In this quickstart, you compare three vector index algorithms (DiskANN, HNSW, and IVF) and three similarity functions (cosine, L2, and inner product) to find the optimal configuration for your search ...

Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

We’re excited to announce a significant update to Semantic Kernel Python’s vector store implementation. Version 1.34 brings a complete overhaul that makes working with vector data simpler, more ...

Vector stores accept multiple model styles so applications can keep the data representation that already fits their validation, memory, and interoperability needs. When you own a model, annotate a ...

This guide covers the major vector store updates introduced in Semantic Kernel version 1.34, which represents a significant overhaul of the vector store implementation to align with the .NET SDK and ...

This repository contains multiple notebooks that demonstrate how to use Azure AI Search for vector and non-vector content in RAG patterns and in traditional search solutions. See What's new in Azure ...