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Trigger cant read fabric data agent
I make an agent in Azure AI Foundry. I use fabric data agent as a knowledge. Everything runs well until I try to use trigger to orchestrate my agent. I have added my trigger identity to fabric workspace where my fabric data agent and my lakehouse located. My trigger can work well and there is no error, but my agent cannot respond as if I do a prompt via the playground. Why?Responses API for gpt-4.1 in Europe
Hello everyone! I'm writing here trying to figure out something about the availability of the "responses" APIs in european regions: https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/responses?tabs=python-key i'm trying to deploy a /responses endpoint for the model we are currently using, gpt-4.1, since i've read that the /completions endpoint will be dismissed by OpenAI starting from august 2026. Our application is currently migrating all the API calls from completions to responses, and we were wondering if we could already do the same for our clients in Europe as well, which have to comply to GDPR and currently use our Azure subscription. In the page linked above, i can see some regions that would fit our needs, in particular: francecentral norwayeast polandcentral swedencentral switzerlandnorth but then, i can also read "Not every model is available in the regions supported by the responses API.", which probably already answers my question: from the Azure AI Foundry Portal, i wasn't able to deploy such endpoint in those regions, except for the o3 model. For the 4.1 model, only the completions endpoint is listed, while searching for "Responses" (in "Deploy base model") returns only o3 and these others: Can you confirm that i'm not doing anything wrong (looking in the wrong place to deploy such endpoint), and currently the gpt-4.1 responses API is not available in any European region? Do you think it's realistic it will be soon (like in 2025)? Any european region would work for us, in the "DataZone-Standard" type of distribution, which already ensures GDPR compliance (no need for a "Standard" one in one specific region). Thank you for your attention, have a nice day or evening,Azure OpenAI: GPT-5-Codex Availability?
Greetings everyone! I just wanted to see if there's any word as to when/if https://openai.com/index/introducing-upgrades-to-codex/ will make it's way to the AI Foundry. It was released on September 15th, 2025, but I have no idea how long Azure tends to follow behind OpenAI's releases. It doesn't really seem like there's any source of information to view whenever new models drop as to what Azure is going to do with them, if any. Any conversation around this would be helpful and appreciated, thanks!
Unable to locate and add a VM (GPU family) to my available VM options.
I am using azure AI foundry and need to run GPU workload but N-series VM options do not appear when i try to add quota Only CPU families like D and E are listed How can i enable or request N-series GPU VMs in my subscription and regionAzure Communication Services - Python SDK Call Media not working with CallConnectionClient
Hi team, I’m working on a FastAPI service that uses Azure Communication Services Call Automation (Python SDK) to handle outbound PSTN calls and real-time speech interaction. So far it is able to make phone calls but not able to do media handling part during conversation. Environment: Python version: 3.12-slim Package: azure-communication-callautomation (version: 1.4.0) Hosting: Azure Container Apps speech cognitive resource is connected to azure communication services https://drive.google.com/file/d/1uC2S-LNx_Ybpp1QwOCtqFS9pwA84mK7h/view?usp=drive_link What I’m trying to do: Place an outbound call to a PSTN number Play a greeting (TextSource) when the call is connected Start continuous speech recognition, forward transcript to an AI endpoint, then play the response back Code snippet: # Play greeting try: call_connection = client.get_call_connection(call_id) call_media = call_connection.call_media() call_media.play_to_all( play_source, operation_context="welcome-play" ) print("Played welcome greeting.") except Exception as e: print("Play Greeting Failed: ", str(e)) # start Recognition participants = list(call_connection.list_participants()) for p in participants: if isinstance(p.identifier, PhoneNumberIdentifier): active_participants[call_id] = p.identifier try: call_connection = client.get_call_connection(call_id) call_media = call_connection.call_media() call_media.start_recognizing_media( target_participant=p.identifier, input_type="speech", interrupt_call_media_operation=True, operation_context="speech-recognition" ) print("Started recognition immediately after call connected.") except Exception as e: print("Recognition start failed:", str(e)) break target_participant = active_participants.get(call_id) if not target_participant: print(f"No PSTN participant found for call {call_id}, skipping recognition.") Issue: When the CallConnected event fires,, I get different errors depending on which method I try: 'CallConnectionClient' object has no attribute 'call_media' 'CallConnectionClient' object has no attribute 'get_call_media_operations' 'CallConnectionClient' object has no attribute 'play_to_all' 'CallConnectionClient' object has no attribute 'get_call_media_client' 'CallConnectionClient' object has no attribute 'get_call_media' Also some import errors: ImportError: cannot import name 'PlayOptions' from 'azure.communication.callautomation' ImportError: cannot import name 'RecognizeOptions' from 'azure.communication.callautomation' ImportError: cannot import name 'CallMediaRecognizeOptions' from 'azure.communication.callautomation' ImportError: cannot import name 'CallConnection' ... Did you mean: 'CallConnectionState'? This makes me unsure which API is the correct/updated way to access play_to_all and start_recognizing_media. https://drive.google.com/file/d/1xI-sWil0OKfAfGwjIgG25eD7CEK95rKc/view?usp=drive_link Questions: What is the current supported way to access call media operations (play / speech recognition) in the Python SDK? Are there breaking changes between SDK versions that I should be aware of? Should I upgrade to a specific minimum version to ensure .call_media works? Thanks in advance!
