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Este documento descreve como gerar métricas do Gemini Code Assist. Por exemplo, é possível gerar métricas que informam o uso ativo diário ou a aceitação de recomendações de código para vários produtos do Google Cloud , incluindo o Cloud Logging, a Google Cloud CLI, o Cloud Monitoring e o BigQuery.
At the bottom of the Google Cloud console, a
Cloud Shell
session starts and displays a command-line prompt. Cloud Shell is a shell environment
with the Google Cloud CLI
already installed and with values already set for
your current project. It can take a few seconds for the session to initialize.
Listar o número de usuários únicos
As instruções a seguir descrevem como usar a CLI gcloud para listar o número de usuários únicos do Gemini Code Assist no período de 28 dias mais recente:
Em um ambiente shell, verifique se você atualizou todos os componentes instalados
da CLI gcloud para a versão mais recente:
gcloudcomponentsupdate
Leia as entradas de registro para usuários e uso do Gemini Code Assist:
As etapas a seguir mostram como usar o Monitoring para criar gráficos de uso diário que mostram o total agregado de usuários ativos diários do Gemini Code Assist e o número de solicitações por dia.
Crie uma métrica do Monitoring com base nos dados de registro que grave o número de usuários do Gemini Code Assist:
No console Google Cloud , acesse a página Análise de registros.
Se você usar a barra de pesquisa para encontrar essa página, selecione o resultado com o subtítulo Logging.
No painel de consultas, insira a consulta a seguir e clique em
Executar consulta:
resource.type="cloudaicompanion.googleapis.com/Instance" AND labels.product="code_assist" AND jsonPayload.@type="type.googleapis.com/google.cloud.cloudaicompanion.logging.v1.ResponseLog"
Na barra de ferramentas, clique em Ações e selecione Criar métrica.
A caixa de diálogo Criar métrica com base em registros aparece.
Configure os seguintes detalhes da métrica:
Verifique se o Tipo de métrica está definido como Contador.
Nomeie a métrica code_assist_example.
Verifique se a Seleção de filtro está definida para apontar para o local em que os registros estão sendo armazenados, seja Projeto ou Bucket.
Faça upgrade do bucket de registros que armazena seus dados para usar a Análise de dados de registros e crie um conjunto de dados vinculado do BigQuery.
Com as duas abordagens, é possível usar o SQL para consultar e analisar os dados de registro e representar os resultados dessas consultas em um gráfico. Se você usa o Log Analytics,
pode salvar seus gráficos em um painel personalizado. No entanto, há diferenças nos preços. Para mais detalhes, consulte
Preços do Log Analytics e
Preços do BigQuery.
Se você usar a barra de pesquisa para encontrar essa página, selecione o resultado com o subtítulo Logging.
Selecione o projeto Google Cloud em que as entradas de registro que você quer encaminhar foram criadas.
Selecione Criar coletor.
No painel Detalhes do coletor, insira os seguintes detalhes:
Em Nome do coletor, forneça um identificador para o coletor. Depois de criar
o coletor, não será possível renomeá-lo, mas você poderá excluí-lo e criar um novo
coletor.
Em Descrição do coletor, descreva a finalidade ou o caso de uso do coletor.
No painel Destino do coletor, configure os seguintes detalhes:
Em Selecionar serviço de coletor, escolha o Conjunto de dados do BigQuery.
Em Selecionar conjunto de dados do BigQuery, crie um conjunto de dados do BigQuery e nomeie-o como code_assist_bq.
Abra o painel Escolher registros para incluir no coletor e, no campo
Criar filtro de inclusão, insira o seguinte:
Opcional: para verificar se você inseriu o filtro correto, selecione
Visualizar registros. O Explorador de registros é aberto em uma nova guia com o filtro pré-preenchido.
Clique em Criar coletor.
Autorizar o coletor de registros a gravar entradas de registro no conjunto de dados
Quando você tem acesso de proprietário ao conjunto de dados do BigQuery, o Cloud Logging concede ao coletor de registros as permissões necessárias para gravar dados de registro.
