A vector database supports vector search by storing, indexing, and querying high-dimensional vectors, which represent data points in a way that enables semantic searches. Unlike traditional databases that store structured data, vector databases handle unstructured data such as text, images, and audio, converting these into vector embeddings. For example, a text document might be transformed into a vector using an embedding model like OpenAI’s text embeddings. Once converted, these vectors are stored in the database. To perform a search, the database uses indexing techniques like HNSW or IVF to quickly locate vectors similar to the query vector. These indexes organize the vectors to minimize the number of comparisons needed during a search, improving the speed and accuracy of the results. Additionally, vector databases often include APIs that allow users to perform similarity searches by comparing vectors using distance metrics such as cosine similarity or Euclidean distance. These systems also offer flexibility, enabling hybrid searches that combine vector-based search with traditional keyword search, giving users more control over the results. For instance, Milvus or Zilliz Cloud allows users to perform semantic searches alongside traditional searches, enhancing the quality of results in applications like recommendation systems or semantic search engines.
How does a vector database support vector search?
- AI & Machine Learning
- Exploring Vector Database Use Cases
- Retrieval Augmented Generation (RAG) 101
- How to Pick the Right Vector Database for Your Use Case
- Information Retrieval 101
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