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Your Video Insights, Promptly Extracted: Azure AI Video Indexer's Preview of Prompt-Ready API
Have you ever watched an online course and wished you could ask questions on the entire course, or have a comprehensive summary of a video? This can all now be achieved with Azure AI Video Indexer and an LLM (Large Language Model) – powering each other. LLMs are powerful language models that can capture the essence of text and allow natural language question-answering and much more. In Azure AI Video Indexer, we understand videos – video content is more than just words, and a single shot can contain a wealth of insights that are critical for its understanding. Coupling these two powerful tools can lead to great results in video understanding, and downstream tasks in natural language. Our new API extracts and processes all the multi-modality insights of a video into prompt-ready format, that can be easily used with LLMs.
tomhirshberg
Jun 06, 2024 Place AI - Azure AI services Blog
7.1KViews
1like
3Comments
Introducing new task-optimized summarization capabilities powered by fine-tuned large-language model
We are expanding our utilization of LLMs to GPT-3.5 Turbo, along with our proprietary z-Code++ models to offer task-optimized summarization using a balance of output accuracy and cost.
YanlingX
Mar 15, 2024 Place AI - Azure AI services Blog
5.1KViews
2likes
5Comments
Accelerate Language Cognitive Services customization with Azure OpenAI
Try out our new data suggestion capabilities in Conversational Language Understanding powered by Azure OpenAI!
HazemElHammamy
Feb 13, 2024 Place AI - Azure AI services Blog
5.3KViews
2likes
5Comments
Document Redaction and Sanitization with ChatGPT on Azure
Privacy and Security are the topmost priorities for Consumers and Businesses. Bad actors can steal the information when it is in transit between the systems, when it is being processed or even when archived. Large Language Models have opened doors for more effective ways of sanitizing the documents. In this article I will provide some examples of how to sanitize the documents using ChatGPT on Azure. What is Document Redaction and Sanitization? It is the process of removing or replacing any information that is considered as sensitive, private or confidential. What types of information needs redaction? Following are some sensitive information types that needs redaction: Personally Identifiable and Information (PII): Anything information that helps identify a person called needs to be redacted. Examples include people's names, addresses, social security numbers, drivers license etc. Protected Health Information(PHI) : Health related information such as patient’s medical records, insurance group numbers, benefit information etc. Business Confidential Information: Organizational information such as employee records, biometric records, business related secrets, contractual information, financial documents, judicial records etc. Using ChatGPT on Azure for Redacting and Sanitizing the documents: Example 1: Sanitize the sensitive information from an Invoice The sample invoice I am going to use is in scanned format. As ChatGPT can only take plain text as input currently, we first have to digitalize the invoice. Form Recognizer service is a very effective way of digitizing the scanned documents. Below is a screenshot from Form Recognizer with the Invoice example: After extracting the data from the invoice using Form Recognizer and some cleansing, we will have the plain text something like below: You will find the responses below from ChatGPT for my redaction and sanitizing instructions: Prompt: Show me all the references of PII below: Prompt: redact all references of PII data below: Prompt: Replace all occurrences of Contoso with LinkedIn: Prompt: convert all dates to MON-DD-YYYY format. show me only dates: Example 2: Sanitize information from a Health Insurance Card for PHI data: Similar to the above example, I used the Form Recognizer service to extract the information from a health insurance card. Below are some examples on how to sanitize the PHI information. Prompt: show me all references of PHI: Prompt: redact all references of people names below: You can continue with various redaction and sanitization activities in the document such as replacing text, removing text, translating text, converting currencies etc. All the Best! Note 1: The responses may vary depending on the hyperparameters like Temperature, Top Probabilities etc. Note 2: I also encourage you all to try the same prompts on text-davinci-003 model also.
Lucky_Pamula_MSFT
Jan 25, 2024 Place AI - Azure AI services Blog
7.2KViews
2likes
0Comments
Introducing Native Document Support for PII Detection (Public Preview)
This capability can now identify, categorize, and redact sensitive information (PII - Personally Identifiable Information) in unstructured text directly from complex documents, allowing users to ensure data privacy compliance within a streamlined workflow. It effortlessly detects and safeguards crucial information, adhering to the highest standards of data privacy and security.
krishnakumar_mk
Nov 15, 2023 Place AI - Azure AI services Blog
11KViews
3likes
0Comments
Building Real-Time, Intelligent AI Copilots with Confluent Cloud, Azure OpenAI & Azure Data Services
Real-time application scenarios such as airline booking systems often provide suboptimal user experiences, with customers having to navigate complex systems and rules to perform simple tasks. In this blog post, we will explain how to build intelligent agents on top of Confluent, Azure data services and Azure Open AI to deliver real-time insights and help customers conduct business transactions.
JamesN
Sep 26, 2023 Place AI - Azure AI services Blog
9.3KViews
1like
0Comments
Microsoft Translator Plugin for Azure OpenAI & Tools for PromptFlow
Microsoft Translator Plugin for Azure OpenAI allows users to translate a source text between any of the 125+ languages and dialects supported by Translator service using either general or custom translation models. Translator and Language Detect tools for PromptFlow helps you to get better responses for various prompts submitted to GPT in non-English languages.
Krishna_Doss
Jun 01, 2023 Place AI - Azure AI services Blog
13KViews
5likes
1Comment
What’s new Azure AI Language | BUILD 2023
We are announcing more new features and capabilities: General Availability (GA) of document and conversation summarization, and public preview of the following features: interactive custom summarization powered by Z-Code++ and Azure OpenAI Service, custom sentiment analysis, enhanced Text Analytics for health, Named Entity Recognition (NER) and Personal Identifiable Information (PII) Detection, increased language coverage, enhanced integration capability, and expanded container support.
YanlingX
May 23, 2023 Place AI - Azure AI services Blog
6.8KViews
5likes
0Comments
Microsoft Azure Text Translation SDK now in Public Preview
Microsoft announces the public preview of its Azure Text Translation SDK designed to help developers easily add text translation capabilities to their applications. In this post, we'll take a closer look at what the Azure Text Translation SDK is, how it works, and how developers can use it.
Charan Mohan
May 23, 2023 Place AI - Azure AI services Blog
3.8KViews
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0Comments
Document Translation now available in the Language Studio
This blog introduces the Document Translation feature in the Language Studio, an UI interface that will let you translate documents using the Azure Cognitive Services Translator service.
krishnakumar_mk
May 23, 2023 Place AI - Azure AI services Blog
6.2KViews
0likes
0Comments

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could ask questions on the entire course, or have a comprehensive summary of a video? This can all now be achieved with Azure AI Video Indexer and an LLM (Large Language Model) – powering each other. \n LLMs are powerful language models that can capture the essence of text and allow natural language question-answering and much more. In Azure AI Video Indexer, we understand videos – video content is more than just words, and a single shot can contain a wealth of insights that are critical for its understanding. Coupling these two powerful tools can lead to great results in video understanding, and downstream tasks in natural language. \n Our new API extracts and processes all the multi-modality insights of a video into prompt-ready format, that can be easily used with LLMs. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":7145},"postTime":"2024-01-15T01:22:32.409-08:00","lastPublishTime":"2024-06-06T07:22:08.588-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" UPDATE June 24: The code for applying the video search over Video Indexer Prompt Content is now available at azure-video-indexer-samples/VideoQnA-Demo/README.md at master · Azure-Samples/azure-video-indexer-samples · GitHub. \n \n Have you ever watched an online course and wished you could ask questions on the entire course, or have a comprehensive summary of a video? This can all now be achieved with Azure AI Video Indexer and LLMs (Large Language Model) – powering each other. \n LLMs are powerful language models that can capture the essence of text, allow natural language question-answering and much more. In Azure AI Video Indexer, we understand videos – video content is more than just words, and a single shot can contain a wealth of insights that are critical for its understanding. Coupling these two powerful tools can lead to great results in video understanding and downstream tasks in natural language. \n Our new API extracts and processes all the multi-modality insights of a video into prompt-ready format, that can be easily used with LLMs. \n This API can be used on already-indexed videos in Azure AI Video Indexer, so there is no need to index videos again to create the prompt-ready format of the videos. \n \n Prompt Content for Video Understanding \n Azure AI Video Indexer has a new algorithm that translates the multi-modality content understanding into an LLM’s prompt-ready format, capturing the important details and insights in the video, which then can be used as-is to create LLM prompts for various tasks, including video summarization or search. \n \n \n Figure 1- Our algorithm flow. Starting by capturing the important details and insights in the video using Azure AI Video Indexer (A, B), then splitting the video and its insights into coherent chapters based on visual context (C), and further splitting the sections based on other insights given the LLM prompt’s limitations (D). \n \n Our new algorithm is based on advanced AI models developed in Azure AI Video Indexer. It effectively integrates all three modalities – visual, audio and text – based on the main insights from Azure AI Video Indexer, processes them and transforms them into an LLM’s prompt-ready format. The method consists of the following steps: \n \n Extracting multi-modal insights: As shown in Figure 1, steps A+B create the insights of the video and allow for a full video understanding. However, having all the insights of the video and its transcript as a prompt for an LLM is problematic. First, because of the prompt size. Second, it’s just too much information, and we need to provide the main insights and separations to the LLM in order to get good results. Therefore, we extract the essence from each insight. For example, we eliminate small OCR, filter visual labels, and more. \n Insights’ “tags”: In order to give more context to the LLM that will ease its video understanding and combining all the insights, we create “tags” that guide the LLM on the insights’ roles within the content. These tags include labels such as [OCR], [Transcript], [Audio effects] and more. An example of a section content with several “tags” is shown in Figure 2. \n Chaptering to sections: We split the video and its insights into coherent chapters, that fit both the essence of the video and the prompt size. We use scene segmentation, which is based on visual content (Figure 1C). However, we don't stop there - we also use the other modalities, such as audio and text, to divide the scenes further smartly into smaller sections to work within the limitations of LLMs. Each section fits to a prompt size and contains the content of the video at that time – including the transcript, audio events (such as clapping, dog barking etc.), and visual content (objects in the video, celebrities, and more). Each part in the video is consolidated, and the matching insights are used to create each section (Figure 1D). We determine the length of the sections, ensuring they are not too long for using them as prompts, and not too short for effective and meaningful content. \n \n The final output is shown in Figure 3, and we call it the Prompt Content Json file. \n Overall, our method combines most of all modalities to provide an effective approach to analyze videos’ content with LLMs. We show examples of two use-cases: Video-to-text summarization and searching within the video content. To exemplify the abilities of combining our prompt content with an LLM, we use videos from AKS Edge Essentials tutorials series. \n \n \n Figure 2 – An example of a section’s content, created with our new algorithm and shown in the output Json file from our new API (Figure 3). \n \n \n Figure 3 – An example of the output Json file from our new API. \n \n \n Figure 4 – The flow from video to downstream tasks, starting with a video, extracting its insights with Azure AI Video Indexer and creating prompt content with our new algorithm. The prompt content can be used with any LLM, such as GPT, for tasks such as summarization and question-answering. \n \n Video to Text Summarization \n Video is more than just words. Today’s methods for video summarization rely mostly on the audio part of videos (transcript). Our approach enriches this information with visual and audio data. By combining these three modalities we can gain a better understanding of the video’s content, hence the potential for a better summarization that captures the essence of the video. We use a simple iterative algorithm for summarization that enables us to summarize long videos with rich content. First, we use our Prompt Content for chaptering and creating prompt-ready sections of the video, where each section includes its matching textual, audio and visual insights (as explained above). Then, at each iteration, we summarize the current section and the cumulative summary of the previous sections (given as additional information in the prompt). We can also control the summary “flavor” – whether we want to create an informative summary, a teaser and much more, by enhancing only the prompt of the final section summary. \n Summarization demo \n \n \n Figure 5 – The video summarization flow. We start by using our new API to create the prompt content, that is divided into sections. Then, we use a simple iterative method for summarization using LLM. \n \n For the summarization example, we will use a video that describes the storage architecture of AKS Edge Essentials. It also demonstrates how to create storage classes with local path and NFS, how to create a PVC, and how to test them on Linux. \n \n Informative summary example – made with our prompt content and Chat GPT \n In this video from AKS Edge Essentials, Lior and a guest demonstrate how to use local path and NFS storage classes with AKS Edge Essentials. The guest demonstrates how to create a Persistent Volume Claim (PVC) using the kubectl apply command and tests it with the kubectl exec command. He also shows how to use the Invoke-AksEdgeNodeCommand feature to interact with the node’s operating system and create a local-path-pvc. They explain the use case for using the NFS storage provisioner for multi-node clusters and how to mount the PVC using the deployment YAML file to store data. In conclusion, they explain how to use the volume-test command to view the PVC and demonstrate how to use it for application development. \n \n As video is more than just words, and specifically in this video, an important part is the live demo showing useful commands. The highlighted sentences came from the visual only. \n \n Teaser summary example – made with our prompt content and Chat GPT \n Check out the latest AKS Edge Essentials video with Lior to learn how to deploy an NFS storage class and use it to store data. Follow along as they demonstrate how to set up persistent storage for applications using persistent volumes, persistent volume claims, and storage classes. Don’t miss this informative and educational video! \n \n Searching within a Video Archive with Natural Language \n \n UPDATE June 2024: The code for creating this capability over Video Indexer Prompt Content is now available in azure-video-indexer-samples/VideoQnA-Demo/README.md at master · Azure-Samples/azure-video-indexer-samples · GitHub \n \n Video archives can be huge and not easy to search. We aim to simplify video archive searching and allow question-answering by combining our Prompt Content file with a document archive search technique. This technique typically involves an embedding model, vector store, search engine, and NLP generative model. For video archive, instead of searched “documents”, we search through sections of the videos, created using our Prompt Content algorithm. Here is how we do it for video archives: \n \n Section creation: We create sections from videos using the Prompt Content algorithm, that are based on multi-modality insights of the videos. These sections are used as the searched “documents”. \n Embedding model: For each section we create embeddings using OpenAI’s ada-002 embedding model. \n Storage: Azure AI Search is employed to store the sections’ content and their corresponding embeddings (vectors). \n Search method: Given a query, we use OpenAI’s ada-002 to create its embeddings, and then we search the N most relevant sections by cosine similarity metric between the vectors. \n Answer generation: Finally, with the top N sections, we utilize OpenAI’s Chat-GPT 3.5 Turbo model to generate a natural language answer. \n \n This approach simplifies the process of searching within video archive and finding answers by leveraging well-established techniques from document searching and LLMs. \n Question-answering demo \n \n \n Figure 6 – The search flow, using Azure AI Video Indexer to create the prompt content, Azure AI Search for the search process and LLMs for embeddings and generating a textual answer based on the top search results. \n \n Question-answering examples - AKS Edge Essentials video archive \n Question: What is the PowerShell command to create a JSON template for AKS edge deployment? \n Answer: The command is 'New-AksEdgeConfig -DeploymentType ScalableCluster -NodeType LinuxAndWindows' 1 . \n Citations: 1. AKS Edge Essentials scheme and Azure Arc (0:03:30) \n \n Question: Please summarize the difference between single and multi-machine cluster? \n Answer: Single machine cluster uses internal virtual switch network architecture and all the IP addresses are defined 12 . Scalable cluster uses external virtual switch network architecture and all the IP addresses need to be set up as part of the deployment 12 . When using single machine cluster, NAT is used, whereas scalable cluster connects directly to a physical network 2 . The DNS and proxy settings are the same for both 2 . Multi-machine cluster is used for industrial scenarios where one device can be used as a backup if another device goes down 3 . \n Citations: 1. AKS Edge Essentials network architecture (0:08:31) 2. AKS Edge Essentials network architecture (0:10:30) 3. AKS Edge Essentials Multi Node (0:05:40) \n \n Video Summarization and Question-Answering Demonstrations \n Check out our demonstrations for the downstream tasks using our new algorithm and API: \n \n Question-answering demo \n Summarization demo \n \n See also Azure AI Search documentation | Microsoft Learn \n Want to explore Video Indexer and stay up to date on all releases? Here are some helpful resources: \n \n Use Azure Video Indexer website to access product website and get a free trial experience. \n Visit Azure Video Indexer Developer Portal to learn about our APIs. \n Search the Azure Video Indexer GitHub repository \n Review our product documentation. \n Get to know the recent features using Azure Video Indexer release notes. \n Use Stack overflow community for technical questions. \n To report an issue with Azure Video Indexer (paid account customers) Go to Azure portal Help + support. Create a new support request. Your request will be tracked within SLA. \n Read our recent blogs in Azure Tech Community. \n 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new task-optimized summarization capabilities powered by fine-tuned large-language model","conversation":{"__ref":"Conversation:conversation:3983822"},"id":"message:3983822","revisionNum":9,"uid":3983822,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:336048"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" We are expanding our utilization of LLMs to GPT-3.5 Turbo, along with our proprietary z-Code++ models to offer task-optimized summarization using a balance of output accuracy and cost. \n \n ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5097},"postTime":"2023-11-15T08:55:26.420-08:00","lastPublishTime":"2024-01-10T17:17:29.099-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" For years, developers around the world have relied on pre-built AI capabilities offered through Azure AI Language, ranging from analyzing sentiment, extracting information, mining opinions and much more. Such pre-built capabilities have accelerated AI building efforts for enterprises looking to support users across geographies. Summarization is one such capability, with many customers using it to optimize their AI workflows. For instance, Beiersdorf uses document summarization and key phrase extraction to power an advanced AI platform that harnesses their resources and condensing essential knowledge around cutting-edge skin care solutions (Customer Story HERE). Arthur D Little, similarly uses abstract summarization to unlock the teams’ collective intelligence (Customer Story HERE) . \n \n Today we are thrilled to announce new capabilities that are designed to accelerate customers’ AI workflows allowing them to build summarization use-cases faster than ever before. We are expanding our utilization of LLMs to GPT-3.5 Turbo, along with our proprietary z-Code++ models to offer task-optimized summarization using a balance of output accuracy and cost. \n \n Here are some of the highlights. All the new public previews are available under api-version 2023-11-15-preview. \n \n [in public preview] More out-of-the-box conversation summarization capabilities, e.g. meeting recap and follow-up tasks, in addition to existing capabilities -- narrative, chapter title, issue and resolution. The “recap” aspect condenses a lengthy meeting or conversation into a concise one-paragraph summary to provide a quick overview, and the “follow-up tasks” aspect summarizes out action items and tasks that arise during a meeting. \n [in public preview] Support the native documents formats, including Word, PDF, PowerPoint in addition to plain text. Customers can now input documents directly without the need for conversion. Please read this post for more. \n [in public preview] Summarize articles and reports for a specific point of interest, as opposed to a general summarization that is currently available. For example, to get targeted summaries for this news about Microsoft merging Activision Blizzard , you can specify “Activision Blizzard” or “Early Settlement