2024:AI Search and the Search Page: Difference between revisions
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Put simply, Azure AI Search will make it easier to keep your documents in '''Grooper'''. To understand how, let's first understand what '''Grooper''' has been. | Put simply, Azure AI Search will make it easier to keep your documents in '''Grooper'''. To understand how, let's first understand what '''Grooper''' has been. | ||
Historically '''Grooper''' has been a transient platform for document processing: documents come in | Historically '''Grooper''' has been a transient platform for document processing: | ||
* documents come in | |||
* data is collected from those documents | |||
* the data and documents are pushed out of '''Grooper''' to some place | |||
It has never been a place to store documents and/or their data. | |||
While it has been possible to keep '''Batches''' and their content in '''Grooper''' it has never been a best practice, nor has it been convenient, to do so. You could, theoretically, devise some kind of hierarchical foldering and naming convention by which you organize '''Batches''' in the node tree, but this is very time consuming and is probably not even that useful. Say you wanted to retrieve all "Invoices" that have a "Total Amount" over "$1,000.00". Without "indexing" the documents and their data, and the ability to "query" | While it has been possible to keep '''Batches''' and their content in '''Grooper''' it has never been a best practice, nor has it been convenient, to do so. You could, theoretically, devise some kind of hierarchical foldering and naming convention by which you organize '''Batches''' in the node tree, but this is very time consuming and is probably not even that useful. Say you wanted to retrieve all "Invoices" that have a "Total Amount" over "$1,000.00". Without "indexing" the documents and their data, and the ability to "query" that index, this would be extremely time consuming at best. | ||
With Azure AI Search you will be able to quickly and efficiently index your documents and their data to allow for ease of | With Azure AI Search you will be able to quickly and efficiently index your documents and their data to allow for ease of retrieval as well as gain a deeper understanding of them. | ||
<div style="padding-left: 1.5em;"> | <div style="padding-left: 1.5em;"> | ||
=== Microsoft Azure AI Search === | === Microsoft Azure AI Search === | ||
[https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search Azure AI Search], formerly known as Azure Cognitive Search, is a cloud-based search-as-a-service solution provided by [https://en.wikipedia.org/wiki/Microsoft_Azure Microsoft Azure]. It | [https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search Azure AI Search], formerly known as Azure Cognitive Search, is a cloud-based search-as-a-service solution provided by [https://en.wikipedia.org/wiki/Microsoft_Azure Microsoft Azure]. It has allowed our developers to build a sophisticated search experience into '''Grooper'''. Here are some key features and capabilities: | ||
* '''Full-Text Search''': Azure AI Search supports full-text search with capabilities like faceting, filtering, and scoring, allowing users to search through large volumes of text efficiently. | * '''Full-Text Search''': Azure AI Search supports full-text search with capabilities like faceting, filtering, and scoring, allowing users to search through large volumes of text efficiently. | ||
* '''Customizable Indexing''': Developers can define custom indexes tailored to their specific data schema. This flexibility allows for a more relevant and precise search experience. | * '''Customizable Indexing''': Developers can define custom indexes tailored to their specific data schema. This flexibility allows for a more relevant and precise search experience. | ||
* '''Faceted Navigation''': The service supports faceted navigation, enabling users to filter and drill down into search results based on predefined categories or attributes. | * '''Faceted Navigation''': The service supports faceted navigation, enabling users to filter and drill down into search results based on predefined categories or attributes. | ||
* '''Search Analytics''': It provides insights into search patterns and behaviors, allowing developers to optimize the search experience based on user interactions. | * '''Search Analytics''': It provides insights into search patterns and behaviors, allowing developers to optimize the search experience based on user interactions. | ||
* '''Scalability''': The service can scale up or down based on the workload, making it suitable for applications of all sizes. | * '''Scalability''': The service can scale up or down based on the workload, making it suitable for applications of all sizes. | ||
* '''Security and Compliance''': Azure AI Search ensures data security and compliance with industry standards, offering features like [https://en.wikipedia.org/wiki/Role-based_access_control role-based access control (RBAC)], data encryption, and integration with [https://en.wikipedia.org/wiki/Active_Directory Active Directory]. | * '''Security and Compliance''': Azure AI Search ensures data security and compliance with industry standards, offering features like [https://en.wikipedia.org/wiki/Role-based_access_control role-based access control (RBAC)], data encryption, and integration with [https://en.wikipedia.org/wiki/Active_Directory Active Directory]. | ||
* '''APIs and SDKs''': Azure AI Search provides [https://en.wikipedia.org/wiki/REST REST APIs] and client libraries for various programming languages, making it easy to integrate with different types of applications. | * '''APIs and SDKs''': Azure AI Search provides [https://en.wikipedia.org/wiki/REST REST APIs] and client libraries for various programming languages, making it easy to integrate with different types of applications. | ||
=== Integration with Grooper === | === Integration with Grooper === | ||
* '''API Integration''': '''Grooper''' can leverage Azure AI Search's REST APIs to automate the indexing of documents and retrieval of search results. This integration can be built into Grooper's workflow to ensure seamless data processing and search capabilities. | * '''API Integration''': '''Grooper''' can leverage Azure AI Search's REST APIs to automate the indexing of documents and retrieval of search results. This integration can be built into '''Grooper's''' workflow to ensure seamless data processing and search capabilities. | ||
