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.
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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 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.
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 utility class from the Hugging Face Transformers library that automatically loads the correct tokenizer for a given pre-trained model. It is crucial for consistent text preprocessing and tokenization, a vital step before generating embeddings for vector database storage.
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.
"all-MiniLM-L6-v2" is a sentence-transformers model that maps sentences and paragraphs to a 384-dimensional dense vector space.
bert, feature-extraction, and text-embeddings-inference categories.