Browsing: Retrieval

In the world of voice AI, the difference between a helpful assistant and an awkward interaction is measured in milliseconds. While text-based Retrieval-Augmented Generation (RAG) systems…

In the current AI landscape, the ‘context window’ has become a blunt instrument. We’ve been told that if we simply expand the memory of a frontier…

A key development in generative AI is AI-powered video generation. Before AI, creating dynamic video content required extensive resources, technical expertise, and significant manual effort. Today,…

OpenViking is an open-source Context Database for AI Agents from Volcengine. The project is built around a simple architectural concept: agent systems should not treat context…

class EverMemAgentOS: def __init__( self, workdir: str = “/content/evermem_agent_os”, db_name: str = “evermem.sqlite”, embedding_model: str = “sentence-transformers/all-MiniLM-L6-v2”, gen_model: str = “google/flan-t5-small”, stm_max_turns: int = 10, ltm_topk:…

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…