Copilot Learning Hub: Your Gateway to Mastering Microsoft Copilot
Have you ever wanted a single place to go to learn all about Microsoft Copilot? The Copilot Learning Hub is designed to be your go-to source for everything related to Microsoft Copilot, with articles, videos, and hands-on labs for all tech areas.
Introducing Vector Search Similarity Capabilities in Azure Cache for Redis Enterprise
The latest wave of generative AI, like large language models, has paved the way for significant advancements in the utilization of vector embeddings and vector similarity search. Large language models, such as OpenAI's GPT, are capable of learning complex patterns and representations from vast amounts of text, enabling them to generate rich semantic embeddings for words, sentences, and documents. By leveraging these learned embeddings, developers can now harness the power of vector similarity search, revolutionizing the way information is organized, retrieved, and analyzed in various domains, including fraud detection, recommendation systems, and information retrieval.Hack Together: RAG Hack - Building RAG Applications with LangChain.js
In the rapidly evolving landscape of Artificial Intelligence and Natural Language Processing, the use of Retrieval Augmented Generation (RAG) has emerged as a powerful solution to enhance the accuracy and relevance of responses generated by language models. In this article, we will explore the talk given during the Hack Together: RAG Hack event, where Glaucia Lemos, a Cloud Advocate at Microsoft, and Yohan Lasorsa, a Senior Cloud Advocate at Microsoft, demonstrated how LangChain.js is revolutionizing the development of RAG applications, making it easier to create intelligent applications that combine large language models (LLMs) with your own data sources.