Date” or other queries as your point of interests. The summary generated will then focus more on these points of interest rather than a general summarization. \n [in public preview] 8 new languages for conversation summarization (namely French, German, Italian, Japanese, Korean, Chinese, Portuguese, Spanish) in addition to English, 2 more languages (namely Hebrew and Polish) for document summarization in addition to those supported with conversations summarization. \n [in public preview] Container solutions with options for connected as well as disconnected, allowing customers to use summarization in more regions and countries beyond what is supported by the cloud offering. For information about container solution, please check on the Summarization Container Blog \n [in public preview] Document Analysis sample flow in Prompt Flow for quick start. In the sample flow we have integrated all building tools for a document analytics use cases where you can translate, PII redact, summarize, extract entities, etc. You can also tailor the flow for your own needs by drag and drop the building tools, even add your own tools if necessary. For more information and getting started, please visit Document Analysis Prompt Flow README. \n Commitment tier pricing making it a cost-effective choice for long-term usage. \n [in public preview] One preview region, Sweden Central, are added for showcasing our latest and continually evolving LLM finetuning techniques, where all summarization capabilities are available. We welcome customers to try it out by creating a resource in this preview region. Your valuable feedback is vital to our continuous enhancement. \n \n \n \n \n \n Document summarization (pre-prompt-engineered, 2 styles, query-focused, length control, 11 Languages, 125K chars, ~ 60 pages, in Prompt Flow, in container) \n \n \n \n \n \n \n \n \n \n \n Conversation summarization (pre-prompt-engineered, 6 aspects, 9 languages, 125K chars, ~3 hours, in container) \n \n \n \n \n \n \n \n \n \n \n We can’t wait to see developers use these new capabilities and remain committed to delivering innovative solutions that enable our customers to achieve their goals. Thank you for your continued trust in our products, and we welcome your feedback as we strive to continuously improve our services. \n For more details and resources, please explore the following links: \n \n Learn more about our summarization solution in our Documentation \n Get started with the summarization container by visiting Documentation \n Explore Azure AI Language and its various capabilities in our \n Access full pricing details on Language Pricing page. \n Try out Language Studio for a code-free experience \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"5031","kudosSumWeight":2,"repliesCount":5,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTgzODIyLTUyODIyOWkxRTI3OUU2NDkyM0Q3RUUw?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTgzODIyLTUyODIyOGlDODQ4RTU5OEUxNjg4MDVD?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTgzODIyLTUyODIzMGlDMjAzMDY1Qzg0NkY4MkUy?revision=9\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTgzODIyLTUyODIzMWkyMUM4Q0EzNTREREY5NzAy?revision=9\"}"}}],"totalCount":4,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3788306":{"__typename":"Conversation","id":"conversation:3788306","topic":{"__typename":"BlogTopicMessage","uid":3788306},"lastPostingActivityTime":"2024-02-13T01:23:08.699-08:00","solved":false},"User:user:1332854":{"__typename":"User","uid":1332854,"login":"HazemElHammamy","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-6.svg?time=0"},"id":"user:1332854"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2OWlFRjg5N0ZEN0I2MDA3OEIx?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2OWlFRjg5N0ZEN0I2MDA3OEIx?revision=3","title":"Suggested Utterances.png","associationType":"TEASER","width":3117,"height":2002,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2OGk5QkM0REZERUExNEUwMzdC?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2OGk5QkM0REZERUExNEUwMzdC?revision=3","title":"Data labelling - CLU.png","associationType":"BODY","width":3122,"height":2020,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2MGlGNDZCQjVCRENBODNCRUM2?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2MGlGNDZCQjVCRENBODNCRUM2?revision=3","title":"Suggest Utterances Pane.png","associationType":"BODY","width":720,"height":2002,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2MWk2QTZEQTIyNDg5RDIzNUJD?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zNzg4MzA2LTQ1Nzg2MWk2QTZEQTIyNDg5RDIzNUJD?revision=3","title":"Suggested Utterances.png","associationType":"BODY","width":3117,"height":2002,"altText":null},"BlogTopicMessage:message:3788306":{"__typename":"BlogTopicMessage","subject":"Accelerate Language Cognitive Services customization with Azure OpenAI","conversation":{"__ref":"Conversation:conversation:3788306"},"id":"message:3788306","revisionNum":3,"uid":3788306,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:1332854"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n \n \n Try out our new data suggestion capabilities in Conversational Language Understanding powered by Azure OpenAI! ","introduction":"","metrics":{"__typename":"MessageMetrics","views":5342},"postTime":"2023-04-05T09:02:40.609-07:00","lastPublishTime":"2023-04-05T09:02:40.609-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" A few weeks ago, we announced the ability to quickly label your Custom NER and Custom text classification projects with generative models. Today, we’re adding more features powered by Azure OpenAI, and introducing utterance suggestions for Conversational Language Understanding. This continues our journey of enhancing Language services with GPT to improve and accelerate our customization experiences. \n \n Conversational Language Understanding allows you to create custom models to detect a user’s intent and extract relevant information from their queries. A significant part of that process is providing data to train machine learning models in the form of utterances that represent what your users say and which intent or entities they belong to. Data is also used to evaluate how accurate your project’s model is when testing against a separate set of examples. Users would have to either create data on the spot, or pull from a source of their data. However, it’s possible that neither option is sufficient to build a comprehensive project. \n \n CLU’s new suggest utterances feature allows you to provide only a few examples to an intent and uses GPT models through Azure OpenAI to recommend similar utterances that can be directly added to your dataset. \n \n To demonstrate, let’s build a new project with CLU and use the new suggest utterances feature. Follow the guide to create a new project in CLU. We’ll build a project related to flight booking requests for an airline. The user intents we’ll build out will be booking new flights or requesting refunds. After adding those intents, we can go to the data labelling page and add the following examples to each intent: \n \n Book Flight:\n \n Book me 2 tickets to Cairo \n I’d like to go from Paris to Montreal \n Are there any flights to Tokyo? \n Get me a plane to Amsterdam on January 3 rd \n I’m interested in going to Beijing first class \n \n \n Request Refund\n \n I’d like a refund for my flight that just got cancelled \n You should issue me a refund as soon as possible \n Why have I not gotten any money back for a flight that was changed last minute? \n Get me a refund for that Tuesday flight please \n I want my money back! \n \n \n \n Make sure to save your changes once you’re done. \n \n \n \n \n Now click on the Suggest utterances button in the middle of the page. This opens a right-hand pane that allows you to select which intent you’d like to generate similar utterances for. \n \n \n \n \n \n To be able to use this feature, you need access to the gated Azure OpenAI resource. You can apply for access here. \n \n Once you’ve gotten access, you should create an Azure OpenAI resource, in the same subscription as your language resource, and a deployment of a GPT model. We recommend using text-davinci-002 or text-davinci-003 for the best results. When you select your Azure OpenAI resource in the suggest utterances pane, you’ll need to click Connect next to the resource to provide access to your Language resource to the Azure OpenAI resource. You may need to add your Language resource the role of Cognitive Services User to your Azure OpenAI resource. \n \n Once connection is complete, go ahead and select the intent you want suggestions for and click on Generate utterances. In a few seconds, you should see suggestions for new utterances show up in your utterance page with a dotted line and an additional note “Generated by AI”. After doing this for both intents, you can now begin reviewing which ones you’d like accept or reject. You’ll notice each suggested utterance has a green accept button or red reject button next to it. It’s up to you to decide which suggestions you’d like to be part of your data. Remember, you decide what qualifies as a good utterance that represents what your users might say! You can also decide to accept all or reject all with the buttons in the toolbar. \n \n \n \n \n In our case, these all look like good suggestions, so we’ll go ahead and accept all of them, and they’ll simply be added to our project. Remember, you have the final decision on whether a suggestion is deemed relevant and accurate to be added to your project’s data, and you must add it to take effect! \n \n You can now repeat this process multiple times, for any intent, and get a project with dozens of more data points within minutes. \n \n We’re excited to continue our path of infusing large language models as part of our authoring experiences to improve productivity and efficiency! 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Redaction and Sanitization with ChatGPT on Azure","conversation":{"__ref":"Conversation:conversation:3765628"},"id":"message:3765628","revisionNum":7,"uid":3765628,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:1002629"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":7206},"postTime":"2023-03-11T12:20:26.309-08:00","lastPublishTime":"2024-01-25T08:43:47.751-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Privacy and Security are the topmost priorities for Consumers and Businesses. Bad actors can steal the information when it is in transit between the systems, when it is being processed or even when archived. Large Language Models have opened doors for more effective ways of sanitizing the documents. In this article I will provide some examples of how to sanitize the documents using ChatGPT on Azure. \n \n What is Document Redaction and Sanitization? \n It is the process of removing or replacing any information that is considered as sensitive, private or confidential. \n \n What types of information needs redaction? \n Following are some sensitive information types that needs redaction: \n Personally Identifiable and Information (PII): Anything information that helps identify a person called needs to be redacted. Examples include people's names, addresses, social security numbers, drivers license etc. \n Protected Health Information(PHI) : Health related information such as patient’s medical records, insurance group numbers, benefit information etc. \n \n Business Confidential Information: Organizational information such as employee records, biometric records, business related secrets, contractual information, financial documents, judicial records etc. \n \n Using ChatGPT on Azure for Redacting and Sanitizing the documents: \n \n Example 1: Sanitize the sensitive information from an Invoice \n The sample invoice I am going to use is in scanned format. As ChatGPT can only take plain text as input currently, we first have to digitalize the invoice. Form Recognizer service is a very effective way of digitizing the scanned documents. \n \n Below is a screenshot from Form Recognizer with the Invoice example: \n \n \n \n After extracting the data from the invoice using Form Recognizer and some cleansing, we will have the plain text something like below: \n \n \n \n You will find the responses below from ChatGPT for my redaction and sanitizing instructions: \n \n Prompt: Show me all the references of PII below: \n \n \n Prompt: redact all references of PII data below: \n \n \n \n Prompt: Replace all occurrences of Contoso with LinkedIn: \n \n \n Prompt: convert all dates to MON-DD-YYYY format. show me only dates: \n \n \n \n \n Example 2: Sanitize information from a Health Insurance Card for PHI data: \n Similar to the above example, I used the Form Recognizer service to extract the information from a health insurance card. \n \n \n \n Below are some examples on how to sanitize the PHI information. \n \n Prompt: show me all references of PHI: \n \n \n Prompt: redact all references of people names below: \n \n \n You can continue with various redaction and sanitization activities in the document such as replacing text, removing text, translating text, converting currencies etc. All the Best! \n \n Note 1: The responses may vary depending on the hyperparameters like Temperature, Top Probabilities etc. \n Note 2: I also encourage you all to try the same prompts on text-davinci-003 model also. 