pengjunjie/ragflow
1
0
Fork 1
mirror of https://github.com/infiniflow/ragflow.git synced 2025-12-08 20:42:30 +08:00
Code Issues Packages Projects Releases Wiki Activity

Add docs for tag sets (#5890)

Browse Source 
### What problem does this PR solve?

#5716, #5529

### Type of change

- [x] Documentation Update
This commit is contained in:
writinwaters
2025-03-11 13:57:36 +08:00
committed by GitHub
parent 715e2b48ca
commit bd3fa317e7
11 changed files with 150 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/guides/dataset/run_retrieval_test.md
View File

@ -9,7 +9,7 @@ Conduct a retrieval test on your knowledge base to check whether the intended ch
---
After your files are uploaded and parsed, it is recommended that you run a retrieval test before proceeding with the chat assistant configuration. Just like fine-tuning a precision instrument, RAGFlow requires careful tuning to deliver optimal question answering performance. Your knowledge base settings, chat assistant configurations, and the specified large and small models can all significantly impact the final results. Running a retrieval test verifies whether the intended chunks can be recovered, allowing you to quickly identify areas for improvement or pinpoint any issue that needs addressing. For instance, when debugging your question answering system, if you know that the correct chunks can be retrieved, you can focus your efforts elsewhere.
After your files are uploaded and parsed, it is recommended that you run a retrieval test before proceeding with the chat assistant configuration. Running a retrieval test is *not* an unnecessary or superfluous step at all! Just like fine-tuning a precision instrument, RAGFlow requires careful tuning to deliver optimal question answering performance. Your knowledge base settings, chat assistant configurations, and the specified large and small models can all significantly impact the final results. Running a retrieval test verifies whether the intended chunks can be recovered, allowing you to quickly identify areas for improvement or pinpoint any issue that needs addressing. For instance, when debugging your question answering system, if you know that the correct chunks can be retrieved, you can focus your efforts elsewhere. For example, in issue [#5627](https://github.com/infiniflow/ragflow/issues/5627), the problem was found to be due to the LLM's limitations.
During a retrieval test, chunks created from your specified chunk method are retrieved using a hybrid search. This search combines weighted keyword similarity with either weighted vector cosine similarity or a weighted reranking score, depending on your settings: