Azure AI Search introduces Multi-Vector Field Support and Scoring Profiles integration with Semantic Ranking, enhancing search relevance and handling complex, multimodal data. These updates enable richer context, precise ranking boosts, and improved search experiences for diverse data types. :
Unlocking Smarter Search with Azure AI Search’s Latest Enhancements
Microsoft recently rolled out two game-changing features in Azure AI Search: Multi-Vector Field Support and Scoring Profiles Integration with Semantic Ranking. These updates are designed to tackle complex, multimodal data and boost search relevance like never before.
What’s New: Multi-Vector Field Support
Previously, vector fields could only live at the top level of your search index. Now, Azure AI Search lets you embed multiple vectors inside nested fields. This means richer context and deeper semantic understanding for your data.
This is a big deal for handling long documents, segmenting them into searchable chunks, or combining text and images in one dataset. You can even query nested vectors directly, letting intelligent ranking pick the most relevant vector per document.
“Multi-Vector Field Support helps you manage detailed, multimodal, and segmented content more effectively.”
For example, imagine a video indexed by scenes, each with its own vector embedding and timestamp. You can now query specific scenes instead of the whole video, making search results way more precise.
Major Update: Scoring Profiles with Semantic Ranking
Scoring profiles have always helped tune search results based on your business needs. What’s new is that these profiles now apply after semantic reranking, not just during initial ranking.
This means boosts for important terms, freshness, or geographic location influence the final results, ensuring your priorities shine through.
“With this enhancement, scoring profiles also apply after semantic reranking, ensuring that your boosts shape the final results.”
Integrating scoring profiles is straightforward. Just add your scoring profile to the semantic query configuration, and it works seamlessly with vector and hybrid searches too.
Why These Updates Matter to You
- Better handling of complex data: Multi-vector fields let you work with segmented documents and multimodal datasets effortlessly.
- More control over relevance: Scoring profiles influence the final ranked results, so you can tailor search outcomes to your needs.
- Improved semantic accuracy: Combining these features leads to smarter, more context-aware search experiences.
Pro Tips for Getting Started
- Use nested vectors for long documents to ensure diverse, unique search hits.
- Combine text and image embeddings in nested vectors for richer insights.
- Experiment with term, freshness, and geographic boosts to fine-tune your results.
Ready to Dive In?
These enhancements are available now in Azure AI Search. Explore the docs on Multi-Vector Field Support and Scoring Profiles with Semantic Ranking to get started.
As search tech evolves, these updates give you powerful tools to build smarter, more relevant search experiences. Don’t miss out!
From the New blog articles in Microsoft Community Hub
Windows Server Devices Now Recognized as a New OS in Intune Microsoft has announced that Windows Server devices are Read more
Microsoft Power Platform is leading the way in AI-generated low-code app development. With the help of AI, users can quickly Read more
Microsoft Intune is an enterprise mobility management platform that helps organizations manage mobile devices, applications, and data. The October edition Read more
Microsoft Intune has released its November edition, featuring new updates to help IT admins better manage their organization’s mobile devices. Read more
