Retrieval
How to Design a Fully Functional Enterprise AI Assistant with Retrieval Augmentation and Policy Guardrails Using Open Source AI Models
In this tutorial, we explore how we can build a compact yet powerful Enterprise AI assistant that runs effortlessly on Colab. We start by integrating retrieval-augmented generation (RAG) using FAISS for document retrieval and FLAN-T5 for text generation, both fully open-source and free. As we progress, we embed enterprise policies such as data redaction, access…
LightOn AI Released GTE-ModernColBERT-v1: A Scalable Token-Level Semantic Search Model for Long-Document Retrieval and Benchmark-Leading Performance
Semantic retrieval focuses on understanding the meaning behind text rather than matching keywords, allowing systems to provide results that align with user intent. This ability is essential across domains that depend on large-scale information retrieval, such as scientific research, legal analysis, and digital assistants. Traditional keyword-based methods fail to capture the nuance of human language,…
This AI Paper from Tel Aviv University Introduces GASLITE: A Gradient-Based Method to Expose Vulnerabilities in Dense Embedding-Based Text Retrieval Systems
Dense embedding-based text retrieval has become the cornerstone for ranking text passages in response to queries. The systems use deep learning models for embedding text into vector spaces that enable semantic similarity measurements. This method has been adopted widely in applications such as search engines and retrieval-augmented generation (RAG), where retrieving accurate and contextually relevant…