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Integrating Azure OpenAI Services
Hello everyone, I'm currently developing an AI to Human Text Converter Free platform aitohumantextconverterfree.net that aims to transform AI text into more human style engaging text. I'm exploring ways to enhance the platform's capabilities and am particularly interested in leveraging Azure's AI services. I've been reading about the Azure OpenAI Service and its various models, such as the o-series and GPT4o, which are designed for advanced reasoning and problem solving tasks. I have a few questions: Model Selection: Which Azure AI models would be most suitable for refining ai generated text to make it more human style? Integration Best Practices: Are there recommended approaches or resources for integrating Azure's ai services into existing web platforms? Customization: Is it possible to fine-tune these models specifically for converting AI-generated text into a more natural, human-like style? Any insights, experiences, or resources you could share would be greatly appreciated. Thank you!How to Build AI Agents in 10 Lessons
Microsoft has released an excellent learning resource for anyone looking to dive into the world of AI agents: "AI Agents for Beginners". This comprehensive course is available free on GitHub. It is designed to teach the fundamentals of building AI agents, even if you are just starting out. What You'll Learn The course is structured into 10 lessons, covering a wide range of essential topics including: Agentic Frameworks: Understand the core structures and components used to build AI agents. Design Patterns: Learn proven approaches for designing effective and efficient AI agents. Retrieval Augmented Generation (RAG): Enhance AI agents by incorporating external knowledge. Building Trustworthy AI Agents: Discover techniques for creating AI agents that are reliable and safe. AI Agents in Production: Get insights into deploying and managing AI agents in real-world applications. Hands-On Experience The course includes practical code examples that utilize: Azure AI Foundry GitHub Models These examples help you learn how to interact with Language Models and use AI Agent frameworks and services from Microsoft, such as: Azure AI Agent Service Semantic Kernel Agent Framework AutoGen - A framework for building AI agents and applications Getting Started To get started, make sure you have the proper set-up. Here are the 10 lessons Intro to AI Agents and Agent Use Cases Exploring AI Agent Frameworks Understanding AI Agentic Design Principles Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Multi-Agent Design Patterns Metacognition in AI Agents AI Agents in Production Multi-Language Support To make learning accessible to a global audience, the course offers multi-language support. Get Started Today! If you are eager to learn about AI agents, this course is an excellent starting point. You can find the complete course materials on GitHub at AI Agents for Beginners.
Building Agentic Solutions with Autogen 0.4
Multi Agent Systems are a consequence of an organized interaction between diverse agents to achieve a goal. Similar to human collaborations, Agentic solutions are expected to collaborate effectively in accordance with the goal to be accomplished. A crucial aspect is adopting the appropriate design pattern depending on the task on hand. Let us look at the design of Agentic Solutions is stages. Stage 1: Determine all the required Agents and define the required tools which can be leveraged by the Agents. The tools may have access requirements which has to be handled with appropriate security constraints. In Autogen, this is supported through multiple patterns which address different requirements. At its core, Autogen provides the ability to leverage LLMs, human inputs, tools or a combination. Autogen 0.4 in particular has provided an high-level API through AgentChat with preset Agents allowing for variations in agent responses. Some of the preset Agents include 1) AssistantAgent is a built-in agent which can use a language model and tools. It can also handle multimodal messages and instructions of the Agents function. 2) UserProxyAgent: An agent that takes user input returns it as responses. 3) CodeExecutorAgent: An agent that can execute code. 4) OpenAIAssistantAgent: An agent that is backed by an OpenAI Assistant, with ability to use custom tools. 5) MultimodalWebSurfer: A multi-modal agent that can search the web and visit web pages for information. 6) FileSurfer: An agent that can search and browse local files for information. 7) VideoSurfer: An agent that can watch videos for information. A Custom Agents can be used when the preset Agents do not address the need. Stage 2: Identify the optimal interaction between the team of agents. This can include a human in the loop proxy agent which serves as an interface for human inputs. Autogen supports multiple interaction patterns 1) GroupChat is a high-level design pattern for interleaved interactions. In Auotgen 0.4, GroupChat got further abstracted with RoundRobinGroupChat or SelectorGroupChat . This means you can choose to go with abstracted options of RoundRobinGroupChat, SelectorGroupChat or customize it to your need with the base GroupChat in the core. RoundRobinGroupChat team configuration where all agents share the same context respond in a round-robin fashion. Broadcasts response to all agents, provides a consistent context. Human In the Loop - UserProxyAgent SelectorGroupChat team