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UHRS
Hi all, I'm Alex and new to this space, Im currently working on the uhrs platform, was looking at a way to contact the admin and found through. I have experience in Ai of self driven cars and human robots from a common vendor, Remotasks. Id be greatful if I get a welcome and a small introduction of this space and what you guys talk about...Thanks in advance .Welcome to the Azure Cognitive Services - Speech AMA!
Welcome to the Azure Cognitive Services - Speech Ask Microsoft Anything (AMA)! This live hour gives you the opportunity to ask questions and provide feedback. Please introduce yourself by replying to this thread. Post your questions in a new thread within theAzure AI AMA space, by clicking on, "Start a New Conversation" at the top of the page.3/10/21 - Announcing an Azure Cognitive Search AMA!
We are very excited to announce an Azure Cognitive Search AMA! The AMA will take place on Wednesday, March 10, 2021 from 9:00 a.m. to 10:00 a.m. PT in theAzure AI AMA space.Add the event to your calendar and view in your time zonehere. An AMA is a live online event similar to a “YamJam” on Yammer or an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. The space is now open for new questions (24 hours before the event), so feel free to post your questions anytime beforehand if it fits your schedule or time zone better.2/10/21 - Announcing an Azure Cognitive Services – Speech AMA!
We are very excited to announce an Azure Cognitive Services – Speech AMA! We'll be answering your questions on how to add capabilities like Text-to-Speech and Custom Neural Voice with Azure Speech service. The AMA will take place on Wednesday, February 10, 2021 from 9:00 a.m. to 10:00 a.m. PT in the Azure AI AMA space.Add the event to your calendar and view in your time zonehere. An AMA is a live online event similar to a “YamJam” on Yammer or an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. Please note, if you are unable to participate on the day of the event, we will open up the space for new questions 24 hours before the event, so feel free to post your questions beforehand if it fits your schedule or time zone better. BEFORE THE EVENT - check out these resources: Learn more about text to speech capability: Build a natural custom voice for your brand (microsoft.com) Check out Friday's AI show featuring Edward Unand Sarah Bird!Azure Open AI Industry Use cases
Azure Open AI Industry Use cases: Content generation Call Center Analytics: Automatically generate responses to customer inquiries Code generation Aircraft company using to convert natural language to SQL for aircraft telemetry data. Consulting service using Azure OpenAI Service to convert natural language to query propriety data models. Semantic search Financial services firm using Azure OpenAI Service to improve search capabilities and the conversational quality of a customer’s Bot experience. Insurance companies extract information from volumes of unstructured data to automate claim handling processes. Summarization International insurance company using Azure OpenAI Service to provide summaries of call center customer support conversation logs. Global bank using Azure OpenAI Service to summarize financial reporting and analyst articles. Government agency using Azure OpenAI Service to extract and summarize key information from their extensive library of rural development reports. Financial services using Azure OpenAI Service to summarize financial reporting for peer risk analysis and customer conversation summarization. Code model use cases: Natural Language to Code Natural Language to SQL Code to Natural Language Code documentation Refactoring Text model use cases: Reason over structured and unstructured data: Classification, Sentiment, Entity Extraction, Search Product feedback sentiment Customer and employee feedback classification Claims and risk analyses Support emails and call transcripts Social media trends Writing assistance Marketing copy/email taglines Long format text Paragraphs from bullets Summarization Call center call transcripts Subject Matter Expert Documents Competitive analysis Peer Analysis Technical reports Product and service feedback Social media trends Conversational AI Smart assists for call centers Tech support chat bots Virtual assistants Use Cases that use multiple model capabilities Contact Centers Classification—route mails to appropriate team Sentiment—prioritize angry customers Entity extraction and search—analyze liability and risk Mail and call transcript summarization Customer response email generation Rapid response marketing campaigns: classification, sentiment, summarization,content generation More details here on Microsoft documentation:Transparency Note for Azure OpenAI - Azure AI services | Microsoft Learn GPT-4 Turbo with Vision: Chat and conversation interaction:Users can interact with a conversational agent that responds with information drawn from trusted documentation such as internal company documentation or tech support documentation. Conversations must be limited to answering scoped questions.Available to internal, authenticated external users, and unauthenticated external users. Chatbot and conversational agent creation:Users