Browsing: Retrieval

In industrial recommendation systems, the shift toward Generative Retrieval (GR) is replacing traditional embedding-based nearest neighbor search with Large Language Models (LLMs). These models represent items…

Perplexity has released pplx-embed, a collection of multilingual embedding models optimized for large-scale retrieval tasks. These models are designed to handle the noise and complexity of…

Large context windows have dramatically increased how much information modern language models can process in a single prompt. With models capable of handling hundreds of thousands—or…

import subprocess, sys, os, json, hashlib def pip(cmd): subprocess.check_call([sys.executable, “-m”, “pip”] + cmd) pip([“uninstall”, “-y”, “pillow”, “PIL”, “torchaudio”, “colpali-engine”]) pip([“install”, “-q”, “–upgrade”, “pip”]) pip([“install”, “-q”, “pillow<12”,…

In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic…

In this tutorial, we build an ultra-advanced agentic AI workflow that behaves like a production-grade research and reasoning system rather than a single prompt call. We…

We are excited to announce the general availability of multimodal retrieval for Amazon Bedrock Knowledge Bases. This new capability adds native support for video and audio…

In this tutorial, we build an advanced agentic AI workflow using LlamaIndex and OpenAI models. We focus on designing a reliable retrieval-augmented generation (RAG) agent that…

Meta researchers have introduced Perception Encoder Audiovisual, PEAV, as a new family of encoders for joint audio and video understanding. The model learns aligned audio, video,…