「ngc」の検索結果

Scaffold Quarkus + LangChain4j projects end-to-end and add agentic components to existing ones — AI services, tools, agents and multi-agent workflows, RAG pipelines, MCP clients and servers (Model Context Protocol), guardrails, and embedding store setups. Use this whenever the user wants a new Quarkus + LangChain4j project or module created, or an agentic component added to an existing project in this stack, including a baseline pom.xml, application.properties, project layout, or starter classes.

日本語の概要は準備中です。原文の説明を表示しています。

Радар фундаментальных публикаций про автономных AI-агентов в блогах AI-лабораторий и продуктовых команд (Anthropic, Claude, OpenAI, Cursor, LangChain, Amp, Cognition). Собирает ленты и HTML-индексы за окно дат, отсеивает продуктовые анонсы и релизы моделей, оставляет глубокие посты про архитектуру агентов: память и контекст, long-running циклы, self-improvement, оркестрация, экономика прогона, эвалы, риски длинных горизонтов. Отдаёт дайджест со ссылками, сутью каждого поста и тем, что забрать в свою работу. Use when (используй когда): пользователь хочет узнать, что нового вышло в блогах про AI-агентов, собрать дайджест за неделю или период, отследить исследования про агентную память. Триггерится на: "радар", "дайджест", "что нового в блогах", "публикации про агентов", "agent research", "что вышло за неделю", "блог Anthropic", "cursor blog", "langchain blog", "long-running agents", "research digest". Do NOT use for (не для): корпоративные вики и трекеры; arXiv; доставка сообщений.

日本語の概要は準備中です。原文の説明を表示しています。

LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing

日本語の概要は準備中です。原文の説明を表示しています。

OpenUI generative UI with LangChain/LangGraph backend. Supports ChatOpenAI and ChatAnthropic.

日本語の概要は準備中です。原文の説明を表示しています。

Configure Messaging for In-App and Web (MIAW) channels in Service Cloud: Messaging Channel, Embedded Service Deployment, CORS/CSP Trusted Sites, pre-chat fields, session routing by Queue or Omni-Channel Flow, Status-Based capacity, Live Agent migration. Trigger keywords: MessagingChannel, EmbeddedServiceConfig, messagingChannelType, EmbeddedMessaging, sessionHandlerQueue, sessionHandlerFlow, embeddedConfig, automatedResponses, CorsWhitelistOrigin, CspTrustedSite, BrandingSet, MessagingSession, MessagingEndUser, enhanced chat, embedded messaging, chat widget, pre-chat form, auto-response. NOT for an Agentforce agent in the chat window - use agentforce/agent-channel-deployment. NOT for Omni-Channel enablement, Service Channels or skills-based routing - use admin/omni-channel-routing-setup.

日本語の概要は準備中です。原文の説明を表示しています。

Build GenAI and agentic apps with genai-tk (tclatos/genai-tk), a YAML-driven IoC toolkit over LangChain, DeepAgents, DeerFlow, and Prefect. Use this skill whenever the user mentions genai-tk, genai_tk, `cli init`, `cli agents run`, `LangchainAgent`, `create_harness`, `get_llm`, `global_config()`, `model_id@provider`, `agents:`/`agent_defaults:` profiles, `RetrieverFactory`, `cli workflow`, or BAML extraction. Also trigger for app_conf.yaml, GENAITK_PROFILE, CodeAct, OpenSandbox, decision models, or "the toolkit".

日本語の概要は準備中です。原文の説明を表示しています。

Expert em LangGraph - o framework de nível produção para construir aplicações de IA com múltiplos atores e estado. Aborda construção de grafos, gerenciamento de estado, ciclos e ramificações, persistência com checkpointers, padrões human-in-the-loop e o padrão ReAct. Usado em produção no LinkedIn, Uber e 400+ empresas. Esta é a abordagem recomendada pelo LangChain para construir agentes. Use quando: langgraph, langchain agent, stateful agent, agent graph, react agent.

