sharedWithMe returns only one item
A request from a standalone application to the https://learn.microsoft.com/en-us/graph/api/drive-sharedwithme endpoint returns only one item. Scopes used for authorization are: files.readwrite.all, group.readwrite.all, sites.readwrite.all, offline_access user.read When calling the same endpoint https://developer.microsoft.com/en-us/graph/graph-explorer, all shared items are returned. Result from application: { "@odata.context": "https://graph.microsoft.com/v1.0/$metadata#Collection(microsoft.graph.driveItem)", "value": [ { "createdDateTime": "2025-11-13T18:48:46Z", "id": "700DF30854FEF749!sd0cb731281c141acb1bf78fed133f933", "lastModifiedDateTime": "2025-11-13T18:48:46Z", "name": "saĝa-simio", "webUrl": "https://onedrive.live.com?cid=700df30854fef749&id=700DF30854FEF749!sd0cb731281c141acb1bf78fed133f933", "size": 0, "createdBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "lastModifiedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "fileSystemInfo": { } Note that only one item is returned. Result from Graph Explorer: { "@odata.context": "https://graph.microsoft.com/v1.0/$metadata#Collection(microsoft.graph.driveItem)", "@microsoft.graph.tips": "Use $select to choose only the properties your app needs, as this can lead to performance improvements. For example: GET me/drive/microsoft.graph.sharedWithMe?$select=audio,bundle", "value": [ { "createdDateTime": "2025-11-13T18:48:46Z", "id": "700DF30854FEF749!sd0cb731281c141acb1bf78fed133f933", "lastModifiedDateTime": "2025-11-13T18:48:46Z", "name": "saĝa-simio", "webUrl": "https://onedrive.live.com?cid=700df30854fef749&id=700DF30854FEF749!sd0cb731281c141acb1bf78fed133f933", "size": 0, "createdBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "lastModifiedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "fileSystemInfo": { "createdDateTime": "2025-11-13T18:48:46Z", "lastModifiedDateTime": "2025-11-13T18:48:46Z" }, "folder": { "childCount": 1, "view": {} }, "remoteItem": { "createdDateTime": "2025-11-13T18:48:46Z", "id": "700DF30854FEF749!sd0cb731281c141acb1bf78fed133f933", "lastModifiedDateTime": "2025-11-13T18:48:46Z", "name": "saĝa-simio", "size": 0, "createdBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "fileSystemInfo": { "createdDateTime": "2025-11-13T18:48:46Z", "lastModifiedDateTime": "2025-11-13T18:48:46Z" }, "folder": { "childCount": 1, "view": {} }, "lastModifiedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } }, "parentReference": { "driveType": "personal", "driveId": "700df30854fef749" }, "shared": { "scope": "users", "sharedDateTime": "2025-11-13T19:16:19Z", "owner": { "user": { "id": "700df30854fef749", "displayName": "Saĝa Simio" } }, "sharedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Saĝa Simio" } } }, "sharepointIds": { "listId": "a5aff78d-98ee-4b34-b35b-f4fe365ebd74", "listItemId": "184", "listItemUniqueId": "d0cb7312-81c1-41ac-b1bf-78fed133f933", "siteId": "87dbe7ee-ae0c-4854-82e7-d56d982fef54", "siteUrl": "https://my.microsoftpersonalcontent.com/personal/700df30854fef749", "webId": "9d8b91c3-b0b9-45d1-83e4-17babde0d1dc" } } }, { "createdDateTime": "2025-11-13T18:24:59Z", "id": "D26CADA368C1131F!116", "lastModifiedDateTime": "2025-11-13T18:24:59Z", "name": "Beastie", "webUrl": "https://onedrive.live.com?cid=d26cada368c1131f&id=D26CADA368C1131F!116", "size": 0, "createdBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Faerie Graceful" } }, "lastModifiedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Faerie Graceful" } }, "fileSystemInfo": { "createdDateTime": "2025-11-13T18:24:59Z", "lastModifiedDateTime": "2025-11-13T18:24:59Z" }, "folder": { "childCount": 1, "view": {} }, "remoteItem": { "createdDateTime": "2025-11-13T18:24:59Z", "id": "D26CADA368C1131F!116", "lastModifiedDateTime": "2025-11-13T18:24:59Z", "name": "Beastie", "size": 0, "createdBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Faerie Graceful" } }, "fileSystemInfo": { "createdDateTime": "2025-11-13T18:24:59Z", "lastModifiedDateTime": "2025-11-13T18:24:59Z" }, "folder": { "childCount": 1, "view": {} }, "lastModifiedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Faerie Graceful" } }, "parentReference": { "driveType": "personal", "driveId": "d26cada368c1131f" }, "shared": { "scope": "users", "sharedDateTime": "2025-11-13T18:41:30Z", "owner": { "user": { "id": "d26cada368c1131f", "displayName": "Faerie Graceful" } }, "sharedBy": { "user": { "email": "email address removed for privacy reasons", "id": "email address removed for privacy reasons", "displayName": "Faerie Graceful" } } }, "sharepointIds": { "listId": "5d31fd1e-38af-4117-b637-c1a0f7d5ca95", "listItemId": "4", "listItemUniqueId": "68c1131f-ada3-206c-80d2-740000000000", "siteId": "74111012-f9e7-4940-ad60-5e53a21e2d3c", "siteUrl": "https://my.microsoftpersonalcontent.com/personal/d26cada368c1131f", "webId": "f1ea2ab8-b5ee-4c93-8ce2-6c12700c226a" } } } ] } Any tips or suggestions are welcome.Configure Embedding Models on Azure AI Foundry with Open Web UI
