3 Repos
Storage engines capable of managing and indexing complex, heterogeneous data types through unified vector embeddings.
Distinguishing note: Focuses on the storage and organization of multimodal data rather than just the search interface.
Explore 3 awesome GitHub repositories matching data & databases · Multimodal Databases. Refine with filters or upvote what's useful.
Milvus is a specialized vector database engine designed for the indexing, management, and high-speed similarity retrieval of high-dimensional vector embeddings. It functions as a similarity search engine capable of identifying nearest neighbors within large-scale vector spaces, supporting the storage and retrieval of billions of data points while maintaining consistent performance. The system utilizes a distributed architecture that decouples storage, query, and coordination into independent services, allowing for horizontal scaling across clusters. It employs a global indexing mechanism that
Acts as a unified storage environment for organizing and retrieving complex data types like text and images.
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
Organizes and stores text, images, audio, and embeddings in a unified format optimized for deep learning.
Marqo is an ecommerce product discovery platform, multimodal vector database, and AI search merchandising tool. It provides infrastructure for implementing semantic search and recommendations, allowing shoppers to find products using natural language and images. The platform distinguishes itself through a hybrid ranking pipeline that combines neural semantic scores with business-defined boosting and pinning rules. It features a conversational commerce engine that uses large language models to process user intent and provides a search performance analytics suite for measuring conversion uplift
Indexes text and images into a shared semantic space for unified multimodal retrieval.