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Found 59 Skills
Guides Qdrant scaling decisions. Use when someone asks 'how many nodes do I need', 'data doesn't fit on one node', 'need more throughput', 'cluster is slow', 'too many tenants', 'vertical or horizontal', 'how to shard', or 'need to add capacity'.
Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
Guides Qdrant deployment selection. Use when someone asks 'how to deploy Qdrant', 'Docker vs Cloud', 'local mode', 'embedded Qdrant', 'Qdrant EDGE', 'which deployment option', 'self-hosted vs cloud', or 'need lowest latency deployment'. Also use when choosing between deployment types for a new project.
Manage the full lifecycle of Alibaba Cloud managed Milvus instances—creation, scaling, configuration management, network management, and status queries. Use this Skill when users want to create a Milvus instance, view instance status, get connection addresses, scale/change configuration, modify settings, enable/disable public network access, set whitelists, release instances, or troubleshoot creation failures. Also applicable when users say "create a Milvus instance", "view instance details", "what's the connection address", "help me check the instance", "scale CU", "change config", "enable public network", "delete instance", etc.
Pinecone integration. Manage Indexs. Use when the user wants to interact with Pinecone data.
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Skills for Upstash Vector features, TypeScript/JavaScript SDK usage, and integrations. Use when users ask how to work with Vector, its TS SDK, features, or supported frameworks.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
Sets up vector databases for semantic search including Pinecone, Chroma, pgvector, and Qdrant with embedding generation and similarity search. Use when users request "vector database", "semantic search", "embeddings storage", "Pinecone setup", or "similarity search".
Implement ReasoningBank adaptive learning with AgentDBs 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.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization