# vector-compression

> Browse 4 items tagged with vector-compression.

## Items

- [nanopq](https://vectordb.ever.works/nl/items/nanopq) — nanopq is a lightweight product quantization library for efficient vector compression and similarity search, which is an important feature for vector databases that need to store and query large-scale vector data efficiently.
- [PQ (Product Quantization)](https://vectordb.ever.works/nl/items/pq-product-quantization) — Product Quantization is a compression and indexing technique for vector search that splits vectors into subspaces and quantizes each part separately, allowing vector databases to store large-scale embeddings compactly while supporting efficient ANN search.
- [Qinco](https://vectordb.ever.works/nl/items/qinco) — Qinco is an open-source implementation from Facebook Research for Residual Quantization with Implicit Neural Codebooks. It provides quantization and indexing methods for compact vector representations to accelerate similarity and nearest neighbor search, making it relevant as a low-level vector indexing and compression component for vector databases and large-scale AI retrieval systems.
- [RaBitQ](https://vectordb.ever.works/nl/items/rabitq) — RaBitQ is an open-source library implementing the "Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search" method, providing vector quantization and compression techniques designed to improve efficiency and accuracy of ANN search engines and vector databases operating in high-dimensional spaces.

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_Canonical page: https://vectordb.ever.works/nl/tags/vector-compression_