can create conversational agents that respond with information drawn from trusted documents such as internal company documentation or tech support documents. For instance, diagrams, charts, and other relevant images from technical documentation can enhance comprehension and provide more accurate responses. Conversations must be limited to answering scoped questions.Limited to internal users only. Code generation or transformation scenarios:Converting one programming language to another or enabling users to generate code using natural language or visual input. For example, users can take a photo of handwritten pseudocode or diagrams illustrating a coding concept and use the application to generate code based on that.Limited to internal and authenticated external users. Reason over structured and unstructured data:Users can analyze inputs using classification, sentiment analysis of text, or entity extraction. Users can provide an image alongside a text query for analysis.Limited to internal and authenticated external users. Summarization:Users can submit content to be summarized for pre-defined topics built into the application and cannot use the application as an open-ended summarizer. Examples include summarization of internal company documentation, call center transcripts, technical reports, and product reviews.Limited to internal, authenticated external users, and unauthenticated external users. Writing assistance on specific topics:Users can create new content or rewrite content submitted by the user as a writing aid for business content or pre-defined topics. Users can only rewrite or create content for specific business purposes or pre-defined topics and cannot use the application as a general content creation tool for all topics. Examples of business content include proposals and reports. May not be selected to generate journalistic content (for journalistic use, select the above Journalistic content use case).Limited to internal users and authenticated external users. Search:Users can search for content in trusted source documents and files such as internal company documentation. The application does not generate results ungrounded in trusted source documentation.Limited to internal users only. Image and Video Tagging:Users can identify and tag visual elements, including objects, living beings, scenery, and actions within an image or recorded video. Users may not attempt to use the service to identify individuals.Limited to internal users and authenticated external users. Image and Video Captioning:Users can generate descriptive natural language captions for visuals. Beyond simple descriptions, the application can identify and provide textual insights about specific subjects or landmarks within images and recorded video. If shown an image of the Eiffel Tower, the system might offer a concise description or highlight intriguing facts about the monument. Generated descriptions of people may not be used to identify individuals.Limited to internal users and authenticated external users. Object Detection:For use to identify the positions of individual or multiple objects in an image by providing their specific coordinates. For instance, in an image that has scattered apples, the application can identify and indicate the location of each apple. Through this application, users can obtain spatial insights regarding objects captured in images. This use case is not yet available for videos.Limited to internal users and authenticated external users. Visual Question Answering:Users can ask questions about an image or video and receive contextually relevant responses. For instance, when shown a picture of a bird, one might ask, "What type of bird is this?" and receive a response like, "It's a European robin." The application can identify and interpret context within images and videos to answer queries. For example, if presented with an image of a crowded marketplace, users can ask, "How many people are wearing hats?" or "What fruit is the vendor selling?" and the application can provide the answers. The system may not be used to answer identifying questions about people.Limited to internal users and authenticated external users. Brand and Landmark recognition:The application can be used to identify commercial brands and popular landmarks in images or videos from a preset database of thousands of global logos and landmarks.Limited to internal users and authenticated external users. GPT-3.5, GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, and/or Embeddings Models: Chat and conversation interaction: Users can interact with a conversational agent that responds with responses drawn from trusted documents such as internal company documentation or tech support documentation; conversations must be limited to answering scoped questions.Available to internal, authenticated external users, and unauthenticated external users. Chat and conversation creation: Users can create a conversational agent that responds with responses drawn from trusted documents such as internal company documentation or tech support documentation; conversations must be limited to answering scoped questions.Limited to internal users only. Code generation or transformation scenarios: For example, converting