Introduction Let’s take a closer look at an exciting development in the AI space. Embedding models are the key to transforming complex data into usable insights, driving innovations like smarter chatbots and tailored recommendations. With Azure AI Foundry, Microsoft’s powerful platform, you’ve got the tools to build and scale these models effortlessly. Add in Open Web UI, a intuitive interface for engaging with AI systems, and you’ve got a winning combo that’s hard to beat. In this article, we’ll explore how embedding models on Azure AI Foundry, paired with Open Web UI, are paving the way for accessible and impactful AI solutions for developers and businesses. Let’s dive in! To proceed with configuring the embedding model from Azure AI Foundry on Open Web UI, please firstly configure the requirements below. Requirements: Setup Azure AI Foundry Hub/Projects Deploy Open Web UI – refer to my previous article on how you can deploy Open Web UI on Azure VM. Optional: Deploy LiteLLM with Azure AI Foundry models to work on Open Web UI - refer to my previous article on how you can do this as well. Deploying Embedding Models on Azure AI Foundry Navigate to the Azure AI Foundry site and deploy an embedding model from the “Model + Endpoint” section. For the purpose of this demonstration, we will deploy the “text-embedding-3-large” model by OpenAI. You should be receiving a URL endpoint and API Key to the embedding model deployed just now. Take note of that credential because we will be using it in Open Web UI. Configuring the embedding models on Open Web UI Now head to the Open Web UI Admin Setting Page > Documents and Select Azure Open AI as the Embedding Model Engine. Copy and Paste the Base URL, API Key, the Embedding Model deployed on Azure AI Foundry and the API version (not the model version) into the fields below: Click “Save” to reflect the changes. Expected Output Now let us look into the scenario for when the embedding model configured on Open Web UI and when it is not. Without Embedding Models configured. With Azure Open AI Embedding models configured. Conclusion And there you have it! Embedding models on Azure AI Foundry, combined with the seamless interaction offered by Open Web UI, are truly revolutionizing how we approach AI solutions. This powerful duo not only simplifies the process of building and deploying intelligent systems but also makes cutting-edge technology more accessible to developers and businesses of all sizes. As we move forward, it’s clear that such integrations will continue to drive innovation, breaking down barriers and unlocking new possibilities in the AI landscape. So, whether you’re a seasoned developer or just stepping into this exciting field, now’s the time to explore what Azure AI Foundry and Open Web UI can do for you. Let’s keep pushing the boundaries of what’s possible!Step-by-step: Integrate Ollama Web UI to use Azure Open AI API with LiteLLM Proxy
Introductions Ollama WebUI is a streamlined interface for deploying and interacting with open-source large language models (LLMs) like Llama 3 and Mistral, enabling users to manage models, test them via a ChatGPT-like chat environment, and integrate them into applications through Ollama’s local API. While it excels for self-hosted models on platforms like Azure VMs, it does not natively support Azure OpenAI API endpoints—OpenAI’s proprietary models (e.g., GPT-4) remain accessible only through OpenAI’s managed API. However, tools like LiteLLM bridge this gap, allowing developers to combine Ollama-hosted models with OpenAI’s API in hybrid workflows, while maintaining compliance and cost-efficiency. This setup empowers users to leverage both self-managed open-source models and cloud-based AI services. Problem Statement As of February 2025, Ollama WebUI, still do not support Azure Open AI API. The Ollama Web UI only support self-hosted Ollama API and managed OpenAI API service (PaaS). This will be an issue if users want to use Open AI models they already deployed on Azure AI Foundry. Objective To integrate Azure OpenAI API via LiteLLM proxy into with Ollama Web UI. LiteLLM translates Azure AI