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Victor Rentea
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UiPathCommunity
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Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe
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apidays
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The presentation was made in “Web3 Fusion: Embracing AI and Beyond” 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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Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
apidays
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Explore how multimodal embeddings work with Milvus. We will see how you can explore a popular multimodal model - CLIP - on a popular dataset - CIFAR 10. You use CLIP to create the embeddings of the input data, Milvus to store the embeddings of the multimodal data (sometimes termed “multimodal embeddings”), and we will then explore the embeddings.
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
Zilliz
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, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.
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Introduction to Multilingual Retrieval Augmented Generation (RAG)
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