* '''Security and Compliance''': Both '''Grooper''' and Azure AI Search offer robust security features. Integrating these ensures that document processing and search operations are secure and compliant with industry standards. | * '''Security and Compliance''': Both '''Grooper''' and Azure AI Search offer robust security features. Integrating these ensures that document processing and search operations are secure and compliant with industry standards. | ||
* '''Indexing Processed Documents''': Once '''Grooper''' processes and extracts data from documents, this data can be sent to Azure AI Search for indexing. This allows users to search through the processed data quickly and efficiently. | * '''Indexing Processed Documents''': Once '''Grooper''' processes and extracts data from documents, this data can be sent to Azure AI Search for indexing. This allows users to search through the processed data quickly and efficiently. | ||
* '''Querying Indexed Documents and Data''': Once Azure | ** Indexing is an intake process that loads content into Azure AI Search service and makes it searchable. Through Azure AI Search, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is [https://en.wikipedia.org/wiki/JSON JSON]. | ||
* '''Querying Indexed Documents and Data''': Once Azure AI Search has indexed documents and their data from '''Grooper''', user's can leverage powerful query syntax like [https://learn.microsoft.com/en-us/azure/search/query-lucene-syntax Lucene] and [https://learn.microsoft.com/en-us/odata/overview OData] to efficiently retrieve the information from their documents. | |||
** Querying can happen once an index is populated with searchable content, when '''Grooper''' sends query requests to a search service and handles responses. All query execution is over a search index that you control. | |||
</div> | </div> | ||
== How To == | == How To == | ||
| Line 56: | Line 46: | ||
=== Index Documents and Data from Grooper === | === Index Documents and Data from Grooper === | ||
<div style="padding-left: 1.5em;"> | |||
==== "Add to Index" Batch Folder Object Command ==== | |||
==== "Create Search Index" Content Type Object Command ==== | |||
==== Execute Activity with "Add to Index" Command ==== | |||
==== Indexing Service ==== | |||
</div> | |||
=== Use the Search Page === | === Use the Search Page === | ||
</div> | </div> | ||
Revision as of 08:58, 13 August 2024
|
2025 BETA |
This article covers new or changed functionality in the current or upcoming beta version of Grooper. Features are subject to change before version 2025's GA release. Configuration and functionality may differ from later beta builds and the final 2025 release. |
Glossary
About
Put simply, Azure AI Search will make it easier to keep your documents in Grooper. To understand how, let's first understand what Grooper has been.
Historically Grooper has been a transient platform for document processing:
- documents come in
- data is collected from those documents
- the data and documents are pushed out of Grooper to some place
It has never been a place to store documents and/or their data.
While it has been possible to keep Batches and their content in Grooper it has never been a best practice, nor has it been convenient, to do so. You could, theoretically, devise some kind of hierarchical foldering and naming convention by which you organize Batches in the node tree, but this is very time consuming and is probably not even that useful. Say you wanted to retrieve all "Invoices" that have a "Total Amount" over "$1,000.00". Without "indexing" the documents and their data, and the ability to "query" that index, this would be extremely time consuming at best.
With Azure AI Search you will be able to quickly and efficiently index your documents and their data to allow for ease of retrieval as well as gain a deeper understanding of them.
Microsoft Azure AI Search
Azure AI Search, formerly known as Azure Cognitive Search, is a cloud-based search-as-a-service solution provided by Microsoft Azure. It has allowed our developers to build a sophisticated search experience into Grooper. Here are some key features and capabilities:
- Full-Text Search: Azure AI Search supports full-text search with capabilities like faceting, filtering, and scoring, allowing users to search through large volumes of text efficiently.
- Customizable Indexing: Developers can define custom indexes tailored to their specific data schema. This flexibility allows for a more relevant and precise search experience.
- Faceted Navigation: The service supports faceted navigation, enabling users to filter and drill down into search results based on predefined categories or attributes.
- Search Analytics: It provides insights into search patterns and behaviors, allowing developers to optimize the search experience based on user interactions.
- Scalability: The service can scale up or down based on the workload, making it suitable for applications of all sizes.
- Security and Compliance: Azure AI Search ensures data security and compliance with industry standards, offering features like role-based access control (RBAC), data encryption, and integration with Active Directory.
- APIs and SDKs: Azure AI Search provides REST APIs and client libraries for various programming languages, making it easy to integrate with different types of applications.
Integration with Grooper
- API Integration: Grooper can leverage Azure AI Search's REST APIs to automate the indexing of documents and retrieval of search results. This integration can be built into Grooper's workflow to ensure seamless data processing and search capabilities.
- Security and Compliance: Both Grooper and Azure AI Search offer robust security features. Integrating these ensures that document processing and search operations are secure and compliant with industry standards.
- Indexing Processed Documents: Once Grooper processes and extracts data from documents, this data can be sent to Azure AI Search for indexing. This allows users to search through the processed data quickly and efficiently.
- Indexing is an intake process that loads content into Azure AI Search service and makes it searchable. Through Azure AI Search, inbound text is processed into tokens and stored in inverted indexes, and inbound vectors are stored in vector indexes. The document format that Azure AI Search can index is JSON.
- Querying Indexed Documents and Data: Once Azure AI Search has indexed documents and their data from Grooper, user's can leverage powerful query syntax like Lucene and OData to efficiently retrieve the information from their documents.
- Querying can happen once an index is populated with searchable content, when Grooper sends query requests to a search service and handles responses. All query execution is over a search index that you control.
How To
Create an Azure AI Search Service
Please refer to the following MSDN article about how to create an Azure AI Search service via their portal.