one programming language to another, generating docstrings for functions, converting natural language to SQL.Limited to internal and authenticated external users. Journalistic content: For use to create new journalistic content or to rewrite journalistic content submitted by the user as a writing aid for pre-defined topics. Users cannot use the application as a general content creation tool for all topics. May not be used to generate content for political campaigns.Limited to internal users. Question-answering: Users can ask questions and receive answers from trusted source documents such as internal company documentation. The application does not generate answers ungrounded in trusted source documentation.Available to internal, authenticated external users, and unauthenticated external users. Reason over structured and unstructured data: Users can analyze inputs using classification, sentiment analysis of text, or entity extraction. Examples include analyzing product feedback sentiment, analyzing support calls and transcripts, and refining text-based search with embeddings.Limited to internal and authenticated external users. Search: Users can search trusted source documents such as internal company documentation. The application does not generate results ungrounded in trusted source documentation.Available to internal, authenticated external users, and unauthenticated external users. Summarization:Users can submit content to be summarized for pre-defined topics built into the application and cannot use the application as an open-ended summarizer. Examples include summarization of internal company documentation, call center transcripts, technical reports, and product reviews.Limited to internal, authenticated external users, and unauthenticated external users. Writing assistance on specific topics: Users can create new content or rewrite content submitted by the user as a writing aid for business content or pre-defined topics. Users can only rewrite or create content for specific business purposes or pre-defined topics and cannot use the application as a general content creation tool for all topics. Examples of business content include proposals and reports. May not be selected to generate journalistic content (for journalistic use, select the above Journalistic content use case).Limited to internal users and authenticated external users. Data generation for fine-tuning:Users can use a model in Azure OpenAI to generate data which is used solely to fine-tune (i) another Azure OpenAI model, using the fine-tuning capabilities of Azure OpenAI, and/or (ii) another Azure AI custom model, using the fine-tuning capabilities of the Azure AI service. Generating data and fine-tuning models is limited to internal users only; the fine-tuned model may only be used for inferencing in the applicable Azure AI service and, for Azure OpenAI service, only for customer’s permitted use case(s) under this form. DALL-E 2 and/or DALL-E 3: Art and Design: For use to generate imagery for artistic purposes only for designs, artistic inspiration, mood boards, or design layouts.Limited to internal and authenticated external users. Communication: For use to create imagery for business-related communication, documentation, essays, bulletins, blog posts, social media, or memos. This use case may not be selected to generate images for political campaigns or journalistic content (for journalistic use, see the Journalistic content use case below).Limited to internal and authenticated external users. Education: For use to create imagery for enhanced or interactive learning materials, either for use in educational institutions or for professional training.Limited to internal users and authenticated external users. Entertainment: For use to create imagery to enhance entertainment content such as video games, movies, TV, videos, recorded music, podcasts, audio books, or augmented or virtual reality. This use case may not be selected to generate images for political campaigns or journalistic content (for journalistic use, see the below Journalistic content use case).Limited to internal and authenticated external users.Integrating Azure OpenAI and Azure Speech Services to Create a Voice-Enabled Chatbot with Python
Artificial intelligence (AI) is changing the way businesses operate, and many organizations are looking for ways to leverage AI to improve their operations and gain a competitive advantage. In this blog post, we’ll explore how to integrate Azure OpenAI service and Azure Speech service to create a chatbot that users can interact with via voice. What is the difference between Azure OpenAI and OpenAI Before we dive into the integration process, let’s first understand what Azure OpenAI Service is. Azure OpenAI Service provides customers with access to advanced language AI capabilities through OpenAI’s GPT-4, GPT-3, Codex, and DALL-E models, all with the added security and enterprise support of Azure. Co-developed with OpenAI, Azure OpenAI ensures compatibility and a seamless transition between the two platforms. By using Azure OpenAI, customers can leverage the same models as OpenAI while benefiting from the security features of Microsoft Azure, such as private networking and regional