The Rise of Embedding Technology and Vector Databases in AI

In recent years, embedding technology has experienced a rapid surge in popularity, particularly since the introduction of Word2vec. This approach, often referred to as “embedding everything,” has gotten in various sectors of machine learning, leading to the emergence of two significant data layers: the raw data layer and the vector data layer. The raw data layer consists of unstructured and certain types of structured data, while the vector layer comprises easily analyzable embeddings derived from the raw data layer through machine learning models. This article explores the advantages of vectorized data over raw data and discusses the growing importance of vector databases in modern data applications.

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Advantages of Vectorized Data:

Compared to raw data, vectorized data offers several distinct advantages:

1. Abstraction and Unified Algebra System: Embedding vectors represent an abstract form of data, allowing the construction of a unified algebra system dedicated to simplifying the complexity of unstructured data.
2. Dense Floating-Point Vectors: Embedding vectors are expressed as dense floating-point vectors, enabling applications to leverage Single Instruction, Multiple Data operations. With SIMD support in modern GPUs and CPUs, vector computations achieve high performance at a relatively low cost.
3. Storage Efficiency: Vector data encoded through machine learning models occupies less storage space than the original unstructured data. This characteristic enhances throughput and enables the efficient handling of large volumes of data.
4. Arithmetic Operations: Embedding vectors facilitate arithmetic operations, enabling various applications. For instance, cross-modal semantic approximate matching utilizes word embeddings to match them with image embeddings, resulting in meaningful connections between different data types.

Vector Databases

The rise of vectorized data necessitates the development of specialized vector databases. Here are some key qualities and features that vector databases should possess:

1. Support for High-Efficiency Vector Operators: A vector database should support different types of vector operators, such as semantic similarity matching and semantic arithmetic. Additionally, it should offer various similarity metrics for calculating the spatial distance between vectors, including Euclidean distance, cosine distance, and inner product distance.
2. Support for Vector Indexing: High-dimensional vector indexes consume significant computing resources. To address this, vector databases should utilize clustering and graph index algorithms while prioritizing matrix and vector operations to leverage hardware acceleration capabilities.
3. Consistent User Experience across Deployment Environments: Vector databases are developed and deployed in diverse environments. They should deliver consistent performance and user experience across different deployment scenarios, ranging from laptops and workstations during the preliminary stages to private clusters or the cloud for full-size database deployment.
4. Support for Hybrid Search: As vector databases become ubiquitous, new applications demand hybrid search capabilities, combining vector data with other types of data. Examples include nearest neighbor search after scalar filtering, multi-channel recall from full-text search and vector search, and hybrid search of spatio-temporal and vector data. Vector databases should offer elastic scalability and query optimization to effectively integrate vector search engines with key-value stores, text search engines, and other search mechanisms.
5. Cloud-Native Architecture: With the exponential growth of data collection, vector data volumes can reach the trillions and require massive storage. Horizontal scalability becomes crucial, necessitating vector databases with cloud-native architectures. Such systems should meet the demands for elasticity, deployment agility, simplified operations, maintenance, and observability using cloud infrastructure. Additional features like multi-tenant isolation, data snapshot and backup, data encryption, and data visualization are also essential.

As embedding technology gains widespread adoption, vector databases are becoming increasingly important in modern data applications. Vectorized data provides numerous advantages over raw data, such as abstraction, computational efficiency, storage savings, and the ability to perform arithmetic operations across vectors. Vector databases, equipped with specialized features and qualities, play a pivotal role in efficiently storing, retrieving, and analyzing vector data. With their support for high-efficiency vector operators, vector indexing, consistent user experience, hybrid search, and cloud-native architecture, these databases empower organizations across various sectors to harness the potential of vectorized data in their data-driven endeavors.