Results for “query-tuning”

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

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

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

Adds autotuning to CuTile kernels using the exhaustive_search API with a tune-once/cache/direct-launch pattern, covering occupancy-only and complex tile-size search spaces.

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

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

Diagnoses and resolves slow Qdrant indexing and data ingestion by optimizing batching, sharding, HNSW parameters, and payload indexing strategies.

Optimizes Qdrant query performance for large limits across multiple shards by using Poisson-distributed subsampling to reduce inter-shard data transfer.

Guides scaling decisions for Qdrant vector databases based on data volume, query throughput, latency, or query volume.

Diagnose and optimize slow-loading, high-memory, or low-FPS USD scenes using a structured workflow with profiling, validation, and mutation phases.

Fine-tune open models or Gemini models using Agent Platform infrastructure, from environment setup through data preparation, job configuration, monitoring, and deployment.

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

Systematically diagnose and resolve performance issues in Power BI models, reports, and queries using a structured troubleshooting methodology.