Synchronize documentation. (#428)

Co-authored-by: RAGFlow Curator <infinitydocs.curator@users.noreply.github.com>

Author: infinityCurator

website/docs/configurations.md

@@ -99,8 +99,8 @@ RAGFlow utilizes MinIO as its object storage solution, leveraging its scalabilit
 - `RAGFLOW-IMAGE`  
   The Docker image edition. Available editions:  
 
-  - `infiniflow/ragflow:v0.21.0-slim` (default): The RAGFlow Docker image without embedding models.  
-  - `infiniflow/ragflow:v0.21.0`: The RAGFlow Docker image with embedding models including:
+  - `infiniflow/ragflow:v0.21.1-slim` (default): The RAGFlow Docker image without embedding models.  
+  - `infiniflow/ragflow:v0.21.1`: The RAGFlow Docker image with embedding models including:
     - Built-in embedding models:
       - `BAAI/bge-large-zh-v1.5` 
       - `maidalun1020/bce-embedding-base_v1`

website/docs/develop/build_docker_image.mdx

@@ -77,7 +77,7 @@ After building the infiniflow/ragflow:nightly-slim image, you are ready to launc
 
 1. Edit Docker Compose Configuration
 
-Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.21.0-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
+Open the `docker/.env` file. Find the `RAGFLOW_IMAGE` setting and change the image reference from `infiniflow/ragflow:v0.21.1-slim` to `infiniflow/ragflow:nightly-slim` to use the pre-built image.
 
 
 2. Launch the Service

website/docs/faq.mdx

@@ -30,17 +30,17 @@ The "garbage in garbage out" status quo remains unchanged despite the fact that
 
 Each RAGFlow release is available in two editions:
 
-- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.21.0-slim`
-- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.21.0`
+- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.21.1-slim`
+- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.21.1`
 
 ---
 
 ### Which embedding models can be deployed locally?
 
-RAGFlow offers two Docker image editions, `v0.21.0-slim` and `v0.21.0`:  
+RAGFlow offers two Docker image editions, `v0.21.1-slim` and `v0.21.1`:  
 
-- `infiniflow/ragflow:v0.21.0-slim` (default): The RAGFlow Docker image without embedding models.  
-- `infiniflow/ragflow:v0.21.0`: The RAGFlow Docker image with the following built-in embedding models:
+- `infiniflow/ragflow:v0.21.1-slim` (default): The RAGFlow Docker image without embedding models.  
+- `infiniflow/ragflow:v0.21.1`: The RAGFlow Docker image with the following built-in embedding models:
   - `BAAI/bge-large-zh-v1.5`
   - `maidalun1020/bce-embedding-base_v1`
 
@@ -510,3 +510,27 @@ See [here](./guides/agent/best_practices/accelerate_agent_question_answering.md)
 
 ---
 
+### How to use MinerU to parse PDF documents?
+
+MinerU PDF document parsing is available starting from v0.21.1. To use this feature, follow these steps:
+
+1. Before deploying ragflow-server, update your **docker/.env** file:  
+   - Enable `HF_ENDPOINT=https://hf-mirror.com`
+   - Add a MinerU entry: `MINERU_EXECUTABLE=/ragflow/uv_tools/.venv/bin/mineru`
+
+2. Start the ragflow-server and run the following commands inside the container:  
+
+```bash
+mkdir uv_tools
+cd uv_tools
+uv venv .venv
+source .venv/bin/activate
+uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple
+```
+
+3. Restart the ragflow-server.
+4. In the web UI, navigate to the **Configuration** page of your dataset. Click **Built-in** in the **Ingestion pipeline** section, select a chunking method from the **Built-in** dropdown, which supports PDF parsing, and slect **MinerU** in **PDF parser**.
+5. If you use a custom ingestion pipeline instead, you must also complete the first three steps before selecting **MinerU** in the **Parsing method** section of the **Parser** component.
+
+
+

website/docs/guides/chat/start_chat.md

@@ -48,7 +48,7 @@ You start an AI conversation by creating an assistant.
      - If no target language is selected, the system will search only in the language of your query, which may cause relevant information in other languages to be missed.
    - **Variable** refers to the variables (keys) to be used in the system prompt. `{knowledge}` is a reserved variable. Click **Add** to add more variables for the system prompt.
       - If you are uncertain about the logic behind **Variable**, leave it *as-is*.
-      - As of v0.21.0, if you add custom variables here, the only way you can pass in their values is to call:
+      - As of v0.21.1, if you add custom variables here, the only way you can pass in their values is to call:
          - HTTP method [Converse with chat assistant](../../references/http_api_reference.md#converse-with-chat-assistant), or
          - Python method [Converse with chat assistant](../../references/python_api_reference.md#converse-with-chat-assistant).
 

website/docs/guides/dataset/configure_knowledge_base.md

@@ -59,7 +59,7 @@ You can also change a file's chunking method on the **Files** page.
 ![change chunking method](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/change_chunking_method.jpg)
 
 <details>
-  <summary>From v0.21.0 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
+  <summary>From v0.21.1 onward, RAGFlow supports ingestion pipeline for customized data ingestion and cleansing workflows.</summary>
 
   To use a customized data pipeline:
 
@@ -138,7 +138,7 @@ See [Run retrieval test](./run_retrieval_test.md) for details.
 
