Tag · 5 posts
#vector
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…
aiEmbedding 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…
aiUsing Azure Database for PostgreSQL as a vector store
When we build LLM applications, there is always a recurring question: “What vector store will we use?”. In Azure, there are several native solutions. Some of them were discussed in previous posts. Azure Cognitive Search: supports vector search but also hybrid search with semantic reranking as discussed here Azure Redis Cache Enterprise: check my blog…
aiUse Azure OpenAI Add your data vector search from code
In the previous post, we looked at using Azure OpenAI Add your data from the Azure OpenAI Chat Playground. It is an easy-to-follow wizard to add documents to a storage account and start asking questions about them. From the playground, you can deploy a chat app to an Azure web app and you are good…
aiStoring and querying for embeddings with Redis
In a previous post, we wrote about using vectorized search and cosine similarity to quickly query a database of blog posts and retrieve the most relevant content to a natural language query. This is achieved using OpenAI’s embeddings API, Pinecone (a vector database), and OpenAI ChatCompletions. For reference, here’s the rough architecture: The steps above…
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