availability. Additionally, Azure OpenAI promotes responsible AI by offering content filtering capabilities. https://learn.microsoft.com/azure/cognitive-services/openai/overview?WT.mc_id=DT-MVP-5001664#comparing-azure-openai-and-openai Access to Azure OpenAI Service is exclusive to approved enterprise customers and partners, including Microsoft MVP. To gain access, registration is required. I feel privileged to have access to Azure OpenAI Service. If you want to access Azure OpenAI Service, you will need to complete the Request Access to Azure OpenAI Service form first. https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xUOFA5Qk1UWDRBMjg0WFhPMkIzTzhKQ1dWNyQlQCN0PWcu Models in Azure OpenAI service The Azure OpenAI service offers users access to a range of different models, each with its own capabilities and price point. The latest models available are theGPT-4models, which are currently in preview. Existing Azure OpenAI customers can apply for access to these models by completing the form below. https://customervoice.microsoft.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR7en2Ais5pxKtso_Pz4b1_xURjE4QlhVUERGQ1NXOTlNT0w1NldTWjJCMSQlQCN0PWcu The GPT-3 base models, including Davinci, Curie, Babbage, and Ada, are available and vary in capability and speed. The Codex series of models, which are trained on natural language and code, can power natural language to code use cases. If your application to access the Azure OpenAI service is approved, then you can create an Azure OpenAI service in your Azure subscription. Azure OpenAI Studio To get started, go tohttps://oai.azure.com/to access Azure OpenAI Studio. Sign in using credentials that have access to your Azure OpenAI resource. You can select the appropriate directory, Azure subscription, and Azure OpenAI resource during or after the sign-in process. Develop a Python program that incorporates Azure OpenAI GPT-4 and Azure Speech functionalities Setting up Azure OpenAI and Azure Speech Services in the Azure portal is quite straightforward. Once created, we can access these services in our code. Let me illustrate this with an example in Python. Installing the necessary Python libraries If you want to integrate the Azure Speech-to-Text and Text-to-Speech functions as well as Azure OpenAI’s language generation capabilities into your Python project, you will need to install the necessary Python libraries. The first library you will need isazure-cognitiveservices-speech, which provides access to Azure’s Speech-to-Text and Text-to-Speech services. You can install this library usingpip, the Python package manager. The second library you will need isopenai, which provides access to Azure OpenAI’s language generation API. Again, you can install this library usingpip. Once you have these libraries installed, you can use them to create a powerful Python program that can recognize speech, generate language, and convert text to speech. Setting Up Azure OpenAI and Speech Services in Python Let’s craft a Python program and configure the Azure OpenAI API credentials, along with the credentials/configurations for Azure’s Speech-to-Text and Text-to-Speech services. import os import azure.cognitiveservices.speech as speechsdk import openai # Set up Azure OpenAI API credentials openai.api_type = "azure" openai.api_base = os.getenv("OPENAI_ENDPOINT") openai.api_version = "2023-03-15-preview" openai.api_key = os.getenv("OPENAI_API_KEY") # Set up Azure Speech-to-Text and Text-to-Speech credentials speech_key = os.getenv("SPEECH_API_KEY") service_region = "eastus" speech_config = speechsdk.SpeechConfig(subscription=speech_key, region=service_region) speech_config.speech_synthesis_language = "en-NZ" # Set up the voice configuration speech_config.speech_synthesis_voice_name = "en-NZ-MollyNeural" speech_synthesizer = speechsdk.SpeechSynthesizer(speech_config=speech_config) First, let’s take a look at the steps involved in setting up the Azure OpenAI service. For the usage of Azure OpenAI service, it is necessary to furnish the endpoint of the previously generated instance, an Azure OpenAI endpoint looks like the following format: https://{your-resource-name}.openai.azure.com/ https://learn.microsoft.com/en-us/azure/cognitive-services/openai/reference?WT.mc_id=DT-MVP-5001664 Moreover, it’s crucial to specify the API version when utilizing the ChatGPT (preview) and GPT-4 (preview) models to generate chat message completions. Please note that chat completions are exclusively accessible with theapi-version=2023–03–15-preview. https://learn.microsoft.com/azure/cognitive-services/openai/reference?WT.mc_id=DT-MVP-5001664#chat-completions To access the Azure OpenAI service from your Python code, the next step involves providing the API key. This key can be located in the Keys and Endpoint panel of your Azure OpenAI service, as shown below. Likewise, it is necessary to establish the Azure Speech service. In this case, I have opted for theen-NZ-MollyNeuralvoice, which emulates the accent of New Zealanders, also known as Kiwis. The link below provides access to information on the languages and voice support available for the Speech service. https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/language-support?tabs=tts&WT.mc_id=DT-MVP-5001664#standard-voices