Docs: RAGFlow does not suppport batch metadata setting (#7795)

### 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


- [x] Documentation Update

docs/guides/chat/set_chat_variables.md

 `{knowledge}` is the system's reserved variable, representing the chunks retrieved from the knowledge base(s) specified by **Knowledge bases** under the **Assistant settings** tab. If your chat assistant is associated with certain knowledge bases, you can keep it as is.
 
 :::info NOTE
-It does not currently make a difference whether you set `{knowledge}` to optional or mandatory, but note that this design will be updated at a later point.
+It currently makes no difference whether  `{knowledge}` is set as optional or mandatory, but please note this design will be updated in due course.
 :::
 
 From v0.17.0 onward, you can start an AI chat without specifying knowledge bases. In this case, we recommend removing the `{knowledge}` variable to prevent unnecessary reference and keeping the **Empty response** field empty to avoid errors.

docs/guides/dataset/best_practices/accelerate_doc_indexing.mdx

 - On the configuration page of your knowledge base, switch off **Use RAPTOR to enhance retrieval**.
 - Extracting knowledge graph (GraphRAG) is time-consuming.
 - Disable **Auto-keyword** and **Auto-question** on the configuration page of your knowledge base, as both depend on the LLM.
-- **v0.17.0+:** If your document is plain text PDF and does not require GPU-intensive processes like OCR (Optical Character Recognition), TSR (Table Structure Recognition), or DLA (Document Layout Analysis), you can choose **Naive** over **DeepDoc** or other time-consuming large model options in the **Document parser** dropdown. This will substantially reduce document parsing time.
+- **v0.17.0+:** If all PDFs in your knowledge base are plain text and do not require GPU-intensive processes like OCR (Optical Character Recognition), TSR (Table Structure Recognition), or DLA (Document Layout Analysis), you can choose **Naive** over **DeepDoc** or other time-consuming large model options in the **Document parser** dropdown. This will substantially reduce document parsing time.

docs/guides/dataset/enable_raptor.md

 
 ### Prompt
 
-The following prompt will be applied recursively for cluster summarization, with `{cluster_content}` serving as an internal parameter. We recommend that you keep it as-is for now. The design will be updated at a later point.
+The following prompt will be applied recursively for cluster summarization, with `{cluster_content}` serving as an internal parameter. We recommend that you keep it as-is for now. The design will be updated in due course.
 
 ```
 Please summarize the following paragraphs... Paragraphs as following:

docs/guides/dataset/select_pdf_parser.md

 ---
-sidebar_position: 0
+sidebar_position: 2
 slug: /select_pdf_parser
 ---
 
     - **Laws**
     - **Presentation**
     - **One**
-- To use a third-party visual model for parsing PDFs, ensure you have set a default image2txt model under **Set default models** on the **Model providers** page.
+- To use a third-party visual model for parsing PDFs, ensure you have set a default img2txt model under **Set default models** on the **Model providers** page.
 
 ## Procedure
 
 
 2. Select the option that works best with your scenario:
 
-- DeepDoc: (Default) The default visual model for OCR, TSR, and DLR tasks.
-- Naive: Skip OCR, TSR, and DLR tasks if *all* your PDFs are plain text.
-- A third-party visual model provided by a specific model provider.
+  - DeepDoc: (Default) The default visual model for OCR, TSR, and DLR tasks, which is time-consuming.
+  - Naive: Skip OCR, TSR, and DLR tasks if *all* your PDFs are plain text.
+  - A third-party visual model provided by a specific model provider.
 
 :::caution WARNING
 Third-party visual models are marked **Experimental**, because we have not fully tested these models for the aforementioned data extraction tasks.

docs/guides/dataset/set_metadata.md

 ---
-sidebar_position: 2
+sidebar_position: 0
 slug: /set_metada
 ---
 
 Ensure that your metadata is in JSON format; otherwise, your updates will not be applied.
 :::
 
-![Image](https://github.com/user-attachments/assets/379cf2c5-4e37-4b79-8aeb-53bf8e01d326)
+![Image](https://github.com/user-attachments/assets/379cf2c5-4e37-4b79-8aeb-53bf8e01d326)
+
+## Frequently asked questions
+
+### Can I set metadata for multiple documents at once?
+
+No, RAGFlow does not support batch metadata setting. If you still consider this feature essential, please [raise an issue](https://github.com/infiniflow/ragflow/issues) explaining your use case and its importance.

docs/guides/models/llm_api_key_setup.md

 5. Click **OK** to confirm your changes.
 
 :::note
-To update an existing model API key at a later point:
+To update an existing model API key:
 ![update api key](https://github.com/infiniflow/ragflow/assets/93570324/0bfba679-33f7-4f6b-9ed6-f0e6e4b228ad)
 :::

docs/quickstart.mdx

 
    ![add llm](https://github.com/infiniflow/ragflow/assets/93570324/10635088-028b-4b3d-add9-5c5a6e626814)
 
-   > Each RAGFlow account is able to use **text-embedding-v2** for free, an embedding model of Tongyi-Qianwen. This is why you can see Tongyi-Qianwen in the **Added models** list. And you may need to update your Tongyi-Qianwen API key at a later point.
-
 2. Click on the desired LLM and update the API key accordingly (DeepSeek-V2 in this case):
 
    ![update api key](https://github.com/infiniflow/ragflow/assets/93570324/4e5e13ef-a98d-42e6-bcb1-0c6045fc1666)

docs/release_notes.md

 - AI chat: Leverages Tavily-based web search to enhance contexts in agentic reasoning. To activate this, enter the correct Tavily API key under the **Assistant settings** tab of your chat assistant dialogue.
 - AI chat: Supports starting a chat without specifying knowledge bases.
 - AI chat: HTML files can also be previewed and referenced, in addition to PDF files.
-- Dataset: Adds a **PDF parser**, aka **Document parser**, dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLA (Document Layout Analysis), OCR (Optical Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options.
+- Dataset: Adds a **PDF parser**, aka **Document parser**, dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLA (Document Layout Analysis), OCR (Optical Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options. See [here](./guides/dataset/select_pdf_parser.md).
 - Agent component: **(x)** or a forward slash `/` can be used to insert available keys (variables) in the system prompt field of the **Generate** or **Template** component.
 - Object storage: Supports using Aliyun OSS (Object Storage Service) as a file storage option.
 - Models: Updates the supported model list for Tongyi-Qianwen (Qwen), adding DeepSeek-specific models; adds ModelScope as a model provider.