where participants take turns broadcasting messages to all other members. A generative model selects the next speaker based on the shared context, enabling dynamic, context-aware collaboration. selector_func argument with a custom selector function to override the default model-based selection. GroupChat in core 2) Sequential Agents Stage 3: Determine the memory and message passing between the Agents Memory can be the context for the Agent which could be the conversation history, RAG which is pulled from a ListMemory or a Custom Memory Store like a Vector DB. Messaging between Agents uses ChatMessage. This message type allows both text and multimodal communication and includes specific types such as TextMessage or MultiModalMessage. Stage 4: Articulate the Termination condition The following Termination options are available in Autogen 0.4 MaxMessageTermination: Stops after a specified number of messages have been produced, including both agent and task messages. TextMentionTermination: Stops when specific text or string is mentioned in a message (e.g., “TERMINATE”). TokenUsageTermination: Stops when a certain number of prompt or completion tokens are used. This requires the agents to report token usage in their messages. TimeoutTermination: Stops after a specified duration in seconds. HandoffTermination: Stops when a handoff to a specific target is requested. Handoff messages can be used to build patterns such as Swarm. This is useful when you want to pause the run and allow application or user to provide input when an agent hands off to them. SourceMatchTermination: Stops after a specific agent responds. ExternalTermination: Enables programmatic control of termination from outside the run. This is useful for UI integration (e.g., “Stop” buttons in chat interfaces). StopMessageTermination: Stops when a StopMessage is produced by an agent. TextMessageTermination: Stops when a TextMessage is produced by an agent. FunctionCallTermination: Stops when a ToolCallExecutionEvent containing a FunctionExecutionResult with a matching name is produced by an agent. Stage 5: Optionally manage the state This is useful in web application where stateless endpoints respond to requests and need to load the state of the application from persistent storage. The state can be saved by using the save_state() call in the AssistantAgent. assistant_agent.save_state() Finally, Logging and Serializing is also available for debugging and sharing. A well-designed Agentic Solution is crucial to be both optimal and effective in accomplishing the assigned goal. References Autogen - https://microsoft.github.io/autogen/stable/index.html
Azure AI Search - Tag Scoring profile on azureopenai extra_body
I created an index on Azure AI Search and connected it to Azure OpenAI using the extra_body. It works perfectly. However, I created a default scoring profile for my index, which boosts documents containing the string "zinc" in the VITAMINS field by a factor of 10. Since doing this, I can no longer run the query that worked previously without issues. Now, the query is asking for a scoringParameter, and when I attempt to pass it, I receive an error. Here is the code that works fine when I remove the scoring function. client.chat.completions.create( model=os.getenv('DEPLOYMENT'), messages=messages, temperature=0.5, extra_body={ "data_sources": [{ "type": "azure_search", "parameters": { "endpoint": os.getenv('ENDPOINT'), "index_name": os.getenv('INDEX'), "semantic_configuration": os.getenv('RANK'), "query_type": "hybrid", "in_scope": True, "role_information": None, "strictness": 1, "top_n_documents": 3, "authentication": { "type": "api_key", "key": os.getenv('KEY') }, "embedding_dependency": { "type": "deployment_name", "deployment_name": os.getenv('ADA_VIT') } } }] } ) However, if I activate the default scoring profile, I get the following error: > An error occurred: Error code: 400 - {'error': 'message': 'An error occurred when calling Azure Cognitive Search: Azure Search Error: 400, message=\'Server responded with status 400. Error message: {"error":{"code":"MissingRequiredParameter","message":"Expected 1 parameter(s) but 0 were supplied.\\\\r\\\\nParameter name: scoringParameter","details":[{"code":"MissingScoringParameter","message":"Expected 1 parameter(s) but 0 were supplied."}]}}\', api-version=2024-03-01-preview\'\nCall to Azure Search instance failed.\nAPI Users: Please ensure you are using the right instance, index_name, and provide admin_key as the api_key.\n'} **If I try to pass the scoringParameter anywhere in the extra_body**, I receive this error: > An error occurred: Error code: 400 - {'error': {'requestid': '', 'code': 400, 'message': 'Validation error at #/data_sources/0/azure_search/parameters/scoringParameter: Extra inputs are not permitted'}} This error is even more confusing. I’ve been looking through various resources, but none of them seem to provide a clear example of how to properly pass the scoring profile or scoring parameters in the extra_body. Here’s how I define my scoring profile using tags: scoring_profiles = [ ScoringProfile( name="my-scoring-profile", functions=[ TagScoringFunction( field_name="VITAMINS", boost=10.0, parameters=TagScoringParameters( tags_parameter="tags", ), ) ] ) ] How to pass the scoring parameters correctly in the `extra_body` on the `client.chat.completions.create`? PS: The only way I can get my code to work is if I delete the scoring profile or do not make it the scoring profile by default by I do want to use it.
