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A cloud data warehouse that can be leveraged to store vector embeddings as List data types, enabling semantic search capabilities through SQL-based similarity functions within an existing data pipeline.

A cloud data platform that offers capabilities for storing and querying various data types, including vector embeddings, often used in conjunction with its data warehousing features.

ACORN is a performant and predicate-agnostic search system for vector embeddings and structured data, enhancing the capability of vector databases to handle complex queries over high-dimensional data efficiently.

Arroy is an open-source library for efficient similarity search and management of vector embeddings, useful in vector database systems.

An open-source library for creating, storing, and searching multimodal data and vector embeddings, supporting AI and ML workflows.

A collection of resources, libraries, and databases focused on handling and searching multidimensional vector data, directly relevant for storing and querying vector embeddings in AI-powered applications.

FastText is an open-source library by Facebook for efficient learning of word representations and text classification. It generates high-dimensional vector embeddings used in vector databases for tasks like semantic search and document clustering.

Gensim is a Python library for topic modeling and vector space modeling, providing tools to generate high-dimensional vector embeddings from text data. These embeddings can be stored and efficiently searched in vector databases, making Gensim directly relevant to vector search use cases.

GloVe is a widely used method for generating word embeddings using co-occurrence statistics from text corpora. These embeddings are commonly used as input to vector databases for semantic search and other vector-based information retrieval tasks.