Restructured guides (#5549)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

docs/configurations.md

@@ -3,7 +3,7 @@ sidebar_position: 1
 slug: /configurations
 ---
 
-# Configurations
+# Configuration
 
 Configurations for deploying RAGFlow via Docker.
 

docs/guides/agent/agent_introduction.md

@@ -22,8 +22,8 @@ Agents and RAG are complementary techniques, each enhancing the other’s capabi
 
 Before proceeding, ensure that:  
 
-1. You have properly set the LLM to use. See the guides on [Configure your API key](../llm_api_key_setup.md) or [Deploy a local LLM](../deploy_local_llm.mdx) for more information.
-2. You have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../configure_knowledge_base.md) for more information.
+1. You have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
+2. You have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
 
 :::
 

docs/guides/agent/embed_agent_into_webpage.md

@@ -7,7 +7,7 @@ slug: /embed_agent_into_webpage
 
 You can use iframe to embed an agent into a third-party webpage.
 
-1. Before proceeding, you must [acquire an API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key); otherwise, an error message would appear.
+1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
 2. On the **Agent** page, click an intended agent **>** **Edit** to access its editing page.
 3. Click **Embed into webpage** on the top right corner of the canvas to show the **iframe** window:
 

docs/guides/agent/general_purpose_chatbot.md

@@ -13,8 +13,8 @@ This document provides guides on creating such a chatbot using our chatbot templ
 
 ## Prerequisites
 
-1. Ensure you have properly set the LLM to use. See the guides on [Configure your API key](../llm_api_key_setup.md) or [Deploy a local LLM](../deploy_local_llm.mdx) for more information.
-2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../configure_knowledge_base.md) for more information.
+1. Ensure you have properly set the LLM to use. See the guides on [Configure your API key](../models/llm_api_key_setup.md) or [Deploy a local LLM](../models/deploy_local_llm.mdx) for more information.
+2. Ensure you have a knowledge base configured and the corresponding files properly parsed. See the guide on [Configure a knowledge base](../dataset/configure_knowledge_base.md) for more information.
 3. Make sure you have read the [Introduction to Agentic RAG](./agent_introduction.md).
 
 ## Create a chatbot agent from template

docs/guides/agent/text2sql_agent.md

@@ -31,10 +31,10 @@ However, traditional Text2SQL solutions often require model fine-tuning, which c
 
 A list of components required:
 
-- [Begin](https://ragflow.io/docs/dev/begin_component)
-- [Interact](https://ragflow.io/docs/dev/interact_component)
-- [Retrieval](https://ragflow.io/docs/dev/retrieval_component)
-- [Generate](https://ragflow.io/docs/dev/generate_component)
+- [Begin](./agent_component_reference/begin.mdx)
+- [Interact](./agent_component_reference/interact.mdx)
+- [Retrieval](./agent_component_reference/retrieval.mdx)
+- [Generate](./agent_component_reference/generate.mdx)
 - ExeSQL
 
 ## Procedure

docs/guides/dataset/_category_.json

@@ -1,5 +1,5 @@
 {
-  "label": "Configure a knowledge base",
+  "label": "Datasets",
   "position": 0,
   "link": {
     "type": "generated-index",

docs/guides/dataset/configure_knowledge_base.md

@@ -3,7 +3,7 @@ sidebar_position: 0
 slug: /configure_knowledge_base
 ---
 
-# Configure a knowledge base
+# Configure knowledge base
 
 Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's AI chats are based on knowledge bases. Each of RAGFlow's knowledge bases serves as a knowledge source, *parsing* files uploaded from your local machine and file references generated in **File Management** into the real 'knowledge' for future AI chats. This guide demonstrates some basic usages of the knowledge base feature, covering the following topics:
 

docs/guides/develop/build_docker_image.mdx

@@ -21,7 +21,6 @@ A guide explaining how to build a RAGFlow Docker image from its source code. By
 - RAM ≥ 16 GB
 - Disk ≥ 50 GB
 - Docker ≥ 24.0.0 & Docker Compose ≥ v2.26.1
-- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 
 ## Build a Docker image
 
