Repository: antgroup/agentUniverse (language: python)

Issue:
ADD: ThreadPoolExecutorWithReturnValue, full error stack in knowledge. CHANGE:  qwen model max token to the newest
ADD: ThreadPoolExecutorWithReturnValue, a ThreadPoolExecutor which can keep FrameworkContext

ADD: thread in insert_knowledge and query will print full error stack now

CHANGE: change the qwen model max token to the newest


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 61dd4a81..2b15507f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -38,9 +38,9 @@ This version introduces new capabilities such as intelligent agent canvas orches
 
 ### Note 
 - Default path optimization for system_db_uri
-The default path is already compatible with the Windows platform, for more details, please refer to [issue142](https://github.com/alipay/agentUniverse/issues/142)
+The default path is already compatible with the Windows platform, for more details, please refer to [issue142](https://github.com/antgroup/agentUniverse/issues/142)
 - Support for configurable chain stop words in ReactAgent
-The ReactAgent YAML configuration now supports the stop_sequence keyword, allowing users to customize chain stop words. For more details, please refer to [issue127](https://github.com/alipay/agentUniverse/issues/127)
+The ReactAgent YAML configuration now supports the stop_sequence keyword, allowing users to customize chain stop words. For more details, please refer to [issue127](https://github.com/antgroup/agentUniverse/issues/127)
 - Added an introduction to RAG principles and a quick guide for building RAG, please pay attention to the corresponding parts in the README and user guide.
 - Added advanced guidance documents for the intelligent agent productization platform, please pay attention to the corresponding parts in the README and user guide.
 - Various code optimizations and documentation updates.
diff --git a/CHANGELOG_zh.md b/CHANGELOG_zh.md
index 9ebb786f..e60a84c9 100644
--- a/CHANGELOG_zh.md
+++ b/CHANGELOG_zh.md
@@ -38,9 +38,9 @@ Note - 对于版本的额外说明。
 
 ### Note 
 - system_db_uri默认路径优化  
-默认路径已兼容windows平台，详情见[issue142](https://github.com/alipay/agentUniverse/issues/142)
+默认路径已兼容windows平台，详情见[issue142](https://github.com/antgroup/agentUniverse/issues/142)
 - ReactAgent支持链停止词配置化  
-ReactAgent yaml配置目前已经支持stop_sequence关键词，用户可以自行配置链停止词,详情见[issue127](https://github.com/alipay/agentUniverse/issues/127)
+ReactAgent yaml配置目前已经支持stop_sequence关键词，用户可以自行配置链停止词,详情见[issue127](https://github.com/antgroup/agentUniverse/issues/127)
 - 新增RAG原理介绍与RAG快速构建指导文档，请关注README与用户指南相应部分。
 - 新增智能体产品化平台高阶指导文档，请关注README与用户指南相应部分。
 - 部分代码优化与文档更新
diff --git a/README.md b/README.md
index e1517d6f..6f7c29b8 100644
--- a/README.md
+++ b/README.md
@@ -12,152 +12,190 @@ Language version: [English](./README.md) | [中文](./README_zh.md) | [日本語
 
 ## What is agentUniverse?
 
-**agentUniverse is a multi-agent framework based on large language models.** agentUniverse provides you with the flexible and easily extensible capability to build single agents. At its core, agentUniverse features a rich set of multi-agent collaboration mode components (which can be viewed as a Collaboration Mode Factory, or Pattern Factory). These components allow agents to maximize their effectiveness by specializing in different domains to solve problems. agentUniverse also focuses on the integration of domain expertise, helping you seamlessly incorporate domain knowledge into the work of your agents.🎉🎉🎉
+**agentUniverse is a multi-agent framework based on large language models. It provides flexible and easily extensible capabilities for building individual agents. The core of agentUniverse is a rich set of multi-agent collaborative pattern components (serving as a collaborative pattern factory), which allows agents to perform their respective duties and maximize their capabilities when solving problems in different fields; at the same time, agentUniverse focuses on the integration of domain experience, helping you smoothly integrate domain experience into the work of intelligent agents.🎉🎉🎉
 
-**🌈🌈🌈agentUniverse helps developers and enterprises to easily build powerful collaborative agents that perform at an expert level in their respective domains.**
+**🌈🌈🌈agentUniverse helps developers and enterprises easily build powerful agents at the domain expert level to work collaboratively for you.**
 
 ![](docs/guidebook/_picture/agent_universe_framework_resize.jpg)
 
-We encourage you to practice and share different domain Patterns within the community. The framework comes pre-loaded with several multi-agent collaboration mode components that have been validated in real-world industries and will continue to expand in the future. The components that will be available soon include:
+We look forward to your practice and communication and sharing of Patterns in different fields through the community. This framework has already placed many useful components that have been tested in real business scenarios in terms of multi-agent cooperation, and will continue to be enriched in the future.
+The pattern components that are currently open for use include:
 
-* PEER Mode Component: This pattern uses agents with different responsibilities—Plan, Execute, Express, and Review—to break down complex problems into manageable steps, execute the steps in sequence, and iteratively improve based on feedback, enhancing the performance of reasoning and analysis tasks. Typical use cases: Event interpretation, industry analysis.
-* DOE Mode Component: This pattern employs three agents—Data-fining, Opinion-inject, and Express—to improve the effectiveness of tasks that are data-intensive, require high computational precision, and incorporate expert opinions. Typical use cases: Financial report generation.
+* PEER pattern component: This pattern uses agents with different responsibilities—Plan, Execute, Express, and Review—to break down complex problems into manageable steps, execute the steps in sequence, and iteratively improve based on feedback, enhancing the performance of reasoning and analysis tasks. Typical use cases: Event interpretation, industry analysis.
+* DOE pattern component: This pattern employs three agents—Data-fining, Opinion-inject, and Express—to improve the effectiveness of tasks that are data-intensive, require high computational precision, and incorporate expert opinions. Typical use cases: Financial report generation.
 
 More patterns are coming soon...
 
 ****************************************
+
+## Citation
+
+The agentUniverse project is supported by the following research achievements.
+
+BibTeX formatted
+```text
+@misc{wang2024peerexpertizingdomainspecifictasks,
+      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
+      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
+      year={2024},
+      eprint={2407.06985},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2407.06985}, 
+}
+```
+Overview: This document introduces in detailed the mechanisms and principles underlying the PEER multi-agent framework. The experimental section assigned scores across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness, with a maximum score of 5 points for each dimension. On average, the PEER model scored higher in each evaluation dimension compared to BabyAGI, and show notable advantages particularly in completeness, relevance, logicality, structure, and comprehensiveness. Furthermore, when tested with the GPT-3.5 Turbo (16k) model, the PEER model achieved a superior accuracy rate of 83% compared to BabyAGI, and with the GPT-4 model, it achieved an accuracy rate of 81%. For more details, please refer to the document. 
+🔗https://arxiv.org/pdf/2407.06985
+
+****************************************
+
 ## Table of Contents
-* [Quick Start](#Quick-Start)
-* [Cases and Example Projects](#Cases-and-Example-Projects)
-* [More](#More)
-  * [Why Use agentUniverse](#Why-Use-agentUniverse)
-  * [Core Features](#Core-Features)
-  * [User Guide](#User-Guide)
-  * [API Reference](#API-Reference)
-  * [Support](#Support)
-  * [Citation](#Citation)
-  * [Acknowledgements](#Acknowledgements)
+
+* [Quick Start](#Quick-Start)  
+* [How to build an agent application](#How-to-build-an-agent-application)
+* [Setup the visual agentic workflow platform](#Setup-the-visual-agentic-workflow-platform)
+* [Why use agentUniverse](#Why-use-agentUniverse)  
+* [Sample Projects](#Sample-Projects)  
+* [Documents](#Documents)  
+* [Support](#Support)
+
 ****************************************
 ## Quick Start
+
+### Installation
 Using pip:
 ```shell
 pip install agentUniverse
 ```
+### Run the first example
+Run your first example, and you can quickly experience the performance of the agents (or agent groups) built by agentUniverse through the tutorial.
 
-We will show you how to:
+Please refer to the document for detail steps: [Run the first example](docs/guidebook/en/Get_Start/Quick_Start.md) 。
 
-* Prepare the environment and application projects
-* Build a simple agent
-* Use mode components for multi-agent collaboration
-* Test and tune the execution effectiveness of an agent
-* Quickly deploy an agent as a service
+****************************************
 
-For more details, please read the [Quick Start](./docs/guidebook/en/1_3_Quick_Start.md).
+## How to build an agent application
+
+### Standard Project Scaffolding
+Setup the standard project: [agentUniverse Standard Project](sample_standard_app)
+
+### Create and use agents
+You can learn about the important components of agents through the [Introduction to Agents](docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent.md). For detailed information on creating agents, refer to [Creating and Using Agents](docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md). You can also deepen your understanding of the creation and usage of agents by exploring official examples, such as the [Python Code Generation and Execution Agent](docs/guidebook/en/Examples/Python_Auto_Runner.md).
+
+### Setting and use knowledgeBase
+In the development of intelligent agent applications, knowledge base construction and recall are indispensable. The agentUniverse framework, based on RAG technology, offers an efficient standard operating procedure for knowledge base construction and managing the retrieval and recall process of RAG. You can learn about its usage through the [Knowledge Introduction](docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge.md) and [Knowledge Definition and Usage](docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md), and further hone your skills in quickly building a knowledge base and creating an agent capable of retrieval and recall through [How to Build RAG Agents](docs/guidebook/en/How-to/How_To_Build_A_RAG_Agent.md).
+
+### Create and use Tools
+In the development of agent applications, agents need to connect to a variety of tools. You should specify a range of tools that they can utilize. You can integrate various proprietary APIs and services as tool plugins through [Tool Creation and Usage](docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Create_And_Use.md). The framework has already integrated LangChain as well as several third-party toolkits. For detailed instructions on how to use thses tools, you can refer to [Integrating LangChain Tools](docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_LangChain_Tools.md) and [Existing Integrated Tools](docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_Tools.md).
+
+### Effectiveness evaluation
+The effectiveness evaluation of agents can be conducted through expert assessments, on the one hand, and by leveraging the evaluation capabilities of the agents themselves, on the other hand. The agentUniverse framework has launched DataAgent (Minimum Viable Product version), which aims to empower your agents with self-evaluation and evolution capabilities utilizing agent intelligence. You can also customize the evaluation criteria within it. For more details, please refer to the documentation:  [DataAgent - Autonomous Data Agents](docs/guidebook/en/In-Depth_Guides/Tutorials/Data_Autonomous_Agent.md).
+
+### agentServe
+agentUniverse provides multiple standard web server capabilities, as well as standard HTTP and RPC protocols. You can further explore the documentation on [Service Registration and Usage](docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md) and the [Web Server](docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md) sections.
 
 ****************************************
-## Using the Product Platform
-agentUniverse provides a local product platform capability. Please follow the steps below for a quick start:
 
-**Install via pip**
+## Setup the visual agentic workflow platform
+
+agentUniverse provides a visual canvas platform for creating agentic workflow. Follow these steps for a quick start:
+
+**Using pip**
 ```shell
 pip install magent-ui ruamel.yaml
 ```
 
 **One-click Run**
 
-Run the [product_application.py](sample_standard_app/boostrap/platform/product_application.py) file located in sample_standard_app/bootstrap for a one-click start.
+Run [product_application.py](sample_standard_app/boostrap/platform/product_application.py) in sample_standard_app/app/bootstrap for quick startup.
 
-For more details, refer to [Quick Start for Product Platform](./docs/guidebook/en/10_1_1_Product%20Platform%20Quick%20Start.md) and the [Advanced Guide](./docs/guidebook/en/10_1_2_Product_Platform_Advancement_Guide.md).
+For more details, refer to [Quick Start for Product Platform](docs/guidebook/en/How-to/Product_Platform_Quick_Start.md) and the [Advanced Guide](docs/guidebook/en/How-to/Product_Platform_Advancement_Guide.md).
 
-This feature is jointly launched by [difizen](https://github.com/difizen/magent) and agentUniverse.
+This feature is jointly developed by [difizen](https://github.com/difizen/magent) and agentUniverse.
 
 ****************************************
 
-## Cases and Example Projects
-### 🌟 Use Cases
-[Legal Consultation Agent v2](./docs/guidebook/en/7_1_1_Legal_Consultation_Case.md)
+## Why use agentUniverse
+
+### Concept
+![](docs/guidebook/_picture/agentuniverse_structure.png)
 
-[Python Code Generation and Execution Agent](./docs/guidebook/en/7_1_1_Python_Auto_Runner.md)
+The core of agentUniverse provides all the essential components needed to build a single intelligent agent, the collaboration mechanisms between multiple agents, and allows for the injection of expert knowledge. The enables developers to effortlessly create intelligent applications equipped with professional know-how.
 
-[Discussion Group Based on Multi-Turn Multi-Agent Mode](./docs/guidebook/en/6_2_1_Discussion_Group.md)
+### Multi Agent Collaboration
+AgentUniverse offers several multi-agent collaboration model components that have been validated in real-world industries. Among these, the "PEER" model stands out as one of the most distinctive.
 
-[Financial Event Analysis Based on PEER Multi-Agent Mode](./docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md)
+The PEER model utilizes agents with four distinct responsibilities: Planning, Executing, Expressing, and Reviewing. This structure allows for the decomposition and step-by-step execution of complex problems and enables autonomous iteration based on evaluation feedback, ultimately enhancing performance in reasoning and analytical tasks. This model is particularly effective in scenarios that require multi-step decomposition and in-depth analysis, such as event interpretation, macroeconomic analysis, and the feasibility analysis of business proposals.
 
-[Andrew Ng's Reflexive Workflow Translation Agent Replication](./docs/guidebook/en/7_1_1_Translation_Case.md)
+The PEER model has achieved impressive results, and the latest research findings and experimental data can be found in the following literature.
 
-#### 🚩 How to Quickly Build an RAG Agent
-The RAG components have been fully upgraded. This tutorial provides a guide on how to quickly build an RAG agent in agentUniverse. You can refer to the documentation on [How to Build a RAG Agent](./docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md). For more theoretical content, please check the documentation on [Introduction to RAG](./docs/guidebook/en/2_2_4_RAG.md).
+### Key Features
+Based on the above introduction, we summarize the main features of agentUniverse as follow:
 
-#### 🚩 DataAgent - Data Autonomous Agent
-agentUniverse has launched DataAgent (Minimum Viable Product Version). DataAgent aims to empower your agent with the capability of self-assessment and evolution through the use of intelligent agent abilities. For more details, please refer to the documentation. [DataAgent - Data Autonomous Agent](./docs/guidebook/en/8_1_1_data_autonomous_agent.md)
+Flexible and Extensible Agent Construction Capability: It provides all the essential components necessary for building agents, all of which support customization to tailor user-specific agents.
 
-### 🌟 Example Projects
-[agentUniverse Example Projects](sample_standard_app)
+Rich and Effective Multi-Agent Collaboration Models: It offers collaborative models such as PEER (Plan/Execute/Express/Review) and DOE (Data-finding/Opinion-inject/Express), which have been validated in the industry. Users can also customize and orchestrate new models to facilitate organic collaboration among multiple agents.
 
-### 🌟 Product Cases Built with agentUniverse
-['Zhi Xiao Zhu' AI Assistant for Financial Professionals](https://zhu.alipay.com/?from=au)
+Easy Integration of Domain Expertise: It offers capabilities for domain prompts, knowledge construction, and management, enabling the orchestration and injection of domain-level SOPs, aligning agents with expert-level domain knowledge.
+
+💡 For additional features:
+see the section on [key features of agentUniverse](docs/guidebook/en/Concepts/Core_Features.md) for more details.
 
 ****************************************
 
-**'Zhi Xiao Zhu' AI Assistant: Facilitate the implementation of large models in rigorous industries to enhance the efficiency of investment research experts**
+## Sample Projects
 
-'Zhi Xiao Zhu' AI Assistant is an efficient solution for the practical application of large models in rigorous industries. It is based on the Finix model, which focuses on precise applications, and the agentUniverse intelligent agent framework, which excels in professional customization. This solution targets a range of professional AI business assistants related to investment research, ESG (Environmental, Social, and Governance), finance, earnings reports, and other specialized areas. It has been extensively validated in large-scale scenarios at Ant Group, enhancing expert efficiency.
+🚩 [Legal Advice Agent v2](docs/guidebook/en/Examples/Legal_Advice.md)
 
+🚩 [Python Code Generation and Execution Agent](docs/guidebook/en/Examples/Python_Auto_Runner.md)
 
-https://private-user-images.githubusercontent.com/39180831/355437700-192f712d-1b03-46a6-8422-1ca10aa94331.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.q1vdSg_Ghxr-DHLXfmQ_fVVRVSFn7H8VMHMi-_2QrjA
+🚩 [Discussion Group Based on Multi-Turn Multi-Agent Mode](docs/guidebook/en/Examples/Discussion_Group.md)
+
+🚩 [Financial Event Analysis Based on PEER Multi-Agent Mode](docs/guidebook/en/Examples/Financial_Event_Analysis.md)
+
+🚩 [Andrew Ng's Reflexive Workflow Translation Agent Replication](docs/guidebook/en/Examples/Translation_Assistant.md)
 
 ****************************************
-## More
-### Why Use agentUniverse
-💡 [Why Use agentUniverse?](./docs/guidebook/en/1_Why_Use_agentUniverse.md)
 
-### Core Features
+## Commercial Product base on agentUniverse
 
-* **Rich Multi-Agent Collaboration Modes:** Provides industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express). It also supports user-defined patterns for new modes, enabling organic collaboration among multiple agents.
-* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents.
-* **Seamless Integration of Domain Expertise:** Offers capabilities for domain-specific prompts, knowledge construction, and management, and supports domain-level SOP orchestration and embedding, aligning agents to the expert level in their fields.
+🔗 [_Zhi Xiao Zhu_-AI Assistant for Financial Professionals](https://zhu.alipay.com/?from=au)
 
-💡 For more features details, see the [Core Features of agentUniverse](./docs/guidebook/en/1_Core_Features.md).
+**_Zhi Xiao Zhu_ AI Assistant: Designed to facilitate the development of large models in rigorous industries to enhance the productively of investment research experts**
+
+_Zhi Xiao Zhu_ AI Assistant an efficient solution for the practical application of large models in rigorous industries. It is built upon the Finix model, which emphasizes precise applications, and leverages the agentUniverse intelligent agent framework, known for its professional customization capabilities. This solution targets a range of professional AI business assistants related to investment research, ESG (environmental, social, and governance), finance, earnings reports, and other specialized domains. It has been extensively validated in large-scale scenarios at Ant Group, significantly improving expert efficiency.
+
+https://private-user-images.githubusercontent.com/39180831/355437700-192f712d-1b03-46a6-8422-1ca10aa94331.mp4?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.q1vdSg_Ghxr-DHLXfmQ_fVVRVSFn7H8VMHMi-_2QrjA
+
+****************************************
+
+## Documents
 
 ### User Guide
-💡 For more detailed information, please read the [User Guide](./docs/guidebook/en/0_index.md).
+💡 For more detailed information, please refer to the [User Guide](./docs/guidebook/en/Contents.md).
 
 ### API Reference
-💡 Please read the [API Reference](https://agentuniverse.readthedocs.io/en/latest/).
+💡 Please consult the [API Reference](https://agentuniverse.readthedocs.io/en/latest/) for technical details.
+
+****************************************
+
+## Support
 
-### Support
-#### Submit Questions via GitHub Issues
-😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days.
+### Submit Questions via GitHub Issues
+😊 We recommend submitting your queries using [GitHub Issues](https://github.com/antgroup/agentUniverse/issues), we typically respond within 2 business days.
 
-#### Contact Us via Discord
+### Contact Us via Discord
 😊 Join our [Discord Channel](https://discord.gg/DHFcdkWAhn) to interact with us.
 
-#### Contact Us via Administrator Email
+### Contact Us via Administrator Email
 😊 Email: 
-[jihan.hanji@antgroup.com](mailto:jihan.hanji@antgroup.com)
-[jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com)
-[jinshi.zjs@antgroup.com](mailto:jinshi.zjs@antgroup.com)
+* [jihan.hanji@antgroup.com](mailto:jihan.hanji@antgroup.com)
+* [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com)
+* [jinshi.zjs@antgroup.com](mailto:jinshi.zjs@antgroup.com)
 
-#### twitter
+### twitter
 ID: [@agentuniverse_](https://x.com/agentuniverse_)
 
-### Citation
-The agentUniverse project is supported by the following research achievements.
-
-BibTeX formatted
-```text
-@misc{wang2024peerexpertizingdomainspecifictasks,
-      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
-      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
-      year={2024},
-      eprint={2407.06985},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2407.06985}, 
-}
-```
-Overview: This document provides a detailed introduction to the mechanisms and principles of the PEER multi-agent framework. In the experimental section, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved a superior rate of 83% over BabyAGI using the GPT-3.5 Turbo (16k) model, and 81% using the GPT-4 model. For more details, please refer to the document.
-https://arxiv.org/pdf/2407.06985
+### Acknowledgements
 
-## Acknowledgements
-This project is partially built on excellent open-source projects such as langchain, pydantic, gunicorn, flask, SQLAlchemy, chromadb, etc. (The detailed dependency list can be found in pyproject.toml). We would like to extend special thanks to the related projects and contributors. 🙏🙏🙏
+This project is partially built upon excellent open-source projects such as Langchain, Pydantic, Gunicorn, Flask, SQLAlchemy, chromadb, etc. (The detailed dependency list can be found in pyproject.toml). We would like to express our heartfelt gratitude to the related projects and their contributors. 🙏🙏🙏
diff --git a/README_jp.md b/README_jp.md
index 66236fc6..4080a466 100644
--- a/README_jp.md
+++ b/README_jp.md
@@ -59,7 +59,7 @@ pip install agentUniverse
 [readthedocs](https://agentuniverse.readthedocs.io/en/latest/)
 
 ## お問い合わせ
-* github: https://github.com/alipay/agentUniverse
+* github: https://github.com/antgroup/agentUniverse
 * gitee: https://gitee.com/agentUniverse/agentUniverse
 * gitcode: https://gitcode.com/agentUniverse
 * Stack Overflow: https://stackoverflowteams.com/c/agentuniverse/questions
diff --git a/README_zh.md b/README_zh.md
index e461ddab..b32f1f86 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -25,35 +25,69 @@
 更多模式组件持续推出中...
 
 ****************************************
+
 ## 目录
-* [快速开始](#快速开始)
-* [案例与样例工程](#案例与样例工程)
-* [更多](#更多)
-  * [为什么使用agentUniverse](#为什么使用agentUniverse)
-  * [核心特性](#核心特性)
-  * [用户指南](#用户指南)
-  * [API参考](#API参考)
-  * [支持](#支持)
-  * [文献](#文献)
-  * [鸣谢](#鸣谢)
+* [快速开始](#快速开始)  
+|  &nbsp; [快速安装](#快速安装) &nbsp; |
+&nbsp; [运行案例](#运行第一个教程案例) &nbsp; |
+* [如何搭建一个智能体应用](#如何搭建一个智能体应用)  
+| &nbsp; [工程化搭建](#工程化搭建) &nbsp; |
+&nbsp; [产品化搭建](#产品化搭建) &nbsp; |
+* [为什么使用agentUniverse](#为什么使用agentUniverse)  
+| &nbsp; [设计思路](#设计思路) &nbsp; | 
+&nbsp; [协同机制](#多智能体协同机制) &nbsp; | 
+&nbsp; [研究文献](#文献) &nbsp; | 
+&nbsp; [核心特性](#核心特性) &nbsp; |
+* [案例与样例工程](#案例与样例工程)  
+| &nbsp; [官方案例](#官方案例) &nbsp; | 
+&nbsp; [样例工程](#示例工程) &nbsp; | 
+&nbsp; [典型产品](#使用aU构建的典型产品) &nbsp; |
+* [用户指南手册](#用户指南手册)
+* [更多](#更多)  
+| &nbsp; Roadmap &nbsp; | 
+&nbsp; [API说明](#API参考) &nbsp; | 
+&nbsp; [项目支持](#支持) &nbsp; |
+&nbsp;&nbsp; [鸣谢](#鸣谢)  &nbsp;&nbsp; |
+
 ****************************************
+
 ## 快速开始
+### 快速安装
 使用pip：
 ```shell
 pip install agentUniverse
 ```
 
-我们将向您展示如何：
-* 进行环境与应用工程准备
-* 构建一个简单的agent
-* 使用模式组件完成多agent协同
-* 对agent执行效果进行测试调优
-* 对agent进行快速服务化
+### 运行第一个教程案例
+
+运行您的第一个案例，您可以通过教程快速体验agentUniverse构建出的智能体(组)运行效果。 
+
+详细步骤请阅读文档: [运行第一个教程案例](./docs/guidebook/zh/开始使用/快速开始.md) 。
 
-详情请阅读[快速开始](docs/guidebook/zh/1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。
 ****************************************
-## 产品化平台使用
-agentUniverse提供基于本地的产品化平台能力，请按照如下步骤快速启动
+
+## 如何搭建一个智能体应用
+
+### 工程化搭建
+
+#### 智能体创建与使用
+您可以通过 [智能体介绍](./docs/guidebook/zh/In-Depth_Guides/原理介绍/智能体/智能体.md) 了解智能体的重要组成部分，通过 [智能体创建与使用](./docs/guidebook/zh/In-Depth_Guides/原理介绍/智能体/智能体创建与使用.md) 了解创建智能体的详细过程。您也可以结合官方案例，如 [Python代码生成与执行Agent](./docs/guidebook/zh/样例文档/Python自动执行案例.md) 进一步理解智能体创建与使用过程。 
+
+#### 知识库构建与使用  
+在智能体应用构建中，知识库构建与召回是不可缺少的，agentUniverse框架以[RAG技术](./docs/guidebook/zh/In-Depth_Guides/原理介绍/RAG.md)为基础提供了高效的知识库构建和RAG检索召回环节的标准作业流程。您可以通过[知识介绍](./docs/guidebook/zh/In-Depth_Guides/原理介绍/知识/知识.md)与[知识定义与使用](./docs/guidebook/zh/In-Depth_Guides/原理介绍/知识/知识定义与使用.md)了解其使用方式，进一步通过[如何构建RAG智能体](./docs/guidebook/zh/How-to/如何构建RAG智能体.md)掌握如何快速构建知识库与构建具有召回能力的智能体。
+
+#### 工具自定义与使用  
+在智能体应用构建中，智能体需要连接各种各样的工具，您需要为其指定可使用的工具范围列表。您可以通过[工具创建与使用](./docs/guidebook/zh/In-Depth_Guides/原理介绍/工具/工具创建与使用.md)将各类自有API与服务以工具插件形式接入。目前框架已经集成了LangChain与部分第三方工具包，详细使用法可阅读[集成LangChain工具](./docs/guidebook/zh/In-Depth_Guides/组件列表/工具列表/集成LangChain工具.md)、[现有集成的工具](./docs/guidebook/zh/In-Depth_Guides/组件列表/工具列表/集成LangChain工具.md)。
+
+#### 效果评估  
+智能体的效果评估一方面可以通过人工专家评定，另一个方面也可以依托智能体的评估能力。agentUniverse推出了DataAgent（Minimum Viable Product版本）, DataAgent旨在使用智能体能力让您的Agent拥有自我评价与演进的能力，您也可以在其中自定义评估标准。详情见文档: [DataAgent - 数据自治智能体](./docs/guidebook/zh/In-Depth_Guides/原理介绍/数据自治智能体.md)。
+
+#### 服务化
+agentUniverse提供多套标准的web serve能力, 同时提供标准的http与rpc协议。您可以进一步关注文档[服务注册与使用](./docs/guidebook/zh/In-Depth_Guides/技术组件/服务化/服务注册与使用.md)与[Web_Server](./docs/guidebook/zh/In-Depth_Guides/技术组件/服务化/Web_Server.md)部分。
+
+### 平台化搭建
+
+agentUniverse提供基于本地的可视化平台搭建能力，请按照如下步骤快速启动。
 
