Plan your QnA Maker app
To plan your QnA Maker app, you need to understand how QnA Maker works and interacts with other Azure services. You should also have a solid grasp of knowledge base concepts.
The QnA Maker service is being retired on the 31st of March, 2025. A newer version of the question and answering capability is now available as part of Azure AI Language. For question answering capabilities within the Language Service, see question answering. Starting 1st October, 2022 you won’t be able to create new QnA Maker resources. For information on migrating existing QnA Maker knowledge bases to question answering, consult the migration guide.
Each Azure resource created with QnA Maker has a specific purpose. Each resource has its own purpose, limits, and pricing tier. It's important to understand the function of these resources so that you can use that knowledge into your planning process.
|QnA Maker resource||Authoring and query prediction|
|Cognitive Search resource||Data storage and search|
|App Service resource and App Plan Service resource||Query prediction endpoint|
|Application Insights resource||Query prediction telemetry|
The free tier,
F0, of each resource works and can provide both the authoring and query prediction experience. You can use this tier to learn authoring and query prediction. When you move to a production or live scenario, reevaluate your resource selection.
Knowledge base size and throughput
When you build a real app, plan sufficient resources for the size of your knowledge base and for your expected query prediction requests.
A knowledge base size is controlled by the:
The knowledge base query prediction request is controlled by the web app plan and web app. Refer to recommended settings to plan your pricing tier.
If you already have some of these resources in use, you may consider sharing resources. See which resources can be shared with the understanding that resource sharing is an advanced scenario.
All knowledge bases created in the same QnA Maker resource share the same test query prediction endpoint.
Understand the impact of resource selection
Proper resource selection means your knowledge base answers query predictions successfully.
If your knowledge base isn't functioning properly, it's typically an issue of improper resource management.
Improper resource selection requires investigation to determine which resource needs to change.
A knowledge base is directly tied its QnA Maker resource. It holds the question and answer (QnA) pairs that are used to answer query prediction requests.
The first knowledge base created on your QnA Maker resource sets the language for the resource. You can only have one language for a QnA Maker resource.
You can structure your QnA Maker resources by language or you can use Translator to change a query from another language into the knowledge base's language before sending the query to the query prediction endpoint.
Ingest data sources
You can use one of the following ingested data sources to create a knowledge base:
- Public URL
- Private SharePoint URL
The ingestion process converts supported content types to markdown. All further editing of the answer is done with markdown. After you create a knowledge base, you can edit QnA pairs in the QnA Maker portal with rich text authoring.
Data format considerations
Because the final format of a QnA pair is markdown, it's important to understand markdown support.
Linked images must be available from a public URL to be displayed in the test pane of the QnA Maker portal or in a client application. QnA Maker doesn't provide authentication for content, including images.
Add a bot personality to your knowledge base with chit-chat. This personality comes through with answers provided in a certain conversational tone such as professional and friendly. This chit-chat is provided as a conversational set, which you have total control to add, edit, and remove.
A bot personality is recommended if your bot connects to your knowledge base. You can choose to use chit-chat in your knowledge base even if you also connect to other services, but you should review how the bot service interacts to know if that is the correct architectural design for your use.
Conversation flow with a knowledge base
Conversation flow usually begins with a salutation from a user, such as
Hello. Your knowledge base can answer with a general answer, such as
Hi, how can I help you, and it can also provide a selection of follow-up prompts to continue the conversation.
You should design your conversational flow with a loop in mind so that a user knows how to use your bot and isn't abandoned by the bot in the conversation. Follow-up prompts provide linking between QnA pairs, which allow for the conversational flow.
Authoring with collaborators
Collaborators may be other developers who share the full development stack of the knowledge base application or may be limited to just authoring the knowledge base.
Knowledge base authoring supports several role-based access permissions you apply in the Azure portal to limit the scope of a collaborator's abilities.
Integration with client applications
Integration with client applications is accomplished by sending a query to the prediction runtime endpoint. A query is sent to your specific knowledge base with an SDK or REST-based request to your QnA Maker's web app endpoint.
To authenticate a client request correctly, the client application must send the correct credentials and knowledge base ID. If you're using an Azure AI Bot Service, configure these settings as part of the bot configuration in the Azure portal.
Conversation flow in a client application
Conversation flow in a client application, such as an Azure bot, may require functionality before and after interacting with the knowledge base.
Does your client application support conversation flow, either by providing alternate means to handle follow-up prompts or including chit-chit? If so, design these early and make sure the client application query is handled correctly by another service or when sent to your knowledge base.
Dispatch between QnA Maker and Language Understanding (LUIS)
A client application may provide several features, only one of which is answered by a knowledge base. Other features still need to understand the conversational text and extract meaning from it.
A common client application architecture is to use both QnA Maker and Language Understanding (LUIS) together. LUIS provides the text classification and extraction for any query, including to other services. QnA Maker provides answers from your knowledge base.
Active learning from a client application
QnA Maker uses active learning to improve your knowledge base by suggesting alternate questions to an answer. The client application is responsible for a part of this active learning. Through conversational prompts, the client application can determine that the knowledge base returned an answer that's not useful to the user, and it can determine a better answer. The client application needs to send that information back to the knowledge base to improve the prediction quality.
Providing a default answer
If your knowledge base doesn't find an answer, it returns the default answer. This answer is configurable on the Settings page in the QnA Maker portal or in the APIs.
This default answer is different from the Azure bot default answer. You configure the default answer for your Azure bot in the Azure portal as part of configuration settings. It's returned when the score threshold isn't met.
The prediction is the response from your knowledge base, and it includes more information than just the answer. To get a query prediction response, use the GenerateAnswer API.
Prediction score fluctuations
A score can change based on several factors:
- Number of answers you requested in response to GenerateAnswer with
- Variety of available alternate questions
- Filtering for metadata
- Query sent to
There's a two-phase answer ranking:
- Cognitive Search - first rank. Set the number of answers allowed high enough that the best answers are returned by Cognitive Search and then passed into the QnA Maker ranker.
- QnA Maker - second rank. Apply featurization and machine learning to determine best answer.
Apply the latest runtime updates to automatically manage service updates.
Scaling, throughput, and resiliency
Analytics with Application Insights
All queries to your knowledge base are stored in Application Insights. Use our top queries to understand your metrics.
The development lifecycle of a knowledge base is ongoing: editing, testing, and publishing your knowledge base.
Knowledge base development of QnA Maker pairs
Your QnA pairs should be designed and developed based on your client application usage.
Each pair can contain:
- Metadata - filterable when querying to allow you to tag your QnA pairs with additional information about the source, content, format, and purpose of your data.
- Follow-up prompts - helps to determine a path through your knowledge base so the user arrives at the correct answer.
- Alternate questions - important to allow search to match to your answer from different forms of the question. Active learning suggestions turn into alternate questions.
Developing a knowledge base to insert into a DevOps pipeline requires that the knowledge base is isolated during batch testing.
A knowledge base shares the Cognitive Search index with all other knowledge bases on the QnA Maker resource. While the knowledge base is isolated by partition, sharing the index can cause a difference in the score when compared to the published knowledge base.
To have the same score on the
production knowledge bases, isolate a QnA Maker resource to a single knowledge base. In this architecture, the resource only needs to live as long as the isolated batch test.