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to build a Copilot for your app? Semantic Kernel & Prompt Flow for Beginners","conversation":{"__ref":"Conversation:conversation:3902524"},"id":"message:3902524","revisionNum":2,"uid":3902524,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:364271"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" I recently co-presented on a developer-focused webinar on Semantic Kernel for beginners. This webinar was part of a series about leveraging Azure OpenAI, with this one diving into how to add intelligent experiences into your new and existing applications. \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":9032},"postTime":"2023-08-17T14:55:26.583-07:00","lastPublishTime":"2023-08-17T14:58:22.429-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Hi there, I’m back with another post, I recently co-presented on a developer-focused webinar on Semantic Kernel for beginners. This webinar was part of a series about leveraging Azure OpenAI, with this one diving into how to add intelligent experiences into your new and existing applications. \n \n In the webinar, we focused on the AI Orchestration layer of the Copilot Stack, which is a framework that helps you design and implement AI solutions using Azure OpenAI. We focused on two tools: Semantic Kernel and Azure Prompt Flow. Semantic Kernel is an SDK that allows you to interact with AI models using natural language commands, while Azure Prompt Flow is a tool that helps you create and manage prompts for AI models. \n \n The focus of my portion of the session (starts at 25:28 in the video) was geared toward developers and the use of the Semantic Kernel SDK to easily add AI into applications. I showed how you can use the SDK in both C# and Python to create plugins, plans, and personas for Semantic Kernel. \n \n Plugins - modules that connect Semantic Kernel to different AI models or services, such as GPT-3, Azure Prompt Flow, or the Microsoft Graph. \n Plans - sequences of actions that Semantic Kernel can execute to achieve a goal, such as booking a flight or writing some content. \n Personas - profiles that define the preferences and personality of Semantic Kernel, such as tone, style, and humor. \n \n I used a demo to introduce these concepts, where I deconstructed the Bing Chat extension for the Edge browser, going through development of a simplified version. I used Semantic Kernel to handle the natural language understanding and generation, as well as to access Deepa’s web service (which she created in Prompt Flow, see below) through plugins. \n \n \n My co-presenter Deepa covered the use of Azure Prompt Flow, which is a graphical interface that lets you create and edit prompts for AI models. Deepa showed how you can use Azure Prompt Flow to create prompts from scratch or from templates, test them on different models and datasets, and deploy them to Semantic Kernel or other applications. \n I thoroughly enjoyed presenting this session and I hope you learned something new and useful from it. If you missed it or want to watch it again, you can find the recording on demand on YouTube. You can also check out the other webinars in the series about Azure OpenAI. \n \n Want to learn more? Here are the \"Resources\" links that we included toward the end of the webinar: \n \n Discord Community – active conversations with weekly office hours https://aka.ms/SKDiscord \n Semantic Kernel on GitHub – source code, backlog, sample apps, notebooks \n Microsoft Learn docs – quick start, tutorials, hackathon materials https://aka.ms/semantic-kernel \n Chat Copilot sample – Build your own integrated large language model chat copilot https://github.com/microsoft/chat-copilot \n Semantic Kernel recipes – Start cooking today https://aka.ms/sk/recipes \n Microsoft Learn doc - https://aka.ms/AML-PF \n RAG Pattern with prompt flow - RAG using PF \n \n 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Azure Health Bot with Azure OpenAI Service","conversation":{"__ref":"Conversation:conversation:3792560"},"id":"message:3792560","revisionNum":8,"uid":3792560,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:203420"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n We are excited to share the preview of a new Azure Health Bot template that allows our customers to experiment with the integration of Azure OpenAI Service into their Health Bot instances for fallback answers. This feature does not aim to facilitate the bot to answer unknown queries in the medical space, rather, it enables organizations to access the Azure OpenAI Service API and decide how to use the model to improve their bot built through the Azure Health Bot service. \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":16868},"postTime":"2023-04-12T05:03:40.736-07:00","lastPublishTime":"2023-04-12T05:03:40.736-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Extending Azure Health Bot with Azure OpenAI Service \n \n Over the past few months, we have witnessed a tremendous shift in the way Artificial Intelligence (AI) is used across every industry. The adoption of Large Language Models (LLM) is gaining traction at an immense speed. \n \n Azure Health Bot empowers healthcare organizations to build and deploy AI-powered, compliant, conversational healthcare experiences at scale. Already today, healthcare organizations are using Azure Health Bot to provide end users with self-serve functionality and easy to access information. The service combines built-in medical intelligence with natural language understanding tuned for clinical terminology in addition to easy customization and extension capabilities. But as with every chatbot, there are limitations to its ability to understand more complex questions or questions beyond its scope. \n \n We are excited to share the preview of a new Azure Health Bot template that allows our customers to experiment with the integration of Azure OpenAI Service into their Health Bot instances for fallback answers. This feature does not aim to facilitate the bot to answer unknown queries in the medical space, rather, it enables organizations to access the Azure OpenAI Service API and decide how to use the