To scale a vector store for a RAG system handling large datasets or high query volumes, three key strategies include sharding, optimized indexing, and tiered storage with caching. Each addresses performance bottlenecks by distributing workloads, improving search efficiency, or reducing redundant computations.
Sharding splits the vector dataset across multiple nodes or clusters to parallelize storage and query processing. For example, vectors can be partitioned by semantic similarity (e.g., using clustering algorithms like k-means) so each shard contains related data. Queries are routed to the most relevant shard(s), reducing the search space per node. Alternatively, hash-based sharding distributes vectors evenly to balance load, but may require querying all shards. Hybrid approaches, such as pre-filtering metadata (e.g., date ranges or categories) before vector search, can further reduce shard contention. Tools like Milvus or Elasticsearch support automated sharding with configurable strategies.
Indexing optimizations focus on balancing speed, accuracy, and resource usage. Hierarchical Navigable Small World (HNSW) graphs provide fast approximate nearest neighbor (ANN) searches with high recall, ideal for latency-sensitive applications. Inverted File Index (IVF) methods group vectors into clusters, accelerating search by narrowing comparisons to a subset of clusters. Adjusting parameters like the number of clusters (IVF) or graph layers (HNSW) tailores performance: fewer clusters speed up queries but reduce accuracy. Quantization techniques (e.g., PQ — Product Quantization) compress vectors into smaller representations, cutting memory usage and enabling larger datasets to fit in RAM. For dynamic datasets, incremental indexing (e.g., FAISS’s add_with_ids) avoids rebuilding the entire index.
Tiered storage and caching reduce redundant computation. Frequently accessed vectors or query results can be cached in-memory (e.g., Redis) or using LRU policies, while less active data resides on SSDs or distributed file systems. Asynchronous background processes can pre-warm caches during off-peak times. Load balancers (e.g., NGINX) distribute incoming queries evenly across nodes, preventing hotspots. For hybrid systems, combining vector search with traditional database filtering (e.g., PostgreSQL’s pgvector with metadata WHERE clauses) minimizes unnecessary vector comparisons. GPU acceleration (via CUDA-enabled libraries like RAPIDS cuDF) further speeds up batch processing for high-throughput scenarios.