To handle millions of sentence embeddings efficiently, focus on three areas: storage optimization, indexing for fast search, and retrieval scalability. Here’s how to approach each:
1. Storage Optimization Sentence embeddings are dense vectors (e.g., 384 or 768 dimensions) that consume significant storage. Use quantization to reduce precision (e.g., 32-bit floats to 8-bit integers), cutting storage by 75% with minimal accuracy loss. For example, Facebook’s FAISS library supports PQ (Product Quantization), which compresses vectors into compact codes. Store embeddings in columnar formats like Parquet or HDF5 for efficient disk I/O, and consider delta encoding if embeddings are updated incrementally. For cloud storage, use chunked compression (e.g., gzip + multipart uploads) to reduce costs. Tools like Apache Arrow or Zarr can manage large datasets in memory-mapped files to avoid loading all data into RAM.
2. Indexing for Fast Search Exact nearest-neighbor search (e.g., brute-force) is impractical at scale. Instead, use approximate nearest neighbor (ANN) algorithms. FAISS provides GPU-accelerated inverted file indexes (IVF) with PQ, balancing speed and accuracy. Hierarchical Navigable Small World (HNSW) graphs (via libraries like hnswlib) offer high recall for high-dimensional data. Partition embeddings into shards (e.g., by topic or language) to reduce index size per node. For hybrid systems, combine ANN with metadata filters (e.g., Elasticsearch for text attributes) to narrow search spaces. Indexes like SCANN or Annoy are also viable, but benchmark them against your data’s dimensionality and query patterns.
3. Retrieval Scalability Deploy distributed systems to parallelize search across nodes. Use frameworks like Ray or Dask to scale ANN queries horizontally. Cache frequently accessed embeddings in-memory (e.g., Redis) or use SSDs for low-latency disk access. For real-time applications, precompute results for common queries and update them asynchronously. Optimize network overhead with batch processing (e.g., query 100 vectors at once instead of 100 separate requests). If using a managed service, leverage vector databases like Pinecone or Qdrant, which handle replication, load balancing, and auto-scaling. Always profile latency/throughput trade-offs—for example, increasing IVF probes improves accuracy but slows queries.
Example Workflow: Store embeddings as 8-bit quantized vectors in Parquet files, index them with FAISS IVF-PQ, and serve queries via a distributed Ray cluster with caching. This balances cost, speed, and accuracy for large-scale applications.