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Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, weâll explore how RAG could work in languages other than English. Weâll explore French, Chinese, and Polish.
Introduction to Multilingual Retrieval Augmented Generation (RAG)
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The presentation was made in âWeb3 Fusion: Embracing AI and Beyondâ is more than a conference; it's a journey into the heart of digital transformation. The conference a provided a platform where the future of technology meets practical application. This three-day hybrid event, set in the heart of innovation, served as a gateway to the latest trends and transformative discussions in AI, Blockchain, IoT, AR/VR, and their collective impact on the information space.
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Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
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This Slide deck talk about how FHIR is being used in Ayushman Bharat Digital Mission (ABDM). It introduces the readers to ABDM and also to FHIR Documents paradigm. This is part of FHIR India community Basics learning initiative.
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