Spelltower scoring algorithm4/4/2023 As stated before, a (custom) analyzer includes a charFilter, tokenizer and tokenFilter. Below is a JSON representation of an index: 1 Particularly the pre-built language analyzers generate a lot of linguistic tokens that are not necessarily helpful at all times. This can be a useful option in the scenario where one is trying to reduce noise in search results. It is even possible to configure separate analyzers for indexing a document and searching (querying) a document. Note that every field can have its own analyzer configured. However, it is possible to create a custom analyzer and configure it for the appropriate fields. In most cases one wouldn’t need to create a custom analyzer and prefer the use of one of the pre-built analyzers, such as the language specific analyzers (nl.microsoft, en.microsoft), or the default analyzer. Index Example of an Azure Cognitive Search IndexĪn Azure Cognitive Search index consists of fields, analyzers, charFilters, tokenizers and tokenFilters. The data either a document or a search query will go through a so called analyzer, more on those later. What’s important to notice is is that the same process for indexing a document aswell as for tokenizing a query is performed. The search engine will then use this so called index to evaluate a given query to it. Indexing documents basically comes down to the creation of a database which stores a set of tokens per field that are generated for each document that has been indexed. Lastly, I will share some practical tips to improve your Azure Search Performance. Secondly, I will explain how a search query is evaluated against the index. First, I will explain the general flow of indexing. This blog post intends to shine some light into that black box. The search engine of Azure Cognitive Search is quite complex and, to be honest, the documentation isn’t really forthcoming as to how documents are being indexed and how the entered query eventually gets processed and scored against the indexed documents. The search engine Azure Cognitive Search Overview In projects with Azure Cognitive Search I have come across way too many questions like: How did this document score this high? And why does it also find these documents? This blog post will help you answer such questions. Performance matters because the user experience of the search is primarily determined by the relevance of the returned documents. This blog post is about unraveling the scoring system, and to help you improve your search performance. This query will be evaluated against the index and the system will return the highest scored documents. Once the content has been indexed, one can query the index with a text query and additionally add facets, filters and sorting. In most cases the indexation takes place on databases such as SQL databases or Cosmos DB, enabling users to index, for instance, their e-commerce platform or knowledge base. The indexing engine then indexes the content based on the index definition that has been configured. It lets you connect multiple data sources such as Cosmos DB collections, SQL databases or even documents such as PDFs. Azure Search Azure Cognitive SearchĪzure Cognitive Search can index various data sources and make them searchable by text queries. I will also provide some practical tips to improve your search performance, but in order to apply those tips correctly you’ll first need to have a deeper understanding of Azure Search itself. In this blog post I intend to explain the inner workings of Azure Search, describing the scoring algorithm and how to tweak it to your advantage. It can be quite a struggle to really understand the ins and outs of Azure Search, how search results are scored, and how scoring profiles with weights and functions add up.
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