Concepts
To create a multi-language question answering solution using Microsoft Azure AI, we can leverage the power
of Azure Cognitive Services, specifically the QnA Maker service. QnA Maker is a cloud-based API service that
enables us to create a knowledge base and utilize natural language processing for question and answer scenarios.
Step 1: Set up Azure Cognitive Services
- Sign in to the Azure portal and create a new QnA Maker resource.
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Follow the on-screen instructions to configure the QnA Maker resource, including specifying a unique name,
subscription, resource group, pricing tier, and location.
Step 2: Create a knowledge base
- Once the QnA Maker resource is provisioned, navigate to the QnA Maker portal.
- Click on “Create a knowledge base” and provide a name for your knowledge base.
- Choose the appropriate language and the type of source you want to use (e.g., files, URLs, or manual entry).
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Provide the necessary content for your knowledge base, including possible questions and corresponding answers
in the desired languages. - Click on “Create your KB” to create your knowledge base.
Step 3: Train and test your knowledge base
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Once your knowledge base is created, QnA Maker will automatically extract relevant information from the provided
sources. - Review and edit the extracted questions and answers to ensure accuracy and clarity.
- Train your knowledge base by clicking on “Train” to enable the model to learn from the provided data.
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Test your knowledge base by asking questions and verifying the answers. QnA Maker provides a test panel to
facilitate this process.
Step 4: Publish the knowledge base
- Once you are satisfied with the performance of your knowledge base, click on “Publish” to deploy it.
- Select the appropriate publication slot, either “Test” or “Production,” based on your deployment needs.
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After publication, QnA Maker will generate an endpoint URL that you can use to access the knowledge base
programmatically.
Step 5: Integrate the QnA Maker service into your application
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To integrate the QnA Maker service into your application, you can use the QnA Maker SDK or the REST-based API
provided by the service. -
Use the provided endpoint URL and an appropriate authentication mechanism (e.g., API key) to access the QnA
Maker service. - Send user queries to the QnA Maker service using the appropriate API call, passing the user’s question as input.
- Handle the response from the QnA Maker service, which includes the most relevant answer and other useful metadata.
Here’s an example of how to use the QnA Maker service in a simple HTML page (assuming you have already obtained
the necessary API key and endpoint URL):
Ask a question:
Answer:
Remember to replace the placeholders (
) in the above code with your actual values.
With this integration, you can now provide a user-friendly interface to ask questions in multiple languages and
receive accurate answers from your QnA Maker knowledge base.
In conclusion, by following the steps outlined above and using Azure Cognitive Services, specifically the QnA
Maker service, you can create a robust multi-language question answering solution. This solution can be integrated
into your applications to provide users with accurate answers to their queries.
Answer the Questions in Comment Section
When designing a multi-language question answering solution in Azure AI, what is the recommended approach for handling translation of user queries?
a) Use Azure Cognitive Services Language Understanding (LUIS) to automatically translate queries.
b) Manually translate queries for each supported language in the application code.
c) Utilize Azure Translator Text API to translate queries on-the-fly.
d) Implement a language detection algorithm to determine the language of the query.
Correct answer: c) Utilize Azure Translator Text API to translate queries on-the-fly.
Which Azure Cognitive Service is best suited for building a language model used in a multi-language question answering solution?
a) Azure Cognitive Services Speech Service
b) Azure Cognitive Services Translator Text API
c) Azure Cognitive Services Language Understanding (LUIS)
d) Azure Cognitive Services QnA Maker
Correct answer: c) Azure Cognitive Services Language Understanding (LUIS)
Which architectural component of Azure AI is responsible for processing user queries in a multi-language question answering solution?
a) Azure Bot Service
b) Azure Cognitive Services Search API
c) Azure Cognitive Services Language Understanding (LUIS)
d) Azure Cognitive Services Translator Text API
Correct answer: c) Azure Cognitive Services Language Understanding (LUIS)
In a multi-language question answering solution, what is the purpose of a knowledge base?
a) To store translated user queries for language analysis.
b) To maintain a collection of frequently asked questions in multiple languages.
c) To store translation models used by the application.
d) To create language models for natural language processing.
Correct answer: b) To maintain a collection of frequently asked questions in multiple languages.
Which Azure service is commonly used for information extraction from unstructured text in a multi-language question answering solution?
a) Azure Cognitive Services Text Analytics
b) Azure Cognitive Services Language Understanding (LUIS)
c) Azure Machine Learning service
d) Azure Cognitive Services Translator Text API
Correct answer: a) Azure Cognitive Services Text Analytics
In a multi-language question answering solution, what is the primary purpose of training a language model?
a) To automate the translation process.
b) To improve the accuracy of language detection.
c) To enhance the efficacy of text analysis.
d) To enable accurate understanding and generation of human-like language.
Correct answer: d) To enable accurate understanding and generation of human-like language.
Which Azure Cognitive Service is designed to provide language translation capabilities in a multi-language question answering solution?
a) Azure Cognitive Services Speaker Recognition
b) Azure Cognitive Services Language Understanding (LUIS)
c) Azure Translator Text API
d) Azure Cognitive Services Text Analytics
Correct answer: c) Azure Translator Text API
When designing an Azure AI solution, what is the recommended approach for handling multiple languages in a question answering system?
a) Implement separate question answering systems for each supported language.
b) Utilize language detection algorithms to direct queries to appropriate language models.
c) Train a single language model that can understand all supported languages.
d) Use Azure Translator Text API to manually translate queries before processing.
Correct answer: b) Utilize language detection algorithms to direct queries to appropriate language models.
In a multi-language question answering solution, what is the advantage of using Azure Cognitive Services Speech Service?
a) It provides real-time translation of user queries.
b) It enables voice-based interaction with the question answering system.
c) It automatically detects the language of user queries.
d) It improves the speed of query processing.
Correct answer: b) It enables voice-based interaction with the question answering system.
Which Azure service is recommended for automating the deployment and management of a multi-language question answering solution?
a) Azure Bot Service
b) Azure Container Instances
c) Azure Batch AI
d) Azure Machine Learning service
Correct answer: a) Azure Bot Service
Great post! This helped me better understand multi-language question answering solutions for AI-102.
Thanks for the detailed information. This is exactly what I needed for my exam prep.
How do you manage the model training for multiple languages? Any tips?
I am confused about integrating this with existing Azure services. Any advice?
Appreciate the blog post!
This is very informative. Thanks!
Not very clear on the deployment steps. Can someone elaborate?
How do you handle language detection before processing the query?