From a640fe512f7a154a2a859b132955d79f1fac05b5 Mon Sep 17 00:00:00 2001 From: "locadex-agent[bot]" <217277504+locadex-agent[bot]@users.noreply.github.com> Date: Tue, 7 Apr 2026 23:30:30 +0000 Subject: [PATCH] docs(locadex): add translations --- docs.json | 14 +- fr/inference/lifecycle.mdx | 4 +- fr/inference/models.mdx | 2 +- fr/models/artifacts/update-an-artifact.mdx | 14 +- .../automations/view-automation-history.mdx | 6 +- fr/models/runs/view-logged-runs.mdx | 2 +- .../app/settings-page/billing-settings.mdx | 18 +- ...w-can-i-see-the-bytes-stored-bytes-tra.mdx | 8 +- .../guides/core-types/prompts-version.mdx | 24 +- fr/weave/guides/integrations.mdx | 1 + .../guides/integrations/vercel_ai_sdk.mdx | 156 + fr/weave/quickstart-inference.mdx | 6 +- gt-lock.json | 6088 +++++++++-------- ja/inference/lifecycle.mdx | 4 +- ja/inference/models.mdx | 40 +- ja/models/artifacts/update-an-artifact.mdx | 10 +- .../automations/view-automation-history.mdx | 16 +- ja/models/runs/view-logged-runs.mdx | 2 +- .../app/settings-page/billing-settings.mdx | 4 + ...w-can-i-see-the-bytes-stored-bytes-tra.mdx | 4 + .../guides/core-types/prompts-version.mdx | 24 +- ja/weave/guides/integrations.mdx | 1 + .../guides/integrations/vercel_ai_sdk.mdx | 156 + ja/weave/quickstart-inference.mdx | 42 +- ko/inference/lifecycle.mdx | 4 +- ko/inference/models.mdx | 2 +- ko/models/artifacts/update-an-artifact.mdx | 10 +- .../automations/view-automation-history.mdx | 10 +- ko/models/runs/view-logged-runs.mdx | 2 +- .../app/settings-page/billing-settings.mdx | 14 +- ...w-can-i-see-the-bytes-stored-bytes-tra.mdx | 4 + .../guides/core-types/prompts-version.mdx | 20 +- ko/weave/guides/integrations.mdx | 1 + .../guides/integrations/vercel_ai_sdk.mdx | 156 + ko/weave/quickstart-inference.mdx | 34 +- 35 files changed, 3705 insertions(+), 3198 deletions(-) create mode 100644 fr/weave/guides/integrations/vercel_ai_sdk.mdx create mode 100644 ja/weave/guides/integrations/vercel_ai_sdk.mdx create mode 100644 ko/weave/guides/integrations/vercel_ai_sdk.mdx diff --git a/docs.json b/docs.json index df1aeba218..0287449e00 100644 --- a/docs.json +++ b/docs.json @@ -1301,7 +1301,6 @@ "tabs": [ { "tab": "Plateforme", - "icon": "/icons/cropped-artifacts.svg", "pages": [ { "group": "Bienvenue sur W&B", @@ -1458,7 +1457,6 @@ }, { "tab": "W&B Models", - "icon": "/icons/cropped-models.svg", "pages": [ "fr/models", "fr/models/quickstart", @@ -1980,7 +1978,6 @@ }, { "tab": "W&B Weave", - "icon": "/icons/cropped-weave.svg", "pages": [ "fr/weave", { @@ -2127,6 +2124,7 @@ "fr/weave/guides/integrations/smolagents", "fr/weave/guides/integrations/pydantic_ai", "fr/weave/guides/integrations/google_adk", + "fr/weave/guides/integrations/vercel_ai_sdk", "fr/weave/guides/integrations/agno", "fr/weave/guides/integrations/koog", "fr/weave/guides/integrations/autogen", @@ -2312,7 +2310,6 @@ }, { "tab": "W&B Inference", - "icon": "/icons/cropped-inference.svg", "pages": [ "fr/inference", "fr/inference/prerequisites", @@ -2352,7 +2349,6 @@ }, { "tab": "W&B Training", - "icon": "/icons/cropped-training.svg", "pages": [ "fr/training", "fr/training/prerequisites", @@ -2383,14 +2379,12 @@ }, { "tab": "Assistance", - "icon": "/icons/cropped-reports.svg", "pages": [ "fr/support" ] }, { "tab": "W&B Launch", - "icon": "/icons/cropped-launch.svg", "hidden": true, "pages": [ "fr/platform/launch", @@ -3360,6 +3354,7 @@ "ja/weave/guides/integrations/smolagents", "ja/weave/guides/integrations/pydantic_ai", "ja/weave/guides/integrations/google_adk", + "ja/weave/guides/integrations/vercel_ai_sdk", "ja/weave/guides/integrations/agno", "ja/weave/guides/integrations/koog", "ja/weave/guides/integrations/autogen", @@ -3614,7 +3609,6 @@ }, { "tab": "サポート", - "icon": "/icons/cropped-reports.svg", "pages": [ "ja/support" ] @@ -4590,6 +4584,7 @@ "ko/weave/guides/integrations/smolagents", "ko/weave/guides/integrations/pydantic_ai", "ko/weave/guides/integrations/google_adk", + "ko/weave/guides/integrations/vercel_ai_sdk", "ko/weave/guides/integrations/agno", "ko/weave/guides/integrations/koog", "ko/weave/guides/integrations/autogen", @@ -4844,7 +4839,6 @@ }, { "tab": "지원팀", - "icon": "/icons/cropped-reports.svg", "pages": [ "ko/support" ] @@ -6300,4 +6294,4 @@ } }, "baseUrl": "https://docs.wandb.ai" -} +} \ No newline at end of file diff --git a/fr/inference/lifecycle.mdx b/fr/inference/lifecycle.mdx index e948acb39f..c732fff4a0 100644 --- a/fr/inference/lifecycle.mdx +++ b/fr/inference/lifecycle.mdx @@ -38,7 +38,9 @@ Les modèles W&B Inference suivants sont **obsolètes** : {/* takeru lifecycle-deprecated - Ce tableau est généré automatiquement, n’y apportez pas de modifications manuelles. */} -*Aucun pour le moment* +| Date d’obsolescence | Date de retrait | ID du modèle | Remplacement recommandé | +| ------------------- | --------------- | ------------------------------------------- | ----------------------- | +| 2026-04-07 | 2026-04-21 | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | `Qwen/Qwen3.5-35B-A3B` |
diff --git a/fr/inference/models.mdx b/fr/inference/models.mdx index 235171fd44..934a271b23 100644 --- a/fr/inference/models.mdx +++ b/fr/inference/models.mdx @@ -16,7 +16,6 @@ W&B Inference donne accès à plusieurs modèles de fondation open source. C | Modèle | ID du modèle (pour l’utilisation de l’API) | Type | Fenêtre de contexte | Paramètres | Description | | ----------------------------- | ---------------------------------------------- | ------------- | ------------------- | -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | DeepSeek V3.1 | `deepseek-ai/DeepSeek-V3.1` | Texte | 161k | 37B-671B (Active-Total) | Un grand modèle hybride qui prend en charge les modes avec et sans raisonnement via des templates de prompt. | -| Meta Llama 4 Scout | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Texte, vision | 64k | 17B-109B (actifs-total) | Modèle multimodal intégrant la compréhension du texte et des images, idéal pour les tâches visuelles et l’analyse combinée. | | Meta Llama 3.3 70B | `meta-llama/Llama-3.3-70B-Instruct` | Texte | 128k | 70B (total) | Modèle multilingue qui excelle dans les tâches conversationnelles, le suivi précis des instructions et le codage. | | Meta Llama 3.1 70B | `meta-llama/Llama-3.1-70B-Instruct` | Texte | 128k | 70B (total) | Modèle conversationnel performant optimisé pour des interactions réactives avec des chatbots multilingues. | | Meta Llama 3.1 8B | `meta-llama/Llama-3.1-8B-Instruct` | Texte | 128k | 8B (au total) | Modèle conversationnel performant, optimisé pour des interactions réactives avec des chatbots multilingues. | @@ -33,6 +32,7 @@ W&B Inference donne accès à plusieurs modèles de fondation open source. C | Qwen3 Coder 480B A35B | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | Texte | 262k | 35B-480B (actifs-total) | Modèle à mélange d’experts optimisé pour les tâches de programmation agentique, comme l’appel de fonctions, l’utilisation d’outils et le raisonnement en contexte long. | | Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` | Texte, vision | 262k | 3B-35B (actifs-total) | Qwen3.5-35B-A3B est un modèle MoE multimodal à poids ouverts, conçu pour une inférence efficace et à haut débit pour le chat, le raisonnement et les tâches agentiques. | | Z.AI GLM 5 | `zai-org/GLM-5-FP8` | Texte | 200k | 40B-744B (Actifs-Total) | Modèle Mixture-of-Experts pour les tâches agentiques à long horizon, avec de solides performances en raisonnement et en programmation. | +| Meta Llama 4 Scout (obsolète) | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Texte, vision | 64k | 17B-109B (actifs-total) | Modèle multimodal intégrant la compréhension du texte et des images, idéal pour les tâches visuelles et l’analyse combinée. |
## Utilisation des ID de modèle diff --git a/fr/models/artifacts/update-an-artifact.mdx b/fr/models/artifacts/update-an-artifact.mdx index 51e5c6139e..cfd341abce 100644 --- a/fr/models/artifacts/update-an-artifact.mdx +++ b/fr/models/artifacts/update-an-artifact.mdx @@ -1,18 +1,18 @@ --- -description: Mettre à jour un Artifact existant dans un Run W&B ou en dehors d'un Run. +description: Mettre à jour un artifact existant pendant qu'un run est actif ou en utilisant uniquement l'API publique. title: Mettre à jour un artifact --- -Définissez les valeurs souhaitées pour mettre à jour la `description`, les `metadata` et l'`alias` d'un artifact. Appelez la méthode `save()` pour mettre à jour l'artifact sur les serveurs W&B. Vous pouvez mettre à jour un artifact pendant un Run W&B ou en dehors d'un Run. +Définissez les valeurs souhaitées pour mettre à jour la `description`, les `metadata` et l'`alias` d'un artifact. Mettez à jour un run précédemment consigné dans W&B avec l'API publique W&B ([`wandb.Api`](/fr/models/ref/python/public-api/api)). Utilisez `wandb.Run.save()` pour mettre à jour un artifact lorsqu'il est initialisé pour la première fois et qu'il est toujours actif. - **Quand utiliser Artifact.save() ou wandb.Run.log_artifact()** + **Quand utiliser wandb.Artifact.save() ou wandb.Run.log_artifact()** - * Utilisez `Artifact.save()` pour mettre à jour un artifact existant sans créer de nouveau run. + * Utilisez `Artifact.save()` pour mettre à jour un artifact existant sans démarrer un nouveau run. * Utilisez `wandb.Run.log_artifact()` pour créer un nouvel artifact et l'associer à un run spécifique. -Utilisez l'API publique W&B ([`wandb.Api`](/fr/models/ref/python/public-api/api)) pour mettre à jour un artifact en dehors d'un run. Utilisez l'API Artifact ([`wandb.Artifact`](/fr/models/ref/python/experiments/artifact)) pour mettre à jour un artifact pendant un run. +Utilisez l'API publique W&B ([`wandb.Api`](/fr/models/ref/python/public-api/api)) pour mettre à jour un artifact. Utilisez la classe wandb.Artifact ([`wandb.Artifact`](/fr/models/ref/python/experiments/artifact)) pendant qu'un run est actif. Vous ne pouvez pas mettre à jour l'alias d'un artifact lié à un modèle dans le Model Registry. @@ -32,8 +32,8 @@ Utilisez l'API publique W&B ([`wandb.Api`](/fr/models/ref/python/public- ``` - - L'exemple de code suivant montre comment mettre à jour la description d'un artifact à l'aide de l'API `wandb.Api` : + + L'exemple suivant met à jour un artifact avec [`wandb.Api`](/fr/models/ref/python/public-api/api) : ```python import wandb diff --git a/fr/models/automations/view-automation-history.mdx b/fr/models/automations/view-automation-history.mdx index b21e5801cd..b31b8234c2 100644 --- a/fr/models/automations/view-automation-history.mdx +++ b/fr/models/automations/view-automation-history.mdx @@ -4,11 +4,7 @@ description: "Consultez l'historique d'exécution de vos W&B Automations pour v --- - Cette fonctionnalité nécessite une licence Enterprise. Elle est disponible uniquement pour : - - * W&B Multi-tenant Cloud - * W&B Dedicated Cloud - * W&B Self-Managed v0.75.0+ + L’historique d’exécution des automatisations est disponible sur W&B Multi-tenant Cloud, W&B Dedicated Cloud et W&B Self-Managed v0.75.0 et versions ultérieures. Cette page explique comment consulter et interpréter l'historique d'exécution de vos [automatisations](/fr/models/automations) W&B, notamment ce qui a déclenché une automatisation, quelles actions ont été effectuées et si elles ont réussi ou échoué. diff --git a/fr/models/runs/view-logged-runs.mdx b/fr/models/runs/view-logged-runs.mdx index 28c4870310..69db9dc617 100644 --- a/fr/models/runs/view-logged-runs.mdx +++ b/fr/models/runs/view-logged-runs.mdx @@ -73,7 +73,7 @@ Utilisez l’onglet **Aperçu** pour consulter les informations spécifiques d * **Auteur** : l’entité W&B qui crée le run. * **Commande** : la commande qui initialise le run. * **Description** : une description du run que vous avez fournie. Ce champ est vide si vous ne renseignez pas de description lorsque vous créez le run. Vous pouvez ajouter une description à un run dans la W&B App ou par programmation avec le SDK Python. -* **Heures comptabilisées** : la durée pendant laquelle le run calcule activement ou enregistre des données, hors pauses et périodes d’attente. Cette métrique vous aide à comprendre le temps de calcul réellement consacré à votre run. +* **Heures comptabilisées** : la durée pendant laquelle le run calcule activement ou enregistre des données, hors pauses et périodes d’attente. Cette métrique vous aide à comprendre le temps de calcul réellement consacré à votre run. Les heures comptabilisées ne vous sont pas facturées, et elles sont illimitées pour tous les forfaits. * **Durée d’exécution** : mesure le temps total entre le début et la fin du run. Il s’agit du temps réel écoulé pour le run, y compris lorsqu’il est en pause ou en attente de ressources. Cette métrique fournit la durée totale écoulée de votre run. * **Dépôt Git** : le dépôt git associé au run. Vous devez [activer git](/fr/platform/app/settings-page/user-settings/#personal-github-integration) pour afficher ce champ. * **Nom d’hôte** : l’endroit où W&B exécute le run. W&B affiche le nom de votre machine si vous initialisez le run localement. diff --git a/fr/platform/app/settings-page/billing-settings.mdx b/fr/platform/app/settings-page/billing-settings.mdx index 80beed72d0..fe4a5dfdff 100644 --- a/fr/platform/app/settings-page/billing-settings.mdx +++ b/fr/platform/app/settings-page/billing-settings.mdx @@ -57,13 +57,13 @@ Un **Administrateur de l’organisation** ou un **administrateur de la facturati Au cours d’une période de facturation donnée, les alertes de dépenses notifient par e-mail les administrateurs de l’organisation et de la facturation lorsque votre organisation atteint un ou plusieurs seuils de dépense personnalisables, exprimés en USD. Vous pouvez configurer jusqu’à 5 alertes de dépenses distinctes par catégorie : -| Catégorie | Description | -| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| Stockage | Frais liés à l’[utilisation](#usage-alerts) du stockage de votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). | -| Heures comptabilisées | Frais liés à l’[utilisation](#usage-alerts) d’entraînement de votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). | -| Weave | Frais liés à l’[utilisation](#usage-alerts) de Weave par votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). Affiché uniquement si Weave est actif dans le déploiement. | -| Inférence | Frais liés à l’[utilisation](#usage-alerts) de l’Inférence de votre organisation pendant la période de facturation. Voir [Tarification de l’inférence](https://wandb.ai/site/pricing/inference/). Affiché uniquement si Inférence est active dans le déploiement. | -| Entraînement | Frais liés à l’[utilisation](#usage-alerts) de Serverless RL de votre organisation pendant la période de facturation. Voir [Tarification du RL serverless](https://wandb.ai/site/pricing/reinforcement-learning/). Affiché uniquement si W&B Training est actif dans le déploiement. | +| Catégorie | Description | +| --------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Stockage | Frais liés à l’[utilisation](#usage-alerts) du stockage de votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). | +| Heures comptabilisées | Frais liés à l’[utilisation](#usage-alerts) d’entraînement de votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). | +| Weave | Frais liés à l’[utilisation](#usage-alerts) de Weave par votre organisation pendant la période de facturation. Voir [Tarification](https://wandb.ai/site/pricing/). Affiché uniquement si Weave est actif dans le déploiement. | +| Inférence | Frais liés à l’[utilisation](#usage-alerts) de l’Inférence de votre organisation pendant la période de facturation. Voir [Tarification de l’inférence](https://wandb.ai/site/pricing/inference/). Affiché uniquement si Inférence est active dans le déploiement. | +| Entraînement | Frais liés à l’[utilisation](#usage-alerts) de Serverless RL de votre organisation pendant la période de facturation. Voir [Tarification du RL serverless](https://wandb.ai/site/pricing/reinforcement-learning/). Affiché uniquement si W&B Training est actif dans le déploiement. | Les messages sont envoyés depuis `support@wandb.com`. @@ -77,6 +77,10 @@ Avec le [plan Pro](https://wandb.ai/site/pricing/) : * Seul l’**administrateur de la facturation** reçoit des alertes de dépenses. * Par défaut, les alertes de dépenses sont configurées à $200, $450, $700 et $1000. + + Vous n’êtes pas facturé pour les heures comptabilisées, qui sont illimitées pour tous les plans. Les heures comptabilisées sont affichées sur la page **Utilisation** à des fins de suivi. + +
#### Afficher les alertes de dépenses
diff --git a/fr/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx b/fr/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx index 024260de60..7b276eaab7 100644 --- a/fr/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx +++ b/fr/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx @@ -11,11 +11,15 @@ Consultez les octets stockés, les octets suivis et les heures de suivi de votre Veillez à remplacer les valeurs entre `<>` par le nom de votre organisation. + + Les heures de suivi sont illimitées pour tous les plans et ne sont pas facturées. Les heures de suivi sont affichées sur la page **Usage** à des fins de suivi uniquement. + + *** -{/* ---- GÉNÉRÉ AUTOMATIQUEMENT: tab badges ---- +{/* ---- GÉNÉRÉ AUTOMATIQUEMENT: badges d’onglet ---- Géré par scripts/knowledgebase-nav/generate_tags.py à partir des mots-clés du front matter. - Ne modifiez pas manuellement le contenu entre ces marqueurs. + Ne modifiez pas manuellement entre ces marqueurs. ---- */} [Administrateur](/fr/support/models/tags/administrator) diff --git a/fr/weave/guides/core-types/prompts-version.mdx b/fr/weave/guides/core-types/prompts-version.mdx index e364ed8099..60ee9035d2 100644 --- a/fr/weave/guides/core-types/prompts-version.mdx +++ b/fr/weave/guides/core-types/prompts-version.mdx @@ -44,7 +44,7 @@ weave://///object/:)` : récupère la version `:latest` d'un prompt. Nécessite d'appeler `weave.init(...)`. -* `weave.ref(:)` : récupère la version spécifiée d'un prompt. Nécessite d'appeler `weave.init(...)`. +* `weave.ref(:)` : récupère un prompt par alias, hash de version ou index de version. Nécessite d'appeler `weave.init(...)`. * `weave.ref()` : récupère le prompt situé à l'[URI de référence pleinement qualifié](#construct-a-fully-qualified-ref-uri) indiquée. Ne nécessite pas d'appeler `weave.init(...)`. L'exemple suivant charge la version `support_prompt:v3` pour que vous puissiez l'utiliser dans votre application : @@ -74,21 +74,21 @@ weave.init("your-team-name/your-project-name") prompt = weave.ref("support_prompt:v3").get() ``` -
- ## Utilisez des versions de prompt en production +
+ ## Utiliser des prompts en production
-Lorsque vous déployez des prompts dans des systèmes de production, fixez une version de prompt spécifique au lieu de référencer la dernière version. Dans votre application de production, chargez cette version avec `weave.ref(:).get()`. +Lorsque vous déployez des prompts dans des systèmes de production, utilisez un [alias](#add-prompt-tags-and-aliases) comme `production` au lieu d’un index de version ou de la dernière version. Dans votre application de production, chargez le prompt avec `weave.ref(:).get()`. -Le fait de fixer une version garantit que le comportement en production reste stable, même si de nouvelles versions de prompt sont publiées par la suite. +L’utilisation d’un alias garantit que le comportement en production reste stable et prévisible. Lorsque vous êtes prêt à promouvoir une nouvelle version, basculez l’alias vers cette version, puis tous les consommateurs récupèrent automatiquement ce changement. -Voici un flux de travail courant : +Un flux de travail courant se présente ainsi : -1. Développez et testez une nouvelle version de prompt. -2. Évaluez le nouveau prompt sur des jeux de données ou des suites d'évaluation. -3. Mettez à jour le système de production pour référencer la nouvelle version. +1. Développez et testez une nouvelle version du prompt. +2. Évaluez le nouveau prompt sur des jeux de données ou des suites d’évaluation. +3. Déplacez l’alias `production` vers la nouvelle version avec `client.set_aliases(new_ref, "production")`. -Cette approche permet aux équipes d'itérer sur les prompts en toute sécurité, sans modifier de façon inattendue le comportement en production. +Cette approche permet aux