1. About this blogHow can Microsoft Fabric help address common challenges in microservice patterns? In this blog, I explore how GraphQL APIs in Microsoft Fabric can help address the communication challenges that often arise in service-to-service interactions within microservices or event-driven architectures, particularly for users who are already leveraging Microsoft Fabric to consolidate data from multiple sources. If you’ve been following my blogs, you might already be familiar with this project that I’m currently working on.

1. About this blog Are you struggling to convert natural language into SQL or not getting the results and accuracy you hoped for? If you find yourself in either of these situations, you’re in the right place! 🚀. All of us today are in a hurry to get things done with LLM and promote it to production. But things doesn’t work in that way. We need to carefully design our system to make it less non-deterministic (it’s already quite non-deterministic 😉).

1. About this blogWelcome to the final episode of this blog series. We are now prepared to work with our agent to develop an autonomous customer care solution. Here are the key takeaways from this blog: Working with Langchain agents 🤖 Understanding function calling concepts 🧠 2. Langchain AgentsLangchain is a open-source framework that simplifies the development of LLM applications. It is all about having set of tools and abstractions that makes it easy to integrate LLM into your workflows, connect them to external data sources and build complex applciations.

1. About this blogWelcome back! In this blog, we’ll explore vector databases and their use cases in building LLM applications. Specifically, we will utilize Azure Cosmos DB for Mongo vCore as our vector database solution. By the end of this blog you should be aware on how to work with Azure Cosmos DB for Mongo vCore and make use of it’s vector search capability. 2. Vector database and embeddings Vector databaseA vector database is a special type of database that stores data as high-dimensional vectors.

1. About this blogWelcome back! In this blog, we will learn how to prepare data for our retrieval process. There are various approaches to this, but we’ll use Azure Document Intelligence to extract information from unstructured documents. By the end, you’ll know how to: Work with the Azure Document Intelligence Layout model 📄 Perform custom chunking and add metadata 📝 Vectorize data into embeddings 🔍 2. DataaaaThe nature of data can vary greatly and often includes unstructured formats such as PDFs, audio, video, and images.

1. About this blogHello, and welcome to the first blog in my DTDL-CustomerCare Series! In this post, I’ll introduce the overall concept and architecture in more depth. But first, let me share how this project began. The idea of developing a chatbot for the customer care department came to me during some discussions with my current client, who expressed interest in this area. I started brainstorming and planning, but due to a busy few months both professionally and personally, I couldn’t dedicate enough time to it.