llamaindex-expert
LlamaIndex for document processing, indexing strategies, and retrieval optimization.
LlamaIndex for document processing, indexing strategies, and retrieval optimization.
Create MongoDB models using Typegoose following project conventions. Use when defining database schemas, creating new models, working with embedded documents, adding indexes for query optimization, or exporting type-safe model instances.
Manage redis cache manager operations. Auto-activating skill for Backend Development. Triggers on: redis cache manager, redis cache manager Part of the Backend Development skill category. Use when working with redis cache manager functionality. Trigger with phrases like "redis cache manager", "redis manager", "redis".
Implement intelligent API response caching with Redis, Memcached, and CDN integration. Use when optimizing API performance with caching. Trigger with phrases like "add caching", "optimize API performance", or "implement cache layer".
Use wagl (DB-first memory) via plugin tools: wagl_recall, wagl_store, wagl_search, wagl_forget.
Use when configuring LibreChat's MongoDB database, Redis caching, MeiliSearch message search, file storage strategy (local vs CDN/S3), or PGVector for RAG. Also use when asked about database backup, connection strings, or storage scaling.
Guidelines for implementing new database backends for rhosocial-activerecord - dialect, type adapter, config, and storage backend
RuVector-powered graph database CLI with Cypher queries, hyperedges, ACID persistence, and 150x faster vector search. Use when managing graph data stores, running Cypher queries, performing vector similarity search, managing database schemas, or building knowledge graphs for AI agents.
Distributed clustering and auto-sharding for RuVector with Raft consensus, node discovery, and rebalancing. Use when building distributed vector databases, adding horizontal scaling to vector search, or coordinating multi-node RuVector deployments with automatic failover.
High-performance HNSW vector database core built in Rust with N-API bindings - 50k+ inserts/sec, sub-ms search. Use when building vector search applications, adding nearest-neighbor indexing to Node.js projects, or needing a fast embedded vector store with metadata filtering.
HNSW vector indexing engine with 50k+ inserts/sec via Rust NAPI bindings. Use when the user needs to build high-performance vector search in Node.js, create HNSW indexes, perform batch vector operations, or integrate similarity search into backend applications.
Advanced extensions for RuVector: embedding generation, admin UI, data export, temporal versioning, and persistence adapters. Use when adding embedding pipelines, visualizing vector data, exporting indexes, or tracking vector changes over time.
Native Node.js graph database bindings with hypergraph support, Cypher queries, and persistence. Use when the user needs a graph database in Node.js, Cypher query execution, vertex/edge CRUD operations, graph traversals, shortest path algorithms, or hypergraph data modeling.
Rust vector database with native NAPI bindings for Node.js, SIMD-accelerated HNSW search, and zero-copy operations. Use when the user needs maximum vector search performance in Node.js, SIMD-optimized distance calculations, native Rust bindings, or low-latency similarity search in server-side applications.
PostgreSQL AI vector database CLI with pgvector-compatible extension, 53+ SQL functions, HNSW/GNN/attention ops. Use when the user needs to manage PostgreSQL vector operations, install the RuVector extension, run vector/sparse/hyperbolic/graph/attention/GNN queries, benchmark PostgreSQL vector performance, or manage a PostgreSQL-backed AI database.
Standalone vector database with SQL, SPARQL, and Cypher query support powered by RuVector WASM. Use when the user needs a lightweight embedded vector database, multi-language query support (SQL/SPARQL/Cypher), standalone vector search without external dependencies, or a portable vector store for applications.
HTTP/gRPC server for RuVector with REST API, streaming, and authentication. Use when the user needs to deploy a vector database server, expose vector search over HTTP or gRPC, configure server authentication, manage collections via REST endpoints, or set up a production vector search service.
High-performance vector database for Node.js with native Rust NAPI and automatic WASM fallback. Use when the user needs to run vector similarity search, manage HNSW indexes, insert embeddings, or use the ruvector CLI for database operations, benchmarking, or ecosystem package management.
Analyzes Redis cache performance including hit rates, key distribution, memory usage, TTL patterns, and tenant isolation compliance
Generate Redis cache service with tenant-prefixed keys to prevent cross-tenant leakage, TTL management, and cache invalidation patterns. Use when caching data or reducing database load.
Best practices dùng Redis: caching patterns, session management, pub/sub, rate limiting và data structures.
Expert knowledge for Azure Cache for Redis development including troubleshooting, best practices, decision making, architecture & design patterns, security, configuration, integrations & coding patterns, and deployment. Use when configuring geo-replication, persistence, VNet/Private Link, CLI/PowerShell automation, or Blob import/export, and other Azure Cache for Redis related development tasks. Not for Azure Managed Redis (use azure-managed-redis), Azure HPC Cache (use azure-hpc-cache), Azure Blob Storage (use azure-blob-storage), Azure Table Storage (use azure-table-storage).
Redis performance optimization and best practices. Use this skill when working with Redis data structures, Redis Query Engine (RQE), vector search with RedisVL, semantic caching with LangCache, or optimizing Redis performance.