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概要と使いどころ
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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Expert guide for long-term episodic memory integration (Mem0 v2, Letta/MemGPT, Zep v2), memory tier architecture, pgvector HNSW storage, and unified context management for autonomous AI agents / Panduan ahli untuk integrasi memori episodik jangka panjang (Mem0 v2, Letta/MemGPT, Zep v2), arsitektur tier memori, penyimpanan pgvector HNSW, dan manajemen konteks terpadu untuk agen AI otonom.
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 guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.
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。
Community knowledge from bivector.net - GA tutorials, discussions, and best practices
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Get a specific vector entry by key. Requires authentication. Use for Agentuity cloud platform operations
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。
List all vector namespaces. Requires authentication. Use for Agentuity cloud platform operations
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。
Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations
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。
Delete one or more vectors by key. Requires authentication. Use for Agentuity cloud platform operations
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。
Get statistics for vector storage. Requires authentication. Use for Agentuity cloud platform operations
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。
AgentDB Vector Search Optimization operates on 3 fundamental principles:
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。
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Advanced AgentDB Vector Search Implementation operates on 3 fundamental principles:
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。
Operate Milvus vector database with pymilvus Python SDK. Use when the user wants to connect to Milvus, create collections, insert vectors, perform similarity search, hybrid search, full-text search, manage indexes, partitions, databases, or RBAC via Python code.
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。
Cloudflare platform management via Wrangler CLI, Agents SDK (v0.13+), and Browser Rendering API. Deploy Pages, Workers, KV, R2, D1, Queues, Vectorize, Workflows, Hyperdrive. Build stateful AI agents with the full SDK stack — Agent, AIChatAgent, Think, Voice, sub-agents, fibers, HITL, scheduling, observability, Code Mode (Dynamic Worker sandboxes). Budget web scraping via cf_browser.py. Use when deploying to Cloudflare, managing CF infrastructure, configuring wrangler.toml/wrangler.jsonc, building AI agents or chat agents on Workers, adding voice, orchestrating sub-agents, or for cheap web scraping. Triggers: Cloudflare, wrangler, Pages, KV, R2, D1, Workers, Vectorize, Queues, Workflows, Hyperdrive, cf_browser, Browser Rendering, Agents SDK, Code Mode, codemode, MCP, Think, AIChatAgent, Voice agent, sub-agents, fibers, HITL, Dynamic Workers.
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 coding skill for the full MapTiler platform — Cloud REST APIs, MapTiler SDK JS (built on MapLibre GL JS), native mobile SDKs, on-premise infrastructure, and vector tile schemas. USE WHEN the user wants to add a map to a web or mobile app, show locations or routes, display geographic data, build a store locator, create data visualizations on maps, add geocoding or address search, do reverse geocoding, look up elevation, do IP geolocation, fetch weather data, transform coordinates between CRS/EPSG, embed static map images, work with vector tilesets (Planet v4, Buildings, Contours, Outdoor, Ocean, Landcover, Cadastre), write MapLibre style expressions or data-driven styling, render 3D terrain, globe view, or 3D buildings extrusion, use satellite imagery, add markers, popups, heatmaps, or clustering, integrate maps in React, Vue, Svelte, Angular, Next.js, Leaflet, OpenLayers, Cesium, or deck.gl, build native maps on iOS (Swift), Android (Kotlin), Flutter, or React Native, self-host with MapTiler Server or generate tiles with MapTiler Engine CLI, or upload/manage tilesets via the Admin API. Also USE WHEN the user mentions MapLibre GL JS — the SDK extends MapLibre with built-in cloud services, helpers, and session billing.
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。
Prove geometric theorems using Euclidean axiomatic methods, coordinate geometry, or vector methods with rigorous step-by-step logical structure. Covers direct proof, proof by contradiction, coordinate proofs, vector proofs, and handling of special cases and degenerate configurations. Use when given a geometric statement to prove, verifying a conjecture, establishing a lemma, converting geometric intuition into a rigorous proof, or comparing the effectiveness of different proof methods.
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。
Work with the full set of Maxwell's equations in integral and differential form to analyze electromagnetic fields, waves, and energy transport. Use when applying Gauss's law, Faraday's law, or the Ampere-Maxwell law to boundary value problems, deriving the electromagnetic wave equation, computing Poynting vector and radiation pressure, solving for fields at material interfaces, or connecting electrostatics and magnetostatics to the unified electromagnetic framework.
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 or porting AArch64 SIMD to SVE or SVE2: arm_sve.h intrinsics, predicates, vector-length-agnostic loops, auto-vectorization, or SVE registers in GDB. Not for NEON: use simd-intrinsics.
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 SurrealDB 3 skill. Use when working with SurrealDB, SurrealQL queries, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, graph traversal, vector search, security and permissions, deployment and operations, performance tuning, SDK integration, Surrealism WASM extensions, SurrealMCP, Surrealist IDE, Surreal-Sync migrations, SurrealFS, SurrealKit, or ecosystem integrations such as n8n and CodeMirror.
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。
Work with any Upstash product, SDK, or tool, including serverless Redis (caching, sessions, leaderboards, key-value storage), Ratelimit (rate limiting and throttling), QStash (message queue, cron schedules, background jobs), Workflow (durable long-running functions), Vector (vector database for embeddings, semantic search, and RAG), Search (full-text and semantic search), Box (sandboxed cloud containers for AI agents, TypeScript/JavaScript and Python), Blob (serverless S3-compatible file storage with direct browser uploads), the Upstash CLI, and no-signup scratch Redis for agents. Use when the user mentions Upstash or needs any of these capabilities in a serverless, edge, or Node.js app.
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 building RAG or LLM applications with LlamaIndex - data loaders, node parsing, vector stores, retrievers and rerankers, query engines, agents and workflows, streaming, or 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。
Chroma vector database -- collection management, automatic embedding, metadata filtering, document storage, query 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。
Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.
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。