AI Agent 
▌Introduction by Guotou On January 9, 2025, at 17:40, the official WeChat account of Tongyi announced the upgrade of the Tongyi Wanxiang model to version 2.1, showcasing significant improvements in both video and image generation capabilities. Let’s take a look at the promotional video from the official Tongyi account. Guotou’s Test Results Based on the … Read more
Editor / A Di Layout / A Di This isA Di’s Official Account‘s108thoriginal article – Introduction – How to make transparent kittens and puppies? They are very beautiful and have received thousands of likes on a certain platform. Some friends have asked me this question. Today, I will teach everyone how to create crystal-clear small … Read more
Paper: https://arxiv.org/abs/2501.00888 Github: https://github.com/Alibaba-NLP/CHRONOS Demo: https://modelscope.cn/studios/vickywu1022/CHRONOS In the digital age, the exponential growth of news information makes it crucial to extract and organize historical event timelines from massive texts. To address this challenge, Alibaba’s Tongyi Lab and researchers from Shanghai Jiao Tong University proposed a new framework for news timeline summarization based on agents—CHRONOS, named … Read more
Large language models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their responsiveness to dynamic real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) serves as a solution by integrating real-time data retrieval to enhance … Read more
If you are building a retrieval-augmented generation (RAG) application, you know how powerful they can be when they work well. But semantic embedding models are not magic. Most RAG implementations rely solely on semantic similarity as the unique retrieval mechanism, throwing every document into a vector database and applying the same retrieval logic to each … Read more
The RAG (Retrieval-Augmented Generation) model, commonly referred to as the RAG system, is widely used in large model applications. The principle of the model is quite simple: it retrieves information from a dataset based on user needs and then uses a large model for reasoning and generation. The advantage of RAG lies in its ability … Read more
A very impressive RAG system introduction. For more resources, please visit: I’m currently discussing interesting topics with friends in the “Large Model Pit Avoidance Guide,” would you like to join? [Code Repository] GitHub
https://arxiv.org/pdf/2501.09136 Overview of Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) represents a significant advancement in the field of artificial intelligence by combining the generative capabilities of Large Language Models (LLMs) with real-time data retrieval. While LLMs excel in natural language processing, their reliance on static pre-trained data often results in outdated or incomplete responses. RAG achieves … Read more
The Retrieval-Augmented Generation (RAG) technology is revolutionizing the AI application field by integrating external knowledge bases with internal knowledge of LLM (Large Language Model), enhancing the accuracy and reliability of AI systems. The knowledge “recall ability” of the multimodal knowledge extractor directly determines whether the large model can obtain accurate professional knowledge when answering reasoning … Read more
In developing the RAG system, the data formats in the knowledge base can be diverse, and most of them are unstructured data content. For example, PDF documents in the knowledge base are likely to contain table data, and our approach to handling this needs special attention to ensure that the table information can be correctly … Read more