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Found 8 Skills
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Use when reranking search candidates is needed with Alibaba Cloud Model Studio rerank models, including hybrid retrieval, top-k refinement, and multilingual relevance sorting.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Alibaba Cloud Tablestore Agent Storage Skill. Use for building and managing Tablestore-based knowledge bases with the `tablestore-agent-storage` Python SDK. Capabilities: - Install and configure the `tablestore-agent-storage` SDK - Create, describe and list knowledge bases (with subspace and custom metadata support) - Upload local files or import OSS documents into a knowledge base - Query document status and list documents - Perform hybrid retrieval (dense vector + full-text) with metadata filtering - Set up local directory sync scripts and scheduled tasks for automatic knowledge base updates Triggers: "知识库", "tablestore", "ots", "表格存储", "agent storage", "knowledge base", "向量检索", "文档上传", "文档导入", "知识库同步", "tablestore-agent-storage", "AgentStorageClient"
Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Use when the user wants embeddings, vector indexing, retrieval, or retrieval-backed answers, including embedding-agent setup, Chroma-backed collections, collection add/query, and KB-to-answer flows.