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kazunori279

1 karma · joined April 13, 2014

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kazunori279··on [dead]
Improving RAG search relevance with new "task type" embeddings developed by the Google DeepMind team. Task type embeddings can effectively bridge the semantic gap between questions and answers, leading to more effective retrieval and enhanced RAG performance.
kazunori279··on What is Multimodal Search: “LLMs with vision” change businesses
What if large language models (LLMs) had "vision", the ability to understand the meaning of images? Just like we have seen the innovation with LLMs with chatbots and text data, the ability would make another huge impact on businesses by letting LLMs look at and organize millions of images in enterprise IT systems. In this post, we will learn how a large vision language model (VLM) works and changes the business in the next couple of years.
kazunori279··on Vertex AI Embeddings for Text: Grounding LLMs Made Easy
Many people are now starting to think about how to bring Gen AI and large language models (LLMs) to production services. You may be wondering "How to integrate LLMs or AI chatbots with existing IT systems, databases and business data?", "We have thousands of products. How can I let LLM memorize them all precisely?", or "How to handle the hallucination issues in AI chatbots to build a reliable service?". Here is a quick solution: grounding with embeddings and vector search.
kazunori279··on An Inside Look at Google BigQuery [pdf]
This is so old! (from the author :)