Embeddings are compressed to reduce their size and computational cost while preserving their usefulness for tasks like similarity search or machine learning. Common approaches include dimensionality reduction, quantization, and pruning. These methods balance efficiency with the retention of meaningful information in the embedding vectors.
Dimensionality reduction techniques like Principal Component Analysis (PCA) project high-dimensional embeddings into a lower-dimensional space. For example, a 512-dimensional embedding might be reduced to 128 dimensions by keeping only the most statistically significant features. Another method is using autoencoders, which train a neural network to reconstruct the original embedding from a compressed representation. Quantization reduces the numerical precision of embedding values. Instead of storing 32-bit floating-point numbers, embeddings might be converted to 8-bit integers (e.g., from [-3.0, 3.0] to integers 0–255). Product quantization divides an embedding into subvectors and replaces each with a codebook index, dramatically shrinking storage needs. For instance, a 128-dimensional vector split into four 32D subvectors could be represented by four 8-bit codes, reducing storage by 75%.
Pruning removes less important dimensions or weights. In sparse embeddings, dimensions with near-zero values across many examples might be discarded. Alternatively, techniques like magnitude-based pruning eliminate weights below a threshold after training. Trade-offs exist: PCA and quantization introduce approximation errors, while pruning risks losing nuanced information. Developers choose methods based on use cases—quantization suits edge devices with limited memory, while pruning benefits sparse retrieval systems. Libraries like FAISS or Scikit-learn provide off-the-shelf tools for these optimizations, allowing developers to experiment with compression ratios and accuracy trade-offs efficiently.