 **通过pip安装**
 ```shell
@@ -64,38 +98,74 @@ pip install magent-ui ruamel.yaml
 
 运行sample_standard_app/boostrap下的[product_application.py](sample_standard_app/boostrap/platform/product_application.py)文件，一键启动。
 
-更多详情参考 [产品化平台快速开始](./docs/guidebook/zh/10_1_1_产品化平台快速开始.md)
-与 [产品化平台进阶指南](./10_1_2_产品化平台进阶指南.md) 。
-
-本功能由 [difizen](https://github.com/difizen/magent) X agentUniverse联合推出。
+更多详情参考 [产品化平台快速开始](./docs/guidebook/zh/How-to/画布式研发平台快速开始.md)
+与 [产品化平台进阶指南](./docs/guidebook/zh/How-to/画布式研发平台进阶指南.md) 。本功能由 🔗[difizen](https://github.com/difizen/magent) 项目组 X agentUniverse 项目组联合推出。
 
 ****************************************
-## 案例与样例工程
-### 🌟 使用案例
-[法律咨询Agent_v2](./docs/guidebook/zh/7_1_1_法律咨询案例.md)
 
-[Python代码生成与执行Agent](./docs/guidebook/zh/7_1_1_Python自动执行案例.md)
+## 为什么使用agentUniverse
+### 设计思路
+
+![](docs/guidebook/_picture/agentuniverse_structure.png)
+
+agentUniverse核心提供了搭建单一智能体的全部关键组件、多智能体之间的协作机制、以及专家经验的注入机制，可以帮助开发者轻松构建具备专业KnowHow的智能应用。
 
-[基于多轮多Agent的讨论小组](./docs/guidebook/zh/6_2_1_讨论组.md)
+### 多智能体协同机制
 
-[基于PEER协同模式的金融事件分析](./docs/guidebook/zh/6_4_1_金融事件分析案例.md)
+agentUniverse提供了若干已在真实产业中验证有效的多智能体协作模式组件，其中，“PEER”是最具特色的模式之一。
 
-[吴恩达反思工作流翻译智能体复刻](./docs/guidebook/zh/7_1_1_翻译案例.md)
+PEER模式通过计划（Planning）、执行（Executing）、表达（Expressing）、评价（Reviewing）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。这一模式显著适用于需要多步拆解、深度分析的场景，比如对于事件的解读、宏中观经济分析、商业方案的可行性分析等。
 
-#### 🚩 如何快速构建RAG智能体
-RAG组件全面升级，教程提供一个在agentUniverse中快速构建一个RAG智能体，可查看文档[如何构建RAG智能体](./docs/guidebook/zh/2_2_4_如何构建RAG智能体.md)，更多原理性内容可查看文档[RAG介绍](./docs/guidebook/zh/2_2_4_RAG.md)。
+PEER模式取得了令人兴奋的效果，最新的研究成果与实验结果我们可以在下列文献中阅读。
 
-#### 🚩 DataAgent - 数据自治智能体
-agentUniverse推出了DataAgent（Minimum Viable Product版本）, DataAgent旨在使用智能体能力让您的Agent拥有自我评价与演进的能力。详情见文档: [DataAgent - 数据自治智能体](./docs/guidebook/zh/8_1_1_数据自治智能体.md)
+### 文献
+
+BibTeX formatted
+```text
+@misc{wang2024peerexpertizingdomainspecifictasks,
+      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
+      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
+      year={2024},
+      eprint={2407.06985},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2407.06985}, 
+}
+```
+文献简介：该文献详细介绍了介绍了PEER多智能体框架的机制原理，同时在实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，PEER模式在每个测评维度的平均分数均高于BabyAGI，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。
+🔗https://arxiv.org/pdf/2407.06985
+
+### 核心特性
+通过上述的介绍我们将其归纳总结，agentUniverse包含如下主要特点：
 
-### 🌟 示例项目
-[agentUniverse 示例项目](sample_standard_app)
+灵活易拓的智能体构建能力： 提供智能体构建所必须的全部关键组件，所有组件均可支持定制供用户增强专属智能体；
 
-### 🌟 使用aU构建的产品案例
-[支小助 金融从业专家AI助手](https://zhu.alipay.com/?from=au)
+丰富有效的多智能体协同模式： 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，支持用户自定义编排新模式，让多个智能体有机合作；
+
+轻松融入领域经验： 提供领域prompt、知识构建与管理的能力，支持领域级SOP编排与注入，将智能体对齐至领域专家级别；
+
+💡 更多特点见[agentUniverse核心特性](./docs/guidebook/zh/设计理念/核心特性.md)部分。
 
 ****************************************
 
+## 案例与样例工程
+### 官方案例
+🚩 [法律咨询Agent_v2](./docs/guidebook/zh/样例文档/法律咨询案例.md)
+
+🚩 [Python代码生成与执行Agent](./docs/guidebook/zh/样例文档/Python自动执行案例.md)
+
+🚩 [基于多轮多Agent的讨论小组](./docs/guidebook/zh/样例文档/讨论组.md)
+
+🚩 [基于PEER协同模式的金融事件分析](./docs/guidebook/zh/样例文档/金融事件分析案例.md)
+
+🚩 [吴恩达反思工作流翻译智能体复刻](./docs/guidebook/zh/样例文档/翻译案例.md)
+
+### 示例工程
+⌨️ [agentUniverse 示例项目](sample_standard_app)
+
+### 使用aU构建的典型产品
+🔗[支小助-金融从业专家AI助手](https://zhu.alipay.com/?from=au)
+
 **投研支小助：助推大模型落地严谨产业，提升投研专家效率**
 
 投研支小助是大模型落地严谨产业的高效解决方案，基于专注严谨应用的凤凰大模型和善于专业定制的agentUniverse智能体框架，主要面向投研、ESG、财经、财报等投研相关细分领域的一系列专业AI业务助手，已在蚂蚁大规模场景充分验证，提升专家效率。
@@ -105,29 +175,20 @@ https://private-user-images.githubusercontent.com/39180831/355437700-192f712d-1b
 
 
 ****************************************
-## 更多
-### 为什么使用agentUniverse
-💡 [为什么使用agentUniverse?](./docs/guidebook/zh/1_为什么选择agentUniverse.md)
-
-### 核心特性
 
-* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作；
+## 用户指南手册
+💡 更多详细信息，请阅读[用户指南手册](docs/guidebook/zh/目录.md) 。
 
-* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体；
-
-* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别；
-
-💡 更多特点见[agentUniverse核心特性](docs/guidebook/zh/1_核心特性.md)部分。
+****************************************
 
-### 用户指南
-💡 更多详细信息，请阅读[用户指南](docs/guidebook/zh/0_%E7%9B%AE%E5%BD%95.md) 。
+## 更多
 
 ### API参考
 💡 请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。
 
 ### 支持
 #### 通过github issue提交疑问
-😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。
+😊 我们建议您使用[github issue](https://github.com/antgroup/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。
 
 #### 通过Discord联系我们
 😊 加入我们的 [Discord频道](https://discord.gg/DHFcdkWAhn) 与我们进行交流。
@@ -137,7 +198,11 @@ https://private-user-images.githubusercontent.com/39180831/355437700-192f712d-1b
 ![](./docs/guidebook/_picture/dingtalk_util20250429.png)
 
 #### 通过管理员Email联系我们
-😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com)
+😊 Email: 
+
+[jihan.hanji@antgroup.com](mailto:jihan.hanji@antgroup.com)
+[jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com)
+[jinshi.zjs@antgroup.com](mailto:jinshi.zjs@antgroup.com)
 
 #### 微信公众号
 
@@ -150,23 +215,5 @@ https://private-user-images.githubusercontent.com/39180831/355437700-192f712d-1b
 #### twitter
 ID: [@agentuniverse_](https://x.com/agentuniverse_)
 
-### 文献
-agentUniverse项目基于以下的研究成果支撑。
-
-BibTeX formatted
-```text
-@misc{wang2024peerexpertizingdomainspecifictasks,
-      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
-      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
-      year={2024},
-      eprint={2407.06985},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2407.06985}, 
-}
-```
-文献简介：该文献详细介绍了介绍了PEER多智能体框架的机制原理，同时在实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，PEER模式在每个测评维度的平均分数均高于BabyAGI，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。
-https://arxiv.org/pdf/2407.06985
-
-## 鸣谢
+### 鸣谢
 本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏
diff --git a/agentuniverse/base/config/configer.py b/agentuniverse/base/config/configer.py
index 6f2dcbc9..2fd3d3d3 100644
--- a/agentuniverse/base/config/configer.py
+++ b/agentuniverse/base/config/configer.py
@@ -18,7 +18,7 @@
 class PlaceholderResolver:
     def __init__(self):
         self._resolvers = []
-        self.register_resolver(r'${(.+?)}',
+        self.register_resolver(r'\${(.+?)}',
                                    lambda match: os.getenv(match.group(1),
                                                            match.group(0)))
     def register_resolver(self, pattern, func):
diff --git a/dataset/README.md b/dataset/README.md
index 7c292a27..c211c534 100644
--- a/dataset/README.md
+++ b/dataset/README.md
@@ -3,7 +3,7 @@
 ## PEER Financial Training Dataset
 We open source 100 common financial questions and a training dataset for use within the PEER framework, containing 100 sets of inputs and outputs of the Planning Agent and the Expressing Agent. The dataset is applicable to agent training, pre-training, and supervised fine-tuning in various financial analysis scenarios. 
 
-For more training data, please provide your email via [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days.
+For more training data, please provide your email via [GitHub Issues](https://github.com/antgroup/agentUniverse/issues), we typically respond within 2 days.
 
 Due to compliance issues, the relevant data of the Executing agent is still in the approval process, please wait patiently.
 
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diff --git a/docs/guidebook/en/0_index.md b/docs/guidebook/en/0_index.md
deleted file mode 100644
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+++ /dev/null
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-# User Guide
-************************************************
-## Table of Contents
-
-**1. Getting Started**
-* 1.1 [Introduction](1_1_Introduction.md)
-* 1.2 [Installation](1_2_Installation.md)
-* 1.3 [Quick Start](1_3_Quick_Start.md)
-* 1.4 [ApplicationStructure](1_4_Application_Engineering_Structure_Explanation.md)
-
-**2. Principle Introduction**
-
-* 2.1 Framework Principles
-* 2.2 [Domain Components](2_2_Domain_Component_Principles.md)
-  * 2.2.1 [Agent](2_2_1_Agent.md)
-    * 2.2.1.1 [Create And Use](2_2_1_Agent_Create_And_Use.md)
-    * 2.2.1.2 [Related Domain Objects](2_2_1_Agent_Related_Domain_Objects.md)
-  * 2.2.2 [LLM](2_2_2_LLM.md)
-    * 2.2.2.1 [Define And Use](2_2_2_LLM_component_define_and_usage.md)
-    * 2.2.2.2 [Related Domain Objects](2_2_2_LLM_Related_Domain_Objects.md)
-  * 2.2.3 [Tool](2_2_3_Tool.md)
-    * 2.2.3.1 [Create And Use](2_2_3_Tool_Create_And_Use.md)
-    * 2.2.3.2 [Related Domain Objects](2_2_3_Tool_Related_Domain_Objects.md)
-  * 2.2.4 [Knowledge](2_2_4_Knowledge.md)
-    * 2.2.4.1 [Define And Use](2_2_4_Knowledge_Define_And_Use.md)
-    * 2.2.4.2 [Related Domain Objects](2_2_4_Knowledge_Related_Domain_Objects.md)
-  * 2.2.5 [Memory](2_2_5_Memory.md)
-    * 2.2.5.1 [Define And Use](2_2_5_Memory_Define_And_Use.md)
-    * 2.2.5.2 [Related Domain Objects](2_2_5_Memory_Related_Domain_Objects.md)
-  * 2.2.6 [Planner](2_2_6_Planner.md)
-    * 2.2.5.1 [Define And Use](2_2_6_Planner_Define_And_Use.md)
-    * 2.2.5.2 [Related Domain Objects](2_2_6_Planner_Related_Domain_Objects.md)
-* 2.3 Technical Components
-  * 2.3.1 [RAG](2_2_4_RAG.md)
-    * 2.3.1.1 [How To Build A RAG Agent](2_2_4_How_To_Build_A_RAG_Agent.md)
-* 2.4 Others
-  * 2.4.1 Service
-    * 2.4.1.1 [Registration and Usage](2_4_1_Service_Registration_and_Usage.md)
-    * 2.4.1.2 [Web Server](2_4_1_Web_Server.md)
-    * 2.4.1.3 [Web API](2_4_1_Web_Api.md)
-    * 2.4.1.4 [Service Information Storage](./2_4_1_Service_Information_Storage.md)
-  * 2.4.2 Prompt Management
-  * 2.4.3 Multi-turn Dialogue
-  * 2.4.4 Logging
-    * 2.4.4.1 [Logging Component](2_6_Logging_Utils.md)
-  * 2.4.5 Data Collection
-    * 2.4.5.1 [Monitor Module](2_5_1_Monitor_Module.md)
-  * 2.4.6 Data Autonomous
-    * 2.4.6.1 [Data Autonomous Agent](8_1_1_data_autonomous_agent.md)
-
-**3. Component Reference Manual**
-* 3.1 Domain Components
-  * 3.1.1 List of Agents
-  * 3.1.2 [List of LLMs](3_1_2_0_List_Of_LLMs.md)
-    * 3.1.2.1 [OpenAI Usage Instructions](3_1_2_OpenAI_LLM_Use.md)
-    * 3.1.2.2 [Qwen Usage Instructions](3_1_2_Qwen_LLM_Use.md)
-    * 3.1.2.3 [WenXin Usage Instructions](3_1_2_WenXin_LLM_Use.md)
-    * 3.1.2.4 [Kimi Usage Instructions](3_1_2_Kimi_LLM_Use.md)
-    * 3.1.2.5 [BaiChuan Usage Instructions](3_1_2_BaiChuan_LLM_Use.md)
-    * 3.1.2.6 [Claude Usage Instructions](3_1_2_Claude_LLM_Use.md)
-    * 3.1.2.7 [Ollama Usage Instructions](3_1_2_Ollama_LLM_Use.md)
-    * 3.1.2.8 [DeepSeek Usage Instructions](3_1_2_DeepSeek_LLM_Use.md)
-    * 3.1.2.9 [General OpenAI Protocol Style Wrapper](3_1_2_OpenAIStyleLLM_Use.md)
-  * 3.1.3 List of Tools
-    * 3.1.3.1 [Integration Tool Details](2_2_3_Integrated_Tools.md)
-    * 3.1.3.2 [LangChain Tool Wrapper](2_2_3_Integrated_LangChain_Tools.md)
-  * 3.1.4 List of Knowledge
-  * 3.1.5 List of Memories
-  * 3.1.6 List of Planners
-* 3.2 Technical Components
-  * 3.2.1 RPC
-    * 3.2.1.1 [gRPC](3_2_1_gRPC.md)
-  * 3.2.2 Store
-    * 3.2.2.1 [SQLDBWrapper](2_3_1_SQLDB_WRAPPER.md)
-    * 3.2.2.2 [Milvus](3_3_1_Milvus.md)
-    * 3.2.2.3 [ChromaDB](3_3_2_ChromaDB.md)
-    * 3.2.2.4 [Sqlite](3_3_3_Sqlite.md)
-  * 3.2.3 Msg
-  * 3.2.4 Logging
-    * 3.2.4.1 [Alibaba Cloud SLS](3_2_4_Alibaba_Cloud_SLS.md)
-
-**[4. API Reference Manual](4_1_API_Reference.md)**
-
-**5. Best Practices**
-* 5.1 Operations and Deployment
-  * 5.1.1 [Docker Containerization Solution](5_1_1_Docker_Container_Deployment.md)
-  * 5.1.2 [K8S Solution](5_1_2_K8S_Deployment.md)
-
-**6. Use Cases**
-* 6.1 RAG-Type Agent Examples
-  * 6.1.1 [Legal Consultation Agent v2](7_1_1_Legal_Consultation_Case.md)
-* 6.2 ReAct-Type Agent Examples
-  * 6.2.1 [Python Code Generation and Execution Agent](7_1_1_Python_Auto_Runner.md)
-* 6.3 [Discussion Group Based on Multi-Turn Multi-Agent Mode](6_2_1_Discussion_Group.md)
-* 6.4 PEER Multi-Agent Cooperation Examples
-  * 6.4.1 [Financial Event Analysis Case](./6_4_1_Financial_Event_Analysis_Case.md)
-* 6.5 [Andrew Ng's Reflexive Workflow Translation Agent Replication](./7_1_1_Translation_Case.md)
-
-**7.Product Platform**
-* 7.1 [Quick Use](./10_1_1_Product Platform Quick Start.md)
-* 7.2 [Advancement Guide](./10_1_2_Product_Platform_Advancement_Guide.md)
-
-**8. Series of Articles**
-
-**9. Frequently Asked Questions (FAQ)**
-
-**[10.Citation](9_1_Citation.md)**
-
-**[11. Contact Us](6_1_Contact_Us.md)**
diff --git a/docs/guidebook/en/10_1_1_Product Platform Quick Start.md b/docs/guidebook/en/10_1_1_Product Platform Quick Start.md
deleted file mode 100644
index d035f7e3..00000000
--- a/docs/guidebook/en/10_1_1_Product Platform Quick Start.md
+++ /dev/null
@@ -1,91 +0,0 @@
-# Product Platform Quick Start
-In this section, we will show you how to:
-
-* Start the agentUniverse productized service
-* Configure the agentUniverse product modules
-* Quickly get started with productized features
-* Debug and experience agents online, optimizing agent performance
-
-## Environment and Application Engineering Preparation
-### Application Engineering Preparation
-We have placed the **product module samples** in the agentUniverse’s sample_standard_app project. You can view them [here](../../../sample_standard_app/platform/difizen/product). This part can be configured in the background through YAML, and of course, these functions can be automatically created and managed through the product page.
-
-### Installing Dependencies
-**Using pip**
-```shell
-pip install magent-ui ruamel.yaml
-```
-
-### Configuration File
-If you have previously used aU’s sample project, please add the following information to the `config.toml` file configuration to include the product module path, as configured in sample_standard_app:
-```toml
-# Ignore the context content.
-[CORE_PACKAGE]
-# Scan and register product components for all paths under this list, with priority over the default.
-product = ['sample_standard_app.platform.difizen.product']
-# Ignore the context content.
-```
-If you are using aU for the first time, you can directly use the latest sample project’s `config.toml`.
-
-#### Private Configuration File
-Of course, when using the agent, you need to pre-configure the various LLM model keys/Tool keys, otherwise, the overall process of the agent cannot be connected. This part is currently not provided in the product page configuration management. Please reuse aU’s original key configuration method. In future versions, we will directly provide key management capabilities for each model in the product.
-
-
-## Using the agentUniverse Product Platform
-### Starting the Product Service
-Run the [product_application](../../../sample_standard_app/boostrap/platform/product_application.py) file located in `sample_standard_app/bootstrap` to start with one click.
-
-![img.png](../_picture/product_start.png)
-
-After a successful start, it will automatically redirect to the product homepage, which includes system presets and your custom Agent/Tool/Knowledge product modules.
-![agentuniverse_product_homepage](../_picture/agentuniverse_product_homepage.png)
-
-### Experience the Agent
-As shown in the image above, click the chat button on the right of the peer multi-agent group to enter the conversation page.
-
-The conversation management system includes the last 10 agent conversation history records, allowing you to directly engage in multi-turn dialogues and experience the capabilities of the peer multi-agent group (default is streaming dialogue, and the multi-agent group includes the intermediate thinking process).
-![agentuniverse_product_agent_chat](../_picture/agentuniverse_product_agent_chat.png)
-
-### Debugging the Agent
-On the product homepage, click the edit button on the left of the agent to enter the online debugging page.
-
-You can debug the agent’s Prompt/Tool/Knowledge/LLM online. Click the save button, and aU-product will automatically save the configuration to the corresponding yaml file.
-![agentuniverse_product_agent_editor](../_picture/agentuniverse_product_agent_editor.png)
-
-Click the debug button in the upper right corner of the image above to view the Trace information, including token consumption, call chain, and latency of the agent’s specific invocation process.
-![agentuniverse_product_agent_trace](../_picture/agentuniverse_product_agent_trace.png)
-
-## Configuring agentUniverse Product Modules
-### Creating Product Modules
-```yaml
-id: demo_rag_agent
-nickname: rag智能体
-type: AGENT
-opening_speech: |
-  欢迎使用rag chatbot！根据您的提问，我将结合实时信息及我个人掌握的知识，给您提供合理的解答。
-  你可以这样问我：
-  问题1: 巴黎奥运会中国获得了几块奖牌
-  问题2: 巴菲特为什么减持苹果股票
-avatar: ../../../resources/rag_agent_logo.png
-metadata:
-  class: AgentProduct
-  module: agentuniverse_product.base.agent_product
-  type: PRODUCT
-```
-
-As shown in the yaml file above:
-
-- `product id` corresponds to `name` in the agent yaml
-- `type` corresponds to `AGENT`
-- `metadata` is configured as the value shown above
-- Other parameters can be configured as needed (the same applies to tool/knowledge modules)
-
-### Configuring Global Config
-In the global `config.toml`, configure the package scan path corresponding to the product module. With one click, you can start the product platform and display the corresponding agent/tool/knowledge information.
-
-## More
-The agentUniverse-product platform is continuously iterating, and more convenient and useful features will be available for community users to experience in the future. We also welcome your valuable suggestions.
-
-This feature is jointly launched by [difizen](https://github.com/difizen/magent) and agentUniverse.
-
-Let’s explore and progress together!
\ No newline at end of file
diff --git a/docs/guidebook/en/10_1_2_Product_Platform_Advancement_Guide.md b/docs/guidebook/en/10_1_2_Product_Platform_Advancement_Guide.md
deleted file mode 100644
index e804fa50..00000000
--- a/docs/guidebook/en/10_1_2_Product_Platform_Advancement_Guide.md
+++ /dev/null
@@ -1,97 +0,0 @@
-# Product Platform Advancement Guide
-In this section, we will show you how to:
-* Workflow Agent Orchestration Feature
-* Table Form Agent Creation Feature
-* Custom Knowledge Insertion Feature
-* Custom Plugin Insertion Feature
-
-## Workflow Agent Orchestration Feature
-``` text
-Note: For old users of the agentUniverse productization platform orchestration capability, please upgrade the magent-ui package version to 0.1.17, and the corresponding aU version must not be lower than 0.0.13. You can use the following command for version-specific installation:
-
-pip install magent-ui==0.1.17 ruamel.yaml --force-reinstall
-```
-### Create Workflow Agent
-On the aU-product homepage, click the "Create Agent" button to the right of the agent tab, and select the workflow type agent.
-![agentuniverse_product_create_workflow_agent](../_picture/create_workflow_agent.png)
-
-### Workflow Node Type
-The current aU product platform supports 7 types of nodes, which are:
-- **Start Node**: define the initial parameters for launching a workflow process.
-- **Tool Node**: provide third-party plugin capabilities for the workflow.
-- **Knowledge Node**: retrieve text content related to the user's query from the knowledge base, which can serve as context for downstream LLM node.
-- **LLM Node**: invoke large language models to answer questions or process natural language.
-- **Agent Node**: invoke and execute existing agent applications.
-- **If-else Node**: split the workflow into multiple branches based on if/else conditional judgments.
-- **End Node**: define the final output content at the end of a workflow process.
-
-### Workflow Agent Orchestration
-On the canvas, connect different types of workflow nodes to build a complete workflow process.
-
-Click the Save button, and aU will automatically generate the corresponding workflow and agent YAML files locally to assist users in completing development work.
-
-``` text
-Note: Please ensure that the logic for passing parameters between nodes is correct; otherwise, it will cause the workflow to fail.
-```
-
-The complete workflow process is shown in the following figure:
-![agentuniverse_product_workflow_agent](../_picture/workflow_agent.png)
-
-The configuration file for the agent generated by aU is shown in the following figure:
-![agentuniverse_product_workflow_agent_yaml](../_picture/workflow_agent_yaml.png)
-
-### Run Workflow Agent
-After clicking the save button, start attempting to run the workflow agent.
-
-The operation process is as shown in the figure:
-![agentuniverse_product_run_workflow_agent](../_picture/run_workflow_agent.png)
-
-
-## Table Form Agent Creation Feature
-### Create Table Form Agent
-On the product homepage, click the 'Create Agent' button to the right of the agent tab, and select the RAG or ReAct type agent.
-
-aU will automatically generate the corresponding agent yaml file locally, assisting users in completing their development work.
-
-![agentuniverse_product_create_agent](../_picture/create_agent.png)
-
-### Run Agent
-After clicking the save button, start attempting to run the created example RAG agent.
-
-The operation process is as shown in the figure:
-![agentuniverse_product_run_demo_rag_agent](../_picture/run_demo_rag_agent.png)
-
-## Custom Knowledge Insertion Feature
-### Create Custom Knowledge
-On the product homepage, switch to the Knowledge tab, and click the 'Add Knowledge' button to the right to create a custom knowledge base.
-
-aU will automatically generate the corresponding knowledge and store yaml files locally, assisting users in completing their development work.
-
-![agentuniverse_product_create_knowledge](../_picture/create_knowledge.png)
-
-### Upload Knowledge
-Specify the knowledge base, click the upload button, and upload the local knowledge file.
-
-After the upload is successful, you can use the corresponding knowledge base in the knowledge node of the workflow agent or the knowledge module of the table form agent to retrieve content related to user questions.
-
-```text
-Currently supported knowledge types are: pdf/docx/pptx/txt
-```
-The upload operation is as shown in the figure:
-![agentuniverse_product_upload_knowledge](../_picture/upload_knowledge.png)
-
-## Custom Plugin Insertion Feature
-### Create Custom Plugin
-On the product homepage, switch to the Plugin tab, and click the 'Create Plugin' button to the right to create a custom plugin.
-![agentuniverse_product_create_plugin](../_picture/create_plugin.png)
-
-After successful creation, you can use the corresponding tools in the tool nodes of the workflow agent or the tool modules of the table form agent, providing powerful third-party plugin capabilities to support the agent.
-
-Plugin schema example reference document: [schema](https://openai.xiniushu.com/docs/plugins/examples)
-
-## More
-The agentUniverse-product platform is continuously iterating, and more convenient and useful features will be available for community users to experience in the future. We also welcome your valuable suggestions.
-
-This feature is jointly launched by [difizen](https://github.com/difizen/magent) and agentUniverse.
-
-Let’s explore and progress together!
\ No newline at end of file
diff --git a/docs/guidebook/en/1_1_Introduction.md b/docs/guidebook/en/1_1_Introduction.md
deleted file mode 100644
index 9943e90b..00000000
--- a/docs/guidebook/en/1_1_Introduction.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# agentUniverse Introduction
-## Introduction
-agentuniverse is a framework for developing applications powered by multi-agent base on large language model.  It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which  serves as a pattern factory that allowing developers to buid and customize multi-agent collaboration patterns. With this framework,  developers can easily construct multi-agent applications, and share the pattern practices from different technical  and business fields.
-
-The framework will come with serveral pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be enriched in the future. Patterns that are currently about to be released include:
-
-- PEER pattern：
-This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback which enhancing performance in reasoning and analytical tasks. 
-
-
-- DOE pattern：
-This pattern consists of three agents: Data-fining agent, which is designed to solve data-intensive and high-computational-precision task; Opinion-inject agent, which combines the data results from first agent and the expert opinions which are pre-collected and structured; the third agent, Express agent generates the final result base on given document type and language style.
-
-More patterns are coming soon...
-
-![](../_picture/agent_universe_framework_resize.jpg)
-
-In addition to rich collaboration modes, agentUniverse also includes the following main features:
-
-* **Fast and Simple Development Experience**: Through configuration and simple interfaces, you can quickly complete single-agent construction, multi-agent collaboration process definition, and service-based applications with this framework.
-* **Rich Components and Custom Extensions**: The framework provides a wide variety of common domain components (LLM, knowledge, tools, memory, collaboration patterns, etc.) and technical components (DB, RPC, Message, etc.) with default implementations. It offers extension standards for all types of components, allowing you to customize any part to enhance your agent capabilities.
-* **Prompt-Friendly and Management**: The framework has built-in a complete set of prompt management mechanisms. You can manage prompts in multiple versions, switch between them, and specialize them based on expert domain knowledge.
-
-## Acknowledgments
-This project is partially based on open-source projects like langchain, pydantic, gunicorn, flask, SQLAlchemy, chromadb, etc. (A detailed dependency list can be found in requirements.txt). Special thanks to the related projects and associated parties.
\ No newline at end of file
diff --git a/docs/guidebook/en/1_2_Installation.md b/docs/guidebook/en/1_2_Installation.md
deleted file mode 100644
index 3c16bc37..00000000
--- a/docs/guidebook/en/1_2_Installation.md
+++ /dev/null
@@ -1,41 +0,0 @@
-# Installation
-## Python version requirements
-- python 3.10+
-
-We recommend that you use python 3.10+ to get the best performance and functionality. The entire project has passed all tests on python 3.10.0. If you are using a python version lower than 3.10, we cannot guarantee that the project will run correctly.
-
-## Installation method
-### Install via pip
-```shell
-pip install agentUniverse
-```
-More version information can be found on the
-[PyPi agentUniverse](https://pypi.org/project/agentUniverse/)
-
-### Install via poetry or other package management tools
-In actual projects, we recommend that you use poetry or other tools to manage project dependencies. You can install it with the following command:
-
-```shell
-poetry add agentUniverse
-```
-
-Or add the following content to your `pyproject.toml` file:
-
-```toml
-[tool.poetry.dependencies]
-agentUniverse = "^0.0.3"
-```
-A standard project's `pyproject.toml` can be found [here](../../../sample_standard_app/pyproject.toml).
-
-Use the `poetry update` command to update dependencies:
-```shell
-poetry update
-```
-
-## Verify installation
-```shell
-pip list | grep agentUniverse
-```
-If you see `agentUniverse` and its version number, the installation was successful.
-
-![image](../_picture/1_2_Installation_0.png)
\ No newline at end of file
diff --git a/docs/guidebook/en/1_4_Application_Engineering_Structure_Explanation.md b/docs/guidebook/en/1_4_Application_Engineering_Structure_Explanation.md
deleted file mode 100644
index 8942b2b0..00000000
--- a/docs/guidebook/en/1_4_Application_Engineering_Structure_Explanation.md
+++ /dev/null
@@ -1,113 +0,0 @@
-# Application Engineering Structure and Explanation
-As you can see, `agentUniverse` is designed with lightness and integrative capabilities in mind, allowing you to incorporate `agentUniverse` into any of your projects for seamless operation.
-
-## Recommended Project Directory Structure and Explanation
-The directory structure provided below is only a suggestion, and you are free to adjust it according to your preferences and actual situation. We will explain this in more detail later in the document.
-
-```
-/
-├── app/
-│   ├── biz/
-│   ├── bootstarp/
-│   │   └── server_application.py
-│   ├── core/
-│   │   ├── agent
-│   │   ├── knowledge
-│   │   ├── llm
-│   │   ├── memory
-│   │   ├── planner
-│   │   ├── service
-│   │   └── tool
-│   ├── test/
-│   └── web/
-├── config
-├── pyproject.toml
-└── other project files...
-```
-
-The specific meanings of each package directory level are as follows:
-* app - The main application program code
-  * biz - Your business code, where you can organize the next level of the directory structure as you wish
-  * bootstrap - The entry layer for starting the web server, for starting details refer to server_application.py
-  * core - The core layer of LLM agent application components
-    * agent - Place the agents you build
-    * knowledge - The knowledge you customize and use
-    * llm - The LLM (Language Model) you customize and use
-    * memory - The memory you customize and use
-    * planner - The collaborative mode you customize and use
-    * service - Service registration directory
-    * tool - The tools you customize and use
-  * test - Directory for tests
-  * web - The upper web layer, currently left blank
-* config - Application configuration code
-
-## Using Any Project Directory Structure
-You can adjust the project directory structure according to your preferences and actual circumstances, but please be sure to follow the rules below.
-
-### Bootstrap Startup Directory
-Regardless of the location of your project's startup script, except for testing, please ensure that the application service is started with the following statement:
-
-```python
-from agentuniverse.agent_serve.web.web_booster import start_web_server
-from agentuniverse.base.agentuniverse import AgentUniverse
-
-
-class ServerApplication:
-    """
-    Server application.
-    """
-
-    @classmethod
-    def start(cls):
-        AgentUniverse().start()
-        start_web_server()
-
-ServerApplication.start()
-
-```
-`ServerApplication.start()` is the server startup method for this framework, which accepts a configuration path `config_path` as an input parameter. The default `config_path` is a file located at `project_root_dir/config/config.toml` under the config directory in the project root path. Please ensure that the config file path is correct. If you've further changed the directory of the config file, please adjust the `config_path` accordingly.
-
-### Config Directory
-As mentioned in the [Bootstrap Startup Directory](#bootstrap-startup-directory), the default config path for the project is `project_root_dir/config/config.toml`. If you have made any adjustments to this, please ensure that the correct config file path is passed to the startup method when the application server is launched.
-
-### Core Directory
-As demonstrated by the recommended directory structure for the project, the project directory within the core directory is mainly used for placing custom domain components such as agents, knowledge, LLM, and others. You are free to position all core components wherever you like and not limited to the same main package. You only need to define it in the `[CORE_PACKAGE]` section of the main configuration file `config/config.toml` of the project, as follows:
-
-```toml
-[CORE_PACKAGE]
-# Perform a full component scan and registration for all the paths under this list.
-default = ['sample_standard_app.intelligence.agentic']
-# Scan and register agent components for all paths under this list, with priority over the default.
-agent = ['sample_standard_app.intelligence.agentic.agent']
-# Scan and register knowledge components for all paths under this list, with priority over the default.
-knowledge = ['sample_standard_app.intelligence.agentic.knowledge']
-# Scan and register llm components for all paths under this list, with priority over the default.
-llm = ['sample_standard_app.intelligence.agentic.llm']
-# Scan and register tool components for all paths under this list, with priority over the default.
-tool = ['sample_standard_app.intelligence.agentic.tool']
-# Scan and register memory components for all paths under this list, with priority over the default.
-memory = ['sample_standard_app.intelligence.agentic.memory']
-# Scan and register service components for all paths under this list, with priority over the default.
-service = ['sample_standard_app.intelligence.service.agent_service']
-# Scan and register prompt components for all paths under this list, with priority over the default.
-prompt = ['sample_standard_app.intelligence.agentic.prompt']
-# Scan and register store components for all paths under this list, with priority over the default.
-store = ['sample_standard_app.intelligence.agentic.knowledge.store']
-# Scan and register rag_router components for all paths under this list, with priority over the default.
-rag_router = ['sample_standard_app.intelligence.agentic.knowledge.rag_router']
-# Scan and register doc_processor components for all paths under this list, with priority over the default.
-doc_processor = ['sample_standard_app.intelligence.agentic.knowledge.doc_processor']
-# Scan and register query_paraphraser components for all paths under this list, with priority over the default.
-query_paraphraser = ['sample_standard_app.intelligence.agentic.knowledge.query_paraphraser']
-# Scan and register memory_compressor components for all paths under this list, with priority over the default.
-memory_compressor = ['sample_standard_app.intelligence.agentic.memory.memory_compressor']
-# Scan and register memory_storage components for all paths under this list, with priority over the default.
-memory_storage = ['sample_standard_app.intelligence.agentic.memory.memory_storage']
-# Scan and register product components for all paths under this list, with priority over the default.
-product = ['sample_standard_app.platform.difizen.product']
-# Scan and register workflow components for all paths under this list, with priority over the default.
-workflow = ['sample_standard_app.platform.difizen.workflow']
-```
-The format for defining packages follows the standard Python package path format. The framework will register, scan, and manage all types of component packages uniformly based on the defined package paths during startup.
-
-**Tips**: The package path starts one level below your project's root directory. For example, in the sample_standard_app example, since the package path is under the agentUniverse level, it starts with sample_standard_app. If you are using sample_standard_app as a project template, then the path should start with app.xxx.
\ No newline at end of file
diff --git a/docs/guidebook/en/1_Core_Features.md b/docs/guidebook/en/1_Core_Features.md
deleted file mode 100644
index 92d7f23e..00000000
--- a/docs/guidebook/en/1_Core_Features.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# Core Features
-* **Agent-Oriented Design:** The framework is designed with agents in mind, customizing agents through the perception, planning, action, and memory modules.
-* **Rich Multi-Agent Collaboration Modes:** Offers industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express), and supports user-defined orchestration of new modes, enabling organic collaboration among multiple agents.
-* **Customizable Components:** All framework components, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents.
-* **Seamless Integration of Domain Expertise:** Provides capabilities for domain-specific prompts, knowledge construction and management, and supports domain-level SOP orchestration and embedding to align agents to expert levels in their fields.
-* **Multi-LLM and Extensible:** Built-in support for multiple models such as the OpenAI series, Qwen series, Baichuan series, Kimi series, etc., while offering standard extensions to integrate any third-party model services and privately deployed models.
-* **Multi-Data Source and Extensible:** Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvu. It also provides standard extension methods to integrate any third-party data services and other databases.
-* **Convenient Ecosystem Integration:** The framework integrates mature and practically tested large model application ecosystem capabilities, shielding most of the underlying configurations and implementations. This includes LLM ecosystem components like LangChain, llama_index, as well as technical components like Grpc and SLS.
-* **Observable and Feedback Capable:** With the monitor component, all service interactions and model interactions within agentUniverse can be recorded and observed. Combined with the framework's automatic agent evaluation capabilities, you can easily grasp the performance gains from agent or model iterations and feed them back into service iterations and model training.
-* **Enterprise Private Component Extensions:** The framework offers a private technical component extension and loading mechanism, allowing internal components like self-developed RPC protocol components, messaging components, and logging components to be integrated via configuration.
-* **One-Click Service Deployment:** Supports one-click startup of a web server through the capabilities of Flask and Gunicorn, making it easy to integrate agent capabilities into any existing system.
-* **Standard Container Delivery:** Provides a standard Docker image and offers recommended integration solutions based on Docker and Kubernetes (K8S) capabilities.
\ No newline at end of file
diff --git a/docs/guidebook/en/1_Why_Use_agentUniverse.md b/docs/guidebook/en/1_Why_Use_agentUniverse.md
deleted file mode 100644
index 3c91c45f..00000000
--- a/docs/guidebook/en/1_Why_Use_agentUniverse.md
+++ /dev/null
@@ -1,10 +0,0 @@
-# Why Use agentUniverse
-With agentUniverse, you can easily build powerful collaborative agents that perform at an expert level in their respective domains.
-
-agentUniverse provides all the essential components for building single agents. All visible components are extensible and customizable, allowing them to seamlessly connect with large model ecosystem components and middleware, helping you build powerful single agents.
-
-agentUniverse features a rich set of multi-agent collaboration capabilities. Different collaboration modes are specialized in solving domain-specific problems, with multi-agents playing different roles and working together towards a common goal. This greatly enhances the agents' performance in completing tasks.
-
-agentUniverse focuses on the construction and management of domain prompts, domain knowledge, and domain SOPs. Through these capabilities, domain expertise is infused into the collaborative work of all agents, ensuring that agents have expert-level proficiency in specific domains.
-
-In addition, agentUniverse already has numerous industry-grade products and practices in the financial sector. We will share and open up collaboration modes, domain expertise, and solutions that have been validated in the financial industry as case studies.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_1_Agent.md b/docs/guidebook/en/2_2_1_Agent.md
deleted file mode 100644
index 8f8f0374..00000000
--- a/docs/guidebook/en/2_2_1_Agent.md
+++ /dev/null
@@ -1,78 +0,0 @@
-# Agent
-An agent is capable of autonomously acting to achieve goals set by humans, with abilities in learning, reasoning, decision-making, and execution. It accomplishes objectives through steps such as task decomposition, using tools and knowledge, and progress control, and then independently concludes its work. Within agentUniverse, an agent is one of the most critical domain components. It integrates a series of other domain components, including tools, knowledge, and plans, into a cohesive whole, ultimately completing the tasks assigned to it by people.
-
-The performance of an agent directly impacts the strength of application service capabilities. In complex service scenarios, often one or multiple outstanding agents are required to complete tasks. In agentUniverse, drawing from the achievements of industry and academia as well as practical experience in industry implementation, the following definition for agents is established, as shown in the figure.
-![](../_picture/agent.jpg)
-
-Let's introduce the roles of the various components within the Agent component separately.
-
-## Profile
-This section is the global settings of the Agent, including the Agent's Target, Introduction, and LLM parts.
-
-### Introduction
-For the description of the Agent's role, for example: You are an AI assistant skilled in information analysis.
-
-### Target
-The goal that the Agent needs to achieve. The Agent will revolve around this goal to complete a series of subsequent tasks.
-  
-```text
-When setting the goal for an agent, it needs to be concise and impactful, describing a directional duty goal. Providing a specific and specialized domain scope is optimal.
-
-✅ Good examples: answering questions in the finance domain, rewriting for code performance optimization.
-
-❌ Bad examples: aimless descriptions such as "just chatting."
-```
-  
-### Instruction
-It includes specific settings for the Agent, such as personality traits, background knowledge, and behavioral guidelines.
-
-```text
-How to write a good Instruction? We can adopt the following paradigm:
-A clear scope of responsibilities the agent excels in (recommended) + Personality setting (fill in as needed) + 
-Background knowledge (fill in as needed) + 
-User input format (fill in as needed) + 
-Behavioral guidance (recommended) + 
-Agent output format (fill in as needed) + 
-Practical examples - fewshot (recommended)
-
-Following the above paradigm, here is an example definition:
-✅ For instance, a good example would be:
-You are Jerry's personal exclusive chatbot [The role played by the agent].
-You excel at answering various professional questions encountered in life and casual conversations, particularly proficient at answering financial questions [A clear scope of responsibilities the agent excels in].
-Your personality is cheerful and lively [Personality setting].
-Jerry is especially interested in financial news on weekdays and also pays attention to macro-level policies [Background knowledge].
-Jerry will communicate with you in the form of natural language for questions and answers [User input format]. 
-Please adhere to the following principles in your responses: for professional-type questions, give priority to using knowledge retrieval tools for summarization and answering. Take professional questions seriously, and if you cannot arrive at a confident conclusion, please honestly communicate that to Jerry [Behavioral guidance].
-Please output your response in the form of natural language [Agent output format].
-Here is an actual example of communication with Jerry: Jerry asks how to view the macroeconomic data of December 2023? Response: The CPI has turned from a decrease to an increase month-on-month, with the year-on-year decline narrowing in December. Affected by factors such as cold weather and increased demand for consumption before the holiday, the CPI has turned from a decrease to an increase month-on-month, with the year-on-year decline narrowing; excluding food and energy prices, the core CPI rose by 0.6% year-on-year, with the increase remaining stable. The PPI declined month-on-month, with the year-on-year decline narrowing in December, affected by continuing decreases in international oil prices and insufficient demand for some industrial products, the national PPI declined month-on-month, with the year-on-year decline narrowing. [Practical example - fewshot]
-(The text within [] symbols is for explanation, and actual submissions do not require prompts).
-
-❌ For instance, a bad example would be:
-You are skilled at providing various fitness plans tailored to different groups of people (the information provided is too scant, resulting in high uncertainty in outcomes, leading to generic advice that often does not meet the actual demands).
-```
-
-### LLM
-The LLM used by the Agent. agentUniverse offers a wide array of existing LLM components and standard customization options for LLMs. You can choose to utilize or define the LLMs you wish to use. We will detail how they work in the LLM section.
-
-## Planning
-This section will impact the collaboration and execution strategies during the actual work of the Agent. It embodies the collaborative and execution ideas of patterns. Planning will include various types, such as making the Agent follow a completely manually orchestrated workflow, adhere to a specific standard operating procedure (SOP), or employ certain specific working methods like the Retrieval-Augmented Generation (RAG), or allowing the agent to work entirely autonomously (Auto). Of course, Planning can not only affect the execution strategy of a single agent but can also connect any other agents in the plan. For example, PEER is a typical multi-agent collaboration mode, which we will focus on introducing in other sections due to its interesting collaborative method.
-
-### Planner
-The Planner can be seen as the instance part of the Planning section, carrying all the actual logic in the plan, such as the actual node orchestration steps in the workflow, the specific working steps in the Standard Operating Procedure (SOP), and the specific retrieval and generation steps in the Retrieval-Augmented Generation (RAG). Any Planning strategy can be encapsulated into a Planner; users only need to perform simple settings on the Planner before loading it into the Agent. Then, the Agent will start working with the specific Planning strategy.
-
-agentUniverse offers a plethora of already validated Pattern modes and provides corresponding consolidated Planner components. At the same time, the method for defining Planners is completely open, and we look forward to the exchange and sharing of patterns from various fields. We will explain specifically how they work in the Planner section.
-
-## Action
-This section mainly contains tools and knowledge. Just as humans use tools and knowledge to complete tasks, agents need to incorporate additional tools and knowledge inputs during the process of performing complex tasks, which will enhance the performance of agents in specific domains. 
-
-### Tool
-Tools available for Agents. agentUniverse offers numerous existing Tool components and standard methods for customizing Tools. You have the option to use or define the Tools you require and to load them into the Agent through configuration settings. Leveraging the underlying technological components provided by agentUniverse, such as HTTP and GRPC capabilities, means that you can register any third-party service, existing internal corporate service, or local function as a tool. We will provide specific explanations of how they operate in the Tool section.
-
-### Knowledge
-Knowledge available for Agents. agentUniverse provides a wide range of existing Knowledge components and standard methods for Knowledge customization. You can opt to use or define the Knowledge you want to use, and then load it into the Agent through configuration. In addition, agentUniverse supports the expanded integration of underlying storage technology middleware, such as SQLite, Chroma, and other storage options, meaning you can access data from any storage medium. We will explain in detail how they work in the Knowledge section.
-
-## Memory
-The memory available for Agents. agentUniverse provides standard optional memory types, and in most cases, users do not need to concern themselves with the specific implementation of memory. The memory component also offers the capability for users to customize it themselves. We will explain in detail how they work in the Memory section.
-
-# Conclusion
-Up to this point, you have gained a comprehensive understanding of the components and principles of an Agent. In the next section, we will specifically introduce the standard definitions of Agent components, how to customize an Agent, and how to use an Agent, among other topics.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_1_Agent_Create_And_Use.md b/docs/guidebook/en/2_2_1_Agent_Create_And_Use.md
deleted file mode 100644
index 4d72787d..00000000
--- a/docs/guidebook/en/2_2_1_Agent_Create_And_Use.md
+++ /dev/null
@@ -1,410 +0,0 @@
-# How to create agents.
-You might have already learned how to quickly create an agent in the [Quick Start](1_3_Quick_Start.md) chapter, or grasped the important components of an agent in the [Principles of Agents](2_2_1_Agent.md). In this section, we will further elaborate on how to create an agent in detail.
-
-Based on the design features of the agentUniverse domain components, creating an agent consists of two parts during the creation process.
-* agent_xx.yaml
-* agent_xx.py
-
-Among them, `agent_xx.yaml` must be created, which includes important attribute information of the agent such as Introduction, Target, Instruction, LLM, etc.; `agent_xx.py` is created as needed, containing specific behaviors of the agent and supporting users to inject custom domain behaviors into the agent. Understanding this principle, let's take a closer look at how to create these two parts.
-
-## Creating Agent Configuration - agent_xx.yaml
-We will provide detailed descriptions of each component in the configuration.
-
-### Setting the basic information of the agent.
-**`info` - basic information of the agent**
-* `name`:  name of the agent
-* `description`:  description of the agent
-
-### Setting the global configurations for the agent.
-**`profile` - Agent global settings.**
-* `introduction`:  introduction to the agent's role
-* `target`:  target of the agent
-* `instruction`: instruction of the agent
-* `llm_model`: The LLM used by the agent
-  * `name`: name of LLM
-  * `model_name`: model_name of LLM
-
-  You can choose any existing LLM or connect to any LLM of your choice. We will not elaborate on this part here; you can refer to the [LLM section](2_2_2_LLM.md) for more details.
-
-### Setting the agent's plan
-**`plan` - plan of agent**
-* `planner` : planner of agent
-  * `name`: name of planner
-  
-  You can choose any existing Planner or connect to any Planner of your choice. We will not elaborate on this part here; you can refer to the Planner section for more details.
-
-### Setting the agent's action
-**`action` - action of agent**
-* `Tool` : Tools available for the agent's use.
-  * tool_name_list，list of tool names, for example:  
-    \- tool_name_a  
-    \- tool_name_b  
-    \- tool_name_c  
-
-  You can choose any existing Tool or connect to any Tool of your choice. We will not elaborate on this part here; you can refer to the [Tool section](2_2_3_Tool.md) for more details.
-
-* `Knowledge` : Knowledge available for the agent's use.
-  * knowledge_name_list，list of knowledge names, for example:   
-    \- knowledge_name_a  
-    \- knowledge_name_b  
-    \- knowledge_name_c
-  
-  You can choose any existing Knowledge or connect to any Knowledge of your choice. We will not elaborate on this part here; you can refer to the Knowledge section for more details.
-
-### Setting up the agent's memory.
-**`memory` - memory of agent**
-
-The agent already has a default memory mode built-in, so you do not need to set it up. Additionally, memory supports customization and type selection, and we will make this capability available in an upcoming feature.
-
-### Setting up component metadata.
-**`metadata` - metadata of component**
-* `type` : type of component，use 'AGENT'
-* `module`: Agent entity package path
-* `class`: Agent entity class name
-
-All provided Agent components will have their corresponding `module` and `class` copied to this section. This section will integrate all configurations and behaviors of the agent into a whole. If you have extended the behavior of the agent, you need to fill in this section according to the actual path. We will further explain this in the "Creating from an Existing Agent Object" section later in the text.
-
-### An actual example of an agent configuration.
-```yaml
-info:
-  name: 'demo_rag_agent'
-  description: 'demo rag agent'
-profile:
-  introduction: You are an AI assistant proficient in information analysis.
-  target: Your goal is to determine whether the answer to a question provides valuable information and to make suggestions and evaluations about the answer.
-  instruction: |
-    The rules you need to follow are:
-
-    1.Must answer the question posed by the user in Chinese, combining the background information and your knowledge.
-    2.Generate structured answers, and when necessary, use blank lines to enhance the reading experience.
-    3.Do not adopt incorrect information from the background.
-    4.Consider the relevance of the answer to the question, and do not provide answers that are not helpful to the question.
-    5.Answer the question comprehensively, with emphasis on the main points, without excessive fancy wording.
-    6.Do not make vague speculations.
-    7.Use numerical information as much as possible. 
-    
-    Background information is: {background} 
-    
-    Begin! 
-    
-    The question to answer is: {input}
-  llm_model:
-    name: 'demo_llm'
-    model_name: 'gpt-4o'
-plan:
-  planner:
-    name: 'rag_planner'
-action:
-  tool:
-    - 'google_search_tool'
-#  knowledge:
-#    - 'knowledge_a'
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.rag_agent_case.demo_rag_agent'
-  class: 'DemoRagAgent'
-```
-The above is an actual example of an agent configuration. In addition to the standard configuration items introduced above, the observant among you may have noticed variables in the prompt like `{background}` and `{input}`. This is a very practical prompt replacement feature, which we will explain in the section [How to dynamically adjust settings based on user input](#How to dynamically adjust settings based on user input).
-
-You can find more agent configuration YAML examples in our sample project under the path `sample_standard_app.intelligence.agentic.agent`.
-
-In addition to this, agentUniverse does not restrict users from extending the YAML configuration content for agents. You can create any custom configuration key according to your own requirements, but please be careful not to duplicate the default configuration keywords mentioned above.
-
-## Creating Agent Domain Behavior Definition agent_xx.py
-In this section, you can orchestrate and customize the behavior of any agent. Of course, if you are completely using existing Agent capabilities, this section is not mandatory.
-
-In this section, we will focus on commonly used definitions of agent domain behavior and the common techniques you may use in the actual process of defining agent domain behaviors.
-
-### Create an Agent class object.
-Create the corresponding Agent class object and inherit the AgentUniverse framework's base class Agent.
-
-### Customize the corresponding agent domain behaviors.
-Common customizable agent behaviors are as follows.
-
-#### input_keys method
-Represents the list of input key parameters from users in the agent's execution process, which needs to be implemented by each custom Agent class.
-
-For example, if the agent runs with only one parameter passed in, such as question=xxx, then the input_keys method should return ['question'].
-
-#### output_keys method
-Represents the list of output key parameters when the agent's execution ends, which needs to be implemented by each custom Agent class.
-
-For example, if the agent outputs a result that includes a response, then the output_keys method should return ['response'].
-
-#### parse_input method
-The input processing node before agent execution; an agent's input can be natural language or structured data such as JSON. You can process any user input in this part, and it needs to be implemented by each custom Agent class.
-
-This section has two input parameters, as follows:
-
-* `input_object`: The original data inputted to the agent
-  * You can retrieve corresponding data from input_object using the `input_object.get_data('input_key')` method.
-  
-    For example, if a user inputs `question=xxx` to the agent, we can obtain the current question from the user by using `input_object.get_data('question')`.
-
-* `agent_input`: The data processed by the agent's input handler, of type `dict`.
-  * `agent_input` includes the following key parameters and data by default.
-    * `chat_history`: Includes historical chat data.
-    * `background`: Includes background.
-    * `date`: Includes the current date.
-  * Append the user input to agent_input.
-    * For example: `agent_input['input'] = input_object.get_data('question')`.
-  
-The output object for the `parse_input` method is generally used `agent_input`.
-
-#### parse_result method
-The output processing node before agent execution, where an agent's output can be natural language or structured data such as JSON. You can manage any content that needs to be output in this part, and it needs to be implemented by each custom Agent class.
-
-This section has one input parameter, as follows:
-* `agent_result`: Agent output data, of type `dict`.  
-* The output object for the `parse_result` method is typically used `agent_result`.
-
-#### execute method
-This method is the core entry point for the agent's execution flow. The agent's base class implements the execution method by default, and users can also override this method to customize the execution method for any agent.
-
-##### Default execute implementation.
-* The default logic for the execute method in the agent base class is as follows:
-
-  * Get the planner name corresponding to the agent YAML configuration
-  * Obtain a Planner instance through the Planner manager
-  * Execute the invoke method of the Planner instance
-
-##### Custom execute implementation.
-You can also customize the execution flow of the agent by overriding the execute method. Overriding execute will override the default implementation of the agent's base class, meaning the agent will lose the ability to automatically select a Planner. If needed, you can refer to the default implementation of the agent's base class to load and use the corresponding planner.
-
-Below is an example of a custom execute implementation:
-```text
-def execute(self, input_object: InputObject, agent_input: dict) -> dict:
-    # Do anything, as exemplified below.
-    
-    # Invoke tool A to get data.
-    
-    # Analyze the data and determine whether it can answer the user's questions.
-    
-    # Organize language to answer questions.
-    
-    # Output the answer.
-    
-    return {'output': 'xxx'}
-```
-
-#### Get the properties of the agent object.
-Through `self.agent_model`, the agent object can access the properties of the agent. In the definition of agent domain behaviors, you can read all the properties configured in the agent configuration section.
-
-A specific example is as follows:
-```text
-def execute(self, input_object: InputObject, agent_input: dict) -> dict:
-    """Execute agent instance.
-
-    Args:
-        input_object (InputObject): input parameters passed by the user.
-        agent_input (dict): agent input parsed from `input_object` by the user.
-
-    Returns:
-        dict: planner result generated by the planner execution.
-    """
-
-    planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name'))
-    planner_result = planner_base.invoke(self.agent_model, agent_input, input_object)
-    return planner_result
-```
-
-In this example, the `plan` section from the agent configuration attributes is accessed within the execution method using `self.agent_model.plan`, and further, the actual name of the `planner` is obtained through the get method.
-
-
-The properties and domain behaviors of the agent are both dependent on the Agent base class in the agentUniverse framework, which is located at `agentuniverse.agent.agent.Agent`. We will also focus on the underlying objects in the sections on agents and related domain objects. If you are interested in the underlying technical implementation, you can further refer to the corresponding code and documentation.
-
-#### An actual example of an agent domain behavior definition.
-```python
-from agentuniverse.agent.agent import Agent
-from agentuniverse.agent.input_object import InputObject
-from agentuniverse.agent.plan.planner.planner import Planner
-from agentuniverse.agent.plan.planner.planner_manager import PlannerManager
-
-class DemoRagAgent(Agent):
-    def input_keys(self) -> list[str]:
-        return ['input']
-
-    def output_keys(self) -> list[str]:
-        return ['output']
-
-    def parse_input(self, input_object: InputObject, agent_input: dict) -> dict:
-        agent_input['input'] = input_object.get_data('input')
-        return agent_input
-
-    def parse_result(self, planner_result: dict) -> dict:
-        return planner_result
-
-    # def execute(self, input_object: InputObject, agent_input: dict) -> dict:    
-    #     planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name'))
-    #     planner_result = planner_base.invoke(self.agent_model, agent_input, input_object)
-    #     return planner_result
-```
-The above is an actual example of an agent domain behavior definition.
-
-## Pay attention to the package path where your defined agent is located
-With the above agent configuration and domain definition part, you have mastered all the steps to create an agent; next, we will use these agents. Before using, please ensure that the created agents are within the correct package scanning path.
-
-In the `config.toml` of the agentUniverse project, you need to configure the package corresponding to the agent file. Please confirm again that the package path where your created file is located is under the `CORE_PACKAGE` in the `agent` path or its subpaths.
-
-Taking the configuration of the example project as a reference, it would be as follows：
-
-```yaml
-[CORE_PACKAGE]
-# Scan and register agent components for all paths under this list, with priority over the default.
-agent = ['sample_standard_app.intelligence.agentic.agent']
-```
-
-## Other techniques for agent development
-### Customize and orchestrate the execution process of agents freely
-In the [Creating Agent Domain Behavior Definitions](#Creating Agent Domain Behavior Definition-agent_xx.py) section of this document, we have already detailed how to customize an execute method. This method is often used in practice customizing processes and inject SOPs (Standard Operating Procedures) according to user demands.
-
-### How to dynamically adjust settings based on user input
-
-**Method 1 (Recommended): Through the standard prompt template variable replacement method.**  
-
-In [An actual example of an agent configuration](#An actual example of an agent configuration.) section of this document, the prompt includes variables like `{background}`,`{input}`, etc. This feature is the prompt variable template replacement function, aimed at dynamically influencing the prompt based on the user's input. One only needs to define the text using `{variable}` format in the agent configuration settings section, and define it in the parse_input's `agent_input` to dynamically replace the corresponding prompt based on the input portion.
-
-For example, in the sample agent `sample_standard_app.intelligence.agentic.agent.agent_instance.rag_agent_case.demo_rag_agent.py`, there is the following `parse_input` method.
-
-```text
-def parse_input(self, input_object: InputObject, agent_input: dict) -> dict:
-    agent_input['input'] = input_object.get_data('input')
-    return agent_input
-```
-
-In its agent settings `sample_standard_app.intelligence.agentic.agent.agent_instance.rag_agent_case.demo_rag_agent.yaml`, in the `instruction` section, we can see the following configuration.
-
-```text
-instruction: |
-    The rules you need to follow are:
-
-    1.Must answer the question posed by the user in Chinese, combining the background information and your knowledge.
-    2.Generate structured answers, and when necessary, use blank lines to enhance the reading experience.
-    3.Do not adopt incorrect information from the background.
-    4.Consider the relevance of the answer to the question, and do not provide answers that are not helpful to the question.
-    5.Answer the question comprehensively, with emphasis on the main points, without excessive fancy wording.
-    6.Do not make vague speculations.
-    7.Use numerical information as much as possible. 
-    
-    Background information is: {background} 
-    
-    Begin! 
-    
-    The question to answer is: {input}
-```
-
-Through this process, we can bring each input, memory or background knowledge, and any information you care about into the interaction between the agent and the LLM using this method.
-
-**Method 2: Adjust the prompt within the parse_input method or the execute method**
-
-Through the agent's `parse_input` method or `execute` method, we can customize the agent in terms of data or behavior. Additionally, by combining the get method in `self.agent_model` with the agent input parameter `input_object`, this means that you can modify any of your settings content through these customization methods.
-
-### Create an agent from an existing agent object
-
-In the actual process of creating applications, we may have many agents of the same type. For example, we might want to create several Retrieval Augmented Generation (RAG) type agents to be used in different question-answering scenarios. By analyzing their similarities and differences, it is not difficult to find that most RAG type agents share the following commonalities and differences:
-
-* Main similarity: They often need to retrieve multiple types of knowledge simultaneously.
-* Main difference: RAG agents in different scenarios need to retrieve using different tools and may have different global agent settings.
-
-Upon further analysis, we can achieve this goal through the same domain behavior and differentiated attribute configurations. This is why configuring settings in YAML is essential during the agent creation process, while domain behavior customization is based on actual needs.
-
-Fortunately, we have a significant number of effective agent types, and they are still expanding. As described in this case, combining the agent domain component setting process, we only need to set different types of agent configuration YAMLs, and complete this goal by setting `metadata`.
-
-
-For example:
-
-**Financial RAG Agent Settings**
-```yaml
-info:
-  name: 'financial_RAG_agent'
-  description: 'demo financial rag agent'
-profile:
-  introduction:  Demo introduction, such as you are a financial RAG agent.
-  target: Demo target, such as use financial tools to retrieve and enhance answer performance.
-  instruction: |
-    demo instruction, xxx, {input}
-  llm_model:
-    name: 'demo_llm'
-    model_name: 'gpt-4o'
-plan:
-  planner:
-    name: 'rag_planner'
-action:
-  tool:
-    - 'financial_data_tool'
-metadata:
-  type: 'AGENT'
-  module: 'agentuniverse.agent.default.rag_agent.rag_agent'
-  class: 'RagAgent'
-```
-
-**Tech RAG Agent Settings**
-```yaml
-info:
-  name: 'tech_RAG_agent'
-  description: 'demo tech rag agent'
-profile:
-  introduction:  Demo introduction, such as you are a tech RAG agent.
-  target: Demo target, such as use tech tools to retrieve and enhance answer performance.
-  instruction: |
-    demo instruction, xxx, {input}
-  llm_model:
-    name: 'demo_llm'
-    model_name: 'gpt-4o'
-plan:
-  planner:
-    name: 'rag_planner'
-action:
-  tool:
-    - 'tech_data_tool'
-metadata:
-  type: 'AGENT'
-  module: 'agentuniverse.agent.default.rag_agent.rag_agent'
-  class: 'RagAgent'
-```
-
-You can find more information about existing agents and understand their roles in the [Learn more about agents](#Learn more about agents) section of this document.
-
-# How to Use an Agent
-In the agentUniverse, all agent entities are managed by a global agent manager. If you need to use an agent during any framework execution process, you can do so through the agent manager. Additionally, leveraging the framework's service capabilities, you can quickly turn an agent into a service and make network calls to it using standard HTTP or RPC protocols.
-
-## Solution 1: Use the agent manager
-
-Through the `get_instance_obj('agent_name_xxx')` method in the agent manager, you can obtain the agent instance with the corresponding name. Meanwhile, the agent can be utilized through its own `run(input='xxx')` method. The `test_rag_agent(self)` method in the test class below demonstrates debugging the agent using this approach.
-
-```python
-import unittest
-
-from agentuniverse.agent.agent import Agent
-from agentuniverse.agent.agent_manager import AgentManager
-from agentuniverse.agent.output_object import OutputObject
-from agentuniverse.base.agentuniverse import AgentUniverse
-
-
-class RagAgentTest(unittest.TestCase):
-    """
-    Test cases for the rag agent
-    """
-
-    def setUp(self) -> None:
-        AgentUniverse().start(config_path='../../config/config.toml')
-
-    def test_rag_agent(self):
-        """Test demo rag agent."""
-        instance: Agent = AgentManager().get_instance_obj('demo_rag_agent')
-        output_object: OutputObject = instance.run(input="What is the reason for the sharp rise in Nvidia's stock?")
-        print(output_object.get_data('output'))
-
-if __name__ == '__main__':
-    unittest.main()
-```
-
-## Solution 2: Utilize the service capabilities of agent_serve
-
-The agentUniverse offers a variety of standard web serve capabilities, along with standard HTTP and RPC protocols. You can further refer to the documentation sections on [Service Registration and Usage](2_4_1_Service_Registration_and_Usage.md) and [Web_Server](2_4_1_Web_Server.md).
-
-# Learn more about agents
-The agents provided by the framework can be found under the `agentuniverse.agent.default` package path. You can further explore the corresponding code or learn more about them in our extension component introduction section.
-
-# Conclusion
-By now, you have mastered all the content related to the creation and use of agents. Let's use this knowledge to customize your own exclusive agent.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md b/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md
deleted file mode 100644
index 490207fc..00000000
--- a/docs/guidebook/en/2_2_4_How_To_Build_A_RAG_Agent.md
+++ /dev/null
@@ -1,110 +0,0 @@
-# How to Build a RAG Agent
-
-This tutorial provides a quick guide to building a RAG (Retrieval-Augmented Generation) agent within agentUniverse. The full structure and content of the case can be found in [this document](7_1_1_Legal_Consultation_Case.md). This document focuses on the construction process, so some non-essential content has been omitted.
-
-## Case Description
-This case is based on RagPlanner and sets up a simple legal consultation agent that provides relevant legal advice by retrieving applicable articles from the Civil Code and Criminal Code and considering the case background.
-The case uses the Qianwen large model and DashScope's embedding and rerank functionalities. Before using it, you need to configure the `DASHSCOPE_API_KEY` in your environment variables.
-
-The knowledge file is defined as follows:
-```yaml
-name: "law_knowledge"
-description: "中国民法与刑法相关的知识库"
-stores:
-    - "civil_law_chroma_store"
-    - "criminal_law_chroma_store"
-    - "civil_law_sqlite_store"
-    - "criminal_law_sqlite_store"
-insert_processors:
-    - "recursive_character_text_splitter"
-post_processors:
-    - "dashscope_reranker"
-readers:
-    pdf: "default_pdf_reader"
-
-metadata:
-  type: 'KNOWLEDGE'
-  module: 'sample_standard_app.intelligence.agentic.knowledge.law_knowledge'
-  class: 'LawKnowledge'
-```
-
-## Building the Knowledge Index
-
-Origin docs：
-- [民法典.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
-- [刑法.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
-
-### Extracting Text from PDFs
-Since the original documents in this case are in PDF format, we configured the Knowledge component as follows:
-```yaml
-readers:
-    pdf: "default_pdf_reader"
-```
-This allows the extraction of text from the PDF. If you want to read more file formats, you can refer to the [Reader component](2_2_4_Reader.md).
-
-### Splitting Long Text
-Since the text content in the original documents is very long, we need to split it into smaller chunks. Here we use `recursive_character_text_splitter` for splitting, configured as follows:：
-```yaml
-insert_processors:
-    - "recursive_character_text_splitter"
-```
-This configuration is a list format, allowing multiple document processors to be configured. The only specified processor in this case, recursive_character_text_splitter, recursively splits the original document according to a specified delimiter until it meets the required length. For more details, refer to [DocProcessor](2_2_4_DocProcessor.md). This document also includes other document processors that you can use or customize.
-
-### Configuring the Store
-This case includes four Stores: the Civil Law and Criminal Law are stored separately in SQLite and ChromaDB. We will take `civil_law_chroma_store` as an example, with other Stores being similar.
-```yaml
-name: 'civil_law_chroma_store'
-description: '保存了中国民法典的所有内容，以文本向量形式存储'
-persist_path: '../../db/civil_law.db'
-embedding_model: 'dashscope_embedding'
-similarity_top_k: 100
-metadata:
-  type: 'STORE'
-  module: 'agentuniverse.agent.action.knowledge.store.chroma_store'
-  class: 'ChromaStore'
-```
-
-The `persist_path` specifies the local storage location of the database file and designates `dashscope_embedding` as the component for vectorizing the text in the database. `similarity_top_k` indicates the number of documents to be retrieved. For more details on Store, refer to [this document](2_2_4_Store.md).
-
-### Executing the Insertion Process
-
-After completing the above configurations, you can execute the following code to build the knowledge base:
-```python
-from agentuniverse.base.agentuniverse import AgentUniverse
-from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
-
-
-if __name__ == '__main__':
-    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
-    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
-    law_knowledge.insert_knowledge(
-        source_path="../resources/刑法.pdf",
-        stores=["civil_law_sqlite_store"]
-    )
-```
-We specify the data to be inserted using the `source_path` parameter in the `insert_knowledge` method and designate different Stores for different documents using the `stores` parameter. The `stores` parameter is optional; if not specified, the data is inserted into all stores by default.
-
-
-## Building the Retrieval Process
-
-### Re-ranking
-We configured the post-processing flow of the knowledge as follows:
-```yaml
-post_processors:
-    - "dashscope_reranker"
-```
-This indicates that we will use Dashscope’s re-ranking service to re-rank the documents. You can also add other post-processing flows here.
-
-## Using RAG in the Agent
-
-```yaml
-info:
-  name: 'law_rag_agent'
-  description: '一个法律顾问，可以根据给出的事件，以及提供的背景知识做出客观的司法判断'
-action:
-  knowledge:
-    - 'law_knowledge'
-```
-We configure the knowledge like this in the YAML file of the Agent.
-
-For the complete case and how to invoke it, please refer to [this document](7_1_1_Legal_Consultation_Case.md).
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_Domain_Component_Principles.md b/docs/guidebook/en/2_2_Domain_Component_Principles.md
deleted file mode 100644
index 713c07d6..00000000
--- a/docs/guidebook/en/2_2_Domain_Component_Principles.md
+++ /dev/null
@@ -1,17 +0,0 @@
-# Domain Component Principles
-agentUniverse includes several domain components, which combine to form the fundamental components of the multi-agent framework, including:
-* Agent
-* LLM
-* Tool
-* Knowledge
-* Memory
-* Planner
-
-All domain components in agentUniverse follow a unified design philosophy, exhibiting the following characteristics:
-
-* Development is configuration-oriented, enhancing the efficiency of component creation and reuse.
-* Possess independent managers, providing a unified and isolated mechanism for component registration and management.
-* Standard domain definitions support the injection of all similar type components in the industry.
-* Components are customizable, opening up capabilities for all domain components to be tailored.
-
-This chapter will specifically introduce the design principles of each domain component and how to use them. Please read the sub-documents within this chapter.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_3_1_SQLDB_WRAPPER.md b/docs/guidebook/en/2_3_1_SQLDB_WRAPPER.md
deleted file mode 100644
index 2051c52c..00000000
--- a/docs/guidebook/en/2_3_1_SQLDB_WRAPPER.md
+++ /dev/null
@@ -1,76 +0,0 @@
-# SQLDB Wrapper
-
-Based on SQLAlchemy and langchain's SQLDatabase, agentUniverse offers a `SQLDBWrapper` class allowing users to conveniently use most SQL-based databases like SQLite, MySQL, Oracle, etc. For details, please refer to the [SQLAlchemy official website](https://docs.sqlalchemy.org/en/20/dialects/). With SQLDBWrapper, you can easily manage multiple different database connection objects at the same time.
-
-## Registration Method
-
-### Step One: Configure Scan Path
-gentUniverse automatically registers the SQLDBWrapper configuration files by scanning the configured paths at application startup. The paths to scan are configured in the `config.toml` file:
-```toml
-[CORE_PACKAGE]
-default = ['default_scan_path']
-sqldb_wrapper = ['sqldb_wrapper_scan_path']
-```
-By default, AgentUniverse scans all paths under `default` or `sqldb_wrapper`, with paths under `sqldb_wrapper` having a higher priority over `default`.
-
-
-### Step Two: Configuration File
-```yaml
-name: 'demo_sql'
-description: 'demo_sql'
-db_uri: "sqlite:///./demo.db"
-sql_database_args:
-  include_tables: ["users"]
-engine_args:
-  pool_size: 10
-metadata:
-  type: 'SQLDB_WRAPPER'
-```
-- **`name`**: The name of the SQLDBWrapper, used to identify a unique instance.
-- **`description`**: A description of the SQLDBWrapper's functionality.
-- **`db_uri`**: A SQLAlchemy-style database URI, used to create a SQLAlchemy engine.
-- **`sql_database_args`**: Optional parameters for the SQLDatabase class in `LangChain`, for specific details refer to [LangChain's official website](https://python.langchain.com/v0.1/docs/integrations/toolkits/sql_database/).
-- **`engine_args`**: Optional parameters, used to configure SQLAlchemy engine parameters. For specific configurable contents, please refer to [SQLAlchemy's webpage](https://docs.sqlalchemy.org/en/20/core/engines.html#sqlalchemy.create_engine).
-- **`metadata`**: Indicates that this configuration is for a `SQLDB_WRAPPER`, no need to change.
-
-## Usage
-The SQLDBWrapper class offers the following functions for users:
-
-### run
-`run` accepts a `str` type parameter command, representing an SQL statement, and executes this SQL via SQLAlchemy, returning the results. The result is a list, if command is a query statement then the list will contain the query results, each element is a dictionary representing a row in the database, with the key as the column name and the value as the corresponding value.
-```python
-from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
-from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
-
-demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
-demo_sqldb_wrapper.run("select * from USERS")
-```
-Example of returned result:
-```text
-[{'age': 30, 'id': 1, 'name': 'Alice'}, {'age': 25, 'id': 2, 'name': 'Bob'}]
-```
-
-### run_with_str_return
-`run_with_str_return` accepts a `str` type parameter command, representing an SQL statement, and executes this SQL via SQLAlchemy, returning the results. The result is a `str`, if command is a query statement then the str consists of all the query results concatenated together, and if the `max_string_length` parameter was configured in `sql_database_args`, the result will be truncated. This function is more convenient for using the results as input for llm.
-```python
-from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
-from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
-
-demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
-demo_sqldb_wrapper.run_with_str_return("select * from USERS")
-```
-Example of returned result:
-```text
-"[(1, 'Alice', 30), (2, 'Bob', 25)]"
-```
-
-### sql_database
-If you wish to use some of the native features of SQLAlchemy or SQLDatabase, you can access the corresponding object through the `sql_database` property of SQLDBWrapper:
-```python
-from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
-from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
-
-demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
-sql_database_ins = demo_sqldb_wrapper.sql_database
-sql_alchemy_engine = sql_database_ins._engine
-```
\ No newline at end of file
diff --git a/docs/guidebook/en/2_4_1_Service_Information_Storage.md b/docs/guidebook/en/2_4_1_Service_Information_Storage.md
deleted file mode 100644
index 6130e76d..00000000
--- a/docs/guidebook/en/2_4_1_Service_Information_Storage.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Service Information Storage
-
-agentUniverse uses a system-level database to store various types of information generated during application runtime, such as the results of asynchronous service requests, which are stored in this database.
-
-## System Database Configuration
-You can configure the system database address in the `config.toml` file:
-```toml
-[DB]
-# A sqlalchemy db uri used for storing various info, for example, service request, generated during application running.
-# If it's empty, agentUniverse will create a local sqlite db as default choice.
-system_db_uri = ''
-```
-Please note that this URI should comply with the URI format specification in SQLAlchemy.
-When this value is empty, a `DB` folder will be created in the project root directory, and a SQLite DB file named `agent_universe.db` will be created in the folder as the default system database.
-If you wish to obtain more information on how to use the system database, you can refer to the section [SQLDB_WRAPPER](2_3_1_SQLDB_WRAPPER.md), where the name of the system database is registered as `__system_db__`.
-
-
-
-## Service Information Table Format
-agentUniverse uses the following ORM to store request information:
-```text
-class RequestORM(Base):
-    """SQLAlchemy ORM Model for RequestDO."""
-    __tablename__ = 'request_task'
-    id = Column(Integer, primary_key=True, autoincrement=True)
-    request_id = Column(String(20), nullable=False)
-    query = Column(Text)
-    session_id = Column(String(50))
-    state = Column(String(20))
-    result = Column(JSON)
-    steps = Column(JSON)
-    additional_args = Column(JSON)
-    gmt_create = Column(DateTime, default=datetime.datetime.now)
-    gmt_modified = Column(DateTime, default=datetime.datetime.now,
-                          onupdate=datetime.datetime.now)
-```
-Where `id` is an auto-incremented primary key, `request_id` is a unique ID for each request, `stat`e represents the execution state of the agent task corresponding to the request, `result` is the execution result, `steps` are the intermediate outputs during the execution process, `session_id` and `additional_args` are reserved fields in the current version.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_4_1_Service_Registration_and_Usage.md b/docs/guidebook/en/2_4_1_Service_Registration_and_Usage.md
deleted file mode 100644
index cb2462fc..00000000
--- a/docs/guidebook/en/2_4_1_Service_Registration_and_Usage.md
+++ /dev/null
@@ -1,31 +0,0 @@
-# Service
-
-AgentUniverse allows developers to register completed Agents as a Service using a simple template definition. This enables the use of the corresponding Agent services through Web API calls after launching the built-in web server.
-
-## Registration Method
-
-### Step1: Configure Scan Path
-AgentUniverse automatically registers Service configuration files by scanning configured paths at application startup. The scanning paths are configured in the `config.toml` file:
-```toml
-[CORE_PACKAGE]
-default = ['default_scan_path']
-service = ['service_scan_path']
-```
-By default, AgentUniverse scans all paths under default or service, with the path configuration under service having higher priority than default.
-
-
-### Step2: Configuration File
-```yaml
-name: 'service_name'
-description: 'description of the service'
-agent: 'agent_name'
-metadata:
-  type: 'SERVICE'
-```
-- **`name`**: The name of the Service, which needs to be provided when calling the service through the Web API.
-- **`description`**: Description of the Service's functionality.
-- **`agent`**: The name of the Agent. For more information about Agents, please refer to [Agent]().
-- **`metadata`**: Indicates that this configuration is for a Service; no changes are needed.
-
-## Usage
-Please refer to [Web API](2_4_1_Web_Api.md).
\ No newline at end of file
diff --git a/docs/guidebook/en/2_6_Logging_Utils.md b/docs/guidebook/en/2_6_Logging_Utils.md
deleted file mode 100644
index 9d22846f..00000000
--- a/docs/guidebook/en/2_6_Logging_Utils.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# Logging Utils
-
-The log component of AgentUniverse is implemented based on loguru, providing a well-packaged global log component and customizable log components. This tutorial will first introduce the usage of the log configuration file in AgentUniverse, then explain the usage of the global log component and customizable log components in turn, and finally describe how to interface with external log service components.
-    
-## Log Configuration
-
-### Configuration File Path
-The configuration path of the log file is specified in the main configuration of AgentUniverse, config.toml.
-```toml
-[SUB_CONFIG_PATH]
-# Log config file path, an absolute path or a relative path based on the dir where the current config file is located.
-log_config_path = './log_config.toml'
-```
- If this parameter is a relative path, its parent directory is the directory where the main configuration file, config.toml, is located.
-If this parameter does not exist, AgentUniverse will use the default log configuration, see [default configuration](#default-configuration).
-
-### Configuration File Options
-The configuration file for logs is in the toml file format; an example configuration is as follows:
-```toml
-[LOG_CONFIG]
-[LOG_CONFIG.BASIC_CONFIG]
-log_level = "INFO"
-log_path = "~/.agent_universe_log"
-log_rotation = "100 MB"
-log_retention = "7 days"
-```
-- **`log_level`**: The default log level, possible values include "TRACE", "DEBUG", "INFO", "SUCCESS", "WARNING", "ERROR", "CRITICAL". The level of the global log component will be the same as this value. When creating a custom log component, if log level is not specified, this default level will be used.
-- **`log_path`**: The log storage path, if this value is empty, a {workdir}/../../logs folder will be created as the log storage path.
-- **`log_rotation`**: This parameter accepts a duration (e.g., "1 week") or file size (e.g., "100 MB"), indicating the condition for log file rotation.
-- **`log_retention`**: This parameter accepts a duration (e.g., "7 days") indicating the retention period for old log files, which will be deleted after they expire.
-
-### Default Configuration
-Configuration not specified in the log configuration file will take the following default settings:
-- **`log_level`**: "INFO"
-- **`log_rotation`**: "10MB"
-- **`log_retention`**: "3 days"
-
-Two default log files, `au_all.log` and `au_error.log`, will be located in the log storage path. 'All' records all logs above the default log level set in the log configuration, while 'error' records only logs at the ERROR level and above. The format for log component output is:
-```python
-log_format: str = ("<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> "
-                       "| <level>{level: <8}</level> "
-                       "| {extra[context_prefix]} "
-                       "| <cyan>{name}</cyan>"
-                       ":<cyan>{function}</cyan>"
-                       ":<cyan>{line}</cyan> "
-                       "| <level>{message}</level>")
-```
-Where `{extra[context_prefix]}` defaults to `default`, When `LOG_CONTEXT` exists in the framework context, its contents will be replaced by the contents of `LOG_CONTEXT`. For more information related to the framework context, please refer to  [Framework_Context](2_7_Framework_Context.md)。
-
-## Global Log Component
-AgentUniverse provides a directly usable log component `logging_util.Logger`, which you can introduce into your project as follows:
-```python
-from agentuniverse.base.util.logging.logging_util import LOGGER
-```
-You can use this object's different member functions to record logs of different levels:
-```python
-from agentuniverse.base.util.logging.logging_util import LOGGER
-
-LOGGER.info("This is an info log.")
-LOGGER.debug("This is a debug log.")
-LOGGER.warn("This is a warn log.")
-LOGGER.error("This is an error log.")
-```
-
-## Custom Log Components
-If you want to output and save certain logs separately, you can create a custom log component using the `get_module_logger` function::
-```python
-from agentuniverse.base.util.logging.logging_util import get_module_logger
-new_logger = get_module_logger("new_module")
-```
-Logs recorded by this new component will be saved in the log storage path, in a log file named `au_{module_name}.log`,  where `{module_name}` is the module name you passed into `get_module_logger`, as in the example above, the corresponding file would be `au_new_module.log`.
-
-## External Log Service
-
-If you want to use more log utils, please refer to [extension logging utils](3_2_4_Alibaba_Cloud_SLS.md).
diff --git a/docs/guidebook/en/3_3_1_Milvus.md b/docs/guidebook/en/3_3_1_Milvus.md
deleted file mode 100644
index 1bb8bee9..00000000
--- a/docs/guidebook/en/3_3_1_Milvus.md
+++ /dev/null
@@ -1,51 +0,0 @@
-## Milvus
-If you want to use the Milvus vector database in aU, you need to:
-
-1. Install the Milvus vector database
-You can refer to the [official Milvus installation documentation](https://milvus.io/docs/install_standalone-docker.md) to install and use Milvus. We recommend starting the Milvus container in Docker with the following commands:
-``` shell
-# Download the installation script
-$ curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh
-
-# Start the Docker container
-$ bash standalone_embed.sh start
-```
-These two commands will pull the Milvus image and start a container, providing database services on port 19530. For more details and other installation methods, please refer to the official documentation.
-
-2. Install the Milvus Python SDK
-```
-pip install pymilvus
-```
-
-### How to Configure Milvus Components
-```yaml
-name: 'milvus_store'
-description: 'a store based on milvus'
-connection_args:
-  host: '127.0.0.1'
-  port: '19530'
-search_args:
-  metric_type: "L2"
-  params:
-    nprobe: 10
-index_params:
-  metric_type: "L2"
-  index_type: "HNSW"
-  params:
-    M: 8
-    efConstruction: 32
-embedding_model: 'dashscope_embedding'
-similarity_top_k: 100
-metadata:
-  type: 'STORE'
-  module: 'agentuniverse.agent.action.knowledge.store.milvus_store'
-  class: 'MilvusStore'
-```
-- connection_args: Parameters for connecting to the Milvus database, including the host address (host) and port number (port).
-- search_args: Search parameters, defining the distance metric type (metric_type) used during searches and related parameters such as nprobe.
-- index_params: Indexing parameters, specifying the index type (index_type), distance metric type (metric_type), and specific parameters for building the index, such as M and efConstruction.
-- embedding_model: The model used to generate embedding vectors, specified here as dashscope_embedding.
-- similarity_top_k: The number of most similar results returned in similarity search.
-### Usage
-[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md)
-
diff --git a/docs/guidebook/en/3_3_2_ChromaDB.md b/docs/guidebook/en/3_3_2_ChromaDB.md
deleted file mode 100644
index b2d6505f..00000000
--- a/docs/guidebook/en/3_3_2_ChromaDB.md
+++ /dev/null
@@ -1,43 +0,0 @@
-## ChromaDB
-
-The agentUniverse already integrates ChromaDB-related dependencies, so you do not need to install additional packages to use ChromaDB's features.
-If you want to learn about the underlying principles of ChromaDB, you can visit the [official ChromaDB website](https://www.trychroma.com/).
-
-### How to Configure ChromaDB Components
-```yaml
-name: 'chroma_store'
-description: 'store based on chroma db'
-persist_path: '../../DB/criminal_law.db'
-embedding_model: 'dashscope_embedding'
-similarity_top_k: 100
-metadata:
-  type: 'STORE'
-  module: 'agentuniverse.agent.action.knowledge.store.chroma_store'
-  class: 'ChromaStore'
-```
-- persist_path: The persistence storage path for the database, used for storing and loading vector data.
-- embedding_model: The model used to generate embedding vectors, specified here as dashscope_embedding.
-- similarity_top_k: The number of most similar results returned in similarity search.
-
-### [ChromaHierarchicalStore](../../../agentuniverse/agent/action/knowledge/store/chroma_hierarchical_store.py)
-ChromaHierarchicalStore is a subclass of ChromaStore, used to store document content that includes hierarchical information.   
-Here is an example of a definition file for ChromaHierarchicalStore:
-```yaml
-name: 'chroma_hierarchical_store'
-description: 'sample chroma hierarchical store'
-persist_path: '../../DB/civil_law_hierarchical.db'
-embedding_model: 'dashscope_embedding'
-search_depth: 2
-similarity_top_k_list:
-  - 2
-  - 10
-metadata:
-  type: 'STORE'
-  module: 'agentuniverse.agent.action.knowledge.store.chroma_hierarchical_store'
-  class: 'ChromaHierarchicalStore'
-```
-- search_depth: Indicates the depth of the search. The search begins at the top level of the document and proceeds through each level.
-- similarity_top_k_list: Specifies the number of documents returned at each search depth. Note that if the first level is set to return 2 documents and the second level is set to return 10, the final result will contain 2 * 10 = 20 documents.
-
-### Usage
-[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/3_3_3_Sqlite.md b/docs/guidebook/en/3_3_3_Sqlite.md
deleted file mode 100644
index 9007a574..00000000
--- a/docs/guidebook/en/3_3_3_Sqlite.md
+++ /dev/null
@@ -1,28 +0,0 @@
-## Sqlite
-
-The SQLite component in the Store module is used to store the text content of Document and build a corresponding inverted index based on Jieba's keyword extraction feature. During the retrieval phase, relevant documents are recalled through keywords, and the relevance between the query and the recalled texts is calculated using the BM25 algorithm, ultimately returning the most similar Top k documents.
-
-### How to Configure the SQLite Component
-
-You can use ChromaDB to store and query knowledge in the [Knowledge Components](2_2_4_Knowledge.md). You can create a storage component using SQLite with the following configuration:
-```yaml
-name: 'sqlite_store'
-description: 'a store based on sqlite'
-db_path: '../../DB/civil_law_sqlite.db'
-k1: 1.5
-b: 0.75
-keyword_extractor: 'jieba_keyword_extractor'
-similarity_top_k: 10
-metadata:
-  type: 'STORE'
-  module: 'agentuniverse.agent.action.knowledge.store.sqlite_store'
-  class: 'SQLiteStore'
-```
-- db_path: The path to the SQLite database file, used for storing and managing text data.
-- k1: The k1 parameter in the BM25 algorithm, which controls the influence of term frequency on the score.
-- b: The b parameter in the BM25 algorithm, which controls the influence of document length on the score.
-- keyword_extractor: The tool used for keyword extraction, specified here as jieba_keyword_extractor.
-- similarity_top_k: Return top k similar documents based on bm25 score.
-
-### Usage
-[Knowledge_Define_And_Use](2_2_4_Knowledge_Define_And_Use.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/6_2_1_Discussion_Group.md b/docs/guidebook/en/6_2_1_Discussion_Group.md
deleted file mode 100644
index 847ec482..00000000
--- a/docs/guidebook/en/6_2_1_Discussion_Group.md
+++ /dev/null
@@ -1,109 +0,0 @@
-# Discussion: Multi-Agent Discussion Group
-In this use case, we will demonstrate how to use agentUniverse for multi-agent discussions.
-
-There are two agent roles: discussion group host (one) and discussion group participants (several).
-
-The user initiates a discussion topic, and the host organizes the participants to start the discussion. In each round, each participant expresses his or her own views according to the topic of the discussion (the views of each participant are not immutable and will be constantly adjusted as the discussion goes on). After several rounds of discussion, the host will summarize the discussion process and return the results of the participants after several rounds of discussion to the user.
-
-Many heads are better than one, imagine that you have a question you want to answer and submit it directly to the agentUniverse discussion group, The host will organize multiple agents with different ideas together, focus on your question, and finally give you a comprehensive and intelligent answer, which is an interesting agent experiment.
-
-## Quick Start
-### Configure API Key
-For example, configure key information in the file `custom_ key.toml` in which agentUniverse manages private key configuration (The agentUniverse discussion group uses GPT as the base model and Serper as the Google search tool by default. The following describes how to use other models or tools).
-```toml
-[KEY_LIST]
-# serper google search key
-SERPER_API_KEY='xxx'
-# openai api key
-OPENAI_API_KEY='xxx'
-```
-### Modify Prompt Version
-Modify the prompt version of agents to the english version, the discussion group multi-agent configuration file is located in the intelligence/agentic/agent/agent_instance/discussion_agent_case directory in the `sample_standard_app` sample project. Find the discussion_group_agent.yaml and change `prompt_version` to `dicussion_group_agent.en`, similarly find the participants yaml file and change the `prompt_version` to `participant_agent.en`.
-
-```yaml
-info:
-  name: 'discussion_group_agent'
-  description: 'discussion group agent'
-profile:
-  llm_model:
-    name: 'qwen_llm'
-    temperature: 0.6
-  total_round: 2
-  participant_names:
-    - 'participant_agent_one'
-    - 'participant_agent_two'
-memory:
-  name: 'demo_memory_b'
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_template.discussion_group_template'
-  class: 'DiscussionGroupTemplate'
-```
-
-
-### Run Discussion Group
-In the sample project of agentUniverse `sample_standard_app`, find the `discussion_chat_bots.py` file in the intelligence/test directory, enter the question you want to answer in the chat method, and run it.
-
-For example, enter the question: Which tastes better, Coca-Cola or Pepsi
-```python
-from agentuniverse.base.agentuniverse import AgentUniverse
-from agentuniverse.agent.agent import Agent
-from agentuniverse.agent.agent_manager import AgentManager
-
-AgentUniverse().start(config_path='../../config/config.toml')
-
-
-def chat(question: str):
-    instance: Agent = AgentManager().get_instance_obj('host_agent')
-    instance.run(input=question)
-
-
-if __name__ == '__main__':
-    chat("Which tastes better, Coca-Cola or Pepsi")
-```
-### Result Demonstration
-Which tastes better, Coca-Cola or Pepsi：
-
-![Picture](../_picture/coca-cola_or_pepsi.png)
-
-## More Details
-### Agent Configuration
-The discussion group multi-agent configuration files are located in the intelligence/agentic/agent/agent_instance/discussion_agent_case directory in the `sample_standard_app` sample project.
-
-The discussion group corresponds to `discussion_group_agent.yaml` and the participant corresponds to `participant_agent_*.yaml`.
-
-
-The prompt is managed by the prompt version. For example, the default prompt_version of discussion group agent is `discussion_group_agent.cn`, and the corresponding prompt file is `discussion_group_agent_cn.yaml` under the intelligence/agentic/prompt directory.
-
-If the LLM Model will be modified to another, such as the Qwen model, change the `llm_model` name information (such as the `qwen_llm` built into the aU system).
-
-The number of discussion rounds in agent defaults to 2, which users can adjust as needed. The default participants are two agents built into aU, which can add discussion group members by creating new agents and configuring the agent name parameter to the `participant_names` of the discussion group agent profile.
-
-```yaml
-info:
-  name: 'discussion_group_agent'
-  description: 'discussion group agent'
-profile:
-  llm_model:
-    name: 'qwen_llm'
-    temperature: 0.6
-  total_round: 2
-  participant_names:
-    - 'participant_agent_one'
-    - 'participant_agent_two'
-memory:
-  name: 'demo_memory_b'
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_template.discussion_group_template'
-  class: 'DiscussionGroupTemplate'
-```
-
-### Memory Configuration
-Participants in the aU discussion group configure `demo_memory_b` in the `sample_standard_app` sample project by default to store common memory information for the entire discussion group.
-
-### Prompt Configuration
-The prompt is managed by the prompt version, and the default prompt files of the aU discussion group agent are configured in the intelligence/agentic/prompt directory. Corresponding to the Chinese and English versions (discussion_group_agent_xx.yaml/participant_agent_xx.yaml), users can change the prompt_version in the agent configuration to complete fast switching.
-
-### Tool Configuration
-Two participants in the aU discussion group are configured with google_search_tool by default. Users can change the `tool name` in the `participant_agent_xxx.yaml` under the intelligence/agentic/agent/agent_instance/discussion_agent_case directory in the `sample_standard_app` sample project to switch tool calls.
diff --git a/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md b/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md
deleted file mode 100644
index 7e7c044e..00000000
--- a/docs/guidebook/en/6_4_1_Financial_Event_Analysis_Case.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# Financial Event Analysis Case
-## Case Description
-This case is based on PeerPlanner and showcases a multi-agent collaborative example for analyzing financial events. Regarding the topic of "Buffett's 2023 Reduction in BYD Shares"， it demonstrates how to use the PEER multi-agent collaboration model in agentUniverse and details the configuration and output examples for each agent in PEER.
-
-This case study utilizes the GPT-4o model by OPENAI. Before using it, you need to configure the `OPENAI_API_KEY` in your environment variables.
-
-## Agents
-### Planning Agent 
-Reference the original code files:
-- [Configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)
-- [Prompt file](../../../sample_standard_app/intelligence/agentic/prompt/planning_agent_cn.yaml)  
-
-The Planning Agent is responsible for breaking down the original financial problem into multiple sub-problems that can be individually solved and provided to the subsequent Executing Agent. In this case, the original question "Analyze the reasons for Buffett's reduction in BYD shares" can be decomposed into several sub-questions as shown in the diagram below:
-![planning_result](../_picture/6_4_1_planning_result.png)
-You can debug the Planning Agent individually in the [test file](../../../sample_standard_app/intelligence/test/test_planning_agent.py).
-
-### Executing Agent 
-Reference the original code files:
-- [Configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_executing_agent.yaml)
-- [Prompt file](../../../sample_standard_app/intelligence/agentic/prompt/executing_agent_cn.yaml)
-
-In this Agent, we provide a tool [google_search_tool](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.py) for searching information on Google. To use this tool, you should configure `SERPER_API_KEY` in your environment. For convenience, if `SERPER_API_KEY ` is not configured, this tool will return a pre-set query result related to this case, which you can find in the [mock_search_tool](../../../sample_standard_app/intelligence/agentic/tool/mock_search_tool.py).
-
-
-The Executing Agent is responsible for solving the sub-problems broken down by the Planning Agent. In this case, the execution results of the Executing Agent are as follows:  
-![executing_result](../_picture/6_4_1_executing_result.png)
-The result is lengthy, so only the execution results of the first two questions are shown here. You can debug the Executing Agent individually in the [test file](../../../sample_standard_app/intelligence/test/test_executing_agent.py) to obtain the complete results.
-
-### Expressing Agent 
-Reference the original code files:
-- [Configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)
-- [Prompt file](../../../sample_standard_app/intelligence/agentic/prompt/expressing_agent_cn.yaml)  
-
-The Expressing Agent is responsible for summarizing all the results output by the Executing Agent and formulating them into an answer to the original question according to the requirements in the prompt file. In this case, the output result of the Expressing Agent is as follows:
-![expressing_result](../_picture/6_4_1_expressing_result.png)
-You can debug the Expressing Agent individually in the [test file](../../../sample_standard_app/intelligence/test/test_expressing_agent.py).
-
-### Reviewing Agent 
-Reference the original code files:
-- [Configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_reviewing_agent.yaml)
-
-The Reviewing Agent is responsible for evaluating whether the result produced by the Expressing Agent is an effective answer to the original question. In this case, the Reviewing Agent accepted the answer from the Expressing Agent:
-![reviewing_result](../_picture/6_4_1_reviewing_result.png)
-You can debug the Reviewing Agent individually in the [test file](../../../sample_standard_app/intelligence/test/test_reviewing_agent.py).
-
-### PEER Agent 
-```yaml
-info:
-  name: 'demo_peer_agent'
-  description: 'demo peer agent'
-plan:
-  planner:
-    name: 'peer_planner'
-    eval_threshold: 60
-    retry_count: 2
-    planning: 'demo_planning_agent'
-    executing: 'demo_executing_agent'
-    expressing: 'demo_expressing_agent'
-    reviewing: 'demo_reviewing_agent'
-metadata:
-  type: 'AGENT'
-  module: 'agentuniverse.agent.default.peer_agent.peer_agent'
-  class: 'PeerAgent'
-```
-Users can configure the four Agents mentioned above into a complete PEER Agent through the `peer_planner` collaboration model. The configurations include:
-- name: Fixed as `peer_planner`, indicating the use of the PEER multi-agent collaboration model.
-- eval_threshold: The minimum score for the Reviewing Agent to accept the answer.
-- retry_count: The number of retries for the PEER Agent if the Reviewing Agent does not accept the answer.
-- planning：The Agent responsible for the Plan part.
-- executing：The Agent responsible for the Execute part.
-- expressing：The Agent responsible for the Express part.
-- reviewing：The Agent responsible for the Review part.
-
-You can run the complete case in the [example file](../../../sample_standard_app/intelligence/test/peer_chat_bot.py).
-
diff --git a/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md b/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md
deleted file mode 100644
index 4b62f4fc..00000000
--- a/docs/guidebook/en/7_1_1_Legal_Consultation_Case.md
+++ /dev/null
@@ -1,84 +0,0 @@
-# Legal Consultation Case
-## Case Description
-This case demonstrates a simple legal consultation agent built using `RagPlanner`. The agent provides legal advice by retrieving relevant provisions from the Civil Law and the Criminal Law, and combining them with the case background.
-
-The case leverages the DashScope embedding and rerank features with the Qwen llm. Before using this, you need to configure the `DASHSCOPE_API_KEY` in your environment variables.
-
-## Components
-### Legal Knowledge Base
-The legal knowledge base is constructed using [Knowledge Components](2_2_4_Knowledge_Related_Domain_Objects.md) from agentUniverse. By storing the original legal provisions in the ChromaDB and Sqlite database, the knowledge base facilitates efficient retrieval and consultation for the agent.
-
-### [Knowledge Definition](../../../sample_standard_app/intelligence/agentic/knowledge/law_knowledge.yaml)
-```yaml
-name: "law_knowledge"
-description: "中国民法与刑法相关的知识库"
-stores:
-    - "civil_law_chroma_store"
-    - "criminal_law_chroma_store"
-    - "civil_law_sqlite_store"
-    - "criminal_law_sqlite_store"
-query_paraphrasers:
-    - "custom_query_keyword_extractor"
-insert_processors:
-    - "recursive_character_text_splitter"
-rag_router: "nlu_rag_router"
-post_processors:
-    - "dashscope_reranker"
-readers:
-    pdf: "default_pdf_reader"
-metadata:
-  type: 'KNOWLEDGE'
-  module: 'sample_standard_app.intelligence.agentic.knowledge.law_knowledge'
-  class: 'LawKnowledge'
-```
-
-### Reader Component
-- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)
-
-### DocProcessor Component
-- [custom_query_keyword_extractor](../../../sample_standard_app/intelligence/agentic/knowledge/doc_processor/query_keyword_extractor.yaml)
-- [recursive_character_text_splitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
-
-### QueryParaphraser Component
-- [custom_query_keyword_extractor](../../../sample_standard_app/intelligence/agentic/knowledge/query_paraphraser/custom_query_keyword_extractor.yaml)
-
-### RagRouter Component
-- [nlu_rag_router](../../../sample_standard_app/intelligence/agentic/knowledge/rag_router/nlu_rag_router.yaml)
-
-### Store Component
-- [civil_law_chroma_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_chroma_store.yaml)
-- [criminal_law_chroma_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_chroma_store.yaml)
-- [civil_law_sqlite_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_sqlite_store.yaml)
-- [criminal_law_sqlite_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_sqlite_store.yaml)
-
-For your convenience, we have placed the databases containing the relevant information in [this directory](../../../sample_standard_app/intelligence/db/). If you want to build the knowledge base from scratch, you can run the following code:
-```python
-from agentuniverse.base.agentuniverse import AgentUniverse
-from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
-
-
-if __name__ == '__main__':
-    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
-    civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"]
-    criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"]
-    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
-    law_knowledge.insert_knowledge(
-        source_path="../resources/刑法.pdf",
-        stores=criminal_store_list
-    )
-    law_knowledge.insert_knowledge(
-        source_path="../resources/民法典.pdf",
-        stores=civil_store_list
-    )
-```
-
-### Law Agent
-This agent involves the following one file:
-- [law_rag_agent.yaml](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/law_rag_agent.yaml): Defines the agent's related prompts
-
-
-### Demonstration Code
-[CodeLink](../../../sample_standard_app/intelligence/test/law_chat_bot.py)
-
-## Demonstration
-![Demonstration Image](../_picture/law_agent_demo.png)
\ No newline at end of file
diff --git a/docs/guidebook/en/7_1_1_Translation_Case.md b/docs/guidebook/en/7_1_1_Translation_Case.md
deleted file mode 100644
index 39bae43b..00000000
--- a/docs/guidebook/en/7_1_1_Translation_Case.md
+++ /dev/null
@@ -1,51 +0,0 @@
-# Translation Case Study
-Recently, Professor Andrew Ng of Stanford University open-sourced an AI agent for machine translation—an agent that employs a reflective workflow for translation. This project showcases an example of machine translation utilizing the reflective agent workflow. The primary steps of the agent include:
-1. Initial translation, utilizing a large model (LLM) to translate text from the source language to the target language;
-2. Reflection, wherein the large model identifies deficiencies in the translation and offers constructive improvement suggestions;
-3. Revision of the translation, where the large model retranslates based on the suggestions derived from the reflection;
-The reflective workflow employed in the project enables the model to identify its own deficiencies and implement targeted revisions, constituting a typical multi-agent collaborative working mechanism. This is also a domain in which agentUniverse has been actively engaged, prompting me to contemplate the migration of Andrew Ng's translation agent to agentUniverse.
-This case is based on the Qianwen large model, and prior to use, you need to configure the `DASHSCOPE_API_KEY` in the environment variables.
-
-## Collaborative Working Process of Multi-Agents
-During the translation process, the priority is to ascertain whether the model's length surpasses the maximum number of tokens it can accommodate. For instances where the length exceeds the limit, the text is initially segmented, followed by the translation of each segment. Nonetheless, the entire translation process adheres to the initial translation -> reflection -> revision workflow.
-![Multi-Agent Collaborative Working Process](../_picture/translation_flow_graph.png)
-
-Implementing this translation in aU involves the following steps:
-1. Define prompts related to translation, with three for short text and three for long text. Relevant files include:
-   - [Short Text Init Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_init_en.yaml)
-   - [Short Text Reflection Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_reflection_en.yaml)
-   - [Short Text Improve Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_improve_en.yaml)
-   - [Long Text Init Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_init_en.yaml)
-   - [Long Text Reflection Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)
-   - [Long Text Improve Prompt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
-2. Define three agents  
-   - [Short Text Translation Work Agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_work_agent.yaml)  
-   - [Short Text Translation Reflection Agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_reflection_agent.yaml)  
-   - [Short Text Translation Improvement Agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_improve_agent.yaml)  
-   These agents switch their prompts based on whether the task involves translating long or short texts. The specific logic for this switching mechanism is detailed in the [agent file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.py).
-
-3. Define the collaborative work process of the three agents  
-The collaborative process is illustrated in the earlier-mentioned flowchart of multi-agent collaboration.  
-![Collaborative Work](../_picture/translation_execute_flow.png)  
-For a deeper dive into the processes, refer to the [detailed code file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_by_token_agent.py).  
-The [configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.yaml) for the collaborative agents.
-
-### Demonstration Code  
-[Code Link](../../../sample_standard_app/intelligence/test/test_translation_agent.py)
-
-[Long Text](../../../sample_standard_app/intelligence/test/translation_data/long_text.txt)  
-[Short Text](../../../sample_standard_app/intelligence/test/translation_data/short_text.txt)  
-
-[Translation Of Long Text](../../../sample_standard_app/intelligence/test/translation_data/short_text_result.txt)  
-[Translation Of Short Text](../../../sample_standard_app/intelligence/test/translation_data/long_text_result.txt)  
-
-### 演示结果
-We can see that using aU maintains consistency with the original translation_agent project results, successfully replicated.
-aU Results:  
-![aU Long Text Result](../_picture/long_translation_au.png)  
-translation_agent Results:  
-![translation Long Text Result](../_picture/long_translation_wu.png)
-
-### Others
-In reference to Professor Andrew Ng's code, when managing segmented translations, to ensure the coherence of the translation, each segment is translated with the corresponding context. However, incorporating all translation content into the context within his code can undermine the original intent of segmentation, and in certain cases, it can result in an excess of tokens beyond the model's capacity. I have raised a related issue on the corresponding repository address: [Issue Link](https://github.com/andrewyng/translation-agent/issues/28)## Implementation in aU
-
diff --git a/docs/guidebook/en/8_1_1_data_autonomous_agent.md b/docs/guidebook/en/8_1_1_data_autonomous_agent.md
deleted file mode 100644
index a71a904d..00000000
--- a/docs/guidebook/en/8_1_1_data_autonomous_agent.md
+++ /dev/null
@@ -1,169 +0,0 @@
-# Data Autonomous Agent
-## Introduction
-The comprehensive view of the Data Autonomous Agent: Through **automated batch execution of multiple rounds of agent invocation**, **offline adaptive data collection**, **production of evaluation datasets**, **production of fine-tuning datasets**, **model inference training**, and **automatic multidimensional assessment of datasets**, a complete workflow is established to enhance the data autonomy process of large language models and Agents.
-
-The current agentUniverse has released the **Data Autonomous MVP (Minimum Viable Product) version**, which, based on the **query sets** specified by the user, performs **automated batch execution of multiple rounds of agent invocation**, **produces evaluation datasets**, **automatically assesses datasets in multiple dimensions**, and **generates evaluation reports**. It conveys the concept of data autonomy to the open-source community, and will gradually open up the complete capabilities of the data autonomous workflow in the future.
-
-## Background
-Currently, the evaluation of the performance of Agents within the industry often relies on the expertise of specialists in specific fields to conduct a significant amount of manual review and annotation, ensuring the high quality and accuracy of the assessment results.
-
-The dataAgent hopes to use the agent's own capabilities to **self-collect**, **self-manage**, and **intelligently evaluate the data of the Agent**.
-
-In the MVP version, you can use the `dataset_build_agent` to make multiple rounds of batch invocation on the query sets to be evaluated, producing evaluation datasets. Then, by using the `dataset_eval_agent`, you can apply the **Fin-Eva** data evaluation standards published by Ant Group based on seven dimensions (relevance, factuality, rationality, timeliness, structure, integrity, and comprehensiveness) to conduct intelligent data assessment and annotation. The results of multiple rounds of evaluation are subjected to a rich statistical analysis to provide users with a more intuitive view of the changes in the Agent's capabilities.
-
-By utilizing a complete set of data autonomy capabilities, you can easily understand the current level of your agent through intelligently generated evaluation reports.
-
-It needs to be particularly emphasized that the scores in the entire evaluation report are reference values. In the actual production environment, we will conduct a comprehensive comparison of scores from multiple rounds to distinguish the effectiveness of the agent.
-
-## DataAgent Flowchart
-![data_agent_flowchart](../_picture/data_agent_flowchart.jpg)
-- data_agent consists of two parts. **dataset_build_agent** is responsible for automating batch execution of multiple rounds of agent invocation and producing evaluation datasets.
-- **dataset_eval_agent** is responsible for automating the multidimensional evaluation of the dataset and generating evaluation report.
-
-## DataAgent Execution Steps
-### Step1 Establish Agent Queryset
-In DataAgent, you first need to construct an agent queryset in the JSONL file format, where each line represents a complete input for an agent invocation.
-
-As shown below, establish a queryset of 3 items, where "input" corresponds to the query information for the agent:
-```jsonl
-{"input": "Help me predict the 2024 European Cup championship"}
-{"input": "Can Tesla's stock still be bought?"}
-{"input": "What's the weather in New York today?"}
-```
-
-### Step2 Configure DataAgent
-Configure the DataAgent as follows, the configuration file, in addition to the basic configuration of the agent, mainly includes two important items: `dataset_builder` corresponds to the name of the agent for producing the evaluation dataset, and `dataset_evaluator` corresponds to the name of the agent for dataset evaluation and annotation.
-```yaml
-info:
-  name: 'data_agent'
-  description: 'data agent'
-plan:
-  planner:
-    dataset_builder: 'dataset_build_agent'
-    dataset_evaluator: 'dataset_eval_agent'
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.data_agent'
-  class: 'DataAgent'
-```
-[data_agent sample configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
-
-[data_agent sample python file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
-
-### Step3 Configure the agent for producing the evaluation dataset.
-Use the **dataset_build_agent** configured in step2 `dataset_builder`, and the following is the configuration file of dataset_build_agent. In addition to the basic configuration of the agent, the configuration file mainly includes two important items: `candidate` configures the name of the agent to be evaluated (for example, to evaluate the effectiveness of demo_rag_agent, candidate is configured as demo_rag_agent), and `concurrency_level` configures the level of concurrency when executing batch agent invocation (for example, setting it to 5 represents calling the candidate agent concurrently with 5).
-```yaml
-info:
-  name: 'dataset_build_agent'
-  description: 'dataset build agent'
-profile:
-  concurrency_level: 1
-plan:
-  planner:
-    candidate: 'demo_rag_agent'
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.dataset_build_agent'
-  class: 'DatasetBuildAgent'
-```
-[dataset_build_agent sample configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
-
-[dataset_build_agent sample python file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
-
-### Step4 Configure the agent for dataset evaluation and annotation
-Use the **dataset_eval_agent** configured in step2 `dataset_evaluator`, and the following is the configuration file of dataset_eval_agent. In addition to the basic configuration of the agent, the configuration file mainly includes two important items: `llm_model` configures the agent model, and `max_eval_lines` configures the number of evaluation data lines (for example, setting it to 10 means only evaluate the first 10 rows of data, to avoid global evaluation and the consumption of a large number of tokens).
-```yaml
-info:
-  name: 'dataset_eval_agent'
-  description: 'dataset eval agent'
-profile:
-  prompt_version: dataset_eval_agent.en
-  max_eval_lines: 10
-  llm_model:
-    name: 'demo_llm'
-    model_name: 'gpt-4o'
-    temperature: 0.1
-metadata:
-  type: 'AGENT'
-  module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.dataset_eval_agent'
-  class: 'DatasetEvalAgent'
-```
-[dataset_eval_agent sample configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
-
-[dataset_eval_agent sample python file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
-
-### step5 Run DataAgent
-Through the [dataAgent code entry](../../../sample_standard_app/intelligence/test/data_agent.py), configure two parameters: `queryset_path` representing the path to the queryset, and `turn` representing the total number of rounds for the queryset execution, to start the dataAgent with one click.
-
-Tips: please configure the queryset and specific evaluation rows reasonably to avoid excessive computational and token consumption.
-
-## DataAgent Example Usage
-Refer to the **DataAgent Execution Steps** mentioned above, and start the configured dataAgent through 5 steps.
-
-### Evaluation Dataset
-First, generate the evaluation dataset, storing the agent's Q&A pairs in the JSONL file format.
-As shown in the figure below:
-- query: agent question 
-- answer: agent answer
-
-![data_agent_dataset](../_picture/data_agent_dataset_en.png)
-
-[dataAgent sample evaluation dataset](../../../sample_standard_app/intelligence/test/data/dataset_turn_1_2024-07-10-15-06-24.jsonl)
-
-
-### Complete Evaluation Results
-After producing the evaluation dataset, dataAgent begins multidimensional data assessment and annotation, generating complete evaluation results (if multiple rounds of dataAgent batch tasks are executed, multiple complete evaluation results will be produced).
-
-As shown in the figure below:
-- Line Number: The line number of the current evaluation data within the dataset.
-- Overall Score: comprehensive score = total of assessment scores from multiple dimensions / number of assessment dimensions (full score is 5, score range 0-5).
-- Query: agent question.
-- Answer: agent answer.
-- Relevance Score: represents the relevance of the agent answer to the question, the higher the score, the more relevant (full score is 5, score range 0-5).
-- Relevance Suggestion: represents issues and suggestions for improvement in the relevance dimension.
-- More dimensions Score/Suggestion: similar to the Relevance dimension.
-![data_agent_eval_result](../_picture/data_agent_eval_result_en.png)
-
-[dataAgent sample eval result](../../../sample_standard_app/intelligence/test/data/eval_result_turn_1_2024-07-10-15-06-24.xlsx)
-
-
-
-### Comprehensive Evaluation Report
-Generate a comprehensive evaluation report based on multiple complete evaluation results.
-
-As shown in the figure below:
-- Line Name: Includes two types, such as `Queryset Turn x` representing the evaluation dataset generated in the x-th round based on the queryset, and `Turn Avg Score` representing the average score of multiple rounds and dimensions.
-- Overall Avg Score: The sum of the overall scores of all data in a single round of the dataset / the amount of data in the dataset (full score is 5, score range 0-5).
-- Relevance Avg Score: The sum of the relevance scores of all data in a single round of the dataset / the amount of data in the dataset (full score is 5, score range 0-5).
-- More dimensions Avg Score: similar to the Relevance dimension.
-
-![data_agent_eval_report](../_picture/data_agent_eval_report_en.png)
-
-[dataAgent sample evaluation report](../../../sample_standard_app/intelligence/test/data/eval_report_2024-07-10-15-06-24.xlsx)
-
-### Comparative Experiment
-Adjust the llm model in demo_rag_agent within aU from the previous `qwen1.5-72b-chat` to `qwen1.5-7b-chat`, and after evaluation by dataAgent, the comprehensive evaluation reports are as follows:
-
-The following figure is the comprehensive evaluation report produced by the data autonomous agent when the model is qwen1.5-7b-chat:
-![data_agent_eval_report](../_picture/data_agent_eval_report_en_qwen7b.png)
-
-The following figure is the comprehensive evaluation report produced by the data autonomous agent when the model is qwen1.5-72b-chat:
-![data_agent_eval_report](../_picture/data_agent_eval_report_en.png)
-Comparing the two comprehensive evaluation reports, it can be observed that after the agent llm model was changed from qwen1.5-7b-chat to qwen1.5-72b-chat, there was a significant increase in the scores across all dimensions in multiple rounds. This method can quickly distinguish the difference in agent effects after the agent configuration was changed.
-
-## DataAgent Detailed Description
-### data_agent
-- [configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
-- [agent file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
-
-### dataset_build_agent
-- [configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
-- [agent file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
-- The evaluation data produced by dataset_build_agent is stored locally in jsonl format (the jsonl file name is dataset_turn_{i}_{date}, `i` represents the round, and `date` represents the generation time)
-
-### dataset_eval_agent
-- [configuration file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
-- [agent file](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
-- [prompt file](../../../sample_standard_app/intelligence/agentic/prompt/dataset_eval_agent_en.yaml)：agentUniverse currently opens six agent evaluation dimensions that are validated in the industry (the MVP version does not open **comprehensive dimension**. The current comprehensive evaluation standard is biased towards the financial field, so it is not mentioned in the open source community)
-- The **complete evaluation results** produced by dataset_eval_agent are stored locally in Excel format (the file name is eval_result_turn_{i}_{date}, `i` represents the round, and `date` represents the generation time)
-- The **comprehensive evaluation report** of dataset_eval_a_agent production is stored locally in Excel format (the file name is eval_report_{date}, and date represents the generation time)
\ No newline at end of file
diff --git a/docs/guidebook/en/9_1_Citation.md b/docs/guidebook/en/Concepts/Citation_PEER.md
similarity index 55%
rename from docs/guidebook/en/9_1_Citation.md
rename to docs/guidebook/en/Concepts/Citation_PEER.md
index 3936b644..f00730f7 100644
--- a/docs/guidebook/en/9_1_Citation.md
+++ b/docs/guidebook/en/Concepts/Citation_PEER.md
@@ -13,5 +13,5 @@ BibTeX formatted
       url={https://arxiv.org/abs/2407.06985}, 
 }
 ```
-Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factualness, logicality, structure, and comprehensiveness (each dimension has a maximum score of 5 points). The PEER model scored higher on average in each evaluation dimension compared to BabyAGI, and demonstrated significant advantages in the dimensions of completeness, relevance, logicality, structure, and comprehensiveness. Additionally, the PEER model achieved an 83% superior rate over BabyAGI using the GPT-3.5 turbo (16k) model, and an 81% superior rate using the GPT-4o model. For more details, please refer to the literature.
+Overview: In the experimental section of this study, scores were assigned across seven dimensions: completeness, relevance, conciseness, factual accuracy, logical coherence, structure, and comprehensiveness (with each dimension having a maximum score of 5 points). On average, the PEER model scored higher in each evaluation dimension compared to BabyAGI, demonstrating significant advantages particularly in the dimensions of completeness, relevance, logical coherence, structure, and comprehensiveness. Furthermore, the PEER model outperformed BabyAGI by an 83% superiority rate when using the GPT-3.5 turbo (16k) model, and by an 81% superiority rate when using the GPT-4.0 model. For further details, please refer to the relevant literature.
 https://arxiv.org/pdf/2407.06985
\ No newline at end of file
diff --git a/docs/guidebook/en/Concepts/Core_Features.md b/docs/guidebook/en/Concepts/Core_Features.md
new file mode 100644
index 00000000..de053218
--- /dev/null
+++ b/docs/guidebook/en/Concepts/Core_Features.md
@@ -0,0 +1,12 @@
+# Core Features
+* **Agent-Oriented Design:** The framework is designed specifically with agents in mind, enabling customization of agents via the perception, planning, action, and memory modules.
+* **Rich Multi-Agent Collaboration Modes:** Offers industry-validated collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express), and supports user-defined orchestration of new modes, enabling organic collaboration among multiple agents.
+* **Customizable Components:** All components of the framework, including LLM, knowledge, tools, and memory, are customizable, allowing users to enhance their dedicated agents.
+* **Seamless Integration of Domain Expertise:** Provides capabilities for domain-specific prompts, knowledge construction and management, and supports domain-level SOP orchestration and embedding to align agents to expert levels in their fields.
+* **LLM Compatibility and Extensibility:** Built-in support for multiple models such as the OpenAI series, Qwen series, Baichuan series, Kimi series, etc., while offering standard extensions to integrate any third-party model services and privately deployed models.
+* **Multi-Data Source Compatibility and Extensibility:** Supports various data sources, whether traditional ones like SQLite, MySQL, Oracle, or vector databases like ChromaDB, Milvus. It also provides standard extension methods to integrate any third-party data services and other databases.
+* **Convenient Ecosystem Integration:** The framework integrates mature and practically tested large model application ecosystem capabilities, shielding most of the underlying configurations and implementations. This includes LLM ecosystem components like LangChain, llama_index, as well as technical components like gRPC and SLS.
+* **Observable and Feedback Capable:** With the monitor component, all service interactions and model interactions within agentUniverse can be recorded and observed. Combined with the framework's automatic agent evaluation capabilities, users can effortlessly comprehend the performance improvements resulting from agent or model iterations, and subsequently incorporate these insights back into service iterations and model training
+* **Enterprise Private Component Extensions:** The framework provides a mechanism for extending and loading private technical components, enabling the integration of internal elements such as custom-developed RPC protocol modules, messaging systems, and logging tools via configuration.
+* **One-Click Service Deployment:** Supports one-click initialization of a web server leveraging Flask and Gunicorn, facilitating the seamless integration of agent capabilities into any existing system.
+* **Standard Container Delivery:** Provides a standard Docker image and offers recommended integration approaches based on Docker and Kubernetes (K8S) functionalities.
\ No newline at end of file
diff --git a/docs/guidebook/en/Concepts/Why_Use_agentUniverse.md b/docs/guidebook/en/Concepts/Why_Use_agentUniverse.md
new file mode 100644
index 00000000..3cd00622
--- /dev/null
+++ b/docs/guidebook/en/Concepts/Why_Use_agentUniverse.md
@@ -0,0 +1,24 @@
+# Why Use agentUniverse
+**Why Choose agentUniverse**
+agentUniverse is a multi-agent framework specifically optimized for tackling complex tasks in real-world business scenarios. It offers not only all the essential components required to build a single agent but also orchestration components for facilitating collaboration between multiple agents (similar to Pattern Factory). Developers can customize these collaboration modes to ensure that agents fulfill their respective duties and solve specialized problems within their domains more effectively. Furthermore, agentUniverse provides a mechanism for injecting expert experience, making it easier for developers and enterprises to build intelligent applications infused with professional know-how.
+
+
+**Architecture Diagram**
+agentUniverse boasts a range of basic capabilities, including single-agent functionalities, orchestration components for multi-agent collaboration modes, and tools and mechanisms for knowledge injection. Through the framework's standardized interfaces, users can seamlessly integrate their own models, knowledge bases, and tools, enabling different agents to leverage these resources effectively.
+
+With these capabilities, you can build your own intelligent applications tailored to your needs. Moreover, the framework further provides key capability components and application solutions specifically designed for large model applications. These components address common challenges in large model applications, such as RAG (Retrieve-and-Generate), tool invocation, and security rigor, helping developers overcome inevitable difficulties. The application solutions, on the other hand, offer end-to-end solutions for typical scenarios of large model applications, such as knowledge Q&A, intelligent search, and analysis and interpretation, thereby enhancing application development efficiency.
+
+
+**Core Features**
+
+Based on the above introduction, we can summarize the core features of agentUniverse as follows:
+
+**· Rich multi-agent collaboration mode:** 
+Provides effective collaboration modes such as PEER (Plan/Execute/Express/Review) and DOE (Data-fining/Opinion-inject/Express) in the industry, and supports users to customize new orchestration modes to allow multiple agents to cooperate organically.
+
+**· Fully Customizable Cumponents:** 
+All framework components, including LLMs (Large Language Models), knowledge bases, tools, and memory, provide customization options for users to enhance their agents according to their specific needs.
+**· Easily integrate domain experience:** 
+Provide domain prompt, knowledge construction and management capabilities, and support domain-level SOP orchestration and injection to align agents to the level of domain experts.
+
+💡 For more features, please refer to the core features of agentUniverse.
\ No newline at end of file
diff --git a/docs/guidebook/en/6_1_Contact_Us.md b/docs/guidebook/en/Contact_Us.md
similarity index 82%
rename from docs/guidebook/en/6_1_Contact_Us.md
rename to docs/guidebook/en/Contact_Us.md
index 64cbba0d..33cb0d99 100644
--- a/docs/guidebook/en/6_1_Contact_Us.md
+++ b/docs/guidebook/en/Contact_Us.md
@@ -1,6 +1,6 @@
 ### Support
 #### Submit Questions via GitHub Issues
-😊 We recommend submitting your queries using [GitHub Issues](https://github.com/alipay/agentUniverse/issues), we typically respond within 2 days.
+😊 We recommend submitting your queries using [GitHub Issues](https://github.com/antgroup/agentUniverse/issues), we typically respond within 2 days.
 
 #### Contact Us via Discord
 😊 Join our [Discord Channel](https://discord.gg/DHFcdkWAhn) to interact with us.
diff --git a/docs/guidebook/en/Contents.md b/docs/guidebook/en/Contents.md
new file mode 100644
index 00000000..c1ae2411
--- /dev/null
+++ b/docs/guidebook/en/Contents.md
@@ -0,0 +1,110 @@
+# User Guide
+************************************************
+## Table of Contents
+
+**1. Getting Started**
+* 1.1 [Introduction](Get_Start/Introduction.md)
+* 1.2 [Installation](Get_Start/Installation.md)
+* 1.3 [Quick Start](Get_Start/Quick_Start.md)
+* 1.5 [ApplicationStructure](Get_Start/Application_Project_Structure_and_Explanation.md)
+
+**2. Principle Introduction**
+
+* 2.1 Framework Principles
+* 2.2 [Domain Components](In-Depth_Guides/Tutorials/Domain_Component_Principles.md)
+  * 2.2.1 [Agent](In-Depth_Guides/Tutorials/Agent/Agent.md)
+    * 2.2.1.1 [Create And Use](In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md)
+    * 2.2.1.2 [Related Domain Objects](In-Depth_Guides/Tutorials/Agent/Agent_Related_Domain_Objects.md)
+  * 2.2.2 [LLM](In-Depth_Guides/Tutorials/LLM/LLM.md)
+    * 2.2.2.1 [Define And Use](In-Depth_Guides/Tutorials/LLM/LLM_component_define_and_usage.md)
+    * 2.2.2.2 [Related Domain Objects](In-Depth_Guides/Tutorials/LLM/LLM_Related_Domain_Objects.md)
+  * 2.2.3 [Tool](In-Depth_Guides/Tutorials/Tool/Tool.md)
+    * 2.2.3.1 [Create And Use](In-Depth_Guides/Tutorials/Tool/Tool_Create_And_Use.md)
+    * 2.2.3.2 [Related Domain Objects](In-Depth_Guides/Tutorials/Tool/Tool_Related_Domain_Objects.md)
+  * 2.2.4 [Knowledge](In-Depth_Guides/Tutorials/Knowledge/Knowledge.md)
+    * 2.2.4.1 [Define And Use](In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md)
+    * 2.2.4.2 [Related Domain Objects](In-Depth_Guides/Tutorials/Knowledge/Knowledge_Related_Domain_Objects.md)
+  * 2.2.5 [Memory](In-Depth_Guides/Tutorials/Memory/Memory.md)
+    * 2.2.5.1 [Define And Use](In-Depth_Guides/Tutorials/Memory/Memory_Define_And_Use.md)
+    * 2.2.5.2 [Related Domain Objects](In-Depth_Guides/Tutorials/Memory/Memory_Related_Domain_Objects.md)
+  * 2.2.6 [Planner](In-Depth_Guides/Tutorials/Plan/Planner.md)
+    * 2.2.5.1 [Define And Use](In-Depth_Guides/Tutorials/Plan/Planner_Define_And_Use.md)
+    * 2.2.5.2 [Related Domain Objects](In-Depth_Guides/Tutorials/Plan/Planner_Related_Domain_Objects.md)
+* 2.3 Technical Components
+  * 2.3.1 [RAG](In-Depth_Guides/Tutorials/RAG.md)
+    * 2.3.1.1 [How To Build A RAG Agent](How-to/How_To_Build_A_RAG_Agent.md)
+* 2.4 Others
+  * 2.4.1 Service
+    * 2.4.1.1 [Registration and Usage](In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md)
+    * 2.4.1.2 [Web Server](In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md)
+    * 2.4.1.3 [Web API](In-Depth_Guides/Tech_Capabilities/Service/Web_Api.md)
+    * 2.4.1.4 [Service Information Storage](In-Depth_Guides/Tech_Capabilities/Service/Service_Information_Storage.md)
+  * 2.4.2 Prompt Management
+  * 2.4.3 Multi-turn Dialogue
+  * 2.4.4 Logging
+    * 2.4.4.1 [Logging Component](In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Logging_Utils.md)
+  * 2.4.5 Data Collection
+    * 2.4.5.1 [Monitor Module](In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Monitor_Module.md)
+  * 2.4.6 Data Autonomous
+    * 2.4.6.1 [Data Autonomous Agent](In-Depth_Guides/Tutorials/Data_Autonomous_Agent.md)
+
+**3. Component Reference Manual**
+* 3.1 Domain Components
+  * 3.1.1 List of Agents
+  * 3.1.2 [List of LLMs](In-Depth_Guides/Components/LLMs/0.List_Of_LLMs.md)
+    * 3.1.2.1 [OpenAI Usage Instructions](In-Depth_Guides/Components/LLMs/OpenAI_LLM_Use.md)
+    * 3.1.2.2 [Qwen Usage Instructions](In-Depth_Guides/Components/LLMs/Qwen_LLM_Use.md)
+    * 3.1.2.3 [WenXin Usage Instructions](In-Depth_Guides/Components/LLMs/WenXin_LLM_Use.md)
+    * 3.1.2.4 [Kimi Usage Instructions](In-Depth_Guides/Components/LLMs/Kimi_LLM_Use.md)
+    * 3.1.2.5 [BaiChuan Usage Instructions](In-Depth_Guides/Components/LLMs/BaiChuan_LLM_Use.md)
+    * 3.1.2.6 [Claude Usage Instructions](In-Depth_Guides/Components/LLMs/Claude_LLM_Use.md)
+    * 3.1.2.7 [Ollama Usage Instructions](In-Depth_Guides/Components/LLMs/Ollama_LLM_Use.md)
+    * 3.1.2.8 [DeepSeek Usage Instructions](In-Depth_Guides/Components/LLMs/DeepSeek_LLM_Use.md)
+    * 3.1.2.9 [GLM Usage Instructions](In-Depth_Guides/Components/LLMs/GLM_LLM_Use.md)
+    * 3.1.2.10 [General OpenAI Protocol Style Wrapper](In-Depth_Guides/Components/LLMs/OpenAIStyleLLM_Use.md)
+  * 3.1.3 List of Tools
+    * 3.1.3.1 [Integration Tool Details](In-Depth_Guides/Components/Tools/Integrated_Tools.md)
+    * 3.1.3.2 [LangChain Tool Wrapper](In-Depth_Guides/Components/Tools/Integrated_LangChain_Tools.md)
+  * 3.1.4 List of Knowledge
+  * 3.1.5 List of Memories
+  * 3.1.6 List of Planners
+* 3.2 Technical Components
+  * 3.2.1 RPC
+    * 3.2.1.1 [gRPC](In-Depth_Guides/Tech_Capabilities/Service/gRPC.md)
+  * 3.2.2 Store
+    * 3.2.2.1 [SQLDBWrapper](In-Depth_Guides/Tech_Capabilities/Storage/SQLDB_WRAPPER.md)
+    * 3.2.2.2 [Milvus](In-Depth_Guides/Tech_Capabilities/Storage/Milvus.md)
+    * 3.2.2.3 [ChromaDB](In-Depth_Guides/Tech_Capabilities/Storage/ChromaDB.md)
+    * 3.2.2.4 [Sqlite](In-Depth_Guides/Tech_Capabilities/Storage/Sqlite.md)
+  * 3.2.3 Msg
+  * 3.2.4 Logging
+    * 3.2.4.1 [Alibaba Cloud SLS](In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Alibaba_Cloud_SLS.md)
+
+**[4. API Reference Manual](In-Depth_Guides/Tech_Capabilities/Others/API_Reference.md)**
+
+**5. Best Practices**
+* 5.1 Operations and Deployment
+  * 5.1.1 [Docker Containerization Solution](In-Depth_Guides/Tech_Capabilities/Deployment/Docker_Container_Deployment.md)
+  * 5.1.2 [K8S Solution](In-Depth_Guides/Tech_Capabilities/Deployment/K8S_Deployment.md)
+
+**6. Use Cases**
+* 6.1 RAG-Type Agent Examples
+  * 6.1.1 [Legal Consultation Agent v2](Examples/Legal_Advice.md)
+* 6.2 ReAct-Type Agent Examples
+  * 6.2.1 [Python Code Generation and Execution Agent](Examples/Python_Auto_Runner.md)
+* 6.3 [Discussion Group Based on Multi-Turn Multi-Agent Mode](Examples/Discussion_Group.md)
+* 6.4 PEER Multi-Agent Cooperation Examples
+  * 6.4.1 [Financial Event Analysis Case](Examples/Financial_Event_Analysis.md)
+* 6.5 [Andrew Ng's Reflexive Workflow Translation Agent Replication](Examples/Translation_Assistant.md)
+
+**7.Product Platform**
+* 7.1 [Quick Use](How-to/Product_Platform_Quick_Start.md)
+* 7.2 [Advancement Guide](How-to/Product_Platform_Advancement_Guide.md)
+
+**8. Series of Articles**
+
+**9. Frequently Asked Questions (FAQ)**
+
+**[10.Citation](Concepts/Citation_PEER.md)**
+
+**[11. Contact Us](Contact_Us.md)**
diff --git a/docs/guidebook/en/Examples/Discussion_Group.md b/docs/guidebook/en/Examples/Discussion_Group.md
new file mode 100644
index 00000000..57d41d61
--- /dev/null
+++ b/docs/guidebook/en/Examples/Discussion_Group.md
@@ -0,0 +1,120 @@
+# Discussion: Multi-Agent Discussion Group
+
+In this use case, we will demonstrate how to utilize agentUniverse for multi-agent discussions.
+There are two agent roles: the discussion group host (one role) and discussion group participants (multiple roles).
+The user initiates a discussion topic, and the host organizes the participants to commence the discussion. In each round, each participant expresses his or her own views based on the discussion topic (note that the views of each participant are not static and will be constantly adjusted as the discussion progresses). After several rounds of discussion, the host will summarize the discussion process and present the participants' consolidated results back to the user.
+Many minds are better than one. Imagine having a question you wish to answer and submitting it directly to an agentUniverse discussion group, The host will assemble multiple agents with diverse perspectives, focus on your question, and ultimately provide you a comprehensive and intelligent response, which is an interesting agent experiment.
+
+## Quick Start
+### Configure API Key
+For example, configure key information in the file `custom_ key.toml` , which agentUniverse uses to manage private key configuration. (The agentUniverse discussion group defaults to using GPT as the base model and Serper as the Google search tool. The following describes how to use alternative models or tools)
+```toml
+[KEY_LIST]
+# serper google search key
+SERPER_API_KEY='xxx'
+# openai api key
+OPENAI_API_KEY='xxx'
+```
+### Modify Prompt Version
+Modify the prompt version of agents to the English version. The configuration file for the multi-agent discussion group are located in the app/core/agent/discussion_agent_case directory within the `sample_standard_app` sample project. Locate the host_agent.yaml file and change the `prompt_version` to `host_agent.en`. Similarly, find the participants' yaml file and change its `prompt_version` to `participant_agent.en`.
+
+```yaml
+info:
+  name: 'host_agent'
+  description: 'host agent'
+profile:
+  prompt_version: host_agent.en
+  llm_model:
+    name: 'demo_llm'
+    temperature: 0.6
+plan:
+  planner:
+    name: 'discussion_planner'
+    round: 2
+    participant:
+      name:
+        - 'participant_agent_one'
+        - 'participant_agent_two'
+memory:
+  name: 'demo_memory'
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.discussion_agent_case.host_agent'
+  class: 'HostAgent'
+```
+
+
+### Run Discussion Group
+In the sample project of agentUnvierse `sample_standard_app`, find the `discussion_chat_bots.py` file in the app/examples directory. Enter the question you wish to pose within the chat function and then run the script.
+For example, you might enter the question: Which tastes better, Coca-Cola or Pepsi?
+```python
+from agentuniverse.base.agentuniverse import AgentUniverse
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.agent_manager import AgentManager
+
+AgentUniverse().start(config_path='../../config/config.toml')
+
+
+def chat(question: str):
+    instance: Agent = AgentManager().get_instance_obj('host_agent')
+    instance.run(input=question)
+
+
+if __name__ == '__main__':
+    chat("Which tastes better, Coca-Cola or Pepsi")
+```
+### Result Demonstration
+Which tastes better, Coca-Cola or Pepsi：
+
+![](../../_picture/coca-cola_or_pepsi.png)
+
+
+## More Details
+### Agent Configuration
+The configuration files for the multi-agent discussion group  are located in the app/core/agent/discussion_agent_case directory within the `sample_standard_app` sample project.
+
+The `host_agent.yaml` file corresponds to the host agent, and the `participant_agent_*.yaml`files corresponds to the participant agents.
+
+
+The prompts are managed by the prompt version. For example, the default prompt_version of the host_agent is `host_agent.cn`, and the corresponding prompt file is `host_agent.cn.yaml` located under the app/core/prompt directory.
+
+If you plan to switch to another LLM Model, such as the Qwen model, you need to update the  `llm_model` name information accordingly(such as the `qwen_llm` built into the agentUniverse system).
+
+The number of discussion rounds in the agent plan defaults to 2, but users can adjust this as needed. By default,  there are two agents built into agentUniverse serving as participants. To add more members to the discussion group, you can create new agents and configuring their name as parameter in the planner.
+
+```yaml
+info:
+  name: 'host_agent'
+  description: 'host agent'
+profile:
+  prompt_version: host_agent.cn
+  llm_model:
+    name: 'qwen_llm'
+    temperature: 0.6
+plan:
+  planner:
+    name: 'discussion_planner'
+    round: 2
+    participant:
+      name:
+        - 'participant_agent_one'
+        - 'participant_agent_two'
+memory:
+  name: 'demo_memory'
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.discussion_agent_case.host_agent'
+  class: 'HostAgent'
+```
+
+### Memory Configuration
+Participants in the agentUniverse discussion group configure `demo_memory` in the `sample_standard_app` sample project by default to store common memory information for the entire discussion group.
+
+### Planner Configuration
+The planner configuration file is located in the app/core/planner directory of the `sample_standard_app` sample project. The `discussion_planner.py` file contains the specific planning process code for the discussion group. If you are curious, you are welcome to review it yourself.
+
+### Prompt Configuration
+The prompts are managed through the prompt_version. The default prompt files for the AgentUniverse (aU) discussion group agents are configured in the app/core/prompt directory. Corresponding to both Chinese and English versions (e.g., host_agent_xx.yaml and participant_agent_xx.yaml), users can modify the prompt_version in the agent configuration to achieve rapid switching.
+
+### Tool Configuration
+Two participants in the agentUniverse discussion group are configured with google_search_tool by default. Users have the option to change the `tool name` in the `participant_agent_xxx.yaml` file, located under the app/core/agent/discussion_agent_case directory within the `sample_standard_app` sample project, in order  to switch between different tool calls.
\ No newline at end of file
diff --git a/docs/guidebook/en/Examples/Financial_Event_Analysis.md b/docs/guidebook/en/Examples/Financial_Event_Analysis.md
new file mode 100644
index 00000000..73da0efb
--- /dev/null
+++ b/docs/guidebook/en/Examples/Financial_Event_Analysis.md
@@ -0,0 +1,84 @@
+# Financial Event Analysis
+## Case Description
+This case study is based on PeerPlanner and presents a multi-agent collaborative example focused on analyzing financial events. Specifically, regarding the topic of "Buffett's 2023 Reduction in BYD Shares"， it demonstrates how to utilize the PEER multi-agent collaboration model within the agentUniverse framework. Additionally, it provides detailed configurations and output examples for each agent in PEER.
+In this case study, we utilizes the GPT-4o model by OPENAI. Prior to its usage, you need to configure the `OPENAI_API_KEY` in your environment variables.
+
+## Agents
+### Planning Agent 
+Reference the original code files:
+- [Configuration file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)
+- [Prompt file](../../../../sample_standard_app/intelligence/agentic/prompt/planning_agent_cn.yaml)  
+
+The Planning Agent is tasked with breaking down the initial  financial problem into multiple sub-problems that can be independently solved and then provided to the subsequent Executing Agent. In this particular scenario, the overarching question "Analyze the reasons for Buffett's reduction in BYD shares" can be dissected into several sub-questions, as illustrated in the diagram below.
+
+![planning_result](../../_picture/6_4_1_planning_result.png)
+
+
+Additionally, you have the ability to debug the Planning Agent independently within the designated [test file](../../../../sample_standard_app/intelligence/test/test_planning_agent.py).
+
+### Executing Agent 
+Reference the original code files:
+- [Configuration file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_executing_agent.yaml)
+- [Prompt file](../../../../sample_standard_app/intelligence/agentic/prompt/executing_agent_cn.yaml)
+
+In this Agent, we provide a tool named[google_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.py) for searching information on Google. To utilize this tool, you should configure `SERPER_API_KEY` in your environment variables. For convenience, if the `SERPER_API_KEY ` is not configured, the tool will return a pre-set query result related to this case, which you can find in the [mock_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/mock_search_tool.py).
+
+
+The Executing Agent is responsible for solving the sub-problems that have been broken down by the Planning Agent. In this particular case, the execution results of the Executing Agent are as follows: 
+![executing_result](../../_picture/6_4_1_executing_result.png)
+
+Due to the length of the result, only the execution results of the first two questions are displayed here. You can debug the Executing Agent independently in the designated[test file](../../../../sample_standard_app/intelligence/test/test_executing_agent.py) to obtain the complete results.
+
+### Expressing Agent 
+Reference the original code files:
+- [Configuration file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)
+- [Prompt file](../../../../sample_standard_app/intelligence/agentic/prompt/expressing_agent_cn.yaml)  
+
+The Expressing Agent is responsible for summarizing all the results outputted by the Executing Agent and formulating them into an answer to the original question, adhering to the requirements specified in the prompt file. In thisinstance, the output result of the Expressing Agent is as follows:
+
+![expressing_result](../../_picture/6_4_1_expressing_result.png)
+
+
+You can debug the Expressing Agent individually in the [test file](../../../../sample_standard_app/intelligence/test/test_expressing_agent.py).
+
+### Reviewing Agent 
+Reference the original code files:
+- [Configuration file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_reviewing_agent.yaml)
+
+The Reviewing Agent is responsible for evaluating whether the answer produced by the Expressing Agent effectively addresses the original question. In this particular case, the Reviewing Agent accepted the answer provided by the Expressing Agent:
+
+![reviewing_result](../../_picture/6_4_1_reviewing_result.png)
+
+
+You can debug the Reviewing Agent individually in the [test file](../../../../sample_standard_app/intelligence/test/test_reviewing_agent.py).
+
+### PEER Agent 
+```yaml
+info:
+  name: 'demo_peer_agent'
+  description: 'demo peer agent'
+plan:
+  planner:
+    name: 'peer_planner'
+    eval_threshold: 60
+    retry_count: 2
+    planning: 'demo_planning_agent'
+    executing: 'demo_executing_agent'
+    expressing: 'demo_expressing_agent'
+    reviewing: 'demo_reviewing_agent'
+metadata:
+  type: 'AGENT'
+  module: 'agentuniverse.agent.default.peer_agent.peer_agent'
+  class: 'PeerAgent'
+```
+Users can configure the four Agents mentioned above into a complete PEER Agent within the agentuniverse through the `peer_planner` collaboration model. The configurations include:
+- name: Set as `peer_planner`, indicating the utilization of the PEER multi-agent collaboration model.
+- eval_threshold: The minimum score threshold that the Reviewing Agent must achieve to accept an answer.
+- retry_count: The number of retry attempts for the PEER Agent if the Reviewing Agent rejects the answer.
+- planning：The Agent responsible for the Plan part.
+- executing：The Agent responsible for the Execute part.
+- expressing：The Agent responsible for the Express part.
+- reviewing：The Agent responsible for the Review part.
+
+You can run the complete case in the [example file](../../../../sample_standard_app/intelligence/test/peer_chat_bot.py).
+
diff --git a/docs/guidebook/en/Examples/Legal_Advice.md b/docs/guidebook/en/Examples/Legal_Advice.md
new file mode 100644
index 00000000..9b983c2f
--- /dev/null
+++ b/docs/guidebook/en/Examples/Legal_Advice.md
@@ -0,0 +1,88 @@
+# Legal Advice
+## Case Description
+This case demonstrates a simple legal consultation agent built utilizing `RagPlanner`. The agent provides legal advice by retrieving relevant provisions from the Civil Law and the Criminal Law, and integrating them with the case background.
+
+The case leverages the embedding and reranking features of DashScope in conjunction with the Qwen llm. Before using this, you need to configure the `DASHSCOPE_API_KEY` in your environment variables.
+
+## Components
+### Legal Knowledge Base
+The legal knowledge base is constructed using [Knowledge Components](../In-Depth_Guides/Tutorials/Knowledge/Knowledge_Related_Domain_Objects.md) from agentUniverse. By storing the original legal provisions in the both ChromaDB and Sqlite database, the knowledge base enables efficient retrieval and consultation for the agent.
+Original legal documents
+- [民法典.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
+- [刑法.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
+
+### [Knowledge Definition](../../../../sample_standard_app/intelligence/agentic/knowledge/law_knowledge.yaml)
+```yaml
+name: "law_knowledge"
+description: "中国民法与刑法相关的知识库"
+stores:
+    - "civil_law_chroma_store"
+    - "criminal_law_chroma_store"
+    - "civil_law_sqlite_store"
+    - "criminal_law_sqlite_store"
+query_paraphrasers:
+    - "custom_query_keyword_extractor"
+insert_processors:
+    - "recursive_character_text_splitter"
+rag_router: "nlu_rag_router"
+post_processors:
+    - "dashscope_reranker"
+readers:
+    pdf: "default_pdf_reader"
+
+metadata:
+  type: 'KNOWLEDGE'
+  module: 'sample_standard_app.app.core.knowledge.law_knowledge'
+  class: 'LawKnowledge'
+```
+
+### Reader Component
+- [default_pdf_reader](../../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)
+
+### DocProcessor Component
+- [custom_query_keyword_extractor](../../../../sample_standard_app/intelligence/agentic/knowledge/doc_processor/query_keyword_extractor.yaml)
+- [recursive_character_text_splitter](../../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
+
+### QueryParaphraser Component
+- [custom_query_keyword_extractor](../../../../sample_standard_app/intelligence/agentic/knowledge/query_paraphraser/custom_query_keyword_extractor.yaml)
+
+### RagRouter Component
+- [nlu_rag_router](../../../../sample_standard_app/intelligence/agentic/knowledge/rag_router/nlu_rag_router.yaml)
+
+### Store Component
+- [civil_law_chroma_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_chroma_store.yaml)
+- [criminal_law_chroma_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_chroma_store.yaml)
+- [civil_law_sqlite_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_sqlite_store.yaml)
+- [criminal_law_sqlite_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_sqlite_store.yaml)
+
+For your convenience, we have placed the databases containing the relevant information in [this directory](../../../../sample_standard_app/db). If you want to build the knowledge base from scratch, you can run the following code:
+```python
+from agentuniverse.base.agentuniverse import AgentUniverse
+from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
+
+
+if __name__ == '__main__':
+    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
+    civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"]
+    criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"]
+    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
+    law_knowledge.insert_knowledge(
+        source_path="../resources/刑法.pdf",
+        stores=criminal_store_list
+    )
+    law_knowledge.insert_knowledge(
+        source_path="../resources/民法典.pdf",
+        stores=civil_store_list
+    )
+```
+
+### Law Agent
+This agent involves the following file:
+- [law_rag_agent.yaml](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/law_rag_agent.yaml): Defines the agent's related prompts
+
+
+### Demonstration Code
+[CodeLink](../../../../sample_standard_app/intelligence/test/law_chat_bot.py)
+
+## Demonstration
+![](../../_picture/law_agent_demo.png)
diff --git a/docs/guidebook/en/7_1_1_Python_Auto_Runner.md b/docs/guidebook/en/Examples/Python_Auto_Runner.md
similarity index 53%
rename from docs/guidebook/en/7_1_1_Python_Auto_Runner.md
rename to docs/guidebook/en/Examples/Python_Auto_Runner.md
index 7024bc30..ba214a30 100644
--- a/docs/guidebook/en/7_1_1_Python_Auto_Runner.md
+++ b/docs/guidebook/en/Examples/Python_Auto_Runner.md
@@ -1,11 +1,11 @@
 # Python Auto Runner
 ## Case Illustration
-This case is based on ReactPlanner, building a simple case that can automatically generate and execute Python code.
-This case is based on the QianWen large language model and the google_search function, requiring you to configure the environment variables DASHSCOPE_API_KEY, SERPER_API_KEY before use.
+This case is built using ReactPlanner, creating a simple application that can automatically generate and execute Python code.
+This case is leverages the QianWen large language model and the google_search function, necessitating the configuration of environment variables DASHSCOPE_API_KEY and SERPER_API_KEY prior to use.
 
