What is the QnA Maker service?

QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.

A client application for QnA Maker is any conversational application that communicates with a user in natural language to answer a question. Examples of client applications include social media apps, chat bots, and speech-enabled desktop applications.

When to use QnA Maker

  • When you have static information - Use QnA Maker when you have static information in your knowledge base of answers. This knowledge base is custom to your needs, which you've built with documents such as PDFs and URLs.
  • When you want to provide the same answer to a request, question, or command - when different users submit the same question, the same answer is returned to both.
  • When you want to filter static information based on meta-information - add metadata tags to provide additional filtering options relevant to your client application's users and the information. Common metadata information includes chit-chat, content type or format, content purpose, and content freshness.
  • When you want to manage a bot conversation that includes static information - your knowledge takes a user's conversational text or command and answers it. If the answer is part of a pre-determined conversation flow, represented in your knowledge base with multi-turn context, the bot can easily provide this flow.

Use QnA Maker knowledge base in a chat bot

Once a QnA Maker knowledge base is published, a client application sends a question to your knowledge base endpoint and receives the results as a JSON response. A common client application for QnA Maker is a chat bot.

Ask a bot a question and get answer from knowledge base content

Step Action
1 The client application sends the user's question (text in their own words), "How do I programmatically update my Knowledge Base?" to your knowledge base endpoint.
2 QnA Maker uses the trained knowledge base to provide the correct answer and any follow-up prompts that can be used to refine the search for the best answer. QnA Maker returns a JSON-formatted response.
3 The client application uses the JSON response to make decisions about how to continue the conversation. These decisions can include showing the top answer or presenting more choices to refine the search for the best answer.

What is a knowledge base?

QnA Maker imports your content into a knowledge base of question and answer sets. The import process extracts information about the relationship between the parts of your structured and semi-structured content to imply relationships between the question and answer sets. You can edit these question and answer sets or add new ones.

The content of the question and answer set includes all the alternate questions for a specific answer, metadata tags used to filter answer choices during the search, and follow-up prompts to continue the search refinement.

Example question and answer with metadata

After you publish your knowledge base, a client application sends a user's question to your endpoint. Your QnA Maker service processes the question and responds with the best answer.

Create, manage, and publish to a bot without code

The QnA Maker portal provides the complete knowledge base authoring experience. You can import documents, in their current form, to your knowledge base. These documents (such as an FAQ, product manual, spreadsheet, or web page) are converted into question and answer sets. Each set is analyzed for follow-up prompts and connected to other sets. The final markdown format supports rich presentation including images and links.

Once your knowledge base is edited, publish the knowledge base to a working Azure Web App bot without writing any code. Test your bot in the Azure portal or download and continue development.

Search quality and ranking provides the best possible answer

QnA Maker's system is a layered ranking approach. The data is stored in Azure search, which also serves as the first ranking layer. The top results from Azure search are then passed through QnA Maker's NLP re-ranking model to produce the final results and confidence score.

QnA Maker improves the conversation process

QnA Maker provides multi-turn prompts and active learning to help you improve your basic question and answer sets.

Multi-turn prompts give you the opportunity to connect question and answer pairs. This connection allows the client application to provide a top answer and provides more questions to refine the search for a final answer.

After the knowledge base receives questions from users at the published endpoint, QnA Maker applies active learning to these real-world questions to suggest changes to your knowledge base to improve the quality.

Development lifecycle

QnA Maker provides authoring, training, and publishing along with collaboration permissions to integrate into the full development life cycle.

How do I start?

Step 1: Create a QnA Maker resource in the Azure portal.

Step 2: Create a knowledge base in the QnA Maker portal. Add files and URLs to create the knowledge base.

Step 3: Publish your knowledge base and test from your custom endpoint using cURL or Postman.

Step 4: From your client application, programmatically call your knowledge base's endpoint and read the JSON response show the best answer to the user.

News and updates

Learn what's new with QnA Maker.

  • June 2019
    • Improved ranker model for French, Italian, German, Spanish, Portuguese
  • April 2019
    • Support website content extraction
    • Sharepoint document support
  • March 2019
    • Active learning
    • Improved NLP ranker model for English,

Next steps

QnA Maker provides everything you need to build, manage, and deploy your custom knowledge base.