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A server that provides text embeddings, serving as a backend for embedding functions used with vector databases.
An embedding function implementation within the ChromaDB Java client (tech.amikos.chromadb.embeddings.hf.HuggingFaceEmbeddingFunction) that utilizes Hugging Face's cloud-based inference API to generate vector embeddings for documents.
A wrapper embedding function for Jina Embedding models, used to generate vector embeddings.
An embedding function that utilizes the OpenAI API to compute vector embeddings, commonly used with vector databases.
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 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 blazing fast inference solution for text embeddings models.
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