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It effortlessly detects and safeguards crucial information, adhering to the highest standards of data privacy and security. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":10648},"postTime":"2023-11-15T09:10:00.021-08:00","lastPublishTime":"2023-11-15T09:10:00.021-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Azure AI Language’s PII (Personally Identifiable Information) detection service has empowered numerous customers in securing sensitive information. It identifies, extracts, and redacts PII entities, such as person names, addresses, and credit card details, within unstructured text. The service accommodates a diverse array of PII categories, including region-specific information, and supports 79 languages. While our current offerings predominantly accept text as input, we have been actively exploring avenues to expand our capabilities further. \n \n In the current data-driven landscape, safeguarding sensitive information within intricate document structures poses a significant challenge. Conventional methods, involving manual pre-processing and post-processing, are time-consuming and prone to errors, compromising compliance. \n \n Today, we are excited to announce the public preview of native document support for PII detection. This capability can now identify, categorize, and redact sensitive information in unstructured text directly from complex documents, allowing users to ensure data privacy compliance within a streamlined workflow. It effortlessly detects and safeguards crucial information, adhering to the highest standards of data privacy and security. The formats currently supported are .pdf, .docx and .txt. \n \n Here is a glimpse of what this new feature is capable of: \n \n Image caption: Comparison of a source PDF document with PII (left) and the redacted output document (right). \n \n Why use this feature? \n \n Skip the need for pre-processing your documents: Perform PII detection and redaction directly on the documents without the hassle of text extraction. \n Preserve layout and structure: Receive the document as an output in the same format, ensuring the preservation of the original layout and structure. \n Perform multiple tasks in one request: Seamlessly execute multiple tasks spanning various Azure AI Language capabilities within a single request. \n Configure redaction to your needs: Tailor the PII detection and redaction process to your preferences, including selecting entity types to redact and defining characters for masking. \n \n How does this work? \n \n Upload your documents to your Azure blob storage. \n Request the service for PII detection/redaction. \n Check the job status. \n Retrieve the completed results from the storage account. \n \n What’s more: Document-based support extended to Azure AI Language capabilities. \n Alongside native document support for PII detection, customers can now leverage document inputs for summarization: learn more. \n Await further updates as we add native document support for more Azure AI Language skills in the future. \n \n Stay tuned: Available in public preview this December '23 \n Visit our documentation to learn more about how you can integrate this cutting-edge solution into your workflow once it's available. \n \n Have thoughts or questions? We value your feedback! Feel free to share your comments and insights below – we're eager to hear from you. 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Real-Time, Intelligent AI Copilots with Confluent Cloud, Azure OpenAI & Azure Data Services","conversation":{"__ref":"Conversation:conversation:3932183"},"id":"message:3932183","revisionNum":5,"uid":3932183,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:821337"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Real-time application scenarios such as airline booking systems often provide suboptimal user experiences, with customers having to navigate complex systems and rules to perform simple tasks. \n In this blog post, we will explain how to build intelligent agents on top of Confluent, Azure data services and Azure Open AI to deliver real-time insights and help customers conduct business transactions. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":9331},"postTime":"2023-09-25T08:52:50.444-07:00","lastPublishTime":"2023-09-26T15:01:13.667-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" \n The Motivation \n Application scenarios such as airline booking systems often provide suboptimal user experiences, with customers having to navigate complex systems and rules to perform simple tasks. The motivation for creating copilots powered by Azure OpenAI stems from the growing need to revolutionize user experiences in every application scenario, such as airline reservation systems, which often leave customers frustrated due to complex processes and rules necessary to successfully use these systems. By harnessing the power of Confluent Cloud, Azure OpenAI, and Azure Data services, we aim to empower businesses to deploy intelligent agents that provide real-time insights and assistance. These AI-driven copilots will not only streamline complex transactions but also enhance customer interactions, ultimately delivering a seamless and efficient user experience that significantly improves customer satisfaction and loyalty. \n \n The copilot application covered in the reference implementation for this blog post offers a set of powerful capabilities for improving the airline booking experience. It can retrieve current flight details from Azure Cognitive Search, check flight status before and after weather events (streamed via Confluent Cloud), answer questions about airline policies related to flight rescheduling, and perform flight rebooking transactions using Cosmos DB. \n \n Confluent Cloud is now available as an Azure Native ISV Service which enables you to easily provision, manage, and tightly integrate independent software vendor (ISV) software and services on Azure. The Azure Native ISV Service is developed and managed by Microsoft and Confluent. Customers can provision the Confluent Cloud resources through a resource provider named Microsoft.Confluent and can also create and manage Confluent Cloud organization resources through the Azure portal, Azure CLI, or Azure SDKs just like any other Azure 1st party resource. \n \n The copilot’s foundation lies in real-time data streaming with Apache Kafka and Confluent Cloud. Flight status updates, airline policy changes, and real-time weather events are continuously streamed, with the ability to filter and modify data before it's sent downstream to various destinations, including Cosmos DB and Azure Cognitive Search for hybrid and vector-based searches. Confluent allows organizations that are building AI applications to create a real-time knowledge base of all their internal data to make their apps smarter. \n \n To enhance user interactions, the application employs Azure OpenAI embedding APIs via Azure Functions to compute text embeddings and vector representations. An intelligent decision-making component, the Language Model (LLM), processes user questions, determining which functions to execute for data retrieval or remote API calls. \n \n Crucially, the application emphasizes the importance of fresh and up-to-date data for the LLM's effectiveness. Confluent Cloud plays a vital role in facilitating real-time data ingestion, processing, and distribution across Azure services, enabling the Copilot to provide timely and accurate assistance to users. \n \n Overview \n In this blog post, we cover and discuss the following: \n \n The motivation for building copilots powered by Azure OpenAI \n Key characteristics of effective copilots and examples \n How to design, build and maintain your own effective and efficient copilots \n Function calling and RAG architecture with Azure OpenAI GPT Models \n Reference architecture for implementing effective copilots with Confluent and Azure \n \n Key Characteristics of Effective Copilots \n \n \n \n Examples of copilot applications in Github and CRM \n \n AI copilots are intelligent software agents designed to collaborate with and assist humans in various tasks and domains and they possess several key characteristics: \n \n Natural Language Understanding: Copilots have a conversational user interface which enables users to interact with it using natural language the same way that they might when talking to an expert. Copilots have the ability to comprehend and generate human language. They can understand and respond to text or speech inputs, making them effective in interacting with users through conversations. Copilots are powered by foundation models: either large language models (LLM) or large multimodal models (LMM). \n \n Context Awareness: Copilots can understand and maintain context during interactions. They remember previous exchanges and use that information to provide relevant responses or suggestions, ensuring a coherent and personalized experience. \n \n User Assistance and Guidance: Copilots are designed to assist and guide users through complex processes, helping them accomplish tasks more effectively and with reduced errors. \n \n Human-AI Collaboration: Rather than replacing humans, Copilots are intended to collaborate with humans. They enhance human capabilities and productivity, fostering a partnership between humans and AI. \n \n Scalability, Integration and Extensibility: Copilots can scale to handle a wide range of tasks and interactions simultaneously. They can assist many users concurrently, making them valuable in scenarios with high demands for user support. These systems are often designed to integrate seamlessly with existing software and systems. They can be extended through APIs and plugins to work with a variety of applications and platforms. \n \n Learning and Adaptation: AI copilots are capable of learning from data and user interactions. They can adapt to changing circumstances, improve their performance over time, and even anticipate user needs based on historical data. \n \n Task Automation: One of the primary functions of AI copilots is to automate repetitive or time-consuming tasks. They can assist users in completing tasks more efficiently, such as data entry, scheduling, or information retrieval. \n \n Decision Support: These AI copilots can provide recommendations and insights to aid users in making informed decisions. They may analyze data, compare