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A feature of Amazon Aurora that enables making calls to ML models like Amazon Bedrock or Amazon SageMaker through SQL functions, allowing direct generation of embeddings within the database and abstracting the vectorization process.
An on-demand, auto-scaling configuration for Amazon Aurora DB instances that automatically adjusts compute and memory capacity based on load, integrated with Knowledge Bases for Amazon Bedrock to simplify vectorization and database capacity management.
A compact and efficient pre-trained sentence embedding model, widely used for generating vector representations of text. It's a popular choice for applications requiring fast and accurate semantic search, often integrated with vector databases.
A server that provides text embeddings, serving as a backend for embedding functions used with vector databases.
A Python library for creating sentence, text, and image embeddings, enabling the conversion of text into high-dimensional numerical vectors that capture semantic meaning. It is essential for tasks like semantic search and Retrieval Augmented Generation (RAG), which often leverage vector databases.
A Python library for generating high-quality sentence, text, and image embeddings. It simplifies the process of converting text into dense vector representations, which are fundamental for similarity search and storage in vector databases.
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.
Amazon Aurora Machine Learning is a feature of Amazon Aurora that integrates machine learning capabilities directly into the database. It allows users to invoke ML models, such as Amazon Bedrock or Amazon SageMaker, by using SQL functions.
Pricing information for Amazon Aurora Machine Learning is not detailed in the provided content.