Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment.
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Use qdrant-performance-optimization to assess [deployment configuration and representative workload] and deliver prioritized recommendations for search latency/throughput, indexing performance, and memory use.
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name: qdrant-performance-optimizationdescription: "Different techniques to optimize the performance of Qdrant, including indexing strategies, query optimization, and hardware considerations. Use when you want to improve the speed and efficiency of your Qdrant deployment."allowed-tools: - Read - Grep - Glob
Qdrant Performance Optimization
There are different aspects of Qdrant performance, this document serves as a navigation hub for different aspects of performance optimization in Qdrant.
Search Speed Optimization
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.
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.
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.