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Docs: exporting created knowledge graphs is not supported (#8465)

### What problem does this PR solve?

_Briefly describe what this PR aims to solve. Include background context
that will help reviewers understand the purpose of the PR._

### Type of change

- [ ] Bug Fix (non-breaking change which fixes an issue)
- [ ] New Feature (non-breaking change which adds functionality)
- [x] Documentation Update
- [ ] Refactoring
- [ ] Performance Improvement
- [ ] Other (please describe):
tags/v0.20.0
writinwaters 4 months ago
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docs/guides/dataset/construct_knowledge_graph.md View File



### Where is the created knowledge graph stored? ### Where is the created knowledge graph stored?


All chunks of the created knowledge graph are stored in RAGFlow's document engine: either Elasticsearch or [Infinity](https://github.com/infiniflow/infinity).
All chunks of the created knowledge graph are stored in RAGFlow's document engine: either Elasticsearch or [Infinity](https://github.com/infiniflow/infinity).

### How to export a created knowledge graph?

Nope. Exporting a created knowledge graph is not supported. If you still consider this feature essential, please [raise an issue](https://github.com/infiniflow/ragflow/issues) explaining your use case and its importance.

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docs/references/http_api_reference.md View File



Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results. Generates five to ten alternative question strings from the user's original query to retrieve more relevant search results.


This operation requires a `Bearer Login Token`, which typically expires with in 24 hours. You can find the it in the Request Headers in your browser easily.
This operation requires a `Bearer Login Token`, which typically expires with in 24 hours. You can find the it in the Request Headers in your browser easily as shown below:

![Image](https://github.com/user-attachments/assets/17a5a0fe-e411-4e35-8251-85c71444468b)


:::tip NOTE :::tip NOTE
The chat model autonomously determines the number of questions to generate based on the instruction, typically between five and ten. The chat model autonomously determines the number of questions to generate based on the instruction, typically between five and ten.

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