Se você não tiver acesso de proprietário ou não encontrar entradas no conjunto de dados, talvez o coletor de registros não tenha as permissões necessárias. Para resolver
essa falha, siga as instruções em
Definir permissões de destino.
Consultas
Use as consultas de exemplo do BigQuery a seguir para gerar dados no nível do usuário e agregados sobre o uso ativo diário e as sugestões geradas.
Antes de usar as consultas de exemplo a seguir, obtenha o caminho totalmente qualificado do coletor recém-criado. Para conseguir o caminho, faça o seguinte:
No console Google Cloud , acesse a página BigQuery.
Na lista de recursos, localize o conjunto de dados chamado code_assist_bq. Esses dados são o destino do coletor.
Selecione a tabela de respostas abaixo de code_assist_bq_dataset, clique no ícone more_vert e em Copiar ID para gerar o ID do conjunto de dados. Anote para usar
nas seções a seguir como a variável GENERATED_BIGQUERY_TABLE.
Substitua GENERATED_BIGQUERY_TABLE pelo caminho totalmente qualificado da tabela de resposta do BigQuery que você anotou nas etapas anteriores para criar um gravador.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-09-04 UTC."],[[["\u003cp\u003eNew Gemini Code Assist customers without prior subscriptions receive credits for up to 50 free licenses in their first month, regardless of the edition, and Gemini Code Assist Enterprise is currently available for $19 per month per user with a 12-month commitment until March 31, 2025.\u003c/p\u003e\n"],["\u003cp\u003eYou can use the \u003ccode\u003egcloud\u003c/code\u003e CLI to list unique Gemini Code Assist users over the most recent 28-day period by reading and analyzing log entries.\u003c/p\u003e\n"],["\u003cp\u003eMonitoring can be used to create daily usage graphs showing the number of active Gemini Code Assist users and their requests, derived from log data.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery can analyze Gemini Code Assist log data, either through creating a log sink to export the data or by upgrading the log bucket to use Log Analytics and then creating a linked BigQuery dataset, allowing for SQL queries and result charting.\u003c/p\u003e\n"],["\u003cp\u003eSample queries are provided to allow for the ability to identify individual and aggregate users by day, as well as for individual and aggregate requests per day by user.\u003c/p\u003e\n"]]],[],null,["This document describes how to generate Gemini Code Assist metrics. For\nexample, you can generate metrics that report the daily active usage or the\nacceptance of code recommendations for a variety of Google Cloud products,\nincluding Cloud Logging, Google Cloud CLI, Cloud Monitoring, and\nBigQuery.\n\nIf you need to enable and view Gemini for Google Cloud\nprompt, response, and metadata logs, see\n[View Gemini for Google Cloud logs](/gemini/docs/log-gemini).\n\nBefore you begin\n\n- Ensure you have [set up Gemini Code Assist](/gemini/docs/discover/set-up-gemini) in your project.\n- Ensure you have\n [enabled Gemini for Google Cloud logging](/gemini/docs/log-gemini#enable)\n in your project.\n\n-\n\n\n In the Google Cloud console, activate Cloud Shell.\n\n [Activate Cloud Shell](https://console.cloud.google.com/?cloudshell=true)\n\n\n At the bottom of the Google Cloud console, a\n [Cloud Shell](/shell/docs/how-cloud-shell-works)\n session starts and displays a command-line prompt. Cloud Shell is a shell environment\n with the Google Cloud CLI\n already installed and with values already set for\n your current project. It can take a few seconds for the session to initialize.\n\n \u003cbr /\u003e\n\nList the number of unique users\n\nThe following instructions describe how to use the gcloud CLI to list\nthe number of unique users of Gemini Code Assist in the most\nrecent 28-day period:\n\n1. In a shell environment, ensure that you have updated all installed components\n of the [gcloud CLI](/sdk/gcloud) to the latest version:\n\n gcloud components update\n\n2. Read the log entries for Gemini Code Assist users and usage:\n\n gcloud logging read 'resource.type=cloudaicompanion.googleapis.com/Instance labels.product=~\"code_assist\"' \\\n --freshness 28d \\\n --project \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e \\\n --format \"csv(timestamp.date('%Y-%m-%d'),labels.user_id)\"\n\n Replace \u003cvar translate=\"no\"\u003ePROJECT_ID\u003c/var\u003e with your Google Cloud project ID.