équipes d’itérer en toute sécurité sur les prompts, sans modifier de façon inattendue le comportement en production. ## Afficher et comparer les versions de prompt @@ -101,7 +101,7 @@ Pour afficher les versions du prompt dans l’UI : ![UI de Prompt Assets, montrant les versions du prompt sélectionné.](/weave/guides/core-types/imgs/prompt-object.png) -5. (Facultatif) Vous pouvez comparer des versions de prompt en cochant les cases à côté des prompts listés, puis en cliquant sur le bouton **Compare** dans la barre d’outils du tableau. Cela vous permet de voir les différences entre vos prompts. +1. (Facultatif) Vous pouvez comparer des versions de prompt en cochant les cases à côté des prompts listés, puis en cliquant sur le bouton **Compare** dans la barre d’outils du tableau. Cela vous permet de voir les différences entre vos prompts. ![UI Compare prompts montrant les différences entre deux versions de prompt.](/weave/guides/core-types/imgs/prompt-comparison.png) diff --git a/fr/weave/guides/integrations.mdx b/fr/weave/guides/integrations.mdx index bac7bb1f0f..4dcb23879d 100644 --- a/fr/weave/guides/integrations.mdx +++ b/fr/weave/guides/integrations.mdx @@ -51,6 +51,7 @@ Les frameworks aident à orchestrer les pipelines d’exécution des application * **[AutoGen](/fr/weave/guides/integrations/autogen)** * **[Verdict](/fr/weave/guides/integrations/verdict)** * **[TypeScript SDK](/fr/weave/guides/integrations/js)** +* **[Vercel AI SDK](/fr/weave/guides/integrations/vercel_ai_sdk)** * **[Agno](/fr/weave/guides/integrations/agno)** * **[Koog](/fr/weave/guides/integrations/koog)** diff --git a/fr/weave/guides/integrations/vercel_ai_sdk.mdx b/fr/weave/guides/integrations/vercel_ai_sdk.mdx new file mode 100644 index 0000000000..e36fd1ab94 --- /dev/null +++ b/fr/weave/guides/integrations/vercel_ai_sdk.mdx @@ -0,0 +1,156 @@ +--- +title: "Vercel AI SDK" +description: "Tracer les appels du Vercel AI SDK dans Weave à l’aide d’OpenTelemetry" +--- + +Vous pouvez tracer les appels de [Vercel AI SDK](https://ai-sdk.dev/) dans Weave à l’aide d’[OpenTelemetry (OTel)](https://opentelemetry.io/). Le Vercel AI SDK est une boîte à outils TypeScript permettant de créer des applications d’IA, avec prise en charge de frameworks comme Next.js, Nuxt, SvelteKit et d’autres frameworks. Il inclut une prise en charge native d’OpenTelemetry via son option `experimental_telemetry`. + +Ce guide vous montre comment configurer OTel pour envoyer des traces du Vercel AI SDK vers Weave. Vous pouvez utiliser le SDK AI avec Next.js ou comme application Node.js autonome. + +Pour plus d’informations sur le traçage OTel dans Weave, voir [Send OTel traces to Weave](../tracking/otel). + +
+ ### Prérequis +
+ +Les exemples Next.js et Node.js de ce guide nécessitent tous deux les mêmes dépendances. Pour commencer : + +1. Installez les bibliothèques Vercel et OTel suivantes : + + ```bash + npm install ai @ai-sdk/openai @opentelemetry/api @opentelemetry/sdk-trace-node @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto @opentelemetry/resources zod + ``` + +2. Définissez les variables d’environnement suivantes : + + ```bash + export WANDB_API_KEY="your-wandb-api-key" + export OPENAI_API_KEY="your-openai-api-key" + ``` + + Vous pouvez obtenir votre clé API W&B dans les [Paramètres utilisateur](https://wandb.ai/settings). + +
+ ## Configurer le traçage OTel pour Next.js +
+ +Cette section explique comment configurer Weave dans une application Next.js. L’exemple ne contient pas d’application complète, seulement la configuration de l’instrumentation et la façon d’invoquer l’instrumentation sur une fonction du Vercel AI SDK, en l’occurrence un simple appel à OpenAI. + +
+ ### Configurer l’instrumentation +
+ +Les applications Next.js utilisent un fichier `instrumentation.ts` pour configurer OTel. Ce fichier s’exécute une seule fois au démarrage du serveur et configure le fournisseur de traceurs utilisé par l’AI SDK. + +Pour intégrer Weave à la fonctionnalité OTel de Vercel, créez un fichier `instrumentation.ts` à la racine de votre projet et ajoutez-y le code suivant, en mettant à jour la fonction `resourceFromAttributes()` avec les noms de votre équipe et de votre projet : + +```typescript instrumentation.ts lines {17-19} +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; + +export function register() { + const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// Configurez l'exportateur OTel pour utiliser le point de terminaison W&B Weave. + const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, + }); + +// Configurez vos identifiants W&B. + const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], + }); + + provider.register(); +} +``` + +Cela crée un exportateur OTLP configuré pour envoyer des données de trace vers le point de terminaison OTel de Weave et s'authentifier avec votre clé API W&B. + + +
+ ### Configurer la télémétrie pour une fonction +
+ +Une fois l’instrumentation ajoutée, utilisez l’option `experimental_telemetry` de Vercel lors de n’importe quel appel de fonction de l’AI SDK pour émettre des spans OTel : + +```typescript route.ts lines {10} +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +export async function POST(req: Request) { + const { prompt } = await req.json(); + +// Ajouter le champ experimental_telemetry à la fonction. + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt, + experimental_telemetry: { isEnabled: true }, + }); + + return Response.json({ text: result.text }); +} +``` + +Tous les appels à `generateText` avec la télémétrie activée produisent des spans OTel exportés vers Weave. + + +
+ ## Configurer le traçage OTel pour Node.js +
+ +Pour les applications Node.js autonomes (sans Next.js), configurez le fournisseur de traceurs au début de votre fichier d’entrée, avant tout appel à l’AI SDK. + +Une fois les prérequis satisfaits, vous pouvez exécuter cet exemple et générer des spans sans configuration supplémentaire. + +```typescript lines {11-14,18-19,31} title="test-app.ts" +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// Configurer l'exportateur OTel pour utiliser le point de terminaison W&B Weave. +const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, +}); + +// Configurer vos identifiants W&B. +const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], +}); + +provider.register(); + +// Ajouter le champ experimental_telemetry à la fonction. +async function main() { + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt: "Explain OpenTelemetry in one sentence.", + experimental_telemetry: { isEnabled: true }, + }); + + console.log(result.text); + + await provider.shutdown(); +} + +main(); +``` + +`BatchSpanProcessor` exporte les spans de manière asynchrone. En bref, dans les processus de courte durée, comme les scripts autonomes, les fonctions serverless ou les outils CLI, appelez `provider.shutdown()` avant que le processus ne se termine pour vous assurer que tous les spans sont envoyés vers Weave. Pour les serveurs de longue durée (comme un serveur de dév Next.js démarré via `instrumentation.ts`), ce n’est pas nécessaire. diff --git a/fr/weave/quickstart-inference.mdx b/fr/weave/quickstart-inference.mdx index b13c092907..edf7b2dd6a 100644 --- a/fr/weave/quickstart-inference.mdx +++ b/fr/weave/quickstart-inference.mdx @@ -280,7 +280,7 @@ W&B Inference permet d’accéder à plusieurs modèles. Utilisez le code su # Créez des instances pour différents modèles llama_model = InferenceModel(model_name="meta-llama/Llama-3.1-8B-Instruct") - deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3-0324") + deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3.1") # Comparez leurs réponses test_question = "Explique l’informatique quantique en un paragraphe pour un lycéen." @@ -321,7 +321,7 @@ W&B Inference permet d’accéder à plusieurs modèles. Utilisez le code su // Créez des instances pour différents modèles const llamaModel = createModel('meta-llama/Llama-3.1-8B-Instruct'); - const deepseekModel = createModel('deepseek-ai/DeepSeek-V3-0324'); + const deepseekModel = createModel('deepseek-ai/DeepSeek-V3.1'); // Comparez leurs réponses const testQuestion = 'Explique l’informatique quantique en un paragraphe pour un lycéen.'; @@ -509,7 +509,7 @@ Ajoutez le code suivant au script que vous avez utilisé dans la section précé // Comparer plusieurs modèles - une fonctionnalité clé du framework d'évaluation de Weave const modelsToCompare = [ { model: llamaModel, name: 'meta-llama/Llama-3.1-8B-Instruct' }, - { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3-0324' }, + { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3.1' }, ]; for (const { model, name } of modelsToCompare) { diff --git a/gt-lock.json b/gt-lock.json index 5256552085..797abfccdd 100644 --- a/gt-lock.json +++ b/gt-lock.json @@ -5,15 +5,15 @@ "01904e13ba5f00993b220ad54efb6a0422949f29c548fcf7cc39dca9c47981e2": { "74394f812d603faf2c2b290252ac51d854d080e0f20dca7be575321350715171": { "ko": { - "updatedAt": "2026-04-03T23:29:55.064Z", + "updatedAt": "2026-04-07T23:28:56.131Z", "postProcessHash": "3618a33d7a71e5aa5773cfbc59cb6d03af3c6bb892cff9c04bd55389870086b0" }, "ja": { - "updatedAt": "2026-04-03T23:29:55.065Z", + "updatedAt": "2026-04-07T23:28:56.131Z", "postProcessHash": "1f3f45dd723a3b5f67431b27519aa9c4aa38457a9b3845c525306a0aa8981473" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.140Z", + "updatedAt": "2026-04-07T23:28:56.148Z", "postProcessHash": "d7c9b441e2f3140d70cdd03a4e375e20cdeb25307d41bd5ecc3d473d010cef02" } } @@ -21,15 +21,15 @@ "01c75a98b1229dd8e61d541d0721d898e7fc3874316af274ac67bc835367c1f6": { "df78ad5b67272b3ed3dbb3a75ebc00ebcff62b1c8c5574f68c4763c0cadf9c72": { "ja": { - "updatedAt": "2026-04-03T23:29:55.184Z", + "updatedAt": "2026-04-07T23:28:56.252Z", "postProcessHash": "3884f66b2711a53a757e58d8e6faa13599a99a0e90dc5528daa5e70e86514501" }, "ko": { - "updatedAt": "2026-04-03T23:29:55.182Z", + "updatedAt": "2026-04-07T23:28:56.250Z", "postProcessHash": "065912ba7ceca24064e672baced5563219f342ce263f9f49a2901c0867154936" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.190Z", + "updatedAt": "2026-04-07T23:28:56.260Z", "postProcessHash": "03ce62cd012dd92c499b1e6a7c6e7c20650e88a04f9675d768bab14db3098386" } } @@ -37,15 +37,15 @@ "01cf7112996b8bfe1df0c431c1c2cb734b3a92f4d7f14ea4f35ba4d625a0d878": { "308087d430d54f0bd77976ee9a20060bb5b683bb4142c379afa4cab1768e1d6a": { "ko": { - "updatedAt": "2026-04-03T23:29:54.985Z", + "updatedAt": "2026-04-07T23:28:56.033Z", "postProcessHash": "eedfff516f9e2b442541713ad3fa53ef0c59ade75f9dda171716712b3b94739f" }, "ja": { - "updatedAt": "2026-04-03T23:29:55.045Z", + "updatedAt": "2026-04-07T23:28:56.107Z", "postProcessHash": "c57d8c835fbe3a17cba9676da7d6216abb31b93980330d92536ba429c38bb9e9" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.075Z", + "updatedAt": "2026-04-07T23:28:56.144Z", "postProcessHash": "917c07d7bb068ce2ec423fabe786a12c46b7f69bc35e7961a7708391ca4a162a" } } @@ -53,15 +53,15 @@ "064ac4db92a1e6e683b7c0110d19f6eaa4fef9ecd8f9679853fd247c2afc627e": { "5a350cc40154d5442585ea26f12f7a38f97b8d4211ad35cf91e528d327691a78": { "ja": { - "updatedAt": "2026-04-03T23:29:55.057Z", + "updatedAt": "2026-04-07T23:28:56.121Z", "postProcessHash": "ad89b69b9c9f5448d324774011128805c4348960bf8e5d222bd259e3d434b749" }, "ko": { - "updatedAt": "2026-04-03T23:29:55.146Z", + "updatedAt": "2026-04-07T23:28:56.206Z", "postProcessHash": "d7d63e889f179440d3126bc2b7171ef4f67104fcea0f84dc511b7b01b9724b0a" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.138Z", + "updatedAt": "2026-04-07T23:28:56.147Z", "postProcessHash": "e5eb9be2b7fb10b41e2ce3cd5dcbeecd2eccce8395f8c3b9418e38cb3ae33332" } } @@ -69,15 +69,15 @@ "06d1ce4e2a0dfe8005200bd43d155a78fb785d6c2d4f8edd841e95d54c004199": { "e4269534fddeda5103fe716c529c536ef26c9e389b86cfbe43727affe01e70f8": { "ko": { - "updatedAt": "2026-04-03T23:29:55.159Z", + "updatedAt": "2026-04-07T23:28:56.229Z", "postProcessHash": "6926b77c58ad4cbb07dd8daf08f29242123e41f319d60a17483bf531b347c2e9" }, "ja": { - "updatedAt": "2026-04-03T23:29:55.164Z", + "updatedAt": "2026-04-07T23:28:56.234Z", "postProcessHash": "cac5c815b4c39afa872856565ff73b03c971420044c6758912141669a443e933" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.194Z", + "updatedAt": "2026-04-07T23:28:56.264Z", "postProcessHash": "c2333c59ce9722debdd5b7b03c324536f65d453f0a62edfd260e0805fe0a0025" } } @@ -85,15 +85,15 @@ "075fc0ef474b35677c97c7f1d8bc82623f0cc76b0ecda2d83e955a55ec00ef43": { "9e1d82e51a0b1d442e891cdd7d5d3b9cc72842335d736abfaab172be31c79241": { "ja": { - "updatedAt": "2026-04-03T23:29:55.080Z", + "updatedAt": "2026-04-07T23:28:56.151Z", "postProcessHash": "ef698826600168e49119c085446c990307208beb50059acf3c290d55734f90f7" }, "ko": { - "updatedAt": "2026-04-03T23:29:55.083Z", + "updatedAt": "2026-04-07T23:28:56.156Z", "postProcessHash": "e387c599085470d921d4d7d6e420688b2cd9dfd067c455631af9b384c3e8fb02" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.072Z", + "updatedAt": "2026-04-07T23:28:56.140Z", "postProcessHash": "9175c76c2d83977c675ff7ffcef092a99d847e24a17c162d3b2d88489f0834db" } } @@ -101,15 +101,15 @@ "0885d4307706aea6dc2ad81e8d648485bc589f3f91c8dedcaa8b88f97f4e5ace": { "4e6076beebc8dd379dccddfc01f045df0effbdc2905452481f42190660b2555d": { "ja": { - "updatedAt": "2026-04-03T23:29:55.147Z", + "updatedAt": "2026-04-07T23:28:56.207Z", "postProcessHash": "337f57678c28dc40154ecda2b91179b380dbb5b0fff73716e71ec391423025f7" }, "ko": { - "updatedAt": "2026-04-03T23:29:55.093Z", + "updatedAt": "2026-04-07T23:28:56.168Z", "postProcessHash": "a0acea414973c39c2e378f5dbd1c8bea804b1de9cda49ce3f659ac11a2ace46f" }, "fr": { - "updatedAt": "2026-04-03T23:29:55.128Z", + "updatedAt": "2026-04-07T23:28:56.191Z", "postProcessHash": "1eb4030cc49511499c374262fb808ade7acffc127a3d4d4e74e92957b21fb9aa" } } @@ -117,15 +117,15 @@ "0aa8902b0a13ee82605486cf34e6efa993c122dd21d47acc104049641fc64301": { "aa074613350c5b93f5cfed07189d8611a730b1af79770bca10f633eb32f65399": { "ko": { - "updatedAt": "2026-04-03T23:29:55.040Z", + "updatedAt": "2026-04-07T23:28:56.101Z", "postProcessHash": "a8e0e5e388d959e159f11b24e156dbbe84a93b23e0913fe688ade008f434bfd4" }, "ja": { - "updatedAt": "2026-04-03T23:29:55.046Z", + "updatedAt": "2026-04-07T23:28:56.107Z", "postProcessHash": "0f9486fa283bc30221421808f38b9e227b54ae1de2d91c5e83a8ab05eb5c980c" }, "fr": { - 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"updatedAt": "2026-04-03T23:29:56.405Z", + "updatedAt": "2026-04-07T23:28:57.733Z", "postProcessHash": "bb4a45271380c38ffbf26aa7099313438fb9ccead1f7300905497a3c78eb1574" } } + }, + "add587d714111ace46ce3ec7279d5072b6010971cfa5eb7def5a5b4755dd4b73": { + "7f60c8e038798278a4987f219926fdfbdf9bdbda0c81f6ef2eb992ab53d3d6d7": { + "ja": { + "updatedAt": "2026-04-07T23:28:57.451Z", + "postProcessHash": "a636aa1d36930c1b71a09645b80b965dce08afc91be223ecdad764c67e17ca1e" + }, + "fr": { + "updatedAt": "2026-04-07T23:28:57.451Z", + "postProcessHash": "85645146ce4f272669c2563d327bf8ddec45b7accdaccb8b439c42cd2707ac7b" + }, + "ko": { + "updatedAt": "2026-04-07T23:28:57.452Z", + "postProcessHash": "4786c690d0edecc20828e41f766dfa07779571a9467b3fa7dc0d644bbeefcac9" + } + } } } }, diff --git a/ja/inference/lifecycle.mdx b/ja/inference/lifecycle.mdx index 46c8e55f01..882c30400d 100644 --- a/ja/inference/lifecycle.mdx +++ b/ja/inference/lifecycle.mdx @@ -38,7 +38,9 @@ W&B Inference の Models では、ライフサイクルの各段階を表す {/* takeru lifecycle-deprecated - この表は自動的に生成されるため、手動で編集しないでください。 */} -*現時点ではありません* +| 非推奨日 | 提供終了日 | モデル ID | 推奨代替モデル | +| ---------- | ---------- | ------------------------------------------- | ---------------------- | +| 2026-04-07 | 2026-04-21 | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | `Qwen/Qwen3.5-35B-A3B` |
diff --git a/ja/inference/models.mdx b/ja/inference/models.mdx index 220cb494db..bcc2864675 100644 --- a/ja/inference/models.mdx +++ b/ja/inference/models.mdx @@ -13,26 +13,26 @@ W&B Inference では、複数のオープンソース基盤モデルを利 {/* takeru inference-models - この表は自動生成されています。手動で編集しないでください。 */} -| モデル | モデル ID (API 使用時) | タイプ | コンテキストウィンドウ | パラメーター | 説明 | -| ----------------------------- | ---------------------------------------------- | --------- | ----------- | ---------------------- | ---------------------------------------------------------------------------------------------- | -| DeepSeek V3.1 | `deepseek-ai/DeepSeek-V3.1` | テキスト | 161k | 37B-671B (アクティブ-合計) | プロンプトテンプレートを通じて、思考モードと思考なしモードの両方をサポートする大規模なハイブリッドモデル。 | -| Meta Llama 4 Scout | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | テキスト、ビジョン | 64k | 17B-109B (アクティブ-合計) | テキストと画像の理解を統合したマルチモーダルモデルで、視覚タスクや統合的な分析に適しています。 | -| Meta Llama 3.3 70B | `meta-llama/Llama-3.3-70B-Instruct` | テキスト | 128k | 70B (合計) | 会話タスク、詳細な指示への追従、コーディングに優れた多言語モデル。 | -| Meta Llama 3.1 70B | `meta-llama/Llama-3.1-70B-Instruct` | テキスト | 128k | 700億 (合計) | 応答性の高い多言語チャットボットとのやり取り向けに最適化された、効率的な会話モデル。 | -| Meta Llama 3.1 8B | `meta-llama/Llama-3.1-8B-Instruct` | テキスト | 128k | 8B (合計) | 応答性の高い多言語チャットボット向けに最適化された、効率的な会話モデル。 | -| Microsoft Phi 4 Mini 3.8B | `microsoft/Phi-4-mini-instruct` | テキスト | 128k | 3.8B (合計) | リソースが限られた環境での高速応答に適した、コンパクトで効率的なモデル。 | -| MiniMax M2.5 | `MiniMaxAI/MiniMax-M2.5` | テキスト | 197k | 10B-230B (アクティブ-合計) | 高スループットかつ低レイテンシ向けに設計された、高度にスパースなアーキテクチャと優れたコーディング性能を備えたMoEモデル。 | -| Moonshot AI Kimi K2.5 | `moonshotai/Kimi-K2.5` | テキスト、画像 | 262k | 32B-1T (アクティブ-合計) | Kimi K2.5 は、320億のアクティブパラメーターと合計1兆のパラメーターを備えた、マルチモーダルのMixture-of-Experts言語モデルです。 | -| NVIDIA Nemotron 3 Super 120B | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8` | テキスト | 262k | 12B-120B (アクティブ-合計) | Nemotron 3 は、優れたエージェント型、自律的推論、対話の各機能を実現するよう設計された LatentMoE モデルです。 | -| OpenAI GPT OSS 120B | `openai/gpt-oss-120b` | テキスト | 131k | 5.1B-117B (アクティブ-合計) | 高度な推論、エージェント型、および汎用のユースケース向けに設計された、効率的なMixture-of-Expertsモデル。 | -| OpenAI GPT OSS 20B | `openai/gpt-oss-20b` | テキスト | 131k | 36億～200億 (アクティブ～総計) | OpenAIのHarmonyレスポンス形式でトレーニングされた、推論能力を備える低レイテンシのMixture-of-Expertsモデル。 | -| OpenPipe Qwen3 14B Instruct | `OpenPipe/Qwen3-14B-Instruct` | テキスト | 32.8k | 14.8B (合計) | OpenPipeがファインチューニングによるエージェント構築向けに最適化した、効率的な多言語対応の高密度な指示チューニング済みモデル。 | -| Qwen3 235B A22B Thinking-2507 | `Qwen/Qwen3-235B-A22B-Thinking-2507` | テキスト | 262k | 22B-235B (アクティブ／合計) | 構造化推論、数学、長文生成向けに最適化された高パフォーマンスのMixture-of-Expertsモデル。 | -| Qwen3 235B A22B-2507 | `Qwen/Qwen3-235B-A22B-Instruct-2507` | テキスト | 262k | 22B-235B (アクティブ-合計) | 論理推論向けに最適化された、効率的な多言語対応の Mixture-of-Experts 型指示チューニング済みモデル。 | -| Qwen3 30B A3B | `Qwen/Qwen3-30B-A3B-Instruct-2507` | テキスト | 262k | 3.3B-30.5B (アクティブ-総計) | Qwen3-30B-A3B-Instruct-2507 は、推論、コーディング、長文コンテキストの理解を強化した、30.5B の MoE 命令チューニング済みモデルです。 | -| Qwen3 Coder 480B A35B | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | テキスト | 262k | 35B-480B (アクティブ-総計) | 関数呼び出し、ツール使用、長いコンテキストでの推論などのエージェント型コーディングタスク向けに最適化されたMixture-of-Expertsモデル。 | -| Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` | テキスト、ビジョン | 262k | 3B-35B (アクティブ-総計) | Qwen3.5-35B-A3Bは、チャット、推論、エージェント型タスクにわたって、効率的かつ高スループットな推論を実現するよう設計された、オープンウェイトのマルチモーダルMoEモデルです。 | -| Z.AI GLM 5 | `zai-org/GLM-5-FP8` | テキスト | 200k | 40B-744B (アクティブ-合計) | 推論とコーディングで高いパフォーマンスを発揮する、長期的なエージェント型タスク向けのMixture-of-Expertsモデル。 | +| モデル | モデル ID (API 使用時) | タイプ | コンテキストウィンドウ | パラメーター | 説明 | +| ----------------------------- | ---------------------------------------------- | --------- | ----------- | --------------------- | ---------------------------------------------------------------------------------------------- | +| DeepSeek V3.1 | `deepseek-ai/DeepSeek-V3.1` | テキスト | 161k | 37B-671B (アクティブ-合計) | プロンプトテンプレートを通じて、思考モードと思考なしモードの両方をサポートする大規模なハイブリッドモデル。 | +| Meta Llama 3.3 70B | `meta-llama/Llama-3.3-70B-Instruct` | テキスト | 128k | 70B (合計) | 会話タスク、詳細な指示への追従、コーディングに優れた多言語モデル。 | +| Meta Llama 3.1 70B | `meta-llama/Llama-3.1-70B-Instruct` | テキスト | 128k | 700億 (合計) | 応答性の高い多言語チャットボットとのやり取り向けに最適化された、効率的な会話モデル。 | +| Meta Llama 3.1 8B | `meta-llama/Llama-3.1-8B-Instruct` | テキスト | 128k | 8B (合計) | 応答性の高い多言語チャットボット向けに最適化された、効率的な会話モデル。 | +| Microsoft Phi 4 Mini 3.8B | `microsoft/Phi-4-mini-instruct` | テキスト | 128k | 3.8B (合計) | リソースが限られた環境での高速応答に適した、コンパクトで効率的なモデル。 | +| MiniMax M2.5 | `MiniMaxAI/MiniMax-M2.5` | テキスト | 197k | 10B-230B (アクティブ-合計) | 高スループットかつ低レイテンシ向けに設計された、高度にスパースなアーキテクチャと優れたコーディング性能を備えたMoEモデル。 | +| Moonshot AI Kimi K2.5 | `moonshotai/Kimi-K2.5` | テキスト、画像 | 262k | 32B-1T (アクティブ-合計) | Kimi K2.5 は、320億のアクティブパラメーターと合計1兆のパラメーターを備えた、マルチモーダルのMixture-of-Experts言語モデルです。 | +| NVIDIA Nemotron 3 Super 120B | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8` | テキスト | 262k | 12B-120B (アクティブ-合計) | Nemotron 3 は、優れたエージェント型、自律的推論、対話の各機能を実現するよう設計された LatentMoE モデルです。 | +| OpenAI GPT OSS 120B | `openai/gpt-oss-120b` | テキスト | 131k | 5.1B-117B (アクティブ-合計) | 高度な推論、エージェント型、および汎用のユースケース向けに設計された、効率的なMixture-of-Expertsモデル。 | +| OpenAI GPT OSS 20B | `openai/gpt-oss-20b` | テキスト | 131k | 36億～200億 (アクティブ～総計) | OpenAIのHarmonyレスポンス形式でトレーニングされた、推論能力を備える低レイテンシのMixture-of-Expertsモデル。 | +| OpenPipe Qwen3 14B Instruct | `OpenPipe/Qwen3-14B-Instruct` | テキスト | 32.8k | 14.8B (合計) | OpenPipeがファインチューニングによるエージェント構築向けに最適化した、効率的な多言語対応の高密度な指示チューニング済みモデル。 | +| Qwen3 235B A22B Thinking-2507 | `Qwen/Qwen3-235B-A22B-Thinking-2507` | テキスト | 262k | 22B-235B (アクティブ／合計) | 構造化推論、数学、長文生成向けに最適化された高パフォーマンスのMixture-of-Expertsモデル。 | +| Qwen3 235B A22B-2507 | `Qwen/Qwen3-235B-A22B-Instruct-2507` | テキスト | 262k | 22B-235B (アクティブ-合計) | 論理推論向けに最適化された、効率的な多言語対応の Mixture-of-Experts 型指示チューニング済みモデル。 | +| Qwen3 30B A3B | `Qwen/Qwen3-30B-A3B-Instruct-2507` | テキスト | 262k | 3.3B-30.5B (アクティブ-総計) | Qwen3-30B-A3B-Instruct-2507 は、推論、コーディング、長文コンテキストの理解を強化した、30.5B の MoE 命令チューニング済みモデルです。 | +| Qwen3 Coder 480B A35B | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | テキスト | 262k | 35B-480B (アクティブ-総計) | 関数呼び出し、ツール使用、長いコンテキストでの推論などのエージェント型コーディングタスク向けに最適化されたMixture-of-Expertsモデル。 | +| Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` | テキスト、ビジョン | 262k | 3B-35B (アクティブ-総計) | Qwen3.5-35B-A3Bは、チャット、推論、エージェント型タスクにわたって、効率的かつ高スループットな推論を実現するよう設計された、オープンウェイトのマルチモーダルMoEモデルです。 | +| Z.AI GLM 5 | `zai-org/GLM-5-FP8` | テキスト | 200k | 40B-744B (アクティブ-合計) | 推論とコーディングで高いパフォーマンスを発揮する、長期的なエージェント型タスク向けのMixture-of-Expertsモデル。 | +| Meta Llama 4 Scout (非推奨) | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | テキスト、ビジョン | 64k | 17B-109B (アクティブ-合計) | テキストと画像の理解を統合したマルチモーダルモデルで、視覚タスクや統合的な分析に適しています。 |
## モデル ID の使用 diff --git a/ja/models/artifacts/update-an-artifact.mdx b/ja/models/artifacts/update-an-artifact.mdx index 9062051682..abe2a578f4 100644 --- a/ja/models/artifacts/update-an-artifact.mdx +++ b/ja/models/artifacts/update-an-artifact.mdx @@ -1,9 +1,9 @@ --- -description: W&B Run の内外で既存の Artifact を更新します。 +description: run がアクティブな間、または Public API のみを使用して既存の artifact を更新します。 title: Artifact を更新する --- -artifact の `description`、`metadata`、`alias` を更新するには、設定したい値を指定します。W&B サーバー上の artifact を更新するには、`save()` method を call します。artifact は W&B Run 中でも Run の外でも更新できます。 +artifact の `description`、`metadata`、`alias` を更新するには、設定したい値を指定します。W&B Public API ([`wandb.Api`](/ja/models/ref/python/public-api/api)) を使用して、以前 W&B にログした run を更新します。artifact が最初に初期化され、引き続きアクティブな場合は、artifact を更新するために `wandb.Run.save()` を使用します。 **Artifact.save() または wandb.Run.log_artifact() を使用するタイミング** @@ -12,7 +12,7 @@ artifact の `description`、`metadata`、`alias` を更新するには、設定 * 新しい artifact を作成して特定の run に関連付けるには、`wandb.Run.log_artifact()` を使用します。 -run の外で artifact を更新するには、W&B Public API ([`wandb.Api`](/ja/models/ref/python/public-api/api)) を使用します。run 中に artifact を更新するには、Artifact API ([`wandb.Artifact`](/ja/models/ref/python/experiments/artifact)) を使用します。 +artifact を更新するには、W&B Public API ([`wandb.Api`](/ja/models/ref/python/public-api/api)) を使用します。run がアクティブな間に artifact を更新するには、wandb.Artifact クラス ([`wandb.Artifact`](/ja/models/ref/python/experiments/artifact)) を使用します。 Model Registry 内のモデルにリンクされた artifact の alias は更新できません。 @@ -32,8 +32,8 @@ run の外で artifact を更新するには、W&B Public API ([`wandb.Api`] ``` - - 次のコード例は、`wandb.Api` API を使用して artifact の description を更新する方法を示しています。 + + 次の例は、[`wandb.Api`](/ja/models/ref/python/public-api/api) を使用して artifact を更新します。 ```python import wandb diff --git a/ja/models/automations/view-automation-history.mdx b/ja/models/automations/view-automation-history.mdx index a1354fa229..83cc755fa3 100644 --- a/ja/models/automations/view-automation-history.mdx +++ b/ja/models/automations/view-automation-history.mdx @@ -4,10 +4,10 @@ description: "ステータス、トリガーイベント、アクション結果 --- -オートメーションの実行履歴は、W&B Multi-tenant Cloud、W&B Dedicated Cloud、および W&B Self-Managed v0.75.0 以降で利用できます。 + オートメーションの実行履歴は、W&B Multi-tenant Cloud、W&B Dedicated Cloud、および W&B Self-Managed v0.75.0 以降で利用できます。 -このページでは、W&B の [automations](/ja/models/automations) の実行履歴を表示して確認する方法について説明します。どのイベントが オートメーション をトリガーしたか、どのようなアクションが実行されたか、またその結果が成功したか失敗したかを確認できます。 +このページでは、W&B の [オートメーション](/ja/models/automations) の実行履歴を表示して確認する方法について説明します。どのイベントが オートメーション をトリガーしたか、どのようなアクションが実行されたか、またその結果が成功したか失敗したかを確認できます。 実行された オートメーション ごとに、次の情報を含むレコードが生成されます。 @@ -23,15 +23,15 @@ description: "ステータス、トリガーイベント、アクション結果 1. プロジェクトのサイドバーで **Registry** をクリックし、レジストリにアクセスします。 2. リストからレジストリを選択します。 - 3. **Automations** タブでレジストリの automations を表示します。**Last execution** の Timestamp をクリックすると、実行履歴の詳細を表示できます。検索バーを使用して オートメーション 名でフィルターしたり、最後にトリガーされた日付で並べ替えて最近実行された automations を見つけたりできます。 + 3. **Automations** タブでレジストリの オートメーション を表示します。**Last execution** の Timestamp をクリックすると、実行履歴の詳細を表示できます。検索バーを使用して オートメーション 名でフィルターしたり、最後にトリガーされた日付で並べ替えて最近実行された オートメーション を見つけたりできます。 4. **Automations history** タブでは、イベント、アクション、ステータスを含むレジストリの オートメーション 実行履歴を新しい順に表示できます。実行 Timestamp をクリックすると、特定の実行の詳細を表示できます。 collection に関連付けられた オートメーション 実行がある場合は、アイコン ![Collection automation execution symbol, a circle with a right-pointing arrow](/images/automations/collection-automation-execution-icon.png) と関連する実行数が表示されます。 - 1. プロジェクトのサイドバーで **Automations** タブをクリックします。プロジェクトの automations が表示されます。**Last execution** の Timestamp をクリックすると、実行履歴の詳細を表示できます。検索バーを使用して オートメーション 名でフィルターしたり、最後にトリガーされた日付で並べ替えて最近実行された automations を見つけたりできます。 - 2. **History** タブでは、プロジェクトの automations のすべての実行を、最新の実行から順に新しい順で表示できます。各実行のメタデータ (イベント、アクション、ステータスを含む) が表示されます。実行 Timestamp をクリックすると、特定の実行の詳細を表示できます。 + 1. プロジェクトのサイドバーで **Automations** タブをクリックします。プロジェクトの オートメーション が表示されます。**Last execution** の Timestamp をクリックすると、実行履歴の詳細を表示できます。検索バーを使用して オートメーション 名でフィルターしたり、最後にトリガーされた日付で並べ替えて最近実行された オートメーション を見つけたりできます。 + 2. **History** タブでは、プロジェクトの オートメーション のすべての実行を、最新の実行から順に新しい順で表示できます。各実行のメタデータ (イベント、アクション、ステータスを含む) が表示されます。実行 Timestamp をクリックすると、特定の実行の詳細を表示できます。 @@ -65,6 +65,6 @@ description: "ステータス、トリガーイベント、アクション結果 ## 次のステップ
-* [オートメーションを作成する](/ja/models/automations/create-automations) -* [Automation events and scopes](/ja/models/automations/automation-events) について確認する -* [シークレットを作成する](/ja/platform/secrets) \ No newline at end of file +* [オートメーションを作成](/ja/models/automations/create-automations) +* [Automation events and scopes](/ja/models/automations/automation-events) を確認する +* [シークレットを作成](/ja/platform/secrets) \ No newline at end of file diff --git a/ja/models/runs/view-logged-runs.mdx b/ja/models/runs/view-logged-runs.mdx index 7cc1b96620..e765f7c04d 100644 --- a/ja/models/runs/view-logged-runs.mdx +++ b/ja/models/runs/view-logged-runs.mdx @@ -73,7 +73,7 @@ description: W&B App または LEET ターミナル UI を使用して、ログ * **Author**: run を作成した W&B entity。 * **Command**: run を初期化するコマンド。 * **Description**: run に対して指定した説明です。run の作成時に説明を指定しなかった場合、このフィールドは空になります。W&B App または Python SDK を使用して、プログラムから run に説明を追加できます。 -* **Tracked Hours**: run が実際に計算を実行している、またはデータをログしている時間です。一時停止中や待機中の時間は含まれません。このメトリクスを使うと、run に実際に費やされた計算時間を把握できます。 +* **Tracked Hours**: run が実際に計算を実行している、またはデータをログしている時間です。一時停止中や待機中の時間は含まれません。このメトリクスを使うと、run に実際に費やされた計算時間を把握できます。Tracked Hours に対して課金されることはなく、すべてのプランで無制限です。 * **Runtime**: run の開始から終了までの合計時間を測定します。これは、run の一時停止中やリソース待ちの時間も含む実時間です。このメトリクスは、run 全体の経過時間を示します。 * **Git repository**: run に関連付けられた git repository。このフィールドを表示するには、[git を有効化](/ja/platform/app/settings-page/user-settings/#personal-github-integration)する必要があります。 * **Host name**: W&B が run を実行する場所です。自分のマシン上でローカルに run を初期化した場合、W&B はそのマシン名を表示します。 diff --git a/ja/platform/app/settings-page/billing-settings.mdx b/ja/platform/app/settings-page/billing-settings.mdx index 7e931af433..589f78323c 100644 --- a/ja/platform/app/settings-page/billing-settings.mdx +++ b/ja/platform/app/settings-page/billing-settings.mdx @@ -77,6 +77,10 @@ title: 課金設定を管理する * **請求管理者**のみが支出アラートを受け取ります。 * デフォルトでは、支出アラートは $200、$450、$700、$1000 に設定されています。 + + トラッキング時間に対して課金されることはなく、すべてのプランで無制限です。トラッキング時間は監視目的で **Usage** ページに表示されます。 + +
#### 支出アラートを表示する
diff --git a/ja/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx b/ja/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx index 5a400d5d5f..dff0530173 100644 --- a/ja/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx +++ b/ja/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx @@ -11,6 +11,10 @@ keywords: ["Administrator"] `<>` で囲まれた値は、組織名に置き換えてください。 + + トラッキング時間はすべてのプランで無制限で、課金対象ではありません。トラッキング時間は監視目的でのみ **Usage** ページに表示されます。 + + *** {/* ---- AUTO-GENERATED: tab badges ---- diff --git a/ja/weave/guides/core-types/prompts-version.mdx b/ja/weave/guides/core-types/prompts-version.mdx index e6fea3e3fb..97ef1d2c51 100644 --- a/ja/weave/guides/core-types/prompts-version.mdx +++ b/ja/weave/guides/core-types/prompts-version.mdx @@ -44,7 +44,7 @@ weave://///object/:)`: プロンプトの `:latest` バージョンを取得します。`weave.init(...)` の呼び出しが必要です。 -* `weave.ref(:)`: 指定したバージョンのプロンプトを取得します。`weave.init(...)` の呼び出しが必要です。 +* `weave.ref(:)`: エイリアス、バージョンハッシュ、またはバージョンインデックスでプロンプトを取得します。`weave.init(...)` の呼び出しが必要です。 * `weave.ref()`: 指定した[完全修飾された ref URI](#construct-a-fully-qualified-ref-uri)にあるプロンプトを取得します。`weave.init(...)` を呼び出す必要はありません。 次の例では、アプリケーションで使用できるように `support_prompt:v3` を読み込みます。 @@ -74,34 +74,34 @@ weave.init("your-team-name/your-project-name") prompt = weave.ref("support_prompt:v3").get() ``` -
- ## 本番環境でプロンプトのバージョンを使う +
+ ## 本番環境でプロンプトを使用する
-本番システムにプロンプトをデプロイする場合は、最新バージョンを参照するのではなく、特定のプロンプトのバージョンを固定してください。本番アプリケーションでは、`weave.ref(:).get()` を使ってそのバージョンを読み込みます。 +本番システムにプロンプトをデプロイする際は、バージョンインデックスや最新バージョンではなく、`production` のような [エイリアス](#add-prompt-tags-and-aliases) を使用します。本番アプリケーションでは、`weave.ref(:).get()` でプロンプトを読み込みます。 -バージョンを固定しておくと、あとから新しいプロンプトのバージョンが公開されても、本番環境の動作を安定した状態に保てます。 +エイリアス を使用すると、本番環境の動作を安定的かつ予測可能に保てます。新しいバージョンを本番に昇格する準備ができたら、そのバージョンに エイリアス を移動します。すると、すべての利用側でその変更が自動的に反映されます。 一般的なワークフローは次のとおりです。 -1. 新しいプロンプトのバージョンを開発してテストします。 -2. 新しいプロンプトをデータセットまたは評価スイートに対して評価します。 -3. 本番システムが新しいバージョンを参照するように更新します。 +1. 新しいバージョンのプロンプトを開発してテストします。 +2. データセットまたは評価スイートに対して新しいプロンプトを評価します。 +3. `client.set_aliases(new_ref, "production")` を使用して、`production` エイリアス を新しいバージョンに移動します。 -この方法により、チームは本番環境の動作を意図せず変えることなく、安全にプロンプトを反復的に改善できます。 +この方法により、チームは本番環境の動作を意図せず変更することなく、安全にプロンプトを繰り返し改善できます。 ## プロンプトのバージョンを表示して比較する UIでプロンプトのバージョンを表示するには、次の手順に従います。 1. [wandb.ai](https://wandb.ai/) にアクセスし、プロジェクトを選択します。 -2. Weave プロジェクトのサイドバーで **Assets** をクリックします。Assets ページが開きます。 +2. Weave のプロジェクトのサイドバーで **Assets** をクリックします。Assets ページが開きます。 3. Assets ページで **Prompts** をクリックします。プロジェクト内のプロンプトが一覧表示された Prompts ページが開きます。 4. **Versions** 列で、表示したいプロンプトの **(x) Versions** をクリックします。プロンプトのバージョン一覧が開きます。 ![選択したプロンプトのバージョンを示す Prompt Assets の UI。](/weave/guides/core-types/imgs/prompt-object.png) -5. (任意) 一覧に表示されたプロンプトの横にあるチェックボックスを選択し、表のツールバーにある **Compare** ボタンをクリックすると、プロンプトのバージョンを比較できます。これにより、プロンプト間の差分を確認できます。 +1. (任意) 一覧に表示されたプロンプトの横にあるチェックボックスを選択し、表のツールバーにある **Compare** ボタンをクリックすると、プロンプトのバージョンを比較できます。これにより、プロンプト間の差分を確認できます。 ![2 つの異なるプロンプト バージョン間の差分を示す Compare prompts UI。](/weave/guides/core-types/imgs/prompt-comparison.png) diff --git a/ja/weave/guides/integrations.mdx b/ja/weave/guides/integrations.mdx index dbcbeea2a8..7af7f5aba1 100644 --- a/ja/weave/guides/integrations.mdx +++ b/ja/weave/guides/integrations.mdx @@ -51,6 +51,7 @@ LLM プロバイダーとは、予測を生成するための大規模言語モ * **[AutoGen](/ja/weave/guides/integrations/autogen)** * **[Verdict](/ja/weave/guides/integrations/verdict)** * **[TypeScript SDK](/ja/weave/guides/integrations/js)** +* **[Vercel AI SDK](/ja/weave/guides/integrations/vercel_ai_sdk)** * **[Agno](/ja/weave/guides/integrations/agno)** * **[Koog](/ja/weave/guides/integrations/koog)** diff --git a/ja/weave/guides/integrations/vercel_ai_sdk.mdx b/ja/weave/guides/integrations/vercel_ai_sdk.mdx new file mode 100644 index 0000000000..a28583eaa7 --- /dev/null +++ b/ja/weave/guides/integrations/vercel_ai_sdk.mdx @@ -0,0 +1,156 @@ +--- +title: "Vercel AI SDK" +description: "OpenTelemetry を使用して Weave で Vercel AI SDK の Call をトレースする" +--- + +[OpenTelemetry (OTel)](https://opentelemetry.io/) を使用すると、Weave で [Vercel AI SDK](https://ai-sdk.dev/) の Call をトレースできます。Vercel AI SDK は、Next.js、Nuxt、SvelteKit などのフレームワークをサポートし、AI を活用したアプリケーションを構築するための TypeScript ツールキットです。`experimental_telemetry` オプションを通じて、OpenTelemetry を組み込みでサポートしています。 + +このガイドでは、Vercel AI SDK から Weave にトレースを送信するように OTel を設定する方法を説明します。AI SDK は、Next.js と組み合わせて使用することも、スタンドアロンの Node.js アプリケーションとして使用することもできます。 + +Weave での OTel トレースの詳細については、[Send OTel traces to Weave](../tracking/otel) を参照してください。 + +
+ ### 前提条件 +
+ +このガイドの Next.js と Node.js の例では、どちらも同じ依存関係が必要です。まず、以下を行います。 + +1. 次の Vercel および OTel ライブラリをインストールします。 + + ```bash + npm install ai @ai-sdk/openai @opentelemetry/api @opentelemetry/sdk-trace-node @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto @opentelemetry/resources zod + ``` + +2. 次の環境変数を設定します。 + + ```bash + export WANDB_API_KEY="your-wandb-api-key" + export OPENAI_API_KEY="your-openai-api-key" + ``` + + W&B APIキーは [User Settings](https://wandb.ai/settings) から取得できます。 + +
+ ## Next.js 向けに OTel トレースを設定する +
+ +このセクションでは、Next.js アプリで Weave を設定する方法を説明します。この例にはアプリ全体は含まれておらず、インストルメンテーションの設定と、Vercel AI SDK の関数でインストルメンテーションを呼び出す方法のみを示しています。この場合は、OpenAI をシンプルに呼び出す例です。 + +
+ ### インストルメンテーションを設定する +
+ +Next.js アプリケーションでは、OTel の設定に `instrumentation.ts` ファイルを使用します。このファイルはサーバーの起動時に 1 回だけ実行され、AI SDK が使用するトレーサープロバイダーを設定します。 + +Weave を Vercel の OTel 機能と統合するには、project ルートに `instrumentation.ts` ファイルを作成し、次のコードを追加します。その際、`resourceFromAttributes()` 関数のチーム名と project 名を更新してください。 + +```typescript instrumentation.ts lines {17-19} +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; + +export function register() { + const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// W&B Weave エンドポイントを使用するよう OTel エクスポーターを設定します。 + const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, + }); + +// W&B の認証情報を設定します。 + const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], + }); + + provider.register(); +} +``` + +これにより、Weave の OTel エンドポイントにトレースデータを送信するよう設定された OTLP エクスポーターが作成され、W&B APIキーを使用して認証されます。 + + +
+ ### 関数でテレメトリーを設定する +
+ +インストルメンテーションを追加したら、任意の AI SDK の関数呼び出しで Vercel の `experimental_telemetry` オプションを使用して、OTel スパンを生成します。 + +```typescript route.ts lines {10} +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +export async function POST(req: Request) { + const { prompt } = await req.json(); + +// functionにexperimental_telemetryフィールドを追加します。 + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt, + experimental_telemetry: { isEnabled: true }, + }); + + return Response.json({ text: result.text }); +} +``` + +テレメトリーを有効にしたすべての `generateText` Call で、Weave にエクスポートされる OTel スパンが生成されます。 + + +
+ ## Node.js 向けに OTel トレースを設定する +
+ +スタンドアロンの Node.js アプリケーション (Next.js を使用しない場合) では、AI SDK を Call する前に、エントリファイルの先頭でトレーサープロバイダーを設定します。 + +前提条件を満たしたら、追加の設定なしでこの例を run し、スパンを生成できます。 + +```typescript lines {11-14,18-19,31} title="test-app.ts" +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// W&B Weave エンドポイントを使用するように OTel エクスポーターを設定します。 +const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, +}); + +// W&B の認証情報を設定します。 +const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], +}); + +provider.register(); + +// 関数に experimental_telemetry フィールドを追加します。 +async function main() { + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt: "Explain OpenTelemetry in one sentence.", + experimental_telemetry: { isEnabled: true }, + }); + + console.log(result.text); + + await provider.shutdown(); +} + +main(); +``` + +`BatchSpanProcessor` は スパン を非同期でフラッシュします。スタンドアロンのスクリプト、サーバーレス関数、CLI ツールのような短命なプロセスでは、すべての スパン が Weave に送信されるよう、プロセスが終了する前に `provider.shutdown()` を呼び出してください。長時間稼働するサーバー (`instrumentation.ts` 経由で起動した Next.js の Dev サーバーなど) では、これは不要です. diff --git a/ja/weave/quickstart-inference.mdx b/ja/weave/quickstart-inference.mdx index 13c37572e4..20bf339e31 100644 --- a/ja/weave/quickstart-inference.mdx +++ b/ja/weave/quickstart-inference.mdx @@ -244,10 +244,10 @@ import WeaveQuickstartPrereqs from "/snippets/ja/_includes/weave-quickstart-prer
- ## 複数のModelsを比較する + ## 複数のモデルを比較する
-W&B Inference では複数のModelsを利用できます。Llama と DeepSeek の応答パフォーマンスを比較するには、次のコードを使用します。 +W&B Inference では複数のモデルを利用できます。Llama と DeepSeek の応答パフォーマンスを比較するには、次のコードを使用します。 @@ -278,9 +278,9 @@ W&B Inference では複数のModelsを利用できます。Llama と DeepSee ) return response.choices[0].message.content - # 異なるModelsのインスタンスを作成 + # 異なるモデルのインスタンスを作成 llama_model = InferenceModel(model_name="meta-llama/Llama-3.1-8B-Instruct") - deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3-0324") + deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3.1") # 応答を比較 test_question = "Explain quantum computing in one paragraph for a high school student." @@ -319,9 +319,9 @@ W&B Inference では複数のModelsを利用できます。Llama と DeepSee }); } - // 異なるModelsのインスタンスを作成 + // 異なるモデルのインスタンスを作成 const llamaModel = createModel('meta-llama/Llama-3.1-8B-Instruct'); - const deepseekModel = createModel('deepseek-ai/DeepSeek-V3-0324'); + const deepseekModel = createModel('deepseek-ai/DeepSeek-V3.1'); // 応答を比較 const testQuestion = 'Explain quantum computing in one paragraph for a high school student.'; @@ -428,7 +428,7 @@ Weave に組み込まれている `EvaluationLogger` を使用して、Q&A ```typescript lines import { EvaluationLogger } from 'weave'; - // シンプルなデータセットを作成する + // Create a simple dataset interface DatasetExample { question: string; expected?: string; @@ -441,7 +441,7 @@ Weave に組み込まれている `EvaluationLogger` を使用して、Q&A { question: 'Name a primary color', expected_one_of: ['red', 'blue', 'yellow'] }, ]; - // スコアラーを定義する + // Define a scorer const accuracyScorer = weave.op(function accuracyScorer(args: { expected: string; output: string; @@ -461,15 +461,15 @@ Weave に組み込まれている `EvaluationLogger` を使用して、Q&A return { correct: isCorrect, score: isCorrect ? 1.0 : 0.0 }; }); - // WeaveのEvaluationLoggerを使用してモデルを評価する + // Evaluate a model using Weave's EvaluationLogger async function evaluateModel( model: (question: string) => Promise, modelName: string, dataset: DatasetExample[] ): Promise { - // トークン使用量を取得するため、モデルを呼び出す前にEvaluationLoggerを初期化する - // W&B Inferenceでコストをトラッキングする際に特に重要 - // モデル名を有効なフォーマットに変換する（英数字以外の文字をアンダースコアに置換） + // Initialize EvaluationLogger BEFORE calling the model to capture token usage + // This is especially important for W&B Inference to track costs + // Convert model name to a valid format (replace non-alphanumeric chars with underscores) const safeModelName = modelName.replace(/\//g, '_').replace(/-/g, '_').replace(/\./g, '_'); const evalLogger = new EvaluationLogger({ name: 'inference_evaluation', @@ -478,45 +478,45 @@ Weave に組み込まれている `EvaluationLogger` を使用して、Q&A }); for (const example of dataset) { - // モデルの予測を取得する + // Get model prediction const output = await model(example.question); - // 予測をログする + // Log the prediction const predLogger = evalLogger.logPrediction( { question: example.question }, output ); - // 出力をスコアリングする + // Score the output const score = await accuracyScorer({ expected: example.expected || '', output: output, expected_one_of: example.expected_one_of }); - // スコアをログする + // Log the score predLogger.logScore('accuracy', score.score); - // この予測のログを完了する + // Finish logging for this prediction predLogger.finish(); } - // サマリーをログする - Weaveが精度スコアを自動集計する + // Log summary - Weave automatically aggregates the accuracy scores await evalLogger.logSummary(); console.log(`Evaluation complete for ${modelName} (logged as: ${safeModelName}). View results in the Weave UI.`); } - // 複数のモデルを比較する - Weaveの評価フレームワークの主要機能 + // Compare multiple models - a key feature of Weave's evaluation framework const modelsToCompare = [ { model: llamaModel, name: 'meta-llama/Llama-3.1-8B-Instruct' }, - { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3-0324' }, + { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3.1' }, ]; for (const { model, name } of modelsToCompare) { await evaluateModel(model, name, dataset); } - // Weave UIのEvalsタブにアクセスして、モデル間の結果を比較する + // In the Weave UI, navigate to the Evals tab to compare results across models ``` diff --git a/ko/inference/lifecycle.mdx b/ko/inference/lifecycle.mdx index d859640819..589c9c75c4 100644 --- a/ko/inference/lifecycle.mdx +++ b/ko/inference/lifecycle.mdx @@ -38,7 +38,9 @@ W&B Inference의 모델는 수명 주기의 각 단계에 대해 다음 용 {/* takeru lifecycle-deprecated - 이 표는 자동 생성되므로 직접 편집하지 마세요. */} -*현재 없음* +| 사용 중단 날짜 | 서비스 종료 날짜 | 모델 ID | 권장 대체 모델 | +| ---------- | ---------- | ------------------------------------------- | ---------------------- | +| 2026-04-07 | 2026-04-21 | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | `Qwen/Qwen3.5-35B-A3B` |