Creating a speech recognizer and starting the recognition To converse with a chatbot powered by GPT-4 in a human-like conversation, the first step is to create a speech recognizer capable of identifying our voice. # Define the speech-to-text function def speech_to_text(): # Set up the audio configuration audio_config = speechsdk.audio.AudioConfig(use_default_microphone=True) # Create a speech recognizer and start the recognition speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config, audio_config=audio_config) print("Say something...") result = speech_recognizer.recognize_once_async().get() if result.reason == speechsdk.ResultReason.RecognizedSpeech: return result.text elif result.reason == speechsdk.ResultReason.NoMatch: return "Sorry, I didn't catch that." elif result.reason == speechsdk.ResultReason.Canceled: return "Recognition canceled." In the code above, we define a function namedspeech_to_textthat uses the Microsoft Azure Speech Service SDK to perform speech-to-text conversion. It sets up the audio configuration and creates a speech recognizer object, which is configured using the Speech Service’s language and authentication credentials. The function prompts the user to speak and starts the recognition process asynchronously using therecognize_once_async()method. Once the recognition is completed, the function checks the “reason” attribute of the “result” object to determine if the speech was recognized successfully or not. If it was recognized, the function returns the recognized text, otherwise it returns an error message. Using Azure OpenAI’s GPT-4 engine to generate text in response to a prompt After getting input from the user using speech-to-text in the previous step, we can use the input as the prompt in Azure OpenAI’s GPT-4 engine. # Define the Azure OpenAI language generation function def generate_text(prompt): response = openai.ChatCompletion.create( engine="chenjd-test", messages=[ {"role": "system", "content": "You are an AI assistant that helps people find information."}, {"role": "user", "content": prompt} ], temperature=0.7, max_tokens=800, top_p=0.95, frequency_penalty=0, presence_penalty=0, stop=None ) return response['choices'][0]['message']['content'] This Python code above defines a function calledgenerate_textthat uses Azure OpenAI's GPT-4 engine to generate text in response to a prompt. The function takes a prompt as input and uses theopenai.ChatCompletion.create()method to generate a response, with parameters like engine, messages, temperature, max_tokens, top_p, frequency_penalty, presence_penalty, and stop. The main input of theopenai.ChatCompletion.create()method is themessagesparameter, which should be an array consisting of message objects. Each message object in the array must include a “role” (which can be either “system”, “user”, or “assistant”) and a “content” field (which contains the message’s content, in this case, the message’s content is the value of prompt). The following link provides more details. https://learn.microsoft.com/azure/cognitive-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions&WT.mc_id=DT-MVP-5001664#working-with-the-chatgpt-and-gpt-4-models-preview Adding Text-to-Speech Functionality to the Chatbot Let’s now include another feature that allows the chatbot to vocalize the text produced by the Azure OpenAI service in a human-like manner. # Define the text-to-speech function def text_to_speech(text): try: result = speech_synthesizer.speak_text_async(text).get() if result.reason == speechsdk.ResultReason.SynthesizingAudioCompleted: print("Text-to-speech conversion successful.") return True else: print(f"Error synthesizing audio: {result}") return False except Exception as ex: print(f"Error synthesizing audio: {ex}") return False This Python code defines a function calledtext_to_speechthat takes atextparameter, which is generated by Azure OpenAI, as input. It uses thespeech_synthesizerobject to asynchronously synthesize the input text and generate speech audio using theen-NZ-MollyNeuralvoice. Follow me:Jiadong Chen Github repo:azure-openai-gpt4-voice-chatbot
SharePoint Online and Cognitive Search Integration
Trying to determine, art of the possible. In my environment, I'm not a global tenant administrator and only have SPO site collection administrator permissions. I do have my own Azure subscription in the tenant so I can leverage Logic Apps, Functions and most other Azure services. My question is, with these limitations would I still be able to build applications and index my SPO content? Or is the index configuration more of a central administration setting.Get Rewarded for Sharing Your Experience with Microsoft Azure AI
We invite our valued Microsoft Azure AI customers to share your firsthand experience developing with Azure AI by writing a review on Gartner Peer Insights. Your review will not only assist other developers and technical decision-makers but also help shape the future of our AI products.Thank you for your time and contribution, and we are excited to hear your thoughts! To Write a Review & Claim Your Reward: Read our blog for next steps.You will receive a $25 gift card, a 3-month subscription to Gartner research, or a donation to a charitable cause as a token of our appreciation.