model to improve their bot built through the Azure Health Bot service. \n \n At this time, we are offering the model in private preview for internal testing and evaluation purposes only. \n \n Building your Health Bot instance in a responsible way \n \n We evaluated how to leverage Large Language Models (LLM) to empower our customers to achieve morewhile at the same time, making sure this was done in a responsible way. \n \n Microsoft is committed to developing AI responsibly and in a way that benefits society as a whole. As a company we follow responsible AI principles for the development and use of AI \n \n With this first step of empowering our customers to integrate Azure OpenAI Service into their Azure Health Bot instance, we are creating a responsible way of using LLM models. Azure Health Bot is already capable of providing patient triage and healthcare related information from clinically validated sources, but in some scenarios the bot does not understand what the end user is trying to ask. In such cases of unrecognized utterances, where the bot does not know what to do, we created a side-by-side integration of Azure OpenAI Service in the Azure Health Bot to help provide fallback answers. \n \n To use the Azure OpenAI Service integration feature in a responsible way, we recommend the following approaches: \n \n Always use the pre-built intents for triage and medical content answers and not disable them, unless you are replacing them with your own intents and flows \n By default, every answer from Azure OpenAI Service will be marked as an AI generated answer, we recommend that you always inform end users that these answers are coming from an AI tool and should be verified before relying on the information. \n Create your own intents, scenarios, and flows for use cases where you want to be in full control of the responses, rather than relying on Azure OpenAI Service. \n Enable continuous clinical assessment by subject matter experts to evaluate the answers. This will give you the insights and the opportunity to improve the responses. \n Ground your AI model from trusted documents and implement a mechanism to have the bot draw responses from these documents, when answering end-user questions. It is the customer responsibility that it only draws from the trusted documents and does not make up answers. \n Monitor and analyze your unrecognized utterances. This will provide you with insights on what your users are asking and give you the information to create relevant scenarios and a more predictable flow. It will also help to mitigate the risk for known limitations of these models, such as potential hallucinations. It is your responsibility to measure and mitigate potential hallucinations to ensure that the bot does not respond outside of the provided trusted documents. Monitoring will also help mitigate harm arising from end users attempting to bypass the safety system (e.g., breaking the metaprompt). \n Act upon user feedback from the Azure OpenAI Service responses. The current implementation contains a simple feedback mechanism with a Thumbs-up and Thumbs-down icon. This feedback information can be stored in your backend or logged into the service. As a customer you should analyze this information to validate if users find the responses valuable or if you need to tweak the responses for better results \n \n \n Importing the Azure OpenAI Service Template into your Azure Health Bot instance. \n \n We are enabling Azure OpenAI Service through the Azure Health Bot Scenario Template Catalog. The catalog allows customers to import validated healthcare scenarios into their Azure Health Bot instance. You can think of these templates as starter kits to decrease time to market. \n \n \n \n The Azure OpenAI Service template allows customers to connect Azure Health Bot with their own Azure OpenAI endpoint. This is done through a secure channel and makes sure all requests and data is kept within the tenant of the customer. Customers who want to access Azure OpenAI Service can request this here. \n \n After a successful import of the Azure OpenAI Service template into see the Azure Health Bot instance, the customers get a new scenario that is ready to use and automatically configured to catch unrecognized utterances through Azure OpenAI Service. \n \n \n \n To provide a tailored experience for the end users, we are using the built-in user context capability from the Azure Health Bot. We use this feature to send context aware information to Azure OpenAI Service which increases the quality of AI generated responses. The user can, at any time, ask the bot to be forgotten. All information of the end-user will then be deleted. \n \n The Azure Health Bot is also able to answer different healthcare related questions. With the help of conversational language understanding, the service is able to catch medical complaints, medication and information requests, condition information, symptoms , etc. We recommend keeping this feature enabled when using the Azure OpenAI integration. \n \n \n \n In the event of an unrecognized utterance, Azure Health Bot will now forward these to Azure OpenAI Service. To provide the most accurate and relevant answer, the Azure Health Bot scenario already implements a small but effective prompt engineering statement: \n \n “you can only answer on medical related content. You need to use information from https://www.cdc.gov/ or https://www.fda.gov/. don't provide an url but a name of where you looked for the content” . \n \n This prompt tells the model to look for answers only from these validated sources and include the sources in the responses. For production use cases we recommend that the customer implement the bot such that it only draws from the trusted documents thereby reducing hallucinations as much as possible. \n \n \n \n \n The Azure OpenAI Service catalogue template will be available in the coming months in preview. We can’t wait to see how customers will experiment with and use this feature. \n \n Learn More: \n Azure Health Bot \n Azure Health Bot documentation \n \n \n 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GPT-3 to Triage Azure DevOps Bugs","conversation":{"__ref":"Conversation:conversation:3754306"},"id":"message:3754306","revisionNum":2,"uid":3754306,"depth":0,"board":{"__ref":"Blog:board:HealthcareAndLifeSciencesBlog"},"author":{"__ref":"User:user:1676800"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" OpenAI’s GPT-3 is a game-changer in the bug hunting industry. Its ability to generate natural language content with high accuracy and coherence makes it an invaluable tool for identifying potential security vulnerabilities in software applications. \n \n In this article, I will explain how you can implement an automation solution to triage a bug and best practices in Azure DevOps. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5602},"postTime":"2023-02-27T16:18:05.953-08:00","lastPublishTime":"2023-02-28T05:59:46.651-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n \n How do you track and manage defects in your code? How do you make sure software problems and customer feedback get addressed quickly to support high-quality software deployments? And, how do you make good progress on new features and address your technical debt? \n \n At a minimum, you need a way to capture your software issues, prioritize them, assign them to a team member, and track progress. And, you want to manage your code defects in ways that align with your Agile practices. \n \n To support these scenarios, Azure Boards provides a specific work item type to track code defects named Bug. \n \n When attempting to resolve bugs, we sometimes don’t always have all the information we need, especially when dealing with urgent production issues. In these cases, we often create a bug in Azure DevOps system with some information e.g. repro steps. \n \n For this kind of urgent issue our development team members need some primary analysis or troubleshooting steps. Developer has to browse the internet and look the solution around the problem in a normal part of the software development cycle (e.g. some kind triage notes). \n \n OpenAI’s GPT-3 is a game-changer in the bug hunting industry. Its ability to generate natural language content with high accuracy and coherence makes it an invaluable tool for identifying potential security vulnerabilities in software applications. \n \n In this article, I will explain how you can implement an automation solution to triage a bug and best practices in Azure DevOps. We can implement this solution using Power Automate, Logic Apps and integration with OpenAI GPT-3 & Azure DevOps. I am going to use Power Automate in this article. \n \n Below are standard steps. \n \n Step 1: Go to link - Microsoft Flow(Power Automate) to setup new workflow. \n \n Step 2: Go to create option to setup new workflow using automate cloud flow option. \n \n \n \n Step 3: First step in the workflow is to add action called “Create a work item” to trigger the workflow (as shown in below screenshot). You can specify your Azure DevOps organization, teams details and specify the type of work item. In my example work item type is “Bug”. So whenever a bug will introduce in system my workflow will be triggered. \n \n \n \n \n Step 4: In next step, we can store the \"repro steps\" of bugs in a separate variable. We are storing it in a separate variable so we can remove the formatting applied to the \"repro steps\" in Azure DevOps editor. \n \n \n \n Note: You can see below sample work item \"Bug\" in Azure DevOps. We can see there are different kind of formatting styles (e.g. bold, italic, bullet, underline) are applied at text and we have to pass the “repro steps” as plain text to Open AI (without any special characters). \n \n \n Step 5: In the next step we will use HTML action to remove the formatting applied to “Repro Steps” and convert it into variable as plain text (look below two steps). \n \n \n \n Step 6: Next step is very important in this workflow. In this step we are calling OpenAI API. We can interact with the API through HTTP requests from any language or actions. The OpenAI API uses API keys for authentication. Visit your API Keys page to retrieve the API key you'll use in your requests. You can refer the documentation how to setup OpenAI free account and setup API keys - API Reference - OpenAI API \n \n In my case I am using “HTTP” action to call the API by passing URL, Headers and Body. You can see in body, I passed variable value as plaintext (which contains bug repro steps). \n \n HTTP actions enable you to interact with APIs and send web requests that perform various operations, such as uploading and downloading data and files. \n \n To send an API request, like POST, GET, PUT, or DELETE, use the Invoke web service action. \n In the action's properties, you must populate the service's URL and the appropriate HTTP method. Additionally, you must choose the request and response content type, such as XML and JSON. You can read more about “HTTP” action at this link - HTTP actions reference - Power Automate | Microsoft Learn. \n \n I am using GPT-3 > text-davinci-003 model in request body which can understand and generate natural language. If you want to know more about GPT-3 models you can refer the link for more details - Models - OpenAI API \n \n \n \n Step 7: In next step we have to store the “choices” and “text” received as response from “HTTP” action (after submitting the POST request) and update the “History” filed of work item “bug”. \n \n \n \n In below example you can see I just passed the work item “Repro steps” as plaintext (e.g. some terraform help) and the response result updated in the discussion section. These triage notes can be very helpful for a team member for initial analysis about the bug. \n \n \n \n Finally our workflow will look like this. You can integrate more steps as per business requirement needs and extend the functionality. \n \n \n \n Conclusion \n \n OpenAI’s GPT-3 is a game-changer in the bug hunting industry. Its ability to generate natural language content with high accuracy and coherence makes it an invaluable tool for identifying potential security vulnerabilities in software applications. \n \n Feel free to leave your comment and share feedback. \n 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