options, and offer suggestions based on predefined criteria or user preferences. \n \n Multi-Modal Capabilities: Copilots can work with various types of data and inputs, including text, speech, images, and other sensory inputs. They can process and generate information in multiple modalities as needed. \n \n In essence, Copilots combine natural language processing, machine learning, and automation to provide intelligent, context-aware assistance, making them valuable assets in various domains, from customer support and healthcare to business operations and beyond. \n \n How to Build Your Own Copilots \n Building a copilot application involves several key steps and considerations for software engineers and architects. Here's a high-level overview of the process: \n \n \n \n \n Define the business flow and features of your copilot: \n Start by clearly defining the problem your copilot application will solve and the specific use case it will address. Understand the needs of your target users and the tasks the copilot will assist with. Define process flow and steps that your copilot needs to follow to achieve the business goals. A best practice is to have a narrow and specialized scope for the business flow with a small number of tasks for a copilot. If your business requires a complex flow, break it down into smaller sub-flows and use multiple copilots under a coordinator agent to implement. \n Define tools and functions that your copilot need to interact: \n To execute tasks, your copilot may need to interact with external systems and tools. This needs to be done via an API layer. For each tool or system, define a wrapper API with description of its purpose and when and how to use it together with its parameters. This needs to be well-aligned with the business flow definition above so that your copilot can pick the tool and use when executing the task. \n Choose the Technology Stack: \n Decide on the programming languages, frameworks, and tools that are suitable for your project. Consider factors like scalability, compatibility, and the availability of relevant libraries and APIs. \n \n Data Collection and Preprocessing: \n Gather the data required to operate your copilot. This could include text data, images, or other relevant information. Ensure data quality and preprocess it as needed, such as cleaning and formatting. This is where Confluent Cloud and Azure Data Services are being leveraged to implement these systems. Confluent cloud has the ability to ingest data in near real time from approximately 200 connectors which allows us to pull real time data from virtually any data source using Kafka connect source connectors, process, enrich or filter it with either Kafka Streams or kSQLDB and then send it to any data store of our choice on Azure using the Kafka connect sink connectors. This is a crucial part of this architecture because it provides the copilot and intelligent AI application with the ability to work with fresh data in environments where the data necessary for enabling the LLMs decisions are constantly changing. \n \n Context Handling: \n Implement mechanisms for handling and maintaining context during user interactions. This allows the copilot to understand user requests and provide relevant responses based on the conversation history. \n \n User Interface (UI): \n Design a user-friendly interface for users to interact with the copilot. This could be a web application, a mobile app, a chatbot interface, or any other suitable platform. \n \n Security and Privacy: \n Implement security measures to protect user data and privacy. Ensure compliance with data protection regulations and best practices in cybersecurity. \n \n Testing and Evaluation: \n Rigorously test your copilot application to identify and fix bugs, errors, and performance issues. Evaluate the copilot's accuracy, responsiveness, and usability. \n \n Deployment: \n Deploy your copilot application to a production environment, whether on the cloud or on-premises. Ensure scalability to handle user traffic and monitor system performance. \n User Feedback and Iteration: \n Gather user feedback and continuously improve your copilot based on user experiences and evolving requirements. This may involve retraining the model with new data. \n \n Maintenance and Updates: \n Maintain the copilot application by addressing any issues that arise and keeping software dependencies up-to-date. Regularly update your models to improve performance. \n \n Documentation: \n Create comprehensive documentation for developers, administrators, and end-users to understand and use your copilot effectively. \n \n Scalability and Extensibility: \n Plan for scalability to handle increased user loads, and consider how the copilot can be extended to support additional use cases or integrate with other systems. \n \n Monitoring and Analytics: \n Implement monitoring and analytics tools to track system performance, user interactions, and other relevant metrics. This data can inform future enhancements. \n \n Compliance and Regulations: \n Ensure that your copilot application complies with legal and regulatory requirements, especially if it handles sensitive data or operates in regulated industries. Building a copilot application is a complex endeavor that requires expertise in machine learning, software development, user experience design, and domain-specific knowledge. Collaboration with cross-functional teams and continuous improvement based on user feedback are essential to create a successful copilot application that meets user needs and delivers value. \n \n Responsible AI Practices \n Building responsible artificial intelligence platforms requires following a pattern that involves asking teams to Identify, Measure, and Mitigate potential harms, and have a plan for how to Operate the AI system as well. In alignment with those practices, these recommendations are organized into the following four stages: \n \n Identify: Identify and prioritize potential harms that could result from your AI system through iterative red-teaming, stress-testing, and analysis. \n Measure: Measure the frequency and severity of those harms by establishing clear metrics, creating measurement test sets, and completing iterative, systematic testing (both manual and automated). \n Mitigate : Mitigate harms by implementing tools and strategies such as prompt engineering and using our content filters. Repeat measurement to test effectiveness after implementing mitigations. \n Operate : Define and execute a deployment and operational readiness plan. \n \n \n Function calling with Azure Open AI GPT 0613 models \n Function calling is an important feature introduced at version 0613 of GPT-4 and GPT-3.5-Turbo models allowing LLMs to interact with external tools and systems. Function calling makes it possible to implement this copilot concept because of the ability to both follow a business flow and use one or multiple functions to execute the flow. \n Implementation of copilot with function calling \n The typical flow to build a copilot by leveraging function calling of Azure OpenAI involves the following key considerations and steps: \n \n Defining the flow and persona \n Defining the function specifications \n Checking if the model needs to use one of the defined functions \n Calling the function or functions if applicable to perform and action \n Retrieving the data from the function and then injecting into the prompt to the LLM \n Retrieving the response from the LLM and sending it to the user \n \n \n 1: Define the Flow and Persona \n \n \n 2: Define the Function Specs \n \n \n \n Overview of Execution Sequence and Interactions \n \n \n Reference implementation of Copilots for Real Time Scenarios \n \n \n \n Summary of Flight Copilot Capabilities: \n \n Retrieval of current flight details from Azure Cognitive Search \n Check flight status before and after an impacting weather event (the weather events are being streamed into Kafka via Confluent Cloud to demonstrate real time updates from remote APIs) \n Question answering regarding airline policies applicable specifically to the passenger’s situation of flight rescheduling. \n Perform flight rebooking transaction against the Cosmos DB datastore \n \n \n In this reference implementation, flight status updates, airline policies changes and real time weather events are constantly being streamed into various Kafka topics on Confluent Cloud. Once the data arrives at the Kafka topics, these events can be filtered, enriched or modified prior to being sent downstream to other topics on Confluent Cloud. These events are then being pushed to the Cosmos DB datastore that powers the booking services our Copilot application is going to leverage later on. \n Some of the data is being sent directly to datastores that the copilot functions will be calling but some of it is also sent to Azure Cognitive Search vector store for hybrid and vector search from within the copilot application. The text content of the documents needs to be sent to Azure OpenAI embedding APIs via Azure Functions to compute the embeddings and vector representations for the text chunks. \n \n Once the vector store (Azure Cognitive Search) and the document store (Cosmos DB) have all the data necessary to power the copilot app, the users can now send questions and queries to the copilot application hosted as an Azure Webapp. This chatbot app receives the questions and sends them to the LLM which analyzes the questions to determine the relevant function that needs to be called to provide the necessary capabilities of either retrieving data or executing remote APIs for CRUD operations. \n \n Finally, it is important to highlight the fact that without useful and valid data the LLM used to power the copilot or intelligent app would not be effective at the tasks and having fresh and up-to-date data is vital and crucial for this to be a reality. Confluent Cloud makes this possible by providing the connectors that allow us to bring in real time data from any source, process it with Kafka streams or KSQLDB and push it to any destination on Azure almost instantaneously. \n \n References \n \n Flight copilot demo repo: OpenAIWorkshop/scenarios/incubations/copilot/realtime_streaming at confluent · microsoft/OpenAIWorkshop (github.com) \n https://learn.microsoft.com/en-us/azure/partner-solutions/apache-kafka-confluent-cloud/overview \n https://learn.microsoft.com/en-us/azure/ai-services/openai/overview \n https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/function-calling \n https://www.confluent.io/hub/ \n https://learn.microsoft.com/en-us/legal/cognitive-services/openai/overview \n https://learn.microsoft.com/en-us/azure/cosmos-db/introduction \n https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"17242","kudosSumWeight":1,"repliesCount":0,"readOnly":true,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkyOWkyRkVGRjlDMzc2NjYyODA3?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkzMGlFRTUyQzc5MzI2NzFERjQ5?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkyOGlCRkQ1MENDNjM5MTc4RUVD?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkzM2k2OEMxMjZGRTVENEREMjMz?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkzMmk5NzgyNzY0MEQ2NDREOUQ1?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkzMWlDNTE1Njg2ODNCNjM0MEM3?revision=5\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDc","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zOTMyMTgzLTUxMDkzNGk1MjM1MDlEMjZGOEQyMzU2?revision=5\"}"}}],"totalCount":7,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3826719":{"__typename":"Conversation","id":"conversation:3826719","topic":{"__typename":"BlogTopicMessage","uid":3826719},"lastPostingActivityTime":"2023-05-31T21:34:40.128-07:00","solved":false},"User:user:968559":{"__typename":"User","uid":968559,"login":"Krishna_Doss","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/dS05Njg1NTktNTExMDk0aUQxMDIyOEREMTlDNzhEOEI"},"id":"user:968559"},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjA3MWkzNEI0MERENDk2NDYzQzVC?