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.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.