diff --git a/ko/inference/models.mdx b/ko/inference/models.mdx index 2fbc0057cf..3fe084b99c 100644 --- a/ko/inference/models.mdx +++ b/ko/inference/models.mdx @@ -16,7 +16,6 @@ W&B Inference는 여러 오픈 소스 파운데이션 모델에 대한 액 | 모델 | 모델 ID(API 사용 시) | 유형 | 컨텍스트 윈도우 | 파라미터 | 설명 | | ----------------------------- | ---------------------------------------------- | -------- | -------- | ----------------------- | --------------------------------------------------------------------------------------------- | | DeepSeek V3.1 | `deepseek-ai/DeepSeek-V3.1` | 텍스트 | 161k | 37B-671B (활성-전체) | 프롬프트 템플릿을 통해 추론 모드와 비추론 모드를 모두 지원하는 대규모 하이브리드 모델입니다. | -| Meta Llama 4 Scout | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | 텍스트, 비전 | 64k | 17B-109B (활성-총계) | 텍스트와 이미지 이해를 결합한 멀티모달 모델로, 시각적 작업과 복합 분석에 적합합니다. | | Meta Llama 3.3 70B | `meta-llama/Llama-3.3-70B-Instruct` | 텍스트 | 128k | 700억 개(총계) | 대화형 작업, 세부 지침 준수, 코딩에 뛰어난 다국어 모델입니다. | | Meta Llama 3.1 70B | `meta-llama/Llama-3.1-70B-Instruct` | 텍스트 | 128k | 70B (전체) | 응답성이 뛰어난 다국어 챗봇 상호작용에 최적화된 효율적인 대화형 모델입니다. | | Meta Llama 3.1 8B | `meta-llama/Llama-3.1-8B-Instruct` | 텍스트 | 128k | 8B (총계) | 빠르게 응답하는 다국어 챗봇 상호작용에 최적화된 효율적인 대화형 모델입니다. | @@ -33,6 +32,7 @@ W&B Inference는 여러 오픈 소스 파운데이션 모델에 대한 액 | Qwen3 Coder 480B A35B | `Qwen/Qwen3-Coder-480B-A35B-Instruct` | 텍스트 | 262k | 35B-480B (활성-총계) | 함수 call, 도구 사용, 긴 컨텍스트 추론과 같은 에이전트형 코딩 작업에 최적화된 MoE(전문가 혼합) 모델. | | Qwen3.5 35B A3B | `Qwen/Qwen3.5-35B-A3B` | 텍스트, 이미지 | 262k | 3B-35B (활성-전체) | Qwen3.5-35B-A3B는 채팅, 추론, agent 작업 전반에서 효율적이고 높은 처리량의 Inference를 위해 설계된 오픈 웨이트 멀티모달 MoE 모델입니다. | | Z.AI GLM 5 | `zai-org/GLM-5-FP8` | 텍스트 | 200k | 40B-744B (활성-전체) | 추론과 코딩에서 뛰어난 성능을 보이는, 장기적인 에이전트 기반 작업을 위한 Mixture-of-Experts 모델. | +| Meta Llama 4 Scout (사용 중단) | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | 텍스트, 비전 | 64k | 17B-109B (활성-총계) | 텍스트와 이미지 이해를 결합한 멀티모달 모델로, 시각적 작업과 복합 분석에 적합합니다. |
## 모델 ID 사용하기 diff --git a/ko/models/artifacts/update-an-artifact.mdx b/ko/models/artifacts/update-an-artifact.mdx index 4d549de55e..4e654fdca9 100644 --- a/ko/models/artifacts/update-an-artifact.mdx +++ b/ko/models/artifacts/update-an-artifact.mdx @@ -1,9 +1,9 @@ --- -description: W&B Run 내부와 외부에서 기존 Artifact를 업데이트합니다. +description: run이 활성 상태일 때 또는 Public API만 사용하여 기존 artifact를 업데이트합니다. title: artifact 업데이트 --- -artifact의 `description`, `metadata`, `alias`를 업데이트하려면 원하는 값을 지정하세요. W&B 서버의 artifact를 업데이트하려면 `save()` 메서드를 call 하세요. W&B Run 중이나 Run 외부에서 artifact를 업데이트할 수 있습니다. +artifact의 `description`, `metadata`, `alias`를 업데이트하려면 원하는 값을 지정하세요. W&B Public API([`wandb.Api`](/ko/models/ref/python/public-api/api))를 사용해 이전에 W&B에 로깅된 run을 업데이트하세요. artifact가 처음 초기화된 후 아직 활성 상태라면 `wandb.Run.save()`를 사용해 업데이트하세요. **Artifact.save() 또는 wandb.Run.log_artifact()를 사용해야 하는 경우** @@ -12,7 +12,7 @@ artifact의 `description`, `metadata`, `alias`를 업데이트하려면 원하 * 새 artifact를 만들고 특정 run에 연결하려면 `wandb.Run.log_artifact()`를 사용하세요. -run 외부에서 artifact를 업데이트하려면 W&B Public API([`wandb.Api`](/ko/models/ref/python/public-api/api))를 사용하세요. run 중에 artifact를 업데이트하려면 Artifact API([`wandb.Artifact`](/ko/models/ref/python/experiments/artifact))를 사용하세요. +artifact를 업데이트하려면 W&B Public API([`wandb.Api`](/ko/models/ref/python/public-api/api))를 사용하세요. run이 활성 상태인 동안에는 wandb.Artifact([`wandb.Artifact`](/ko/models/ref/python/experiments/artifact)) Class를 사용하세요. Model Registry의 모델에 연결된 artifact의 alias는 업데이트할 수 없습니다. @@ -32,8 +32,8 @@ run 외부에서 artifact를 업데이트하려면 W&B Public API([`wandb.Ap ``` - - 다음 코드 예제는 `wandb.Api` API를 사용해 artifact의 설명을 업데이트하는 방법을 보여줍니다: + + 다음 예제는 [`wandb.Api`](/ko/models/ref/python/public-api/api)를 사용해 artifact를 업데이트합니다: ```python import wandb diff --git a/ko/models/automations/view-automation-history.mdx b/ko/models/automations/view-automation-history.mdx index 8bea210d6b..d051834f01 100644 --- a/ko/models/automations/view-automation-history.mdx +++ b/ko/models/automations/view-automation-history.mdx @@ -4,7 +4,7 @@ description: "상태, 트리거 이벤트, 작업 결과를 확인할 수 있도 --- -자동화 실행 기록은 W&B Multi-tenant Cloud, W&B Dedicated Cloud, W&B Self-Managed v0.75.0 이상에서 사용할 수 있습니다. + 자동화 실행 기록은 W&B Multi-tenant Cloud, W&B Dedicated Cloud, W&B Self-Managed v0.75.0 이상에서 사용할 수 있습니다. 이 페이지에서는 무엇이 자동화를 트리거했는지, 어떤 작업이 수행되었는지, 그리고 성공 또는 실패 여부를 포함해 W&B [automations](/ko/models/automations)의 실행 이력을 보고 이해하는 방법을 설명합니다. @@ -31,7 +31,7 @@ description: "상태, 트리거 이벤트, 작업 결과를 확인할 수 있도 1. 프로젝트 사이드바에서 **Automations** 탭을 클릭합니다. 프로젝트의 자동화가 표시됩니다. 실행 이력 세부 정보를 보려면 **Last execution** Timestamp를 클릭합니다. 검색창을 사용해 자동화 name으로 필터링할 수 있으며, 마지막 트리거 날짜순으로 정렬해 최근 실행된 자동화를 찾을 수 있습니다. - 2. **History** 탭에서 가장 최근 실행부터 프로젝트 자동화의 모든 실행을 최신순으로 확인합니다. 각 실행의 메타데이터가 표시되며, 여기에는 이벤트, 작업, 상태가 포함됩니다. 특정 실행의 세부 정보를 보려면 실행 Timestamp를 클릭합니다. + 2. **이력** 탭에서 가장 최근 실행부터 프로젝트 자동화의 모든 실행을 최신순으로 확인합니다. 각 실행의 메타데이터가 표시되며, 여기에는 이벤트, 작업, 상태가 포함됩니다. 특정 실행의 세부 정보를 보려면 실행 Timestamp를 클릭합니다. @@ -65,6 +65,6 @@ description: "상태, 트리거 이벤트, 작업 결과를 확인할 수 있도 ## 다음 단계
-* [자동화 생성하기](/ko/models/automations/create-automations) -* [Automation events and scopes](/ko/models/automations/automation-events) 알아보기 -* [시크릿 생성하기](/ko/platform/secrets) \ No newline at end of file +* [자동화 만들기](/ko/models/automations/create-automations) +* [Automation events and scopes](/ko/models/automations/automation-events)에 대해 알아보기 +* [시크릿 만들기](/ko/platform/secrets) \ No newline at end of file diff --git a/ko/models/runs/view-logged-runs.mdx b/ko/models/runs/view-logged-runs.mdx index 433827bd41..53c490c6bf 100644 --- a/ko/models/runs/view-logged-runs.mdx +++ b/ko/models/runs/view-logged-runs.mdx @@ -73,7 +73,7 @@ description: W&B App 또는 LEET 터미널 UI를 사용해 특정 로깅된 run * **Author**: run을 생성한 W&B entity입니다. * **Command**: run을 초기화하는 command입니다. * **Description**: 사용자가 제공한 run 설명입니다. run을 생성할 때 설명을 지정하지 않으면 이 필드는 비어 있습니다. W&B App에서 또는 Python SDK를 사용해 프로그래밍 방식으로 run에 설명을 추가할 수 있습니다. -* **Tracked Hours**: 일시 중지되거나 대기한 시간을 제외하고, run이 실제로 계산하거나 데이터를 로깅한 시간입니다. 이 metric은 run에 실제로 소요된 계산 시간을 파악하는 데 도움이 됩니다. +* **Tracked Hours**: 일시 중지되거나 대기한 시간을 제외하고, run이 실제로 계산하거나 데이터를 로깅한 시간입니다. 이 metric은 run에 실제로 소요된 계산 시간을 파악하는 데 도움이 됩니다. Tracked Hours에는 요금이 청구되지 않으며, 모든 플랜에서 무제한으로 제공됩니다. * **Runtime**: run 시작부터 종료까지의 전체 시간을 측정합니다. 여기에는 run이 일시 중지되었거나 리소스를 기다린 시간도 포함됩니다. 즉, run의 실제 경과 시간(wall-clock time)입니다. 이 metric은 run의 전체 소요 시간을 보여줍니다. * **Git repository**: run과 연결된 git 저장소입니다. 이 필드를 보려면 [git을 활성화](/ko/platform/app/settings-page/user-settings/#personal-github-integration)해야 합니다. * **Host name**: W&B가 run을 계산하는 위치입니다. 사용자의 머신에서 로컬로 run을 초기화하면 W&B는 해당 머신의 이름을 표시합니다. diff --git a/ko/platform/app/settings-page/billing-settings.mdx b/ko/platform/app/settings-page/billing-settings.mdx index b71fcc6f5f..6bbf51cfe4 100644 --- a/ko/platform/app/settings-page/billing-settings.mdx +++ b/ko/platform/app/settings-page/billing-settings.mdx @@ -57,12 +57,12 @@ title: billing 설정 관리 특정 청구 기간 동안 지출 알림은 조직이 USD 기준의 사용자 지정 가능한 지출 임계값에 하나 이상 도달하면 조직 관리자와 청구 관리자에게 이메일로 알립니다. 카테고리별로 서로 다른 지출 알림을 최대 5개까지 설정할 수 있습니다: -| Category | Description | -| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| Category | Description | +| --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 저장소 | 청구 기간 동안 조직의 저장소 [사용](#usage-alerts)에 대한 요금입니다. [가격](https://wandb.ai/site/pricing/)을 참조하세요. | -| 추적 시간 | 청구 기간 동안 조직의 트레이닝 [사용](#usage-alerts)에 대한 요금입니다. [가격](https://wandb.ai/site/pricing/)을 참조하세요. | -| Weave | 청구 기간 동안 조직의 Weave [사용](#usage-alerts)에 대한 요금입니다. [가격](https://wandb.ai/site/pricing/)을 참조하세요. 배포에서 Weave가 활성화된 경우에만 표시됩니다. | -| Inference | 청구 기간 동안 조직의 Inference [사용](#usage-alerts)에 대한 요금입니다. [Inference Pricing](https://wandb.ai/site/pricing/inference/)을 참조하세요. 배포에서 Inference가 활성화된 경우에만 표시됩니다. | +| 추적 시간 | 청구 기간 동안 조직의 트레이닝 [사용](#usage-alerts)에 대한 요금입니다. [가격](https://wandb.ai/site/pricing/)을 참조하세요. | +| Weave | 청구 기간 동안 조직의 Weave [사용](#usage-alerts)에 대한 요금입니다. [가격](https://wandb.ai/site/pricing/)을 참조하세요. 배포에서 Weave가 활성화된 경우에만 표시됩니다. | +| Inference | 청구 기간 동안 조직의 Inference [사용](#usage-alerts)에 대한 요금입니다. [Inference Pricing](https://wandb.ai/site/pricing/inference/)을 참조하세요. 배포에서 Inference가 활성화된 경우에만 표시됩니다. | | 트레이닝 | 청구 기간 동안 조직의 Serverless RL [사용](#usage-alerts)에 대한 요금입니다. [Serverless RL Pricing](https://wandb.ai/site/pricing/reinforcement-learning/)을 참조하세요. 배포에서 W&B Training이 활성화된 경우에만 표시됩니다. | 메시지는 `support@wandb.com`에서 발송됩니다. @@ -77,6 +77,10 @@ title: billing 설정 관리 * **청구 관리자**만 지출 알림을 받습니다. * 기본적으로 지출 알림은 $200, $450, $700, $1000으로 설정됩니다. + + 추적 시간에는 요금이 청구되지 않으며, 모든 플랜에서 무제한으로 제공됩니다. 추적 시간은 모니터링 목적으로 **Usage** 페이지에 표시됩니다. + +
#### 지출 알림 보기
diff --git a/ko/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx b/ko/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx index 05b7957d17..42c31e705c 100644 --- a/ko/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx +++ b/ko/support/models/articles/how-can-i-see-the-bytes-stored-bytes-tra.mdx @@ -11,6 +11,10 @@ keywords: ["관리자"] `<>`로 둘러싸인 값은 조직 이름으로 바꾸세요. + + 추적 시간은 모든 플랜에서 무제한이며 요금이 청구되지 않습니다. 추적 시간은 모니터링 목적으로만 **Usage** 페이지에 표시됩니다. + + *** {/* ---- AUTO-GENERATED: tab badges ---- diff --git a/ko/weave/guides/core-types/prompts-version.mdx b/ko/weave/guides/core-types/prompts-version.mdx index 6879f14543..9d890329ae 100644 --- a/ko/weave/guides/core-types/prompts-version.mdx +++ b/ko/weave/guides/core-types/prompts-version.mdx @@ -44,7 +44,7 @@ weave://///object/:)`: 프롬프트의 `:latest` 버전을 조회합니다. `weave.init(...)`을 호출해야 합니다. -* `weave.ref(:)`: 지정한 프롬프트 버전을 조회합니다. `weave.init(...)`을 호출해야 합니다. +* `weave.ref(:)`: 별칭, 버전 해시 또는 버전 인덱스로 프롬프트를 조회합니다. `weave.init(...)`을 호출해야 합니다. * `weave.ref()`: 지정한 [전체 ref URI](#construct-a-fully-qualified-ref-uri)에 있는 프롬프트를 조회합니다. `weave.init(...)`을 호출할 필요가 없습니다. 다음 예제는 애플리케이션에서 사용할 수 있도록 `support_prompt:v3` 버전을 불러옵니다. @@ -74,21 +74,21 @@ weave.init("your-team-name/your-project-name") prompt = weave.ref("support_prompt:v3").get() ``` -
- ## 프로덕션에서 프롬프트 버전 사용하기 +
+ ## 프로덕션에서 프롬프트 사용
