This package serves as a workspace for exploring state of the art RAG approaches in Go. The initial focus is on the GraphRAG approach from Microsoft, but other approaches may be explored in the future.
- GraphRAG
- dsRAG
GraphRAG is seems to be mostly marketing spin from Microsoft. There are some legitimately cool ideas in there such as using LLMs to extract graph relationships from a given text.
The heavy use of multi-stage summarisation, however, produces results that are not accurate to the original text or useful for an executive or research audience because they too general and lacking concrete details.
Source: https://github.com/D-Star-AI/dsRAG
There are three key methods used to improve performance over vanilla RAG systems:
Semantic sectioning AutoContext Relevant Segment Extraction (RSE)
We will implement these in Go to explore them further.