{"id":20254,"date":"2024-04-08T08:00:40","date_gmt":"2024-04-08T15:00:40","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/microsoft365dev\/?p=20254"},"modified":"2024-12-11T02:34:25","modified_gmt":"2024-12-11T10:34:25","slug":"building-custom-copilots-in-microsoft-teams-with-teams-app-test-tool-and-dev-proxy","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/microsoft365dev\/building-custom-copilots-in-microsoft-teams-with-teams-app-test-tool-and-dev-proxy\/","title":{"rendered":"Building custom copilots in Microsoft Teams with Teams App Test Tool and Dev Proxy"},"content":{"rendered":"

Custom copilots are AI assistants that have been tailored to meet the specific needs of an organization. Microsoft Teams is where employees communicate and collaborate across the organization daily. Many organizations are interested in building bots for Microsoft Teams to integrate custom copilots they build. However, building a custom copilot is not trivial. By using Teams App Test Tool, you\u2019ll build and test your custom copilots quicker, saving time and money.<\/p>\n

Custom copilots on Microsoft Cloud<\/h2>\n

Many organizations are building custom copilots grounded in organizational data using the Retrieval Augmented Generation (RAG) pattern. Organizations can integrate information retrieval with large language models (LLMs) making the responses more contextually relevant and useful.<\/p>\n

The ChatGPT + Enterprise data with Azure OpenAI and AI Search<\/a> sample is an example of how to implement RAG on the Microsoft Cloud.<\/p>\n

\"DiagramRetrieval Augmented Generation (RAG) architecture<\/em><\/p>\n

Employees use natural language to ask questions and receive answers based on data from a fictitious company called Contoso Electronics. Allowing them to ask questions about the benefits, internal policies, as well as job descriptions and roles.<\/p>\n

\"Chat<\/p>\n

Chat interface in the web application<\/em><\/p>\n

Requests are sent to a back-end API when employees submit queries. Answers are returned in natural language along with document citations and follow-up questions in the response which are displayed in the user interface.<\/p>\n

Custom copilots in Microsoft Teams<\/h2>\n

In many organizations, daily communication and collaboration takes place in Microsoft Teams. Exposing conversational AI bots in Microsoft Teams gives employees a familiar experience that can be accessed on desktop, tablet, and mobile devices through the Microsoft Teams clients, meeting employees where they are, and in the flow of work.<\/p>\n

In the ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search in Microsoft Teams<\/a> sample, which builds on top of the previous sample, employees post questions as messages to a bot in Microsoft Teams, as an alternative to using the web-based app.<\/p>\n

The bot sends requests to the same back-end API to return answers from the LLM.<\/p>\n

\"ChatChat interface in Microsoft Teams bot<\/em><\/p>\n

The answers, along with document citations and action buttons, are returned in Adaptive Cards. In addition, follow-up questions are displayed as suggested actions.<\/p>\n

Removing platform dependencies<\/h2>\n

To run and develop the two sample projects that we\u2019ve just seen, you need to setup and configure several cloud services, as well as have access to a Microsoft 365 tenant.<\/p>\n

\"AMicrosoft Teams bot with RAG architecture<\/em><\/p>\n

During development this can be inefficient, in both time and money. It takes time to get everything in place and each request you send to the LLM incurs a cost as does keeping these services up and running.<\/p>\n

During development, consider the following:<\/p>\n