revert white-space changes in docs

docs/basics/rag.md

@@ -86,22 +86,22 @@ They are highly consistent at the technical base (e.g., vector retrieval, keywor
 
 RAG has demonstrated clear value in several typical scenarios:
 
-1. Enterprise Knowledge Q&A and Internal Search
+1. Enterprise Knowledge Q&A and Internal Search  
    By vectorizing corporate private data and combining it with an LLM, RAG can directly return natural language answers based on authoritative sources, rather than document lists. While meeting intelligent Q&A needs, it inherently aligns with corporate requirements for data security, access control, and compliance.
-2. Complex Document Understanding and Professional Q&A
+2. Complex Document Understanding and Professional Q&A  
    For structurally complex documents like contracts and regulations, the value of RAG lies in its ability to generate accurate, verifiable answers while maintaining context integrity. Its system accuracy largely depends on text chunking and semantic understanding strategies.
-3. Dynamic Knowledge Fusion and Decision Support
+3. Dynamic Knowledge Fusion and Decision Support  
    In business scenarios requiring the synthesis of information from multiple sources, RAG evolves into a knowledge orchestration and reasoning support system for business decisions. Through a multi-path recall mechanism, it fuses knowledge from different systems and formats, maintaining factual consistency and logical controllability during the generation phase.
 
 ## The future of RAG
 
 The evolution of RAG is unfolding along several clear paths:
 
-1. RAG as the data foundation for Agents
+1. RAG as the data foundation for Agents  
    RAG and agents have an architecture vs. scenario relationship. For agents to achieve autonomous and reliable decision-making and execution, they must rely on accurate and timely knowledge. RAG provides them with a standardized capability to access private domain knowledge and is an inevitable choice for building knowledge-aware agents.
-2. Advanced RAG: Using LLMs to optimize retrieval itself
+2. Advanced RAG: Using LLMs to optimize retrieval itself  
    The core feature of next-generation RAG is fully utilizing the reasoning capabilities of LLMs to optimize the retrieval process, such as rewriting queries, summarizing or fusing results, or implementing intelligent routing. Empowering every aspect of retrieval with LLMs is key to breaking through current performance bottlenecks.
-3. Towards context engineering 2.0
+3. Towards context engineering 2.0  
    Current RAG can be viewed as Context Engineering 1.0, whose core is assembling static knowledge context for single Q&A tasks. The forthcoming Context Engineering 2.0 will extend with RAG technology at its core, becoming a system that automatically and dynamically assembles comprehensive context for agents. The context fused by this system will come not only from documents but also include interaction memory, available tools/skills, and real-time environmental information. This marks the transition of agent development from a "handicraft workshop" model to the industrial starting point of automated context engineering.
 
 The essence of RAG is to build a dedicated, efficient, and trustworthy external data interface for large language models; its core is Retrieval, not Generation. Starting from the practical need to solve private data access, its technical depth is reflected in the optimization of retrieval for complex unstructured data. With its deep integration into agent architectures and its development towards automated context engineering, RAG is evolving from a technology that improves Q&A quality into the core infrastructure for building the next generation of trustworthy, controllable, and scalable intelligent applications.

docs/configurations.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   sidebarIcon: LucideCog
 }
 ---
-
 # Configuration
 
 Configurations for deploying RAGFlow via Docker.

docs/contribution/contributing.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucideBookA
 }
 ---
-
 # Contribution guidelines
 
 General guidelines for RAGFlow's community contributors.
@@ -35,7 +34,7 @@ The list below mentions some contributions you can make, but it is not a complet
 1. Fork our GitHub repository.
 2. Clone your fork to your local machine:
 `git clone [email protected]:<yourname>/ragflow.git`
-3. Create a local branch:
+3. Create a local branch: 
 `git checkout -b my-branch`
 4. Provide sufficient information in your commit message
 `git commit -m 'Provide sufficient info in your commit message'`

docs/develop/acquire_ragflow_api_key.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucideKey
 }
 ---
-
 # Acquire RAGFlow API key
 
 An API key is required for the RAGFlow server to authenticate your HTTP/Python or MCP requests. This documents provides instructions on obtaining a RAGFlow API key.

docs/develop/build_docker_image.mdx

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucidePackage
 }
 ---
-
 # Build RAGFlow Docker image
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';

docs/develop/launch_ragflow_from_source.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucideMonitorPlay
 }
 ---
-
 # Launch service from source
 
 A guide explaining how to set up a RAGFlow service from its source code. By following this guide, you'll be able to debug using the source code.
@@ -39,7 +38,7 @@ cd ragflow/
 ### Install Python dependencies
 
