The performance of a vector store is heavily influenced by embedding dimensions and index type, both of which directly affect retrieval speed, accuracy, and resource usage. Embedding dimension determines the size of each vector representation. Larger dimensions (e.g., 1024 vs. 768) can capture finer semantic details but increase computational overhead during similarity searches. For example, comparing 1024-dimensional vectors requires more calculations than lower-dimensional ones, slowing down retrieval. However, higher dimensions might improve result quality in complex tasks like dense text retrieval. Conversely, smaller dimensions reduce memory and compute costs but risk oversimplifying data relationships, potentially leading to less precise matches. Designers must balance these trade-offs based on use case requirements.
The index type dictates how efficiently vectors are stored and queried. Flat indexes (exhaustive search) guarantee perfect accuracy but scale poorly—searching 1 million vectors takes O(n) time, becoming impractical at scale. Approximate Nearest Neighbor (ANN) indexes like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File) sacrifice some accuracy for speed. HNSW creates a layered graph structure to enable fast traversal with high recall, making it suitable for low-latency applications. IVF partitions data into clusters, reducing the search space by focusing on a subset of vectors. For instance, IVF with 100 clusters might search only 10% of the dataset per query, drastically improving speed. However, IVF’s performance depends on cluster quality, which degrades if data distribution shifts over time. Each index type has distinct memory, build time, and query latency characteristics that must align with system constraints.
For a RAG system requiring quick retrievals, the interplay between embedding size and index type is critical. A high-dimensional embedding paired with a flat index would be too slow for real-time use, while a low-dimensional embedding with HNSW might offer fast but less accurate results. For example, a 384-dimensional embedding combined with HNSW could provide sub-50ms latency for 1M vectors, but if the task demands higher precision (e.g., legal document retrieval), a 768-dimensional embedding with IVF-PQ (Product Quantization) might better balance speed and accuracy. Design choices should prioritize measurable metrics: if latency is capped at 100ms, lower dimensions and optimized indexes are mandatory. Additionally, index parameters (e.g., HNSW’s “efSearch” or IVF’s “nprobe”) require tuning to match the embedding characteristics. Pre-filtering steps or hybrid indexes might also be necessary to handle dynamic data or multi-modal queries efficiently. Ultimately, testing combinations under real-world loads is key to optimizing the trade-offs.
