Results for “github-search”

11 skills

qdrant-search-quality-diagnosis

Diagnoses Qdrant search quality issues by isolating causes like HNSW approximation, quantization, embedding model, or search pipeline problems.

qdrant-search-speed-optimization

Diagnoses and resolves slow Qdrant search performance issues including high latency, low throughput, and slow filtered searches.

qdrant-search-strategies

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

qdrant-minimize-latency

Guides optimization of Qdrant query latency by tuning segments, memory, quantization, and search parameters.

qdrant-performance-optimization

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

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qdrant-clients-sdk

Integrate Qdrant vector search into applications using officially supported client SDKs for Python, JavaScript, Rust, Go, .NET, and Java.

qdrant-monitoring

Guides monitoring and observability setup for Qdrant vector search deployments, including Prometheus scraping, health checks, and metric-based debugging of production issues.

postgresql-optimization

Optimize PostgreSQL databases with advanced features, performance tuning, and best practices for JSONB, full-text search, window functions, and indexing.

qdrant-model-migration

Guides embedding model migration in Qdrant without downtime, covering alias swap, side-by-side, and hybrid search strategies.

qdrant-sliding-time-window

Guides scaling Qdrant vector search with time-based data rotation using shard rotation, collection rotation, or filter-and-delete strategies.

qdrant-scaling-qps

Guides scaling Qdrant query throughput (QPS) through performance tuning, horizontal scaling with read replicas, and disk I/O optimization.