日本語の概要は準備中です。原文の説明を表示しています。

Framework para construir aplicações com LLM usando agentes, chains e RAG. Suporta múltiplos provedores (OpenAI, Anthropic, Google), 500+ integrações, agentes ReAct, tool calling, gerenciamento de memória e recuperação de vector stores. Use para construir chatbots, sistemas de perguntas e respostas, agentes autônomos ou aplicações RAG. Ideal para prototipagem rápida e deployments em produção.

日本語の概要は準備中です。原文の説明を表示しています。

AI Workflow Automation IA — Expert en automatisation de workflows IA (n8n AI nodes, LangChain, Dify, Flowise, pipelines custom)

日本語の概要は準備中です。原文の説明を表示しています。

Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guidance for LangChain and LangGraph development with Python, covering chain composition, agents, memory, and RAG implementations.

日本語の概要は準備中です。原文の説明を表示しています。

Guide for using the Tinker Python SDK APIs — ServiceClient, TrainingClient, SamplingClient, RestClient, forward_backward, optim_step, sampling, and async patterns. Use when the user asks about Tinker API basics, how to call training/sampling, or how the SDK works.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.

日本語の概要は準備中です。原文の説明を表示しています。

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

日本語の概要は準備中です。原文の説明を表示しています。

visionOS / iOS 空間コンピューティングリファレンス。RealityKit、ARKit、visionOS SwiftUI API。 Entity, ModelEntity, AnchorEntity, Component, RealityView, MeshResource, Material, System, EntityAction, GaussianSplatComponent, ComputeGraphComponent, PhysicsJointsComponent, ForceEffectComponent, ClothBodyComponent, NavigationComponent, NavigationMeshComponent, NavigationController, ImmersiveSpace, ImmersionStyle, Model3D, WindowGroup, VolumetricWindowStyle, ObjectTrackingProvider, RoomTrackingProvider, ARSession, ARWorldTrackingConfiguration, ARKitSession, WorldTrackingProvider, HandTrackingProvider, SceneReconstructionProvider。

Wear OS (Kotlin) の androidx.wear API・Watch Face Format (WFF) リファレンス。 Wear Compose の CurvedLayout, rotary input, ambient mode, ScalingLazyColumn。 Tiles / ProtoLayout, ComplicationDataSourceService と ComplicationData 型, Health Services (MeasureClient, ExerciseClient, PassiveMonitoringClient), WFF の XML 要素・式・コンプリケーションスロット・ユーザー設定。

NVIDIA DGX Spark User Guide リファレンス。GB10 Grace Blackwell Superchip ハードウェア仕様、first-boot / UEFI 設定、Spark Stacking クラスタ、 DGX OS / NGC / container runtime ソフトウェアスタック、 PXE / cloud-init プロビジョニング、fleet lifecycle 運用、 apt / fwupdmgr 更新、system recovery、PSIRT サポート、 vLLM / TensorRT-LLM / Ollama / ComfyUI / NIM / NeMo fine-tune / NCCL 等の build.nvidia.com/spark playbook、複数 Spark 接続・Tailscale・VS Code、 NVIDIA Developer Forums 由来の known issues / troubleshooting。

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.

日本語の概要は準備中です。原文の説明を表示しています。

Goのコード作成・レビュー・リファクタリングを、言語の慣習に沿って支援します。エラー処理、並行処理、パッケージ構成を整え、読みやすく保守しやすい実装を目指します。

• Goの慣習に沿ってコードをレビュー
• 既存コードのエラー処理を改善したいとき
• キャンセル可能な並行処理を設計

NVIDIA GPU上で大規模言語モデルの応答生成を高速化し、APIとして提供する設定を支援します。量子化や一括処理、複数GPUへの分散も扱います。

• 言語モデルをチャットAPIで提供したいとき
• 多数のプロンプトを一括処理したいとき
• 量子化でメモリ使用量を抑えたいとき

文章などを数値化して似た内容を探す検索をQdrantで実装。属性による絞り込みやAI回答用の資料検索から、大規模運用の設定・調整まで支援します。

• AI回答用の参考資料を検索したいとき
• カテゴリや日時で絞る類似検索
• リアルタイム推薦の実装

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

日本語の概要は準備中です。原文の説明を表示しています。

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

日本語の概要は準備中です。原文の説明を表示しています。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

日本語の概要は準備中です。原文の説明を表示しています。