「vector search」の検索結果

Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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。

Route fastdup workflows for visual dataset curation, annotation-driven analysis, embeddings and search, and video or exchange plumbing.

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 FlashRAG for retrieval-augmented generation research workflows: config/data validation, retrieval and index building, generation/refinement components, RAG pipelines/method reproduction, evaluation, and WebUI 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。

Use DeepFace for face recognition, verification, embeddings, face detection, demographic analysis, datastore search, API serving, model selection, and troubleshooting.

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 the DeepXiv SDK for citation-aware academic and web research through its Python Reader, deepxiv CLI, progressive paper APIs, optional local LangGraph agent, PMC, bioRxiv/medRxiv, and trending workflows.

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 DeepMotionEditing BVH animation, skeleton-aware motion retargeting, motion style transfer, and Blender visualization workflows for research use.

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 code-review-graph for local-first CLI/MCP code knowledge graphs, graph-backed code review, structural search, and repository-analysis integrations.

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 CLIP-as-service client, server, and CLIP search workflows for text/image embeddings, cross-modal ranking, and retrieval services.

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。

Routes DisCo Researcher to Cream-family vision NAS, compression, distillation, and relative-position-encoding workflows across the AutoFormer, AutoFormerV2, Cream, CDARTS, EfficientViT, MiniViT, TinyCLIP, TinyViT, and iRPE project families.

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。

Route auto-sklearn AutoML estimator, data validation, search, custom component, and metadata-maintenance workflows.

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 AutoKeras for Keras-based AutoML task APIs, custom AutoModel graphs, tuner search, and model export workflows.

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。

Route Airweave tasks to the most specific sub-skill: local-development, backend-api, source-connectors, frontend-dashboard, connect-widget, mcp-search, or monke-e2e.

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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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 production-ready LLM applications, RAG systems, and intelligent agents. Use when implementing AI features, chatbots, vector search, or agent orchestration.

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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

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。

Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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。

Assists with building real-time reactive backends using Convex. Use when creating databases with automatic client sync, reactive queries, file storage, scheduled functions, or full-text and vector search. Trigger words: convex, reactive backend, real-time database, useQuery, useMutation, convex functions, convex schema.

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-engineer workflow skill. Use this skill when the user needs Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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-engineer workflow skill. Use this skill when the user needs Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.

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 RAG retrieval for LLM grounding: chunking, BM25+vector hybrid, reranking, GraphRAG. Use when adding embeddings to SQLite FTS5 search or running held-out retrieval eval.

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 Knowledge Graphs, GraphRAG, Microsoft GraphRAG, Neo4j Text2Cypher, multi-hop relational retrieval, and hybrid vector-graph search / Panduan ahli Knowledge Graph, GraphRAG, dan pencarian relasional multi-hop.

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。

Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, SDK usage, and vector search. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database 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。

Run the Upstash CLI (`upstash`) from a terminal, shell script, or CI job when no Upstash MCP tools are in the session. Do not load this skill when Upstash MCP tools are available (the Upstash plugin registers mcp.upstash.com) - they already cover creating, listing, renaming, and deleting Redis databases, running Redis commands, usage stats, backups, Vector and Search indexes, QStash schedules and messages, Blob buckets, and Box, so call them directly. Use this skill for shell work the MCP does not do - a CI step or provisioning script that needs `upstash` commands with JSON output, piping results into other commands, `upstash auth login` and API-key setup, team and member management, changing plans, regions, TLS, eviction, auto-upgrade, or budgets, minting temporary S3 credentials for a Blob bucket, connecting a coding agent to Upstash with `upstash setup` (the Upstash plugin, or the remote MCP server plus skills), or when the user explicitly asks for the CLI or for managing Upstash without the console.

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。