How do you decide which search engine to pick? Do you look which languages it supports, compatibility with devices or the ease of use? How does a search engine work? Can a search engine be more than a full-text search? Would you like to get more information and analytics out of your mammoth size data? Will the search engine look for relationships in the data? How does your search engine fare against competitors? Wait no further, here we present to you an all-in-one compilation of the Azure Cognitive Search from Microsoft.
Azure Cognitive Search, renamed from Azure Search, is referred to the use of cognitive skills and AI processing in core operations. It uses the natural language stack which was being used in Bing and Office and the AI services across vision, language, and speech. It allows you to add search operations to your web/mobile/enterprise applications even if you are not a search engine expert. It is comparatively easy to search a structured data, but Azure search can search through un-structured data too, and extract meaning out of it.
Azure Cognitive Search offers a multitude of great features:
It provides fully managed cloud search service through simple REST API or through .NET SDK and provides services such as scoring, geo-search, faceting, auto-completion, and synonym search. At Cazton, we help clients with debugging index corruption, monitoring for service availability and scaling the indexing process. However, Azure Search automates all that for you. That’s a value add and helps save time and enhances productivity.
The Azure AI package can turn raw, unstructured information into searchable content using its aptness in vision, speech, and language processing. All these intelligent information support can be enabled within the search configuration with ease and is accessible all around the world in more than 50 languages with 99.9% availability. Additionally, there exists ways to filter out search information specific to the industry and business requirements. Not only this, all this power is encrypted throughout the indexing pipeline from malicious activities. Cognitive search works with several file formats including not only Microsoft Word, PowerPoint, Excel, but Adobe PDF, PNG, RTF, JSON, HTML and XML, Cosmos DB or Azure Blob Storage.
Continue reading to dig deeper into Azure Cognitive Search. Contact us today to learn more about what our experts can do for you.
Azure Search uses Lucene for full text search through four stages of Lucene query execution: query parsing, lexical analysis, document retrieval and scoring. Understanding about what they are can help us in knowing what is happening behind the scenes.
It can be applied on:
| Sr No. | Skills | Description |
|---|---|---|
| 1 | Custom Entity | Looks for a user-defined set of words and phrases and supports fuzzy matching. |
| 2 | Key Phrases | It uses a pretrained model to extract data which detects important phrases based on how unusual a term is within the document, term placement in a document, proximity to other terms and linguistic rules. It is useful when you would like to know the main points in the record (max length 50000 characters). |
| 3 | Language Detection | It detects the language of each document. It is useful when you need to provide the language of the text as input to other skills like Sentiment Analysis, or Text Split skill. |
| 4 | Merge | Combines text from multiple fields to a single field. A common use case is to extract a caption from the OCR skill and then merge it with the content field of a document. |
| 5 | Entity Recognition | Identify people, location, organization, emails, datetime, URLs fields. |
| 6 | PII Detection | Extract personally identifiable information from a given text. |
| 7 | Sentiment Analysis | Score (0 to 1) positive or negative on a record by record skill or a neutral score if the sentiment could not be identified. |
| 8 | Text Split | Splits the text into sentences or pages of a specific length. to enrich or augment content incrementally. |
| 9 | Translate | Translate a text into a variety of languages for normalizing or localizing use case. It is useful when you know that all the documents are not in a same language. |
| 10 | Image Analysis | Identify content of image and generate text description, generate tags or identify celebrities or landmarks. |
| 11 | OCR | Optical Character Recognition supports a maximum width and height of 10000 pixels for English and 4200 pixels for other languages. OCR API is used for non-English document, a new API ‘Read’ is used for English documents for the same purpose. |
| 12 | Conditional | Allow filtering, merging, or assigning a default value based on a condition, like searching only for Spanish documents, or setting a default value for a value that doesn’t exist. |
| 13 | Document Extraction | Extracts content from a document. |
| 14 | Shaper | Maps output to a complex form which could then be used as a combination for search. It basically allows you to create a structure, define the name of the members of that structure, and assign values to each member. |
| 15 | Web API | Extension of AI enrichment pipeline by making a HTTP call to custom web API. |
AI enrichment is a capability Azure Cognitive search indexing which can be used to extract text from images, blobs or any other unstructured data. The enrichment store makes a data more searchable in an index or in a knowledge store. It passed through the following pipeline:
A knowledge store collects the information about how the data is connected internally and is projected in form of Table Storage (tables), or Blob Storage (JSON objects, and images extracted from documents called files). This representation can now be used to create a data visualization in a tool like PowerBI with say, Power Query. It can generate relationships within and across different projection types. Any tool or process that can connect to Azure Storage like PowerBI, Azure Storage Explorer, or Azure Data Factory can now consume the contents of this knowledge store. Also, this can be accessed through a REST API. Pretty cool, right!
| Azure Cognitive Search | Elasticsearch |
|---|---|
| Free version is limited and modeled for Commercial use. | Free and open source standalone software. (Need to pay for Elastic cloud) |
| Supports about 50 languages. | Supports about 35 languages. |
| Supported in large number of devices. | Supported in a smaller number of devices than Azure Search. |
| No in-memory capability. | Memcached and Redis in-memory integration. |
| All shown features of Elasticsearch along with cognitive (AI) search capabilities. | Features like full-text search, auto-complete, geo-search, bucket-aggregation for faceted navigation, and relevance. |
While developing a search functionality, it is essential to have the right team and understand how to manage them. Expertise, experience and our company's history of success is crucial in making a project successful. Delay in projects not only reduces the competitive edge of companies, but can also result in massive layoffs. We, at Cazton, work with you ensure you are successful both as an individual by rising higher in your career and as a company by staying innovative and ahead of the competition.
Our experts can consult you with best practices and implementation strategies for Azure Cognitive Search. We can help you improve your search experience by implementing various features including autocomplete, spell-checking, hit-highlighting, paging and throttling and AI skills such as Natural Language Processing (including Entity Recognition, language detection, key-phrase extraction, text manipulation, sentiment detection and PII detection). We have the expertise in implementing multi-dimensional search algorithms. We also provide on-demand Azure Cognitive Search training. Contact us today to learn more about what our experts can do for you.
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