Agent in Azure AI Foundry not able to access SharePoint data via C# (but works in Foundry portal)
Hi Team, I created an agent in Azure AI Foundry and added a knowledge source using the SharePoint tool. When I test the agent inside the Foundry portal, it works correctly; it can read from the SharePoint site and return file names/data. However, when I call the same agent using C# code, it answers normal questions fine, but whenever I ask about the SharePoint data, I get the error: Sorry, something went wrong. Run status: failed I also referred to the official documentation and sample here: https://learn.microsoft.com/en-us/azure/ai-foundry/agents/how-to/tools/sharepoint-samples?pivots=rest I tried the cURL samples as well, and while the agent is created successfully, the run status always comes back as failed. Has anyone faced this issue? Do I need to configure something extra for SharePoint when calling the agent programmatically (like additional permissions or connection binding)? Any help on this would be greatly appreciated. Thanks!
Chaining and Streaming with Responses API in Azure
Responses API is an enhancement of the existing Chat Completions API. It is stateful and supports agentic capabilities. As a superset of the Chat Completions class, it continues to support functionality of chat completions. In addition, reasoning models, like GPT-5 result in better model intelligence when compared to Chat Completions. It has input flexibility, supporting a range of input types. It is currently available in the following regions on Azure and can be used with all the models available in the region. The API supports response streaming, chaining and also function calling. In the examples below, we use the gpt-5-nano model for a simple response, a chained response and streaming responses. To get started update the installed openai library. pip install --upgrade openai Simple Message 1) Build the client with the following code from openai import OpenAI client = OpenAI( base_url=endpoint, api_key=api_key, ) 2) The response received is an id which can then be used to retrieve the message. # Non-streaming request resp_id = client.responses.create( model=deployment, input=messages, ) 3) Message is retrieved using the response id from previous step response = client.responses.retrieve(resp_id.id) Chaining For a chained message, an extra step is sharing the context. This is done by sending the response id in the subsequent requests. resp_id = client.responses.create( model=deployment, previous_response_id=resp_id.id, input=[{"role": "user", "content": "Explain this at a level that could be understood by a college freshman"}] ) Streaming A different function call is used for streaming queries. client.responses.stream( model=deployment, input=messages, # structured messages ) In addition, the streaming query response has to be handled appropriately till end of event stream for event in s: # Accumulate only text deltas for clean output if event.type == "response.output_text.delta": delta = event.delta or "" text_out.append(delta) # Echo streaming output to console as it arrives print(delta, end="", flush=True) The code is available in the following github link - https://github.com/arunacarunac/ResponsesAPI Additional details are available in the following links - Azure OpenAI Responses API - Azure OpenAI | Microsoft Learn
Azure OpenAI: gpt-5-mini chat/completions streaming returns empty response.
Summary When calling gpt-5-mini via Chat Completions with "stream": true, the server opens the stream but no assistant tokens are emitted and the final JSON is empty (choices: [], created: 0, empty id/model). The same code path streams correctly for gpt-5 and gpt-4o deployments. Also, non-streaming ("stream": false) with gpt-5-mini returns valid content as expected. Environment API: POST /openai/deployments/{deployment}/chat/completions?api-version=2025-01-01-preview Model / Deployment: gpt-5-mini (Azure OpenAI deployment) Date/Time observed: 26 Aug 2025, ~13:00 IST (UTC+05:30) Region: useast2 Note: Same client, headers, and network path work for gpt-5 and gpt-4o streaming. Request Endpoint /openai/deployments/gpt-5/chat/completions?api-version=2025-01-01-preview Body { "messages": [ { "role": "system", "content": "give the best result you can" }, { "role": "user", "content": "Hello" } ], "stream": true } Actual Response (final aggregated JSON after stream ends) { "choices": [], "created": 0, "id": "", "model": "", "object": "", "prompt_filter_results": [ { "prompt_index": 0, "content_filter_results": { "hate": { "filtered": false, "severity": "safe" }, "jailbreak": { "filtered": false, "detected": false }, "self_harm": { "filtered": false, "severity": "safe" }, "sexual": { "filtered": false, "severity": "safe" }, "violence": { "filtered": false, "severity": "safe" } } } ] } Notes: No delta tokens arrive on the SSE stream. No assistant message content is ever emitted. Content filter result is safe across categories. Expected Behavior With "stream": true, server should emit SSE chunks with assistant delta tokens and finish with a populated final message in choices[0].message.content. Azure OpenAI: gpt-5-mini chat/completions streaming returns empty response (choices: [], created: 0) while other models stream fineDo you have experience fine tuning GPS OSS models?
Hi I found this space called Affine. It is a daily reinforcement learning competition and I'm participating in it. One thing that I am looking for collaboration on is with fine tuning GPT OSS models to score well on the evaluations. I am wondering if anyone here is interested in mining? I feel that people here would have some good reinforcement learning tricks. These models are evaluated on a set of RL-environments with validators looking for the model which dominates the Pareto frontier. I'm specifically looking to see any improvements in the coding deduction environment and the new ELR environment they made. I would like to use a GPT OSS model here but its hard to fine-tune these models in GRPO. Here is the information I found on Affine: https://www.reddit.com/r/reinforcementlearning/comments/1mnq6i0/comment/n86sjrk/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button
Image Dataset in Azure AI Asking for Tabular Format During Training
Hi everyone, I’m working on an image-based project in Azure AI. My images (PNG) are stored in Azure Blob Storage, and I registered them as a folder in Data Assets. When I start training, the UI asks for a tabular dataset instead. Since my data is images, I’m unsure how to proceed or whether I need to convert or register the dataset differently. What’s the correct way to set up image data for training in Azure AI?
