azure-search-vector-samples

Why we included this project

This is the most direct set of working examples for vector search on Azure AI Search that we have seen for teams already building on Azure. Alongside Python notebooks you will find console apps in several other languages, notably .NET, Java, and JavaScript, that trace the whole pipeline end to end: chunking source documents, embedding text or image content with Azure OpenAI, building the vector index, and running vector or hybrid queries in a RAG pattern. The integrated vectorization samples stand out because they show chunking and embedding happening automatically inside the search service through indexers and skillsets, rather than in your own code. The same material works as a migration reference too, since the repo tracks the breaking changes between the older preview REST API and current versions. And if you are prototyping an Azure knowledge search stack, the quantization and storage examples give you a concrete way to weigh index size trade-offs before you commit to a schema.