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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 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.
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.
JinaEmbeddingFunction is a Go wrapper embedding function designed for Jina Embedding models, used to generate vector embeddings. It is part of the chroma-go project and is available as a Go module.
EmbeddingRequest, EmbeddingResponse, and EmbeddingType.Pricing information is not available for this open-source Go package.