 ## Quick Start
 ### Configure API Key
-For example, configure key information in the file custom_key.toml where agentUniverse manages private keys (the default discussion group uses qwen as the base model and serper as the google search tool).
+For instance, configure key information in the custom_key.toml file, which is where agentUniverse manages private keys (the default setup uses qwen as the base model and serper as the google search tool).
 ```toml
 [KEY_LIST]
 # serper google search key
@@ -39,13 +39,13 @@ metadata:
 ```
 
 Here we used two tools: google_search_tool and python_runner. The relevant tool code links are as follows:
-- [google_search_tool](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)
-- [python_runner](../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)
+- [google_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)
+- [python_runner](../../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)
 
 
 ### Case Run
 1. Test Case Run
-Directly run with test code[test_case](../../../sample_standard_app/intelligence/test/test_react_agent.py)
+Directly run with test code[test_case](../../../../sample_standard_app/intelligence/test/test_react_agent.py)
 2. Interface Run
 After configuring the related keys, start the web service and use the following curl for testing.
 ```shell
@@ -62,11 +62,11 @@ curl --location --request POST 'http://localhost:8888/service_run' \
 ```
 
 ### Result
-![test_case](../_picture/react_demo_step.png)
+![test_case](../../_picture/react_demo_step.png)
 
-In the image, React went through three steps in total:  
-Step 1: The model provided a piece of Python code and handed it over to the Python Runner tool for execution, but the execution failed due to the failure to use print to output the execution result.  
-Step 2: The model realized the mistake and proactively modified the code. It then used the Python Runner tool again for execution, which was successful.  
+In the image, React underwent a total of three steps:
+Step 1: The model provided a piece of Python code and submitted it to the Python Runner tool for execution. However,  the execution failed due to an inability to use print to output the execution result.
+Step 2: The model recognized the mistake and proactively revised the code. It then resubmitted the code to the Python Runner tool for execution, which was successful this time.
 Step 3: The model conveyed the successfully executed code to the user.  
 
 ### Please note
diff --git a/docs/guidebook/en/Examples/Translation_Assistant.md b/docs/guidebook/en/Examples/Translation_Assistant.md
new file mode 100644
index 00000000..9527ef2e
--- /dev/null
+++ b/docs/guidebook/en/Examples/Translation_Assistant.md
@@ -0,0 +1,60 @@
+# Translation Assistant
+Recently, Professor Andrew Ng of Stanford University open-sourced an AI agent for machine translation, which employs a reflective workflow for translation process. This project serve as an example of machine translation utilizing the reflective agent workflow. The primary steps of the agent are as follow:
+1. Initial translation, where a large model (LLM) is utilized to translate text from the source language to the target language.
+2. Reflection, in which the large model identifies deficiencies in the translation and proposes constructive improvement suggestions.
+3. Revision of the translation, where the large model retranslates the text based on the suggestions derived from the reflection process.
+
+The reflective workflow employed in this project enables the model to identify its own deficiencies and implement targeted revisions, constituting a typical multi-agent collaborative working mechanism. This is also an area in which agentUniverse has been actively engaged, prompting me to contemplate the migration of Andrew Ng's translation agent to agentUniverse framework.
+This case is based on the Qianwen large model. Prior to use, you need to configure the `DASHSCOPE_API_KEY` in the environment variables.
+
+## Collaborative Working Process of Multi-Agents
+During the translation process, the priority is to ascertain whether the model's length surpasses the maximum number of tokens it can accommodate. For instances where the length exceeds the limit, the text is initially segmented, followed by the translation of each segment. However, the entire translation process adheres to the initial translation -> reflection -> revision workflow.
+![](../../_picture/translation_flow_graph.png)
+
+
+Implementing this translation in agentUniverse involves the following steps:
+1. Define prompts related to translation, with three for short text and three for long text. Relevant files include:
+   - [Short Text Init Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_init_en.yaml)
+   - [Short Text Reflection Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_reflection_en.yaml)
+   - [Short Text Improve Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_improve_en.yaml)
+   - [Long Text Init Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_init_en.yaml)
+   - [Long Text Reflection Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)
+   - [Long Text Improve Prompt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
+2. Define three agents  
+   - [Short Text Translation Work Agent](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_work_agent.yaml)
+   - [Short Text Translation Reflection Agent](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_reflection_agent.yaml)  
+   - [Short Text Translation Improvement Agent](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_improve_agent.yaml)  
+   These agents switch their prompts based on whether the task involves translating long or short texts. The specific logic for this switching mechanism is detailed in the [agent file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.py).
+
+3. Define the collaborative work process of the three agents
+The collaborative process is illustrated in the earlier-mentioned flowchart of multi-agent collaboration. 
+
+![](../../_picture/translation_execute_flow.png)    
+
+
+For a deeper dive into the processes, refer to the [detailed code file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_by_token_agent.py).  
+The [configuration file](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.yaml) for the collaborative agents.
+
+### Demonstration Code  
+[Code Link](../../../../sample_standard_app/intelligence/test/test_translation_agent.py)
+
+[Long Text](../../../../sample_standard_app/intelligence/test/translation_data/long_text.txt)  
+[Short Text](../../../../sample_standard_app/intelligence/test/translation_data/short_text.txt)  
+
+[Translation Of Long Text](../../../../sample_standard_app/intelligence/test/translation_data/short_text_result.txt)  
+[Translation Of Short Text](../../../../sample_standard_app/intelligence/test/translation_data/long_text_result.txt)  
+
+### Demonstration Results
+We can see that using agentUniverse maintains consistency with the results of the orginal translation_agent project, which hasa been successfully replicated.
+agentUniverse Results:  
+
+![](../../_picture/long_translation_au.png)    
+
+translation_agent Results:  
+
+![](../../_picture/long_translation_wu.png)  
+
+
+### Others
+In reference to Professor Andrew Ng's code, when managing segmented translations, to ensure the coherence of the translation, each segment is translated with its corresponding context. However, incorporating all translation content into the context within his code can undermine the original intent of segmentation, and in certain cases, it can result in exceeding the model's taken capacity. I have raised a related issue on the corresponding repository address:  [Issue Link](https://github.com/andrewyng/translation-agent/issues/28)## Implementation in agentUniverse.
+
diff --git a/docs/guidebook/en/Get_Start/Application_Project_Structure_and_Explanation.md b/docs/guidebook/en/Get_Start/Application_Project_Structure_and_Explanation.md
new file mode 100644
index 00000000..c9982579
--- /dev/null
+++ b/docs/guidebook/en/Get_Start/Application_Project_Structure_and_Explanation.md
@@ -0,0 +1,137 @@
+# Application Project Structure and Explanation
+As you can see, `agentUniverse`  is designed with lightweight and integration capabilities in mind, allowing you to incorporate `agentUniverse`  into any of your projects for seamless operation.
+
+## Recommended Project Directory Structure and Explanation
+The directory structure provided below is only a suggestion, and you are free to adjust it according to your preferences and actual situation. We will explain this in more detail later in the document.
+
+```
+/
+├── bootstrap/
+│   ├── intelligence/
+│   │   └── server_application.py
+│   ├── platform/
+│   │   └── product_application.py
+├── intelligence/
+│   ├── agentic/
+│   │   ├── agent
+│   │   │   └── agent_instance
+│   │   │   └── agent_template
+│   │   ├── knowledge
+│   │   │   └── store/
+│   │   │   └── rag_router/
+│   │   │   └── doc_processor/
+│   │   ├── llm
+│   │   ├── prompt
+│   │   ├── memory
+│   │   ├── tool
+│   │   └── work_pattern
+│   ├── service/
+│   │   └── agent_service
+│   │   └── classic_service
+│   ├── dal/
+│   ├── integration/
+│   ├── utils/
+│   └── test/
+├── platform/
+├── config
+├── pyproject.toml
+└── other project files...
+```
+
+Here's what each package directory level means:
+* bootstrap: The entry layer for starting the web server
+  * intelligence - The entry layer for starting Intelligent web server
+  * platform - The entry layer for productization web server
+* intelligence: Intelligent project layer, used for agent construction, component customization, and service implementation.
+  * agentic: Intelligent domain layer, where related domain components of agentUniverse are placed.
+    * agent - Agent layer, corresponding to the agent component of agentUniverse, which can be used to build agent templates and instances.
+      * agent_template - Agent template layer, abstracted from business understanding, to help users quickly build agents. After the template is built, users only need to configure specific attributes based on the template to execute the corresponding logic. 
+      * agent_instance - Agent instance layer.
+    * knowledge - Knowledge layer, corresponding to the knowledge component of agentUniverse, including knowledge injection and search capabilities.
+      * store - Knowledge store, corresponding to the store component of agentUniverse.
+      * rag_router - Knowledge RAG router, corresponding to the rag_router component of agentUniverse.
+      * doc_processor - Knowledge document processor, corresponding to the doc_processor component of agentUniverse.
+    * llm - LLM layer, corresponding to the llm component of agentUniverse, providing llm services such as ChatGPT/Qwen.
+    * prompt - Prompt layer, corresponding to the prompt component of agentUniverse, providing prompt templates and generation capabilities.
+    * memory - Memory layer, corresponding to the memory component of agentUniverse, providing agent memory capabilities, including memory compression and storage.
+    * tool - Tool layer, corresponding to the tool component of agentUniverse, providing auxiliary tools such as API services.
+    * work_pattern - Work pattern layer, providing typical single-agent and multi-agent collaboration patterns, including PEER/DOE/MAP, etc.
+  * service: Intelligent project service layer.
+    * agent_service - Intelligent service layer, corresponding to the service component of agentUniverse, associating agent instances and providing external services.
+    * classic_service - Traditional service layer, handling non-agent services such as file services and session services.
+  * dal: Data access layer, providing data source access interfaces.
+  * integration: Integration layer, used to connect and coordinate different systems, services, and components.
+  * utils: Basic utility layer, providing small and general-purpose helper functions.
+  * test: Unit tests.
+* platform: Platform project layer, handling platform capabilities outside intelligent project.
+* config - Application configuration code
+
+## Using Any Project Directory Structure
+You can adjust the project directory structure according to your preferences and actual circumstances, but please ensure you follow the rules below.
+
+### Bootstrap Startup Directory
+Regardless of the location of your project's startup script, except for testing, you should start the application service with the following statement:
+
+```python
+from agentuniverse.agent_serve.web.web_booster import start_web_server
+from agentuniverse.base.agentuniverse import AgentUniverse
+
+
+class ServerApplication:
+    """
+    Server application.
+    """
+
+    @classmethod
+    def start(cls):
+        AgentUniverse().start()
+        start_web_server()
+
+ServerApplication.start()
+
+```
+`ServerApplication.start()` is the server startup method for this framework, which accepts a configuration path `config_path` as an input parameter. The default `config_path` points to a file named 'config.toml', located in the config directory under the project root path(`project_root_dir/config/config.toml`). Ensure that the config file path is correct; if you have further changed the directory of the config file, adjust the `config_path` accordingly.
+
+### Config Directory
+As mentioned in the [Bootstrap Startup Directory](#bootstrap-startup-directory), the default config path for the project is `project_root_dir/config/config.toml`. If you have made any adjustments to this, please ensure that the correct config file path is provided to the startup method when the application server is launched.
+
+### Core Directory
+As shown in the recommended directory structure, the agentic directory within intelligence is primarily used to place domain components related to agents, knowledge, and LLMs. If you want to customize the location of core components, you can specify the paths of the domain components in the [CORE_PACKAGE] section of the main configuration file config/config.toml as follows:
+```toml
+[CORE_PACKAGE]
+# Perform a full component scan and registration for all the paths under this list.
+default = ['sample_standard_app.intelligence.agentic']
+# Scan and register agent components for all paths under this list, with priority over the default.
+agent = ['sample_standard_app.intelligence.agentic.agent']
+# Scan and register knowledge components for all paths under this list, with priority over the default.
+knowledge = ['sample_standard_app.intelligence.agentic.knowledge']
+# Scan and register llm components for all paths under this list, with priority over the default.
+llm = ['sample_standard_app.intelligence.agentic.llm']
+# Scan and register tool components for all paths under this list, with priority over the default.
+tool = ['sample_standard_app.intelligence.agentic.tool']
+# Scan and register memory components for all paths under this list, with priority over the default.
+memory = ['sample_standard_app.intelligence.agentic.memory']
+# Scan and register service components for all paths under this list, with priority over the default.
+service = ['sample_standard_app.intelligence.service.agent_service']
+# Scan and register prompt components for all paths under this list, with priority over the default.
+prompt = ['sample_standard_app.intelligence.agentic.prompt']
+# Scan and register store components for all paths under this list, with priority over the default.
+store = ['sample_standard_app.intelligence.agentic.knowledge.store']
+# Scan and register rag_router components for all paths under this list, with priority over the default.
+rag_router = ['sample_standard_app.intelligence.agentic.knowledge.rag_router']
+# Scan and register doc_processor components for all paths under this list, with priority over the default.
+doc_processor = ['sample_standard_app.intelligence.agentic.knowledge.doc_processor']
+# Scan and register query_paraphraser components for all paths under this list, with priority over the default.
+query_paraphraser = ['sample_standard_app.intelligence.agentic.knowledge.query_paraphraser']
+# Scan and register memory_compressor components for all paths under this list, with priority over the default.
+memory_compressor = ['sample_standard_app.intelligence.agentic.memory.memory_compressor']
+# Scan and register memory_storage components for all paths under this list, with priority over the default.
+memory_storage = ['sample_standard_app.intelligence.agentic.memory.memory_storage']
+# Scan and register product components for all paths under this list, with priority over the default.
+product = ['sample_standard_app.platform.difizen.product']
+# Scan and register workflow components for all paths under this list, with priority over the default.
+workflow = ['sample_standard_app.platform.difizen.workflow']
+```
+The format for specifying package paths in the configuration follows the standard Python package path format. The framework will register, scan, and manage all types of component packages uniformly based on the defined package paths during startup.
+
+**Tips**: The package path should be specified relative to one level below your project's root directory. For example, in the sample_standard_app project, since the package path is specified under the project's top-level directory, it starts with sample_standard_app. If you are using sample_standard_app as a project template and organizing your components under an app subdirectory, then the package path should start with app.xxx.”
\ No newline at end of file
diff --git a/docs/guidebook/en/Get_Start/Installation.md b/docs/guidebook/en/Get_Start/Installation.md
new file mode 100644
index 00000000..577d8fa4
--- /dev/null
+++ b/docs/guidebook/en/Get_Start/Installation.md
@@ -0,0 +1,43 @@
+# Installation
+## Python version requirements
+- python 3.10+
+
+We recommend using python 3.10 or later to ensure optimal performance and functionality. The entire project has successfully passed all tests on Python 3.10.0 and later versions. If you are using a Python version lower than 3.10, we cannot guarantee that the project will run correctly.
+
+
+
+## Installation method
+### Install via pip
+```shell
+pip install agentUniverse
+```
+More version information can be found on the
+[PyPi agentUniverse](https://pypi.org/project/agentUniverse/)
+
+### Install via poetry or other package management tools
+In actual projects, we recommend using Poetry or other tools to manage your project dependencies. You can install agentUniverse using the following command:
+
+```shell
+poetry add agentUniverse
+```
+
+Alternatively, you can add the following content to your `pyproject.toml` file:
+
+```toml
+[tool.poetry.dependencies]
+agentUniverse = "^0.0.3"
+```
+A standard `pyproject.toml` file for a standard project can be found [here](../../../../sample_standard_app/pyproject.toml).
+
+To update your dependencies, use the `poetry update` command:
+```shell
+poetry update
+```
+
+## Verify installation
+```shell
+pip list | grep agentUniverse
+```
+If you see `agentUniverse` and its version numberin the output, the installation was successful.
+
+![](../../_picture/1_2_Installation_0.png)
diff --git a/docs/guidebook/en/Get_Start/Introduction.md b/docs/guidebook/en/Get_Start/Introduction.md
new file mode 100644
index 00000000..6c80deb0
--- /dev/null
+++ b/docs/guidebook/en/Get_Start/Introduction.md
@@ -0,0 +1,26 @@
+# agentUniverse Introduction
+## Introduction
+agentuniverse is a framework for developing applications powered by multi-agent based on large language models. It provides all the essential components for building a single agent, and a multi-agent collaboration mechanism which serves as a pattern factory that allowing developers to build and customize multi-agent collaboration patterns. With this framework, developers can easily construct multi-agent applications, and share the pattern practices from different technical and business fields.
+
+
+The framework will come with several pre-install multi-agent collaboration patterns which have been proven effective in real business scenarios, and will continue to be expanded and enriched in the future. Patterns that are currently soon to be released include:
+
+- PEER pattern：
+This pattern utilizes four distinct agent roles: Plan, Execute, Express, and Review, to achieve a multi-step breakdown and sequential execution of a complex task. It also performs autonomous iteration based on evaluative feedback thereby enhancing performance in reasoning and analytical tasks.
+
+
+- DOE pattern：
+This pattern consists of three agents: the Data-finding agent, which is designed to solve data-intensive and high-computational-precision task; the Opinion-inject agent, which combines the data results from the first agent with expert opinions that are pre-collected and structured; and the third agent, the Express agent，which generates the final result based on the given document type and language style.
+
+More patterns are coming soon...
+
+![](../../_picture/agent_universe_framework_resize.jpg)
+
+In addition to rich collaboration modes, agentUniverse also includes the following main features:
+
+* **Fast and Simple Development Experience**: Through configuration and simple interfaces, you can quickly complete single-agent construction, multi-agent collaboration process definition, and service-based applications using this framework.
+* **Rich Components and Custom Extensions**: The framework provides a wide variety of common domain components (LLM, knowledge, tools, memory, collaboration patterns, etc.) and technical components (DB, RPC, Message, etc.) with default implementations. It offers extension standards for all types of components, allowing you to customize any part to enhance your agent capabilities.
+* **Prompt-Friendly and Management**: The framework has built-in a complete set of prompt management mechanisms. You can manage prompts in multiple versions, switch between them, and specialize them based on expert domain knowledge.
+
+## Acknowledgments
+This project is partially based on open-source projects like Langchain, Pydantic, Gunicorn, Flask, SQLAlchemy, Chromadb, and others (a detailed dependency list can be found in requirements.txt). Special thanks are given to the creators and contributors of these related projects and associated parties.
\ No newline at end of file
diff --git a/docs/guidebook/en/Get_Start/Quick_Start.md b/docs/guidebook/en/Get_Start/Quick_Start.md
new file mode 100644
index 00000000..bdb62d88
--- /dev/null
+++ b/docs/guidebook/en/Get_Start/Quick_Start.md
@@ -0,0 +1,160 @@
+# Quick Start
+Through this document, you will learn how to quickly run examples of agentUniverse and personally experience its effects.
+
+## 1.Preparation Work
+Before running the example case, ensure that you have installed the necessary runtime environment and completed the project setup.
+
+### 1.1 Preparation of the environment
+agentUniverse supports three operating system platforms: `macos`, `linux`, and `windows`. The configuration details for each platform may vary slightly. We won't go into detail here, but you can check the documentation for the necessary steps to ensure all requirements are met, and search for corresponding software configuration tutorials based on your specific platform.
+
+#### 1.1.1 Preparing Python environment
+Using **Python 3.10** version
+
+Tips: If you have other Python version environments installed on your computer, it is strongly recommended that you use environment management tools like conda to isolate different multiple Python environments.
+
+#### 1.1.2 Development Tool Preparation
+AgentUniverse is designed for standard containerized delivery and includes standard images. You can run and modify the source code via shell during the development phase, but this method is not very efficient.
+
+Although this section is not mandatory, we strongly recommend that you install a Python-oriented IDE tool. For an optimal Python development experience, we recommend using PyCharm, followed by VSCode (although configuring VSCode for Python development can be more cumbersome, so it is not advisable for beginners in Python development).
+
+Don't forget to configure the Python interpreter in your chosen IDE tool.
+
+### 1.2 Project Setup
+#### 1.2.1 Sample Project Setup
+You can download the agentUniverse source code from the project portal webpage or by using `git clone`. After completing this section, you will obtain the following project structure, where the `sample_standard_app` folder contains the sample project code, and this folder is located one level below the root directory, which is `agentUniverse`.
+
+```text
+➜  agentUniverse ✗ tree -L 1
+.
+├── CHANGELOG.md
+├── CHANGELOG_zh.md
+├── LICENSE
+├── README.md
+├── README_PYPI.md
+├── README_jp.md
+├── README_zh.md
+├── agentuniverse
+├── agentuniverse_connector
+├── agentuniverse_extension
+├── agentuniverse_product
+├── dataset
+├── dist
+├── docs
+├── poetry.toml
+├── pyproject.toml
+├── sample_standard_app
+└── tests
+```
+
+Tips: To simplify the setup, please do not modify any project directories or rename folders for now. AgentUniverse supports arbitrary project directory structures, which requires some additional configuration. We will explain its usage in other sections.
+
+#### 1.2.2 Install the corresponding framework package
+```shell
+# framework package
+pip install agentUniverse
+
+# Visualization Interface Package
+pip install magent-ui ruamel.yaml
+```
+
+#### 1.2.3 AK Configuration
+This framework supports key injection through environment variables, file configuration, and other methods. We will explain the complete usage in a separate chapter on key settings. Here, we will demonstrate the steps for configuring keys through a file. Please carefully check whether the following steps are followed in order：
+
+##### step1. Confirm that the config file enables automatic reading of the external key file
+Locate the main configuration file in the project (path: agentUniverse/sample_standard_app/config/config.toml). Find the SUB_CONFIG_PATH configuration item, and uncomment the custom_key_path configuration item as shown in the picture below:
+![config_ak_path](../../_picture/config_ak_path.png)
+
+This step will enable the project to automatically read configuration files and batch load keys. We can see that the custom_key_path configuration option already includes one default path. To simplify the tutorial, we will not modify the path here.
+
+##### step2. Create an external key file based on the template
+The external key file template is located at the same level as the config path (specifically at sample_standard_app/config/custom_key.toml.sample). We will copy custom_key.toml.sample and create a new file, naming it custom_key.toml in accordance with the default configuration of custom_key_path from step 1, as shown in the image below:
+![config_ak_create_file](../../_picture/config_ak_create_file.png)
+
+Tips: The external key file generally contains all of your access keys (AK), which are very private and need to be strictly protected. This file should never be leaked or managed on code platforms like Git. In actual production projects, we typically separate this file from the project and implement strong system-level permission controls. The steps in the key configuration of this framework are mainly for the sake of production security.
+
+##### step3. In the external key file, configure your commonly used model AK
+The key file contains dozens of common model service AK formats. You can fill in your own keys according to your needs, and don't forget to uncomment them. In subsequent tutorials, we will use the Qwen and GPT models as the LLM for the tutorial agent, so here we will configure the corresponding AK for Qianwen and GPT as shown in the image below:
+![config_ak_demo_key](../../_picture/config_ak_demo_key.png)
+
+## 2. Run the first example
+The agentUniverse currently includes 5 official tutorial cases, located at agentUniverse/sample_standard_app/app/examples.
+
+In this section, we will run the first example, choosing the law_chat_bot as our first tutorial case.
+
+### 2.1 Determine the agent used behind the example and its configuration
+For instance, in the case of law_chat_bot (path: agentUniverse/sample_standard_app/app/examples/law_chat_bot.py), we first identify the corresponding agent_name in the script. For the law_chat_bot, it is law_rag_agent:
+![demo_case_law_rag_agent](../../_picture/demo_case_law_rag_agent.png)
+
+After determining the agent used in the example, we go to the project agent directory (the directory path is: agentUniverse/sample_standard_app/app/core/agent) and find the corresponding agent configuration file law_rag_agent.yaml. Note that the name field in the agent configuration corresponds to the agent name in law_chat_bot.
+![demo_case_law_rag_agent_yaml](../../_picture/demo_case_law_rag_agent_yaml.png)
+
+Let's further examine the other configuration details in the law_rag_agent.yaml file, focusing on the llm_model configuration item. This item specifies the LLM used by the agent. By default, law_rag_agent uses the qwen series, specifically qwen-max, as the model core. If you configured a different series model type during the key configuration stage, you'll need to replace it here. You can refer to the following common model replacements. You can copy the default settings, and if needed, you can replace the model_name according to the official model codes of the service provider.
+
+Qwen Model
+```text
+  llm_model:
+    name: 'qwen_llm'
+    model_name: 'qwen-max'
+```
+
+gpt Model
+```text
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+```
+
+WenXin Model
+```text
+  llm_model:
+    name: 'wenxin_llm'
+    model_name: 'ERNIE-3.5-8K'
+```
+
+kimi Model
+```text
+  llm_model:
+    name: 'kimi_llm'
+    model_name: 'moonshot-v1-8k'
+```
+
+BaiChuan Model
+```text
+  llm_model:
+    name: 'baichuan_llm'
+    model_name: 'Baichuan2-Turbo'
+```
+
+DeepSeek Model
+```text
+  llm_model:
+    name: 'deep_seek_llm'
+    model_name: 'deepseek-chat'
+```
+
+Tips: To simplify the configuration process, we only list a selection of commonly used model services here. In addition to model service providers, local deployment models can also be configured. We will not demonstrate this in this chapter, but users with such needs can further refer to the chapters related to LLM configuration.
+
+
+### 2.2 Run the Example
+By following the steps above, you have completed all the preparatory work. Now, let's run it and see the results. Locate the agentUniverse/sample_standard_app/app/examples/law_chat_bot.py file and run it in your IDE or in the shell.
+
+![demo_case_law_rag_agent_run](../../_picture/demo_case_law_rag_agent_run.png)
+
+When you see the following results, it indicates that the case has run successfully.
+
+![demo_case_law_rag_agent_answer](../../_picture/demo_case_law_rag_agent_answer.png)
+
+
+## 3. Run the other example
+You have completed the execution of the first case. In other official examples, we used the google_search_tool, which provides your agent with web search capabilities. To experience other use cases, we recommend that you apply for a SERPER API key and pre-configure the search tool's API key. Serper has provided thousands of free search credits for your experience.
+
+You need to apply for the SERPER_API_KEY on the official Serper website and configure it. The official website address is:  https://serper.dev
+
+After applying, find the corresponding key in the custom_key.toml configuration file and uncomment it as shown.
+
+```toml
+#Google search
+SERPER_API_KEY='xxxxxx'
+```
+
+Follow the steps in Section 2 to execute other cases. You can now reference and run all official cases.
diff --git a/docs/guidebook/en/1_3_Quick_Start.md b/docs/guidebook/en/Get_Start/Start_Tutorial.md
similarity index 99%
rename from docs/guidebook/en/1_3_Quick_Start.md
rename to docs/guidebook/en/Get_Start/Start_Tutorial.md
index e60490c7..2706a6f2 100644
--- a/docs/guidebook/en/1_3_Quick_Start.md
+++ b/docs/guidebook/en/Get_Start/Start_Tutorial.md
@@ -1,4 +1,4 @@
-# Quick Start
+# Start Tutorial
 we will show you how to:
 
 * Prepare the environment and application engineering
@@ -12,7 +12,7 @@ we will show you how to:
 - python 3.10+
 
 ### Application Preparation
-We provide a standard project template which you can access [here](../../../sample_standard_app) . 
+We provide a standard project template which you can access [here](../../../../sample_standard_app) . 
 
