Tags: #multimodal

LightRAG is a simple and fast Retrieval-Augmented Generation (RAG) system designed for efficient and scalable knowledge retrieval and generation with Large Language Models.

A unified, production-ready inference API for effortlessly deploying and serving open-source language, speech, and multimodal AI models across various environments.

An AI-driven platform that transforms research papers, text, or topics into editable scientific figures, technical diagrams, and presentation slides with universal file support.

A comprehensive toolkit providing state-of-the-art embedding and reranker models for efficient information retrieval and Retrieval-Augmented Generation (RAG) applications.

A lightweight Python library for building modular, asynchronous, and composable AI pipelines, enabling efficient, parallel, and multimodal content processing for Generative AI applications.

An open-source framework for building real-time multimodal conversational AI agents.

A comprehensive, full-stack guide to Retrieval-Augmented Generation (RAG) technology, covering theory, practice, and engineering best practices for building LLM applications.

Deep Lake is an AI data runtime and database optimized for deep learning, offering multimodal data storage, querying, vector search, and streaming for LLM and deep learning applications.

SimpleMem provides an efficient, lifelong, and multimodal memory solution for LLM agents, featuring semantic lossless compression and retrieval.

A comprehensive and continuously updated collection of the world's best resources for Large Language Models (LLMs), covering various aspects from data to advanced applications.

A high-performance data engine for AI and multimodal workloads, processing diverse data types at scale with Python and Rust.

An open-source implementation for efficiently fine-tuning Alibaba Cloud's Qwen-VL series of multimodal large language models using HuggingFace and Liger-Kernel.

Morphik Core is an AI-native platform providing accurate document search and storage for building robust AI applications, specifically designed to handle complex, visually rich, and multimodal data, overcoming the limitations of traditional RAG.

A Python library for extracting clean markdown, multimodal media, and structured data from complex documents using vision-language models.

A series of video foundation models and large-scale datasets designed for comprehensive multimodal video understanding and generation.

A comprehensive, curated collection of research papers and resources focused on enhancing and understanding the reasoning abilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs).