In a noteworthy development, Azure AI Search has been integrated with Haystack, enabling the creation of an interactive review agent tailored for retrieving and analyzing customer reviews. By leveraging the combined ability of Azure AI Search’s hybrid retrieval and Haystack’s modular architecture, users can delve deeper into customer insights using sophisticated tools for sentiment analysis and summarization. This collaborative effort between Khye Wei from Azure AI Search and Amna Mubashar from Haystack seeks to streamline evaluation processes and enhance the interpretability of customer data.
The functionality provided by Azure AI Search is robust, with an enterprise-focused retrieval system designed to accommodate high-performance GenAI applications. At its core, it blends keyword-based and vector-based search techniques to optimize search results, leveraging semantic ranking through deep learning models. Haystack augments these capabilities with its flexible pipeline design, enabling customizable AI applications adaptable across various uses and data flows. This collaboration is especially powerful due to Haystack’s tools that allow AI models to interact with external functions, facilitating complex and intelligent analyses.
For those eager to venture into AI-driven applications, detailed instructions and a working code example are available, utilizing an open-source dataset on Kaggle. The process involves converting datasets to Haystack Documents, indexing with semantic search configurations in Azure AI Search, and deploying a query pipeline enhanced with aspect-based sentiment analysis and summarization tools. This impactful collaboration exemplifies the power of combining search capabilities with AI-driven tools to refine customer review assessments.
News: Building an Interactive Feedback Review Agent with Azure AI Search and Haystack
Documentation: Azure AI Search Documentation
