How do you optimize embeddings for low-latency retrieval?

To optimize embeddings for low-latency retrieval, focus on three key areas: embedding model efficiency, indexing strategies, and infrastructure optimizations. Start by ensuring your embedding model is lightweight and tailored to your data. For example, use a smaller pre-trained model like DistilBERT instead of BERT for text embeddings, or reduce the embedding dimensions (e.g., from 768 to 128) to shrink memory usage without sacrificing too much accuracy. Quantization—converting embeddings from 32-bit floats to 8-bit integers—can further reduce storage and computation costs. These steps directly lower the computational overhead during inference and retrieval.

Next, use efficient indexing structures designed for high-speed similarity search. Approximate Nearest Neighbor (ANN) algorithms like FAISS, Annoy, or ScaNN trade a small accuracy loss for significant speed gains. For instance, FAISS uses techniques like inverted file indexing (IVF) to cluster embeddings and limit searches to the most relevant clusters. When implementing this, you might create an IVF index with 100 clusters and search 10 nearest clusters per query, reducing comparisons by 90%. HNSW (Hierarchical Navigable Small World) graphs, another ANN method, organize embeddings into layers for fast traversal. These methods avoid brute-force comparisons, which are impractical at scale.

Finally, optimize the retrieval pipeline’s infrastructure. Use batch processing for embedding generation to maximize GPU/CPU utilization, and deploy models with frameworks like ONNX Runtime or TensorRT for hardware acceleration. Cache frequently accessed embeddings in-memory (e.g., Redis) to avoid recomputation. Ensure embeddings are normalized (unit vectors) to enable efficient dot-product calculations instead of slower Euclidean distance computations. For example, if using cosine similarity, pre-normalize embeddings so a dot product equals the cosine value. Combined, these steps reduce latency at every stage, from embedding creation to final retrieval, making real-time applications feasible.