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjA3MWkzNEI0MERENDk2NDYzQzVC?revision=3","title":"Krishna_Doss_0-1684701442025.png","associationType":"BODY","width":975,"height":548,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjgwNWlGQjc2QUNENUI0QkZGMjNB?revision=3\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjgwNWlGQjc2QUNENUI0QkZGMjNB?revision=3","title":"Krishna_Doss_0-1684817763568.png","associationType":"BODY","width":1884,"height":1267,"altText":null},"BlogTopicMessage:message:3826719":{"__typename":"BlogTopicMessage","subject":"Microsoft Translator Plugin for Azure OpenAI & Tools for PromptFlow","conversation":{"__ref":"Conversation:conversation:3826719"},"id":"message:3826719","revisionNum":3,"uid":3826719,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:968559"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Microsoft Translator Plugin for Azure OpenAI allows users to translate a source text between any of the 125+ languages and dialects supported by Translator service using either general or custom translation models. Translator and Language Detect tools for PromptFlow helps you to get better responses for various prompts submitted to GPT in non-English languages. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":13344},"postTime":"2023-05-23T09:00:00.035-07:00","lastPublishTime":"2023-05-23T09:00:00.035-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Seattle—May 23, 2023—Today, Microsoft Azure Cognitive Services (ACS) Translator announces a) ‘Microsoft Translator’ plugin for Azure OpenAI, and b) Translator and Language Detect tools for PromptFlow in private preview. \n \n ‘Microsoft Translator’ plugin for Azure OpenAI \n On customer prompts requiring translation, OpenAI GPT models do good general domain translation among languages which have good web presence. However, it doesn’t provide a simple way to get translation customized for your organization and domain terminologies. Also, GPT models have limited language coverage and translation accuracy drops for low resource languages. \n \n Microsoft Translator Plugin for Azure OpenAI allows users to translate a source text between any of the 125+ languages and dialects supported by Translator service. The plugin will also get you customized translation by using custom machine translation model which you have built and published using Azure AI Custom Translator portal. Using Microsoft Translator plugin for AOAI you could have uniform translation experience across your organization, when you are translating with any of your application built with AOAI API, ACS Translator API, or any of Microsoft products. \n \n You could configure the Microsoft Translator plugin with your own Azure Translator resource and credentials. Whenever the user sends a prompt request to AOAI, the GPT model will decide and invoke the Microsoft Translator plug-in as required. \n \n In the example below, GPT understands that the prompt asked to complete the task in ‘Tamil’ language. Hence it invokes Microsoft Translator plugin and gets the response translated. \n \n \n \n Translator and Language Detect tools for PromptFlow \n Translator and Language Detect tools for PromptFlow helps you to get better responses for various prompts submitted to GPT in non-English languages. You need to configure your Azure Translator resource in PromptFlow to enable this ability. A typical App workflow is as follows: \n \n Preprocess the prompt before sending it to GPT. \n Preprocessing could include detecting the language of prompt text and translating it into English if required. Optionally determine what content of the prompt text requires translation. \n Submit the pre-processed prompt to GPT. \n Post process the response from GPT before returning final response. \n Post processing could include translation of GPT response into the original language of the prompt. \n \n In the example below, the language of the prompt is detected. On detecting the language as Tamil, the prompt is translated into English before making a GPT call. The response from GPT is translated back from English to Tamil. \n \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2757","kudosSumWeight":5,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjA3MWkzNEI0MERENDk2NDYzQzVC?revision=3\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuMXwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI2NzE5LTQ3MjgwNWlGQjc2QUNENUI0QkZGMjNB?revision=3\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3828842":{"__typename":"Conversation","id":"conversation:3828842","topic":{"__typename":"BlogTopicMessage","uid":3828842},"lastPostingActivityTime":"2023-05-23T11:22:34.379-07:00","solved":false},"BlogTopicMessage:message:3828842":{"__typename":"BlogTopicMessage","subject":"What’s new Azure AI Language | BUILD 2023","conversation":{"__ref":"Conversation:conversation:3828842"},"id":"message:3828842","revisionNum":4,"uid":3828842,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:336048"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" We are announcing more new features and capabilities: General Availability (GA) of document and conversation summarization, and public preview of the following features: interactive custom summarization powered by Z-Code++ and Azure OpenAI Service, custom sentiment analysis, enhanced Text Analytics for health, Named Entity Recognition (NER) and Personal Identifiable Information (PII) Detection, increased language coverage, enhanced integration capability, and expanded container support. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":6778},"postTime":"2023-05-23T08:53:50.968-07:00","lastPublishTime":"2023-05-23T11:22:34.379-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n At Azure AI Language (aka. Azure Cognitive Service for Language), we believe that language is at the core of human intelligence. In line with Microsoft’s mission to empower every person and every organization on the planet to achieve more, we are dedicated to providing natural language processing services that break down language barriers. Azure AI Language products are powered by state-of-the-art language models, including Z-Code++ and Azure OpenAI Service, empowering you to retrieve and analyze meaningful insights from a full range of text content. Join us as we explore the latest developments in our language AI products and how they can transform the way you work with text data. \n \n Today we're thrilled to announce more new features and capabilities designed to make your workflow more seamless and efficient than ever before at this year’s Microsoft Build: General Availability (GA) of document and conversation summarization, and public preview of the following features: interactive custom summarization powered by Z-Code++ and Azure OpenAI Service, custom sentiment analysis, enhanced Text Analytics for health, Named Entity Recognition (NER) and Personal Identifiable Information (PII) Detection, increased language coverage, enhanced integration capability, and expanded container support. \n \n \n \n Here are some of the highlights. \n General Availability (GA) of document and conversation summarization \n Since the launch of document and conversation summarization in public preview at Build and Ignite 2022, customers across industries have been utilizing these features to effectively extract and condense essential information from their documents and conversations. With this GA announcement, customers can now confidently implement use-cases in their production environments, with Azure SLA and enterprise-grade security in mind. \n \n This GA encompasses various summarization capabilities and will go live on June 19th: \n Extractive and abstractive document summarizations – These features provide different styles of summarization that users can choose from, allowing them either to preserve the original text by extracting key sentences directly from the input (extractive) or to receive a concise (TL;DR) summary based on an understanding of the input (abstractive). \n \n Conversation summarization – This feature supports four aspects (issue, resolution, chapter title, and narrative) which are specially tailored for conversational content. These features provide useful conversational artifacts in addition to textual summarization that help users locate relevant contexts. \n \n The issue and resolution summarizations are designed to meet the needs of, for instance, customer support and call centers, and aim to summarize customer-agent conversations by identifying key customer issue and relevant resolutions. \n The chapter title and narrative summarizations are more general-purpose and can be used for conversations with multiple participants. The chapter title feature segments a conversation into sections with each section headed by a title, while the narrative generates a summary for each segment. For each summary/title, there is a \"context\" field that displays the range of the input conversation from which the respective element was generated. \n \n \n Public Preview of interactive custom summarization powered by Z-Code++ and Azure OpenAI Service \n At Azure AI Language, we understand that every customer has unique needs when it comes to language processing. That is why we have developed a powerful customization capability that allows you to tailor our products to your specific requirements. With custom features, you can create custom models that extract, analyze, and retrieve meaningful insights for business cases. \n \n To make the customization process even easier and more accessible, we introduced auto labelling to significantly reduce the complexity and time-consumption of data labelling efforts. In March, we introduced an accelerated auto labelling experience powered by Azure OpenAI Service for custom named entity recognition (NER), custom text classification, and conversational language understanding (CLU). You can find more details in the blog posts published at the time, custom NER and custom text classification's post and CLU's post. \n \n Today, we are excited to announce this accelerated auto labeling experience is expanding to support custom summarization, which will go live on June 1 st . With this new addition, you have even more power to fine-tune summarization models. \n \n Public Preview of interactive custom sentiment analysis \n Today, sentiment analysis helps you understand a person’s sentiment within text using a collection of machine learning and AI algorithms. However, identifying user sentiment can be unique depending on the specific domain and context. To address this, we’re excited to announce that our new, interactive custom sentiment analysis feature will be available in public preview on June 1 st . With this new feature, you can train your own AI model tailored to your users and your business needs. Using our new auto-labelling experience in the Language Studio, you can upload your datasets and label them for training and testing your custom model. The new auto-labeling experience is powered by our prebuilt sentiment analysis model. Within our prebuilt model, we are pleased to introduce a suite of AI quality updates, enhanced with model training on both conversation and document data for English and Italian. \n \n Public Preview of enhanced Text Analytics for Health \n Text Analytics for health helps you extract and label relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records. \n In this release, we are previewing several new capabilities: \n \n New entity categories for social determinants of health \n Support for relationship extraction and assertion detection for the new social determinants of health entity categories \n New assertion type for temporal context indicating whether an entity is from past, present, or future. \n \n Custom Text Analytics