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I like its ability to deliver highly relevant results using its AI powered features such as Semantic Search and Natural Language Processing. Moreover, it handles large volumes of traffic and unstructured datasets efficiently and effectively without the need of manual labour.
It works really well within the Azure ecosystem (easy to connect to storage, AI services, etc.). It also has good support for both traditional and vector-based search use cases, and it scales well and handles large datasets without much issue. Also, the flexible indexing and schema design options are really good
Its AI enrichment pipeline, successfully converts complex, unstructured data like PDFs and images into fully searchable content making it the ideal secure knowledge base for grounding our Generative AI applications.
The initial setup and tuning of search indexes can be complex for people with a non-technical background. Advanced features may require additional services offered by Azure which may increase the overall cost of the system.
Documentation can be confusing at times, especially for beginners. Not enough practical examples for real-world implementations are present. Debugging search relevance or tuning results isnt straightforward and requires a lot of deep diving. Understanding indexes, weightages etc.
Achieving product excellence also requires significant technical expertise and a steep learning curve to correctly configure and maintain the complex indexing pipelines and AI enrichment features.