5种Embedding压缩技术详解与工程实践

5 embedding compression techniques, clearly explained: (bookmark this) Ten million 1,536-dimensional embeddings will occupy: - 62 GB in float32 - 15 GB in int8 - 2 GB as packed bits This only covers the raw vector payload, and an in-memory system also needs space for the ANN index, metadata, and allocator overhead. Embedding compression works along two axes: - the number of dimensions stored - the number of bits used for each dimension The five techniques in the visual reduce different parts of the payload. 1) PCA applies a post-training transform: It learns the directions with the most variance from a representative sample, then projects existing embeddings into a smaller space. It works with any embedding model, although the projection must be fitted and applied consistently to indexed vectors and queries. 2) MRL alters the training objective: The model is trained so that selected prefixes (say, the first dimensions) of the embedding remain useful independently. You can therefore truncate an MRL embedding at inference without training a separate model for every target dimension. OpenAI reports that text-embedding-3-large at 256 dimensions still outperforms the 1,536-dimensional text-embedding-ada-002 on MTEB. 3-4) Scalar and binary quantization keep the dimension count fixed and reduce the representation used for each value. Scalar quantization typically maps float32 values to int8, giving a 4x reduction in the raw vector payload. The scale and offset add a small amount of metadata. Binary quantization keeps one bit per dimension, which gives a 32x reduction. Similarity search can then use XOR and a population count instead of floating-point distance calculations. 5) Product Quantization encodes subvectors as centroid IDs: It splits a vector into subvectors and replaces each subvector with the ID of its nearest centroid. Query distances are then approximated through centroid lookup tables. In all of these setups, the compressed representation does not need to produce the final ranking. Usually, you retrieve extra candidates from the compressed index, then recompute similarity using higher-precision document embeddings. This is especially useful with binary quantization. One bit can preserve enough coarse structure but loses magnitude information. Rescoring improves the ordering, but it cannot recover an item missed by the initial compressed-retrieval stage. Also, these methods can also work together. An MRL embedding can be truncated first and quantized afterward, reducing both dimension and precision. I built the binary retrieval pipeline end-to-end, including the rescoring pass. In the tested setup, it searches 36 million vectors in under 30 ms. Read it below.