This article is approximately 5400 words, and it is recommended to read in 10minutes From neural search to multimodal applications, here neural search refers to the use of neural network models in search systems. When it comes to neural search, multimodal data inevitably comes to mind because the greatest advantage of neural networks over traditional … Read more
Have you ever been troubled by these questions? Searching through a cluttered computer hard drive for an assignment you saved, clearly remembering the general content, but unable to find it by name because the file name has little to do with the content; or when writing a paper, you remember a piece of literature supporting … Read more
In a previous article titled RAG Mastery Manual: Is RAG Sounding the Death Knell? Does Long Context in Large Models Mean Vector Retrieval is No Longer Important, we introduced the indispensability of RAG in solving the hallucination problem of large models, and reviewed how to enhance the practical effects of RAG using vector databases. Today, … Read more
Click on the “AI Youdao” above to select the “Top” public account Heavyweight content delivered first-hand This article is reproduced from Big Data Digest, secondary reproduction is prohibited Translated by Zhang Qiuyue, Yihang, Gao Yan, Long Xincheng Embedding is one of the most fascinating ideas in machine learning. If you have ever used Siri, Google … Read more
In today’s business landscape, corporate data exhibits high diversity and complexity. Emails, invoices, resumes, support tickets, log messages, and tabular data all contain intricate conceptual relationships and contextual information. However, traditional single-vector embedding models struggle to capture and understand this complex multidimensional data structure, posing significant challenges for data retrieval and mining. The Current State … Read more
You may have heard of the concept of word embedding, which represents semantics using numerical vectors. The closer the numerical vectors are, the more similar the corresponding statements or words are in meaning. LlamaIndex also uses embeddings to represent documents. The embedding model takes text as input and returns a long string of numbers that … Read more
This is the sixth article in the large model programming series, and also my notes from the free course on some cloud large model engineer ACA certification[1]. This course is really good, highly recommended! 👍🏻 If you’re interested in the course, please click the link at the bottom to view the original article. Here are … Read more
Big Data DigestProduced by Author: Jay Alammar Embedding is one of the most fascinating ideas in machine learning. If you have ever used Siri, Google Assistant, Alexa, Google Translate, or even your smartphone keyboard for next word prediction, you have likely benefited from this idea that has become central to natural language processing models. Over … Read more
LLM (Large Language Model) is a powerful new platform, but they are not always trained on data relevant to our tasks or the latest data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private or up-to-date data). It allows LLMs to use external data to generate … Read more