A vector index and a vector database serve different purposes in managing and querying vector data, though they are often used together in AI and machine learning applications.
A vector index is a data structure optimized for fast similarity search (e.g., nearest neighbor search) in high-dimensional vector spaces. It organizes vectors to enable efficient retrieval without scanning the entire dataset. Common types include:
Example: An HNSW (Hierarchical Navigable Small World) index speeds up image retrieval by organizing embeddings for quick similarity matching.
A vector database is a specialized system that stores, manages, and queries vector data at scale. It integrates vector indexes, provides APIs for CRUD operations, and supports advanced features like metadata filtering, versioning, and scalability.
Example: A vector database can store product image embeddings, allowing e-commerce platforms to retrieve visually similar items using metadata filters (e.g., "red shoes under $50").
| Feature | Vector Index | Vector Database |
|---|---|---|
| Purpose | Optimizes similarity search | Manages and queries vector data |
| Scope | Focuses on search efficiency | Handles storage, indexing, and APIs |
| Features | Limited to search logic | Supports metadata, scalability, etc. |
For scalable vector search, Tencent Cloud’s VectorDB combines efficient indexing (e.g., HNSW) with database capabilities, enabling fast similarity search for AI applications like recommendation systems, image retrieval, and semantic search. It supports hybrid queries (vector + metadata) and auto-scaling for dynamic workloads.
Example: A video platform uses Tencent Cloud VectorDB to index video embeddings, allowing users to find similar content based on visual and textual features while filtering by tags or upload date.