retrieval

In industrial recommendation systems, the shift towards Generative Retrieval (GR) Traditional embedding-based nearest neighbor search is being replaced by large language models (LLMs). These models represent objects Semantic ID (SID)—discrete …

confusion continues pplx-embedA collection of multilingual embedding models optimized for large-scale retrieval tasks. These models are designed to handle the noise and complexity of web-scale data, providing a production-ready alternative …

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 even millions of …

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”, “torchaudio==2.8.0”)) pip((“install”, “-q”, “colpali-engine”, …

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

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 quick call. We asynchronously ingest real web …

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 can reason on evidence, …

Author(s): Ayyub Nainiya Originally published on Towards AI. RAG is not a recovery problem, it is a system design problem. The sooner you start treating it as one, the sooner …

Author(s): abi Originally published on Towards AI. A practical guide to making recovery a learnable decision layer with code, architecture, and production trade-offs. Vector search works great for “one question, …

Meta Researchers Introduce Perception Encoder Audiovisual, PEAVAs a new family of encoders for joint audio and video understanding. The model learns aligned audio, video and text representations in a single …