Azure AI Search
Retrieve And Rank Relevant Information
As the volume of documents continues to increase daily, the task of analyzing such vast datasets becomes increasingly challenging. One well-known use case is the search for relevant documents. Fortunately, the widespread adoption of large language models in recent months has provided us with tangible benefits in coping with this data overload. An application built upon this task is Retrieval Augmented Generation (RAG), which involves utilizing a vector database that leverages embeddings and search mechanisms to identify closely related documents.
AI Search
Azure AI Search, formerly known as Cognitive Search (even earlier as Azure Search), is a cloud-based search service that provides developers with infrastructure, APIs, and tools for creating a robust search experience across diverse content. AI Search provides powerful search capabilities, including full-text search, fuzzy matching, faceted navigation, filtering, and sorting. It supports advanced search features like language analyzers, stemming, synonyms, and Geo-spatial search. Among the numerous features offered by AI Search, this article focuses on retrieval and ranking, which are particularly relevant for RAGs.
The query notation options for Azure AI Search include OData and Simple Query Syntax. In AI Search, an…