Face API to detect race or ethnicity or skin color from a given image
Currently Face API gives multiple attributes of faces detected in a picture that include Age, Gender, Hair Type, Hair Color, etc,. But it doesn't give any attribute to detect race or ethnicity or skin color. Is there any plan when these attributes will be available?6/3: Announcing an Azure Cognitive Search AMA!
We are very excited to announce an Azure Cognitive Search AMA! The AMA will take place on Thursday, June 3, 2021 from 9:00 a.m. to 10:00 a.m. PT in theAzure AI AMA space.Add the event to your calendar and view in your time zonehere. An AMA is a live text-based online event similar to a “YamJam” on Yammer or an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. The space will be open 24 hours before the event, so feel free to post your questions anytime beforehand if it fits your schedule or time zone better.
Cost of Custom Neural Voice
We are a startup, and we're building a digital assistant. We would love the ability to use a custom neural voice to be able to differentiate and brand our assistant, but my understanding is that the cost of building a custom neural voice is in the $100k range and almost $3,000 just for the endpoint hosting. Is this the case?4/28/21 AMA - Conversational AI on Bot Framework Composer and the Telephony Channel
We are very excited to announce aConversational AI with Bot Framework Composer and the Telephony Channel AMA! The AMA will take place on Wednesday, April 28, 2021 from 9:00 a.m. to 10:00 a.m. PT in theAzure AI AMA space.Add the event to your calendar and view in your time zonehere. An AMA is a live text-based online event similar to a “YamJam” on Yammer or an “Ask Me Anything” on Reddit. This AMA gives you the opportunity to connect with Microsoft product experts who will be on hand to answer your questions and listen to feedback. The space will be open24 hours before the event, so feel free to post your questions anytime beforehand during that period if it fits your schedule or time zone better.Building Multi-Agentic Workflows in AutoGen Studio: A Low-Code Platform
In the dynamic world of software development, speed and efficiency are paramount. Enter AutoGen Studio, an innovative platform introduced by Microsoft Research, designed to streamline the creation of multi-agent workflows. As a low-code interface, AutoGen Studio simplifies the development process, making it accessible to a broader audience, including those with limited coding experience. Low-Code Advantage One of the most significant benefits of AutoGen Studio is its low-code nature. This approach reduces the need for extensive programming knowledge, enabling users to create complex workflows through a more intuitive interface. This is particularly advantageous for rapid prototyping, where speed and adaptability are crucial. By lowering the barrier to entry, AutoGen Studio empowers users to experiment with different workflow configurations and iterate quickly based on feedback. Prototyping, Not Production While AutoGen Studio excels in the prototyping phase, it is important to note its limitations for production use. The platform's primary design is to facilitate quick and efficient workflow development, which may not always align with the robustness and scalability required for production environments. Users should consider this when planning their development cycles, ensuring that workflows created in AutoGen Studio are thoroughly tested and adapted before deployment in a live setting. Current Capabilities of AutoGen Studio AutoGen Studio is designed to lower the barrier to building multi-agent applications, facilitate rapid prototyping, and foster a community for sharing and reusing technology. With its early release (v0.1.0), it offers several key capabilities: Rapid Workflow Authoring: Users can quickly assemble workflows from a library of pre-defined agents, customizing them with models, prompts, and skills via a graphical interface. Workflows can be sequential or autonomous, driven by custom logic or a large language model. Debugging and Testing: AutoGen Studio allows immediate testing of workflows, providing insights into agent actions, costs, and outcomes. Developers can review artifacts and the internal logic of workflows to ensure they function as intended. Artifact Reuse and Deployment: Skills, agents, and workflows can be downloaded, shared, and reused. The platform supports exporting workflows as APIs, JSON files, or Dockerfiles for seamless integration and deployment on cloud services. Installation Instructions in Conda Environment To get started with AutoGen Studio in a Conda environment, follow these steps: Create a New Conda Environment: conda create --name autogen-studio python=3.11 conda activate autogen-studio Install AutoGen Framework and AutoGen Studio: pip install pyautogen pip install autogenstudio Run AutoGen Studio: autogenstudio ui --port 8081 With these steps, you'll have AutoGen Studio set up and ready to use in your Conda environment. Tutorial: Critic - Writer Reflection Scenario One of the engaging scenarios you can create with AutoGen Studio is the Critic-Writer reflection workflow. This scenario involves two agents: a Critic, who reviews and critiques content, and a Writer, who creates or revises content based on the feedback. Here's a step-by-step guide to setting up this scenario: 1. Model Creation: Begin by binding the appropriate language models (LLMs) to your workflow. Set the API key, endpoint and API version for Azure OpenAI Service and add a binding for the gpt-4o model. 2.Create Critic and Writer Agents: Navigate to the "Agents" section in AutoGen Studio and add the Critic and Writer agents from the library of pre-defined agents. Configure and customize both agents with initial prompts and guidelines for content creation. 3. Create Workflow: Set up the interaction between the Critic and Writer agents. Choose the simplest interaction mode, "Autonomous," to facilitate dialogue between the agents. Name the workflow appropriately. Once you hit the "Create workflow" button, navigate to the "Agents" subsection where you need to choose the Critic agent as the initiator and the Writer agent as the receiver. 4.Testing in the Playground: Click on the "Playground" section above and create a new session by choosing the appropriate workflow. For example, give a query like "Write a concise but engaging blog post about Customer Support with an agentic approach." The Critic and Writer agents will start collaborating: the Critic sends a query to the Writer, who then drafts the blog post. The Critic reviews the draft and provides feedback, which the Writer uses to revise the blog post. This process continues until the final content is satisfactory. You can see a short demo video below by clicking the provided link. Critic-Writer Workflow Chat Test By following these steps, you can effectively set up and utilize the Critic-Writer reflection scenario in AutoGen Studio, demonstrating the platform's capabilities in creating dynamic, multi-agent workflows. Conclusion AutoGen Studio represents a significant step forward in the realm of workflow automation, offering a low-code solution that democratizes the development process. Its strengths in rapid prototyping make it an invaluable tool for teams looking to innovate quickly and efficiently. However, developers should be mindful of its limitations in production settings and plan accordingly. By leveraging AutoGen Studio's unique capabilities, users can create powerful, multi-agent workflows that streamline operations and drive productivity. In summary, AutoGen Studio is an exciting addition to the toolkit of any developer or organization looking to enhance their workflow automation capabilities. Its low-code interface, multi-agent support, and ease of integration make it a versatile and practical solution for modern software development challenges. Resources Microsoft AutoGen Microsoft AutoGen Studio Introducing AutoGen Studio: A low-code interface for building multi-agent workflows Building AI Agent Applications Series - Using AutoGen to build your AI Agents
Adding searching SharePoint pages to chatbot (page contents not uploaded files)
Hi , I am new to Azure bot frameworks and Cognitive Services and am working on creating a chatbot to signpost staff to various in-house services. I have created a QnA bot using the QnA maker but I want to extend the bot to be able to search our SharePoint Intranet and link users to appropriate entries on our site. These are not files but SharePoint web pages. Assuming this is possible, what is the best way of achieving this?
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