Vector search in Azure Cosmos DB

• นำไปใช้กับ:

  ✅ NoSQL, ✅ MongoDB vCore, ✅ PostgreSQL

Vector search is a method that helps you find similar items based on their data characteristics rather than by exact matches on a property field. This technique is useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. It works by taking the vector embeddings of your data and query, and then measuring the distance between the data vectors and your query vector. The data vectors that are closest to your query vector are the ones that are found to be most similar semantically.

Examples

This interactive visualization shows some examples of closeness and distance between vectors.

Algorithms

Two major types of vector search algorithms are k-nearest neighbors (kNN) and approximate nearest neighbor (ANN). Between kNN and ANN, the latter offers a balance between accuracy and efficiency, making it better suited for large-scale applications. Some well-known ANN algorithms include Inverted File (IVF), Hierarchical Navigable Small World (HNSW), and the state-of-the-art DiskANN.

Using an integrated vector search feature in a fully featured database (as opposed to a pure vector database) offers an efficient way to store, index, and search high-dimensional vector data directly alongside other application data. This approach removes the necessity of migrating your data to costlier alternative vector databases and provides a seamless integration of your AI-driven applications.

Related content

• What is a vector database?
• Retrieval Augmented Generation (RAG)
• Vector database in Azure Cosmos DB NoSQL
• Vector database in Azure Cosmos DB for MongoDB
• LLM tokens
• Vector embeddings
• Distance functions
• kNN vs ANN vector search algorithms
• Multi-tenancy for Vector Search