\n\n You can use the Unix command `uniq` to uniquely identify users on a per-day\n basis.\n\n The output is similar to the following: \n\n 2024-10-30,user1@company.com\n 2024-10-29,user2@company.com\n 2024-10-29,user2@company.com\n 2024-10-29,user2@company.com\n 2024-10-29,user1@company.com\n 2024-10-28,user1@company.com\n\nCreate a chart that displays daily usage\n\nThe following steps show how to use Monitoring to create daily use\ngraphs that show the aggregate total of daily active Gemini Code Assist\nusers and the number of their requests per day.\n\n1. Create a Monitoring metric from your log data that records\n the number of Gemini Code Assist users:\n\n 1. In the Google Cloud console, go to the **Logs Explorer** page:\n\n [Go to **Logs Explorer**](https://console.cloud.google.com/logs/query)\n\n \u003cbr /\u003e\n\n If you use the search bar to find this page, then select the result whose subheading is\n **Logging**.\n 2. In the query pane, enter the following query, and then click\n **Run query**:\n\n resource.type=\"cloudaicompanion.googleapis.com/Instance\" AND labels.product=\"code_assist\" AND jsonPayload.@type=\"type.googleapis.com/google.cloud.cloudaicompanion.logging.v1.ResponseLog\"\n\n | **Note:** The default time period value is **Last 1 hour** , but you can set it to a longer time period (such as **Last 7 days**).\n 3. In the toolbar, click **Actions** , and then select **Create metric**.\n\n The **Create log-based metric** dialog appears.\n 4. Configure the following metric details:\n\n - Ensure the **Metric Type** is set to **Counter**.\n - Name the metric `code_assist_example`.\n - Ensure **Filter selection** is set to point to\n the location where your logs are being stored, either **Project** or\n **Bucket**.\n\n For information about generating Monitoring metrics from\n your log data, see\n [Log-based metrics overview](/logging/docs/logs-based-metrics).\n 5. Click **Create metric**.\n\n A success banner is displayed, explaining the metric was created.\n 6. In that success banner, click **View in Metrics explorer**.\n\n Metrics Explorer opens and displays a preconfigured chart.\n | **Note:** It may take up to 10 minutes for data to populate on the chart. For more information, see [Metric is missing logs data](/logging/docs/logs-based-metrics/troubleshooting#slow-startup).\n2. Save the chart to a dashboard:\n\n 1. In the toolbar, click **Save chart**.\n 2. Optional: Update the chart title.\n 3. Use the **Dashboard** menu either to select an existing custom dashboard or to create a new dashboard.\n 4. Click **Save chart**.\n\nAnalyze usage by using BigQuery\n\nThe following steps show how to use BigQuery to analyze your\nlog data.\n\nThere are two approaches that you can use to analyze your log data in\nBigQuery:\n\n- [Create a log sink](#create-sink) and export your log data to a BigQuery dataset.\n- Upgrade the log bucket that stores your log data to use [Log Analytics](/logging/docs/log-analytics#analytics), and then create a linked BigQuery dataset.\n\nWith both approaches, you can use SQL to query and analyze your log data, and\nyou can chart the results of those queries. If you use Log Analytics,\nthen you can save your charts to a custom dashboard. However, there are\ndifferences in pricing. For details, see\n[Log Analytics pricing](/stackdriver/pricing#logging-costs) and\n[BigQuery pricing](/bigquery/pricing).\n\nThis section describes how to create a log sink to export select log entries\nto BigQuery, and it provides a list of sample queries.\nIf you want to know more about Log Analytics, see\n[Query and analyze logs with Log Analytics](/logging/docs/analyze/query-and-view)\nand [Query a linked BigQuery dataset](/logging/docs/analyze/query-linked-dataset).