-프로덕션 시스템에 프롬프트를 배포할 때는 최신 버전을 참조하지 말고 특정 프롬프트 버전에 고정하세요. 프로덕션 애플리케이션에서는 `weave.ref(:).get()`으로 해당 버전을 불러옵니다. +프로덕션 시스템에 프롬프트를 배포할 때는 버전 인덱스나 최신 버전 대신 `production`과 같은 [별칭](#add-prompt-tags-and-aliases)을 사용하세요. 프로덕션 애플리케이션에서는 `weave.ref(:).get()`으로 프롬프트를 로드하세요. -버전을 고정하면 나중에 새 프롬프트 버전이 게시되더라도 프로덕션 동작을 안정적으로 유지할 수 있습니다. +별칭을 사용하면 프로덕션 동작을 안정적이고 예측 가능하게 유지할 수 있습니다. 새 버전을 프로덕션으로 승격할 준비가 되면 별칭을 해당 버전으로 옮기세요. 그러면 모든 사용자가 자동으로 변경 사항을 반영합니다. 일반적인 워크플로는 다음과 같습니다: 1. 새 프롬프트 버전을 개발하고 테스트합니다. -2. 데이터셋이나 평가 스위트를 대상으로 새 프롬프트를 평가합니다. -3. 프로덕션 시스템이 새 버전을 참조하도록 업데이트합니다. +2. 데이터셋 또는 평가 스위트를 기준으로 새 프롬프트를 평가합니다. +3. `client.set_aliases(new_ref, "production")`으로 `production` 별칭을 새 버전으로 이동합니다. -이 방식을 사용하면 팀이 프로덕션 동작을 예기치 않게 바꾸지 않으면서도 프롬프트를 안전하게 반복 개선할 수 있습니다. +이 접근 방식을 사용하면 팀이 프로덕션 동작을 예기치 않게 변경하지 않고도 프롬프트를 안전하게 반복 개선할 수 있습니다. ## 프롬프트 버전 보기 및 비교 @@ -101,7 +101,7 @@ UI에서 프롬프트 버전을 보려면 다음 단계를 따르세요. ![선택한 프롬프트의 버전을 보여주는 Prompt Assets UI.](/weave/guides/core-types/imgs/prompt-object.png) -5. (선택) 나열된 프롬프트 옆의 체크박스를 클릭한 다음 테이블 툴바에서 **Compare** 버튼을 클릭하면 프롬프트 버전을 비교할 수 있습니다. 이렇게 하면 프롬프트 간 차이점을 확인할 수 있습니다. +1. (선택) 나열된 프롬프트 옆의 체크박스를 클릭한 다음 테이블 툴바에서 **Compare** 버튼을 클릭하면 프롬프트 버전을 비교할 수 있습니다. 이렇게 하면 프롬프트 간 차이점을 확인할 수 있습니다. ![두 개의 서로 다른 프롬프트 버전 간 차이점을 보여주는 Compare prompts UI.](/weave/guides/core-types/imgs/prompt-comparison.png) diff --git a/ko/weave/guides/integrations.mdx b/ko/weave/guides/integrations.mdx index 58bd67368a..5766f55378 100644 --- a/ko/weave/guides/integrations.mdx +++ b/ko/weave/guides/integrations.mdx @@ -51,6 +51,7 @@ LLM 제공업체는 예측을 생성할 수 있도록 대규모 언어 모델에 * **[AutoGen](/ko/weave/guides/integrations/autogen)** * **[Verdict](/ko/weave/guides/integrations/verdict)** * **[TypeScript SDK](/ko/weave/guides/integrations/js)** +* **[Vercel AI SDK](/ko/weave/guides/integrations/vercel_ai_sdk)** * **[Agno](/ko/weave/guides/integrations/agno)** * **[Koog](/ko/weave/guides/integrations/koog)** diff --git a/ko/weave/guides/integrations/vercel_ai_sdk.mdx b/ko/weave/guides/integrations/vercel_ai_sdk.mdx new file mode 100644 index 0000000000..95e7e37c9f --- /dev/null +++ b/ko/weave/guides/integrations/vercel_ai_sdk.mdx @@ -0,0 +1,156 @@ +--- +title: "Vercel AI SDK" +description: "OpenTelemetry를 사용해 Weave에서 Vercel AI SDK Call을 트레이스하세요" +--- + +[OpenTelemetry (OTel)](https://opentelemetry.io/)를 사용해 Weave에서 [Vercel AI SDK](https://ai-sdk.dev/) Call을 트레이스할 수 있습니다. Vercel AI SDK는 Next.js, Nuxt, SvelteKit 및 기타 프레임워크를 지원하며, AI 기반 애플리케이션을 구축하기 위한 TypeScript 툴킷입니다. `experimental_telemetry` 옵션을 통해 OpenTelemetry를 기본적으로 지원합니다. + +이 가이드에서는 Vercel AI SDK의 트레이스를 Weave로 전송하도록 OTel을 설정하는 방법을 설명합니다. AI SDK는 Next.js와 함께 사용하거나 독립형 Node.js 애플리케이션으로 사용할 수 있습니다. + +Weave에서 OTel 트레이싱에 대한 자세한 내용은 [Send OTel traces to Weave](../tracking/otel)를 참조하세요. + +
+ ### 사전 요구 사항 +
+ +이 가이드의 Next.js 및 Node.js 예제에는 동일한 의존성이 필요합니다. 시작하려면 다음을 수행하세요. + +1. 다음 Vercel 및 OTel 라이브러리를 설치하세요. + + ```bash + npm install ai @ai-sdk/openai @opentelemetry/api @opentelemetry/sdk-trace-node @opentelemetry/sdk-trace-base @opentelemetry/exporter-trace-otlp-proto @opentelemetry/resources zod + ``` + +2. 다음 환경 변수를 설정하세요. + + ```bash + export WANDB_API_KEY="your-wandb-api-key" + export OPENAI_API_KEY="your-openai-api-key" + ``` + + W&B API 키는 [User Settings](https://wandb.ai/settings)에서 확인할 수 있습니다. + +
+ ## Next.js용 트레이싱 설정 +
+ +이 섹션에서는 Next.js 앱에서 Weave를 설정하는 방법을 설명합니다. 이 예제에는 전체 앱이 아니라 계측 설정과 Vercel AI SDK 함수에서 계측을 호출하는 방법만 포함되어 있습니다. 여기서는 OpenAI에 대한 단순한 호출을 예시로 사용합니다. + +
+ ### 계측 설정 +
+ +Next.js 애플리케이션은 OTel을 설정하기 위해 `instrumentation.ts` 파일을 사용합니다. 이 파일은 서버가 시작될 때 한 번 실행되며, AI SDK가 사용하는 트레이서 프로바이더를 구성합니다. + +Weave를 Vercel의 OTel 기능과 통합하려면 프로젝트 루트에 `instrumentation.ts` 파일을 만들고 다음 코드를 추가하세요. 이때 `resourceFromAttributes()` 함수의 팀 이름과 프로젝트 이름을 사용 중인 값으로 업데이트하세요: + +```typescript instrumentation.ts lines {17-19} +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; + +export function register() { + const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// W&B Weave 엔드포인트를 사용하도록 OTel 익스포터를 설정합니다. + const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, + }); + +// W&B 자격 증명을 설정합니다. + const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], + }); + + provider.register(); +} +``` + +이렇게 하면 W&B API 키를 사용해 인증하고, 트레이스 데이터를 Weave의 OTel 엔드포인트로 전송하도록 구성된 OTLP 익스포터를 생성합니다. + + +
+ ### 함수에 텔레메트리 설정하기 +
+ +계측을 추가한 후에는 AI SDK의 함수 호출에서 Vercel의 `experimental_telemetry` 옵션을 사용해 OTel 스팬을 내보내세요: + +```typescript route.ts lines {10} +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +export async function POST(req: Request) { + const { prompt } = await req.json(); + +// 함수에 experimental_telemetry 필드를 추가합니다. + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt, + experimental_telemetry: { isEnabled: true }, + }); + + return Response.json({ text: result.text }); +} +``` + +텔레메트리가 활성화된 모든 `generateText` Call은 Weave로 내보내는 OTel 스팬을 생성합니다. + + +
+ ## Node.js용 OTel 트레이싱 설정 +
+ +독립 실행형 Node.js 애플리케이션(Next.js 제외)의 경우, AI SDK를 호출하기 전에 엔트리 파일의 맨 위에서 트레이서 프로바이더를 설정하세요. + +사전 요구 사항을 충족한 후에는 추가 설정 없이 이 예제를 실행해 스팬을 생성할 수 있습니다. + +```typescript lines {11-14,18-19,31} title="test-app.ts" +import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node"; +import { BatchSpanProcessor } from "@opentelemetry/sdk-trace-base"; +import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-proto"; +import { resourceFromAttributes } from "@opentelemetry/resources"; +import { openai } from "@ai-sdk/openai"; +import { generateText } from "ai"; + +const WANDB_API_KEY = process.env.WANDB_API_KEY!; + +// W&B Weave 엔드포인트를 사용하도록 OTel 익스포터를 설정합니다. +const exporter = new OTLPTraceExporter({ + url: "https://trace.wandb.ai/otel/v1/traces", + headers: { "wandb-api-key": WANDB_API_KEY }, +}); + +// W&B 자격 증명을 설정합니다. +const provider = new NodeTracerProvider({ + resource: resourceFromAttributes({ + "wandb.entity": "", + "wandb.project": "", + }), + spanProcessors: [new BatchSpanProcessor(exporter)], +}); + +provider.register(); + +// 함수에 experimental_telemetry 필드를 추가합니다. +async function main() { + const result = await generateText({ + model: openai("gpt-4o-mini"), + prompt: "Explain OpenTelemetry in one sentence.", + experimental_telemetry: { isEnabled: true }, + }); + + console.log(result.text); + + await provider.shutdown(); +} + +main(); +``` + +`BatchSpanProcessor`는 스팬을 비동기적으로 플러시합니다. 독립형 스크립트, 서버리스 함수, CLI 도구처럼 수명이 짧은 프로세스에서는 프로세스가 종료되기 전에 `provider.shutdown()`을 호출해 모든 스팬이 Weave로 전송되도록 하세요. 반면 `instrumentation.ts`를 통해 시작한 Next.js dev 서버처럼 장시간 실행되는 서버에서는 이 작업이 필요하지 않습니다. diff --git a/ko/weave/quickstart-inference.mdx b/ko/weave/quickstart-inference.mdx index 5e89ac43d3..59f5b49cd9 100644 --- a/ko/weave/quickstart-inference.mdx +++ b/ko/weave/quickstart-inference.mdx @@ -280,7 +280,7 @@ W&B Inference를 사용하면 여러 모델에 액세스할 수 있습니다 # 서로 다른 모델 인스턴스를 생성합니다 llama_model = InferenceModel(model_name="meta-llama/Llama-3.1-8B-Instruct") - deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3-0324") + deepseek_model = InferenceModel(model_name="deepseek-ai/DeepSeek-V3.1") # 응답을 비교합니다 test_question = "Explain quantum computing in one paragraph for a high school student." @@ -321,7 +321,7 @@ W&B Inference를 사용하면 여러 모델에 액세스할 수 있습니다 // 서로 다른 모델 인스턴스를 생성합니다 const llamaModel = createModel('meta-llama/Llama-3.1-8B-Instruct'); - const deepseekModel = createModel('deepseek-ai/DeepSeek-V3-0324'); + const deepseekModel = createModel('deepseek-ai/DeepSeek-V3.1'); // 응답을 비교합니다 const testQuestion = 'Explain quantum computing in one paragraph for a high school student.'; @@ -428,7 +428,7 @@ Weave의 내장 `EvaluationLogger`를 사용해 Q&A 작업에서 모델의 ```typescript lines import { EvaluationLogger } from 'weave'; - // 간단한 데이터셋 생성 + // Create a simple dataset interface DatasetExample { question: string; expected?: string; @@ -441,7 +441,7 @@ Weave의 내장 `EvaluationLogger`를 사용해 Q&A 작업에서 모델의 { question: 'Name a primary color', expected_one_of: ['red', 'blue', 'yellow'] }, ]; - // scorer 정의 + // Define a scorer const accuracyScorer = weave.op(function accuracyScorer(args: { expected: string; output: string; @@ -461,15 +461,15 @@ Weave의 내장 `EvaluationLogger`를 사용해 Q&A 작업에서 모델의 return { correct: isCorrect, score: isCorrect ? 1.0 : 0.0 }; }); - // Weave의 EvaluationLogger를 사용하여 모델 평가 + // Evaluate a model using Weave's EvaluationLogger async function evaluateModel( model: (question: string) => Promise, modelName: string, dataset: DatasetExample[] ): Promise { - // 토큰 사용량을 캡처하려면 모델 호출 전에 EvaluationLogger를 초기화해야 합니다 - // W&B Inference에서 비용을 추적할 때 특히 중요합니다 - // 모델 이름을 유효한 형식으로 변환합니다 (영숫자가 아닌 문자를 밑줄로 대체) + // Initialize EvaluationLogger BEFORE calling the model to capture token usage + // This is especially important for W&B Inference to track costs + // Convert model name to a valid format (replace non-alphanumeric chars with underscores) const safeModelName = modelName.replace(/\//g, '_').replace(/-/g, '_').replace(/\./g, '_'); const evalLogger = new EvaluationLogger({ name: 'inference_evaluation', @@ -478,45 +478,45 @@ Weave의 내장 `EvaluationLogger`를 사용해 Q&A 작업에서 모델의 }); for (const example of dataset) { - // 모델 예측 결과 조회 + // Get model prediction const output = await model(example.question); - // 예측 로깅 + // Log the prediction const predLogger = evalLogger.logPrediction( { question: example.question }, output ); - // 출력 채점 + // Score the output const score = await accuracyScorer({ expected: example.expected || '', output: output, expected_one_of: example.expected_one_of }); - // 점수 로깅 + // Log the score predLogger.logScore('accuracy', score.score); - // 이 예측에 대한 로깅 완료 + // Finish logging for this prediction predLogger.finish(); } - // 요약 로깅 - Weave가 자동으로 accuracy 점수를 집계합니다 + // Log summary - Weave automatically aggregates the accuracy scores await evalLogger.logSummary(); console.log(`Evaluation complete for ${modelName} (logged as: ${safeModelName}). View results in the Weave UI.`); } - // 여러 모델 비교 - Weave 평가 프레임워크의 핵심 기능 + // Compare multiple models - a key feature of Weave's evaluation framework const modelsToCompare = [ { model: llamaModel, name: 'meta-llama/Llama-3.1-8B-Instruct' }, - { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3-0324' }, + { model: deepseekModel, name: 'deepseek-ai/DeepSeek-V3.1' }, ]; for (const { model, name } of modelsToCompare) { await evaluateModel(model, name, dataset); } - // Weave UI에서 Evals 탭으로 이동하여 모델 간 결과를 비교하세요 + // In the Weave UI, navigate to the Evals tab to compare results across models ```