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46 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
upstash-vector-js
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
upstash
pgvector-semantic-search
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
timescale
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
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。
trailofbits
redis-vector-search
Redis vector search guidance covering HNSW vs FLAT algorithm choice, vector index configuration (dims, distance metric, datatype), filtered hybrid search combining vector similarity with TAG or NUMERIC filters, and the RAG retrieval pattern with RedisVL. Use when defining a VECTOR field in FT.CREATE, integrating embeddings (OpenAI, Cohere, sentence-transformers), tuning HNSW parameters (M, EF_CONSTRUCTION, EF_RUNTIME), building a retrieval-augmented generation pipeline, or filtering vector results by attribute.
redisqdrant
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
event-sourcing
Implement event sourcing and CQRS patterns using event stores, aggregates, and projections. Use when building audit trails, temporal queries, or systems requiring full history.
aj-geddes
memory-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
ray-data
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
orchestra-research
elasticsearch-ingest
Load CSV and JSON files into Elasticsearch indices using the bulk API and explicit mappings when field types matter. Use when batch-importing local files, converting CSV rows or JSON arrays to NDJSON bulk format, or verifying document counts and mappings after ingest — not for Logstash pipelines, Beats, custom scripts, or index-to-index reindex.
elastic
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
vss-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).
nvidia
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
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).
marketcalls
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
hindsight-local
Store user preferences, learnings from tasks, and procedure outcomes. Use to remember what works and recall context before new tasks. (user)
vectorize-io
jevgrep
Use for questions about how, why, or where behavior works in a repository, including questions that name a function or setting. Start behavioral discovery with jg before broad text searches or git history. When delegating repository discovery, instruct the subagent to start with jg. Jevgrep returns relevant files and source excerpts. For exact symbol definitions, string matches, or filenames, use grep or file search instead.
dzhngnemotron-retrieval-recipes
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron `embed`/`rerank` retrieval recipes.
nvidia
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics
agiprolabs
openviking-memory
Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.
volcengine
cli-anything-chromadb
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.
hkudsgoogle-cloud-storage-basics
Stores, retrieves, and manages data as objects in Cloud Storage (Google Cloud Storage, or GCS) buckets. Use when you need to interact with Cloud Storage — create or configure buckets, upload, download, stream, or transfer data, organize objects with folders, generate signed URLs, control access (IAM, ACLs, public access prevention), set storage classes and tiering (Standard, Nearline, Coldline, Archive), manage cost and lifecycle, protect data (versioning, encryption/CMEK, retention and Bucket Lock, object holds, soft delete), host static websites, trigger Pub/Sub notifications on object changes, mount buckets as a file system (gcsfuse), or optimize storage performance at any scale. Covers the gcloud storage / gsutil CLI, JSON and XML APIs, client libraries, Terraform, and Cloud Storage MCP servers. Don't use for block storage (Persistent Disk), data warehousing/analytics (BigQuery), or databases (Cloud SQL, Spanner, Bigtable, Firestore).
google