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Creating a Copilot declarative agent with VS Code and the Teams Toolkit
If you are a Microsoft 365 Copilot user, you have probably seen that the words “agent” and “Copilot agent” are popping up here and there. For example, if you chat with Copilot there is an Agents section in the top right corner: Above, there is a Visual Creator agent that’s built-in. It’s an agent dedicated…
Using your own message broker with Diagrid Catalyst
In a previous post, I wrote about Diagrid Catalyst. Catalyst provides services like pub/sub and state stores to support the developer in writing distributed applications. In the post, we discussed a sample application that processes documents and extracts fields with an LLM (gpt-4o structured extraction). Two services, upload and process, communicate via the pub/sub pattern.…
Writing an multi-service document extractor with the help of Diagrid’s Catalyst
Many enterprises have systems in place that take documents, possibly handwritten, that contain data that needs to be extracted. In this post, we will create an application that can extract data from documents that you upload. We will make use of an LLM, in this case gpt-4o. We will use model version 2024-08-06 and its…
Token consumption in Microsoft’s Graph RAG
In the previous post, we discussed Microsoft’s Graph RAG implementation. In this post, we will take a look at token consumption to query the knowledge graph, both for local and global queries. Note: this test was performed with gpt-4o. A few days after this blog post, OpenAI released gpt-4o-mini. Initital tests with gpt-4o-mini show that…
Trying out Microsoft’s Graph RAG
Whenever we build applications on top of LLMs such as OpenAI’s gpt-4o, we often use the RAG pattern. RAG stands for retrieval augmented generation. You use it to let the LLM answer questions about data it has never seen. To answer the question, you retrieve relevant information and hand it over to the LLM to…
Embracing the Age of AI Transformation: A New Era for Innovation and Expertise
⚠️ This is an AI-generated article based on a video to transcript generator I created to summarise Microsoft Build sessions. This article is used as an example for a LinkedIn post. This article is based on the Microsoft Build keynote delivered on Tuesday, May 21st, 2024. It was created with gpt-4o. The post is unedited…
Load balancing OpenAI API calls with LiteLLM
If you have ever created an application that makes calls to Azure OpenAI models, you know there are limits to the amount of calls you can make per minute. Take a look at the settings of a GPT model below: Above, the tokens per minute (TPM) rate limit is set to 60 000 tokens. This…
Use Azure OpenAI on your data with Semantic Kernel
I have written before about Azure OpenAI on your data. For a refresher, see Microsoft Learn. In short, Azure OpenAI on your data tries to make it easy to create an Azure AI Search index that supports advanced search mechanisms like vector search, potentially enhanced with semantic reranking. On of the things you can do…
So you want a chat bot to talk to your SharePoint data?
It’s a common request we hear from clients: “We want a chatbot that can interact with our data in SharePoint!” The idea is compelling – instead of relying on traditional search methods or sifting through hundreds of pages and documents, users could simply ask the bot a question and receive an instant, accurate answer. It promises to be a much more efficient and user-friendly experience. The appeal is clear: Improved user experience Time savings Increased productivity But…
Embedding flows created with Microsoft Prompt Flow in your own applications
A while ago, I wrote about creating your first Prompt Flow in Visual Studio Code. In this post, we will embed such a flow in a Python application built with Streamlit. The application allows you to search for images based on a description. Check the screenshot below: There are a few things we need to…
