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Supercharge Your Search with Azure AI Search & OpenAI

Science & Technology

Introduction

Welcome, everyone, to our session aimed at revolutionizing your search capabilities using Azure AI Search and OpenAI. My name is Peter Carson, and I am the President of Externet User Manager in Envision IT, as well as a Microsoft M365 MVP. Today, we will be discussing how to build your own chat solutions incorporating enterprise data utilizing Azure services.

Introduction to Externet User Manager

For those unfamiliar with Externet User Manager, our mission is to simplify Microsoft services, particularly by facilitating external sharing scenarios for organizations. While today's focus will not be on that topic specifically, I will showcase our product as an example of how we're integrating Azure AI Search and OpenAI into our offerings.

Imaginary Scenario: Travel Recommendations

To illustrate the capabilities of AI and search, let’s envision a scenario. When traveling, many seek local recommendations—from restaurants to attractions. Imagine if a hotel concierge could leverage generative AI to provide personalized recommendations, complete with booking details. This potential is indicative of where AI is steering various sectors, including search.

Today's session will focus on the integration of search and AI. We will touch on Microsoft Copilot from the M365 suite and dive into building your enterprise chat solution. This will be primarily centered on how to utilize Azure OpenAI and AI Search effectively.

Audience Interaction: Search Habits Poll

To get a sense of the audience's search preferences, I encourage everyone to share if they turn to ChatGPT for answers before consulting Google. This shift in behavior highlights a growing trend towards using generative AI in everyday search tasks.

Utilizing Microsoft Copilot

Microsoft Copilot has gained traction within the Microsoft 365 suite. It aggregates enterprise data while promising sensitive and secure interactions, unlike other public AI models where data privacy is uncertain. Copilot can summarize emails, manage calendars, and more, showcasing a tangible benefit in efficiency.

Live Demonstration: Microsoft's Copilot

Let’s now transition to a live demonstration using Microsoft Copilot. I often utilize it to view my calendar. For example, asking, “What’s on my schedule today?” allows Copilot to access my calendar via Microsoft Graph, showcasing how Copilot grounds its response in actual enterprise data. Through those interactions, context and semantic understanding underpin the models, ensuring relevant responses.

The Architecture Behind AI Solutions

One of the crucial aspects of the demonstrations is understanding the architecture of retrieval-augmented generation. When a user asks a question, the model processes the query while grounding it using the appropriate data sources within the organization, such as Microsoft Graph.

Building a Chat Interface

The retrieval-augmented generation model allows for a seamless integration of chat functionalities. We can facilitate conversations that pull from various data sources to provide informative responses.

Azure AI Search

As a provider, we utilize Azure AI Search to build a searchable interface for our events. The AI Search capabilities allow us to index and search through documents efficiently. This facilitates solid search experiences—crucial for effective AI interactions.

Transitioning from traditional keyword searching to semantic search methodologies—including vector search—is key. The latter allows us to query documents that relate to the underlying meaning of the query rather than the exact keywords. This advancement serves global audiences better by accommodating language differences, synonyms, and contextual relevance.

Demonstrating Search Capabilities

Let’s utilize an AI Search interface to query about past events. Upon entering a term like “Traffic Light Protocol,” we can quickly retrieve relevant content. The integration of chat capabilities makes for a more user-friendly experience—allowing follow-up questions and providing layers of information.

Cost Considerations

However, utilizing these advanced models comes at a cost. Both OpenAI and Azure AI Search have unique pricing structures based on token usage and service hours. Understanding the potential expenses linked to facilitating AI experiences is vital for planning and budgeting.

Categorizing Data Sensitivity

When rolling out chat features on public websites, it is essential to assess the sensitivity of the content. We must ensure that personal or confidential data remains protected and compliant when exposed to the available chat capabilities.

In conclusion, the prospect of leveraging both Azure AI Search and OpenAI poses significant enhancements to productivity, decision-making, and cost savings. Moreover, organizations can control the use of AI tools without losing compliance or security.

Keywords

  • Azure AI Search
  • OpenAI
  • Generative AI
  • Semantic Search
  • Copilot
  • Enterprise Data
  • Vector Search
  • Data Sensitivity
  • Chat Interface
  • Retrieval-Augmented Generation

FAQ

1. What is Azure AI Search? Azure AI Search is a cloud integrated search service that allows organizations to create efficient search experiences using AI and semantic understanding.

2. How does OpenAI integrate with Azure services? OpenAI models can be used within Azure to build applications that leverage artificial intelligence for chat solutions, utilizing secure enterprise data.

3. What are the cost implications of using these services? Both OpenAI and Azure AI Search come with distinct pricing based on usage. It is essential to evaluate potential costs to avoid unexpected expenses.

4. How do we ensure the security of sensitive data? Security can be maintained by ensuring that only necessary permissions are granted and sensitive information is filtered appropriately during chat interactions.

5. What is retrieval-augmented generation? Retrieval-augmented generation is a method that combines search capabilities with large language models to provide contextually relevant responses drawn from specific information sources.

Thank you for participating in this session today! We are excited about the future of search and AI integration in our environments.