 1. Install uv:
-
+   
    ```bash
    pipx install uv
    ```
@@ -91,13 +90,13 @@ docker compose -f docker/docker-compose-base.yml up -d
    ```
 
 3. **Optional:** If you cannot access HuggingFace, set the HF_ENDPOINT environment variable to use a mirror site:
-
+ 
    ```bash
    export HF_ENDPOINT=https://hf-mirror.com
    ```
 
 4. Check the configuration in **conf/service_conf.yaml**, ensuring all hosts and ports are correctly set.
-
+   
 5. Run the **entrypoint.sh** script to launch the backend service:
 
    ```shell
@@ -126,10 +125,10 @@ docker compose -f docker/docker-compose-base.yml up -d
 3. Start up the RAGFlow frontend service:
 
    ```bash
-   npm run dev
+   npm run dev 
    ```
 
-   *The following message appears, showing the IP address and port number of your frontend service:*
+   *The following message appears, showing the IP address and port number of your frontend service:*  
 
    ![](https://github.com/user-attachments/assets/0daf462c-a24d-4496-a66f-92533534e187)
 

docs/develop/mcp/launch_mcp_server.md

@@ -5,20 +5,19 @@ sidebar_custom_props: {
   categoryIcon: LucideTvMinimalPlay
 }
 ---
-
 # Launch RAGFlow MCP server
 
 Launch an MCP server from source or via Docker.
 
 ---
 
-A RAGFlow Model Context Protocol (MCP) server is designed as an independent component to complement the RAGFlow server. Note that an MCP server must operate alongside a properly functioning RAGFlow server.
+A RAGFlow Model Context Protocol (MCP) server is designed as an independent component to complement the RAGFlow server. Note that an MCP server must operate alongside a properly functioning RAGFlow server. 
 
-An MCP server can start up in either self-host mode (default) or host mode:
+An MCP server can start up in either self-host mode (default) or host mode: 
 
-- **Self-host mode**:
+- **Self-host mode**:  
   When launching an MCP server in self-host mode, you must provide an API key to authenticate the MCP server with the RAGFlow server. In this mode, the MCP server can access *only* the datasets of a specified tenant on the RAGFlow server.
-- **Host mode**:
+- **Host mode**:  
   In host mode, each MCP client can access their own datasets on the RAGFlow server. However, each client request must include a valid API key to authenticate the client with the RAGFlow server.
 
 Once a connection is established, an MCP server communicates with its client in MCP HTTP+SSE (Server-Sent Events) mode, unidirectionally pushing responses from the RAGFlow server to its client in real time.
@@ -32,9 +31,9 @@ Once a connection is established, an MCP server communicates with its client in
 If you wish to try out our MCP server without upgrading RAGFlow, community contributor [yiminghub2024](https://github.com/yiminghub2024) 👏 shares their recommended steps [here](#launch-an-mcp-server-without-upgrading-ragflow).
 :::
 
-## Launch an MCP server
+## Launch an MCP server 
 
-You can start an MCP server either from source code or via Docker.
+You can start an MCP server either from source code or via Docker. 
 
 ### Launch from source code
 
@@ -51,7 +50,7 @@ uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base-url=http://127.0
 # uv run mcp/server/server.py --host=127.0.0.1 --port=9382 --base-url=http://127.0.0.1:9380 --mode=host
 ```
 
-Where:
+Where: 
 
 - `host`: The MCP server's host address.
 - `port`: The MCP server's listening port.
@@ -97,7 +96,7 @@ The MCP server is designed as an optional component that complements the RAGFlow
         #   - --no-json-response # Disables JSON responses for the streamable-HTTP transport
 ```
 
-Where:
+Where: 
 
