To efficiently compress or store large sets of sentence embeddings, developers can use a combination of compression techniques, optimized storage formats, and specialized databases. Sentence embeddings, often represented as high-dimensional vectors (e.g., 768 dimensions from models like BERT), require strategies that balance storage efficiency with retrieval speed and accuracy. The approach depends on whether the focus is reducing memory usage, enabling fast queries, or minimizing disk space.
Compression Techniques A common method is quantization, which reduces the precision of floating-point numbers in embeddings. For example, converting 32-bit floats to 16-bit or 8-bit integers can cut storage size by 50–75% with minimal accuracy loss. Product quantization (PQ) splits vectors into subvectors and replaces them with compact codes, as seen in libraries like FAISS. For extreme compression, binary hashing converts vectors into compact binary codes (e.g., using Locality-Sensitive Hashing), though this may sacrifice some retrieval accuracy. These methods are often combined—for instance, using 8-bit quantization with PQ—to achieve smaller sizes while preserving search performance.
Storage Formats and File Structures Choosing the right file format is critical. HDF5 is ideal for large datasets, supporting chunked storage and compression algorithms like GZIP. Parquet, a columnar format, efficiently compresses numerical data and integrates with big data tools (e.g., Apache Spark). For simplicity, binary formats like NumPy’s .npy or PyTorch’s .pt can store tensors directly but lack built-in compression. Combining formats with compression—such as saving embeddings as a compressed Parquet file—reduces disk usage while enabling parallel processing. For temporary storage, in-memory databases like Redis can cache embeddings using serialization protocols like MessagePack.
Databases and Vector Storage Solutions Specialized vector databases optimize both storage and retrieval. FAISS (Facebook AI Similarity Search) compresses vectors using PQ and stores them in inverted indexes for fast approximate searches. Milvus or Pinecone offer scalable, distributed storage with built-in compression and support for hybrid queries (e.g., filtering by metadata). Traditional databases like PostgreSQL with the pgvector extension handle smaller datasets but lack the scalability of dedicated vector DBs. For cloud-based solutions, AWS S3 paired with a query layer (e.g., Amazon Athena) can store compressed embeddings cost-effectively, though latency may be higher. The choice depends on use case: FAISS suits static datasets, while Milvus excels in dynamic, large-scale environments.