A vector database is a specialized type of database designed to store, manage, and efficiently query vector embeddings. These embeddings are high-dimensional numerical representations (vectors) of unstructured data, such as text, images, audio, or video, that capture their semantic meaning or characteristics. By transforming complex data into these numerical vectors, a vector database can perform highly efficient similarity searches, finding items that are “semantically similar” or “contextually related” based on the proximity of their vectors in a multi-dimensional space.
Key Concepts:
- Vector Embeddings: Numerical arrays (lists of numbers) generated by machine learning models (e.g., neural networks) that encode the meaning or features of data. Data with similar meanings will have vectors that are “close” to each other in this multi-dimensional space.
- Similarity Search (Nearest Neighbor Search): The core functionality where the database takes a query vector and finds the most similar stored vectors (and thus the original data items) based on distance metrics like cosine similarity or Euclidean distance. This is typically achieved using Approximate Nearest Neighbor (ANN) algorithms for speed and scalability.
- Use Cases: Powering applications like semantic search, recommendation systems, anomaly detection, image recognition, and large language models (LLMs) for retrieving relevant context.