RAGLight supports three RAG modes:
- Standard RAG Pipes: Combining document retrieval and generation, relevant document fragments are retrieved by similarity search and fed into the LLM as context to generate answers.
- Agentic RAG: By
AgenticRAGPipelineimplementation, adding intelligent body functions to support multi-step reasoning and dynamic adjustment of retrieval strategies. - RAT (Retrieval Augmented Thinking): By
RATPipelinerealization, adding a reflection step (viareflection(parameter control) to improve the logic and accuracy of the answer.
Users can choose different modes according to their needs, such as Agentic RAG or RAT mode when complex reasoning is required.
This answer comes from the articleRAGLight: Lightweight Retrieval Augmentation Generation Python LibraryThe
May not be reproduced without permission:AI productivity tools " What RAG modes does RAGLight offer? What is the difference between them?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil