Post Overview

  • Improvements in Azure OpenAI Add your data

    Improvements in Azure OpenAI Add your data

    In a previous post, I talked about the Add your data feature in the Azure OpenAI Chat playground. Recently, there have been some updates to this feature, including vector search. Let’s take a look at the updated experience and focus on vector search. Starting point I have some PDF documents in a storage account container.… Read more

  • Use the Azure Add your data feature from code

    Use the Azure Add your data feature from code

    In a previous post, we looked at Azure Add your data (preview) that can be configured from the Azure OpenAI Chat playground. In a couple of steps, you can point to or upload files of a supported file type (PDF, Word, …) and create an Azure Cognitive Search index. That index can subsequently be queried… Read more

  • Building a chatbot in Azure that works with your data

    Building a chatbot in Azure that works with your data

    When I talk to customers about Azure OpenAI, I am often asked how to build a chatbot that uses your own data in the simplest way possible while still allowing for some customization. In Azure, there are a few solutions. We will look at one of the solutions in this post. Note: don’t feel like… Read more

  • Semantic Kernel Planner 101

    Semantic Kernel Planner 101

    Introduction If you are a developer who wants to build AI-first apps with natural language processing and large language models, you might be interested in Semantic Kernel (SK), a lightweight and open-source SDK that aims to simplify the integration of AI with conventional programming languages. SK is part of the CoPilot Stack and Microsoft is… Read more

  • Enhancing Semantic Search with a Streamlit UI

    Enhancing Semantic Search with a Streamlit UI

    In a previous blog post, we discussed two Python programs, upload_vectors.py and search_vectors.py. These programs were used to create and search vectors, respectively. The upload_vectors.py script created vectors from chunks of a larger text and stored them in Pinecone, while the search_vectors.py script enabled semantic search on the text. In this blog post, we will… Read more

  • Enhancing Blog Post Search with Chunk-based Embeddings and Pinecone

    Enhancing Blog Post Search with Chunk-based Embeddings and Pinecone

    In this blog post, we’ll show you a different approach to searching through a large database of blog posts. The previous approach involved creating a single embedding for the entire article and storing it in a vector database. The new approach is much more effective, and in this post, we’ll explain why and how to… Read more