Creating custom question answering projects in Azure involves building knowledge bases that can respond to user queries with relevant answers. This capability is part of Azure AI Language service, formerly known as QnA Maker. Here's how to create these projects:
**Setting Up the Project**
First, c…Creating custom question answering projects in Azure involves building knowledge bases that can respond to user queries with relevant answers. This capability is part of Azure AI Language service, formerly known as QnA Maker. Here's how to create these projects:
**Setting Up the Project**
First, create an Azure AI Language resource in the Azure portal. Navigate to Language Studio and select 'Custom question answering' to begin your project. You'll need to define a project name and select the language for your knowledge base.
**Adding Knowledge Sources**
You can populate your knowledge base through multiple methods:
- Upload documents (PDF, Word, Excel)
- Add URLs to extract FAQ content from websites
- Manually enter question-answer pairs
- Import existing knowledge bases
**Structuring Q&A Pairs**
Each entry consists of a question, an answer, and optional metadata. You can add alternative phrasings to questions, helping the system recognize different ways users might ask the same thing. Metadata tags enable filtering responses based on context.
**Testing and Training**
Use the built-in test panel to evaluate responses. The system uses machine learning to match user queries with appropriate answers based on semantic similarity. You can add follow-up prompts to create multi-turn conversations, guiding users through complex topics.
**Deployment and Integration**
Once satisfied with your knowledge base, deploy it to create a REST API endpoint. This endpoint can be integrated into applications, chatbots, or websites. Azure Bot Service integration allows seamless connection to various channels like Teams, Slack, or web chat.
**Active Learning**
Enable active learning to improve accuracy over time. The system suggests alternative questions based on user interactions, which you can review and approve to enhance the knowledge base continuously.
**Best Practices**
Organize content logically, use clear and concise answers, include varied question phrasings, and regularly review analytics to identify gaps in your knowledge base coverage.
Creating Custom Question Answering Projects
Why It Is Important
Custom question answering is a core component of Azure AI Language services that enables developers to build conversational AI solutions. For the AI-102 exam, understanding how to create and configure question answering projects is essential because it demonstrates your ability to implement natural language processing solutions that can automatically respond to user queries. This capability is widely used in chatbots, virtual assistants, and customer support applications.
What It Is
Custom question answering is a cloud-based Natural Language Processing (NLP) feature within Azure AI Language that allows you to create a knowledge base of question-answer pairs. This knowledge base can be built from existing content such as FAQ documents, URLs, product manuals, or custom QnA pairs that you define manually. The service uses machine learning to match user questions with the most appropriate answers from your knowledge base.
How It Works
1. Create a Language Resource: First, you need to provision an Azure AI Language resource in the Azure portal with the custom question answering feature enabled.
2. Create a Project: Using Language Studio or the REST API, you create a new question answering project and specify a default answer for cases when no match is found.
3. Add Knowledge Sources: You can populate your knowledge base by: - Importing FAQ URLs - Uploading documents (PDF, Word, Excel, TXT) - Adding manual QnA pairs - Using chit-chat datasets for conversational responses
4. Configure Settings: Set up synonyms, add alternate questions, define metadata for filtering, and configure multi-turn conversations for complex dialogues.
5. Test and Train: Use the test pane to validate responses and improve accuracy by adding alternate phrasings.
6. Deploy: Deploy the knowledge base to a production endpoint for integration with applications or Azure Bot Service.
Key Components to Remember
- QnA Pairs: The fundamental unit consisting of a question and its corresponding answer - Alternate Questions: Different ways users might ask the same question - Metadata: Key-value pairs used to filter responses - Multi-turn Conversations: Follow-up prompts that enable conversational flows - Confidence Score: A value between 0 and 1 indicating how well a question matches - Default Answer: The response returned when confidence is below the threshold
Exam Tips: Answering Questions on Creating Custom Question Answering Projects
1. Know the Resource Requirements: Remember that custom question answering requires an Azure AI Language resource with the feature enabled during creation. You also need an Azure Search resource for the knowledge base index.
2. Understand Source Types: Be familiar with all supported source types including URLs, files (PDF, DOCX, XLSX, TXT), and manual entries. Know file size limits and supported formats.
3. Confidence Threshold: Questions may ask about configuring the confidence threshold. The default is 0.3, and responses below this threshold return the default answer.
4. Multi-turn vs Single-turn: Understand when to use follow-up prompts for complex scenarios requiring additional context from users.
5. Active Learning: Know that active learning suggests alternate questions based on user queries to improve the knowledge base over time.
6. Metadata Filtering: Remember that metadata allows you to filter responses at query time, useful for multi-tenant or context-specific scenarios.
7. Deployment Options: Understand that you can have multiple deployment slots and that the production deployment is what client applications consume.
8. Integration Points: Be prepared for questions about integrating with Azure Bot Service using the question answering connector.
9. REST API Knowledge: Familiarize yourself with the authoring and runtime APIs, including endpoints for creating projects, adding sources, and querying the knowledge base.
10. Chit-chat Feature: Remember that pre-built chit-chat personalities (Professional, Friendly, Witty, Caring, Enthusiastic) can be added to enhance conversational experiences.