To efficiently store or compress large sets of sentence embeddings, consider three approaches: compression techniques, specialized storage systems, and efficient serialization formats. Each method balances trade-offs between storage size, retrieval speed, and accuracy.
Compression techniques reduce the size of embeddings while preserving their utility. Dimensionality reduction methods like PCA or UMAP lower the number of dimensions, sacrificing some information for smaller storage. Quantization converts high-precision floats (e.g., 32-bit) to lower-precision types (e.g., 8-bit integers) or binary codes, which cuts storage requirements significantly. Product quantization (PQ) divides vectors into subvectors, encodes them using codebooks, and stores compact indices. For example, converting 1024-dimensional float32 embeddings to 8-bit integers reduces storage by 75%. However, aggressive compression can impact downstream tasks like similarity search, so testing accuracy trade-offs is critical.
Specialized storage systems optimize for efficient querying and scalability. Vector databases like FAISS, Milvus, or Pinecone use indexing structures (e.g., HNSW, IVF) to enable fast nearest-neighbor searches and manage large datasets in memory or on disk. FAISS is ideal for static datasets, while Milvus supports dynamic updates and distributed storage. For relational data, PostgreSQL with pgvector integrates embeddings into SQL workflows, though it may lack the speed of dedicated vector databases. Solutions like Annoy (Approximate Nearest Neighbors Oh Yeah) offer lightweight indexing for smaller datasets. The choice depends on scalability needs, query latency, and integration with existing infrastructure.
Efficient serialization formats minimize storage overhead and improve I/O performance. Binary formats like Protocol Buffers, Parquet, or HDF5 store embeddings more compactly than text-based formats (JSON, CSV). Parquet’s columnar storage and compression (e.g., Snappy) reduce file sizes and speed up batch reads. For instance, HDF5 supports chunked storage, enabling partial loading of large datasets without memory overload. Applying general-purpose compression (e.g., gzip, Brotli) to serialized files can further reduce size by 50-70%, though it adds CPU overhead during read/write. Chunking data into smaller files (e.g., 100k vectors per file) simplifies parallel processing and cloud storage. Combining these methods ensures compact storage while maintaining accessibility for downstream tasks.