 - `mcp-host`: The MCP server's host address.
 - `mcp-port`: The MCP server's listening port.
@@ -122,13 +121,13 @@ Run `docker compose -f docker-compose.yml up` to launch the RAGFlow server toget
   docker-ragflow-cpu-1  | Starting MCP Server on 0.0.0.0:9382 with base URL http://127.0.0.1:9380...
   docker-ragflow-cpu-1  | Starting 1 task executor(s) on host 'dd0b5e07e76f'...
   docker-ragflow-cpu-1  | 2025-04-18 15:41:18,816 INFO     27 ragflow_server log path: /ragflow/logs/ragflow_server.log, log levels: {'peewee': 'WARNING', 'pdfminer': 'WARNING', 'root': 'INFO'}
-  docker-ragflow-cpu-1  |
+  docker-ragflow-cpu-1  | 
   docker-ragflow-cpu-1  | __  __  ____ ____       ____  _____ ______     _______ ____
   docker-ragflow-cpu-1  | |  \/  |/ ___|  _ \     / ___|| ____|  _ \ \   / / ____|  _ \
   docker-ragflow-cpu-1  | | |\/| | |   | |_) |    \___ \|  _| | |_) \ \ / /|  _| | |_) |
   docker-ragflow-cpu-1  | | |  | | |___|  __/      ___) | |___|  _ < \ V / | |___|  _ <
   docker-ragflow-cpu-1  | |_|  |_|\____|_|        |____/|_____|_| \_\ \_/  |_____|_| \_\
-  docker-ragflow-cpu-1  |
+  docker-ragflow-cpu-1  |     
   docker-ragflow-cpu-1  | MCP launch mode: self-host
   docker-ragflow-cpu-1  | MCP host: 0.0.0.0
   docker-ragflow-cpu-1  | MCP port: 9382
@@ -141,13 +140,13 @@ Run `docker compose -f docker-compose.yml up` to launch the RAGFlow server toget
   docker-ragflow-cpu-1  | 2025-04-18 15:41:23,263 INFO     27 init database on cluster mode successfully
   docker-ragflow-cpu-1  | 2025-04-18 15:41:25,318 INFO     27 load_model /ragflow/rag/res/deepdoc/det.onnx uses CPU
   docker-ragflow-cpu-1  | 2025-04-18 15:41:25,367 INFO     27 load_model /ragflow/rag/res/deepdoc/rec.onnx uses CPU
-  docker-ragflow-cpu-1  |         ____   ___    ______ ______ __
+  docker-ragflow-cpu-1  |         ____   ___    ______ ______ __               
   docker-ragflow-cpu-1  |        / __ \ /   |  / ____// ____// /____  _      __
   docker-ragflow-cpu-1  |       / /_/ // /| | / / __ / /_   / // __ \| | /| / /
-  docker-ragflow-cpu-1  |      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ /
-  docker-ragflow-cpu-1  |     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/
-  docker-ragflow-cpu-1  |
-  docker-ragflow-cpu-1  |
+  docker-ragflow-cpu-1  |      / _, _// ___ |/ /_/ // __/  / // /_/ /| |/ |/ / 
+  docker-ragflow-cpu-1  |     /_/ |_|/_/  |_|\____//_/    /_/ \____/ |__/|__/                             
+  docker-ragflow-cpu-1  | 
+  docker-ragflow-cpu-1  |     
   docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 RAGFlow version: v0.18.0-285-gb2c299fa full
   docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 project base: /ragflow
   docker-ragflow-cpu-1  | 2025-04-18 15:41:29,088 INFO     27 Current configs, from /ragflow/conf/service_conf.yaml:
@@ -156,12 +155,12 @@ Run `docker compose -f docker-compose.yml up` to launch the RAGFlow server toget
   docker-ragflow-cpu-1  |  * Running on all addresses (0.0.0.0)
   docker-ragflow-cpu-1  |  * Running on http://127.0.0.1:9380
   docker-ragflow-cpu-1  |  * Running on http://172.19.0.6:9380
-  docker-ragflow-cpu-1  |   ______           __      ______                     __
+  docker-ragflow-cpu-1  |   ______           __      ______                     __            
   docker-ragflow-cpu-1  |  /_  __/___ ______/ /__   / ____/  _____  _______  __/ /_____  _____
   docker-ragflow-cpu-1  |   / / / __ `/ ___/ //_/  / __/ | |/_/ _ \/ ___/ / / / __/ __ \/ ___/
-  docker-ragflow-cpu-1  |  / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /
-  docker-ragflow-cpu-1  | /_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/
-  docker-ragflow-cpu-1  |
+  docker-ragflow-cpu-1  |  / / / /_/ (__  ) ,<    / /____>  </  __/ /__/ /_/ / /_/ /_/ / /    
+  docker-ragflow-cpu-1  | /_/  \__,_/____/_/|_|  /_____/_/|_|\___/\___/\__,_/\__/\____/_/                               
+  docker-ragflow-cpu-1  |     
   docker-ragflow-cpu-1  | 2025-04-18 15:41:34,501 INFO     32 TaskExecutor: RAGFlow version: v0.18.0-285-gb2c299fa full
   docker-ragflow-cpu-1  | 2025-04-18 15:41:34,501 INFO     32 Use Elasticsearch http://es01:9200 as the doc engine.
   ...
@@ -173,11 +172,11 @@ Run `docker compose -f docker-compose.yml up` to launch the RAGFlow server toget
 This section is contributed by our community contributor [yiminghub2024](https://github.com/yiminghub2024). 👏
 :::
 
