infiniflow · infinityCurator · Oct 23, 2025 · Oct 23, 2025
diff --git a/website/docs/configurations.md b/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`

diff --git a/website/docs/develop/build_docker_image.mdx b/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

diff --git a/website/docs/faq.mdx b/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.
+
+
+
diff --git a/website/docs/guides/chat/start_chat.md b/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).
 

diff --git a/website/docs/guides/dataset/configure_knowledge_base.md b/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)
 

diff --git a/website/docs/guides/dataset/select_pdf_parser.md b/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.
 :::

diff --git a/website/docs/guides/manage_files.md b/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. 
diff --git a/website/docs/guides/manage_users_and_services.md b/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:

diff --git a/website/docs/guides/tracing.mdx b/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_
 :::
 

diff --git a/website/docs/guides/upgrade_ragflow.mdx b/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
    ```
diff --git a/website/docs/quickstart.mdx b/website/docs/quickstart.mdx
@@ -44,7 +44,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 
    `vm.max_map_count`. This value sets the maximum number of memory map areas a process may have. Its default value is 65530. While most applications require fewer than a thousand maps, reducing this value can result in abnormal behaviors, and the system will throw out-of-memory errors when a process reaches the limitation.
 
-   RAGFlow v0.21.0 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
+   RAGFlow v0.21.1 uses Elasticsearch or [Infinity](https://github.com/infiniflow/infinity) for multiple recall. Setting the value of `vm.max_map_count` correctly is crucial to the proper functioning of the Elasticsearch component.
 
 <Tabs
   defaultValue="linux"
@@ -184,13 +184,13 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
    ```bash
    $ git clone https://github.com/infiniflow/ragflow.git
    $ cd ragflow/docker
-   $ git checkout -f v0.21.0
+   $ git checkout -f v0.21.1
    ```
 
 3. Use the pre-built Docker images and start up the server:
 
    :::tip NOTE
-   The command below downloads the `v0.21.0-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.0-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.0` for the full edition `v0.21.0`.
+   The command below downloads the `v0.21.1-slim` edition of the RAGFlow Docker image. Refer to the following table for descriptions of different RAGFlow editions. To download a RAGFlow edition different from `v0.21.1-slim`, update the `RAGFLOW_IMAGE` variable accordingly in **docker/.env** before using `docker compose` to start the server. For example: set `RAGFLOW_IMAGE=infiniflow/ragflow:v0.21.1` for the full edition `v0.21.1`.
    :::
 
    ```bash
@@ -207,8 +207,8 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 
 | RAGFlow image tag   | Image size (GB) | Has embedding models and Python packages? | Stable?                  |
 | ------------------- | --------------- | ----------------------------------------- | ------------------------ |
-| `v0.21.0`           | &approx;9       | :heavy_check_mark:                        | Stable release           |
-| `v0.21.0-slim`      | &approx;2       | ❌                                        | Stable release           |
+| `v0.21.1`           | &approx;9       | :heavy_check_mark:                        | Stable release           |
+| `v0.21.1-slim`      | &approx;2       | ❌                                        | Stable release           |
 | `nightly`           | &approx;9       | :heavy_check_mark:                        | *Unstable* nightly build |
 | `nightly-slim`      | &approx;2       | ❌                                        | *Unstable* nightly build |
 
@@ -217,7 +217,7 @@ This section provides instructions on setting up the RAGFlow server on Linux. If
 ```
 
 :::danger IMPORTANT
-The embedding models included in `v0.21.0` and `nightly` are:
+The embedding models included in `v0.21.1` and `nightly` are:
 
 - BAAI/bge-large-zh-v1.5
 - maidalun1020/bce-embedding-base_v1

diff --git a/website/docs/references/glossary.mdx b/website/docs/references/glossary.mdx
@@ -19,7 +19,7 @@ import TOCInline from '@theme/TOCInline';
 
 ### Cross-language search
 
-Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.21.0. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
+Cross-language search (also known as cross-lingual retrieval) is a feature introduced in version 0.21.1. It enables users to submit queries in one language (for example, English) and retrieve relevant documents written in other languages such as Chinese or Spanish. This feature is enabled by the system’s default chat model, which translates queries to ensure accurate matching of semantic meaning across languages.
 
 By enabling cross-language search, users can effortlessly access a broader range of information regardless of language barriers, significantly enhancing the system’s usability and inclusiveness.
 

diff --git a/website/docs/release_notes.md b/website/docs/release_notes.md
@@ -9,8 +9,8 @@ Key features, improvements and bug fixes in the latest releases.
 
 :::info
 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`
 :::
 
 :::danger IMPORTANT
@@ -22,6 +22,23 @@ The embedding models included in a full edition are:
 These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
 :::
 
+## v0.21.1
+
+Released on October 23, 2025.
+
+### New features
+
+- Experimental: Adds support for PDF document parsing using MinerU. See [here](./faq.mdx#how-to-use-mineru-to-parse-pdf-documents).
+
+### Improvements
+
+- Enhances UI/UX for the dataset and personal center pages.
+- Upgrades RAGFlow's document engine, [Infinity](https://github.com/infiniflow/infinity), to v0.6.1.
+
+### Fixed issues
+
+- An issue with video parsing.
+
 ## v0.21.0
 
 Released on October 15, 2025.

diff --git a/website/versioned_docs/version-v0.21.1/_category_.json b/website/versioned_docs/version-v0.21.1/_category_.json
@@ -0,0 +1,8 @@
+{
+  "label": "Get Started",
+  "position": 1,
+  "link": {
+    "type": "generated-index",
+    "description": "RAGFlow Quick Start"
+  }
+}
Original file line number	Diff line number	Diff line change
Expand Up		@@ -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.