@@ -35,8 +34,9 @@ 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 maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine as well.
+:::danger IMPORTANT
+- 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.
+- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 :::
 
 ```bash
@@ -53,8 +53,9 @@ 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 maintain RAGFlow Docker images for ARM. However, you can build an image yourself on a `linux/arm64` or `darwin/arm64` host machine.
+:::danger IMPORTANT
+- 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.
+- For ARM64 platforms, please upgrade the `xgboost` version in **pyproject.toml** to `1.6.0` and ensure **unixODBC** is properly installed.
 :::
 
 ```bash

docs/guides/manage_files.md

@@ -3,7 +3,7 @@ sidebar_position: 5
 slug: /manage_files
 ---
 
-#  Manage files
+# Files
 
 Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's file management allows you to upload files individually or in bulk. You can then link an uploaded file to multiple target knowledge bases. This guide showcases some basic usages of the file management feature. 
 

docs/guides/models/_category_.json

@@ -0,0 +1,8 @@
+{
+  "label": "Models",
+  "position": -1,
+  "link": {
+    "type": "generated-index",
+    "description": "Guides on model settings."
+  }
+}

docs/guides/models/deploy_local_llm.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 7
+sidebar_position: 2
 slug: /deploy_local_llm
 ---
 

docs/guides/models/llm_api_key_setup.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 6
+sidebar_position: 1
 slug: /llm_api_key_setup
 ---
 
@@ -9,7 +9,7 @@ An API key is required for RAGFlow to interact with an online AI model. This gui
 
 ## Get model API key
 
-RAGFlow supports most mainstream LLMs. Please refer to [Supported Models](./references/supported_models.mdx) for a complete list of supported models. You will need to apply for your model API key online. Note that most LLM providers grant newly-created accounts trial credit, which will expire in a couple of months, or a promotional amount of free quota.
+RAGFlow supports most mainstream LLMs. Please refer to [Supported Models](../../references/supported_models.mdx) for a complete list of supported models. You will need to apply for your model API key online. Note that most LLM providers grant newly-created accounts trial credit, which will expire in a couple of months, or a promotional amount of free quota.
 
 :::note
 If you find your online LLM is not on the list, don't feel disheartened. The list is expanding, and you can [file a feature request](https://github.com/infiniflow/ragflow/issues/new?assignees=&labels=feature+request&projects=&template=feature_request.yml&title=%5BFeature+Request%5D%3A+) with us! Alternatively, if you have customized or locally-deployed models, you can [bind them to RAGFlow using Ollama, Xinference, or LocalAI](./deploy_local_llm.mdx).
@@ -31,8 +31,7 @@ You have two options for configuring your model API key:
    - Update `base_url` if you use a proxy to connect to the remote service.
 3. Reboot your system for your changes to take effect.
 4. Log into RAGFlow.
-   
-   *After logging into RAGFlow, you will find your chosen model appears under **Added models** on the **Model providers** page.*
+   _After logging into RAGFlow, you will find your chosen model appears under **Added models** on the **Model providers** page._
 
 ### Configure model API key after logging into RAGFlow
 

docs/guides/run_health_check.md

@@ -3,7 +3,7 @@ sidebar_position: 7
 slug: /run_health_check
 ---
 
-# Run dependency health check
+# Monitoring
 
 Double-check the health status of RAGFlow's dependencies.
 

docs/guides/start_chat.md

@@ -3,9 +3,9 @@ sidebar_position: 1
 slug: /start_chat
 ---
 
-# Start an AI-powered chat
+# Chat
 
-Initiate a chat with a configured chat assistant.
+Initiate an AI-powered chat with a configured chat assistant.
 
