The evolution of enterprise search technology
For decades, the term “search” has been ubiquitous with exploring, finding, and making sense of the information at your disposal. It started with keyword search, allowing users to find specific information that matched a certain keyword or phrase. Then, it evolved into more effective solutions powered by advances in natural language processing and other machine learning technologies. Features like fuzzy search, spellers, autosuggest, etc. significantly improved the quality and relevance of search results.
Now, there is cognitive search, which broadens the mediums users can explore effectively (PDFs, spreadsheets, images, audio files, etc.), and moves from “finding” to “understanding.” This is made possible by a combination of natural language processing, computer vision, and new advances in machine learning technology.
We formally started on this journey with the “cognitive search” feature in Azure Search, as a way to talk about the new Cognitive Services-based capabilities in the enrichment pipeline. But AI is deeply embedded into the entire product, from ingestion to exploration:
Azure Search will continue to have significant elements of machine learning powering content understanding, ranking, and more, and we needed a name to reflect that: Azure Cognitive Search. This single name captures both our core value prop (“Search”) and our approach to making it better and more broadly applicable with AI (“Cognitive”).
What’s possible with Azure Cognitive Search
We’re empowering developers to create cognitive search solutions by simplifying the process into to three main steps:
Ingest more file formats from more data sources: As announced at our recent Ignite conference, we are expanding the scope of data sources supported by our pull indexers. We have added built-in support for ADLS Gen 2, Cosmos DB Gremlin API, and Cosmos DB Cassandra API. We have also introduced the ability to extract content and metadata from a document as a skill in case you need to apply any transformations to your content before that stage. Also, we've made it possible for you to create custom document cracking skills.
AI management: As discussed earlier, the biggest pain points in the AI space come with the management and orchestration of several machine-powered services together. Our enrichment pipeline streamlines this process, and we’ve made it even simpler in the latest updates.
Explore: At the end of the day, the point of creating a sophisticated Cognitive Search solution is often to be able to apply it to an application. With that in mind, we’ve improved the process of moving from raw content to exploration with the following:
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