AI

This article aims to explore the architectural design and specific code implementation of the RAG algorithm through the interpretation of papers and source code. This article mainly discusses GraphRAG, LightRAG and RAPTOR RAG, and also mentions Contextual Retrieval proposed by Anthropic and the evaluation method of the RAG algorithm. In the end, it is recommended that different methods be selected according to the size of the knowledge base document.

With the popularity of the Transformer architecture, many Embedding and Rerank models are now based on this architecture. Taking this opportunity, we will sort out the process and history of the research, and take stock of the architectures adopted by several well-known Rerank models and the companies that developed them. Finally, we will return to the topic and briefly discuss whether Rerank should be used in RAG scenarios.

This article mainly sorts out the relevant concepts of the hybrid expert model (MoE), and introduces the architectures and optimization methods of several open source MoE models, such as GShard, Switch Transformers, DeepSeek-MoE, and LLaMA-MoE. The characteristics and optimization methods of these models are also introduced.