Knowledge Mining Solution Accelerator Guide
About this repository
Welcome to the Knowledge Mining Solution Accelerator! This accelerator was built to provide developers with all of the resources needed to quickly build an initial Knowledge Mining prototype with Azure Cognitive Search. Use this accelerator to jump start your development efforts with your own data or as a learning tool to better understand how Cognitive Search can be leveraged for the unique needs of your business.
In this repository, we've provided you with all of the artifacts needed to quickly create a Cognitive Search Solution including: templates for deploying the appropriate Azure resources, assets for creating your first search index, templates for custom skills, a basic web app, and PowerBI reports to monitor search solution performance. We've infused best practices throughout the documentation to help further guide you. With Cognitive Search, both digital data (i.e. documents and text files) and analog data (i.e. images and scanned documents) can be easily indexed.
Note: Cognitive Search is an AI feature in Azure Search rather than a standalone product. See this doc for more information on the subject. Cognitive Search allows you to ingest all kinds of data (documents, text files, images, scanned docs, etc.), extract the content out of your data, enrich and transform it, and then index it for exploration purposes.
Once finished, you'll have a web app ready to search against your data like in the image below:
In order to successfully complete your solution, you will need to have access to and or provisioned the following:
- Access to an Azure subscription
- Visual Studio 2017 or newer
- Postman for making API calls
- Documents uploaded to any data source supported by Azure Search Indexers. This solution accelerator uses Azure Blob Storage as a container for source data files. Sample documents are provided in the sample_documents folder.
The directions provided for this repository assume fundemental working knowledge of Azure Portal, Azure Functions, Azure Search, Visual Studio and Postman.
For additional training and support, please see:
Getting Started and Process Overview
Clone/download this repo onto your computer and then walk through each of these folders in order, following the steps outlined in each of the README files. After completion of all steps, you will have a working end-to-end solution with the following architecture:
The resources in this folder can be used to deploy the required resources into your Azure Subscription. This can be done either via the Azure Portal or by using the PowerShell script included in the resource deployment folder.
Alternatively, you can deploy the resources using this button:
This folder contains a Postman collection that can be used to create a search index. The collection is pre-configured to take advantage of a majority of the out of the box Cognitive Search functionality.
We recommend using this collection to create an initial index and then iterrating by editing the postman collection and adding custom skills as needed.
This folder contains a basic Web UI Template, written in .NET Core, which can be configured to query your search index. Simply follow the steps outlined in the Web UI Template README file to integrate your new search index into the web app.
This folder contains examples and templates to add your own custom skills to your solution. These custom skills help to align the solution to the needs of your particular use case, but is entirely optional and may be skipped if not needed.
This folder contains optional pre-built PowerBI reports that can be used to monitor your solution and to understand user search behavior. They leverage data captured via Application Insights and can be modified to meet your particular business objectives.
This folder contains a small data set in a variety of file formats that can be used to build your solution if you do not have another data set available.
Special thanks Microsoft's Early Access innovation Program (EAiP) and to Derek Legenzoff, Christopher Romero, Lauren Mills, and Chris Lill who wrote a large majority of the code here.
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