AI Agents: Mastering Agentic RAG - Part 5
This blog post, Part 5 of a series on AI agents, explores Agentic RAG (Retrieval-Augmented Generation), a paradigm shift in how LLMs interact with external data. Unlike traditional RAG, Agentic RAG allows LLMs to autonomously plan their information retrieval process through an iterative loop of actions and evaluations. The post highlights the importance of the LLM "owning" the reasoning process, dynamically selecting tools and refining queries. It covers key implementation details, including iterative loops, tool integration, memory management, and handling failure modes. Practical use cases, governance considerations, and code examples demonstrating Agentic RAG with AutoGen, Semantic Kernel, and Azure AI Agent Service are provided. The post concludes by emphasizing the transformative potential of Agentic RAG and encourages further exploration through linked resources and previous blog posts in the series.[pt2] Choosing the right Data Storage Source (Under Preview) for Azure AI Search
This blog introduces new preview data sources for Azure AI Search, including Fabric OneLake Files, Azure Cosmos DB for Gremlin, Azure Cosmos DB for MongoDB, SharePoint, and Azure Files. Each data source supports incremental indexing, metadata extraction, and AI enrichment, making Azure AI Search more powerful for enterprise search applications.[pt1] Choosing the right Data Storage Source (Generally available) for Azure AI Search
When integrating Azure AI Search into your solutions, choosing the right data storage and data sources is crucial for efficient and scalable indexing. This blog dives into three primary data source connectors for Azure AI Search: Azure Blob Storage, Azure Cosmos DB for NoSQL, and Azure SQL Database. Each data source type offers distinct advantages and use cases depending on the structure of your data and the desired search functionality.
RoomRadar.ai: Revolutionising Hotel Search with Azure Maps and Azure AI Services
Explore how RoomRadar.ai leverages Azure Maps and AI Search to transform hotel hunting. From interactive map views with route planning to finding hotels based on visual preferences with AI, RoomRadar.ai showcases the future of travel planning. Dive into the technical details for this UCL-Microsoft collaboration.Enhancing Applications with AI - RoomRadar.Ai's Chatbot, Search, and Recommendation Systems
Learn how to build AI-powered applications using Microsoft Azure’s AI services. This article discusses RoomRadar.Ai, a hotel search system, as an example to guide developers to help create personalized user experiences with AI technologies. Within, the GPT-4o chatbot, search, and recommendation (similarity search) systems are discussed.Teach ChatGPT to Answer Questions: Using Azure AI Search & Azure OpenAI (Semantic Kernel)
In this two-part series, we will explore how to build intelligent service using Azure. In Series 1, we'll use Azure AI Search to extract keywords from unstructured data stored in Azure Blob Storage. In Series 2, we'll Create a feature to answer questions based on PDF documents using Azure OpenAIProtect PII information in your Microsoft Fabric Lakehouse with Responsible AI
Data analysts and data scientists need to protect the personally identifiable information (PII) of their clients, such as names, addresses, emails, phone numbers, or social security numbers, that they use to build reports and dashboards. PII can pose risks to both the data subjects and the data holders and can introduce biases that affect the decisions made based on the data. One way to protect PII is to use Responsible AI, which is a set of principles and practices that help to mask PII with synthetic or anonymized data that preserves the statistical properties and structure of the original data but does not reveal the identity or attributes of the individuals.The future of LLM: Scopriamo la RAG
La Retrieval Augmented Generation (RAG) rappresenta una svolta paradigmatica nell’ambito dell’intelligenza artificiale, offrendo una soluzione rivoluzionaria ai tradizionali Large Language Models (LLM). Mentre gli LLM hanno dimostrato un’eccezionale capacità di generare testi coerenti e informativi, la RAG spinge i confini dell’AI combinando queste capacità con un meccanismo avanzato di recupero delle informazioni da fonti esterne.
