dsRAG achieves breakthrough improvements in retrieval effectiveness through the following methodology:
1. Semantic segmentation
LLM analyzes the semantic structure of documents and divides lengthy content into logical paragraphs. For example, when dealing with legal contracts, the system recognizes structural units such as "definitional clauses" and "obligation clauses", enabling subsequent searches to pinpoint the relevant sections.
2. Automatic context generation
Dynamically generate metadata for each text block containing the following elements:
- Document title and section path
- Summary of preceding and following paragraphs
- Field keyword tagging
This enhanced embedding enables similarity search to understand contextual associations.
3. Extraction of relevant segments
The query is processed in two stages:
- Retrieve the most relevant text segments first
- and then automatically find semantically related neighboring paragraphs
With this adaptive extension, the final results returned remain both focused and have full context. Experiments show that this method improves the accuracy of long quiz tasks by 411 TP3T.
This answer comes from the articledsRAG: A Retrieval Engine for Unstructured Data and Complex QueriesThe
