Vector search, or vector similarity search, is a technique to find similar items within a dataset by representing them as high-dimensional vectors. Unlike keyword search, which relies on exact term matching, vector search evaluates semantic relationships, enabling it to retrieve results based on meaning or context. For instance, a search for "apple" could return results about the fruit or the company based on contextual clues. The core idea is to measure how "close" vectors are in a multi-dimensional space to identify related items. This process involves mapping data—like text, images, or audio—into numerical vectors. These vectors reside in a high-dimensional space where similar items cluster together. The similarity between vectors is determined using metrics such as Euclidean distance or cosine similarity. For example, two similar images might have vectors that are closer together than those of unrelated images. Indexing methods like k-d trees or HNSW graphs help efficiently organize and retrieve vectors. Applications of vector search are vast. It's used in recommendation systems (e.g., Netflix suggesting movies), semantic search engines (e.g., retrieving contextually relevant documents), and image retrieval systems (e.g., finding visually similar photos). By leveraging the power of embeddings and proximity-based comparisons, vector search offers precise and contextually relevant results, bridging the gap between raw data and actionable insights.
What is vector search?
- Getting Started with Milvus
- Exploring Vector Database Use Cases
- Evaluating Your RAG Applications: Methods and Metrics
- Embedding 101
- AI & Machine Learning
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the difference between community-driven and vendor-driven open-source?
Community-driven and vendor-driven open-source projects differ primarily in their governance, funding, and the motivatio
What are the trade-offs between speed and accuracy in vector search?
In vector search, there is often a trade-off between speed and accuracy, influenced by various factors such as the size
In what ways do companies leverage Sentence Transformer embeddings for enterprise search solutions within their internal document repositories?
Companies use Sentence Transformer embeddings in enterprise search solutions to improve the accuracy and relevance of do