_id = @driver_id;" ] }, { "cell_type": "markdown", "id": "r3XfIhXBDSFb", "metadata": { "id": "r3XfIhXBDSFb" }, "source": [ " ## Step 03: Perform the top-K embeddings similarity matching" ] }, { "cell_type": "code", "execution_count": 20, "id": "YgDM8KHmEzgc", "metadata": { "executionInfo": { "elapsed": 528, "status": "ok", "timestamp": 1696862488063, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "YgDM8KHmEzgc" }, "outputs": [], "source": [ "from google.cloud import bigquery\n", "import pandas as pd\n", "\n", "client = bigquery.Client()" ] }, { "cell_type": "code", "execution_count": 21, "id": "P4YvoOJOE_xn", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 351, "status": "ok", "timestamp": 1696862508494, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "P4YvoOJOE_xn", "outputId": "f713593c-90a6-4b66-f34b-21c49fe59e6a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SQL: \n", "FOR customer_embedding_data IN (SELECT customer_id,llm_customer_quantitative_analysis_embedding FROM `rideshare_llm_curated.customer` WHERE ARRAY_LENGTH(llm_customer_quantitative_analysis_embedding) !=0 AND ARRAY_LENGTH(prefered_drivers) = 0 LIMIT 10 )\n", "DO\n", "UPDATE `rideshare_llm_curated.customer` AS customer\n", " SET prefered_drivers = child.matched_drivers\n", " FROM (\n", " WITH top_drivers AS\n", " (SELECT AS STRUCT\n", " customer_embedding_data.customer_id,\n", " customer_embedding_data.llm_customer_quantitative_analysis_embedding,\n", " driver.llm_driver_quantitative_analysis_embedding,\n", " driver.driver_id,\n", " ML.DISTANCE(customer_embedding_data.llm_customer_quantitative_analysis_embedding, driver.llm_driver_quantitative_analysis_embedding, 'COSINE') AS distance\n", " FROM\n", " rideshare_llm_curated.driver AS driver\n", " WHERE\n", " ARRAY_LENGTH(driver.llm_driver_quantitative_analysis_embedding) !=0\n", " ORDER BY distance ASC\n", " --top 3 matches\n", " LIMIT 3)\n", " SELECT ARRAY_AGG(driver_id) matched_drivers FROM top_drivers) AS child\n", " WHERE customer.customer_id = customer_embedding_data.customer_id;\n", "END FOR;\n", " \n" ] } ], "source": [ "batch_size = 10\n", "update_sql=\"\"\"\n", "FOR customer_embedding_data IN (SELECT customer_id,llm_customer_quantitative_analysis_embedding FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.customer` WHERE ARRAY_LENGTH(llm_customer_quantitative_analysis_embedding) !=0 AND ARRAY_LENGTH(prefered_drivers) = 0 LIMIT {batch_size} )\n", "DO\n", "UPDATE `${project_id}.${bigquery_rideshare_llm_curated_dataset}.customer` AS customer\n", " SET prefered_drivers = child.matched_drivers\n", " FROM (\n", " WITH top_drivers AS\n", " (SELECT AS STRUCT\n", " customer_embedding_data.customer_id,\n", " customer_embedding_data.llm_customer_quantitative_analysis_embedding,\n", " driver.llm_driver_quantitative_analysis_embedding,\n", " driver.driver_id,\n", " ML.DISTANCE(customer_embedding_data.llm_customer_quantitative_analysis_embedding, driver.llm_driver_quantitative_analysis_embedding, 'COSINE') AS distance\n", " FROM\n", " `${project_id}.${bigquery_rideshare_llm_curated_dataset}.driver` AS driver\n", " WHERE\n", " ARRAY_LENGTH(driver.llm_driver_quantitative_analysis_embedding) !=0\n", " ORDER BY distance ASC\n", " --top 3 matches\n", " LIMIT 3)\n", " SELECT ARRAY_AGG(driver_id) matched_drivers FROM top_drivers) AS child\n", " WHERE customer.customer_id = customer_embedding_data.customer_id;\n", "END FOR;\n", " \"\"\".format(batch_size=batch_size)\n", "\n", "print(\"SQL: {update_sql}\".format(update_sql=update_sql))" ] }, { "cell_type": "code", "execution_count": 22, "id": "UFTxVXbV4tsR", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 338957, "status": "ok", "timestamp": 1696862862562, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "UFTxVXbV4tsR", "outputId": "a5cd09d4-08f9-45b4-e1d0-541b218a5a14" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Remaining