Awesome GitHub RepositoriesMultimodal Data Storage

Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea

DeepLake is AI data infrastructure consisting of a multimodal data lake, a hybrid search engine, and a serverless vector database. It provides a PostgreSQL-based AI data runtime that combines multimodal storage with streaming pipelines to load and shuffle datasets from cloud storage directly into deep learning training pipelines. The system utilizes lazy indexing to store and slice images, audio, and video without loading entire files into memory. It enables retrieval-augmented generation by persisting high-dimensional embeddings in a serverless vector store and implementing hybrid search tha

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

Lance is a columnar data format and storage layer designed for high-performance random access and the persistence of multimodal data. It functions as a vector database storage system, a multimodal data store, and a versioned dataset manager. The project distinguishes itself as a hybrid search engine that combines vector similarity search and full-text indexing on a single dataset. It provides unified storage for diverse data types including images, audio, and video, utilizing a system that lazy-loads large binary objects only when requested. The system manages dataset evolution through schem

Lance is a versioned columnar data format and storage engine designed as a multimodal AI lakehouse. It serves as a vector database storage engine and a cloud object store dataset manager, organizing images, video, audio, and embeddings into a unified format optimized for machine learning workflows. The project distinguishes itself by combining a columnar layout for structured data with a specialized blob store for large multimodal tensors. It implements a hybrid search engine that integrates vector similarity search, full-text search, and SQL analytics on a single dataset, supported by a stor

Este proyecto es un kit de desarrollo de software (SDK) y una herramienta de gestión de clústeres para PHP. Sirve como SDK de búsqueda de texto completo e interfaz de búsqueda vectorial, permitiendo a las aplicaciones realizar búsquedas léxicas, difusas y semánticas sobre datos indexados. La librería implementa un cliente HTTP PSR 7 para garantizar la compatibilidad entre entornos mediante interfaces de mensajería estandarizadas. Proporciona una interfaz especializada para recuperar embeddings y realizar flujos de trabajo de recuperación semántica utilizando datos vectoriales. Su superficie de capacidades abarca una amplia gama de tareas administrativas y operativas, incluyendo la gestión de índices de búsqueda, monitoreo de salud del clúster y operaciones del ciclo de vida de documentos. Admite diversos métodos de consulta como SQL, EQL y ES|QL, junto con agregación de datos y análisis geoespacial. Además, proporciona herramientas para la orquestación de machine learning, detección de anomalías y gestión de identidad y acceso.

Daft is a distributed dataframe library and multimodal data processor designed to handle large-scale structured and unstructured data. It functions as a vectorized execution engine that processes tables alongside images, audio, and video, utilizing a unified schema to manage diverse data types. The project distinguishes itself by combining distributed data engineering with large-scale AI inference. It provides an AI data pipeline for batch-optimizing model prompts and generating high-dimensional text embeddings, while utilizing zero-copy memory sharing to execute custom Python functions witho

Infinity es una base de datos vectorial distribuida y un almacén vectorial multimodal diseñado para gestionar datasets a gran escala para recuperación y búsqueda por similitud. Sirve como backend para aplicaciones de modelos de lenguaje grandes y pipelines de generación aumentada por recuperación (RAG) almacenando y recuperando vectores densos, vectores dispersos y datos de texto completo. El sistema funciona como un motor de búsqueda híbrido, combinando embeddings vectoriales y búsqueda de texto completo con algoritmos de reranking para identificar los documentos más relevantes. Admite el almacenamiento de datos multimodal, permitiendo el mantenimiento de diversos tipos de datos, incluyendo tensores, cadenas y numéricos, dentro de un único entorno. La base de datos ofrece capacidades para gestionar esquemas y registros, incluyendo importación, exportación y consultas estructuradas. Incluye herramientas para la gestión de índices y optimización de almacenamiento, y ofrece recuperación de estado mediante snapshots del sistema o de tablas. La base de datos puede desplegarse como un binario único o mediante Docker, y es accesible a través de una API HTTP y un SDK de Python.

This research framework provides a deep learning driving simulator and a multimodal data pipeline for autonomous vehicle research. It centers on the creation of synchronized autonomous vehicle datasets, which combine high-frequency vehicle telemetry with camera frames to train neural networks. The project implements a convolutional neural network trainer specifically designed to predict steering angles and vehicle transition states from visual data. It features generative capabilities, using autoencoders and transition models to synthesize driving environments and simulate future vehicle move

OM1 is a multimodal AI agent runtime and orchestration framework designed to connect large language models to physical robot hardware and sensors. It provides an execution environment that processes audio, video, and sensor data to drive autonomous decisions and actions in real-world settings. The system integrates a robotics SLAM and navigation stack with a hardware abstraction layer, allowing high-level AI commands to be translated into low-level motor and actuator instructions. It distinguishes itself by incorporating blockchain-based governance to enforce immutable operational rules and p