Tag: NLP embeddings
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Implementing RAG Embedding in AI Models
Retrieval-Augmented Generation (RAG) relies heavily on embeddings to establish a shared semantic space for efficient retrieval and generation of information. Embedding in RAG transforms textual or multimodal data into dense vector representations that encapsulate contextual and semantic relationships. These embeddings form the foundation for retrieving relevant information from external knowledge bases, thereby enriching the generative…
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Open source Embedding in AI Systems
Embeddings have revolutionized the field of artificial intelligence (AI) by providing a robust way to represent high-dimensional data like text, images, and audio in a continuous vector space. Open-source embeddings have become indispensable tools for AI practitioners, enabling rapid experimentation and deployment of machine learning models. These embeddings, freely available to the community, allow researchers…