GENPERF04-BP01 Test vector store features for latency and relevant performance
Optimizing a data retrieval system for generative AI typically has more to do with data architecture and meta data than the foundation model selected. This best practice encourages high data quality and data architecture to accelerate data-driven generative AI workloads.
Desired outcome: When implemented, this best practice facilitates expedient data storage and access, with accurate and relevant data retrieval.
Benefits of establishing this best practice: Consider mechanical sympathy - Optimizing a data storage system for a generative AI workload can be as simple as changing vector indexes or modifying the chunking strategy. Familiarize yourself with how the system performs data storage and retrieval to best optimize the database.
Level of risk exposed if this best practice is not established: Medium
Implementation guidance
Optimizing vector store features for generative AI requires a holistic approach to search architecture. Begin with effective chunking and embedding strategies, as these have greater effects on performance and can only be addressed before data enters the search engine. There are several popular chunking strategies to select from, including fixed-size, hierarchical, or semantic. Some vector base solutions like Amazon Bedrock allow for custom chunking strategies. There are several factors to consider when selecting a chunking strategy. Evaluate the available options when configuring a vector store.
When selecting an approximate nearest neighbor (ANN) algorithm, consider the trade-offs between accuracy, speed, memory usage, and scalability. Common options include locality-sensitive hashing (LSH) for fast indexing, hierarchical navigable small world (HNSW) for high accuracy, inverted file index (IVF) for balance, and product quantization (PQ) for compact storage. Benchmark multiple algorithms with your specific dataset to find the optimal balance.
Organize indices hierarchically, with top-level indices for general information and lower-level indices for detailed data. This approach generally outperforms single, all-encompassing indices.
For search optimization in AI-driven queries, focus on machine-to-machine interactions. Implement query expansion using AI-generated context, and shift fuzzy matching towards semantic similarity. Leverage hybrid search approaches that combine semantic understanding with traditional retrieval techniques to enhance result relevance.
Continually monitor performance across all system components, including embedding generation, index construction, query processing, and result retrieval. Track latency, throughput, and resource utilization. Prepare for scenarios where performance bottlenecks may shift between layers as your system scales and usage patterns change.
Maintain data quality through regular assessments of freshness, accuracy, and representativeness. Monitor for data drift and implement processes for continuous data ingestion and periodic re-embedding. Use automated checks, human review, and AI output analysis to maintain data quality. Establish clear governance policies, and maintain version control of your vector store.
Remember that optimizations in one area can affect the entire system. Stay adaptable to new techniques and algorithms to maintain a high-performing, efficient knowledge retrieval system that delivers accurate, contextually relevant information for your generative AI application.
Implementation steps
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Identify the most important performance KPI for this workload (for example, accuracy, speed, memory usage, or scalability). Consider implementing a custom search algorithm that supports this KPI.
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Organize indices based on a hierarchy, where more detail is introduced towards the bottom of the hierarchy.
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Establish query latency monitoring on the data retrieval system to verify the database latency is consistently monitored and alerted upon.
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Perform regular data quality checks, verifying that data is assessed for quality before being placed into a database.
Resources
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