 The "sample_standard_app" folder contains a standard project template that you can modify according to your own needs. You can also copy the "sample_standard_app" folder to use as the root directory of your application project.
 
@@ -226,7 +226,7 @@ python server_application.py
 ```
 
 When the command line shows that the service is listening successfully, the service is started. By default, the service listens on the address `127.0.0.1` and port `8000`, with 5 workers. You can modify the configuration in `config/gunicorn_config.toml`.(Note that on Windows systems and when directly using Flask to start, the default listening port is currently 8888.)
-![image](../_picture/1_3_Quick%20Start_0.png)
+![image](../../_picture/1_3_Quick%20Start_0.png)
 
 ### Access the Service
 #### Local Access
diff --git a/docs/guidebook/en/How-to/How_To_Build_A_RAG_Agent.md b/docs/guidebook/en/How-to/How_To_Build_A_RAG_Agent.md
new file mode 100644
index 00000000..c9a05870
--- /dev/null
+++ b/docs/guidebook/en/How-to/How_To_Build_A_RAG_Agent.md
@@ -0,0 +1,106 @@
+# How to Build a RAG Agent
+
+This tutorial offers a quick guide to constructing a RAG (Retrieval-Augmented Generation) agent within the agentUniverse framework. The comprehensive structure and detailed content of the case can be found in [this document](../Examples/Legal_Advice.md). However, this document specifically focuses on the construction process, so some non-essential details have been omitted for brevity.
+
+## Case Description
+This case is based on RagPlanner and sets up a simple legal consultation agent. This agent provides relevant legal advice by retrieving applicable articles from the Civil Code and Criminal Code, while also considering the case background. The case utilizes the Qianwen large model and DashScope's embedding and reranking functionalities. Before using it, you need to configure the  `DASHSCOPE_API_KEY` in your environment variables.
+The knowledge file is defined as follows:
+```yaml
+name: "law_knowledge"
+description: "中国民法与刑法相关的知识库"
+stores:
+    - "civil_law_chroma_store"
+    - "criminal_law_chroma_store"
+    - "civil_law_sqlite_store"
+    - "criminal_law_sqlite_store"
+insert_processors:
+    - "recursive_character_text_splitter"
+post_processors:
+    - "dashscope_reranker"
+readers:
+    pdf: "default_pdf_reader"
+
+metadata:
+  type: 'KNOWLEDGE'
+  module: 'sample_standard_app.app.core.knowledge.law_knowledge'
+  class: 'LawKnowledge'
+```
+
+## Building the Knowledge Index
+
+Origin docs：
+- [民法典.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
+- [刑法.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
+
+### Extracting Text from PDFs
+Since the original documents in this case are in PDF format, we configured the Knowledge component as follows:
+```yaml
+readers:
+    pdf: "default_pdf_reader"
+```
+This allows the extraction of text from the PDF. If you want to read more file formats, you can refer to the [Reader component](../In-Depth_Guides/Tutorials/Knowledge/Reader.md).
+
+### Splitting Long Text
+Since the text content in the original documents is very long, we need to split it into smaller chunks. Here, we use the `recursive_character_text_splitter` ffor splitting, configured as follows:
+```yaml
+insert_processors:
+    - "recursive_character_text_splitter"
+```
+This configuration is in list format, allowing multiple document processors to be specified. In this case, the only processor specified is recursive_character_text_splitter, which recursively splits the original document according to a specified delimiter until the required length is met. For more details, refer to the  [DocProcessor](../In-Depth_Guides/Tutorials/Knowledge/DocProcessor.md)documentation. Additionally, this framework (agentUniverse) includes other document processors that you can use or customize.
+
+### Configuring the Store
+This case includes four Stores: the Civil Law and Criminal Law are stored separately in SQLite and ChromaDB. We will use the `civil_law_chroma_store` as an example, with the other Stores being similar.
+```yaml
+name: 'civil_law_chroma_store'
+description: '保存了中国民法典的所有内容，以文本向量形式存储'
+persist_path: '../../DB/civil_law.db'
+embedding_model: 'dashscope_embedding'
+similarity_top_k: 100
+metadata:
+  type: 'STORE'
+  module: 'agentuniverse.agent.action.knowledge.store.chroma_store'
+  class: 'ChromaStore'
+```
+
+The `persist_path` specifies the local storage location of the database file and designates `dashscope_embedding` as the component responsible for vectorizing the text within the database. The `similarity_top_k` parameter indicates the number of documents to be retrieved. For more details on Stores, refer to [this document](../In-Depth_Guides/Tutorials/Knowledge/Store.md) or the agentuniverse framework documentation..
+
+### Executing the Insertion Process
+
+After completing the above configurations, you can execute the following code to build the knowledge base:
+```python
+from agentuniverse.base.agentuniverse import AgentUniverse
+from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
+
+
+if __name__ == '__main__':
+    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
+    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
+    law_knowledge.insert_knowledge(
+        source_path="../resources/刑法.pdf",
+        stores=["civil_law_sqlite_store"]
+    )
+```
+We specify the data to be inserted using the `source_path` parameter in the `insert_knowledge` method, and we designate different Stores for different documents by using the  `stores` parameter. The `stores` parameter is optional; if it is not specified, the data will be inserted into all stores by default.
+
+## Building the Retrieval Process
+
+### Re-ranking
+We have configured the post-processing flow for the knowledge within the agentuniverse as follows:
+```yaml
+post_processors:
+    - "dashscope_reranker"
+```
+This configuration indicates that we will utilize Dashscope's re-ranking service to reorder the documents based on relevance. Additionally, you have the option to incorporate other post-processing steps here as needed.
+
+## Using RAG in the Agent
+
+```yaml
+info:
+  name: 'law_rag_agent'
+  description: '一个法律顾问，可以根据给出的事件，以及提供的背景知识做出客观的司法判断'
+action:
+  knowledge:
+    - 'law_knowledge'
+```
+The specific configuration for the knowledge, as outlined, is defined in the YAML file of the Agent within the agentunivers.
+For the complete case and how to invoke it, please refer to  [this document](../Examples/Legal_Advice.md).
\ No newline at end of file
diff --git a/docs/guidebook/en/How-to/Product_Platform_Advancement_Guide.md b/docs/guidebook/en/How-to/Product_Platform_Advancement_Guide.md
new file mode 100644
index 00000000..5bc1d012
--- /dev/null
+++ b/docs/guidebook/en/How-to/Product_Platform_Advancement_Guide.md
@@ -0,0 +1,93 @@
+# Product Platform Advancement Guide
+In this section, we will guide you on how to:
+1. Utilize the Workflow Agent Orchestration Feature
+2. Create a Table Form Agent Using the Feature
+3. Insert Custom Knowledge Utilizing the Feature
+4. Integrate Custom Plugins with the Feature
+
+
+## Workflow Agent Orchestration Feature
+``` text
+Note: For old users of the agentUniverse productization platform orchestration capability, please upgrade the magent-ui package version to 0.1.17, and the corresponding aU version must not be lower than 0.0.13. You can use the following command for version-specific installation:
+
+pip install magent-ui==0.1.17 ruamel.yaml --force-reinstall
+```
+### Create Workflow Agent
+On the aU-product homepage, click the "Create Agent" button to the right of the agent tab, and select the workflow type agent.
+![agentuniverse_product_create_workflow_agent](../../_picture/create_workflow_agent.png)
+
+### Workflow Node Type
+The current agentUniverse product platform supports seven types of nodes, which are:
+● Start Node: define the initial parameters for initiating a workflow process.
+● Tool Node: provide third-party plugin capabilities to enhance the workflow.
+● Knowledge Node: retrieve text content related to the user's query from the knowledge base, serving as context for downstream LLM nodes.
+● LLM Node: invoke large language models to answer questions or process natural language inputs.
+● Agent Node: invokes and executes existing agent applications within the workflow.
+● If-else Node: split the workflow into multiple branches based on if/else conditional judgments.
+● End Node: define the final output content upon completion of a workflow process.
+
+
+### Workflow Agent Orchestration
+On the canvas, connect various types of workflow nodes to construct a complete workflow process.
+Click the Save button, and agentUniverse will automatically generate the corresponding workflow and agent YAML files locally, aiding users in completing their development tasks.
+
+``` text
+Note: Please ensure that the logic for passing parameters between nodes is correct; otherwise, it will cause the workflow to fail.
+```
+
+The complete workflow process is illustrated in the following figure:
+![agentuniverse_product_workflow_agent](../../_picture/workflow_agent.png)
+
+The agent configuration file generated by agentUniverse is depicted in the following figure:
+![agentuniverse_product_workflow_agent_yaml](../../_picture/workflow_agent_yaml.png)
+
+### Run Workflow Agent
+After clicking the save button, start attempting to run the workflow agent.
+The operation process is as shown in the figure:
+![agentuniverse_product_run_workflow_agent](../../_picture/run_workflow_agent.png)
+
+
+## Table Form Agent Creation Feature
+### Create Table Form Agent
+On the product homepage, click the 'Create Agent' button to the right of the agent tab, and then select either the RAG or ReAct type agent.
+
+agentUniverse will automatically generate the corresponding agent YAML file locally, thereby assisting users in completing their development tasks.
+
+![agentuniverse_product_create_agent](../../_picture/create_agent.png)
+
+### Run Agent
+After clicking the save button, proceed to attempt running the created example RAG agent.
+The operational process is illustrated in the figure below:
+![agentuniverse_product_run_demo_rag_agent](../../_picture/run_demo_rag_agent.png)
+
+## Custom Knowledge Insertion Feature
+### Create Custom Knowledge
+On the product homepage, naviagte to the Knowledge tab, and click the 'Add Knowledge' button located to the right to create a custom knowledge base.
+
+agentUniverse will automatically generate the corresponding knowledge and storage YAML files locally, thereby assisting users in completing their development tasks.
+
+![agentuniverse_product_create_knowledge](../../_picture/create_knowledge.png)
+
+### Upload Knowledge
+Specify the knowledge base, then click the upload button to upload the local knowledge file.
+Once the upload is successful, you can utilize the corresponding knowledge base within the knowledge node of the workflow agent or the knowledge module of the table form agent to retrieve content pertinent to user inquiries.
+
+```text
+Currently supported knowledge types are: pdf/docx/pptx/txt
+```
+The upload operation process is illustrated in the figure below:
+![agentuniverse_product_upload_knowledge](../../_picture/upload_knowledge.png)
+
+## Custom Plugin Insertion Feature
+### Create Custom Plugin
+On the product homepage, navigate to the Plugin tab, and click the 'Create Plugin' button located to the right to create a custom plugin.
+![agentuniverse_product_create_plugin](../../_picture/create_plugin.png)
+
+Once created successfully, you can utilize the corresponding tools within the tool nodes of the workflow agent or the tool modules of the table form agent. These tools provide powerful third-party plugin capabilities to enhance the agent's functionality.
+
+Plugin schema example reference document: [schema](https://openai.xiniushu.com/docs/plugins/examples)
+
+## More
+The agentUniverse product platform is continuously iterating, and in the future, more convenient and useful features will be available for community users to experience. We also welcome your valuable suggestions and feedback.
+This feature is jointly launched by [difizen](https://github.com/difizen/magent) and agentUniverse.
+Let’s explore and progress together!
\ No newline at end of file
diff --git a/docs/guidebook/en/How-to/Product_Platform_Quick_Start.md b/docs/guidebook/en/How-to/Product_Platform_Quick_Start.md
new file mode 100644
index 00000000..2198e341
--- /dev/null
+++ b/docs/guidebook/en/How-to/Product_Platform_Quick_Start.md
@@ -0,0 +1,87 @@
+# Product Platform Quick Start
+In this section, we will show you how to:
+
+● Initiate the productized service of agentUniverse
+● Configure the modules within the agentUniverse framework
+● Quickly begin utilizing the productized features
+● Debug and test agents online, optimizing agent performance
+
+## Environment and Application Engineering Preparation
+### Application Engineering Preparation
+We have placed the sample of product module within the sample_standard_app project of agentUniverse. You can access and view them [here](../../../../sample_standard_app/platform/difizen/product). These modules can be configured in the background using YAML file, and additionally, the functions can be automatically created and managed via the product page.
+
+
+### Installing Dependencies
+**Using pip**
+```shell
+pip install magent-ui ruamel.yaml
+```
+
+### Configuration File
+If you have previously used agentUniverse’s sample project, please add the following information to the `config.toml`  file  to include the product module path as configured in sample_standard_app:
+```toml
+# Ignore the context content.
+[CORE_PACKAGE]
+# Scan and register product components for all paths under this list, with priority over the default.
+product = ['sample_standard_app.app.core.product']
+# Ignore the context content.
+```
+If you are using agentUniverse for the first time, you can directly adopt the latest sample project’s `config.toml` file.
+
+#### Private Configuration File
+Of course, when utilizing the agent, you need to preconfigure the various LLM model keys/Tool keys; otherwise, the agent's overall process will fail to connect. Currently, this configuration is not available in the product page's configuration management. Please continue to use agentUniverse’s original key configuration method. In future versions, we will directly provide key management capabilities for each model within the product.
+
+
+## Using the agentUniverse Product Platform
+### Starting the Product Service
+To start the product service with a single click, run the [product_application](../../../../sample_standard_app/boostrap/platform/product_application.py) file located in `sample_standard_app/app/bootstrap` .
+![img.png](../../_picture/product_start.png)
+
+Upon successful initiation, it will automatically redirect you to the product homepage, which features system presets as well as your customized Agent, Tool and Knowledge product modules.
+![agentuniverse_product_homepage](../../_picture/agentuniverse_product_homepage.png)
+
+### Experience the Agent
+As shown in the image above, click the chat button positioned on the right side of the peer multi-agent group to access the conversation page.
+The conversation management system incorporates the last 10 conversation history records from agents, enabling you to engage in multi-turn dialogues seamlessly and experience the capabilities of the peer multi-agent group (by default, it is set to streaming dialogue, and the multi-agent group showcases the intermediate thinking process).
+![agentuniverse_product_agent_chat](../../_picture/agentuniverse_product_agent_chat.png)
+
+### Debugging the Agent
+On the product homepage, click the edit button on the left of the agent to access the online debugging page.
+You have the ability to debug the agent's Prompt, Tool, Knowledge, and LLM online. Simply click the save button, and the aU-product (AgentUniverse framework) will automatically save the configuration to the corresponding YAML file.
+![agentuniverse_product_agent_editor](../../_picture/agentuniverse_product_agent_editor.png)
+
+To view the Trace information, including token consumption, call chain, and latency of the agent's specific invocation process, click the debug button located in the upper right corner of the image provided above.
+![agentuniverse_product_agent_trace](../../_picture/agentuniverse_product_agent_trace.png)
+
+## Configuring agentUniverse Product Modules
+### Creating Product Modules
+```yaml
+id: demo_rag_agent
+nickname: rag智能体
+type: AGENT
+opening_speech: |
+  欢迎使用rag chatbot！根据您的提问，我将结合实时信息及我个人掌握的知识，给您提供合理的解答。
+  你可以这样问我：
+  问题1: 巴黎奥运会中国获得了几块奖牌
+  问题2: 巴菲特为什么减持苹果股票
+avatar: ../../../resources/rag_agent_logo.png
+metadata:
+  class: AgentProduct
+  module: agentuniverse_product.base.agent_product
+  type: PRODUCT
+```
+
+As shown in the yaml file above:
+
+- `product id` corresponds to `name` in the agent yaml
+- `type` corresponds to `AGENT`
+- `metadata` is configured as the value shown above
+- Other parameters can be configured as needed (the same applies to tool/knowledge modules)
+
+### Configuring Global Config
+In the `config.toml`file for global settings, configure the package scan path that corresponds to the product module. With a single click, you can launch the product platform and view the corresponding agent, tool, and knowledge information.
+
+## More
+The AgentUniverse product platform is undergoing continuous iteration, and in the future, even more convenient and useful features will be available for community users to experience. We warmly welcome your valuable suggestions and feedback.
+This feature has been jointly launched by [difizen](https://github.com/difizen/magent) and agentUniverse.
+Let's explore and make progress together!
\ No newline at end of file
diff --git a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/0.List_Of_LLMs.md
similarity index 55%
rename from docs/guidebook/en/3_1_2_0_List_Of_LLMs.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/0.List_Of_LLMs.md
index c716e927..0d6d0c05 100644
--- a/docs/guidebook/en/3_1_2_0_List_Of_LLMs.md
+++ b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/0.List_Of_LLMs.md
@@ -1,20 +1,20 @@
 # List of LLMs
 The platform has currently integrated the following list of models; please refer to this section for detailed usage instructions for each LLM.
 
-| LLM (Series) Name                     |
-|---------------------------------------|
-| [OpenAI](3_1_2_OpenAI_LLM_Use.md)     |
-| [Qwen](3_1_2_Qwen_LLM_Use.md)         |
-| [WenXin](3_1_2_WenXin_LLM_Use.md)     |
-| [Kimi](3_1_2_Kimi_LLM_Use.md)         |
-| [BaiChuan](3_1_2_BaiChuan_LLM_Use.md) |
-| [Claude](3_1_2_Claude_LLM_Use.md)     |
-| [ollama](3_1_2_Ollama_LLM_Use.md)     |
-| [DeepSeek](3_1_2_DeepSeek_LLM_Use.md) |
-| [GLM](3_1_2_GLM_LLM_Use.md)      |
+| LLM (Series) Name         |
+|---------------------------|
+| [OpenAI](OpenAI_LLM_Use.md) |
+| [Qwen](Qwen_LLM_Use.md)   |
+| [WenXin](WenXin_LLM_Use.md) |
+| [Kimi](Kimi_LLM_Use.md)   |
+| [BaiChuan](BaiChuan_LLM_Use.md) |
+| [Claude](Claude_LLM_Use.md) |
+| [ollama](Ollama_LLM_Use.md) |
+| [DeepSeek](DeepSeek_LLM_Use.md) |
+| [GLM](GLM_LLM_Use.md)     |
 
-To facilitate the integration of models with OpenAI-style protocols, we offer a wrapper object based on the OpenAI general protocol. See [OpenAIStyleLLM](3_1_2_OpenAIStyleLLM_Use.md).
+To facilitate the integration of models with OpenAI-style protocols, we offer a wrapper object based on the OpenAI general protocol. See [OpenAIStyleLLM](OpenAIStyleLLM_Use.md).
 
-For LLM not included in the list, you can customize the integration of any LLM according to the steps provided in the [LLM definition](2_2_3_Tool_Create_And_Use.md).
+For LLM not included in the list, you can customize the integration of any LLM according to the steps provided in the [LLM definition](../../Tutorials/LLM/LLM_component_define_and_usage.md).
 
 At the same time, we are adding the integration methods for common LLMs such as Gemini, Llama, ZhipuAI, etc. If you have used these LLMs in your projects and have created your own instances, we welcome you to submit a PR to become a contributor to agentUniverse.
\ No newline at end of file
diff --git a/docs/guidebook/en/3_1_2_BaiChuan_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/BaiChuan_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_BaiChuan_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/BaiChuan_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_Claude_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/Claude_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_Claude_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/Claude_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/DeepSeek_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_DeepSeek_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/DeepSeek_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_GLM_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/GLM_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_GLM_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/GLM_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_Kimi_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/Kimi_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_Kimi_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/Kimi_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_Ollama_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/Ollama_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_Ollama_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/Ollama_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/OpenAIStyleLLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_OpenAIStyleLLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/OpenAIStyleLLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_OpenAI_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/OpenAI_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_OpenAI_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/OpenAI_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_Qwen_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/Qwen_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_Qwen_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/Qwen_LLM_Use.md
diff --git a/docs/guidebook/en/3_1_2_WenXin_LLM_Use.md b/docs/guidebook/en/In-Depth_Guides/Components/LLMs/WenXin_LLM_Use.md
similarity index 100%
rename from docs/guidebook/en/3_1_2_WenXin_LLM_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Components/LLMs/WenXin_LLM_Use.md
diff --git a/docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md b/docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_LangChain_Tools.md
similarity index 93%
rename from docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md
rename to docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_LangChain_Tools.md
index b1bfe45b..6df6ff8d 100644
--- a/docs/guidebook/en/2_2_3_Integrated_LangChain_Tools.md
+++ b/docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_LangChain_Tools.md
@@ -9,7 +9,7 @@ For the second category of tools, we have implemented a LangChainTool base class
 Note: If you want to directly use the description from LangChain, the description in the configuration file must be set to empty.
 
 An Example of Tool Initialization:
-[Tool Address](../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/human_input_run.yaml)
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/human_input_run.yaml)
 ```yaml
 name: 'human_input_run'
 description: ''
@@ -38,7 +38,7 @@ Parameter Description:
     If you completely override the `init_langchain_tool` method, then you do not need to configure this part.
 
 ## 1. Integrate the DuckDuckGo Tool from LangChain
-[Tool Address](../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/duckduckgo_search.yaml)
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/duckduckgo_search.yaml)
 ```yaml
 name: 'duckduckgo_search'
 description: 'DuckDuckGo Search tool'
diff --git a/docs/guidebook/en/2_2_3_Integrated_Tools.md b/docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_Tools.md
similarity index 81%
rename from docs/guidebook/en/2_2_3_Integrated_Tools.md
rename to docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_Tools.md
index 97405d5e..5be14ad4 100644
--- a/docs/guidebook/en/2_2_3_Integrated_Tools.md
+++ b/docs/guidebook/en/In-Depth_Guides/Components/Tools/Integrated_Tools.md
@@ -1,11 +1,11 @@
 # Integrated Tools
 
-In the current au's sample project, the following tools are integrated.
+In the current agentUniverse's sample project, the following tools are integrated.
 
 ## 1. Search Tools
 
 ### 1.1 Google Search
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)  
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)  
 Detailed Configuration Information:
 
 ```yaml
@@ -25,11 +25,12 @@ metadata:
 To use this API, you must apply for a BING_SUBSCRIPTION_KEY at https://serper.dev and configure it in your environment variables. 
 Configuration method:
 1. Configure via Python code You must configure: SERPER_API_KEY
+
 ```python
 import os
 os.environ['SERPER_API_KEY'] = 'xxxx'
 ```
-2. Configure via configuration file In the custom_key.toml file located in the config directory of the project, add the configuration:  
+2. Configure via configuration file In the custom_key.toml file located in the config directory of the project, add the configuration:
 ```toml
 SERPER_API_KEY="xxxx"
 ```
@@ -37,7 +38,7 @@ SERPER_API_KEY="xxxx"
 
 ### 1.2 Bing Search 
 Currently, it integrates with the official Bing search.
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/bing_search_tool.yaml)  
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/bing_search_tool.yaml)  
 Tool configuration:
 ```yaml
 name: 'bing_search_tool'
@@ -67,8 +68,8 @@ BING_SUBSCRIPTION_KEY="xxxx"
 
 ### 1.3 Search API
 Supports multiple search tools, such as: 
-- [Baidu search](../../../sample_standard_app/intelligence/agentic/tool/search_api_baidu_tool.yaml)
-- [Bing search](../../../sample_standard_app/intelligence/agentic/tool/search_api_bing_tool.yaml)  
+- [Baidu search](../../../../../../sample_standard_app/intelligence/agentic/tool/search_api_baidu_tool.yaml)
+- [Bing search](../../../../../../sample_standard_app/intelligence/agentic/tool/search_api_bing_tool.yaml)  
 Other search engines also include: Google search, Amazon search, YouTube search, etc. For more information, please refer to: https://www.searchapi.io/
 Tool configuration:
 ```yaml
@@ -87,7 +88,7 @@ metadata:
 ```
 Parameter description:
 
-search_type: Represents the format of the expected search results, where json represents the expectation for JSON format and common represents the expectation for string string format.
+search_type: Represents the format of the expected search results, where json represents the expectation for JSON format and common represents the expectation for string format.
 search_params: Represents additional parameters that need to be passed to the search engine, such as in Baidu search, num represents the number of returned search results, detailed parameters need to be referenced at [https://www.searchapi.io/].
 engine: The search engine you expect to use, including baidu, google, bing, amazon, youtube, ... To use this API, you must apply for SEARCH_API_KEY from the official website ([https://www.searchapi.io/]) and configure it in environment variables.
 Configuration method:
@@ -107,7 +108,7 @@ SEARCHAPI_API_KEY="xxxxxx"
 ## 2. Code Tool
 
 ### 2.1 PythonRepl
-[Tool address](../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)  
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)  
 This tool can execute a piece of Python code, the configuration information of the tool:  
 ```yaml
 name: 'python_runner'
@@ -137,7 +138,7 @@ This tool can be used directly without any key, but for system security, please
 ## 3.HTTP Tool
 
 ### 3.1 HTTP GET
-[Tool address](../../../sample_standard_app/intelligence/agentic/tool/request_get_tool.yaml)
+[Tool Path](../../../../../../sample_standard_app/intelligence/agentic/tool/request_get_tool.yaml)
 The tool can send a GET request, with its configuration information being:
 ```yaml
 name: 'requests_get'
@@ -181,9 +182,9 @@ metadata:
   class: 'RequestTool'
 ```
 Parameter Description:
-    method: The method of the request, such as GET, POST, PUT, etc.
-    headers: The HTTP headers required for sending the request.
-    json_parse: Specifies whether the input parameters need to be parsed by HTTP. It should be set to True for POST requests and False for GET requests.
-    response_content_type: The parsing method for the HTTP request result. If set to 'json', the result will be returned in JSON format; if set to 'text', the result will be returned as text.
-This tool can be used directly without any keys required.
+    method: the method of the request, such as GET, POST, PUT, etc.
+    headers: the HTTP headers neccessary for sending the request.
+    json_parse: indicates whether the input parameters should be serialized as JSON and sent in the request body (True for POST requests) or not (False for GET requests, where parameters are typically sent as a query string).
+    response_content_type: the output format for the HTTP request result. If set to 'json', the result will be returned in JSON format; if set to 'text', it will be returned as plain text.
+This tool can be used directly without  requiring any keys.
 
diff --git a/docs/guidebook/en/5_1_1_Docker_Container_Deployment.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/Docker_Container_Deployment.md
similarity index 62%
rename from docs/guidebook/en/5_1_1_Docker_Container_Deployment.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/Docker_Container_Deployment.md
index ec2aec02..530310e8 100644
--- a/docs/guidebook/en/5_1_1_Docker_Container_Deployment.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/Docker_Container_Deployment.md
@@ -1,35 +1,35 @@
 # Docker container deployment
 
-AgentUniverse provides standard work environment images for containerized deployment of AgentUniverse projects. This document will explain how to deploy your own project based on the such images. You can get full tag list in [this site](https://cr.console.aliyun.com/repository/cn-hangzhou/agent_universe/agent_universe/images).
+agentUniverse provides standard work environment images for the containerized deployment of agentUniverse projects. This document will explain how to deploy your own project using such images. You can find the full tag list on  [this site](https://cr.console.aliyun.com/repository/cn-hangzhou/agent_universe/agent_universe/images).
 
 ## Preparations
-1.  Build your own project according to the standard directory structure of AgentUniverse, referring to [Application_Engineering_Structure_Explanation](1_4_Application_Engineering_Structure_Explanation.md)。For ease of explanation, this document assumes the project name and project directory are `sample_standard_app`.
-2.   Get the required version of the AgentUniverse image.
+1.  Build your own project according to the standard directory structure of agentUniverse, referring to the [Application_Engineering_Structure_Explanation](../../../Get_Start/Application_Project_Structure_and_Explanation.md). For ease of explanation, this document assumes the project name and project directory are `sample_standard_app`.
+2.   Obtain the required version of the AagentUniverse image.
 ```shell
 docker pull registry.cn-hangzhou.aliyuncs.com/agent_universe/agent_universe:0.0.9_centos8
 ```
 
 
 ## Method 1: Mount the host path
-You can mount your project to a path inside the container by mounting the host directory, with the reference command as follows:
+You can mount your project to a path inside the container by mounting the host directory. The reference command is as follows:
 ```shell
 docker run -d -p 8888:8888 -v ./sample_standard_app/:/usr/local/etc/workspace/project/sample_standard_app registry.cn-hangzhou.aliyuncs.com/agent_universe/agent_universe:0.0.9_centos8
 ```
-The`-p 8888:8888`represents the port mapping for the Web Server. The first 8888 indicates that the web server inside the container is started on port 8888, and the latter indicates that it is mapped to port 8888 on the host machine. Adjust it according to the actual startup conditions of the application as needed.  
+The`-p 8888:8888`represents the port mapping for the Web Server. The first 8888 indicates that the web server inside the container is started on port 8888, and the latter indicates that it is mapped to port 8888 on the host machine. Adjust it according to the actual startup conditions of your application as needed.  
 `-v {local_dir}:/usr/local/etc/workspace/project/{local_dir_name}`indicates that the `local_dir` directory on the host is mounted to the `/usr/local/etc/workspace/project`within the container. The directory path inside the container is a fixed value and cannot be modified. `local_dir_name` stands for the last pattern of the `local_dir`.
 
 ### Precautions:：
-If you need multiple containers to mount the same directory, it's advised to pay attention to the following points when using this method:：
+If you need multiple containers to mount the same directory, consider the following points when using this method:：
 1. Specify a SQLite database file address outside the mounted path `/usr/local/etc/workspace/project`in the config.toml, such as inside `/usr/local/etc` in the container, for example,
     ```toml
     system_db_uri = 'sqlite:////usr/local/etc/agent_universe.db'
     ```
-    This can reduce the problems with SQLite concurrency (as SQLite does not handle concurrency very well). Alternatively, you can directly use MySQL, which will offer better concurrency performance.
-2. Specify a directory outside the mounted path in the log_config.toml, like
+    This can reduce issue with SQLite concurrency (as SQLite does not handle concurrency very well). Alternatively, you can directly use MySQL, which offers better concurrency performance.
+2. Specify a directory outside the mounted path in the log_config.toml, like:
     ```toml
     log_path = "/usr/local/etc/au_log"
     ```
-    To prevent all containers from logging in the same file, which makes it difficult to read.
+    To prevent all containers from logging to the same file, which makes it difficult to read.
 
 ## Method 2: Pull the project from Github
 The image already has the git command installed. You can modify the image's entrypoint to git clone your project and then copy the entire project to a specified path. For example:
@@ -38,8 +38,8 @@ docker run -d -p 8888:8888 --entrypoint=/bin/bash registry.cn-hangzhou.aliyuncs.
 ````
 Where `repo_addr` is the address of your git project，and `project_dir` is the project directory, for example，if `sample_standard_app` is in the `project`directory within your git project, then`project_dir` would be `project/sample_standard_app`。
 ## Result Verification
-Taking the example of port 8888, you can verify whether the service has started correctly using the curl command:
+Using port 8888 as an example, you can verify whether the service has started correctly using the curl command:
 ```shell
 curl http://127.0.0.1:8888/echo
 ```
-Try to access the web service, and if it returns the word "Welcome" it means the service has started successfully.
+Try to access the web service. If it returns the word "Welcome", it means the service has started successfully.
diff --git a/docs/guidebook/en/5_1_2_K8S_Deployment.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/K8S_Deployment.md
similarity index 65%
rename from docs/guidebook/en/5_1_2_K8S_Deployment.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/K8S_Deployment.md
index 0606bd67..bb1d9187 100644
--- a/docs/guidebook/en/5_1_2_K8S_Deployment.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Deployment/K8S_Deployment.md
@@ -1,9 +1,9 @@
 # High Availability Deployment with K8S
-AgentUniverse provides standard working environment images, designed to support containerized deployments on Kubernetes (K8S) clusters. This guide will show you how to utilize these working environment images to deploy and set up a cluster on K8S. You can get full tag list in [this site](https://cr.console.aliyun.com/repository/cn-hangzhou/agent_universe/agent_universe/images).
+agentUniverse provides standard working environment images specifically designed to support containerized deployments on Kubernetes (K8S) clusters. This guide will demonstrate how to utilize these images to deploy and set up a cluster within a K8S  environment. You can get the full list of tags on [this site](https://cr.console.aliyun.com/repository/cn-hangzhou/agent_universe/agent_universe/images).
 Official K8S Documentation: [Kubernetes Setup Documentation](https://kubernetes.io/docs/setup/)
 
 ## 1. Resource Configuration
-First, you need to configure the necessary resource files. Below is an example used to define the required Namespace, Deployment, and Service resources using a YAML configuration file:
+Firstly, you need to configure the necessary resource files. Below is an example YAML configuration file used to define the required Namespace, Deployment, and Service resources:
 
 ```yaml
 apiVersion: v1
@@ -34,7 +34,7 @@ spec:
         ports:
         - containerPort: 8888
         command: ["/bin/bash", "-c"]
-        args: ["git clone git@github.com:alipay/agentUniverse.git; mv agentUniverse/sample_standard_app /usr/local/etc/workspace/project; /bin/bash --login /usr/local/etc/workspace/shell/start.sh"]
+        args: ["git clone git@github.com:antgroup/agentUniverse.git; mv agentUniverse/sample_standard_app /usr/local/etc/workspace/project; /bin/bash --login /usr/local/etc/workspace/shell/start.sh"]
         # Uncomment and replace "XXX" with your key to configure the agent
         # env:
         # - name: OPENAI_API_KEY
@@ -58,11 +58,11 @@ spec:
 
 #### Method 1 (Recommended)
 
-In the resource configuration file, uncomment the `env` section and replace `value` with your key. For additional security considerations, it's recommended to use K8S officially recommended methods, such as ConfigMap. See the [ConfigMap Configuration Documentation](https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/).
+In the resource configuration file, uncomment the `env` section and replace `value` with your key. For additional security considerations, it's recommended to use officially recommended K8S methods, such as ConfigMap. Pliase refer to the [ConfigMap Configuration Documentation](https://kubernetes.io/docs/tasks/configure-pod-container/configure-pod-configmap/) for more details.
 
 #### Method 2
 
-Please refer to the description at the beginning of the configuration file: [Quick Start Guide](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/zh/1_3_快速开始.md)
+Please refer to the description at the beginning of the configuration file: [Quick Start Guide](../../../Get_Start/Quick_Start.md)
 
 ## 2. Building Resources
 
@@ -80,7 +80,7 @@ Verify that all resources have been correctly deployed:
 kubectl get all -n agent-namespace
 ```
 
-![Resource Deployment Status](../_picture/k8s_resource.png)
+![Resource Deployment Status](../../../../_picture/k8s_resource.png)
 
 ## 4. Accessing AgentUniverse Services from Inside the Cluster
 
@@ -98,7 +98,7 @@ kubectl exec -it [Pod Name] -n agent-namespace -- curl http://agentuniverse-serv
 kubectl exec -it agentuniverse-deployment-55cfd778d-g7d9d -n agent-namespace -- curl http://agentuniverse-service:9999/echo
 ```
 
-![Connectivity Test](../_picture/k8s_hello.png)
+![Connectivity Test](../../../../_picture/k8s_hello.png)
 
 #### 4.1.2 Q&A Test
 
@@ -106,4 +106,4 @@ kubectl exec -it agentuniverse-deployment-55cfd778d-g7d9d -n agent-namespace --
 kubectl exec -it agentuniverse-deployment-55cfd778d-g7d9d -n agent-namespace -- curl -X POST -H "Content-Type: application/json" -d '{"service_id":"demo_service","params":{"input":"(18+3-5)/2*4=?"}}' http://agentuniverse-service:9999/service_run
 ```
 
-![Q&A Test](../_picture/k8s_question.png)
+![Q&A Test](../../../../_picture/k8s_question.png)
diff --git a/docs/guidebook/en/3_2_4_Alibaba_Cloud_SLS.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Alibaba_Cloud_SLS.md
similarity index 82%
rename from docs/guidebook/en/3_2_4_Alibaba_Cloud_SLS.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Alibaba_Cloud_SLS.md
index a370019f..bcc319d9 100644
--- a/docs/guidebook/en/3_2_4_Alibaba_Cloud_SLS.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Alibaba_Cloud_SLS.md
@@ -32,7 +32,7 @@ sls_log_send_interval = 3.0
 - **`sls_log_store`**: The log store in Aliyun sls.
 - **`access_key_id`**: The access key id for Aliyun sls.
 - **`access_key_secret`**: The access key secret for Aliyun sls.
-- **`sls_log_queue_max_size`**: The AgentUniverse adopts a mechanism of timing batch uploading of logs. This parameter signifies the maximum number of log entries that can be cached during each upload interval.
+- **`sls_log_queue_max_size`**: The AgentUniverse employs a mechanism for timed batch uploading of logs, where this parameter signifies the maximum number of log entries that can be cached during each upload interval.
 - **`sls_log_send_interval`**: The interval for uploading logs to sls, in seconds.
 
- After configuration, you can directly use the global log component or your custom log components to record logs. AgentUniverse will automatically upload the recorded content to your sls.
\ No newline at end of file
+ After configuration, you can directly use the global log component or your custom log components to record logs. AgentUniverse will automatically upload the recorded logs to your specified sls project and log store.
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Logging_Utils.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Logging_Utils.md
new file mode 100644
index 00000000..cf23c393
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Logging_Utils.md
@@ -0,0 +1,74 @@
+# Logging Utils
+
+The log component of agentUniverse is implemented based on loguru, offering a well-packaged global log component and customizable log components. This tutorial will first introduce the usage of the log configuration file within agentUniverse, then sequentially explain the utilization of the global log component and customizable log components, and finally describe how to intergrate with external log service components.
+    
+## Log Configuration
+
+### Configuration File Path
+The configuration path of the log file is specified in the main configuration of AgentUniverse, config.toml.
+```toml
+[SUB_CONFIG_PATH]
+# Log config file path, an absolute path or a relative path based on the dir where the current config file is located.
+log_config_path = './log_config.toml'
+```
+  If this parameter is a relative path, it refers to the directory that contains the main configuration file, config.toml. If this parameter does not exist, agentUniverse will use the default log configuration, see [default configuration](#default-configuration).
+
+### Configuration File Options
+The configuration file for logs is in the toml file format. An example configuration is as follows:
+```toml
+[LOG_CONFIG]
+[LOG_CONFIG.BASIC_CONFIG]
+log_level = "INFO"
+log_path = "~/.agent_universe_log"
+log_rotation = "100 MB"
+log_retention = "7 days"
+```
+- **`log_level`**: The default log level, possible values include "TRACE", "DEBUG", "INFO", "SUCCESS", "WARNING", "ERROR", "CRITICAL". The global log component's level will default to this value. When creating a custom log component and a log level is not specified, this default level will be utilized.
+- **`log_path`**: The log storage path, if this value is empty, a {workdir}/../../logs folder will be created as the log storage path.
+- **`log_rotation`**: This parameter accepts a duration (e.g., "1 week") or file size (e.g., "100 MB"), indicating the condition for log file rotation.
+- **`log_retention`**: This parameter accepts a duration (e.g., "7 days") specifying the retention period for old log files, which will be deleted after they expire.
+
+### Default Configuration
+Configuration setting not specified in the log configuration file will default to the following values:
+- **`log_level`**: "INFO"
+- **`log_rotation`**: "10MB"
+- **`log_retention`**: "3 days"
+
+Two default log files, `au_all.log` and `au_error.log`, will be located in the log storage path. The au_all.logfile records all logs above the default log level specified in the log configuration file, while the au_error.log file records only logs at the ERROR level and above. The format for log component is:
+```python
+log_format: str = ("<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> "
+                       "| <level>{level: <8}</level> "
+                       "| {extra[context_prefix]} "
+                       "| <cyan>{name}</cyan>"
+                       ":<cyan>{function}</cyan>"
+                       ":<cyan>{line}</cyan> "
+                       "| <level>{message}</level>")
+```
+Where `{extra[context_prefix]}` defaults to `default`, and if `LOG_CONTEXT` exists in the framework context, its contents will be used to replaced the default contents.  For more information related to the framework context, please refer to  the  [Framework_Context](../Others/Framework_Context.md) documentation.
+
+## Global Log Component
+agentUniverse provides a directly usable log component  `logging_util.Logger`, which you can integrate into your project as follows:
+```python
+from agentuniverse.base.util.logging.logging_util import LOGGER
+```
+You can utilize this object's various member functions to record logs of different severity levels:
+```python
+from agentuniverse.base.util.logging.logging_util import LOGGER
+
+LOGGER.info("This is an info log.")
+LOGGER.debug("This is a debug log.")
+LOGGER.warn("This is a warn log.")
+LOGGER.error("This is an error log.")
+```
+
+## Custom Log Components
+If you wish to output and save specific logs separately, you can create a customized log component by utilizing the `get_module_logger` function:
+```python
+from agentuniverse.base.util.logging.logging_util import get_module_logger
+new_logger = get_module_logger("new_module")
+```
+Logs recorded by this newly created component will be stored in the designated log storage path, within a log file named `au_{module_name}.log`. Here, `{module_name}` represents the module name you provided to the `get_module_logger`function. For instance,  in the example provided above, the corresponding log file would be named `au_new_module.log`.
+
+## External Log Service
+
+If you want to use more log utils, please refer to [extension logging utils](Alibaba_Cloud_SLS.md).
diff --git a/docs/guidebook/en/2_5_1_Monitor_Module.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Monitor_Module.md
similarity index 73%
rename from docs/guidebook/en/2_5_1_Monitor_Module.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Monitor_Module.md
index e77c88ba..fae43344 100644
--- a/docs/guidebook/en/2_5_1_Monitor_Module.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Log_And_Monitor/Monitor_Module.md
@@ -1,12 +1,10 @@
 # Monitor Module
 
-agentUniverse monitor module, mainly around the agent runtime LLM invocation/token expenses for data tracking and
-recording. In this document, we will provide a detailed introduction on how to use the **LLM and Agent invocation tracking
-capabilities** that are currently open in the agentUniverse.
+The agentUniverse monitor module focuses primarily on tracking and recording the LLM invocation and token expenses during the agent's runtime. In this document, we will provide a detailed introduction on how to utilize the LLM and agent invocation tracking capabilities that are currently available in agentUniverse.
 
 ## Monitor Configuration
 
-Configure the monitor module settings in the main configuration file `config.toml` of agentUniverse, as follows:
+Configure the settings for the monitor module in the main configuration file`config.toml` oof agentUniverse as follows:
 
 ```toml
 [MONITOR]
@@ -14,19 +12,14 @@ activate = true
 dir = './monitor'
 ```
 
-- **`activate`**: The master switch for the monitor module is set to off by default. When set to true, it will turn on
-  the LLM and Agent invocation tracking functions.
-- **`dir`**: The local storage directory corresponding to the monitor module is, by default, the 'monitor' directory one
-  level above the runtime directory. Users can customize the configuration of the directory path.
+- **`activate`**: The master switch for the monitor module is set tooff by default. When set to true, it will activate the LLM and agent invocation tracking functions.
+- **`dir`**: The local storage directory for the monitor module, by default, is the 'monitor' directory located one level above the runtime directory. Users can customize the path of this directory.
 
 ### LLM Tracing Configuration
 
-For the LLM invocation tracking capability, agentUniverse also supports model granularity configuration. Once the main
-switch of the monitor module is turned on, the invocation tracking function for specific models can be selectively
-disabled through the LLM's yaml file.
+For the LLM invocation tracking capability in agentUniverse, the framework supports model granularity configuration. Once the main switch of the monitor module is activated, the invocation tracking function for specific models can be selectively disabled through the corresponding YAML file for the LLM.
 
-For example, for a user-defined model such as `demo_llm`, if the tracing is set to false, then the invocation tracking
-function for this model will be turned off.
+For instance, if you have a user-defined model named demo_llm and you set the tracing option to false in its YAML configuration, the invocation tracking function for this model will be deactivated.
 
