Copilot Studio and Azure Labs

• 1: Overview
• 2: Concepts
• 2.1: Copilot Studio
• 2.2: Azure OpenAI
• 2.3: Azure AI Search
• 2.4: Azure Storage
• 3: Contribution Guidelines

1 - Overview

⚠️ Work in Progress

This documentation is currently under development and may contain incomplete or placeholder content. Information provided here is subject to change without notice. Please check back regularly for updates.

Description

Copilot Studio and Azure Labs solution is a new offering that has different delivery modes. You can use it as a presales activity to demonstrate to customer the various Azure Open AI features that are accessible with minimal coding effort or you can deliver it as a Non-technical Hands on. This is intended for executives or other non-technical leads. The goal is to show different solutions where customers can test and proceed with further steps such as VBD engagements or new AOAI opportunities.

Pre-requisites

To use this solution, you will need the following:

• An Azure subscription
• A User, Service Principal or Managed Identity with the following permissions:
  • Contributor role on the Azure subscription
• Azure CLI v2.53.0 or later
• Terraform v1.6.0 or later

Objectives

By the end of this delivery, you will be able to:

• Configure and deploy the Copilot Studio and Azure Labs solution
• Use OpenAI cases to understand the solution’s capabilities
• Upload custom data files and perform vector search
• Extend the solution with plugins
• Manage the charge back functionality
• Secure and monitor the solution
• Scale and integrate the solution with other services

High-level Architecture

The following diagram shows the high-level architecture of the Copilot Studio and Azure Labs solution:

Delivery Guidance

This delivery involves the the following steps:

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Closing

By the end of the Copilot Studio and Azure Labs delivery, stakeholders will have a strong and innovative perspective of some AOAI services which can be used separately or together to improve their document retrieval efficiency, enhance their user experience, increase their productivity, share their knowledge, gain data insights and analytics, and comply with security standards.

2 - Concepts

2.1 - Copilot Studio

⚠️ Work in Progress

This documentation is currently under development and may contain incomplete or placeholder content. Information provided here is subject to change without notice. Please check back regularly for updates.

Learn more about Copilot Studio: What is Copilot Studio?

2.2 - Azure OpenAI

Azure OpenAI Service provides REST API access to OpenAI’s powerful language models including the GPT-4, GPT-35-Turbo, and Embeddings model series. In addition, the new GPT-4 and gpt-35-turbo model series have now reached general availability. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.

Important concepts about Azure OpenAI:

• Azure OpenAI Studio

• Models available

  • GPT-35-Turbo series: typical “chatGPT” model, recommended for most of the Azure OpenAI projects. When we might need more capability, GPT4 can me considered (take into account it will imply more latency and cost)
  • GPT-4 series: they are the most advanced language models, available once you fill in the GPT4 Request Form
  • Embeddings series: embeddings make it easier to do machine learning on large inputs representing words by capturing the semantic similarities in a vector space. Therefore, you can use embeddings to determine if two text chunks are semantically related or similar, and provide a score to assess similarity.

  Take into account that not all models are available in all Azure Regions, for Regional availability check the documentation: Model summary table and region availability

• Deployment: once you instantiate a specific model, it will be available as deployment. You can create and delete deployments of available models as you wish. This is managed through the AOAI Studio.

• Quotas: the quotas available in Azure are allocated per model and per region, within a subscription. Learn more about quotas. In the documentation you can find best practices to manage your quota.

Copilot Studio and Azure Labs uses Azure OpenAI Embeddings model to vectorize the content and ChatGPT model to conversate with that content.

More information at the official documentation: What is Azure OpenAI

2.3 - Azure AI Search

Azure AI Search (formerly known as “Azure Search”) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.

Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you’ll work with the following capabilities:

• A search engine for full text and vector search over a search index containing user-owned content
• Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
• Rich query syntax for vector queries, text search, fuzzy search, autocomplete, geo-search and more
• Programmability through REST APIs and client libraries in Azure SDKs
• Azure integration at the data layer, machine learning layer, and AI (Azure AI services)

Copilot Studio and Azure Labs uses Azure AI Search to serve an index of vectorized content, that will be used by our LLM (ChatGPT) to respond to user’s query.

Learn more at the official documentation: What is Azure AI Search?.

Learning Path:Implement knowledge mining with Azure AI Search

2.4 - Azure Storage

Azure Blob Storage is Microsoft’s object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn’t adhere to a particular data model or definition, such as text or binary data.

Blob Storage is designed for:

• Serving images or documents directly to a browser.
• Storing files for distributed access.
• Streaming video and audio.
• Writing to log files.
• Storing data for backup and restore, disaster recovery, and archiving.
• Storing data for analysis by an on-premises or Azure-hosted service.

Copilot Studio and Azure Labs uses Blob Storage to store the documents (PDFs) that will be then vectorized, indexed or analyzed.

Learn more about Azure Blob Storage: What is Azure Blob Storage?

3 - Contribution Guidelines

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repositories using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.