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Fix typos (#11208)

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### What problem does this PR solve?

As title

### Type of change

- [x] Documentation Update

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
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Jin Hai
2025-11-12 14:20:04 +08:00
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parent 20b6dafbd8
commit 8406a5ea47
21 changed files with 34 additions and 34 deletions
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docs/guides/dataset/enable_raptor.md
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@ -13,7 +13,7 @@ RAPTOR (Recursive Abstractive Processing for Tree Organized Retrieval) is an enh
![document_clustering](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/document_clustering_as_preprocessing.jpg)
Our tests with this new approach demonstrate state-of-the-art (SOTA) results on question-answering tasks requiring complex, multi-step reasoning. By combining RAPTOR retrieval with our built-in chunking methods and/or other retrieval-augmented generation (RAG) approaches, you can further improve your question-answering accuracy.
Our tests with this new approach demonstrate state-of-the-art (SOTA) results on question-answering tasks requiring complex, multistep reasoning. By combining RAPTOR retrieval with our built-in chunking methods and/or other retrieval-augmented generation (RAG) approaches, you can further improve your question-answering accuracy.
:::danger WARNING
Enabling RAPTOR requires significant memory, computational resources, and tokens.
@ -29,7 +29,7 @@ The recursive clustering and summarization capture a broad understanding (by the
## Scenarios
For multi-hop question-answering tasks involving complex, multi-step reasoning, a semantic gap often exists between the question and its answer. As a result, searching with the question often fails to retrieve the relevant chunks that contribute to the correct answer. RAPTOR addresses this challenge by providing the chat model with richer and more context-aware and relevant chunks to summarize, enabling a holistic understanding without losing granular details.
For multi-hop question-answering tasks involving complex, multistep reasoning, a semantic gap often exists between the question and its answer. As a result, searching with the question often fails to retrieve the relevant chunks that contribute to the correct answer. RAPTOR addresses this challenge by providing the chat model with richer and more context-aware and relevant chunks to summarize, enabling a holistic understanding without losing granular details.
:::tip NOTE
Knowledge graphs can also be used for multi-hop question-answering tasks. See [Construct knowledge graph](./construct_knowledge_graph.md) for details. You may use either approach or both, but ensure you understand the memory, computational, and token costs involved.