What is a Vector Database?

Summary

Vector databases store and manage high-dimensional vector representations of data, grouping them by similarity rather than traditional row-and-column organization. They support semantic and similarity-based retrieval by converting text, images, audio, or video into embeddings, indexing them with methods such as product quantization and HNSW, and comparing query vectors with indexed vectors. These capabilities underpin use cases including RAG, hybrid search, recommendations, NLP, image and video recognition, and anomaly detection, while sharding, partitioning, caching, and replication support scale. The article distinguishes a vector database from a standalone index because it also provides storage, CRUD, metadata filtering, horizontal scaling, and data management functions, and describes Databricks AI Search as an integrated implementation with automatic scaling, optimization, security, and governance.

Context

Traditional databases are well suited to structured data in rows and columns, while increasingly common unstructured data such as text, images, and video requires representations and retrieval methods based on semantic similarity. Vector databases support AI systems that need fast similarity searches, including some LLM applications and RAG workflows.

Approach / What changed

Data is vectorized into embeddings, indexed using methods such as product quantization or HNSW, and queried by comparing an input vector with indexed vectors. Vector databases add storage, CRUD operations, metadata filtering, horizontal scaling, and data management capabilities; Databricks AI Search provides an integrated implementation with security, governance, automatic scaling, and optimization.

Takeaways

• Embeddings convert multimodal data into numerical vectors, and the quality of those vector representations critically affects machine-learning performance.
• HNSW and product quantization create data structures for faster similarity or nearest-neighbor searches than scanning vectors without specialized indexing.
• A vector database extends standalone vector indexing with storage, CRUD operations, metadata filtering, horizontal scaling, data integrity, and security capabilities.

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