 ## Search for dataset
 
-As of RAGFlow v0.21.0, the search feature is still in a rudimentary form, supporting only dataset search by name.
+As of RAGFlow v0.21.1, the search feature is still in a rudimentary form, supporting only dataset search by name.
 
 ![search dataset](https://raw.githubusercontent.com/infiniflow/ragflow-docs/main/images/search_datasets.jpg)
 

website/docs/guides/dataset/select_pdf_parser.md

@@ -35,8 +35,31 @@ RAGFlow isn't one-size-fits-all. It is built for flexibility and supports deeper
 
   - DeepDoc: (Default) The default visual model performing OCR, TSR, and DLR tasks on PDFs, which can be time-consuming.
   - Naive: Skip OCR, TSR, and DLR tasks if *all* your PDFs are plain text.
+  - MinerU: An experimental feature.
   - A third-party visual model provided by a specific model provider.
 
+:::danger IMPORTANG
+MinerU PDF document parsing is available starting from v0.21.1. To use this feature, follow these steps:
+
+1. Before deploying ragflow-server, update your **docker/.env** file:  
+   - Enable `HF_ENDPOINT=https://hf-mirror.com`
+   - Add a MinerU entry: `MINERU_EXECUTABLE=/ragflow/uv_tools/.venv/bin/mineru`
+
+2. Start the ragflow-server and run the following commands inside the container:  
+
+```bash
+mkdir uv_tools
+cd uv_tools
+uv venv .venv
+source .venv/bin/activate
+uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple
+```
+
+3. Restart the ragflow-server.
+4. In the web UI, navigate to the **Configuration** page of your dataset. Click **Built-in** in the **Ingestion pipeline** section, select a chunking method from the **Built-in** dropdown, which supports PDF parsing, and slect **MinerU** in **PDF parser**.
+5. If you use a custom ingestion pipeline instead, you must also complete the first three steps before selecting **MinerU** in the **Parsing method** section of the **Parser** component.
+:::
+
 :::caution WARNING
 Third-party visual models are marked **Experimental**, because we have not fully tested these models for the aforementioned data extraction tasks.
 :::

website/docs/guides/manage_files.md

@@ -87,4 +87,4 @@ RAGFlow's file management allows you to download an uploaded file:
 
 ![download_file](https://github.com/infiniflow/ragflow/assets/93570324/cf3b297f-7d9b-4522-bf5f-4f45743e4ed5)
 
-> As of RAGFlow v0.21.0, bulk download is not supported, nor can you download an entire folder. 
+> As of RAGFlow v0.21.1, bulk download is not supported, nor can you download an entire folder. 

website/docs/guides/manage_users_and_services.md

@@ -46,7 +46,7 @@ The Admin CLI and Admin Service form a client-server architectural suite for RAG
 2. Install ragflow-cli.
 
    ```bash
-   pip install ragflow-cli==0.21.0
+   pip install ragflow-cli==0.21.1
    ```
 
 3. Launch the CLI client:

website/docs/guides/tracing.mdx

@@ -18,7 +18,7 @@ RAGFlow ships with a built-in [Langfuse](https://langfuse.com) integration so th
 Langfuse stores traces, spans and prompt payloads in a purpose-built observability backend and offers filtering and visualisations on top.  
 
 :::info NOTE
-• RAGFlow **≥ 0.21.0** (contains the Langfuse connector)  
+• RAGFlow **≥ 0.21.1** (contains the Langfuse connector)  
 • A Langfuse workspace (cloud or self-hosted) with a _Project Public Key_ and _Secret Key_
 :::
 

website/docs/guides/upgrade_ragflow.mdx

@@ -66,10 +66,10 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
    git clone https://github.com/infiniflow/ragflow.git
    ```
 
-2. Switch to the latest, officially published release, e.g., `v0.21.0`:
+2. Switch to the latest, officially published release, e.g., `v0.21.1`:
 
    ```bash
-   git checkout -f v0.21.0
+   git checkout -f v0.21.1
    ```
 
 3. Update **ragflow/docker/.env**:
@@ -83,14 +83,14 @@ To upgrade RAGFlow, you must upgrade **both** your code **and** your Docker imag
   <TabItem value="slim">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.0-slim
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1-slim
 ```
 
   </TabItem>
   <TabItem value="full">
 
 ```bash
-RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.0
+RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1
 ```
 
   </TabItem>
@@ -114,10 +114,10 @@ No, you do not need to. Upgrading RAGFlow in itself will *not* remove your uploa
 1. From an environment with Internet access, pull the required Docker image.
 2. Save the Docker image to a **.tar** file.
    ```bash
-   docker save -o ragflow.v0.21.0.tar infiniflow/ragflow:v0.21.0
+   docker save -o ragflow.v0.21.1.tar infiniflow/ragflow:v0.21.1
    ```
 3. Copy the **.tar** file to the target server.
 4. Load the **.tar** file into Docker:
    ```bash
-   docker load -i ragflow.v0.21.0.tar
+   docker load -i ragflow.v0.21.1.tar
    ```