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A pre-trained model used for extracting embeddings from content like PDFs, videos, and transcripts, which are then stored in vector databases for faster search.
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 collection of examples and guides from OpenAI, including best practices for working with embeddings, which are fundamental to vector search and vector database applications.
An embedding function that utilizes the OpenAI API to compute vector embeddings, commonly used with vector databases.
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.
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 pre-trained model used for extracting embeddings from content like PDFs, videos, and transcripts, which are then stored in vector databases for faster search.
The provided content is a university project report on a "Gamified Code Learning Platform with NLP" and does not contain specific feature details for "OpenAI’s text-embedding-ada-002". It broadly discusses AI-powered tools and state-of-the-art models in the context of natural language to code translation.
Pricing information for "OpenAI’s text-embedding-ada-002" is not available in the provided content.