Adopting Hybrid Search with Azure Cosmos DB
In an era where data accessibility and retrieval are crucial, Azure Cosmos DB introduces Hybrid Search, a cutting-edge feature that merges the capabilities of Vector Search and Full-Text Search. This integration enhances search relevance by combining semantic understanding with traditional keyword-based methods, making it ideal for diverse applications such as e-commerce, content management, and AI-driven chatbots. The blog provides a comprehensive guide on enabling and configuring Hybrid Search within Azure Cosmos DB, detailing the processes for setting up Vector Search and Full-Text Search. It also explores the underlying mechanics of Hybrid Search, which utilizes Reciprocal Rank Fusion (RRF) to combine multiple scoring functions for more accurate search results. Additionally, practical use cases and a step-by-step project example demonstrate how to implement an enterprise knowledge management system using Nest.js integrated with Azure Cosmos DB's Hybrid Search capabilities. This powerful combination offers developers and businesses the tools needed to create sophisticated, efficient, and intelligent search experiences within their applications.[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.Full-Text Search in Azure Cosmos DB
Full-Text Search in Azure Cosmos DB: A Powerful Way to Enhance Search Capabilities Searching through vast amounts of unstructured data can be challenging, but Full-Text Search simplifies this by allowing advanced querying beyond simple keyword matching. Now available in Azure Cosmos DB for NoSQL (Preview), this feature enables faster, more accurate searches using techniques like tokenization, stemming, stop-word removal, and indexing. How It Works Full-text search operates in two stages: Indexing – Text is analyzed, broken into tokens, and indexed for efficient retrieval. Searching – Queries are run against the index using functions like FullTextContains, FullTextContainsAll, and FullTextScore, allowing ranked and relevant results.Develop a Library Web API: Integrating Azure Cosmos DB for MongoDB with ASP.NET Core
As a software developer, you’re always seeking ways to build scalable, high-performance applications. Azure Cosmos DB for MongoDB offers the flexibility of MongoDB with the reliability and global reach of Azure. In this blog, we’ll explore how to integrate Azure Cosmos DB for MongoDB with your ASP.NET Core application, walking through the key steps for setting up a simple API to perform CRUD operations. By leveraging this powerful combination, you can streamline your development process and unlock new possibilities for your data-driven projects. In our previous blog, we delved into the capabilities of azure cosmos DB for MongoDB using Open MongoDB shell in Azure portal. I highly recommend checking it out to understand the fundamentalsBuild a chatbot service to ensure safe conversations: Using Azure Content Safety & Azure OpenAI
This tutorial is ideal for anyone who wants to build a chatbot service with strong content moderation capabilities. In this tutorial, you will learn how to build a chatbot service that interacts with users using Azure Cosmos DB, Azure Content Safety, and Azure OpenAI. This service provides the following features: 1. Analyze user messages for safety: Analyze messages entered by users using Azure Content Safety to evaluate them for hate, self-harm, sexual content, and violence. 2. Conversations with chatbot: Conduct conversations about safe messages using Azure OpenAI. 3. Manage conversation history: Store a user's conversation history in Azure Cosmos DB and load or clear the history as needed.
