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weaviate.io | ||
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amirmalik.net
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| | | | | An introduction to Retrieval-Augmented Generation (RAG) and how embeddings, chunking, and vector search work together in the context of LLM search. | |
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www.milanjovanovic.tech
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| | | | | Vector search finds information based on meaning rather than exact keywords, delivering more intuitive results by converting content into numerical vectors that capture semantic relationships. | |
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lantern.dev
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| | | | | Hybrid vector search combines the strengths of sparse and dense vector searches to improve search quality. We evaluate its performance on several datasets from the BEIR framework using Postgres. | |
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amatria.in
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| | | In the landscape of Generative AI (GenAI), we often find ourselves amazed at the rapidity and scale of advancements. GPT-4 stands as a shining example, pushing the boundaries of linguistic understanding and generation. Yet, as we move forward, a compelling new horizon emerges: the Multimodal Generative AI Revolution. By melding GPT-4's textual capabilities with multimodality-integrating diverse data types such as images, voice, and video-we're not just opening a door, but unleashing a tidal wave of transformative potential that promises to redefine our digital experiences. | ||