Learning: AgentChat Swarm
I am trying to use autogen agentchat swarm team, specifically in websocket based application. Facing issues with async setup and swarm usage. If someone has done work in agentchat or swarm domain, have some sort of tutorial code and can share, that will be of great help. Thanks!
Do not see option Add your data in Azure AI chat playground for DeepSeek model
Issue 1: I am evaluating different models in Azure AI Foundry against my own data in Azure AI search and do not see option to add your data when DeepSeek-R1 model is selected in chat playground. It used to be there but disappeared recently ( precisely on Feb 18 ET). However see option to add your data when gpt models are selected in chat play ground. Issue 2: When add your data option was available for DeepSeek-R1 model (prior to Feb 18,2025), I was getting following error in chat playground "An error occurred when calling Azure OpenAI: Server responded with status 400. Error message: {'error': {'code': 'unknown_model', 'message': 'Unknown model: chatgpt', 'details': 'Unknown model: chatgpt'}}"Image-to-Image generation using Stable-Diffuson-3.5 Large Model
Has anybody been able to generate an image with 'Image-to-Image' mode of 'Stable-Diffusion 3.5' ( deployed as a serverless api) ? I tried with text-to-image option (via Python + POST request ) and was able to generate the image but 'image-to-image' option does not seem to work. Infact even Azure Playground does not list parameters for this option. But the model information on Azure does list it supports image-to-image mode and accepts an image input. Any leads on this will be greatly appreciated.
Principle Does not have Access to API/Operation
Hi all, I am trying to connect Azure OpenAI service to Azure AI Search service to Azure Gen 2 Data lake. In the Azure AI Foundry studio Chat Playground, I am able to add my data source, which is a .csv file in the data lake that has been indexed successfully. I use "System Assigned Managed Identity". The following RBAC has been applied: AI Search service has Cognitive Services OpenAI Contributor in Azure Open AI service Azure OpenAI service has Search Index Data Reader in AI Search Service Azure OpenAI service has Search Service Contributor in AI Search Service AI Search Service has Storage Blob Data Reader in Storage account (Data Lake) As mentioned when adding the data source it passes validation but when I try to ask a question, I get the error "We couldn't connect your data Principal does not have access to API/Operation"Azure AI speech studio - synthesis failed
Hi, in my TTS project all files created so far cause a failure when I hit the Play button. I get following error msg: Response status code does not indicate success: 400 (Synthesis failed. StatusCode: FailedPrecondition, Details: '=' is an unexpected token. The expected token is ';'. Line 1, position 535..). Connection ID: c2e319c0-c447-11ef-8937-33bd13f92760 Changing voices does not solve it. Location of the speech service is "Germany West Central"Integration between AI Agent and D365 Finance and Operations
We have a requirement to fire a database query using the AI agent to find and retrieve certain information. I understood from our team that retrieving the information from a specific table may be possible, but the team is facing difficulty when retrieving the information from multiple related tables and when there are multiple conditions to consider. Any pointer here is greatly appreciated.
