Distributing your Search Ingest pipeline using Dapr

In the ever-evolving landscape of cloud-native applications, the need for scalable and resilient architectures has never been more critical. Traditional monolithic systems often fall short when it comes to handling large volumes of data and complex workflows. Enter Dapr: Distributed Application Runtime, a powerful framework that helps you build highly scalable and resilient microservices architecture. In this blog post, we'll explore how you can leverage Dapr to distribute your search ingest pip »

Optimizing Azure AI Search: Comparing Push and Pull approaches with performance recommendations

The Azure Search Index can be populated in two different ways. You can either directly push data into the index via the REST API/SDK (left image), or leverage the built-in Azure Search Indexer, which pulls data from a chosen DataSource and adds them to a specified index (right image) Choosing between the pull and push models depends on the requirements you have. Do you care most about how fast your data gets indexed, or is security your top priority? Maybe you need the flexibility to work with »

Transforming PDF Chaos into clarity: Add PDF Insights with Azure AI Search & RAG

A great way to add insights to your unstructured PDFs is by ingesting them into an Azure AI Search Index and adding an LLM on top, also known as the RAG pattern as described in: Enhancing Your Data with Azure: Unlocking Insights through Retrieval-Augmented Generation. This blog posts shows various code snippets, on how to achieve these different steps using Python. The first bit is to setup and populate the Search Index, and the last sections show how to query the search index and enrich the da »