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Fix errors (#11795)

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### What problem does this PR solve?

- typos
- IDE warnings

### Type of change

- [x] Refactoring

---------

Signed-off-by: Jin Hai <haijin.chn@gmail.com>
This commit is contained in:
Jin Hai
2025-12-08 09:42:10 +08:00
committed by GitHub
parent 8de6b97806
commit 6546f86b4e
13 changed files with 35 additions and 35 deletions
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4
docs/guides/agent/best_practices/accelerate_agent_question_answering.md
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@ -45,13 +45,13 @@ Click the light bulb icon above the *current* dialogue and scroll down the popup
| Item name | Description |
| ----------------- | --------------------------------------------------------------------------------------------- |
| ----------------- |-----------------------------------------------------------------------------------------------|
| Total | Total time spent on this conversation round, including chunk retrieval and answer generation. |
| Check LLM | Time to validate the specified LLM. |
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Tune question | Time to optimize the user query using the context of the multi-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |

2
docs/guides/chat/best_practices/accelerate_question_answering.mdx
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@ -37,7 +37,7 @@ Please note that rerank models are essential in certain scenarios. There is alwa
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Tune question | Time to optimize the user query using the context of the multi-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |

2
docs/guides/manage_users_and_services.md
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@ -8,7 +8,7 @@ slug: /manage_users_and_services
The Admin CLI and Admin Service form a client-server architectural suite for RAGflow system administration. The Admin CLI serves as an interactive command-line interface that receives instructions and displays execution results from the Admin Service in real-time. This duo enables real-time monitoring of system operational status, supporting visibility into RAGflow Server services and dependent components including MySQL, Elasticsearch, Redis, and MinIO. In administrator mode, they provide user management capabilities that allow viewing users and performing critical operations—such as user creation, password updates, activation status changes, and comprehensive user data deletion—even when corresponding web interface functionalities are disabled.
The Admin CLI and Admin Service form a client-server architectural suite for RAGFlow system administration. The Admin CLI serves as an interactive command-line interface that receives instructions and displays execution results from the Admin Service in real-time. This duo enables real-time monitoring of system operational status, supporting visibility into RAGFlow Server services and dependent components including MySQL, Elasticsearch, Redis, and MinIO. In administrator mode, they provide user management capabilities that allow viewing users and performing critical operations—such as user creation, password updates, activation status changes, and comprehensive user data deletion—even when corresponding web interface functionalities are disabled.

2
docs/guides/models/deploy_local_llm.mdx
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@ -305,7 +305,7 @@ With the Ollama service running, open a new terminal and run `./ollama pull <mod
</TabItem>
</Tabs>
### 4. Configure RAGflow
### 4. Configure RAGFlow
To enable IPEX-LLM accelerated Ollama in RAGFlow, you must also complete the configurations in RAGFlow. The steps are identical to those outlined in the *Deploy a local model using Ollama* section: