Restructured guides (#5549)

### What problem does this PR solve?

### Type of change

- [x] Documentation Update

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