for health is the custom version of Text Analytics for health, which allows you to take advantage of state-of-the-art machine learning models to develop a custom entity extraction model tailored to your unique healthcare domain data. Your training and testing data uploaded for this purpose will be stored in Azure Blob Storage linked to your Custom Text Analytics for health project. The new feature uses all the prebuilt entity categories from Text Analytics for Health and allows you to extend these categories with your own custom vocabulary. To learn more about this exciting new offering, be sure to check out our custom Text Analytics for Health post. \n \n Public Preview of enhancements to Named Entity Recognition and Personally Identifiable Information Detection \n Named Entity Recognition (NER) and Personally Identifiable Information (PII) Detection are getting the following of improvements in terms of model quality, extraction granularity, and API response customization. \n NER will allow you to control the response returning from the service by letting you specify a list of entities to selectively include or exclude from the service’s response. Additionally, you can now decide the service’s overlap policy which determines how overlapping entities (entities occurring in the same span) should be handled. For example, the address “143 Rodeo Drive” has 2 entities: the number “143” and the address “143 Rodeo Drive” overlapping each other. You can decide if you allow overlap or should match the longest possible span of entities. The former policy will return the Number entity in “143” along with the full Address “143 Rodeo Drive” entity, predicting all the possible entities in a response; the latter will return the entity with the longest span. The default behavior is the longest span. \n Predicted entities now come with metadata and tags. Metadata includes any additional information for an entity, such as an ISO date resolution for “Jan 3rd\" as “2023-01-03\". Tags provide more granular identifiers to an entity. These include identifying if a location is a City, State, Country, Continent, or Airport. To learn more, please read our NER documentation \n The set of languages is now 94 in NER. \n \n The new PII Detection preview model offers a better way to identify financial entities in text, such as SWIFT code, IBAN, ABA Routing number and Credit Card Number. Unlike the previous rule-based approach, the new model uses AI technology to detect these entities using their context rather than reliance on regular expressions. Other enhancements exclusion list, and customizable redaction character are possible on PII. \n The Conversational PII has also been updated with 4 new languages, an exclusion list support, and customizable redaction character. \n \n Public Preview of improvements in integration capability \n We’re always looking to expand the interoperability of capabilities within Azure Cognitive Services for Language with other Microsoft products, to deliver more seamless experiences to customers. With this in mind, we are excited to announce preview features that significantly improve our integration capabilities: \n \n Language Studio's data labelling is now fully compatible with Azure Machine Learning, making it easier for you to incorporate data labelling into your workflow. Please read our Language Studio and Azure Machine Learning blog post to learn more. This compatibility improvement allows you to use the labeling experience in Azure Machine Learning Studio for easier collaboration with more flexibility and enables you to outsource labeling tasks to external labeling vendors through the Azure Marketplace. \n Additionally, we've integrated conversational language understanding (CLU) with Power Virtual Agent (PVA), simplifying the process of creating intelligent chatbots and virtual agents. Please read our PVA blog post for more details. This improvement enables you to use Conversational Language Understanding (CLU) to get higher quality of intent triggering and entity extraction with multilingual support, then build your bot with dialog flows in low-code/no-code by connecting your Power Virtual Agents (PVA) with CLU. \n \n These enhancements provide more seamless experiences for our customers, allowing you to create even more powerful solutions with our products. \n \n Public preview of expanded language support \n We understand the importance of catering to a global audience. Regardless of whether you are a multinational corporation or a startup, understanding your customers and effectively delivering your solutions requires you to process and comprehend customer inputs in a variety of languages. This is why we have taken steps to expand our language support, providing you with tools to connect users around the world. Our expanded language support encompasses multiple language skills. \n \n Document summarization, both extractive and abstractive, supports 9 languages \n Named entity recognition supports 94 (with 55 new) languages \n Personally Identifiable Information (PII) Detection supports 94 languages for names and addresses, with significantly improved AI quality across multiple entities (including financial) \n Conversation PII Detection supports 4 ( with 3 new) languages \n Language Detection supports 120 (with 6 new) languages and 12 Romanized Indic languages \n \n \n Public preview of connected container for custom Named Entity Recognition \n Finally, we're excited to announce the expansion of connected containers to now support custom Named Entity Recognition (NER)! With this feature, you can manage and deploy your custom named entity recognition model in a containerized environment, making it simpler and more efficient to maintain your infrastructure. To learn more, please read our custom NER connected container post. \n \n We look forward to seeing customers use these capabilities to enhance productivity and decision-making, and we remain committed to delivering innovative solutions that enable our customers to achieve their goals. Thank you for your continued trust in our products, and we welcome your feedback as we strive to continuously improve our services. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"12512","kudosSumWeight":5,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PWlHRVIybVhudE5rfDB8MjU7MjV8fA","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=iGER2mXntNk","thumbnail":"https://i.ytimg.com/vi/iGER2mXntNk/hqdefault.jpg","uploading":false,"height":225,"width":400,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3826717":{"__typename":"Conversation","id":"conversation:3826717","topic":{"__typename":"BlogTopicMessage","uid":3826717},"lastPostingActivityTime":"2023-05-23T09:00:00.041-07:00","solved":false},"User:user:109248":{"__typename":"User","uid":109248,"login":"Charan Mohan","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/m_assets/avatars/default/avatar-7.svg?time=0"},"id":"user:109248"},"BlogTopicMessage:message:3826717":{"__typename":"BlogTopicMessage","subject":"Microsoft Azure Text Translation SDK now in Public Preview","conversation":{"__ref":"Conversation:conversation:3826717"},"id":"message:3826717","revisionNum":25,"uid":3826717,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:109248"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Microsoft announces the public preview of its Azure Text Translation SDK designed to help developers easily add text translation capabilities to their applications. In this post, we'll take a closer look at what the Azure Text Translation SDK is, how it works, and how developers can use it. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":3836},"postTime":"2023-05-23T09:00:00.041-07:00","lastPublishTime":"2023-05-23T09:00:00.041-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" The Azure Text Translation SDK is a cloud-based REST API feature of the Translator service that uses neural machine translation. It enables quick and accurate source-to-target text translation across all supported languages in real-time. The SDK simplifies the developer experience and reduces the need to assimilate API definitions. \n \n The Azure Text Translation SDK offers several key benefits for developers, including- \n - Quick and accurate translations in real-time across all supported languages. \n - Easy to integrate with your application or workflow. \n - Customization options for your specific needs. \n - Scalable in handling a large volume of text for translation. \n \n The client libraries provided in the section below enable developers to \n \n Return a list of languages supported by Translate, Transliterate, and Dictionary operations. \n Render a single source-language text to multiple target languages in a single request. \n Convert text of a source language to different scripts. \n Return equivalent words for the source term in the target language. \n \n Client Libraries and details \n We are now announcing the public previews for client libraries across .NET, Java, JavaScript, and Python. But before we dive in let's take a quick look at some of the key concepts. \n \n Key concepts \n \n TextTranslationClient \n A TextTranslationClient is a primary interface for developers using the Text Translation client library. It provides both synchronous and asynchronous operations to make calls, such as getting supported languages and translating text. \n \n Input \n A text element (string) is a single unit of input to be processed by the translation models in the Translator service. Operations on TextTranslationClient may take a single text element or a collection of text elements. For text element length limits, maximum requests size, and supported text encoding refer to https://learn.microsoft.com/azure/cognitive-services/translator/request-limits \n \n Return value \n Return values, such as Response<IReadOnlyList<TranslatedTextItem>>, result from a Text Translation operation. It contains an array with one result for each string in the input array. A request return value also may include additional information about the input text element (for example source language when auto-detect is used). \n \n Thread safety \n \n Typically, all client instance methods are thread-safe and independent of each other (guideline). This ensures that the recommendation of reusing client instances is safe, even across threads. \n \n Additional concepts \n Client options | Accessing the response | Long-running operations | Handling failures | Diagnostics | Mocking | Client lifetime \n \n Getting started with .NET \n The following section provides several code snippets for a specific user scenario and covers the main features present in this client library. Although most of the snippets below make use of asynchronous service calls, keep in mind that Azure. AI.Translation.Text package supports both synchronous and asynchronous APIs. \n \n Scenario \n As a developer for a retail company, say your goal is to localize content in a product catalog. In this case, the company wants to expand its global reach and therefore sell products in multiple markets. As a developer solving this business case, you will need to ensure that the product descriptions and other content are translated accurately and appropriately for each language the market supports. Moreover, there could be a requirement for some product names in the catalog to remain as-is or be presented differently in the target language. Text translation SDK provides the capability to enable this business scenario. Follow the steps below to develop your solution. \n \n Prerequisites Completion \n You need an active Azure subscription and an existing or new Translator service / Cognitive service resource to request translations from our general domain. You can follow the instructions in our online documentation to successfully create an Azure translator resource. You can also use create a Cognitive Services resource