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Deep Lake is a vector database designed as a data lake for AI, capable of storing and managing vector embeddings, text, images, and videos. It utilizes a tensor format for efficient querying and integration with AI algorithms, making it suitable for similarity search and machine learning workflows. It is open-source and tailored for handling unstructured and multimodal data, with seamless integration with frameworks like PyTorch and TensorFlow.
Marqo is an open-source neural search engine that leverages vector representations to enable semantic search over textual data. It abstracts vector database complexity and provides a high-level interface for building advanced search applications.
Vearch is a distributed vector search engine designed for AI-native applications, enabling scalable and efficient similarity search across large datasets.
A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
An open-source library for creating, storing, and searching multimodal data and vector embeddings, supporting AI and ML workflows.
Havenask is an open-source distributed search engine with support for vector search, designed for large-scale AI and search applications.
Deep Lake is an open-source vector database designed as a data lake for AI applications. It is built to store and manage various types of unstructured and multimodal data, such as vector embeddings, text, images, videos, PDFs, and audio files. Deep Lake utilizes a tensor format for efficient querying and seamless integration with AI algorithms and frameworks like PyTorch and TensorFlow. It is suitable for similarity search, machine learning workflows, and connecting data to large language models.
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