 ---
 
@@ -80,13 +80,13 @@ Hover over an intended chat assistant **>** **Edit** to show the chat configurat
 
 RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
 
-- [Acquire a RAGFlow API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key)
-- [HTTP API reference](https://ragflow.io/docs/dev/http_api_reference)
-- [Python API reference](https://ragflow.io/docs/dev/python_api_reference)
+- [Acquire a RAGFlow API key](./models/llm_api_key_setup.md)
+- [HTTP API reference](../references/http_api_reference.md)
+- [Python API reference](../references/python_api_reference.md)
 
 You can use iframe to embed the created chat assistant into a third-party webpage:
 
-1. Before proceeding, you must [acquire an API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key); otherwise, an error message would appear.
+1. Before proceeding, you must [acquire an API key](./models/llm_api_key_setup.md); otherwise, an error message would appear.
 2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
 
    ![chat-embed](https://github.com/user-attachments/assets/13ea3021-31c4-4a14-9b32-328cd3318fb5)

docs/quickstart.mdx

@@ -3,7 +3,7 @@ sidebar_position: 0
 slug: /
 ---
 
-# Quick start
+# Get started
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 import APITable from '@site/src/components/APITable';

docs/references/http_api_reference.md

@@ -5,7 +5,7 @@ slug: /http_api_reference
 
 # HTTP API
 
-A complete reference for RAGFlow's RESTful API. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](https://ragflow.io/docs/dev/acquire_ragflow_api_key).
+A complete reference for RAGFlow's RESTful API. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](../guides/models/llm_api_key_setup.md).
 
 ---
 

docs/references/python_api_reference.md

@@ -5,7 +5,7 @@ slug: /python_api_reference
 
 # Python API
 
-A complete reference for RAGFlow's Python APIs. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](https://ragflow.io/docs/dev/acquire_ragflow_api_key).
+A complete reference for RAGFlow's Python APIs. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](../guides/models/llm_api_key_setup.md).
 
 :::tip NOTE
 Run the following command to download the Python SDK:

docs/release_notes.md

@@ -3,7 +3,7 @@ sidebar_position: 2
 slug: /release_notes
 ---
 
-# Release notes
+# Releases
 
 Key features, improvements and bug fixes in the latest releases.
 
@@ -17,7 +17,7 @@ Released on February 6, 2025.
 - GraphRAG refactor: Knowledge graph is dynamically built on an entire knowledge base (dataset) rather than on an individual file, and automatically updated when a newly uploaded file starts parsing. See [here](https://ragflow.io/docs/dev/construct_knowledge_graph).
 - Adds an **Iteration** agent component and a **Research report generator** agent template. See [here](./guides/agent/agent_component_reference/iteration.mdx).
 - New UI language: Portuguese.
-- Allows setting metadata for a specific file in a knowledge base to enhance AI-powered chats. See [here](./guides/configure_knowledge_base/set_metadata.md).
+- Allows setting metadata for a specific file in a knowledge base to enhance AI-powered chats. See [here](./guides/dataset/set_metadata.md).
 - Upgrades RAGFlow's document engine [Infinity](https://github.com/infiniflow/infinity) to v0.6.0.dev3.
 - Supports GPU acceleration for DeepDoc (see [docker-compose-gpu.yml](https://github.com/infiniflow/ragflow/blob/main/docker/docker-compose-gpu.yml)).
 - Supports creating and referencing a **Tag** knowledge base as a key milestone towards bridging the semantic gap between query and response.
@@ -30,8 +30,8 @@ The **Tag knowledge base** feature is *unavailable* on the [Infinity](https://gi
 
 #### Added documents
 
-- [Construct knowledge graph](./guides/configure_knowledge_base/construct_knowledge_graph.md)
-- [Set metadata](./guides/configure_knowledge_base/set_metadata.md)
+- [Construct knowledge graph](./guides/dataset/construct_knowledge_graph.md)
+- [Set metadata](./guides/dataset/set_metadata.md)
 - [Begin component](./guides/agent/agent_component_reference/begin.mdx)
 - [Generate component](./guides/agent/agent_component_reference/generate.mdx)
 - [Interact component](./guides/agent/agent_component_reference/interact.mdx)