Key take-aways:
- Vector search in Azure Cognitive Search: A new feature that enables searching for information based on vector embeddings of data, such as text, image, audio, and video. It allows customers to use pretrained or custom models to generate embeddings.
- Generative AI applications with RAG pattern: A way to integrate large language models (LLMs) with custom data sources using retrieval, analysis, and generation steps. It enables organizations to create innovative and contextually relevant applications with domain-specific knowledge. It leverages the semantic meaning and the textual features of the data.
- Hybrid search for supercharged results: A combination of vector and keyword search methods that delivers better quality and relevance than either method alone
- Azure AI integration and recognition: A seamless connection between Azure Cognitive Search and other Azure AI services, such as Azure OpenAI Service and Azure AI Vision. It allows customers to build multi-modal and chat-based applications with LLMs. It also showcases Microsoft’s leadership in the insight engines market.
Note that hybrid search offers “supercharged results”:
Overview:
