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From qdrant
Optimizes Qdrant vector search performance covering indexing strategies, query optimization, search speed, indexing performance, and memory usage. Use to improve speed and efficiency of Qdrant deployment.
How this skill is triggered — by the user, by Claude, or both
Slash command
/qdrant:qdrant-performance-optimizationThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.
There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.
There are two different criteria for search speed: latency and throughput. Latency is the time it takes to get a response for a single query, while throughput is the number of queries that can be processed in a given time frame. Depending on your use case, you may want to optimize for one or both of these metrics.
More on search speed optimization can be found in the Search Speed Optimization skill.
Qdrant needs to build a vector index to perform efficient similarity search. The time it takes to build the index can vary depending on the size of your dataset, hardware, and configuration.
More on indexing performance optimization can be found in the Indexing Performance Optimization skill.
Vector search can be memory intensive, especially when dealing with large datasets. Qdrant has a flexible memory management system, which allows you to precisely control which parts of storage are kept in memory and which are stored on disk. This can help you optimize memory usage without sacrificing performance.
More on memory usage optimization can be found in the Memory Usage Optimization skill.
npx claudepluginhub qdrant/skills --plugin qdrantTunes vector indexes for latency, recall, and memory using HNSW parameters, quantization strategies, and scaling guidelines up to billions of vectors.
Optimizes vector index performance by tuning HNSW parameters, selecting quantization strategies, and balancing latency, recall, and memory for production-scale vector search.
Diagnoses Qdrant search relevance issues (poor results, low precision/recall) and guides tuning of embedding models, HNSW parameters, query strategies, and hybrid search with reranking.