 ```yaml
 name: 'demo_llm'
@@ -41,17 +34,14 @@ metadata:
   class: 'DefaultOpenAILLM'
 ```
 
-Special note: In the main configuration file of agentUniverse, the monitor module's master switch has the **highest
-priority**. If the `activate` configuration is set to false, the model granularity configuration will not take effect.
+**Special note:** In the main configuration file (config.toml) of agentUniverse, the master switch for the monitor module has the highest priority. If the activate configuration is set to false, any model granularity configurations will not take effect.
 
 ### Agent Tracing Configuration
 
-For the agent invocation tracking capability, agentUniverse also supports agent granularity configuration. Once the main
-switch of the monitor module is turned on, the invocation tracking function for specific agent can be selectively
-disabled through the agent's yaml file.
+FFor the agent invocation tracking capability, agentUniverse also supports agent-granularity configuration. Once the main
+switch of the monitor module is activated, the invocation tracking function for specific agents can be selectively disabled through the corresponding YAML file for each agent.
 
-For example, for a user-defined agent such as `demo_agent`, if the tracing is set to false, then the invocation tracking
-function for this agent will be turned off.
+For instance, for a user-defined agent named demo_agent, if the tracing is set to false in its YAML configuration, the invocation tracking function for this agent will be deactivated.
 
 ```yaml
 info:
@@ -76,22 +66,18 @@ metadata:
   class: 'DemoRagAgent'
 ```
 
-Special note: In the main configuration file of agentUniverse, the monitor module's master switch has the **highest
-priority**. If the `activate` configuration is set to false, the agent granularity configuration will not take effect.
+**Special note:** In the main configuration file of agentUniverse, the master switch for the monitor module holds the highest priority. If the activate configuration option is set to false, any agent-granularity configurations will not take effect.
 
 
 ## Monitor Module Introduction
 
 ### LLM Invocation Tracking
 
-If the user utilizes the **default model class** supported by the agentUniverse, the feature can be used after the
-configuration as described above is completed.
+If the user utilizes the **default model class** supported by agentUniverse, the feature can be enabled after the configuration, as described above, is completed.
 
 #### trace_llm Decorator
 
-If a user customizes the extension based on the agentUniverse's model base class `LLM` to implement a custom model
-invocation, it is necessary to add the `@trace_llm` decorator to the call/acall method of the custom model class.
-
+If a user customizes an extension based on the agentUniverse's model base class LLM to implement a custom model invocation, it is necessary to add the @trace_llm decorator to the call/acall method of the custom model class.
 Custom model example code as follows:
 
 ```python
@@ -112,15 +98,11 @@ class DemoLLM(LLM):
         pass
 ```
 
-agentUniverse implements the `@trace_llm` decorator, which, when the agent calls the corresponding LLM call/acall
-methods, collects the model's prompt and response, serializes the information into JSON, and stores it in the local
-storage directory corresponding to the monitor configuration in the form of a jsonl file, divided by the hour.
+agentUniverse implements the `@trace_llm` decorator. When an agent calls the corresponding call/acall methods of the LLM, this decorator collects the model's prompt and response, serializes the information into JSON format, and stores it in the local storage directory corresponding to the monitor configuration. The data is stored in the form of jsonl files, divided by the hour.
 
 #### Demo Effect
 
-Call the `demo_rag_agent` in the sample project of agentUniverse, using the gpt-4o model, with the query to analyze the
-reasons behind Warren Buffett's reduction of his stake in BYD. The model invocation tracking function captures the
-following data:
+When calling the `demo_rag_agent` iin the sample project of agentUniverse, using the gpt-4omodel with a query to analyze the reasons behind Warren Buffett's reduction of his stake in BYD, the model invocation tracking function captures the following data:
 
 ```json
 {
@@ -151,22 +133,19 @@ following data:
 
 - **`source`**: the source of LLM invocation during agent runtime.
 - **`date`**: LLM invocation time
-- **`llm_input`**: LLM invocation messages and model parameters
-- **`llm_output`**: LLM output text
+- **`llm_input`**: The messages and model parameters used for the LLM invocation.
+- **`llm_output`**: The text output by the LLM.
+
 
 ### Agent Invocation Tracking
 
 #### trace_agent Decorator
 
-agentUniverse implements the `@trace_agent` decorator, which, when the agent calls the run
-method, collects the agent's input and output parameters, serializes the information into JSON, and stores it in the local
-storage directory corresponding to the monitor configuration in the form of a jsonl file, divided by the hour and the specific agent.
+agentUniverse implements the `@trace_agent` decorator. When an agent calls its run method, this decorator collects the agent's input and output parameters, serializes the information into JSON format, and stores it in the local storage directory that corresponds to the monitor configuration. The data is stored in the form of jsonl files, divided by both the hour and the specific agent.
 
 #### Demo Effect
 
-Call the `demo_rag_agent` in the sample project of agentUniverse, using the gpt-4o model, with the query to analyze the
-reasons behind Warren Buffett's reduction of his stake in BYD. The agent invocation tracking function captures the
-following data:
+When you call the `demo_rag_agent`  in the sample project of agentUniverse, using the gpt-4o model with a query to analyze the reasons behind Warren Buffett's reduction of his stake in BYD, the agent invocation tracking function captures the following data:
 
 ```json
 {
diff --git a/docs/guidebook/en/4_1_API_Reference.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Others/API_Reference.md
similarity index 100%
rename from docs/guidebook/en/4_1_API_Reference.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Others/API_Reference.md
diff --git a/docs/guidebook/en/2_7_Framework_Context.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Others/Framework_Context.md
similarity index 54%
rename from docs/guidebook/en/2_7_Framework_Context.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Others/Framework_Context.md
index 784879c8..d3876f5d 100644
--- a/docs/guidebook/en/2_7_Framework_Context.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Others/Framework_Context.md
@@ -1,6 +1,6 @@
 # Framework Context
 
-AgentUniverse provides a context management tool that is independent on threads and coroutines level, allowing the storage and retrieval of context variables in a key-value format that need to be managed separately within threads or coroutines.
+agentUniverse provides a context management tool that is independent of both threads and coroutines, enabling the storage and retrieval of context variables in a key-value format. These context variables need to be managed independently within threads or coroutines.
 
 ## Usage
 
@@ -13,15 +13,14 @@ with FrameworkContext({"context_var1": 1,
                        "context_var2":"context_var"}):
     pass
 ```
-By using the `with` keyword to create a `FrameworkContext` instance, which accepts a `dict` type parameter, AgentUniverse will save all key-value pairs from the parameters to the context manager within the scope of with, and remove them upon exit.
-
+By using the `with` keyword to create a `FrameworkContext` instance, which accepts a parameter of  type `dict`, agentUniverse will store all key-value pairs from the parameters within the context manager's scope during with block, and will remove them upon exit from the block.
 #### Method 2:
 ```python
 from agentuniverse.base.context.framework_context_manager import FrameworkContextManager
 
 FrameworkContextManager().set_context("context_var", "value")
 ```
-Create a new context variable with `FrameworkContextManager` directly. Variables created in this way will exist throughout the lifecycle of the thread or coroutine and can be manually deleted with following code:
+You can create a new context variable directly using `FrameworkContextManager` within the AgentUniverse framework. Variables created in this manner will persist throughout the entire lifecycle of the thread or coroutine in which they are created. These variables can be manually deleted using the following code:
 ```python
 from agentuniverse.base.context.framework_context_manager import FrameworkContextManager
 
@@ -34,4 +33,4 @@ from agentuniverse.base.context.framework_context_manager import FrameworkContex
 
 FrameworkContextManager().get_context("context_var", default_value=None)
 ```
-The `default_value` is `None` by default. When there is no corresponding key in the context, `default_value` is returned.
\ No newline at end of file
+The `default_value` is `None` by default. When there is no corresponding key in the context, the `default_value` will be returned.
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Information_Storage.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Information_Storage.md
new file mode 100644
index 00000000..1f34ea08
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Information_Storage.md
@@ -0,0 +1,40 @@
+# Service Information Storage
+
+agentUniverse utilizes a system-level database to store various types of information generated during application runtime, including the results of asynchronous service requests, which are stored within this database.
+
+## System Database Configuration
+You can configure the system database address in the `config.toml` file:
+```toml
+[DB]
+# A sqlalchemy db uri used for storing various info, for example, service request, generated during application running.
+# If it's empty, agentUniverse will create a local sqlite db as default choice.
+system_db_uri = ''
+```
+Please note that this URI must comply with the URI format specification required by SQLAlchemy. If this value is left empty, a DB folder will be created in the project's root directory, and within that folder, a SQLite database file named `agent_universe.db` will be created to serve as the default system database. If you wish to obtain more information on how to use the system database, you can refer to the [SQLDB_WRAPPER](../Storage/SQLDB_WRAPPER.md) section, where the system database is registered with the name `__system_db__`.
+
+
+
+## Service Information Table Format
+agentUniverse uses the following ORM to store request information:
+```text
+class RequestORM(Base):
+    """SQLAlchemy ORM Model for RequestDO."""
+    __tablename__ = 'request_task'
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    request_id = Column(String(20), nullable=False)
+    query = Column(Text)
+    session_id = Column(String(50))
+    state = Column(String(20))
+    result = Column(JSON)
+    steps = Column(JSON)
+    additional_args = Column(JSON)
+    gmt_create = Column(DateTime, default=datetime.datetime.now)
+    gmt_modified = Column(DateTime, default=datetime.datetime.now,
+                          onupdate=datetime.datetime.now)
+```
+● id: An auto-incremented primary key.
+● request_id: A unique identifier for each request.
+● state: Represents the execution state of the agent task associated with the request.
+● result: The execution result of the task.
+● steps: Intermediate outputs generated during the execution process.
+● session_id and additional_args: Reserved fields in the current version of the framework.
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md
new file mode 100644
index 00000000..0aabbdcb
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md
@@ -0,0 +1,30 @@
+# Service
+
+agentUniverse enables developers to register completed Agents as services using a straightforward template definition. This functionality allows for the utilization of the corresponding Agent services via Web API calls once the built-in web server has been launched.
+
+## Registration Method
+
+### Step1: Configure Scan Path
+agentUniverse automatically registers service configuration files by scanning configured paths during application startup. The scanning paths are specified in the `config.toml` file:
+```toml
+[CORE_PACKAGE]
+default = ['default_scan_path']
+service = ['service_scan_path']
+```
+By default, AgentUniverse scans all paths under both default and service directories, with the path configuration under service having higher priority than that under default.
+
+### Step2: Configuration File
+```yaml
+name: 'service_name'
+description: 'description of the service'
+agent: 'agent_name'
+metadata:
+  type: 'SERVICE'
+```
+- **`name`**: The name of the Service, which must be provided when invoking the service through the Web API.
+- **`description`**: A description of the Service's functionality.
+- **`agent`**: The name of the Agent associated with this Service. For more information about Agents, please refer to the Agent documentation.
+- **`metadata`**: This field indicates that this configuration is intended for a Service; no modifications are required.
+
+## Usage
+Please refer to [Web API](Web_Api.md).
\ No newline at end of file
diff --git a/docs/guidebook/en/2_4_1_Web_Api.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Api.md
similarity index 56%
rename from docs/guidebook/en/2_4_1_Web_Api.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Api.md
index 2864a63b..6b903876 100644
--- a/docs/guidebook/en/2_4_1_Web_Api.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Api.md
@@ -10,12 +10,11 @@ agent: 'demo_agent'
 metadata:
   type: 'SERVICE'
 ```
-The `demo_agent` accepts a string type input parameter,
-and returns a string: `"Your input is {input}."`
+The `demo_agent` accepts a string type input parameter and returns a string in the format:: `"Your input is {input}."`
 
 ## /service_run
-This POST interface calls the Agent service in a synchronous manner, and the call will block until the target Agent service returns a result.
-An example call is as follows:
+This POST interface invokes the Agent service in a synchronous manner, meaning that the call will block until the targeted Agent service responds with a result.
+An example response structure is as follows:
 ```shell
 curl -X POST -H "Content-Type: application/json" -d '{"service_id":"demo_service","params":{"input":"Hello!"}}' http://127.0.0.1:8888/service_run
 ```
@@ -28,10 +27,10 @@ The expected return value example is as follows:
         "request_id": "7dd7d737b6b64c3c92addf541e73e97c"
 }
 ```
-- **`success`**: Indicates whether the Agent call was successful or not, with values `true` and `false`.。
-- **`message`**: When the `success` value is `false`, this value represents the error message, and it is `null` when successful.
-- **`result`**: Represents the result of the execution when the Agent call is successful.
-- **`request_id`**: A random string, used for a unique request. It can be used in the [/service_run_result](#servicerunresult) interface to query the result of the corresponding request.
+- **`success`**:  Indicates whether the Agent invocation was successful. It takes the values `true` or `false`.
+- **`message`**: When `success` is `false`, this field contains the error message. It is `null` when the operation is successful.
+- **`result`**: Represents the outcome of the execution when the Agent invocation is successful.
+- **`request_id`**: A randomly generated string serving as a unique identifier for the request. It can be utilized in the [/service_run_result](#service_run_result) interface to retrieve the result of the corresponding request.
 
 ## /service_run_stream
 
@@ -39,14 +38,14 @@ This POST interface is similar to `/service_run`, and its call method is consist
 ```shell
 curl -X POST -H "Content-Type: application/json" -d '{"service_id":"demo_service","params":{"input":"Hello!"}}' http://127.0.0.1:8888/service_run_stream
 ```
-However, the return result of the Agent will be returned in a streaming manner:
+However, there is one key difference: the return result of the Agent will be streamed back to the client. 
 ```text
 # agentuniverse.agent_serve_web.flask_server.service_run_stream
 
 response = Response(task.stream_run(), mimetype="text/event-stream")
 response.headers['X-Request-ID'] = task.request_id
 ```
-`request_id` will be included in the response header X-Request-ID.
+Additionally, the `request_id` will be included in the response header as X-Request-ID.
 
 ## /service_run_async
 This POST interface calls the Agent service in an asynchronous manner. The calling method is as follows:
@@ -62,11 +61,11 @@ The interface will return immediately:
         "request_id": "7dd7d737b6b64c3c92addf541e73e97c"
 }
 ```
-The return result will only contain the success indicating whether the call was successful or not, and the request_id indicating this call.
-For the result of the call, you need to use the request_id to query in the [/service_run_result](#servicerunresult) interface.
+The response will only include an indication of success (whether the call was successful or not) and a request_id that uniquely identifies this specific call.
+To retrieve the result of the call, you need to use the request_id to query the [/service_run_result](#service_run_result) interface.
 
 ## /service_run_result
-This GET interface allows users to check the request status with request_id, an example call is as follows:
+This GET interface enables users to check the status of a request using the request_id. An example of how to make such a call is as follows:
 ```shell
  curl 'http://127.0.0.1:8888/service_run_result?request_id=8e6f17dbe7ff4730a62b4a2914d73c74'
 ```
@@ -83,8 +82,8 @@ The expected return value example is as follows:
   "success":true}
 
 ```
-The `result` contains three parts: `result` indicates the result of the Agent service execution, `state` indicates the state of the Agent service execution, and `steps` indicates the intermediate process of the Agent service execution.  
-`state` represents the task status and includes the following scenarios:
+The `result` comprises three parts: `result` indicates the outcome of the Agent service execution, `state` represents the status of the Agent service execution, and `steps` details the intermediate processes involved in the Agent service execution.  
+The `state` field indicates the task status and encompasses the following scenarios:
 ```text
 # agentuniverse.agent_serve.web.request_task.TaskStateEnum
 
diff --git a/docs/guidebook/en/2_4_1_Web_Server.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md
similarity index 50%
rename from docs/guidebook/en/2_4_1_Web_Server.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md
index 997912fe..86fd2c3a 100644
--- a/docs/guidebook/en/2_4_1_Web_Server.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md
@@ -1,10 +1,10 @@
 # Web Server
 
-AgentUniverse provides a web server based on Flask. You can call the developed Agent services through the preset APIs in the web server.
+agentUniverse provides a web server based on Flask, allowing users to invoke the developed agent services through the predefined APIs on the server.
 
 ## Web Server Launch
-You need to start the web server after launching the AgentUniverse application.
-Here is an example of how to launch it:
+You need to start the web server after launching the AgentUniverse application. 
+Here is an example demonstrating how to launch it:
 ```python
 from agentuniverse.agent_serve.web.web_booster import start_web_server
 from agentuniverse.base.agentuniverse import AgentUniverse
@@ -12,7 +12,7 @@ from agentuniverse.base.agentuniverse import AgentUniverse
 AgentUniverse().start()
 start_web_server()
 ```
-Flask server default port is 8888，you can change it by edit `bind` param:
+The default port for the Flask server is 8888. You can change it by editing the `bind` parameter.
 ```python
 from agentuniverse.agent_serve.web.web_booster import start_web_server
 from agentuniverse.base.agentuniverse import AgentUniverse
@@ -21,16 +21,16 @@ AgentUniverse().start()
 start_web_server(bind="127.0.0.1:8002")
 ```
 
-If you need to configure more details or pursue production stability, you can use Gunicorn as the HTTP server.
+If you require more detailed configuration or aim for production stability, you can utilize Gunicorn as the HTTP server.
 
 ## Gunicorn Configuration
-You can set Gunicorn as your HTTP server by configuring it in the configuration file as follows:
+You can set Gunicorn as your HTTP server by configuring it in the configuration file, as detailed below:
 ```toml
 [GUNICORN]
 activate = 'true'
 ```
 
-The configuration of the Gunicorn server consists of four levels in order of increasing priority: native Gunicorn default configuration, AgentUniverse default configuration, configuration file settings, and startup configuration. For Gunicorn's native default configuration, please refer to the official documentation for the corresponding version of Gunicorn.
+The configuration of the Gunicorn server comprises four levels of priority, in ascending order: the native default configuration of Gunicorn, the default configuration provided by AgentUniverse, settings specified in the configuration file, and startup configuration. For information on Gunicorn's native default configuration, please refer to the official documentation for the relevant version of Gunicorn.
 
 ### AgentUniverse Default Configuration
 ```python
@@ -44,16 +44,16 @@ DEFAULT_GUNICORN_CONFIG = {
     'keepalive': 10
 }
 ```
-AgentUniverse has pre-configured some parameters based on the framework's working characteristics, which you can modify through the configuration file below or parameters when launching the web server.
+agentUniverse has pre-configured certain parameters based on the framework's operational characteristics. These parameters can be modified either through the configuration file provided below or by specifying parameters when launching the web server.
 
 ### Configuration File Path
-The configuration path for the web server is specified in AgentUniverse's main configuration file `config.toml`.
+The configuration path for the web server is designated in AgentUniverse's main configuration file,  `config.toml`.
 ```toml
 [GUNICORN]
 # Gunicorn config file path, an absolute path or a relative path based on the dir where the current config file is located.
 gunicorn_config_path = './gunicorn_config.toml'
 ```
-If this parameter is in relative path format, the corresponding parent directory is the directory where the main configuration file config.toml is located.
+If the path is specified in a relative format, the corresponding parent directory is the one where the main configuration file, config.toml, is located.
 
 ### Configuration File
 ```toml
@@ -66,7 +66,7 @@ workers = 5
 timeout = 60
 keepalive = 10
 ```
-The configuration file is written in TOML format, and the specific configuration items are not limited to the content in the example above. You can refer to the official Gunicorn documentation for more detailed configurations.
+The configuration file is formatted in TOML, and the specific configuration items included are not exhaustive, as they are merely illustrative examples. For more comprehensive and detailed configurations, you can refer to the official Gunicorn documentation.
 
 ### Startup Configuration
 You can also configure the web server at startup as follows:
@@ -77,7 +77,7 @@ from agentuniverse.base.agentuniverse import AgentUniverse
 AgentUniverse().start()
 start_web_server(bind="127.0.0.1:8002", threads=4)
 ```
-If you have certain functions you want to execute after gunicorn fork child processes, you can add them in a way similar to the following:
+If you have certain functions you that you wish to execute after Gunicorn fork child processes, you can add them in a manner similar to the following:
 ```python
 from agentuniverse.agent_serve.web.post_fork_queue import add_post_fork
 from agentuniverse.agent_serve.web.web_booster import start_web_server
@@ -90,4 +90,4 @@ add_post_fork(print_hello, "name")
 AgentUniverse().start()
 start_web_server(bind="127.0.0.1:8002")
 ```
-You should observe the same amount output "hello name" as gunicorn workers。
\ No newline at end of file
+You should observe an equal unmber of "hello name" output as there are Gunicorn workers processes.
\ No newline at end of file
diff --git a/docs/guidebook/en/3_2_1_gRPC.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/gRPC.md
similarity index 68%
rename from docs/guidebook/en/3_2_1_gRPC.md
rename to docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/gRPC.md
index 68f2d20d..688b8d20 100644
--- a/docs/guidebook/en/3_2_1_gRPC.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Service/gRPC.md
@@ -1,5 +1,5 @@
 ## gRPC
-In addition to HTTP API, we also provide a built-in GRPC Agent service server.
+In addition to HTTP API, we also offer a built-in GRPC Agent service server.
 
 ### Starting gRPC server
 If you need to enable GRPC server, you can configure it in the config.toml file as follows:
@@ -9,11 +9,11 @@ activate = 'true'
 max_workers = 10
 server_port = 50051
 ```
-- **activate**: The gRPC server starts only when this value is set to `true`.
-- **max_workers**: The maximum number of threads in the gRPC server thread pool, with a default of 10.
+- **activate**: The gRPC server will only start when this value is set to `true`.
+- **max_workers**: TThe maximum number of threads in the gRPC server thread pool, with a default of 10.
 - **server_port**: The service port of the gRPC server, with a default of 50051.
 
-And then start the gRPC server:
+And then，proceed to start the gRPC server:
 ```python
 from agentuniverse.agent_serve.web.web_booster import start_web_server
 from agentuniverse.base.agentuniverse import AgentUniverse
@@ -56,7 +56,7 @@ message AgentResultRequest {
 }
 ```
 \
-Similar to the [Web API](2_4_1_Web_Api.md), the gRPC service includes three interfaces:
+Similar to the [Web API](Web_Api.md), the gRPC service includes three interfaces:
 ```text
 service AgentUniverseService {
   rpc service_run(AgentServiceRequest) returns (AgentServiceResponse);
@@ -64,12 +64,12 @@ service AgentUniverseService {
   rpc service_run_result(AgentResultRequest) returns (AgentServiceResponse);
 }
 ```
-- **service_run**: Synchronously calls an Agent service, blocking during the call until the Agent returns results.
-- **service_run_async**: Asynchronously calls an Agent service, initially returning a `request_id`, and the result of the Agent service can be queried later using the `service_run_result` interface with this ID.
-- **service_run_result**: Queries the result of the Agent service.。
+- **service_run**: Synchronously invokes an Agent service, blocking until the Agent returns results.
+- **service_run_async**:  Asynchronously invokes an Agent service, initially returning a `request_id`. The result of the Agent service can be queried later using the  `service_run_result` interface with this ID.
+- **service_run_result**: Queries the result of the Agent service.
 
 \
-The request body structure for calling an Agent service is as follows:：
+The request body structure for invoking an Agent service is as follows:：
 ```text
 message AgentServiceRequest {
   string service_id = 1;
@@ -77,9 +77,9 @@ message AgentServiceRequest {
   bool saved = 3;
 }
 ```
-- **service_id**: The model service id registered in the application.。
-- **params**: The service input parameters in JSON String format, which will be parsed by `json.loads` into the form of `**kwargs` passed to the underlying Agent.
-- **saved**: Whether to save the result of this request. If the value is `false`, the result of this request cannot be queried in `service_run_result`.
+- **service_id**: The unique identifier for the model service registered in the application.
+- **params**: The service input parameters in JSON String format, which will be parsed by  `json.loads` into the form of `**kwargs` passed to the underlying Agent.
+- **saved**: Whether to save the result of this request. If set to `false`, the result of this request will not be available for querying via the `service_run_result`interface.
 
 \
 The request body structure for querying the result of an Agent service is as follows:
@@ -100,10 +100,10 @@ message AgentServiceResponse {
   string result = 4;
 }
 ```
-- **message**: Detailed error information when the request fails.
+- **message**: Detailed error information provided when the request fails.
 - **success**: Indicates whether the request was successfully executed.
 - **request_id**: The ID of this request.
-- **result**: The result of executing the Agent service. It is empty in the asynchronous interface `service_run_async`.
+- **result**: The outcome of executing the Agent service.  In the asynchronous interface `service_run_async`, this field is initially empty as the result is not immediately available.
 