using the Azure CLI to create the cognitive resources using the Azure CLI. \n \n Note - Text translation supports resource creation for multi-service and single-service access. Create a Cognitive Services resource if you plan to access multiple Cognitive Services under a single endpoint and API key. To access the features of the Text translation service only, create a Text translation service resource. \n \n Authenticate the client \n Interaction with the service using the client library begins with creating an instance of the TextTranslationClient class. You will need an API key or TokenCredential to instantiate a client object. For more information regarding authentication options with Cognitive Services, see Authenticate Requests to Translator Service. \n \n Get an API key \n You can get the endpoint, API key, and Region from the Cognitive Services resource or Translator service resource information in the Azure Portal. \n Alternatively, use the Azure CLI snippet below to get the API key from the Translator service resource. \n \n \n \n \n \n az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name> \n \n \n \n Create a TextTranslationClient using an API key and Region credential. Once you have the value for the API key and Region, create an AzureKeyCredential. This will allow you to update the API key without creating a new client. \n With the value of the endpoint, AzureKeyCredential, and a Region, you can create the TextTranslationClient. \n Create a Text Translation Client \n Firstly, install the package the Azure Text Translation client library for .NET with NuGet \n \n \n \n dotnet add package Azure.AI.Translation.Text –prerelease \n \n \n Create a TextTranslationClient using an API key from the Global Text Translator resource. \n Once you have the value for the API key, create an AzureKeyCredential. With the value of the AzureKeyCredential, you can create the TextTranslationClient \n \n \n \n \n AzureKeyCredential credential = new(\"<apiKey>\");\nTextTranslationClient client = new(credential);\n \n \n \n \n Replace <apiKey> with a value created in Creating Cognitive Services resource. \n \n There are also other ways to create a text translation client such as using - \n \n API key and Region credential \n Custom Subdomain and API Key \n Token Authentication \n \n For How-to, refer to creating a Text Translation client. \n \n Translate \n Once you have set up the Text translation client, we can now complete the localization use case by translating the input product catalog text into various languages. For this use case, let’s look at an example code on how to translate text into multiple target languages. You can provide multiple target languages which results in the input element being translated to all those target languages. You can specify the input text language or let the service auto-detect it. \n \n \n try\n{\n IEnumerable<string> targetLanguages = new[] { \"cs\", \"es\", \"de\"};\n IEnumerable<string> inputTextElements = new[]\n {\n \"This is a test.\"\n };\n\n Response<IReadOnlyList<TranslatedTextItem>> response = await client.TranslateAsync(targetLanguages, inputTextElements).ConfigureAwait(false);\n IReadOnlyList<TranslatedTextItem> translations = response.Value;\n\n foreach (TranslatedTextItem translation in translations)\n {\n Console.WriteLine($\"Detected languages of the input text: {translation?.DetectedLanguage?.Language} with score: {translation?.DetectedLanguage?.Score}.\");\n\n Console.WriteLine($\"Text was translated to: '{translation?.Translations?.FirstOrDefault().To}' and the result is: '{translation?.Translations?.FirstOrDefault()?.Text}'.\");\n }\n}\ncatch (RequestFailedException exception)\n{\n Console.WriteLine($\"Error Code: {exception.ErrorCode}\");\n Console.WriteLine($\"Message: {exception.Message}\");\n}\n \n \n \n \n You now have a working Text translation client! \n \n Note - For the above product catalog localization use-case, the translation workflow can also include Transliterating certain product names or validating if the target language is supported (supported languages). More capabilities can be leveraged depending on the intended translation scenarios. \n \n Other capabilities are listed below for your reference: \n \n Translate Text \n Translate with Auto detection of input text language. \n Translate with transliteration. \n Translate multiple text inputs \n Translate multiple target languages \n Translate different text types. \n Non-Translation of Entities names in a text \n Apply dictionary for a specific entity name in a text \n Profanity handling \n Include alignment in translation (some languages supported for research) \n Translate using sentence length \n Translate with your custom models. \n \n You can learn more about them in Translate text samples. \n \n Dictionary Lookup \n Once you have a working text translation client that can detect the language of the input text and translate it across multiple languages, you might need to ensure that certain product names in the catalog are accurately returned for the target language. To achieve this, we can leverage the dictionary lookup. \n \n \n \n \n try\n{\n string sourceLanguage = \"en\";\n string targetLanguage = \"es\";\n IEnumerable<string> inputTextElements = new[]\n {\n \"fly\"\n };\n\n Response<IReadOnlyList<DictionaryLookupItem>> response = await client.LookupDictionaryEntriesAsync(sourceLanguage, targetLanguage, inputTextElements).ConfigureAwait(false);\n IReadOnlyList<DictionaryLookupItem> dictionaryEntries = response.Value;\n DictionaryLookupItem dictionaryEntry = dictionaryEntries.FirstOrDefault();\n\n Console.WriteLine($\"For the given input {dictionaryEntry?.Translations?.Count} entries were found in the dictionary.\");\n Console.WriteLine($\"First entry: '{dictionaryEntry?.Translations?.FirstOrDefault()?.DisplayTarget}', confidence: {dictionaryEntry?.Translations?.FirstOrDefault()?.Confidence}.\");\n\n}\ncatch (RequestFailedException exception)\n{\n Console.WriteLine($\"Error Code: {exception.ErrorCode}\");\n Console.WriteLine($\"Message: {exception.Message}\");\n}\n \n \n \n \n \n You have now completed the localization use case for a retail client involving the translation of product catalog entities to multiple Target languages. However, while doing so you also ensured that for certain specific product names, you adhered to the business pre-determined translation via Dictionary lookup. \n \n Other Client Libraries \n The same use case above can be implemented in Python, JavaScript, and Java apart from .Net. You can use the respective SDKs to bootstrap your development. \n \n In summary, the Microsoft Azure Text Translation SDK is a powerful tool that allows developers to easily add text translation capabilities to their applications. With support for multiple languages to meet your market need, an easy-to-use API, and scalable cloud infrastructure, the SDK offers a range of benefits for developers looking to integrate text translation capabilities into their business solutions. \n Following the steps outlined in this post, developers can quickly get started with the Azure Text Translation SDK to build translation solutions that reflect their business, industry, and domain-specific terminology and style. These solutions seamlessly integrate into your existing applications, workflows, and websites. \n \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"12141","kudosSumWeight":0,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"coverImage":null,"coverImageProperties":{"__typename":"CoverImageProperties","style":"STANDARD","titlePosition":"BOTTOM","altText":""}},"Conversation:conversation:3825711":{"__typename":"Conversation","id":"conversation:3825711","topic":{"__typename":"BlogTopicMessage","uid":3825711},"lastPostingActivityTime":"2023-05-23T08:30:00.053-07:00","solved":false},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI1NzExLTQ3MTYyN2k5Mzg1MjU3OEIzMzk2NUND?revision=13\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI1NzExLTQ3MTYyN2k5Mzg1MjU3OEIzMzk2NUND?revision=13","title":"MicrosoftTeams-image (1).png","associationType":"TEASER","width":1920,"height":1080,"altText":null},"AssociatedImage:{\"url\":\"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI1NzExLTQ3MTYzM2k4M0QwMTk0REREMkI4OTA3?revision=13\"}":{"__typename":"AssociatedImage","url":"https://techcommunity.microsoft.com/t5/s/gxcuf89792/images/bS0zODI1NzExLTQ3MTYzM2k4M0QwMTk0REREMkI4OTA3?revision=13","title":"Drawing1.png","associationType":"BODY","width":3376,"height":553,"altText":null},"BlogTopicMessage:message:3825711":{"__typename":"BlogTopicMessage","subject":"Document Translation now available in the Language Studio","conversation":{"__ref":"Conversation:conversation:3825711"},"id":"message:3825711","revisionNum":13,"uid":3825711,"depth":0,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"author":{"__ref":"User:user:1869077"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" \n This blog introduces the Document Translation feature in the Language Studio, an UI interface that will let you translate documents using the Azure Cognitive Services Translator service. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":6182},"postTime":"2023-05-23T08:30:00.053-07:00","lastPublishTime":"2023-05-23T08:30:00.053-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introducing Document Translation in Language Studio - a user-friendly UI experience designed to simplify the process of translating rich documents. With Document Translation in Language Studio, users can take advantage of the powerful Azure Translator service through a streamlined interface. \n \n Since it is a web app, users can access the feature across multiple operating systems & browsers, making it a convenient and efficient solution for document translation needs. Plus, with Azure Active Directory (AAD) authentication and the ability to use existing Azure accounts and translator resources, users can scale and control usage with secure data handling. Enterprises can adopt this ready-to-use UI consumption experience across their organization by simply authorizing specific employees or employee groups to access the Translator resource. \n \n In addition, the feature allows users to translate documents directly from the local storage. This hides the complexity of uploading the source documents to cloud storage before translation. The locally uploaded documents are stored within the user’s configured storage account, ensuring their preferences on regional data residency, privacy and security are met. Likewise, the translated documents could be either downloaded to your local system or stored in Azure blob storage. \n \n For more customized translation results, optional settings include the use of custom translation models for context-specific translations or glossaries to enforce and skip translation. This flexibility allows customers to tailor the translation service to their specific needs. \n \n How to use this experience? \n \n Prerequisites: \n The experience requires the users to complete a one-time configuration to use the experience. Users need two Azure resources: \n \n Translator resource. \n Azure storage account. \n \n Once you have the above two, configure the following: \n Step 1: Enable system-assigned managed identity on the Translator resource. \n Step 2: Add ‘Storage blob Data Contributor’ role to the Translator resource on the Storage account. \n \n Steps to use the experience: \n \n Sign into the language studio using Azure credentials. \n Configure by choosing Translator resource & Azure Storage account. \n Select source & target language(s). \n Choose the source document(s) from your local file system or blob storage. \n Choose the destination for the translated files. \n Translate! \n \n Demo (one-time configuration & the user experience): \n \n \n \n For more information: Documentation 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