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1. Semantic Search & Vector Support: Delivers highly relevant results using natural language and vector embeddings for deep contextual understanding. 2. Scalable & Secure Architecture: Easily handles enterprise workloads with built-in encryption, role-based access, and global availability. 3. Cognitive Enrichment: Integrates AI-powered skills like OCR, entity recognition, and language detection for enriched indexing and discovery.
speed, flexibility amd customisable indexing.
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.
1. Limited Debugging Visibility: Troubleshooting indexing and query issues can be opaque without deeper telemetry or logs 2. Complex Schema Evolution: Updating index structures post deployment often requires reindexing, which can be resource intensive 3. Restricted Regional Availability: Certain advanced features (e.g., vector search) may not be supported across all Azure regions.
tool some time to get it working in a useful way for me and the client.
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.