\n\nCreate a log sink\n\n1. In the Google Cloud console, go to the **Log Router** page:\n\n [Go to **Log Router**](https://console.cloud.google.com/logs/router)\n\n \u003cbr /\u003e\n\n If you use the search bar to find this page, then select the result whose subheading is\n **Logging**.\n2. Select the Google Cloud project in which the log entries that you want to route originate.\n3. Select **Create sink**.\n4. In the **Sink details** panel, enter the following details:\n\n - For **Sink name**, provide an identifier for the sink. After you create\n the sink, you can't rename the sink but you can delete it and create a new\n sink.\n\n - For **Sink description**, describe the purpose or use case for the sink.\n\n5. In the **Sink destination** panel, configure the following details:\n\n - For **Select sink service** , select **BigQuery dataset**.\n - For **Select BigQuery dataset** , create a new BigQuery dataset and name it `code_assist_bq`.\n6. Open the **Choose logs to include in sink** panel, and in the\n **Build inclusion filter** field, enter the following:\n\n resource.type=\"cloudaicompanion.googleapis.com/Instance\" AND labels.product=\"code_assist\"\n\n7. Optional: To verify that you entered the correct filter, select\n **Preview logs**. The Logs Explorer opens in a new tab with the filter\n pre-populated.\n\n8. Click **Create sink**.\n\nAuthorize the log sink to write log entries to the dataset\n\nWhen you have Owner access to the BigQuery dataset,\nCloud Logging grants the log sink the necessary permissions to write log\ndata.\n\nIf you don't have Owner access or if you don't see any entries in your\ndataset, then the log sink might not have the required permissions. To resolve\nthis failure, follow the instructions in\n[Set destination permissions](/logging/docs/export/configure_export_v2#dest-auth).\n\nQueries\n\nYou can use the following sample BigQuery queries to generate\nuser- and aggregate-level data for daily active use and suggestions generated.\n\nBefore using the following sample queries, you must obtain the fully qualified\npath for the [newly created sink](#create-sink). To obtain the path, do the following:\n\n1. In the Google Cloud console, go to the **BigQuery** page.\n\n [Go to BigQuery](https://console.cloud.google.com/bigquery)\n2. In the resources list, locate the dataset named `code_assist_bq`. This\n data is the [sink destination](#sink_destination).\n\n3. Select the responses table from beneath the `code_assist_bq_dataset`, click\n the more_vert icon, and then click\n **Copy ID** to generate the dataset ID. Make note of it so that you can use\n it in the following sections as the \u003cvar translate=\"no\"\u003eGENERATED_BIGQUERY_TABLE\u003c/var\u003e variable.\n\nList individual users by day \n\n SELECT DISTINCT labels.user_id as user, DATE(timestamp) as use_date\n FROM \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eGENERATED_BIGQUERY_TABLE\u003c/span\u003e\u003c/var\u003e\n ORDER BY use_date\n\nReplace \u003cvar translate=\"no\"\u003eGENERATED_BIGQUERY_TABLE\u003c/var\u003e with the fully qualified path of the\nBigQuery response table you noted in the\n[previous steps for creating a sink](#create-sink).\n\nList aggregate users by day \n\n SELECT COUNT(DISTINCT labels.user_id) as total_users, DATE(timestamp) as use_date\n FROM \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eGENERATED_BIGQUERY_TABLE\u003c/span\u003e\u003c/var\u003e\n GROUP BY use_date\n ORDER BY use_date\n\nList individual requests per day by user \n\n SELECT COUNT(*), DATE(timestamp) as use_date, labels.user_id as user\n FROM \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eGENERATED_BIGQUERY_TABLE\u003c/span\u003e\u003c/var\u003e\n GROUP BY use_date, user\n ORDER BY use_date\n\nList aggregate requests per day by date \n\n SELECT COUNT(*), DATE(timestamp) as use_date\n FROM \u003cvar translate=\"no\"\u003e\u003cspan class=\"devsite-syntax-n\"\u003eGENERATED_BIGQUERY_TABLE\u003c/span\u003e\u003c/var\u003e\n GROUP BY use_date\n ORDER BY use_date\n\nWhat's next\n\n- Learn more about [Gemini for Google Cloud logging](/gemini/docs/log-gemini).\n- Learn more about [Gemini for Google Cloud monitoring](/gemini/docs/monitor-gemini)."]]
