![]() ![]() ![]() The data vectors that are closest to your query vector are the ones that are found to be most similar semantically.īy integrating vector search capabilities natively, you can now unlock the full potential of your data in your intelligent applications. It then measures the distance between the data vectors and your query vector. ![]() It works by taking the vector representations (lists of numbers) of your data that you have created using an ML model, or an embeddings API such as Azure OpenAI Service Embeddings or Hugging Face on Azure. This is especially useful in applications such as searching for similar text, finding related images, making recommendations, or even detecting anomalies. Vector search is a method that helps you find similar items based on their data characteristics rather than exact matches on a property field. With Vector Search, you’ll be able to unlock new insights from your data that were previously hidden or hard to find, leading to more accurate and powerful applications. This comprehensive solution streamlines your AI application development by reducing complexity and enhancing efficiency. You can store, index, and query high dimensional vector data stored directly in Azure Cosmos DB for MongoDB vCore, eliminating the need to transfer your data to more expensive alternatives for vector similarity search capabilities. With Vector Search, you can now seamlessly integrate AI-based applications, including those using OpenAI embeddings, with your data already stored in Cosmos DB. This innovative feature opens a world of new opportunities for building intelligent AI-powered applications and makes Azure Cosmos DB for MongoDB vCore the first among MongoDB-compatible offerings to feature Vector Search! We are thrilled to announce the release of Vector Search in Azure Cosmos DB for MongoDB vCore, which will be showcased at Microsoft Build. ![]()
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