-1. Prepare all MCP-specific files and directories.
-   i. Copy the [mcp/](https://github.com/infiniflow/ragflow/tree/main/mcp) directory to your local working directory.
-   ii. Copy [docker/docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml) locally.
-   iii. Copy [docker/entrypoint.sh](https://github.com/infiniflow/ragflow/blob/main/docker/entrypoint.sh) locally.
-   iv. Install the required dependencies using `uv`:
+1. Prepare all MCP-specific files and directories.  
+   i. Copy the [mcp/](https://github.com/infiniflow/ragflow/tree/main/mcp) directory to your local working directory.  
+   ii. Copy [docker/docker-compose.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose.yml) locally.  
+   iii. Copy [docker/entrypoint.sh](https://github.com/infiniflow/ragflow/blob/main/docker/entrypoint.sh) locally.  
+   iv. Install the required dependencies using `uv`:  
        - Run `uv add mcp` or
        - Copy [pyproject.toml](https://github.com/infiniflow/ragflow/blob/main/pyproject.toml) locally and run `uv sync --python 3.12`.
 2. Edit **docker-compose.yml** to enable MCP (disabled by default).
@@ -197,19 +196,19 @@ docker logs docker-ragflow-cpu-1
 
 ## Security considerations
 
-As MCP technology is still at early stage and no official best practices for authentication or authorization have been established, RAGFlow currently uses [API key](./acquire_ragflow_api_key.md) to validate identity for the operations described earlier. However, in public environments, this makeshift solution could expose your MCP server to potential network attacks. Therefore, when running a local SSE server, it is recommended to bind only to localhost (`127.0.0.1`) rather than to all interfaces (`0.0.0.0`).
+As MCP technology is still at early stage and no official best practices for authentication or authorization have been established, RAGFlow currently uses [API key](./acquire_ragflow_api_key.md) to validate identity for the operations described earlier. However, in public environments, this makeshift solution could expose your MCP server to potential network attacks. Therefore, when running a local SSE server, it is recommended to bind only to localhost (`127.0.0.1`) rather than to all interfaces (`0.0.0.0`). 
 
 For further guidance, see the [official MCP documentation](https://modelcontextprotocol.io/docs/concepts/transports#security-considerations).
 
 ## Frequently asked questions
 
 ### When to use an API key for authentication?
 
-The use of an API key depends on the operating mode of your MCP server.
+The use of an API key depends on the operating mode of your MCP server. 
 
-- **Self-host mode** (default):
-  When starting the MCP server in self-host mode, you should provide an API key when launching it to authenticate it with the RAGFlow server:
-  - If launching from source, include the API key in the command.
+- **Self-host mode** (default):  
+  When starting the MCP server in self-host mode, you should provide an API key when launching it to authenticate it with the RAGFlow server:  
+  - If launching from source, include the API key in the command. 
   - If launching from Docker, update the API key in **docker/docker-compose.yml**.
-- **Host mode**:
+- **Host mode**:  
   If your RAGFlow MCP server is working in host mode, include the API key in the `headers` of your client requests to authenticate your client with the RAGFlow server. An example is available [here](https://github.com/infiniflow/ragflow/blob/main/mcp/client/client.py).

docs/develop/mcp/mcp_client_example.md

@@ -6,7 +6,6 @@ sidebar_custom_props: {
 }
 
 ---
-
 # RAGFlow MCP client examples
 
 Python and curl MCP client examples.
@@ -39,11 +38,11 @@ When interacting with the MCP server via HTTP requests, follow this initializati
 
 1. **The client sends an `initialize` request** with protocol version and capabilities.
 2. **The server replies with an `initialize` response**, including the supported protocol and capabilities.
-3. **The client confirms readiness with an `initialized` notification**.
+3. **The client confirms readiness with an `initialized` notification**.  
    _The connection is established between the client and the server, and further operations (such as tool listing) may proceed._
 
 :::tip NOTE
-For more information about this initialization process, see [here](https://modelcontextprotocol.io/docs/concepts/architecture#1-initialization).
+For more information about this initialization process, see [here](https://modelcontextprotocol.io/docs/concepts/architecture#1-initialization). 
 :::
 
 In the following sections, we will walk you through a complete tool calling process.

docs/develop/mcp/mcp_tools.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucideToolCase
 }
 ---
-
 # RAGFlow MCP tools
 
 The MCP server currently offers a specialized tool to assist users in searching for relevant information powered by RAGFlow DeepDoc technology:

docs/develop/switch_doc_engine.md

@@ -5,7 +5,6 @@ sidebar_custom_props: {
   categoryIcon: LucideShuffle
 }
 ---
-
 # Switch document engine
 
 Switch your doc engine from Elasticsearch to Infinity.