「vector database」の検索結果

Expert knowledge for Azure Cosmos DB development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using NoSQL/Mongo/Cassandra APIs, change feed, multi-region HA, vector search, or Cosmos DB for PostgreSQL, and other Azure Cosmos DB related development tasks. Not for Azure Table Storage (use azure-table-storage), Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Data Explorer (use azure-data-explorer).

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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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。

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。

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。

Auto-activate for pytest_databases, Docker DB fixtures, PostgreSQL/pgvector/ParadeDB, MySQL/MariaDB, Oracle/SQL Server, CockroachDB, MongoDB, Redis/Valkey, BigQuery/Spanner, MinIO, or RustFS. Not for mocked DBs.

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 working with Chroma — chroma vector database management, collection inspection, embedding analysis, and query performance monitoring. Covers collection metadata, document counts, distance metrics, index health, and tenant/database configuration. Read this skill before any Chroma 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。

Use when doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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。

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

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 building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

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。

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

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。

SQL injection testing skill for offensive security assessments and bug bounty hunting. Covers error-based, UNION-based, boolean/time-based blind, out-of-band, second-order, NoSQL, GraphQL, WebSocket, and JSON-operator SQLi. Includes WAF bypass techniques, database-specific exploitation (MySQL, MSSQL, PostgreSQL, Oracle), cloud-native attack paths, ORM CVE tracking, and SQLmap automation. Use when performing web application SQL injection testing, database enumeration, privilege escalation via SQLi, or assessing injection vectors in APIs and modern stacks.

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。

文書や会話、仕事の情報を内容に合う保存先へ整理し、ローカルファイルや知識ベース、GitHubなどを横断して重複確認、同期、検索を進めるスキル。

• 文書や会話を知識ベースに保存したいとき
• 重複するメモの整理
• 複数の保存先にある知識の同期

追加の学習データを用意せず、文章で指定した候補に画像を分類したり、内容に合う画像を検索したりします。画像と文章の類似度比較や、複数画像の一括処理も扱うスキルです。

• 文章のラベルで写真を分類したいとき
• 画像の内容を言葉で指定して探したいとき
• 画像と説明文の対応を比較したいとき

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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 advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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 ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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 advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

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。

Command-line interface for ChromaDB - A stateless CLI for managing vector database collections, documents, and semantic search. Designed for AI agents and automation via the ChromaDB HTTP API v2.

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 efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval 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。

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external 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。

Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB MCP tools for automated database operations including AI-powered search and vector capabilities.

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 dealing with cognee database migrations — understanding when they run automatically, checking or repairing migration state with cognee-cli upgrade/downgrade/stamp/current, a write blocked by a failed migration, authoring a new Alembic (relational schema) revision or a graph/vector data migration, or moving data between systems (relational DB import, memory export/import).

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 the user wants to connect cognee to external services — switching LLM or embedding providers (OpenAI, Azure, Gemini, Anthropic, Ollama, OpenRouter), changing databases (Postgres, PGVector, Neo4j, Neptune, Turso), S3 storage, or the MCP server for IDE 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。