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49 results for "storing and querying vectors"
storing-and-querying-vectors
使用 Amazon S3 Vectors（一种经济高效的长周期向量存储服务，拥有自己的 API 命名空间 s3vectors）存储和查询向量嵌入。触发条件：创建 S3 向量存储桶、向量索引、存储嵌入、语义搜索、RAG 向量存储、相似性搜索、向量数据库、从其他向量数据库迁移。不适用于：查询表格数据（请使用 querying-data-lake）、S3 对象存储或数百/数千的持续 QPS（请使用 OpenSearch）。
awscreating-data-lake-table
使用 Amazon S3 Tables（s3tables API 命名空间）创建托管 Iceberg 表，支持自动压缩和快照管理。设置表桶、命名空间、表、模式、Glue 目录注册、分区、IAM 访问控制。触发条件：创建表、数据湖表、分析表、结构化数据存储、S3 Tables、Iceberg、Athena 表、分区策略、访问权限。请勿用于：导入文件（使用 ingesting-into-data-lake）、向量存储（使用 storing-and-querying-vectors）、查询现有表（使用 querying-data-lake）或查找现有表（使用 finding-data-lake-assets）。
aws
langchain4j-vector-stores-configuration
Provides configuration patterns for LangChain4J vector stores in RAG applications. Use when building semantic search, integrating vector databases (PostgreSQL/pgvector, Pinecone, MongoDB, Milvus, Neo4j), implementing embedding storage/retrieval, setting up hybrid search, or optimizing vector database performance for production AI applications.
giuseppe-trisciuoglio
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
ruvnetqdrant
Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
giuseppe-trisciuoglio
qdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
qdrant
vector-forge
基于突变的测试向量生成。查找密码算法或协议的实现，运行突变测试以识别逃逸突变体，然后生成刻意覆盖未覆盖代码路径的新测试向量。比较突变杀死率的前后差异以证明向量的有效性。适用于生成密码测试向量、衡量 Wycheproof 覆盖缺口、通过突变测试发现逃逸突变体、创建跨实现测试套件，或改进密码原语的测试向量覆盖。
trailofbitsmemory-management
AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
ruvnet
vss-manage-video-io-storage
Use to call the VIOS REST API (sensor list, timelines, clip extraction, snapshots, add/delete sensors and streams). Not for VLM inference or search.
nvidia
vectorbt-expert
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
marketcallsvss-search-archive
Use this skill to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).
nvidianemotron-retrieval-recipes
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.
nvidiaqdrant-scaling-data-volume
Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
qdrantoptimize
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
marketcallsnemo-retriever
Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (`.jpg` `.png` `.tiff`), Office (`.docx` `.pptx`), text (`.html` `.txt`), audio (`.mp3` `.wav` `.m4a`), or video (`.mp4` `.mov`). Prefer this over native Read / Grep for multi-file or non-PDF corpora. Not for: editing files, web browsing, single-file plain-text lookups, fine-tuning.
nvidia
tiledbvcf
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
k-dense-airag
Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.
giuseppe-trisciuoglio
github-traffic
获取、存储和可视化 GitHub 仓库流量数据（浏览量、克隆量、引荐来源、星标数），并生成趋势图表。需要仓库的推送权限。
zc277584121
rag-architect
设计并实现生产级RAG系统，包括文档分块、生成嵌入向量、配置向量存储、构建混合搜索管道、应用重排序以及评估检索质量。在构建RAG系统、向量数据库或需要语义搜索、文档检索、上下文增强、相似性搜索或基于嵌入索引的知识驱动AI应用时使用。
jeffallan
valyu-best-practices
适用于AI代理的完整Valyu API工具包。当需要执行跨网页、学术、医疗、交通、金融来源的实时搜索、从URL提取内容、带引用的AI驱动答案或全面的深度研究报告时，请使用此技能。
valyuaibacktest
快速回测某个策略在某个标的上的表现。生成完整的 .py 脚本，包含数据获取、信号生成、回测、统计和图表。
marketcalls
redis-search
Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lexical and vector ranking, RAG pipelines, zero-downtime index updates via aliases, and debugging with FT.PROFILE and FT.EXPLAIN. Use when defining a search index on Hash or JSON documents, writing FT.SEARCH queries with filters, sorting, aggregation, or vector KNN, tuning HNSW parameters, building a RAG retrieval pipeline, or troubleshooting slow or empty search results.
redis
dt-app-notebooks
Work with Dynatrace notebooks - create, modify, query, and analyze notebook JSON including sections, DQL queries, and visualizations.
dynatrace
google-cloud-solution-hybrid-search-alloydb
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.
google