records to process: 100 out of 100 batch_size: 10\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state RUNNING\n", "Job ea7a4f42-b3a4-4351-baa2-56999950245a is currently in state DONE\n", "Remaining records to process: 90 out of 100 batch_size: 10\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state RUNNING\n", "Job 3242bb10-9544-4046-80e0-6810a9a7a034 is currently in state DONE\n", "Remaining records to process: 80 out of 100 batch_size: 10\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state RUNNING\n", "Job 33e98312-8e14-495b-b4f3-094b95a40428 is currently in state DONE\n", "Remaining records to process: 70 out of 100 batch_size: 10\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state RUNNING\n", "Job c57bac58-3b53-4140-9ec8-da01cb8371cc is currently in state DONE\n", "Remaining records to process: 60 out of 100 batch_size: 10\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state RUNNING\n", "Job e34552d8-718a-4f91-af85-8d5ede924fab is currently in state DONE\n", "Remaining records to process: 50 out of 100 batch_size: 10\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state RUNNING\n", "Job 88b8a973-2fe0-4418-a308-12779ab82d1c is currently in state DONE\n", "Remaining records to process: 40 out of 100 batch_size: 10\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state RUNNING\n", "Job c4a5915e-37a3-4676-90ce-e07ef2b79a7e is currently in state DONE\n", "Remaining records to process: 30 out of 100 batch_size: 10\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state RUNNING\n", "Job df43380f-b7ee-456d-a54b-661701b9a2ad is currently in state DONE\n", "Remaining records to process: 20 out of 100 batch_size: 10\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state RUNNING\n", "Job 7995073f-1faa-4883-88b3-00f7062efbbd is currently in state DONE\n", "Remaining records to process: 10 out of 100 batch_size: 10\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state RUNNING\n", "Job 69a0ae92-efdd-4001-b1ac-09a4907d3e06 is currently in state DONE\n", "Remaining records to process: 0 out of 100 batch_size: 10\n" ] } ], "source": [ "import time\n", "\n", "done = False\n", "displayed_first_sql = False\n", "original_record_count = 0\n", "\n", "while done == False:\n", " # Get the count of records to match\n", " sql = \"\"\"\n", " SELECT COUNT(*) AS cnt\n", " FROM `${project_id}.${bigquery_rideshare_llm_curated_dataset}.customer` customer\n", " WHERE\n", " ARRAY_LENGTH(customer.llm_customer_quantitative_analysis_embedding) !=0 AND ARRAY_LENGTH(customer.prefered_drivers) = 0 ;\n", " \"\"\"\n", "\n", " df_record_count = client.query(sql).to_dataframe()\n", " cnt = df_record_count['cnt'].head(1).item()\n", " if displayed_first_sql == False:\n", " original_record_count = cnt\n", " displayed_first_sql = True\n", "\n", " print(\"Remaining records to process: \", cnt, \" out of\", original_record_count, \" batch_size: \", batch_size)\n", "\n", "\n", " if cnt == 0:\n", " done = True\n", " else:\n", " # https://github.com/googleapis/python-bigquery/tree/master/samples\n", " job_config = bigquery.QueryJobConfig(priority=bigquery.QueryPriority.INTERACTIVE)\n", " query_job = client.query(update_sql, job_config=job_config)\n", "\n", " # Check on the progress by getting the job's updated state.\n", " query_job = client.get_job(\n", " query_job.job_id, location=query_job.location\n", " )\n", " print(\"Job {} is currently in state {}\".format(query_job.job_id, query_job.state))\n", "\n", " while query_job.state != \"DONE\":\n", " time.sleep(5)\n", " query_job = client.get_job(\n", " query_job.job_id, location=query_job.location\n", " )\n", " print(\"Job {} is currently in state {}\".format(query_job.job_id, query_job.state))" ] }, { "cell_type": "markdown", "id": "JoF8nkv0Pt7-", "metadata": { "id": "JoF8nkv0Pt7-" }, "source": [ "Lets check one matched customer-driver pair" ] }, { "cell_type": "code", "execution_count": 23, "id": "UZp-1IZHsCLs", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 270, "referenced_widgets": [ "268ce8a8c00b410aad93a53f655d6dbe", "20d98b4bd5ed4b41a7cc9e34bed7cf0c", "f56d692b950d492f99645f2612984c2d", "8622d7d4ea0941d8a30f06e55ac493f2", "949979326a3a41f5a4a6d80d2217558c", "e70fb75c86e6458aaa6cdd5b0df0403c", "76bfe63771f742f0ba0de7dbb1387da0", "8fa92530fd0e42ba97b3ea256d875d9f", "2e12153f8985465aa9636b44bbc8c337", "ee87506af7b846d995ad49afafba7d57", "ef00c1658ea749cbb3d15edcdcc24709", "e6b152fd0ee442d59e30d6c5c472bf6b", "1729ed0250dc43fca9491c5ca8cfa0cc", "1a01811ea5fe4a4a998d37691f036e62", "a50d3f2e5ed249c1a628cd8275117759", "3a2c61c1c5ca411584589a55c6838079", "069e5544f8414b67b543fd0ed87ea181", "42df62d2a84c4007bb9a46a1a4917056", "9f0fb8741f104fb8aafc652615bdd311", "142dc8da74f0497b8e898bea58460407", "612ddbf55a4148a4a837897485fada03", "beac3002f47d465b89c3821b8955cf40" ] }, "executionInfo": { "elapsed": 2390, "status": "ok", "timestamp": 1696862867430, "user": { "displayName": "", "userId": "" }, "user_tz": -120 }, "id": "UZp-1IZHsCLs", "outputId": "3e33fde3-6661-4821-c004-57f6faeba364" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "268ce8a8c00b410aad93a53f655d6dbe", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Query is running: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e6b152fd0ee442d59e30d6c5c472bf6b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| |" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", " <div id=\"df-93c36d4f-f1be-4367-a8a8-d2719bca7ed5\" class=\"colab-df-container\">\n", " <div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>customer_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1745</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4027</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3184</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>387</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>240</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>\n", " <div class=\"colab-df-buttons\">\n", " \n", " <div class=\"colab-df-container\">\n", " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93c36d4f-f1be-4367-a8a8-d2719bca7ed5')\"\n", " title=\"Convert this dataframe to an interactive table.\"\n", " style=\"display:none;\">\n", " \n", " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n", " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n", " </svg>\n", " </button>\n", " \n", " <style>\n", " .colab-df-container {\n", " display:flex;\n", " gap: 12px;\n", " }\n", "\n", " .colab-df-convert {\n", " background-color: #E8F0FE;\n", " border: none;\n", " border-radius: 50%;\n", " cursor: pointer;\n", " display: none;\n", " fill: #1967D2;\n", " height: 32px;\n", " padding: 0 0 0 0;\n", " width: 32px;\n", " }\n", "\n", " .colab-df-convert:hover {\n", " background-color: #E2EBFA;\n", " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", " fill: #174EA6;\n", " }\n", "\n", " .colab-df-buttons div {\n", " margin-bottom: 4px;\n", " }\n", "\n", " [theme=dark] .colab-df-convert {\n", " background-color: #3B4455;\n", " fill: #D2E3FC;\n", " }\n", "\n", " [theme=dark] .colab-df-convert:hover {\n", " background-color: #434B5C;\n", " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", " fill: #FFFFFF;\n", " }\n", " </style>\n", "\n", " <script>\n", " const buttonEl =\n", " document.querySelector('#df-93c36d4f-f1be-4367-a8a8-d2719bca7ed5 button.colab-df-convert');\n", " buttonEl.style.display =\n", " google.colab.kernel.accessAllowed ? 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colab-enterprise/rideshare_llm_ai_lakehouse_embeddings.ipynb (8,357 lines of code) (raw):