 ### Call Example
 ```python
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/ChromaDB.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/ChromaDB.md
new file mode 100644
index 00000000..f25e2536
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/ChromaDB.md
@@ -0,0 +1,23 @@
+## ChromaDB
+
+The agentUniverse already integrates ChromaDB-related dependencies, so there is no need for you to install additional packages to utilize ChromaDB's features.
+If you wish to gain insights into the underlying principles of ChromaDB, you may visit the  [official ChromaDB website](https://www.trychroma.com/).
+
+### How to Configure ChromaDB Components
+```yaml
+name: 'chroma_store'
+description: 'store based on chroma db'
+persist_path: '../../DB/criminal_law.db'
+embedding_model: 'dashscope_embedding'
+similarity_top_k: 100
+metadata:
+  type: 'STORE'
+  module: 'agentuniverse.agent.action.knowledge.store.chroma_store'
+  class: 'ChromaStore'
+```
+- persist_path: The path for persistence storage of the database, utilized for saving and retrieving vector data.
+- embedding_model: The model employed to generate embedding vectors, specified here as dashscope_embedding.
+- similarity_top_k: The number of most similar results returned during a similarity search.
+
+### Usage
+[Knowledge_Define_And_Use](../../../In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/Milvus.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/Milvus.md
new file mode 100644
index 00000000..bb020d7f
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/Milvus.md
@@ -0,0 +1,53 @@
+## Milvus
+If you wish to utilize the Milvus vector database within agentUniverse, you need to follow these steps:
+
+1. Install the Milvus vector database
+You can consult the [official Milvus installation documentation](https://milvus.io/docs/install_standalone-docker.md) for detailed instructions on installing and using Milvus. As a recommendation, you can start the Milvus container in Docker by executing the following commands:
+
+``` shell
+# Download the installation script
+$ curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh
+
+# Start the Docker container
+$ bash standalone_embed.sh start
+```
+These commands will download the Milvus image and initiate a container, thereby offering database services on port 19530. For additional information and alternative installation methods, kindly refer to the official documentation.
+
+2. Install the Milvus Python SDK
+```
+pip install pymilvus
+```
+
+### How to Configure Milvus Components
+```yaml
+name: 'milvus_store'
+description: 'a store based on milvus'
+connection_args:
+  host: '127.0.0.1'
+  port: '19530'
+search_args:
+  metric_type: "L2"
+  params:
+    nprobe: 10
+index_params:
+  metric_type: "L2"
+  index_type: "HNSW"
+  params:
+    M: 8
+    efConstruction: 32
+embedding_model: 'dashscope_embedding'
+similarity_top_k: 100
+metadata:
+  type: 'STORE'
+  module: 'agentuniverse.agent.action.knowledge.store.milvus_store'
+  class: 'MilvusStore'
+```
+- connection_args: Parameters required for connecting to the Milvus database, encompassing the host address (host) and port number (port).
+- search_args: Search parameters, defining the type of distance metric (metric_type) utilized during searches, along with related parameters such as nprobe.
+- index_params: Indexing parameters, specifying the index type (index_type), distance metric type (metric_type), and specific parameters pertinent to index construction, such as M and efConstruction.
+- embedding_model: The model employed to generate embedding vectors, specified here as dashscope_embedding.
+- similarity_top_k: The number of the most similar results returned in a similarity search.
+
+### Usage
+[Knowledge_Define_And_Use](../../../In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md)
+
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/SQLDB_WRAPPER.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/SQLDB_WRAPPER.md
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+# SQLDB Wrapper
+
+Based on SQLAlchemy and Langchain's SQLDatabase, agentUniverse offers a `SQLDBWrapper` class that allows users to conveniently utilize most SQL-based databases, such as SQLite, MySQL, Oracle, and more. For detailed information, please refer to the [SQLAlchemy official website](https://docs.sqlalchemy.org/en/20/dialects/). With SQLDBWrapper, users can easily manage multiple different database connection objects simultaneously.
+
+## Registration Method
+
+### Step One: Configure Scan Path
+agentUniverse automatically registers SQLDBWrapper configuration files by scanning the configured paths during application startup. The paths to be scanned are specified in the `config.toml` file:
+```toml
+[CORE_PACKAGE]
+default = ['default_scan_path']
+sqldb_wrapper = ['sqldb_wrapper_scan_path']
+```
+By default, AgentUniverse scans all paths under either `default` or `sqldb_wrapper`sections in the configurtion, with paths under `sqldb_wrapper` having a higher priority than those under `default`.
+
+
+### Step Two: Configuration File
+```yaml
+name: 'demo_sql'
+description: 'demo_sql'
+db_uri: "sqlite:///./demo.db"
+sql_database_args:
+  include_tables: ["users"]
+engine_args:
+  pool_size: 10
+metadata:
+  type: 'SQLDB_WRAPPER'
+```
+- **`name`**: The name of the SQLDBWrapper, used to uniquely identify an instance.
+- **`description`**: A brief description of the functionality of the SQLDBWrapper's.
+- **`db_uri`**: A SQLAlchemy-style database URI, which is used to create a SQLAlchemy engine.
+- **`sql_database_args`**: Optional parameters for the SQLDatabase class in `LangChain`. For specific details, refer to [LangChain's official website](https://python.langchain.com/v0.1/docs/integrations/toolkits/sql_database/).
+- **`engine_args`**:  Optional parameters used to configure SQLAlchemy engine settings. For specific configurable options, please refer to [SQLAlchemy's webpage](https://docs.sqlalchemy.org/en/20/core/engines.html#sqlalchemy.create_engine).
+- **`metadata`**: Indicates that this configuration is specific to the `SQLDB_WRAPPER`and should not be changed.
+
+## Usage
+The SQLDBWrapper class offers the following functions for users:
+
+### run
+The `run` function accepts a `str` type parameter command, which represents an SQL statement. It executes this SQL statement via SQLAlchemy and returns the results. If the command is a query statement, the result will be a list, where each element is a dictionary representing a row in the database. The dictionary keys correspond to column names, and the values correspond to the respective data in those columns.
+```python
+from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
+from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
+
+demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
+demo_sqldb_wrapper.run("select * from USERS")
+```
+Example of returned result:
+```text
+[{'age': 30, 'id': 1, 'name': 'Alice'}, {'age': 25, 'id': 2, 'name': 'Bob'}]
+```
+
+### run_with_str_return
+The `run_with_str_return` function accepts a `str` type parameter command, which represents an SQL statement. It executes this SQL statement via SQLAlchemy and returns the results as a `str`. If the command is a query statement, the returned string consists of all the query results concatenated together. If the `max_string_length` parameter was configured in `sql_database_args`, the result will be truncated to this length. This function is particularly convenient for using the concatenated results as input for an LLM.
+```python
+from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
+from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
+
+demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
+demo_sqldb_wrapper.run_with_str_return("select * from USERS")
+```
+Example of returned result:
+```text
+"[(1, 'Alice', 30), (2, 'Bob', 25)]"
+```
+
+### sql_database
+If you wish to utilize some of the native features of SQLAlchemy or the SQLDatabase, you can access the corresponding objects through the `sql_database` property of the SQLDBWrapper class in the AgentUniverse framework.
+```python
+from agentuniverse.database.sqldb_wrapper import SQLDBWrapper
+from agentuniverse.database.sqldb_wrapper_manager import SQLDBWrapperManager
+
+demo_sqldb_wrapper:SQLDBWrapper = SQLDBWrapperManager().get_instance_obj("demo_sqldb_wrapper")
+sql_database_ins = demo_sqldb_wrapper.sql_database
+sql_alchemy_engine = sql_database_ins._engine
+```
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/Sqlite.md b/docs/guidebook/en/In-Depth_Guides/Tech_Capabilities/Storage/Sqlite.md
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+## Sqlite
+
+The SQLite component within the Store module is utilized to store the textual content of Documents and construct a corresponding inverted index based on the keyword extraction capability of Jieba. During the retrieval phase, relevant documents are retrieved using keywords, and the relevance score between the query and the retrieved texts is computed using the BM25 algorithm. Ultimately, the system returns the Top k most similar documents.
+
+### How to Configure the SQLite Component
+
+You can use ChromaDB to store and query knowledge in the [Knowledge Components](../../../In-Depth_Guides/Tutorials/Knowledge/Knowledge.md). You can create a storage component using SQLite with the following configuration:
+```yaml
+name: 'sqlite_store'
+description: 'a store based on sqlite'
+db_path: '../../DB/civil_law_sqlite.db'
+k1: 1.5
+b: 0.75
+keyword_extractor: 'jieba_keyword_extractor'
+similarity_top_k: 10
+metadata:
+  type: 'STORE'
+  module: 'agentuniverse.agent.action.knowledge.store.sqlite_store'
+  class: 'SQLiteStore'
+```
+- db_path: The path to the SQLite database file, utilized for storing and managing textual data.
+- k1:  The k1 parameter in the BM25 algorithm, which regulates the impact of term frequency on the scoring.
+- b: The b parameter in the BM25 algorithm, which modulates the influence of document length on the scoring.
+- keyword_extractor: The tool designated for keyword extraction, specified here as jieba_keyword_extractor.
+- similarity_top_k: Returns the top k most similar documents based on the BM25 score.
+
+### Usage
+[Knowledge_Define_And_Use](../../../In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent.md
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+# Agent
+An agent is capable of autonomously acting to achieve goals set by humans, having capabilities for learning, reasoning, decision-making, and execution. It accomplishes objectives through steps such as task decomposition, using tools and knowledge, and progress control, and then independently completes its tasks. Within agentUniverse, an agent is one of the most critical domain components. It integrates a series of other domain components, including tools, knowledge, and plans, into a cohesive and efficient system, ultimately completing the tasks assigned to it by people.
+The performance of an agent directly impacts the effectiveness of application service. In complex service scenarios, often one or multiple competent agents are required to complete tasks. In agentUniverse, utilizing the achievements of industry and academia as well as practical experience in industry implementation, the following definition for agents is established, as shown in the figure.
+![](../../../../_picture/agent.jpg)
+
+Let's introduce the roles of the various components within the Agent component separately.
+
+## Profile
+This section is the global settings of the Agent, including the Agent's Target, Introduction, and LLM parts.
+
+### Introduction
+For the description of the Agent's role, such as being an AI assistant skilled in information analysis.
+
+### Target
+The goal that the Agent needs to achieve. The Agent will focus on this goal to complete a series of subsequent tasks.
+  
+```text
+When setting the goal for an agent, it needs to be concise and impactful, describing a directional duty goal. Providing a specific and specialized domain scope is optimal.
+
+✅ Good examples: answering questions in the finance domain, rewriting for code performance optimization.
+
+❌ Bad examples: aimless descriptions such as "just chatting."
+```
+  
+### Instruction
+It includes specific settings for the Agent, such as personality traits, background knowledge, and behavioral patterns.
+
+```text
+How to write a good Instruction? We can adopt the following paradigm:
+A clear scope of responsibilities the agent excels in (recommended) + Personality setting (fill in as needed) + 
+Background knowledge (fill in as needed) + 
+User input format (fill in as needed) + 
+Behavioral guidance (recommended) + 
+Agent output format (fill in as needed) + 
+Practical examples - fewshot (recommended)
+
+Following the above paradigm, here is an example definition:
+✅ For instance, a good example would be:
+You are Jerry's personal exclusive chatbot [The role played by the agent].
+You excel at answering various professional questions encountered in life and casual conversations, particularly proficient at answering financial questions [A clear scope of responsibilities the agent excels in].
+Your personality is cheerful and lively [Personality setting].
+Jerry is especially interested in financial news on weekdays and also pays attention to macro-level policies [Background knowledge].
+Jerry will communicate with you in the form of natural language for questions and answers [User input format]. 
+Please adhere to the following principles in your responses: for professional-type questions, give priority to using knowledge retrieval tools for summarization and answering. Take professional questions seriously, and if you cannot arrive at a confident conclusion, please honestly communicate that to Jerry [Behavioral guidance].
+Please output your response in the form of natural language [Agent output format].
+Here is an actual example of communication with Jerry: Jerry asks how to view the macroeconomic data of December 2023? Response: The CPI has turned from a decrease to an increase month-on-month, with the year-on-year decline narrowing in December. Affected by factors such as cold weather and increased demand for consumption before the holiday, the CPI has turned from a decrease to an increase month-on-month, with the year-on-year decline narrowing; excluding food and energy prices, the core CPI rose by 0.6% year-on-year, with the increase remaining stable. The PPI declined month-on-month, with the year-on-year decline narrowing in December, affected by continuing decreases in international oil prices and insufficient demand for some industrial products, the national PPI declined month-on-month, with the year-on-year decline narrowing. [Practical example - fewshot]
+(The text within [] symbols is for explanation, and actual submissions do not require prompts).
+
+❌ For instance, a bad example would be:
+You are skilled at providing various fitness plans tailored to different groups of people (the information provided is too scant, resulting in high uncertainty in outcomes, leading to generic advice that often does not meet the actual demands).
+```
+
+### LLM
+The LLM utilized by the Agent. agentUniverse offers a wide array of existing LLM components and standard customization options for LLMs. You can choose to use or define the LLMs you need. We will detail how they work in the LLM section.
+
+## Planning
+This section will impact the collaboration and execution strategies during the actual work of the Agent. It embodies the collaborative and execution ideas of patterns. Planning will include various types, such as making the Agent follow a completely manually orchestrated workflow, adhere to a specific standard operating procedure (SOP), or use certain specific working methods like the Retrieval-Augmented Generation (RAG), or allowing the agent to work entirely autonomously (Auto). Of course, Planning can not only affect the execution strategy of a single agent but can also involve or coordinate any other agents in the plan. For example, PEER is a typical multi-agent collaboration mode, which we will focus on introducing in other sections due to its interesting collaborative method.
+
+### Planner
+The Planner can be seen as the instance part of the Planning section, containing all the actual logic in the plan, such as the actual node orchestration steps in the workflow, the specific working steps in the Standard Operating Procedure (SOP), and the specific retrieval and generation proccess in the Retrieval-Augmented Generation (RAG). Any Planning strategy can be encapsulated into a Planner; users only need to configure the Planner before loading it into the Agent. Then, the Agent will start working with the specific Planning strategy.
+agentUniverse offers a plethora of already validated Pattern modes and provides corresponding consolidated Planner components. At the same time, the method for defining Planners is completely open, and we look forward to the exchange and sharing of Planner configurations from various fields. We will explain specifically how they work in the Planner section.
+
+## Action
+This section primarily consists of tools and knowledge. Just as humans use tools and knowledge to complete tasks, agents need to acquire additional knowledge and skills, as well as incorporate additional tools, during the process of performing complex tasks, which will improve their performance in specific domains. 
+
+### Tool
+Tools available to Agents. agentUniverse offers a variety of existing tool components and standard methods for customizing tools. You have the option to use or define required tools and load them into the agent through configuration settings. Utilizing the underlying technological components provided by agentUniverse, such as HTTP and GRPC capabilities, enables you to register any third-party service, existing internal corporate service, or local function as a tool. Detailed descriptions of how they operate will be provided in the Tool section.
+
+### Knowledge
+Knowledge accessible to Agents. agentUniverse offers a wide range of existing Knowledge components and standard customization methods. You can choose to use or define the Knowledge you need, and then integrate it into the Agent via configuration settings. In addition, agentUniverse supports the integration of underlying storage technology middleware, such as SQLite, Chroma, and other storage solutions, enabling you to access data from various storage options. We will explain in detail how they work in the Knowledge section.
+
+## Memory
+The memory resources available to agents. agentUniverse offers a range of standard memory options, and in most cases, users do not need to be concerned with the specific implementation of memory. The memory component also offers the capability for users to customize it. We will explain in detail how they work in the memory section.
+
+# Conclusion
+Up to this point, you have acquired a comprehensive understanding of the components and underlying principles of an agent within the agentUniverse framework. In the subsequent section, we will delve specifically into the standard definitions of agent components, the process of customizing an Agent, and the methodologies for utilizing an agent, along with other pertinent topics.
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md
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+# How to create agents.
+You may have already learned how to quickly create an agent from the [Quick Start](../../../Get_Start/Quick_Start.md) chapter, or you may have grasped the essential components of an agent from the [Principles of Agents](../../../In-Depth_Guides/Tutorials/Agent/Agent.md) section.
+
+Based on the design features of the agentUniverse framework's domain components, the creation process of an agent involves two main parts.
+* agent_xx.yaml
+* agent_xx.py
+
+Among them, the `agent_xx.yaml` file must be created, which contains crucial attribute information for the agent, such as Introduction, Target, Instruction, LLM, and so forth. The `agent_xx.py` file, on the other hand, is created as needed and includes specific behaviors of the agent, allowing users to inject custom domain-specific behaviors into the agent. With this understanding, let's delve deeper into the creation of these two essential parts.
+
+## Creating Agent Configuration - agent_xx.yaml
+We will provide detailed descriptions of each component within the configuration file.
+
+### Setting the basic information of the agent.
+**`info` - basic information of the agent**
+* `name`:  the name of the agent
+* `description`:  a description of the agent's purpose or funtion 
+
+### Setting the global configurations for the agent.
+**`profile` - Agent global settings.**
+* `introduction`:  an introduction to the agent's role and responsibilities
+* `target`:  the agent's target or intended purpose
+* `instruction`: instruction or guidelines for the agent;s behavior
+* `llm_model`: the LLM used by the agent
+  * `name`: the name of the LLM
+  * `model_name`: the specific model name of the LLM
+
+  Note: You can choose any existing LLM or connect to any LLM of your choice. We will not elaborate on this part here; for more details, please refer to the [LLM section](../../../In-Depth_Guides/Tutorials/LLM/LLM.md) .
+  
+### Setting the agent's plan
+**`plan` - plan of agent**
+* `planner` : the planner utilized by the agent
+* `name`: name of planner
+  
+  Note: You can choose any existing planner or connect to any planner of your choice. We will not elaborate on this part here; for more details, please refer to the planner section .
+
+### Setting the agent's action
+**`action` - action of agent**
+* `Tool` : Tools available for the agent's use.
+  * tool_name_list，list of tool names, for example:  
+    \- tool_name_a  
+    \- tool_name_b  
+    \- tool_name_c  
+
+  Note: You can choose any existing tool or connect to any tool of your choice. We will not elaborate on this part here; you can refer to the [Tool section](../../../In-Depth_Guides/Tutorials/Tool/Tool.md) for more details.
+
+* `Knowledge` : Knowledge available for the agent's use.
+  * knowledge_name_list，list of knowledge names, for example:   
+    \- knowledge_name_a  
+    \- knowledge_name_b  
+    \- knowledge_name_c
+  
+  Note: You can choose any existing Knowledge or connect to any knowledge of your choice. We will not elaborate on this part here; you can refer to the knowledge section for more details.
+
+### Setting up the agent's memory.
+**`memory` - memory of agent**
+
+The agent already has a default memory mode built-in, so there is no need to set it up separately. Additionally,  the memory supports customization and type selection, a feature that will be available in a future release.
+
+### Setting up component metadata.
+**`metadata` - metadata of component**
+* `type` : the type of component，use 'AGENT'
+* `module`: the package path for the agent entity
+* `class`: the class name of the agent entity
+
+All provided Agent components will have their corresponding `module` and `class` details copied into this section. This section serves to integrate all configurations and behaviors of the agent into a cohesive whole. If you have extended the behavior of the agent, you need to fill in this section according to the actual path and class name. We will further elaborate on this  in the "Creating from an Existing Agent Object" section later in the document.
+
+### An actual example of an agent configuration.
+```yaml
+info:
+  name: 'demo_rag_agent'
+  description: 'demo rag agent'
+profile:
+  introduction: You are an AI assistant proficient in information analysis.
+  target: Your goal is to determine whether the answer to a question provides valuable information and to make suggestions and evaluations about the answer.
+  instruction: |
+    The rules you need to follow are:
+
+    1.Must answer the question posed by the user in Chinese, combining the background information and your knowledge.
+    2.Generate structured answers, and when necessary, use blank lines to enhance the reading experience.
+    3.Do not adopt incorrect information from the background.
+    4.Consider the relevance of the answer to the question, and do not provide answers that are not helpful to the question.
+    5.Answer the question comprehensively, with emphasis on the main points, without excessive fancy wording.
+    6.Do not make vague speculations.
+    7.Use numerical information as much as possible. 
+    
+    Background information is: {background} 
+    
+    Begin! 
+    
+    The question to answer is: {input}
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+plan:
+  planner:
+    name: 'rag_planner'
+action:
+  tool:
+    - 'google_search_tool'
+#  knowledge:
+#    - 'knowledge_a'
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.rag_agent_case.demo_rag_agent'
+  class: 'DemoRagAgent'
+```
+The above is an actual example of an agent configuration. Besides the standard configuration items introduced above, those of you who are observant may have noticed variables in the prompt, such as `{background}` and `{input}`. This is a very practical prompt replacement feature, which we will explain further in the section titled "[How to dynamically adjust settings based on user input](#How to dynamically adjust settings based on user input)".
+
+You can find more examples of agent configuration YAML files in our sample project, located under the path `sample_standard_app.app.core.agent`.
+
+In addition, agentUniverse does not restrict users from extending the YAML configuration content for agents. You can create any custom configuration keys according to your own requirements, but please ensure that you do not duplicate the default configuration keywords mentioned above.
+
+## Creating Agent Domain Behavior Definition agent_xx.py
+In this section, you can orchestrate and customize the behavior of any agent. Of course, if you are solely utilizing existing agent capabilities, this section may not be mandatory.
+
+In this section, we will focus on commonly used definitions for agent domain behavior and the techniques you may employ in the actual process of defining these behaviors.
+
+### Create an Agent class object.
+Create the corresponding agent class object by inheriting from the base class agent provided by the agentUniverse framework.
+
+### Customize the corresponding agent domain behaviors.
+Common customizable agent behaviors include the  follows:
+
+#### input_keys method
+This method represents the list of input key parameters from users during the agent's execution process. It must be implemented by each custom Agent class. 
+
+For instance, if the agent operates with only one parameter passed in, such as question=xxx, then the input_keys method should return ['question'].
+
+#### output_keys method
+This method represents the list of output key parameters when the agent's execution completes. It must be implemented by each custom Agent class. 
+
+For example, if the agent produces an output that contains a response, then the output_keys method should return ['response'].
+
+#### parse_input method
+The parse_input method serves as the input processing stage before agent execution. An agent's input can be in the form of natural language or structured data such as JSON. This part allows you to process any user input, and it must be implemented by each custom Agent class.
+
+This method has two input parameters:
+
+* `input_object`: The raw data inputted to the agent.
+  * You can retrieve corresponding data from input_object using the `input_object.get_data('input_key')` method.
+  
+    For instance, if a user inputs `question=xxx` to the agent, you can obtain the user's current question by using `input_object.get_data('question')`.
+
+* `agent_input`: The data processed by the agent's input handler, which is of type `dict`.
+  * By default, `agent_input` includes the following key parameters and data:
+    * `chat_history`:  Contains historical chat data.
+    * `background`: Contains background information.
+    * `date`: Contains the current date.
+  * Append the user input to agent_input.
+    * For example: `agent_input['input'] = input_object.get_data('question')`.
+  
+The output object for the `parse_input` method is typically the modified `agent_input`.
+
+#### parse_result method
+The parse_result method serves as the output processing node before agent execution. An agent's output can be in the form of natural language or structured data such as JSON. This part allows you to manage any content that needs to be outputted, and it must be implemented by each custom Agent class.
+
+This method has one input parameter:
+* `agent_result`: Agent output data, of type `dict`.  
+  The output object for the `parse_result` method is typically used `agent_result`.
+
+#### execute method
+This method is the core entry point for the agent's execution flow. The agent's base class provides a default implementation of the execution method, and users can also override this method to customize the execution method for any agent.
+
+##### Default execute implementation.
+* The default logic for the execute method in the agent base class is as follows:
+
+  *   Retrieve the planner name corresponding to the agent's YAML configuration.
+  *   Obtain a planner instance through the planner manager
+  *   Execute the invoke method of the obtained planner instance
+
+##### Custom execute implementation.
+You can also customize the execution flow of the agent by overriding the execute method. Overriding the execute method will replace the default implementation provided by the agent's base class, meaning the agent will no longer automatically select a planner. If needed, you can refer to the default implementation in the agent's base class to load and use the corresponding planner.
+
+Below is an example of a custom execute method implementation:
+```text
+def execute(self, input_object: InputObject, agent_input: dict) -> dict:
+    # Do anything, as exemplified below.
+    
+    # Invoke tool A to get data.
+    
+    # Analyze the data and determine whether it can answer the user's questions.
+    
+    # Organize language to answer questions.
+    
+    # Output the answer.
+    
+    return {'output': 'xxx'}
+```
+
+#### Get the properties of the agent object.
+Through `self.agent_model`, the agent object can access its properties. In the definition of agent domain behaviors, you can read all the properties configured in the agent's configuration section.
+
+A specific example is as follows:
+```text
+def execute(self, input_object: InputObject, agent_input: dict) -> dict:
+    """Execute agent instance.
+
+    Args:
+        input_object (InputObject): input parameters passed by the user.
+        agent_input (dict): agent input parsed from `input_object` by the user.
+
+    Returns:
+        dict: planner result generated by the planner execution.
+    """
+
+    planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name'))
+    planner_result = planner_base.invoke(self.agent_model, agent_input, input_object)
+    return planner_result
+```
+
+In this example, within the execution method, the `plan` section from the agent's configuration attributes is accessed using `self.agent_model.plan`, and further, the actual name of the `planner` is obtained through the get method.
+
+
+The properties and domain behaviors of the agent are both dependent on the Agent base class in the agentUniverse framework, which is located at `agentuniverse.agent.agent.Agent`. We will also delve into the underlying objects in the sections discussing agents and related domain objects. If you are interested in the underlying technical implementation, you can further refer to the corresponding code and documentation.
+
+#### An actual example of an agent domain behavior definition.
+```python
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.input_object import InputObject
+from agentuniverse.agent.plan.planner.planner import Planner
+from agentuniverse.agent.plan.planner.planner_manager import PlannerManager
+
+class DemoRagAgent(Agent):
+    def input_keys(self) -> list[str]:
+        return ['input']
+
+    def output_keys(self) -> list[str]:
+        return ['output']
+
+    def parse_input(self, input_object: InputObject, agent_input: dict) -> dict:
+        agent_input['input'] = input_object.get_data('input')
+        return agent_input
+
+    def parse_result(self, planner_result: dict) -> dict:
+        return planner_result
+
+    # def execute(self, input_object: InputObject, agent_input: dict) -> dict:    
+    #     planner_base: Planner = PlannerManager().get_instance_obj(self.agent_model.plan.get('planner').get('name'))
+    #     planner_result = planner_base.invoke(self.agent_model, agent_input, input_object)
+    #     return planner_result
+```
+The above provides an actual example of an agent domain behavior definition.
+
+
+## Pay attention to the package path where your defined agent is located
+With the agent configuration and domain definition provided above, you have now mastered all the steps required to create an agent. Next, we will proceed to use these agents. Before doing so, please ensure that the created agents are located within the correct package scanning path.
+
+In the `config.toml` file of the agentUniverse project, you need to configure the package that corresponds to your agent file. Please confirm once again that the package path where your created file is located falls under the `CORE_PACKAGE` in the `agent` path or one of its subpaths.
+
+Taking the configuration of the example project as a reference, it would look something like this:
+
+```yaml
+[CORE_PACKAGE]
+# Scan and register agent components for all paths under this list, with priority over the default.
+agent = ['sample_standard_app.app.core.agent']
+```
+
+## Other techniques for agent development
+### Customize and orchestrate the execution process of agents freely
+In the [Creating Agent Domain Behavior Definitions](#Creating Agent Domain Behavior Definition-agent_xx.py) section of this document, we have already detailed how to customize an execute method. This method is often used in practice to customize processes and inject SOPs (Standard Operating Procedures) according to user demands.
+
+### How to dynamically adjust settings based on user input
+
+**Method 1 (Recommended): Through the standard prompt template variable replacement method.**  
+
+In the [An actual example of an agent configuration](#An actual example of an agent configuration.) section of this document, the prompt includes variables like `{background}`,`{input}`, etc. This feature is the prompt variable template replacement function, aimed at dynamically influencing the prompt based on the user's input. One only needs to define the text using  `{variable}` format in the agent configuration settings section and then define the corresponding variables in the `agent_input` method's agent_input to dynamically replace the prompt based on the input portion.
+
+For example, in the sample agent `sample_standard_app.app.core.agent.rag_agent_case.demo_rag_agent.py`, there is the following `parse_input` method.
+
+```text
+def parse_input(self, input_object: InputObject, agent_input: dict) -> dict:
+    agent_input['input'] = input_object.get_data('input')
+    return agent_input
+```
+
+In its agent settings file `sample_standard_app.app.core.agent.rag_agent_case.demo_rag_agent.yaml`, in the `instruction` section, we can see the following configuration.
+
+```text
+instruction: |
+    The rules you need to follow are:
+
+    1.Must answer the question posed by the user in Chinese, combining the background information and your knowledge.
+    2.Generate structured answers, and when necessary, use blank lines to enhance the reading experience.
+    3.Do not adopt incorrect information from the background.
+    4.Consider the relevance of the answer to the question, and do not provide answers that are not helpful to the question.
+    5.Answer the question comprehensively, with emphasis on the main points, without excessive fancy wording.
+    6.Do not make vague speculations.
+    7.Use numerical information as much as possible. 
+    
+    Background information is: {background} 
+    
+    Begin! 
+    
+    The question to answer is: {input}
+```
+
+Through this process, we can incorporate each input, memory or background knowledge, and any other information you care about into the interaction between the agent and the LLM using this method.
+
+**Method 2: Adjust the prompt within the parse_input method or the execute method**
+
+Through the agent's `parse_input` method or `execute` method, we can customize the agent's  data or behavior. Additionally, by combining the get method in `self.agent_model` with the agent input parameter `input_object`, this means that you can modify any of your settings content through these customization methods.
+
+### Create an agent from an existing agent object
+
+In the actual process of creating applications, we may have many agents of the same type. For instance, we might want to create several Retrieval Augmented Generation (RAG) type agents for use in various question-answering scenarios. By analyzing their similarities and differences, it is not difficult to discern that most RAG type agents share certain commonalities while also exhibiting distinct differences:
+
+* Main similarity: They often need to retrieve multiple types of knowledge simultaneously.
+* Main difference: RAG agents deployed in different scenarios require the use of different retrieval tools and may have distinct global agent settings.
+
+Upon further analysis, we realize that we can achieve this differentiation through the use of a common domain behavior definition coupled with differentiated attribute configurations. This underscores the importance of configuring settings in YAML during the agent creation process, while domain behavior customization should be based on actual needs.
+
+Fortunately, we have a substantial number of effective agent types available, and this list is continually expanding. As illustrated in this case, by leveraging the agent domain component setting process, we simply need to create different types of agent configuration YAMLs and set the appropriate metadata to accomplish our goals.
+
+
+For example:
+
+**Financial RAG Agent Settings**
+```yaml
+info:
+  name: 'financial_RAG_agent'
+  description: 'demo financial rag agent'
+profile:
+  introduction:  Demo introduction, such as you are a financial RAG agent.
+  target: Demo target, such as use financial tools to retrieve and enhance answer performance.
+  instruction: |
+    demo instruction, xxx, {input}
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+plan:
+  planner:
+    name: 'rag_planner'
+action:
+  tool:
+    - 'financial_data_tool'
+metadata:
+  type: 'AGENT'
+  module: 'agentuniverse.agent.default.rag_agent.rag_agent'
+  class: 'RagAgent'
+```
+
+**Tech RAG Agent Settings**
+```yaml
+info:
+  name: 'tech_RAG_agent'
+  description: 'demo tech rag agent'
+profile:
+  introduction:  Demo introduction, such as you are a tech RAG agent.
+  target: Demo target, such as use tech tools to retrieve and enhance answer performance.
+  instruction: |
+    demo instruction, xxx, {input}
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+plan:
+  planner:
+    name: 'rag_planner'
+action:
+  tool:
+    - 'tech_data_tool'
+metadata:
+  type: 'AGENT'
+  module: 'agentuniverse.agent.default.rag_agent.rag_agent'
+  class: 'RagAgent'
+```
+
+You can find more information about existing agents and understand their roles in the [Learn more about agents](#Learn more about agents) section of this document.
+
+# How to Use an Agent
+In the agentUniverse, all agent entities are managed by a global agent manager. If you need to utilize an agent during any framework execution process, you can do so through the agent manager. Additionally, by leveraging the framework's service capabilities, you can quickly turn an agent into a service and make network calls to it using standard HTTP or RPC protocols.
+
+## Solution 1: Use the agent manager
+
+Through the `get_instance_obj('agent_name_xxx')` method in the agent manager, you can obtain the agent instance with the corresponding name. 
+Furthermore, the agent can be utilized through its own `run(input='xxx')` method. The `test_rag_agent(self)` method in the test class below demonstrates how to debug the agent using this approach.
+
+```python
+import unittest
+
+from agentuniverse.agent.agent import Agent
+from agentuniverse.agent.agent_manager import AgentManager
+from agentuniverse.agent.output_object import OutputObject
+from agentuniverse.base.agentuniverse import AgentUniverse
+
+
+class RagAgentTest(unittest.TestCase):
+    """
+    Test cases for the rag agent
+    """
+
+    def setUp(self) -> None:
+        AgentUniverse().start(config_path='../../config/config.toml')
+
+    def test_rag_agent(self):
+        """Test demo rag agent."""
+        instance: Agent = AgentManager().get_instance_obj('demo_rag_agent')
+        output_object: OutputObject = instance.run(input="What is the reason for the sharp rise in Nvidia's stock?")
+        print(output_object.get_data('output'))
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+## Solution 2: Utilize the service capabilities of agent_serve
+
+The agentUniverse offers a variety of standard web server capabilities, along with standard HTTP and RPC protocols. You can further refer to the documentation sections on [Service Registration and Usage](../../../In-Depth_Guides/Tech_Capabilities/Service/Service_Registration_and_Usage.md) , as well as  [Web_Server](../../../In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md) section.
+
+# Learn more about agents
+The agents provided by the framework can be found under the `agentuniverse.agent.default` package path. You can further explore the corresponding code or learn more about them in our extension component introduction section.
+
+# Conclusion
+By now, you have mastered all the content related to the creation and use of agents. Let's use this knowledge to customize your own exclusive agent.
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_1_Agent_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Related_Domain_Objects.md
similarity index 59%
rename from docs/guidebook/en/2_2_1_Agent_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Related_Domain_Objects.md
index 60f3aeae..c419b4c4 100644
--- a/docs/guidebook/en/2_2_1_Agent_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Agent/Agent_Related_Domain_Objects.md
@@ -1,5 +1,5 @@
 # Agent and Related Domain Objects
-In this chapter, we will highlight agent and related core domain objects. We have omitted their corresponding code parts. If you wish to learn more, please read the [API Reference](4_1_API_Reference.md) Section.
+In this chapter, we will highlight the agent and its related core domain objects. Note that we have omitted their corresponding code parts for brevity. If you wish to learn more, please refer to the [API Reference](../../../In-Depth_Guides/Tech_Capabilities/Others/API_Reference.md) section.
 
 ## Agent Base Class
 Package path: `agentuniverse.agent.agent.Agent`
diff --git a/docs/guidebook/en/In-Depth_Guides/Tutorials/Data_Autonomous_Agent.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Data_Autonomous_Agent.md
new file mode 100644
index 00000000..de0b9ae2
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Data_Autonomous_Agent.md
@@ -0,0 +1,174 @@
+# Data Autonomous Agent
+## Introduction
+The comprehensive view of the Data Autonomous Agent: By **automated batch execution of multiple rounds of agent invocation**, **offline adaptive data collection**, **production of evaluation datasets**, **production of fine-tuning datasets**, **model inference training**, and **automatic multidimensional assessment of datasets**, a complete workflow is established to enhance the data autonomy process of large language models and Agents.
+
+The current agentUniverse has released the **Data Autonomous MVP (Minimum Viable Product) version**.  This version, based on the **query sets** specified by the user, performs **automated batch execution of multiple rounds of agent invocation**, **produces evaluation datasets**, **automatically assesses datasets in multiple dimensions**, and **generates evaluation reports**. It introduces the concept of data autonomy to the open-source community, and will gradually unlock the full capabilities of the data autonomous workflow in the future.
+
+## Background
+Currently, the evaluation of agent performance within the industry often relies heavily on the expertise of specialists in specific fields, who conduct extensive manual review and annotation to ensure the high quality and accuracy of assessment results.
+
+The dataAgent, within the agentUniverse framework, aims to leverage the agent's own capabilities for **self-collect**, **self-manage**, and **intelligently evaluate the data of the Agent**.
+
+In the MVP version of agentUniverse, the  `dataset_build_agent` can be utilized to make multiple rounds of batch invocation on the query sets that need to be evaluated, thereby producing evaluation datasets. Subsequently,  the  `dataset_eval_agent` applies the **Fin-Eva** data evaluation standards published by Ant Group, which encompass seven dimensions (relevance, factuality, rationality, timeliness, structure, integrity, and comprehensiveness), to conduct intelligent data assessment and annotation. The results from multiple rounds of evaluation undergo comprehensive statistical analysis,  providing users with a more intuitive understanding of the Agent's capabilities changes.
+
+By harnessing the complete set of data autonomy capabilities within agentUniverse, users can effortlessly ascertain their Agent's current performance level through intelligently generated evaluation reports.
+It is particularly important to note that the scores in the entire evaluation report serve as reference values. In an actual production environment, a comprehensive comparison of scores from multiple rounds will be conducted to accurately assess the Agent's effectiveness.
+
+## DataAgent Flowchart
+![data_agent_flowchart](../../../_picture/data_agent_flowchart.jpg)
+- data_agent consists of two parts. **dataset_build_agent** iis responsible for automating batch execution of multiple rounds of agent invocation and producing evaluation datasets.
+- **dataset_eval_agent** is responsible for automating the multidimensional evaluation of the dataset and generating evaluation report.
+
+## DataAgent Execution Steps
+### Step1 Establish Agent Queryset
+In the DataAgent framework, the initial step involves constructing an agent queryset in the JSONL (JSON Lines) file format. Each line within this file represents a complete input for an individual agent invocation. 
+
+To illustrate, consider establishing a queryset that contains 3 items. Each item should include an "input" field, which corresponds to the query information specifically for the agent. Here's how it might look:
+```jsonl
+{"input": "Help me predict the 2024 European Cup championship"}
+{"input": "Can Tesla's stock still be bought?"}
+{"input": "What's the weather in New York today?"}
+```
+
+### Step2 Configure DataAgent
+CWhen configuring the DataAgent within the AgentUniverse framework, the configuration file, in addition to the basic agent settings, primarily includes two crucial items:  `dataset_builder` , which corresponds to the name of the agent responsible for generating the evaluation dataset, and `dataset_evaluator`, which corresponds to the name of the agent tasked with dataset evaluation and annotation.
+```yaml
+info:
+  name: 'data_agent'
+  description: 'data agent'
+plan:
+  planner:
+    dataset_builder: 'dataset_build_agent'
+    dataset_evaluator: 'dataset_eval_agent'
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.data_agent_case.data_agent'
+  class: 'DataAgent'
+```
+[data_agent sample configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml)
+
+[data_agent sample python file](../../../../../sample_standard_app/app/core/agent/data_agent_case/data_agent.py)
+
+### Step3 Configure the agent for producing the evaluation dataset.
+Utilize the **dataset_build_agent** configured in the `dataset_builder`step of step 2 within the agentuniverse framework. Below is the configuration file for the dataset_build_agent. In addition to the basic agent configuration,  the configuration file primarily comprises two key items: 
+1. `candidate` , which specifies the name of the agent to be evaluated (for instance, to assess the effectiveness of demo_rag_agent, candidate should be set to demo_rag_agent).
+2. `concurrency_level` ,  which configures the concurrency level during batch agent invocation (for example, setting it to 5 means that the candidate agent will be invoked concurrently with 5 instances).
+```yaml
+info:
+  name: 'dataset_build_agent'
+  description: 'dataset build agent'
+profile:
+  concurrency_level: 1
+plan:
+  planner:
+    candidate: 'demo_rag_agent'
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.data_agent_case.dataset_build_agent'
+  class: 'DatasetBuildAgent'
+```
+[dataset_build_agent sample configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml)
+
+[dataset_build_agent sample python file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py)
+
+### Step4 Configure the agent for dataset evaluation and annotation
+Utilize the **dataset_eval_agent** configured in the `dataset_evaluator`step of step 2 within the AgentUniverse framework. Below is the configuration file for the dataset_eval_agent. In addition to the basic agent configuration, the configuration file primarily contains two key items:
+1. `llm_model` , which configures the agent's model.
+2. `max_eval_lines` , which specifies the maximum number of evaluation data lines (for instance, setting it to 10 means that only the first 10 rows of data will be evaluated, to avoid a full-scale evaluation and the consumption of a large number of tokens).
+```yaml
+info:
+  name: 'dataset_eval_agent'
+  description: 'dataset eval agent'
+profile:
+  prompt_version: dataset_eval_agent.en
+  max_eval_lines: 10
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+    temperature: 0.1
+metadata:
+  type: 'AGENT'
+  module: 'sample_standard_app.app.core.agent.data_agent_case.dataset_eval_agent'
+  class: 'DatasetEvalAgent'
+```
+[dataset_eval_agent sample configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml)
+
+[dataset_eval_agent sample python file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py)
+
+### step5 Run DataAgent
+Using the [dataAgent code entry](../../../../../sample_standard_app/app/examples/data_agent.py), configure two parameters:
+1. `queryset_path` , which representing the path to the queryset.
+2. `turn` r, which specifies the total number of rounds for the queryset execution. With these configurations, you can start the dataAgent with a single click.
+
+Tips: please configure the queryset and specific the evaluation rows reasonably to avoid excessive computational and token consumption.
+
+## DataAgent Example Usage
+Please refer to the **DataAgent Execution Steps** mentioned previously, and follow these five steps to start the configured dataAgent.
+
+### Evaluation Dataset
+First, you need to generate the evaluation dataset. This dataset should store the agent's Q&A pairs in the JSONL file format. Each line in the file will represent a single Q&A pair, formatted as follows:
+- query: agent question 
+- answer: agent answer
+
+![data_agent_dataset](../../../_picture/data_agent_dataset_en.png)
+
+[dataAgent sample evaluation dataset](../../../../../sample_standard_app/app/examples/data/dataset_turn_1_2024-07-10-15-06-24.jsonl)
+
+
+### Complete Evaluation Results
+After generating the evaluation dataset, the dataagent begins multidimensional data assessment and annotation, producing complete evaluation results. (If multiple rounds of dataAgent batch tasks are executed, multiple set of complete evaluation results will be generated.)
+
+As illustrated in the figure below:
+- Line Number: Indicates the line number of the current evaluation data within the dataset.
+- Overall Score:  A comprehensive score calculated as the total of assessment scores from multiple dimensions divided by the number of assessment dimensions (with a full score of 5 and a score range of 0-5).
+- Query: The question posed to the agent.
+- Answer: The agent's response to the question.
+- Relevance Score: Represents the relevance of the agent's answer to the question. A higher score indicates greater relevance (with a full score of 5 and a score range of 0-5).
+- Relevance Suggestion: Provides issues and suggestions for improvement in the relevance dimension.
+- Additional Dimension Scores/Suggestions: Similar to the Relevance dimension, but for other assessment dimensions.
+![data_agent_eval_result](../../../_picture/data_agent_eval_result_en.png)
+
+[dataAgent sample eval result](../../../../../sample_standard_app/app/examples/data/eval_result_turn_1_2024-07-10-15-06-24.xlsx)
+
+
+
+### Comprehensive Evaluation Report
+Generate a comprehensive evaluation report based on multiple sets of complete evaluation results.
+
+As illustrated in the figure below:
+- Line Name: Includes two types of entries. One is `Queryset Turn x`, representing the evaluation dataset generated in the x-th round based on the queryset. The other is `Turn Avg Score`,which represents the average score across multiple rounds and dimensions.
+- Overall Avg Score: This is calculated as the sum of the overall scores of all data entries in a single round of the dataset divided by the total number of data entries in that dataset (with a full score of 5 and a score range of 0-5).
+- Relevance Avg Score: This is calculated as the sum of the relevance scores of all data entries in a single round of the dataset divided by the total number of data entries in that dataset (with a full score of 5 and a score range of 0-5).
+- Additional Dimension Avg Scores: These are similar to the Relevance Avg Score but pertain to other assessment dimensions.
+
+![data_agent_eval_report](../../../_picture/data_agent_eval_report_en.png)
+
+[dataAgent sample evaluation report](../../../../../sample_standard_app/app/examples/data/eval_report_2024-07-10-15-06-24.xlsx)
+
+### Comparative Experiment
+Adjust the LLM model in the demo_rag_agent within agentUniverse from the previous `qwen1.5-72b-chat` to `qwen1.5-7b-chat`, and after evaluation by dataAgent, the comprehensive evaluation reports are as follows:
+
+The following figure presents the comprehensive evaluation report generated by the data autonomous agent when the model is qwen1.5-7b-chat:
+
+![data_agent_eval_report](../../../_picture/data_agent_eval_report_en_qwen7b.png)
+
+The subsequent figure displays the comprehensive evaluation report produced by the data autonomous agent when the model is 
+![data_agent_eval_report](../../../_picture/data_agent_eval_report_en.png)
+Upon comparing the two comprehensive evaluation reports, it is evident that after switching the agent's LLM model from qwen1.5-7b-chat to qwen1.5-72b-chat, there was a notable increase in scores across all dimensions and in multiple rounds. This approach allows for a swift distinction in agent performance following changes to the agent configuration.
+
+## DataAgent Detailed Description
+### data_agent
+- [configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/data_agent.yaml)
+- [agent file](../../../../../sample_standard_app/app/core/agent/data_agent_case/data_agent.py)
+
+### dataset_build_agent
+- [configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.yaml)
+- [agent file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_build_agent.py)
+- The evaluation data produced by dataset_build_agent is stored locally in jsonl format (the jsonl file name is dataset_turn_{i}_{date}, i represents the round, and `date` represents the generation time)
+
+### dataset_eval_agent
+- [configuration file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.yaml)
+- [agent file](../../../../../sample_standard_app/app/core/agent/data_agent_case/dataset_eval_agent.py)
+- [prompt file](../../../../../sample_standard_app/app/core/prompt/dataset_eval_agent_en.yaml)：agentUniverse currently offers six agent evaluation dimensions that are industry-validated (note that the MVP version does not include the **comprehensive dimension**. The current comprehensive evaluation standard is tailored towards the financial field, and therefore, it is not discussed in the open source community).
+- The complete **evaluation results** generated by **dataset_eval_agent** are stored locally in Excel format. The file naming convention is **eval_result_turn_{i}_{date}**, where  `i` represents the evaluation round and `date` represents the generation timestamp.
+- The comprehensive **evaluation report** produced by **dataset_eval_a_agent** (assuming it's a typo and should be dataset_eval_agent or a specific variant) is also stored locally in Excel format. The file naming convention for the report is **eval_report_{date}**, where **{date}** represents the generation timestamp.
\ No newline at end of file
diff --git a/docs/guidebook/en/In-Depth_Guides/Tutorials/Domain_Component_Principles.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Domain_Component_Principles.md
new file mode 100644
index 00000000..efa000bd
--- /dev/null
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Domain_Component_Principles.md
@@ -0,0 +1,17 @@
+# Domain Component Principles
+agentUniverse includes several domain components, which combine to form the fundamental components of the multi-agent framework, including:
+* Agent
+* LLM
+* Tool
+* Knowledge
+* Memory
+* Planner
+
+All domain components within agentUniverse adhere to a unified design philosophy, exhibiting the following key characteristics:
+
+* Configuration-oriented development enhances the efficiency of component creation and reuse.
+* Each component has an independent manager, providing a unified and isolated mechanism for component registration, management, and integration.
+* Standardized domain definitions support the seamless integration of similar components from across the industry.
+* Components are highly customizable, allowing for tailored configurations to meet specific domain requirements.
+
+This chapter will delve into the design principles of each domain component and provide guidance on how to effectively utilize them. Please refer to the sub-documents within this chapter for detailed information.
diff --git a/docs/guidebook/en/2_2_4_DocProcessor.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/DocProcessor.md
similarity index 88%
rename from docs/guidebook/en/2_2_4_DocProcessor.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/DocProcessor.md
index 9e95dbbf..40415b2f 100644
--- a/docs/guidebook/en/2_2_4_DocProcessor.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/DocProcessor.md
@@ -1,6 +1,6 @@
 # DocProcessor
 
-The DocProcessor is responsible for various processing tasks on Document, such as text splitting, keyword extraction, etc. The input and output of DocProcessor are both List[Document], ensuring that multiple DocProcessors can be stacked to form a processing pipeline for the Document.
+The DocProcessor is responsible for various processing tasks on Document, such as text splitting, keyword extraction. The input and output of DocProcessor are both List[Document], ensuring that multiple DocProcessors can be stacked to form a processing pipeline for the Document.
 
 The Document is defined as follows:
 ```python
@@ -60,7 +60,7 @@ class DocProcessor(ComponentBase):
 ```
 Users need to mainly override the _process_docs function in their custom DocProcessor to implement the specific logic for processing Document.
 
-After writing the corresponding code, you can refer to the following YAML configuration to register your DocProcessor as an aU component:
+After writing the corresponding code, you can refer to the following YAML configuration to register your DocProcessor as an agentUniverse component:
 ```yaml
 name: 'dashscope_reranker'
 description: 'reranker use dashscope api'
@@ -82,7 +82,7 @@ doc_processor = ['sample_standard_app.intelligence.agentic.knowledge.doc_process
 
 
 ## The following DocProcessors are built into agentUniverse:
-### [CharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
+### [CharacterTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
 This component splits the original text based on the number of characters.
 The component definition file is as follows:
 ```yaml
@@ -100,7 +100,7 @@ metadata:
 - chunk_overlap: The length of the overlapping part between adjacent chunks.
 - separators: The specified separator.
 
-### [TokenTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
+### [TokenTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
 This component splits the text based on the specified tokenizer, splitting the text into multiple segments according to the set chunk_size and chunk_overlap, where each segment contains a specified number of tokens.
 
 The component definition file is as follows:
@@ -120,7 +120,7 @@ metadata:
 - chunk_overlap: The number of overlapping tokens between adjacent chunks.
 - tokenizer: The specified tokenizer used to split the text into tokens.
 
-### [RecursiveCharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
+### [RecursiveCharacterTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
 
 This component recursively splits the original text based on the specified separators. It first tries to split the text using the separator with the highest priority. If it fails to meet the chunk_size requirement, it will recursively use the next separator until the text is successfully split.
 
@@ -142,7 +142,7 @@ metadata:
 - chunk_overlap: The length of the overlapping part between adjacent chunks.
 - separators: A list of specified separators. The component will try to split using the separators in order. If the first separator cannot meet the conditions, it will recursively use the next separator.
 
-### [JiebaKeywordExtractor](../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml)
+### [JiebaKeywordExtractor](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml)
 This component uses the Jieba segmentation library to extract keywords from the text. It can extract the most important keywords based on the set top_k parameter, which can be used later to build an inverted index.
 The component definition file is as follows:
 ```yaml
@@ -156,7 +156,7 @@ metadata:
 ```
 - top_k: The number of keywords to extract from the text, meaning the  top_k keywords will be extracted.
 
-### [DashscopeReranker](../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml)
+### [DashscopeReranker](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml)
 
 This component uses the DashScope API to rerank texts, sorting the content recalled by the Store based on the relevance to the Query content.
 
@@ -171,7 +171,7 @@ metadata:
 ```
 This component requires configuring DASHSCOPE_API_KEY in the environment variables.
 
-### [HierarchicalRegexTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/hierarchical_regex_text_spliter.py)
+### [HierarchicalRegexTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/hierarchical_regex_text_spliter.py)
 
 This component splits the original text into multiple hierarchical levels using specified regex rules, forming a tree-like document structure. Users need to create a custom definition file, with an example as follows:
 ```yaml
diff --git a/docs/guidebook/en/2_2_4_Knowledge.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge.md
similarity index 98%
rename from docs/guidebook/en/2_2_4_Knowledge.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge.md
index 9622389b..af3bbea4 100644
--- a/docs/guidebook/en/2_2_4_Knowledge.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge.md
@@ -10,7 +10,7 @@ agentUniverse defines a standard knowledge format, which includes various knowle
 ## Knowledge Structure
 
 In agentUniverse, the overall architecture of Knowledge and its related domain objects is illustrated in the following diagram:
-![agentUniverse Knowledge architecture](../_picture/knowledge_structure.png)
+![agentUniverse Knowledge architecture](../../../../_picture/knowledge_structure.png)
 The upper part of the diagram, from Reader to Store, represents the Knowledge injection process, while the lower part represents the Knowledge query process.
 
 ### Knowledge Injection Process
diff --git a/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md
similarity index 93%
rename from docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md
index 3d8b9f3a..b3fbebbf 100644
--- a/docs/guidebook/en/2_2_4_Knowledge_Define_And_Use.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Define_And_Use.md
@@ -38,7 +38,7 @@ metadata:
 ## Creating Knowledge Domain Behavior Definition - knowledge_xx.py
 agentUniverse provides a standard Knowledge class that you can use directly in the YAML definition file or extend by overriding some of its methods.
 
-### [Knowledge Class Definition:](../../../agentuniverse/agent/action/knowledge/knowledge.py)
+### [Knowledge Class Definition:](../../../../../../agentuniverse/agent/action/knowledge/knowledge.py)
 
 - _load_data(self, *args: Any, **kwargs: Any) -> List[Document]
 : Loads the data source and selects an appropriate reader based on the type of data source (file or URL) to load the document data.
@@ -79,7 +79,7 @@ knowledge = ['sample_standard_app.intelligence.agentic.knowledge']
 
 # How to Use the Knowledge Component
 ## Configuring Knowledge in an Agent
-Based on the content in [Creating and Using Agents](2_2_1_Agent_Create_And_Use.md), you can set any created knowledge under the knowledge action in the agent.
+Based on the content in [Creating and Using Agents](../../../In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md), you can set any created knowledge under the knowledge action in the agent.
 
 ## Using the Knowledge Manager
 You can obtain the Knowledge instance corresponding to the name via the `.get_instance_obj(xx_knowledge_name)` method in the Knowledge Manager, and call it using the `query_knowledge` method.
@@ -93,4 +93,4 @@ knowledge.query_knowledge()
 ```
 
 ## Learn More About Existing Knowledge
-[Legal Consultation Case](7_1_1_Legal_Consultation_Case.md)
\ No newline at end of file
+[Legal Consultation Case](../../../Examples/Legal_Advice.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Related_Domain_Objects.md
similarity index 72%
rename from docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Related_Domain_Objects.md
index abd510af..17c3c9ae 100644
--- a/docs/guidebook/en/2_2_4_Knowledge_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Knowledge_Related_Domain_Objects.md
@@ -4,25 +4,25 @@ In this section, we will list the knowledge and its related core domain objects.
 
 ### Knowledge
 Package Path: `agentuniverse.agent.action.knowledge.knowledge.Knowledge`  
-Documentation Link: [Knowledge](2_2_4_知识定义与使用.md)
+Documentation Link: [Knowledge](Knowledge.md)
 
 ### Reader
 Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-Documentation Link: [Reader](2_2_4_Reader.md)
+Documentation Link: [Reader](Reader.md)
 
 ### DocProcessor
 Package Path: `agentuniverse.agent.action.knowledge.doc_processor.doc_processor.DocProcessor`  
-Documentation Link: [DocProcessor](2_2_4_DocProcessor.md)
+Documentation Link: [DocProcessor](DocProcessor.md)
 
 ### Store
 Package Path: `agentuniverse.agent.action.knowledge.store.store.Store`  
-Documentation Link: [Store](2_2_4_Store.md)
+Documentation Link: [Store](Store.md)
 
 The following new components are involved in this process:
 ### QueryParaphraser
 Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-Documentation Link: [QueryParaphraser](2_2_4_QueryParaphraser.md)
+Documentation Link: [QueryParaphraser](QueryParaphraser.md)
 
 ### RagRouter
 Package Path: `agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-Documentation Link: [RagRouter](2_2_4_RagRouter.md)
+Documentation Link: [RagRouter](RagRouter.md)
diff --git a/docs/guidebook/en/2_2_4_QueryParaphraser.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/QueryParaphraser.md
similarity index 96%
rename from docs/guidebook/en/2_2_4_QueryParaphraser.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/QueryParaphraser.md
index e52a5c50..04c7b9f1 100644
--- a/docs/guidebook/en/2_2_4_QueryParaphraser.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/QueryParaphraser.md
@@ -71,7 +71,7 @@ query_paraphraser = ['sample_standard_app.intelligence.agentic.knowledge.query_p
 ```
 
 ## The following QueryParaphraser are built into agentUniverse:
-### [query_keyword_extractor](../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml)
+### [query_keyword_extractor](../../../../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml)
 This component extracts keywords from the original text in the Query and saves them into the keywords field of the Query.
 The component definition file is as follows:
 ```yaml
diff --git a/docs/guidebook/en/2_2_4_RagRouter.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/RagRouter.md
similarity index 94%
rename from docs/guidebook/en/2_2_4_RagRouter.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/RagRouter.md
index dc4f563d..73904ea7 100644
--- a/docs/guidebook/en/2_2_4_RagRouter.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/RagRouter.md
@@ -49,7 +49,7 @@ rag_router = ['sample_standard_app.intelligence.agentic.knowledge.rag_router']
 
 
 ## The following RagRouters are built into agentUniverse:
-### [base_router](../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml)
+### [base_router](../../../../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml)
 This component's primary function is to route the query to all the stores in the system, ensuring that the query can find answers in all possible resources. It is also the default RagRouter in Knowledge.
 ```yaml
 name: 'base_router'
@@ -60,7 +60,7 @@ metadata:
   class: 'BaseRouter'
 ```
 
-### [nlu_rag_router](../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py)
+### [nlu_rag_router](../../../../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py)
 This component selects the most relevant stores by analyzing the correlation between the description of all stores and the original query text using a configured language model (LLM).
 
 Users need to create their own YAML file for this component, with a format similar to the following:
diff --git a/docs/guidebook/en/2_2_4_Reader.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Reader.md
similarity index 69%
rename from docs/guidebook/en/2_2_4_Reader.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Reader.md
index c913e410..c5dacf3d 100644
--- a/docs/guidebook/en/2_2_4_Reader.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Reader.md
@@ -48,9 +48,9 @@ reader = ['sample_standard_app.intelligence.agentic.knowledge.reader']
 ```
 
 ## Prebuilt Readers in agentUniverse:
-- [default_docx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml): Reads text content from local Docx files.
-- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml): Reads text content from local PDF files.
-- [default_pptx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml): Reads text content from local PPTX files.
-- [default_txt_reader](../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml): Reads text content from TXT files.
-- [default_web_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml): Reads text content from PDF files found on the web.
-- [default_markdown_reader](../../../agentuniverse/agent/action/knowledge/reader/file/markdown_reader.yaml): Reads text content from local Markdown files. 
\ No newline at end of file
+- [default_docx_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml): Reads text content from local Docx files.
+- [default_pdf_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml): Reads text content from local PDF files.
+- [default_pptx_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml): Reads text content from local PPTX files.
+- [default_txt_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml): Reads text content from TXT files.
+- [default_web_pdf_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml): Reads text content from PDF files found on the web.
+- [default_markdown_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/markdown_reader.yaml): Reads text content from local Markdown files. 
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_4_Store.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Store.md
similarity index 87%
rename from docs/guidebook/en/2_2_4_Store.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Store.md
index 5325dc1e..97c2ad16 100644
--- a/docs/guidebook/en/2_2_4_Store.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Knowledge/Store.md
@@ -52,7 +52,7 @@ class Store(ComponentBase):
     def update_document(self, documents: List[Document], **kwargs):
         raise NotImplementedError
 ```
-- `_new_client` and `_new_async_client` are used to create database connections. These methods are added to the [post_fork](2_4_1_Web_Server.md) execution list during the component registration phase to ensure that the database connections created are independent in Gunicorn mode child processes.
+- `_new_client` and `_new_async_client` are used to create database connections. These methods are added to the [post_fork](../../../In-Depth_Guides/Tech_Capabilities/Service/Web_Server.md) execution list during the component registration phase to ensure that the database connections created are independent in Gunicorn mode child processes.
 - The `query`function is called by the knowledge component during a query, responsible for searching the store for relevant content based on the passed Query instance and returning the results in the form of Document objects.
 - The `Store` also includes CRUD (Create, Read, Update, Delete) operations for Document data, serving as the management interface for knowledge storage.
 
@@ -77,6 +77,6 @@ store = ['sample_standard_app.intelligence.agentic.knowledge.store']
 ```
 
 ## Prebuilt Stores in agentUniverse:
-- [Chroma](3_3_2_ChromaDB.md)
-- [Milvus](3_3_1_Milvus.md)
-- [Sqlite](3_3_3_Sqlite.md)
\ No newline at end of file
+- [Chroma](../../../In-Depth_Guides/Tech_Capabilities/Storage/ChromaDB.md)
+- [Milvus](../../../In-Depth_Guides/Tech_Capabilities/Storage/Milvus.md)
+- [Sqlite](../../../In-Depth_Guides/Tech_Capabilities/Storage/Sqlite.md)
\ No newline at end of file
diff --git a/docs/guidebook/en/2_2_2_LLM.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM.md
similarity index 100%
rename from docs/guidebook/en/2_2_2_LLM.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM.md
diff --git a/docs/guidebook/en/2_2_2_LLM_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_Related_Domain_Objects.md
similarity index 74%
rename from docs/guidebook/en/2_2_2_LLM_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_Related_Domain_Objects.md
index 1627b449..170adc52 100644
--- a/docs/guidebook/en/2_2_2_LLM_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_Related_Domain_Objects.md
@@ -1,5 +1,5 @@
 # LLM and Related Domain Objects
-In this chapter, we will highlight LLM and related core domain objects. We have omitted their corresponding code parts. If you wish to learn more, please read the [API Reference](4_1_API_Reference.md) Section.
+In this chapter, we will highlight LLM and related core domain objects. We have omitted their corresponding code parts. If you wish to learn more, please read the [API Reference](../../../In-Depth_Guides/Tech_Capabilities/Others/API_Reference.md) Section.
 
 ## LLM Base Class
 Package path: `agentuniverse.llm.llm.LLM`
diff --git a/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_component_define_and_usage.md
similarity index 99%
rename from docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_component_define_and_usage.md
index aaea05b8..a7d47dd7 100644
--- a/docs/guidebook/en/2_2_2_LLM_component_define_and_usage.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/LLM/LLM_component_define_and_usage.md
@@ -484,7 +484,7 @@ You can find more LLM metadata in the [Understanding More Existing LLM Component
 
 # How to Use Model LLM Components
 ## Configure for use in an Agent
-You can set up any LLM you have created in the llm_model of your agent according to the contents of [Agent Creation and Usage section](2_2_1_Agent_Create_And_Use.md).
+You can set up any LLM you have created in the llm_model of your agent according to the contents of [Agent Creation and Usage section](../Agent/Agent_Create_And_Use.md).
 
 Refer to the example: `demo_multillm_agent`, with the specific file path being `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_multillm_agent.yaml`.
 
diff --git a/docs/guidebook/en/2_2_5_Memory.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory.md
similarity index 84%
rename from docs/guidebook/en/2_2_5_Memory.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory.md
index 2494dc41..dffa85c4 100644
--- a/docs/guidebook/en/2_2_5_Memory.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory.md
@@ -22,7 +22,7 @@ operation process of the agent, and so on.
 
 In the agentUniverse, the overall architecture diagram of memory is shown as follows:
 
-![agentUniverse memory architecture](../_picture/memory.jpg)
+![agentUniverse memory architecture](../../../../_picture/memory.jpg)
 Including multi-agent memory transfer, adding memory, retrieving memory, and pruning and compressing memory processes.
 
 ### Multi-Agent Memory Transfer Process
@@ -35,7 +35,7 @@ Agent B's corresponding memory.
 #### Multi-Agent Memory Transfer Code Example
 
 During the runtime of Agent A, specific memory information is transferred to Agent B. For a specific application
-example, you can refer to the [Multi-Agent Discussion Group](6_2_1_Discussion_Group.md) in the aU sample project.
+example, you can refer to the [Multi-Agent Discussion Group](../../../Examples/Discussion_Group.md) in the aU sample project.
 
 ```python
 from typing import List
@@ -57,7 +57,7 @@ instance are obtained. The memory message list, agent ID (agent_id), session ID
 and other information are stored in multiple storages.
 
 Currently, agentUniverse comes with three built-in memory storage
-methods: [ChromaDB](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py), [SqlAlchemy](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py), [LocalMemory](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py).
+methods: [ChromaDB](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py), [SqlAlchemy](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py), [LocalMemory](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py).
 Users can choose the appropriate memory storage method based on their actual needs.
 
 Special Reminder: In the `pre_parse_input` method of the agent base class, aU will automatically read the `name`
@@ -81,10 +81,10 @@ In the memory retrieval process, the memory_retrieval_storage domain component c
 obtained (if the user has not configured it, the first memory storage component in memory_storages is used by default).
 Memory is retrieved based on the agent ID (agent_id), session ID (session_id), and source (memory source).
 
-[ChromaDB](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py) can perform vector retrieval
+[ChromaDB](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py) can perform vector retrieval
 based on the query,
-[SqlAlchemy](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
-and [LocalMemory](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py) filter based on
+[SqlAlchemy](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
+and [LocalMemory](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py) filter based on
 chronological conditions to retrieve the specified memory information.
 
 ### Memory Pruning and Compression Process
diff --git a/docs/guidebook/en/2_2_5_MemoryCompressor.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryCompressor.md
similarity index 94%
rename from docs/guidebook/en/2_2_5_MemoryCompressor.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryCompressor.md
index 185601f4..1f807d9e 100644
--- a/docs/guidebook/en/2_2_5_MemoryCompressor.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryCompressor.md
@@ -43,7 +43,7 @@ metadata:
 
 agentUniverse provides a base MemoryCompressor class with a default memory compression method. You can either use this class directly in the yaml definition file or inherit it and override the `compress_memory` function.
 
-#### [Definition of MemoryCompressor Class:](../../../agentuniverse/agent/memory/memory_compressor/memory_compressor.py)
+#### [Definition of MemoryCompressor Class:](../../../../../../agentuniverse/agent/memory/memory_compressor/memory_compressor.py)
 
 - compress_memory(self, new_memories: List[Message], max_tokens: int = 500, existing_memory: str = '', **kwargs) -> str:
   : Memory compression, combines the memories to be compressed with the existing memory information, and compresses it to meet the maximum token limit for the final memory information.
@@ -72,7 +72,7 @@ memory_compressor = ['sample_standard_app.intelligence.agentic.memory.memory_com
 
 ## agentUniverse currently has the following built-in MemoryCompressor:
 
-### [default_memory_compressor](../../../agentuniverse/agent/memory/memory_compressor/default_memory_compressor.yaml)
+### [default_memory_compressor](../../../../../../agentuniverse/agent/memory/memory_compressor/default_memory_compressor.yaml)
 
 The component configuration file is as follows:
 
diff --git a/docs/guidebook/en/2_2_5_MemoryStorage.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryStorage.md
similarity index 90%
rename from docs/guidebook/en/2_2_5_MemoryStorage.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryStorage.md
index f09874a1..1771579a 100644
--- a/docs/guidebook/en/2_2_5_MemoryStorage.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/MemoryStorage.md
@@ -45,7 +45,7 @@ metadata:
 agentUniverse provides a base MemoryStorage class. You need to inherit it and override the add/delete/get functions to
 complete the storage, deletion, and retrieval of memory information.
 
-#### [Definition of MemoryStorage Class:](../../../agentuniverse/agent/memory/memory_storage/memory_storage.py)
+#### [Definition of MemoryStorage Class:](../../../../../../agentuniverse/agent/memory/memory_storage/memory_storage.py)
 
 - add(self, message_list: List[Message], session_id: str = '', agent_id: str = '', **kwargs) -> None:
   : Memory information storage, stores the memory message list, agent ID (agent_id), session ID (session_id), source (
@@ -87,7 +87,7 @@ memory_storage = ['sample_standard_app.intelligence.agentic.memory.memory_storag
 
 ## agentUniverse currently has the following built-in MemoryStorage components:
 
-### [local_memory_storage](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)
+### [local_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)
 
 Local memory storage, the system's built-in component configuration file is as follows:
 
@@ -100,7 +100,7 @@ metadata:
   class: 'LocalMemoryStorage'
 ```
 
-### [chroma_memory_storage](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
+### [chroma_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
 
 ChromaDB Memory Storage, which includes vector retrieval and conditional retrieval when retrieving memories. The **example** component configuration file in the sample project is as follows:
 
@@ -118,9 +118,9 @@ metadata:
 
 Here, `collection_name` is the name of the Chroma collection, `persist_path` is the persistence path for Chroma, and `embedding_model` is the name of the embedding domain component used for vector retrieval.
 
-### [sql_alchemy_memory_storage](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
+### [sql_alchemy_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
 
-SqlAlchemy Memory Storage, which includes another domain component of aU, [SQLDB_WRAPPER](2_3_1_SQLDB_WRAPPER.md). The **example** component configuration file in the sample project is as follows:
+SqlAlchemy Memory Storage, which includes another domain component of aU, [SQLDB_WRAPPER](../../Tech_Capabilities/Storage/SQLDB_WRAPPER.md). The **example** component configuration file in the sample project is as follows:
 
 ```yaml
 name: 'mysql_memory_storage'
diff --git a/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Define_And_Use.md
similarity index 77%
rename from docs/guidebook/en/2_2_5_Memory_Define_And_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Define_And_Use.md
index 457a49ce..7df93b1b 100644
--- a/docs/guidebook/en/2_2_5_Memory_Define_And_Use.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Define_And_Use.md
@@ -42,14 +42,14 @@ metadata:
 
 The aU sample project includes two examples of memory configurations:
 
-1. [demo_memory_with_chroma_storage](../../../sample_standard_app/intelligence/agentic/memory/demo_memory_a.yaml): An example of memory with a chroma storage mounted
-2. [demo_memory_with_local_storage](../../../sample_standard_app/intelligence/agentic/memory/demo_memory_b.yaml): An example of memory with a local memory storage mounted
+1. [demo_memory_with_chroma_storage](../../../../../../sample_standard_app/intelligence/agentic/memory/demo_memory_a.yaml): An example of memory with a chroma storage mounted
+2. [demo_memory_with_local_storage](../../../../../../sample_standard_app/intelligence/agentic/memory/demo_memory_b.yaml): An example of memory with a local memory storage mounted
 
 ## Creating Memory Domain Behavior Definition - xx_memory.py
 
 agentUniverse provides a standard Memory class, which you can directly use in the yaml definition file or inherit it and override some of its methods.
 
