Chatbots are becoming increasingly popular for interacting with users, providing information, entertainment, and assistance. However, building chatbots that can handle diverse and complex user queries is still a challenging task. One of the main difficulties is finding relevant and reliable information from large and noisy data sources. In this talk, I will present some of the latest advances...
Will discuss how to use multimodal embedding and large generative multimodal models that can see, hear, read, and feel data(!), to perform cross-modal search (searching audio with images, videos with text, etc.) and multimodal retrieval augmented generation (MM-RAG).
Today, we've gotten used to natural language search and recommendation systems. We expect to get what we search for without remembering the exact keywords.To solve these problems at scale we need a database that understands our data, this is where vector databases take center stage and really shine!
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