https://www.chenshaowen.com/en/tags/rag/Recent content in RAG on Shaowen Chen's WebsiteHugo -- gohugo.ioen©2016 - {year}, All Rights Reserved.Sun, 12 Jan 2025 00:00:00 +0000weeklyhttps://www.chenshaowen.com/en/blog/ai-application-development-tech-stack.htmlSun, 12 Jan 2025 00:00:00 +0000Sun, 12 Jan 2025 00:00:00 +0000https://www.chenshaowen.com/en/blog/ai-application-development-tech-stack.htmlEmbedding Models Embedding models map high-dimensional data into a lower-dimensional space, which makes the data easier to process and analyze. Text Models Here is a leaderboard, https://huggingface.co/spaces/mteb/leaderboard The leaderboard above gives each model’s score, parameter count, memory usage, vector dimension, maximum tokens, and other information. Below are some commonly used open-source models:微信公众号AIApplication DevelopmentTech StackLLMR&DLearningRAGEmbeddingBest Practiceshttps://www.chenshaowen.com/en/blog/large-model-application-design-and-implementation-guide.htmlSat, 23 Dec 2023 11:22:55 +0000Sat, 23 Dec 2023 11:22:55 +0000https://www.chenshaowen.com/en/blog/large-model-application-design-and-implementation-guide.html1. Problems with Using LLMs Directly Unstable output One characteristic of generative AI is the diversity of its output. Ask an LLM the same question several times and you may get different answers. This uncertainty in output is a pleasant surprise for users in conversation and creative scenarios. But in微信公众号LLMAIBest PracticesR&DArchitectureLearningRAGAgent