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Updated template description (#4744)

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### What problem does this PR solve?



### Type of change


- [x] Documentation Update
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writinwaters
2025-02-06 17:14:13 +08:00
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2
docs/guides/accelerate_question_answering.mdx
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@ -23,7 +23,7 @@ Please note that some of your settings may consume a significant amount of time.
- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, disabling **Multi-turn optimization** will reduce the time required to get an answer from the LLM.
- In the **Prompt Engine** tab of your **Chat Configuration** dialogue, leaving the **Rerank model** field empty will significantly decrease retrieval time.
- In the **Assistant Setting** tab of your **Chat Configuration** dialogue, disabling **Keyword analysis** will reduce the time to get get an answer from the LLM.
- In the **Assistant Setting** tab of your **Chat Configuration** dialogue, disabling **Keyword analysis** will reduce the time to receive an answer from the LLM.
- When chatting with your chat assistant, click the light bulb icon above the *current* dialogue and scroll down the popup window to view the time taken for each task:
![enlighten](https://github.com/user-attachments/assets/fedfa2ee-21a7-451b-be66-20125619923c)

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docs/guides/develop/build_docker_image.mdx
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@ -35,7 +35,7 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
This image is approximately 2 GB in size and relies on external LLM and embedding services.
:::tip NOTE
While we also test RAGFlow on ARM64 platforms, we do not plan to maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
:::
```bash
@ -51,7 +51,7 @@ docker build --build-arg LIGHTEN=1 -f Dockerfile -t infiniflow/ragflow:nightly-s
This image is approximately 9 GB in size. As it includes embedding models, it relies on external LLM services only.
:::tip NOTE
While we also test RAGFlow on ARM64 platforms, we do not plan to maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine.
While we also test RAGFlow on ARM64 platforms, we do not maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine.
:::
```bash