Vector Database (Vector Database) is a database system dedicated to storing and querying vector data. Vector databases usually use efficient vector indexing technology, support query and retrieval based on vector similarity, and can be applied to image search, natural language processing, recommendation system, machine learning and other fields.
Different from traditional relational databases, vector databases usually use a vector-based data model, using vectors as the core representation of data. The vector database can store and process a large amount of vector data, and supports efficient vector similarity calculation and query. Common vector indexing technologies include KD-Tree, LSH, HNSW, etc., which can quickly locate and retrieve vector data in high-dimensional spaces. In addition, the vector database also supports operations such as clustering, dimensionality reduction, and normalization on vector data for better data processing and analysis.
Vector databases have many application scenarios, such as:
- Image search: convert images into vector representations, store them in a vector database, and query similar images based on vector similarity.
- Natural language processing: convert text into vector representation, store it in a vector database, and query similar text based on vector similarity.
- Recommendation system: Convert users and products into vector representations, store them in a vector database, and recommend similar products based on vector similarity.
- Machine learning: Convert training data and model parameters into vector representations, store them in a vector database, and perform tasks such as classification and clustering based on vector similarity.
Some popular vector database systems include Faiss, Milvus, Annoy, NMSLIB, etc., which provide rich functions and excellent performance, and can help users process and analyze large-scale vector data.
A typical application example is an image search engine. The image search engine can find a group of images most similar to the query image through the vector data stored in the vector database of the image similarity search engine.
Specifically, image search engines can use deep learning models such as convolutional neural networks to convert images into vector representations. Each image can be viewed as being in a high-dimensional vector space