API requests into OpenAI-style requests on Ollama Web UI allowing users to use OpenAI models deployed on Azure AI Foundry. If you haven’t hosted Ollama WebUI already, follow my other step-by-step guide to host Ollama WebUI on Azure. Proceed to the next step if you have Ollama WebUI deployed already. Step 1: Deploy OpenAI models on Azure Foundry. If you haven’t created an Azure AI Hub already, search for Azure AI Foundry on Azure, and click on the “+ Create” button > Hub. Fill out all the empty fields with the appropriate configuration and click on “Create”. After the Azure AI Hub is successfully deployed, click on the deployed resources and launch the Azure AI Foundry service. To deploy new models on Azure AI Foundry, find the “Models + Endpoints” section on the left hand side and click on “+ Deploy Model” button > “Deploy base model” A popup will appear, and you can choose which models to deploy on Azure AI Foundry. Please note that the o-series models are only available to select customers at the moment. You can request access to the o-series models by completing this request access form, and wait until Microsoft approves the access request. Click on “Confirm” and another popup will emerge. Now name the deployment and click on “Deploy” to deploy the model. Wait a few moments for the model to deploy. Once it successfully deployed, please save the “Target URI” and the API Key. Step 2: Deploy LiteLLM Proxy via Docker Container Before pulling the LiteLLM Image into the host environment, create a file named “litellm_config.yaml” and list down the models you deployed on Azure AI Foundry, along with the API endpoints and keys. Replace "API_Endpoint" and "API_Key" with “Target URI” and “Key” found from Azure AI Foundry respectively. Template for the “litellm_config.yaml” file. model_list: - model_name: [model_name] litellm_params: model: azure/[model_name_on_azure] api_base: "[API_ENDPOINT/Target_URI]" api_key: "[API_Key]" api_version: "[API_Version]" Tips: You can find the API version info at the end of the Target URI of the model's endpoint: Sample Endpoint - https://example.openai.azure.com/openai/deployments/o1-mini/chat/completions?api-version=2024-08-01-preview Run the docker command below to start LiteLLM Proxy with the correct settings: docker run -d \ -v $(pwd)/litellm_config.yaml:/app/config.yaml \ -p 4000:4000 \ --name litellm-proxy-v1 \ --restart always \ ghcr.io/berriai/litellm:main-latest \ --config /app/config.yaml --detailed_debug Make sure to run the docker command inside the directory where you created the “litellm_config.yaml” file just now. The port used to listen for LiteLLM Proxy traffic is port 4000. Now that LiteLLM proxy had been deployed on port 4000, lets change the OpenAI API settings on Ollama WebUI. Navigate to Ollama WebUI’s Admin Panel settings > Settings > Connections > Under the OpenAI API section, write http://127.0.0.1:4000 as the API endpoint and set any key (You must write anything to make it work!). Click on “Save” button to reflect the changes. Refresh the browser and you should be able to see the AI models deployed on the Azure AI Foundry listed in the Ollama WebUI. Now let’s test the chat completion + Web Search capability using the "o1-mini" model on Ollama WebUI. Conclusion Hosting Ollama WebUI on an Azure VM and integrating it with OpenAI’s API via LiteLLM offers a powerful, flexible approach to AI deployment, combining the cost-efficiency of open-source models with the advanced capabilities of managed cloud services. While Ollama itself doesn’t support Azure OpenAI endpoints, the hybrid architecture empowers IT teams to balance data privacy (via self-hosted models on Azure AI Foundry) and cutting-edge performance (using Azure OpenAI API), all within Azure’s scalable ecosystem. This guide covers every step required to deploy your OpenAI models on Azure AI Foundry, set up the required resources, deploy LiteLLM Proxy on your host machine and configure Ollama WebUI to support Azure AI endpoints. You can test and improve your AI model even more with the Ollama WebUI interface with Web Search, Text-to-Image Generation, etc. all in one place.A Comprehensive Guide to Getting Started with Data API Builder for Azure SQL Database or SQL Server
Learn about the capabilities of Data API Builder for Azure SQL Database or SQL Server. Follow our step-by-step guide on provisioning, deploying, and testing your API, as well as learning how to link your database to a simple HTML page. With Data API Builder, you can unleash the strength of your data and improve your productivity.
Understanding Resource SKU restriction reason codes
Hello, I am using the SKU REST API (https://docs.microsoft.com/en-us/rest/api/compute/resource-skus/list?tabs=HTTP) to query available VM sizes for each region and I do not understand ResourceSkuRestrictionsReasonCode. There are two codes: NotAvailableForSubscription and QuotaId. Now, for NotAvailableForSubscription I see many deprecated sizes listed which makes perfect sense, but I also do see some sizes to disappear/reapper from time to time when I do multiple requests and I wonder if this reason code is also used when capacity for such VM size is reached. The problem is, I want to get full list of all possible (not deprecated) VM sizes, I am not interested in what the actual capacity is and I believe this REST API is the right one to use. Can someone explain me this flag? The second code, QuotaId, is also a bit weird. From the name, it looks like it should restrict VM sizes my account don't have quotas for, however, on my account I only have basic quotas, yet I see all of the VM sizes. I have literally zero quota restrictions. The documentation is not really good for this REST API, I hope to find more information somewhere. Thanks!
SharePoint API won't return readOnly fields
Hello everyone, we try to get all the attributes from a file in SharePoint using the API. When we call /_api/web/lists(...)/items and filter for the wanted file we get all attributes like name, id, the last modify date and even custom fields, but none like file size and file type. We figured that fields that are meant to be readOnly won't be delivered with the calls we make. But we also don't want to make more calls than necessary to get the attributes. Is there a way or call to get all attributes a file has at once?Invoke-WebRequest : { "type": "Client error", "title": "Missing HTTP body" } - Error Api invoke
Dear all, I need your support about the error indicated in the subject. The Body field in the cmdlet contain the Json data but it is not possible to execute. Invoke-WebRequest -Uri 'xxxxx' -Method POST -Headers $headers -ContentType 'application/problem+json' -body $body
OneDrive API - createLink with type "embed" returns dead urls
Hello. I've just started using OneDrive Rest API (I'm using https://github.com/OneDrive/onedrive-sdk-python if that's important). What I'm trying to accomplish is to get embed url for some pictures from OneDrive. I get an error while using https://docs.microsoft.com/en-us/onedrive/developer/rest-api/api/driveitem_createlink?view=odsp-graph-online with type = "embed". I receive an url in format "https://onedrive.live.com/embed?resid=abcdef&authkey=123456" that leads to non-working page: If I try to get link from onedrive website interface (using "Embed" option) it works fine but it's in different format (https://yfzapq.am.files.1drv.com/abcdefg123456?height=504&width=504). Am I missing something? Is there way to get working embed urls via OneDrive API? PS I've found that some people also receiving the same issue - https://stackoverflow.com/questions/58805214/onedrive-rest-api-embed-download-url What I've also found that in 2016 ember urls were unavailable in API (https://github.com/OneDrive/onedrive-api-docs/issues/102). Maybe nothing changed since then.
