「embedding」の検索結果

Bridges UIKit and SwiftUI with UIViewRepresentable, UIViewControllerRepresentable, UIHostingController, UIHostingConfiguration, coordinator delegates, and UIKit automatic observation tracking for shared @Observable state. Use when wrapping UIKit-only or third-party UIKit views/controllers in SwiftUI, embedding SwiftUI in UIKit, integrating mail/share/document/PDF/text-view surfaces, or migrating UIKit apps to SwiftUI incrementally.

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。

Tokenize, tag, and analyze natural language text using Apple's NaturalLanguage framework and translate between languages with the Translation framework. Use when adding language identification, sentiment analysis, named entity recognition, part-of-speech tagging, text embeddings, or in-app translation to iOS/macOS/visionOS apps.

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。

Display and manipulate PDF documents using PDFKit. Use when embedding PDFView to show PDF files, creating or modifying PDFDocument instances, adding annotations (highlights, notes, signature widgets), extracting text with PDFSelection, navigating pages, generating thumbnails, filling PDF forms, or wrapping PDFView in SwiftUI.

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。

Add drawings, shapes, and a consistent markup experience using PaperKit. Use when integrating PaperMarkupViewController for markup editing, adding shape recognition, working with PaperMarkup data models, embedding markup tools in document editors, or building annotation features that need the system-standard markup toolbar. New in iOS 26.

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。

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

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。

AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.

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。

RF Toolbox and RF Blockset in MATLAB -- S-parameter I/O, network conversions (S/Z/Y/ABCD/T/H/G, mixed-mode), cascade/de-embedding, rfbudget analysis, circuit composition, matching networks, amplifier stability, mixer spurs, rational fitting, SI channels, baseband processing, Circuit Envelope simulation. Trigger: sparameters, Touchstone, .s2p, .s4p, rfplot, smithplot, rfparam, rfwrite, zparameters, yparameters, abcdparameters, s2sdd, cascadesparams, deembedsparams, rfbudget, noise figure, OIP3, IIP3, amplifier, modulator, nport, rffilter, attenuator, seriesRLC, shuntRLC, lcladder, txline, circuit, setports, clone, matchingnetwork, stabilityk, stabilitymu, powergain, gammams, gammaml, mixerIMT, OpenIF, rational, rationalfit, stepresp, txlineWRLGC, rf.Amplifier, rf.Mixer, rf.Filter, rf.Sparameter, rfsystem, RF Blockset.

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 and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

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。

HLSL code generation, StringBuilder patterns, builtin headers, and DXIL embedding.

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。

Rebuild or restyle editable Microsoft Visio diagrams from reference images and existing .vsdx files, then export deliverables as .vsdx, PNG, SVG, PDF, or PPTX. Use when the user asks Codex to open Visio, recreate a diagram from a PNG/JPG/screenshot/reference image, match a scientific model figure, update colors/typography/layout in a .vsdx, calibrate panel coordinates to avoid shifted or overlapping submodules, or make publication/presentation outputs while preserving Visio native editable shapes rather than embedding the reference image as a flat picture.

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。

Inspects and configures the security headers a Power Pages site sends to browsers — Content Security Policy, frame and clickjacking protection, cross-origin sharing, cookie behavior, and related site settings. Identifies gaps and walks the user through fixes. Use when the user wants to review headers, fix CSP errors, allow embedding in another site, control cross-origin access, harden cookie settings, or asks "are my browser settings safe?", "fix my CSP", "set up CORS" — even if they only mention a specific header name without saying "security headers".

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。

Use when writing test fixtures for @copilotkit/aimock — mock LLM responses, tool call sequences, error injection, multi-turn agent loops, embeddings, structured output, sequential responses, or debugging fixture mismatches

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。

Designs production-grade RAG systems with document chunking, embedding generation, vector store configuration, hybrid search pipelines, reranking, and retrieval quality evaluation.

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。

Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR, HIPAA), conducting threat modeling and risk assessments, managing security operations and incident response, and embedding security throughout the SDLC.

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。

Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.

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。

Design discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject content.

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。

Design a structured outdoor learning sequence embedding curriculum objectives in an available outdoor space. Use when planning lessons in school grounds, parks, or local natural environments.

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。

RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.

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。

Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).

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。

SwiftUI framework skills — data flow (identity, Observation, state ownership), layout & containers (Layout protocol, lazy-stack performance), AlarmKit, 3D charts, rich text editing, customizable toolbars, and WebKit embedding. Use for SwiftUI state/rendering bugs, custom layouts, scroll performance, or these feature areas.

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 knowledge for Microsoft Foundry Local (aka Azure AI Foundry Local) development including best practices, decision making, configuration, and integrations & coding patterns. Use when compiling HF models with Olive, using Foundry Local CLI, embeddings/chat/audio APIs, or OpenAI-style integrations, and other Microsoft Foundry Local related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Azure Local (use azure-local), Azure Content Understanding in Foundry Tools (use azure-content-understanding).

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 knowledge for Azure Health Bot development including best practices, security, configuration, and integrations & coding patterns. Use when wiring Teams/SMS/WhatsApp channels, embedding web chat, calling external APIs, configuring handoff, or enforcing consent, and other Azure Health Bot related development tasks. Not for Azure AI Bot Service (use azure-bot-service), Azure Communication Services (use azure-communication-services), Azure Functions (use azure-functions).

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。

千问AI平台（Qianwen AI Platform / DashScope）官方文档离线知识库，用于检索并回答模型选择、API Key、OpenAI 兼容接口、DashScope SDK、文本与多模态生成、图像/视频/语音、Realtime API、Embedding、Reranking、Function Calling、MCP、批量调用、计费、Token Plan、API/SDK/CLI 参考和模型更新等问题，也可基于官方文档生成示例代码与排障建议。当用户提到千问AI平台、Qianwen AI Platform、DashScope、通义千问 API、百炼 API、DASHSCOPE_API_KEY 或具体千问/万相模型接入时使用；千问聊天产品的一般使用、模型原理论文解读或无关的阿里云产品问题不使用。

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。

LlamaIndex 官方用户文档离线知识库，用于检索并回答 LlamaIndex Python 框架的安装、RAG、数据加载、索引、检索与查询、Agent、Workflow、模型、Embedding、向量库、评估、可观测性、部署、LlamaCloud 和 LlamaParse 等问题，也可生成有文档依据的示例代码与排障建议。当用户提到 LlamaIndex、llama-index、LlamaParse、VectorStoreIndex、QueryEngine、Retriever、AgentWorkflow 或相关集成用法时使用；纯源码贡献、内部实现审查或与 LlamaIndex 无关的通用 AI 问题不使用。

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。