# concepts-definitions

> Browse 12 items in concepts-definitions.

## Items

- [PQ (Product Quantization)](https://vectordb.ever.works/es/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.
- [Deep Learning for Search](https://vectordb.ever.works/es/items/deep-learning-for-search) — Applied book on using deep learning for search, including dense vector representations, semantic search, and neural ranking, all directly relevant to building applications on top of vector databases.
- [Foundations of Multidimensional and Metric Data Structures](https://vectordb.ever.works/es/items/foundations-of-multidimensional-and-metric-data-structures) — Technical book covering theory and practice of multidimensional and metric data structures for similarity search, forming a theoretical basis for index structures used in vector databases.
- [Machine Learning Crash Course: Embeddings](https://vectordb.ever.works/es/items/machine-learning-crash-course-embeddings) — Module of Google’s Machine Learning Crash Course that explains word and text embeddings, how they are obtained, and the difference between static and contextual embeddings, giving essential background for using vector representations in vector databases and similarity search systems.
- [K-means Tree](https://vectordb.ever.works/es/items/k-means-tree) — K-means Tree is a clustering-based data structure that organizes high-dimensional vectors for fast similarity search and retrieval. It is used as an indexing method in some vector databases to optimize performance for vector search operations.
- [Locality-Sensitive Hashing](https://vectordb.ever.works/es/items/locality-sensitive-hashing) — Locality-Sensitive Hashing (LSH) is an algorithmic technique for approximate nearest neighbor search in high-dimensional vector spaces, commonly used in vector databases to speed up similarity search while reducing memory footprint.
- [M-tree](https://vectordb.ever.works/es/items/m-tree) — M-tree is a dynamic index structure for organizing and searching large data sets in metric spaces, enabling efficient nearest neighbor queries and dynamic updates, which are important features for vector databases handling high-dimensional vectors.
- [Online Product Quantization (O-PQ)](https://vectordb.ever.works/es/items/online-product-quantization-o-pq) — Online Product Quantization (O-PQ) is a variant of product quantization designed to support dynamic or streaming data. It enables adaptive updating of quantization codebooks and codes in real-time, making it suitable for vector databases that handle evolving datasets.
- [Optimized Product Quantization (OPQ)](https://vectordb.ever.works/es/items/optimized-product-quantization-opq) — Optimized Product Quantization (OPQ) enhances Product Quantization by optimizing space decomposition and codebooks, leading to lower quantization distortion and higher accuracy in vector search. OPQ is widely used in advanced vector databases for improving recall and search quality.
- [R-tree](https://vectordb.ever.works/es/items/r-tree) — R-tree is a tree data structure widely used for indexing multi-dimensional information such as vectors, supporting efficient spatial queries like nearest neighbor and range queries, which are essential in vector databases.
- [Spectral Hashing](https://vectordb.ever.works/es/items/spectral-hashing) — Spectral Hashing is a method for approximate nearest neighbor search that uses spectral graph theory to generate compact binary codes, often applied in vector databases to enhance retrieval efficiency on large-scale, high-dimensional data.
- [Vector Database](https://vectordb.ever.works/es/items/vector-database) — A vector database is a specialized database designed to store, index, and retrieve unstructured data represented as high-dimensional vectors, enabling efficient semantic search, similarity search, and powering applications such as LLM long-term memory, semantic search, and recommendation systems.

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