-### [Memory Class Definition:](../../../agentuniverse/agent/memory/memory.py)
+### [Memory Class Definition:](../../../../../../agentuniverse/agent/memory/memory.py)
 
 
 - add(self, message_list: List[Message], session_id: str = '', agent_id: str = '', **kwargs) -> None:
@@ -66,7 +66,7 @@ agentUniverse provides a standard Memory class, which you can directly use in th
 
 ## Configuring and Using in Agent
 
-You can set up your memory instance in the agent's memory according to the content in [Creating and Using Agents](2_2_1_Agent_Create_And_Use.md). For example, if you have created the `demo_memory` instance mentioned above, you can configure it in the agent like this:
+You can set up your memory instance in the agent's memory according to the content in [Creating and Using Agents](../Agent/Agent_Create_And_Use.md). For example, if you have created the `demo_memory` instance mentioned above, you can configure it in the agent like this:
 
 ```yaml
 info:
@@ -80,13 +80,13 @@ memory:
 
 
 ### Case Study
-For example, in the PEER working mode, configure the `demo_memory` instance created above for the three agents in the aU sample project: [demo_planning_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml), [demo_expressing_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml), and [demo_peer_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_peer_agent.yaml). When making a peer call, pass in the `session_id` as `peer_1`. After the call is completed, check the contents of the memory storage as follows:
+For example, in the PEER working mode, configure the `demo_memory` instance created above for the three agents in the aU sample project: [demo_planning_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml), [demo_expressing_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml), and [demo_peer_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_peer_agent.yaml). When making a peer call, pass in the `session_id` as `peer_1`. After the call is completed, check the contents of the memory storage as follows:
 
 #### mysql_memory_storage
-![mysql_memory](../_picture/mysql_memory.png)
+![mysql_memory](../../../../_picture/mysql_memory.png)
 
 #### chroma_memory_storage
-![chroma_memory](../_picture/chroma_memory.png)
+![chroma_memory](../../../../_picture/chroma_memory.png)
 
 ## Using the Memory Manager
 
@@ -101,6 +101,6 @@ memory = MemoryManager().get_instance_obj(component_instance_name=memory_name)
 
 # Summary
 
-By now, you have mastered the basic usage of Memory. For specific details on the `memory_compressor` and `memory_storage` domain components, you can refer to the [MemoryCompressor Documentation](2_2_5_MemoryCompressor.md) and [MemoryStorage Documentation](2_2_5_MemoryStorage.md).
+By now, you have mastered the basic usage of Memory. For specific details on the `memory_compressor` and `memory_storage` domain components, you can refer to the [MemoryCompressor Documentation](MemoryCompressor.md) and [MemoryStorage Documentation](MemoryStorage.md).
 
 Go ahead and try using Memory now.
diff --git a/docs/guidebook/en/2_2_5_Memory_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Related_Domain_Objects.md
similarity index 84%
rename from docs/guidebook/en/2_2_5_Memory_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Related_Domain_Objects.md
index 91c11786..2421a1bc 100644
--- a/docs/guidebook/en/2_2_5_Memory_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Memory/Memory_Related_Domain_Objects.md
@@ -7,7 +7,7 @@ code sections; if you wish to learn more, please refer to the source code based
 
 Package Path: `agentuniverse.agent.memory.memory.Memory`
 
-Document link: [Memory](2_2_5_Memory_Define_And_Use.md)
+Document link: [Memory](../../../In-Depth_Guides/Tutorials/Memory/Memory_Define_And_Use.md)
 
 ## Memory Manager
 
@@ -21,7 +21,7 @@ Package Path: `agentuniverse.agent.memory.message.Message`
 
 Package Path: `agentuniverse.agent.memory.memory_storage.memory_storage.MemoryStorage`
 
-Document link: [MemoryStorage](2_2_5_MemoryStorage.md)
+Document link: [MemoryStorage](MemoryStorage.md)
 
 ## Memory Storage Corresponding Manager
 
@@ -31,7 +31,7 @@ Package Path: `agentuniverse.agent.memory.memory_storage.memory_storage_manager.
 
 Package Path: `agentuniverse.agent.memory.memory_compressor.memory_compressor.MemoryCompressor`
 
-Document Link: [MemoryCompressor](2_2_5_MemoryCompressor.md)
+Document Link: [MemoryCompressor](MemoryCompressor.md)
 
 ## Memory Compressor Corresponding Manager
 
diff --git a/docs/guidebook/en/2_2_6_Planner.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner.md
similarity index 100%
rename from docs/guidebook/en/2_2_6_Planner.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner.md
diff --git a/docs/guidebook/en/2_2_6_Planner_Define_And_Use.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Define_And_Use.md
similarity index 96%
rename from docs/guidebook/en/2_2_6_Planner_Define_And_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Define_And_Use.md
index 90ed8c9b..ff08e715 100644
--- a/docs/guidebook/en/2_2_6_Planner_Define_And_Use.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Define_And_Use.md
@@ -184,7 +184,7 @@ planner = ['sample_standard_app.app.core.planner']
 
 # How to Use the Planner Component
 ## Configuring Use in the Agent
-Following the [Creating and Using Agents guide](2_2_1_Agent_Create_And_Use.md), you can set up any created Planner component in the agent's planner section. Refer to the example: `demo_rag_agent`, with the specific file path `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_rag_agent.yaml`.
+Following the [Creating and Using Agents guide](../Agent/Agent_Create_And_Use.md), you can set up any created Planner component in the agent's planner section. Refer to the example: `demo_rag_agent`, with the specific file path `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_rag_agent.yaml`.
 
 ## Using the Planner Manager
 You can get the Planner instance corresponding to its name using the `.get_instance_obj(xx_planner_name)` method in the Planner Manager and call it using the `invoke` method.
diff --git a/docs/guidebook/en/2_2_6_Planner_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Related_Domain_Objects.md
similarity index 85%
rename from docs/guidebook/en/2_2_6_Planner_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Related_Domain_Objects.md
index 5e75afc1..0e48aee9 100644
--- a/docs/guidebook/en/2_2_6_Planner_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Plan/Planner_Related_Domain_Objects.md
@@ -1,5 +1,5 @@
 # Planner and Related Domain Objects
-In this chapter, we will focus on listing the Planner and related core domain objects. We have omitted their corresponding code parts. If you would like to learn more, please refer to the [API Reference Section](4_1_API_Reference.md).
+In this chapter, we will focus on listing the Planner and related core domain objects. We have omitted their corresponding code parts. If you would like to learn more, please refer to the [API Reference Section](../../Tech_Capabilities/Others/API_Reference.md).
 
 ## Planner Base Class
 Package Path:`agentuniverse.agent.plan.planner.planner.Planner`
diff --git a/docs/guidebook/en/2_2_4_RAG.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/RAG.md
similarity index 85%
rename from docs/guidebook/en/2_2_4_RAG.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/RAG.md
index 4e57aa64..9a081050 100644
--- a/docs/guidebook/en/2_2_4_RAG.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/RAG.md
@@ -3,7 +3,7 @@
 Retrieval-Augmented Generation (RAG) is a cutting-edge natural language processing (NLP) technique that significantly enhances the accuracy and diversity of text generation tasks by combining information retrieval and generative models. The core idea of RAG is to first retrieve the most relevant content from a large document corpus based on the input query and then pass this retrieved information as context to the generative model. This allows the generative model to not only rely on its training data but also dynamically access up-to-date external knowledge, enabling the agent to generate responses that are more contextually appropriate.
 
 ## General RAG Workflow
-![rag_structure](../_picture/rag_structure.png)
+![rag_structure](../../../_picture/rag_structure.png)
 
 According to [this review](https://arxiv.org/pdf/2312.10997), the majority of current RAG workflows can be summarized into three parts:
 
@@ -14,9 +14,9 @@ According to [this review](https://arxiv.org/pdf/2312.10997), the majority of cu
 ## RAG in agentUniverse
 In contrast to the general RAG workflow described above, agentUniverse breaks down RAG into two parts:
 
-1. Knowledge Component: This includes the ability to build a knowledge base from documents and retrieve knowledge based on a query. You can refer to [Knowledge](2_2_4_知识.md) for detailed usage instructions.
-2. Agent-Driven Response Generation Using Knowledge: Once the knowledge component is built in the first step, the agent can use it to retrieve documents relevant to the query and then pass these, along with the prompt and context, to a large model to generate the final response. For specific usage methods, you can refer to [Agent Creation and Use ](2_2_1_Agent_Create_And_Use.md) and set any knowledge you’ve created under the agent's action knowledge section.
+1. Knowledge Component: This includes the ability to build a knowledge base from documents and retrieve knowledge based on a query. You can refer to [Knowledge](../../In-Depth_Guides/Tutorials/Knowledge/Knowledge.md) for detailed usage instructions.
+2. Agent-Driven Response Generation Using Knowledge: Once the knowledge component is built in the first step, the agent can use it to retrieve documents relevant to the query and then pass these, along with the prompt and context, to a large model to generate the final response. For specific usage methods, you can refer to [Agent Creation and Use ](../../In-Depth_Guides/Tutorials/Agent/Agent_Create_And_Use.md) and set any knowledge you’ve created under the agent's action knowledge section.
 
-If you want to quickly build a RAG workflow using your own documents and use it in an agent, you can refer to [How to Build a Knowledge-Based RAG Agent](2_2_4_How_To_Build_A_RAG_Agent).
+If you want to quickly build a RAG workflow using your own documents and use it in an agent, you can refer to [How to Build a Knowledge-Based RAG Agent](../../How-to/How_To_Build_A_RAG_Agent.md).
 
 
diff --git a/docs/guidebook/en/2_2_3_Tool.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool.md
similarity index 100%
rename from docs/guidebook/en/2_2_3_Tool.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool.md
diff --git a/docs/guidebook/en/2_2_3_Tool_Create_And_Use.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Create_And_Use.md
similarity index 99%
rename from docs/guidebook/en/2_2_3_Tool_Create_And_Use.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Create_And_Use.md
index 9e59daa7..a0174afd 100644
--- a/docs/guidebook/en/2_2_3_Tool_Create_And_Use.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Create_And_Use.md
@@ -86,7 +86,7 @@ tool = ['sample_standard_app.intelligence.agentic.tool']
 
 # How to Use the Tool Component
 ## Configure for use in an Agent
-You can set up any tool you have created in the tool of your agent according to the contents of [Agent Creation and Usage section](2_2_1_Agent_Create_And_Use.md).
+You can set up any tool you have created in the tool of your agent according to the contents of [Agent Creation and Usage section](../Agent/Agent_Create_And_Use.md).
 
 Refer to the example: `demo_rag_agent`, with the specific file path being `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_rag_agent.yaml`.
 
diff --git a/docs/guidebook/en/2_2_3_Tool_Related_Domain_Objects.md b/docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Related_Domain_Objects.md
similarity index 78%
rename from docs/guidebook/en/2_2_3_Tool_Related_Domain_Objects.md
rename to docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Related_Domain_Objects.md
index 23b83614..b39ad0a8 100644
--- a/docs/guidebook/en/2_2_3_Tool_Related_Domain_Objects.md
+++ b/docs/guidebook/en/In-Depth_Guides/Tutorials/Tool/Tool_Related_Domain_Objects.md
@@ -1,5 +1,5 @@
 # Tool and Related Domain Objects
-In this chapter, we will highlight tool and related core domain objects. We have omitted their corresponding code parts. If you wish to learn more, please read the [API Reference](4_1_API_Reference.md) Section.
+In this chapter, we will highlight tool and related core domain objects. We have omitted their corresponding code parts. If you wish to learn more, please read the [API Reference](../../Tech_Capabilities/Others/API_Reference.md) Section.
 
 ## Tool Base Class
 Package path: `agentuniverse.agent.action.tool.tool.Tool`
diff --git "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md" "b/docs/guidebook/zh/0_\347\233\256\345\275\225.md"
deleted file mode 100644
index 1c7a728a..00000000
--- "a/docs/guidebook/zh/0_\347\233\256\345\275\225.md"
+++ /dev/null
@@ -1,113 +0,0 @@
-# 使用指南
-************************************************
-## 目录
-
-**1.开始使用**
-
-* 1.1 [简介](1_1_简介.md)
-
-* 1.2 [安装](1_2_安装.md)
-
-* 1.3 [快速开始](1_3_快速开始.md)
-
-* 1.4 [应用结构说明](1_4_应用工程结构及说明.md)
-
-**2.原理介绍**
-
-* 2.1 框架原理
-* 2.2 [领域组件](2_2_领域组件原理.md)
-  * 2.2.1 [智能体(Agent)](2_2_1_智能体.md)
-    * 2.2.1.1 [智能体创建与使用](2_2_1_智能体创建与使用.md)
-    * 2.2.1.2 [智能体及相关领域对象](2_2_1_智能体及相关领域对象.md)
-  * 2.2.2 [模型(LLM)](2_2_2_模型.md)
-    * 2.2.2.1 [模型定义与使用](2_2_2_模型及相关领域对象.md)
-    * 2.2.2.2 [模型及相关领域对象](2_2_2_模型及相关领域对象.md)
-  * 2.2.3 [工具(Tool)](2_2_3_工具.md)
-    * 2.2.3.1 [工具创建与使用](2_2_3_工具创建与使用.md)
-    * 2.2.3.2 [工具及相关领域对象](2_2_3_工具及相关领域对象.md)
-  * 2.2.4 [知识(Knowledge)](2_2_4_知识.md)
-    * 2.2.4.1 [知识定义与使用](2_2_4_知识定义与使用.md)
-    * 2.2.4.2 [知识及相关领域对象](2_2_4_知识及相关领域对象.md)
-  * 2.2.5 [记忆(Memory)](2_2_5_记忆.md)
-    * 2.2.5.1 [记忆定义与使用](2_2_5_记忆定义与使用.md)
-    * 2.2.5.2 [记忆及相关领域对象](2_2_5_记忆及相关领域对象.md)
-  * 2.2.6 [计划(Planner)](2_2_6_计划.md)
-    * 2.2.6.1 [计划定义与使用](2_2_6_计划定义与使用.md)
-    * 2.2.6.2 [计划及相关领域对象](2_2_6_计划及相关领域对象.md)
-* 2.3 技术组件
-  * 2.3.1 [RAG](2_2_4_RAG.md)
-    * 2.3.1.1 [如何构建RAG智能体](2_2_4_如何构建RAG智能体.md)
-* 2.4 其他
-  * 2.4.1 服务化
-    * 2.4.1.1 [注册与使用](2_4_1_服务注册与使用.md)
-    * 2.4.1.2 [Web Server](2_4_1_Web_Server.md)
-    * 2.4.1.3 [服务API](2_4_1_服务Api.md)
-    * 2.4.1.4 [服务信息存储](2_4_1_服务信息存储.md)
-  * 2.4.2 prompt管理
-  * 2.4.3 多轮会话
-  * 2.4.4 日志体系
-    * 2.4.4.1 [日志组件](2_4_4_日志组件.md)
-  * 2.4.5 数据采集
-    * 2.4.5.1 [monitor模块](2_5_1_监控模块.md)
-  * 2.4.6 数据自治
-    * 2.4.6.1 [数据自治智能体](8_1_1_数据自治智能体.md)
-
-**3.组件参考手册**
-* 3.1 领域组件
-  * 3.1.1 智能体列表(Agent)
-  * 3.1.2 [模型列表(LLM)](3_1_2_0_模型列表.md)
-    * 3.1.2.1 [OpenAI使用说明](3_1_2_OpenAI使用.md)
-    * 3.1.2.2 [通义千问使用说明](3_1_2_Qwen使用.md)
-    * 3.1.2.3 [文心一言使用说明](3_1_2_文心使用.md)
-    * 3.1.2.4 [Kimi使用说明](3_1_2_Kimi使用.md)
-    * 3.1.2.5 [百川使用说明](3_1_2_百川使用.md)
-    * 3.1.2.6 [Claude使用说明](3_1_2_Claude使用.md)
-    * 3.1.2.7 [Ollama使用说明](3_1_2_Ollama使用.md)
-    * 3.1.2.8 [DeepSeek使用说明](3_1_2_DeepSeek使用.md)
-    * 3.1.2.9 [通用OpenAI协议风格包装类型](3_1_2_OpenAIStyleLLM使用.md)
-  * 3.1.3 工具列表(Tool)
-    * 3.1.3.1 [集成工具列表](2_2_3_集成的工具.md)
-    * 3.1.3.2 [LangChain工具包装类型](2_2_3_集成LangChain工具.md)
-  * 3.1.4 知识列表(Knowledge)
-  * 3.1.5 记忆列表(Memory)
-  * 3.1.6 计划列表(Planner)
-* 3.2 技术组件
-  * 3.2.1 RPC
-    * 3.2.1.1 [gRPC](3_2_1_gRPC.md)
-  * 3.2.2 存储 (Store)
-    * 3.2.2.1 [SQLDBWrapper](2_3_1_SQLDB_WRAPPER.md)
-    * 3.2.2.2 [Milvus](3_3_1_Milvus.md)
-    * 3.2.2.3 [ChromaDB](3_3_2_ChromaDB.md)
-    * 3.2.2.4 [Sqlite](3_3_3_Sqlite.md)
-  * 3.2.3 消息(Msg)
-  * 3.2.4 日志(Logging)
-    * 3.2.4.1 [阿里云SLS](3_2_4_阿里云SLS.md)
-
-**[4.API参考手册](4_1_API参考.md)**
-
-**5.最佳实践**
-* 5.1 运维部署
-  * 5.1.1 [Docker容器化方案](5_1_1_Docker容器化部署.md)
-  * 5.1.2 [K8S部署方案](5_1_2_K8S部署.md)
-
-**6.使用案例**
-* 6.1 RAG类Agent案例
-  * 6.1.1 [法律咨询Agent_v2](7_1_1_法律咨询案例.md)
-* 6.2 ReAct类Agent案例
-  * 6.2.1 [Python代码生成与执行Agent](7_1_1_Python自动执行案例.md)
-* 6.3 [基于多轮多Agent的讨论小组](6_2_1_讨论组.md)
-* 6.4 PEER多Agent协作案例
-  * 6.4.1 [金融事件分析案例](./6_4_1_金融事件分析案例.md)
-* 6.5 [吴恩达反思工作流翻译智能体复刻](./7_1_1_翻译案例.md)
-
-**7.产品化平台**
-* 7.1 [快速使用](./10_1_1_产品化平台快速开始.md)
-* 7.2 [进阶指南](./10_1_2_产品化平台进阶指南.md)
-
-**8.系列文章**
-
-**9.常见问题FAQ**
-
-**[10.研究文献](9_1_研究文献.md)**
-
-**[11.联系我们](6_1_联系我们.md)**
diff --git "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md" "b/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md"
deleted file mode 100644
index 9047edce..00000000
--- "a/docs/guidebook/zh/1_1_\347\256\200\344\273\213.md"
+++ /dev/null
@@ -1,94 +0,0 @@
-## agentUniverse是什么？
-
-**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉
-
-**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。**
-
-![](../_picture/agent_universe_framework_resize.jpg)
-
-我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括：
-* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析
-* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成
-****************************************
-## 目录
-* [快速开始](#快速开始)
-* [案例与样例工程](#案例与样例工程)
-* [更多](#更多)
-  * [为什么使用agentUniverse](#为什么使用agentUniverse)
-  * [核心特性](#核心特性)
-  * [用户指南](#用户指南)
-  * [API参考](#API参考)
-  * [支持](#支持)
-  * [鸣谢](#鸣谢)
-****************************************
-## 快速开始
-使用pip：
-```shell
-pip install agentUniverse
-```
-
-我们将向您展示如何：
-* 进行环境与应用工程准备
-* 构建一个简单的agent
-* 使用模式组件完成多agent协同
-* 对agent执行效果进行测试调优
-* 对agent进行快速服务化
-
-详情请阅读[快速开始](1_3_%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 。
-****************************************
-## 案例与样例工程
-### 🌟 使用案例
-[法律咨询Agent](7_1_1_法律咨询案例.md)
-
-[Python代码生成与执行Agent](7_1_1_Python自动执行案例.md)
-
-[基于多轮多Agent的讨论小组](6_2_1_讨论组.md)
-
-### 🌟 示例项目
-[agentUniverse 示例项目](../../../sample_standard_app)
-
-### 🌟 使用aU构建的产品案例
-[支小助 金融从业专家AI助手](https://zhu.alipay.com/)
-****************************************
-## 更多
-### 为什么使用agentUniverse
-💡[为什么使用agentUniverse?](1_为什么选择agentUniverse.md)
-
-### 核心特性
-
-* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作；
-
-* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体；
-
-* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别；
-
-💡更多特定见[agentUniverse核心特性](1_核心特性.md)部分。
-
-### 用户指南
-💡更多详细信息，请阅读[用户指南](0_%E7%9B%AE%E5%BD%95.md) 。
-
-### API参考
-💡请阅读[readthedocs](https://agentuniverse.readthedocs.io/en/latest/) 。
-
-### 支持
-#### 通过github issue提交疑问
-😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。
-
-#### 通过Discord联系我们
-😊 加入我们的 [Discord频道](https://discord.gg/VfhEvJzQ) 与我们进行交流。
-
-#### 通过钉钉群联系我们
-😊 加入我们的钉钉答疑群与我们联系。
-![](../_picture/dingtalk_util20250429.png)
-
-#### 通过管理员Email联系我们
-😊 Email: [jerry.zzw@antgroup.com](mailto:jerry.zzw@antgroup.com)
-
-#### 微信公众号
-
-😊 公众号ID：**agentUniverse智多星**
-
-更多相关的文章与资讯你可以在微信公众号中获取。
-
-## 鸣谢
-本项目部分基于langchain、pydantic、gunicorn、flask、SQLAlchemy、chromadb等（详细依赖列表可见pyproject.toml）优秀开源项目实现，在此特别感谢相关项目与关联方。 🙏🙏🙏
\ No newline at end of file
diff --git "a/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" "b/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md"
deleted file mode 100644
index e5f0e4d7..00000000
--- "a/docs/guidebook/zh/1_\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md"
+++ /dev/null
@@ -1,10 +0,0 @@
-# 为什么选择agentUniverse
-通过agentUniverse您可以轻松构建出领域专家级别的强大智能体协同为您工作。
-
-agentUniverse提供了搭建单智能体的全部关键组件，所有可见的组件都是可扩展与定制的，它们可以非常轻松的连接大模型生态组件与中间件，帮助您构建能强大的单智能体。
-
-agentUniverse拥有丰富的多智能体协同能力，不同的协同模式专精于解决专属场景下的领域问题，多智能体将扮演不同的角色将各司其职围绕着任务目标协同工作，这将大大提升智能体们完成任务的表现；
-
-agentUniverse专注于领域prompt、领域知识、领域SOP构建管理的能力，通过这些能力将领域经验注入到所有智能体的协同工作中去，保证智能体在特定领域拥有对齐人工专家的能力；
-
-此外agentUniverse在金融领域已经有非常多的产业级产品与实践，我们会将金融领域经过产业验证的协同模式、领域经验、解决方案作为案例分享开放。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md" "b/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md"
deleted file mode 100644
index 17bd1b31..00000000
--- "a/docs/guidebook/zh/3_1_2_0_\346\250\241\345\236\213\345\210\227\350\241\250.md"
+++ /dev/null
@@ -1,20 +0,0 @@
-# 模型（LLM）列表
-目前平台已经接入了如下模型列表，详细使用方式请关注本章节各LLM的说明。
-
-| 模型LLM(系列)名称                     |
-|---------------------------------|
-| [OpenAI](3_1_2_OpenAI使用.md)     |
-| [通义千问](3_1_2_Qwen使用.md)         |
-| [文心一言](3_1_2_文心使用.md)           |
-| [Kimi](3_1_2_Kimi使用.md)         |
-| [百川](3_1_2_百川使用.md)             |
-| [Claude](3_1_2_Claude使用.md)     |
-| [ollama](3_1_2_Ollama使用.md)     |
-| [DeepSeek](3_1_2_DeepSeek使用.md) |
-| [智谱GLM](3_1_2_智谱GLM使用.md)    |
-
-为方便用户接入OpenAI风格协议的模型，提供基于OpenAI通用协议接入包装对象 [OpenAIStyleLLM](3_1_2_OpenAIStyleLLM使用.md)。
-
-未在列表中的模型您可以根据[模型定义](2_2_2_模型定义与使用.md)中的步骤自定义接入任何模型。
-
-同时我们正在添加Gemini、Llama、ZhipuAI等常见LLM的接入方式，如果您在工程中使用了这些LLM并创建了属于自己的实例，欢迎您提交pr成为agentUniverse的contributor。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" "b/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md"
deleted file mode 100644
index 91ca1157..00000000
--- "a/docs/guidebook/zh/7_1_1_\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md"
+++ /dev/null
@@ -1,88 +0,0 @@
-# 法律咨询案例
-## 案例说明
-本案例基于RagPlanner，搭建了一个简单的法律咨询智能体，通过检索民法典和刑法中的相关条例并结合案件背景给出相关的法律建议。
-
-该案例基于千问大模型和DashScope的embedding和rerank功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。
-
-## 构成
-### 法律知识库
-法律知识库基于agentUniverse中的[知识组件](2_2_4_知识及相关领域对象.md)，通过将法律条款原文存储至ChromaDB和Sqlite中，构建方便智能体查阅检索的知识库。  
-法律书籍原本：
-- [民法典.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
-- [刑法.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
-
-### [知识定义](../../../sample_standard_app/intelligence/agentic/knowledge/law_knowledge.yaml)
-```yaml
-name: "law_knowledge"
-description: "中国民法与刑法相关的知识库"
-stores:
-    - "civil_law_chroma_store"
-    - "criminal_law_chroma_store"
-    - "civil_law_sqlite_store"
-    - "criminal_law_sqlite_store"
-query_paraphrasers:
-    - "custom_query_keyword_extractor"
-insert_processors:
-    - "recursive_character_text_splitter"
-rag_router: "nlu_rag_router"
-post_processors:
-    - "dashscope_reranker"
-readers:
-    pdf: "default_pdf_reader"
-
-metadata:
-  type: 'KNOWLEDGE'
-  module: 'sample_standard_app.intelligence.agentic.knowledge.law_knowledge'
-  class: 'LawKnowledge'
-```
-
-### Reader组件
-- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)
-
-### DocProcessor组件
-- [custom_query_keyword_extractor](../../../sample_standard_app/intelligence/agentic/knowledge/doc_processor/query_keyword_extractor.yaml)
-- [recursive_character_text_splitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
-
-### QueryParaphraser组件
-- [custom_query_keyword_extractor](../../../sample_standard_app/intelligence/agentic/knowledge/query_paraphraser/custom_query_keyword_extractor.yaml)
-
-### RagRouter组件
-- [nlu_rag_router](../../../sample_standard_app/intelligence/agentic/knowledge/rag_router/nlu_rag_router.yaml)
-
-### Store组件
-- [civil_law_chroma_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_chroma_store.yaml)
-- [criminal_law_chroma_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_chroma_store.yaml)
-- [civil_law_sqlite_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_sqlite_store.yaml)
-- [criminal_law_sqlite_store](../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_sqlite_store.yaml)
-
-为了方便您使用，我们已经将存储有相关信息的数据库文件放在[该目录下](../../../sample_standard_app/intelligence/db/)，如果您想从头构建知识库的话，您可以运行如下代码：
-```python
-from agentuniverse.base.agentuniverse import AgentUniverse
-from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
-
-
-if __name__ == '__main__':
-    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
-    civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"]
-    criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"]
-    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
-    law_knowledge.insert_knowledge(
-        source_path="../resources/刑法.pdf",
-        stores=criminal_store_list
-    )
-    law_knowledge.insert_knowledge(
-        source_path="../resources/民法典.pdf",
-        stores=civil_store_list
-    )
-```
-
-### 法律智能体
-该智能体涉及如下1个文件： 
-- [law_rag_agent.yaml](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/law_rag_agent.yaml): 定义了智能体的相关提示词
-
-
-### 演示代码
-[代码链接](../../../sample_standard_app/intelligence/test/law_chat_bot.py)
-
-## 效果演示
-![演示图片](../_picture/law_agent_demo.png)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/How-to/\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md"
similarity index 81%
rename from "docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md"
rename to "docs/guidebook/zh/How-to/\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md"
index 68f5a2dc..178c3319 100644
--- "a/docs/guidebook/zh/2_2_4_\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md"
+++ "b/docs/guidebook/zh/How-to/\345\246\202\344\275\225\346\236\204\345\273\272RAG\346\231\272\350\203\275\344\275\223.md"
@@ -1,6 +1,6 @@
 # 如何构建RAG智能体
 
-本教程提供一个在agentUniverse中快速构建一个RAG智能体。案例的完整结构和内容可在[该文档](7_1_1_法律咨询案例.md)中查看，本文档侧重在构建过程的教学，因此省略了部分非必要的内容。
+本教程提供一个在agentUniverse中快速构建一个RAG智能体。案例的完整结构和内容可在[该文档](../样例文档/法律咨询案例.md)中查看，本文档侧重在构建过程的教学，因此省略了部分非必要的内容。
 
 ## 案例说明
 本案例基于RagPlanner，搭建了一个简单的法律咨询智能体，通过检索民法典和刑法中的相关条例并结合案件背景给出相关的法律建议。
@@ -31,8 +31,8 @@ metadata:
 ## 构建知识索引
 
 法律书籍原本：
-- [民法典.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
-- [刑法.pdf](../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
+- [民法典.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
+- [刑法.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/刑法.pdf)
 
 ### 抽取PDF中的文本内容
 因为本案例中，原始文档是pdf格式，因此我们在Knowledge中进行了如下配置：
@@ -40,7 +40,7 @@ metadata:
 readers:
     pdf: "default_pdf_reader"
 ```
-这样就可以将pdf中的文本抽取出来。如果您想要读取更多格式的文件，您可以参考[Reader组件](2_2_4_Reader.md)中的内容
+这样就可以将pdf中的文本抽取出来。如果您想要读取更多格式的文件，您可以参考[Reader组件](../In-Depth_Guides/原理介绍/知识/Reader.md)中的内容
 
 ### 拆分长文本
 因为原始文档中的文本内容非常长，因此我们需要将其拆分为小的片段。这里我们选用`recursive_character_text_splitter`对其进行拆分，配置如下：
@@ -48,7 +48,7 @@ readers:
 insert_processors:
     - "recursive_character_text_splitter"
 ```
-该配置项是一个list形式，可以配置多种处理文档的处理器，这个案例中指定的唯一处理器`recursive_character_text_splitter`会根据指定的分隔符递归的拆分原始文档直到满足指定长度，具体内容可以参考[DocProcessor](2_2_4_DocProcessor.md)。该文档中也包含了其它的文档处理器，您可以直接使用或是编写自己的处理器。
+该配置项是一个list形式，可以配置多种处理文档的处理器，这个案例中指定的唯一处理器`recursive_character_text_splitter`会根据指定的分隔符递归的拆分原始文档直到满足指定长度，具体内容可以参考[DocProcessor](../In-Depth_Guides/原理介绍/知识/DocProcessor.md)。该文档中也包含了其它的文档处理器，您可以直接使用或是编写自己的处理器。
 
 ### 配置Store
 本案例中包含四个Store：民法和刑法分别存储至sqlite以及chromadb中。我们仅以`civil_law_chroma_store`作为例子，其它Store类似。  
@@ -65,7 +65,7 @@ metadata:
   class: 'ChromaStore'
 ```
 
-其中`persist_path`指定了数据库本地文件的存储位置，并指定`dashscope_embedding`作为数据库向量化文本的组件，`similarity_top_k`表示召回的文档数量。关于`Store`的具体内容您可以在[该文档](2_2_4_Store.md)中查看。
+其中`persist_path`指定了数据库本地文件的存储位置，并指定`dashscope_embedding`作为数据库向量化文本的组件，`similarity_top_k`表示召回的文档数量。关于`Store`的具体内容您可以在[该文档](../In-Depth_Guides/原理介绍/知识/Store.md)中查看。
 
 ### 执行插入流程
 在完成上述的配置后，我们可以执行下面的代码完成知识库的构建：
@@ -107,4 +107,4 @@ action:
 ```
 我们将knowledge像这样配置在Agent中的yaml中即可。
 
-完整的案例以及调用方法请参考[文档](7_1_1_法律咨询案例.md)。
\ No newline at end of file
+完整的案例以及调用方法请参考[文档](../样例文档/法律咨询案例.md)。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/10_1_1_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md" "b/docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md"
similarity index 82%
rename from "docs/guidebook/zh/10_1_1_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md"
rename to "docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md"
index b7d2f77e..73748080 100644
--- "a/docs/guidebook/zh/10_1_1_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md"
+++ "b/docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\345\277\253\351\200\237\345\274\200\345\247\213.md"
@@ -1,4 +1,4 @@
-# 产品化平台快速开始
+# 画布式研发平台进阶指南
 在本部分我们将向您展示如何：
 * 启动agentUniverse产品化服务
 * 配置agentUniverse产品模块
@@ -7,7 +7,7 @@
 
 ## 环境与应用工程准备
 ### 应用工程准备
-我们将**产品化模块样例**放在了agentUniverse的sample_standard_app工程中，你可以到 [这里](../../../sample_standard_app/platform/difizen/product) 查看。这部分可以在后台通过yaml配置，当然通过产品页面可以自动创建和管理这些功能。
+我们将**产品化模块样例**放在了agentUniverse的sample_standard_app工程中，你可以到 [这里](../../../../sample_standard_app/platform/difizen/product) 查看。这部分可以在后台通过yaml配置，当然通过产品页面可以自动创建和管理这些功能。
 
 ### 安装依赖
 **通过pip安装**
@@ -32,27 +32,27 @@ product = ['sample_standard_app.platform.difizen.product']
 
 ## 使用agentUniverse产品化平台
 ### 启动产品化服务
-运行sample_standard_app/boostrap下的[product_application](../../../sample_standard_app/boostrap/platform/product_application.py)文件，一键启动。
+运行sample_standard_app/boostrap下的[product_application](../../../../sample_standard_app/boostrap/platform/product_application.py)文件，一键启动。
 
-![img.png](../_picture/product_start.png)
+![img.png](../../_picture/product_start.png)
 
 启动成功后，自动跳转产品化主页，主页包含系统预制和您自定义的Agent/Tool/Knowledge产品模块。
-![agentuniverse_product_homepage](../_picture/agentuniverse_product_homepage.png)
+![agentuniverse_product_homepage](../../_picture/agentuniverse_product_homepage.png)
 
 ### 体验智能体
 如上图所示，点击peer多智能体组的右一聊天按钮，即进入会话页面。
 
 会话管理系统包含近10次智能体会话历史记录，可以直接进行多轮对话，体验peer多智能体组能力（默认流式对话，多智能体组包含运行中间思考过程）。
-![agentuniverse_product_agent_chat](../_picture/agentuniverse_product_agent_chat.png)
+![agentuniverse_product_agent_chat](../../_picture/agentuniverse_product_agent_chat.png)
 
 ### 调试智能体
 在产品化主页，点击智能体的左一编辑按钮，即进入在线调试页面。
 
 可以在线调试智能体的Prompt/Tool/Knowledge/LLM等，点击保存按钮，aU-product将配置自动保存到对应的yaml文件中。
-![agentuniverse_product_agent_editor](../_picture/agentuniverse_product_agent_editor.png)
+![agentuniverse_product_agent_editor](../../_picture/agentuniverse_product_agent_editor.png)
 
 点击上图中右上角的调试按钮，可查看智能体具体调用过程的Token消耗/调用链条/时延等Trace信息。
-![agentuniverse_product_agent_trace](../_picture/agentuniverse_product_agent_trace.png)
+![agentuniverse_product_agent_trace](../../_picture/agentuniverse_product_agent_trace.png)
 
 ## 配置agentUniverse产品模块
 ### 创建产品模块
diff --git "a/docs/guidebook/zh/10_1_2_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md" "b/docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md"
similarity index 83%
rename from "docs/guidebook/zh/10_1_2_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md"
rename to "docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md"
index d3613bb1..ccd638dc 100644
--- "a/docs/guidebook/zh/10_1_2_\344\272\247\345\223\201\345\214\226\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md"
+++ "b/docs/guidebook/zh/How-to/\347\224\273\345\270\203\345\274\217\347\240\224\345\217\221\345\271\263\345\217\260\350\277\233\351\230\266\346\214\207\345\215\227.md"
@@ -14,7 +14,7 @@ pip install magent-ui==0.1.17 ruamel.yaml --force-reinstall
 ```
 ### 创建workflow智能体
 产品化主页，点击智能体的右一创建智能体按钮，选择workflow类型智能体。
-![agentuniverse_product_create_workflow_agent](../_picture/create_workflow_agent.png)
+![agentuniverse_product_create_workflow_agent](../../_picture/create_workflow_agent.png)
 
 ### workflow节点类型
 当前aU产品化平台支持7种节点类型，分别是：
@@ -36,16 +36,16 @@ pip install magent-ui==0.1.17 ruamel.yaml --force-reinstall
 ```
 
 workflow完整流程如下图：
-![agentuniverse_product_workflow_agent](../_picture/workflow_agent.png)
+![agentuniverse_product_workflow_agent](../../_picture/workflow_agent.png)
 
 aU自动生成的智能体配置文件如下图：
-![agentuniverse_product_workflow_agent_yaml](../_picture/workflow_agent_yaml.png)
+![agentuniverse_product_workflow_agent_yaml](../../_picture/workflow_agent_yaml.png)
 
 ### 运行workflow智能体
 点击保存按钮后，开始尝试运行workflow智能体。
 
 运行流程如图所示：
-![agentuniverse_product_run_workflow_agent](../_picture/run_workflow_agent.png)
+![agentuniverse_product_run_workflow_agent](../../_picture/run_workflow_agent.png)
 
 
 ## 表单智能体搭建功能
@@ -54,13 +54,13 @@ aU自动生成的智能体配置文件如下图：
 
 aU将在本地自动生成对应的agent yaml文件，帮助用户完成开发工作。
 
-![agentuniverse_product_create_agent](../_picture/create_agent.png)
+![agentuniverse_product_create_agent](../../_picture/create_agent.png)
 
 ### 运行智能体
 点击保存按钮后，开始尝试运行创建的示例RAG智能体。
 
 运行流程如图所示：
-![agentuniverse_product_run_demo_rag_agent](../_picture/run_demo_rag_agent.png)
+![agentuniverse_product_run_demo_rag_agent](../../_picture/run_demo_rag_agent.png)
 
 ## 自定义知识录入
 ### 创建自定义知识库
@@ -68,7 +68,7 @@ aU将在本地自动生成对应的agent yaml文件，帮助用户完成开发
 
 aU将在本地自动生成对应的knowledge和store yaml文件，帮助用户完成开发工作。
 
-![agentuniverse_product_create_knowledge](../_picture/create_knowledge.png)
+![agentuniverse_product_create_knowledge](../../_picture/create_knowledge.png)
 
 ### 上传知识
 指定知识库，点击上传按钮，上传本地知识文件。
@@ -79,12 +79,12 @@ aU将在本地自动生成对应的knowledge和store yaml文件，帮助用户
 当前支持知识类型为：pdf/docx/pptx/txt
 ```
 上传操作如图所示：
-![agentuniverse_product_upload_knowledge](../_picture/upload_knowledge.png)
+![agentuniverse_product_upload_knowledge](../../_picture/upload_knowledge.png)
 
 ## 自定义插件录入
 ### 创建自定义插件
 产品化主页，切换到插件tab，点击右一创建插件按钮，创建自定义插件。
-![agentuniverse_product_create_plugin](../_picture/create_plugin.png)
+![agentuniverse_product_create_plugin](../../_picture/create_plugin.png)
 
 创建成功后，即可在workflow智能体的工具节点或表单智能体的工具模块中使用对应工具，为智能体提供强大的第三方插件能力支持。
 
diff --git a/docs/guidebook/zh/2_2_4_RAG.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/RAG.md"
similarity index 84%
rename from docs/guidebook/zh/2_2_4_RAG.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/RAG.md"
index 737f7485..091bfe71 100644
--- a/docs/guidebook/zh/2_2_4_RAG.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/RAG.md"
@@ -3,7 +3,7 @@
 Retrieval-Augmented Generation (RAG) 是一种前沿的自然语言处理（NLP）技术，通过结合信息检索和生成模型，显著提升了文本生成任务的准确性和多样性。RAG 的核心思想在于，在生成文本之前，先从大规模文档库中检索出与输入查询最相关的内容，并将这些检索到的信息作为上下文，传递给生成模型。这样，生成模型不仅仅依赖于训练数据，还可以动态获取最新的外部知识，从而让智能体生成更加符合语境的响应。
 
 ## 通用RAG流程
-![rag_structure](../_picture/rag_structure.png)
+![rag_structure](../../../_picture/rag_structure.png)
 
 参考[这篇综述](https://arxiv.org/pdf/2312.10997)，我们可以将现在大多数的RAG流程概括为三个部分：
 1. 文档的索引构建：从原始的大规模文档库中的Documents构建出便于检索召回的形式，一般是将文本拆分为小段的chunks后通过embedding转为向量索引的形式存储在向量数据库中。
@@ -12,9 +12,9 @@ Retrieval-Augmented Generation (RAG) 是一种前沿的自然语言处理（NLP
 
 ## agentUniverse中的RAG
 对比上述的通用RAG流程，agentUniverse将RAG拆分为了两部分：
-1. 知识组件：包含从文档构建知识库以及根据Query检索知识的能力。您可以参考[知识](2_2_4_知识.md)了解详细的使用方式。
-2. 智能体利用知识生成回答：在第一步构建完成知识组件后，智能体可以利用知识组件召回和Query相关的文档，和提示词以及上下文一同传递给大模型，生成最终的回答。具体使用方法您可以参考[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的action下的knowledge中设置您创建的任意知识。
+1. 知识组件：包含从文档构建知识库以及根据Query检索知识的能力。您可以参考[知识](./知识/知识.md)了解详细的使用方式。
+2. 智能体利用知识生成回答：在第一步构建完成知识组件后，智能体可以利用知识组件召回和Query相关的文档，和提示词以及上下文一同传递给大模型，生成最终的回答。具体使用方法您可以参考[智能体创建与使用](./智能体/智能体创建与使用.md)中的内容在agent的action下的knowledge中设置您创建的任意知识。
 
-如果您想快速利用自己的文档构建RAG流程并在智能体中使用的话，您可以参考[如何构建基于知识的RAG智能体](2_2_4_如何构建RAG智能体.md)。
+如果您想快速利用自己的文档构建RAG流程并在智能体中使用的话，您可以参考[如何构建基于知识的RAG智能体](../../How-to/如何构建RAG智能体.md)。
 
 
diff --git "a/docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217.md"
similarity index 85%
rename from "docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217.md"
index af4079f4..cf939d9f 100644
--- "a/docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217.md"
@@ -4,7 +4,7 @@
 
 ## 如何定义工作模式
 
-### [工作模式基础类定义](../../../agentuniverse/agent/work_pattern/work_pattern.py)
+### [工作模式基础类定义](../../../../../../agentuniverse/agent/work_pattern/work_pattern.py)
 
 包含配置信息如下：
 
@@ -21,7 +21,7 @@
 
 ### 工作模式具体实现类的定义
 
-以agentUniverse内置的[PEER工作模式](../../../agentuniverse/agent/work_pattern/peer_work_pattern.py)为例，定义了四个属性：
+以agentUniverse内置的[PEER工作模式](../../../../../../agentuniverse/agent/work_pattern/peer_work_pattern.py)为例，定义了四个属性：
 
 - planning: plan拆解节点
 - executing: execute执行节点
@@ -31,11 +31,11 @@
 invoke和async_invoke分别为工作模式的同步和异步执行方法。工作模式执行逻辑**只包含各节点间的流转逻辑，相比于智能体模版，可以理解为更高层级的系统抽象**。
 
 ## 如何使用工作模式
-以上文提及的PEER工作模式为例，其调用关系存在于[PEER智能体模版](../../../agentuniverse/agent/template/peer_agent_template.py)中，
+以上文提及的PEER工作模式为例，其调用关系存在于[PEER智能体模版](../../../../../../agentuniverse/agent/template/peer_agent_template.py)中，
 在智能体模版的执行逻辑中，获取指定工作模式实例，装配属性并执行。
 
 流程图如下：
-![peer工作模式](../_picture/peer_work_pattern.png)
+![peer工作模式](../../../../_picture/peer_work_pattern.png)
 
 通过调用peer智能体模版对应的智能体实例，开启整体流程；peer智能体模版在做好前置校验工作后，调用工作模式，工作模式执行各节点成员间的流转逻辑，最终将结果返回给peer智能体模版，进行具体的拼装、后置处理等，最终结束整体流程。
 
@@ -52,7 +52,7 @@ work_pattern = WorkPatternManager().get_instance_obj(component_instance_name=wor
 
 ## agentUniverse目前内置有以下工作模式:
 
-### [PeerWorkPattern](../../../agentuniverse/agent/work_pattern/peer_work_pattern.py)
+### [PeerWorkPattern](../../../../../../agentuniverse/agent/work_pattern/peer_work_pattern.py)
 
 内置系统配置如下：
 
diff --git "a/docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 83%
rename from "docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index e610ed6b..64197d40 100644
--- "a/docs/guidebook/zh/2_2_8_\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\344\275\234\346\250\241\345\274\217/\345\267\245\344\275\234\346\250\241\345\274\217\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,5 +1,5 @@
 # 工作模式与相关领域对象
-在本章节我们会重点列出WorkPattern与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。
+在本章节我们会重点列出WorkPattern与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## WorkPattern基础类
 包路径：`agentuniverse.agent.work_pattern.work_pattern.WorkPattern`
diff --git "a/docs/guidebook/zh/2_2_3_\345\267\245\345\205\267.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267.md"
similarity index 100%
rename from "docs/guidebook/zh/2_2_3_\345\267\245\345\205\267.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267.md"
diff --git "a/docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
similarity index 96%
rename from "docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
index 6718f1a0..bb1962b3 100644
--- "a/docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
@@ -86,7 +86,7 @@ tool = ['sample_standard_app.intelligence.agentic.tool']
 
 # 如何使用工具Tool组件
 ## 在Agent中配置使用
-您可以根据[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的action下的tool中设置您创建的任意工具。
+您可以根据[智能体创建与使用](../智能体/智能体创建与使用.md)中的内容在agent的action下的tool中设置您创建的任意工具。
 
 可参考示例：`demo_rag_agent`， 具体文件路径为 `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_rag_agent.yaml`。
 
diff --git "a/docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 81%
rename from "docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index 7239eba1..0d90adb4 100644
--- "a/docs/guidebook/zh/2_2_3_\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\345\267\245\345\205\267/\345\267\245\345\205\267\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,5 +1,5 @@
 # 工具与相关领域对象
-在本章节我们会重点列出Tool与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。
+在本章节我们会重点列出Tool与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## Tool基础类
 包路径：`agentuniverse.agent.action.tool.tool.Tool`
diff --git "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md"
similarity index 72%
rename from "docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md"
index 1aea0e92..4c683540 100644
--- "a/docs/guidebook/zh/8_1_1_\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\225\260\346\215\256\350\207\252\346\262\273\346\231\272\350\203\275\344\275\223.md"
@@ -19,7 +19,7 @@ dataAgent希望通过智能体本身的能力，对Agent数据进行**自采集
 
 
 ## dataAgent流程图
-![data_agent_flowchart](../_picture/data_agent_flowchart.jpg)
+![data_agent_flowchart](../../../_picture/data_agent_flowchart.jpg)
 - data_agent包含两部分，**dataset_build_agent**负责自动化批量执行多轮智能体调用，生产评测数据集
 - **dataset_eval_agent**负责自动化多维度评估数据集，产出评测报告
 
@@ -48,9 +48,9 @@ metadata:
   module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.data_agent'
   class: 'DataAgent'
 ```
-[data_agent配置文件样例地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
+[data_agent配置文件样例地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
 
-[data_agent代码地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
+[data_agent代码地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
 
 ### step3 配置生产评测数据集的智能体
 以step2中`dataset_builder`配置的**dataset_build_agent**，如下所示为dataset_build_agent的配置文件，配置文件除智能体基本配置外，主要包含两个重要项，`candidate`配置待评测的智能体名称（如评测demo_rag_agent效果，candidate配置demo_rag_agent），`concurrency_level`配置批量执行智能体调用时的并发度（如配置5，代表并发5调用candidate智能体）
@@ -68,9 +68,9 @@ metadata:
   module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.dataset_build_agent'
   class: 'DatasetBuildAgent'
 ```
-[dataset_build_agent配置文件样例地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
+[dataset_build_agent配置文件样例地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
 
-[dataset_build_agent代码地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
+[dataset_build_agent代码地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
 
 ### step4 配置数据集评估标注的智能体
 以step2中`dataset_evaluator`配置的**dataset_eval_agent**，如下所示为dataset_eval_agent的配置文件，配置文件除智能体基本配置外，主要包含两个重要项，`llm_model`配置智能体模型，`max_eval_lines`配置评测数据行数（如配置10代表只评估前10条评测数据，避免全局评测，消耗巨大token量）
@@ -90,12 +90,12 @@ metadata:
   module: 'sample_standard_app.intelligence.agentic.agent.agent_instance.data_agent_case.dataset_eval_agent'
   class: 'DatasetEvalAgent'
 ```
-[dataset_eval_agent配置文件样例地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
+[dataset_eval_agent配置文件样例地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
 
-[dataset_eval_agent代码地址](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
+[dataset_eval_agent代码地址](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
 
 ### step5 运行dataAgent
-通过 [dataAgent代码入口](../../../sample_standard_app/intelligence/test/data_agent.py)，分别配置两个参数，`queryset_path`代表问题集路径，`turn`代表问题集总体执行轮次，一键启动dataAgent。
+通过 [dataAgent代码入口](../../../../../sample_standard_app/intelligence/test/data_agent.py)，分别配置两个参数，`queryset_path`代表问题集路径，`turn`代表问题集总体执行轮次，一键启动dataAgent。
 
 tips: 请合理配置问题集及具体评测行数，以免造成大量算力及token损耗。
 
@@ -108,9 +108,9 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力
  - query: 评测数据问题
  - answer: 评测数据答案
 
-![data_agent_dataset](../_picture/data_agent_dataset.png)
+![data_agent_dataset](../../../_picture/data_agent_dataset.png)
 
-[dataAgent生产的评测数据集样例地址](../../../sample_standard_app/intelligence/test/data/dataset_turn_1_2024-07-10-10-48-30.jsonl)
+[dataAgent生产的评测数据集样例地址](../../../../../sample_standard_app/intelligence/test/data/dataset_turn_1_2024-07-10-10-48-30.jsonl)
 
 ### 完整评测结果
 生产评测数据集后，dataAgent开始数据多维度评估标注，产出完整评测结果（若执行多轮dataAgent跑批任务，则产出多个完整评测结果）。
@@ -123,13 +123,13 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力
  - 相关性 Score: 代表评测数据答案与问题的相关性，分数越高越相关（满分5分，分数范围0-5）
  - 相关性 Suggestion: 代表相关性维度存在的问题和改进建议
  - 更多维度 Score/Suggestion 以此类推
-![data_agent_eval_result](../_picture/data_agent_eval_result.png)
+![data_agent_eval_result](../../../_picture/data_agent_eval_result.png)
 
 评估节点发现的问题：
  - 例如第1条数据在**相关性维度的suggestion**: 虽然回答了关于北京天气的问题，但提供的温度单位为华氏度，与国内用户习惯的摄氏度不符，建议转换为摄氏度并提供更全面的天气信息，如湿度、风力等。
  - 例如第3条数据在**事实性维度的suggestion**: 回答中包含事实错误，如将阿根廷球星莱昂内尔·梅西错误地归入英格兰队。优化建议是确保所有提及的数据和事实准确无误，尤其是在涉及具体人物和事件时。
 
-[dataAgent生产的完整评测结果样例地址](../../../sample_standard_app/intelligence/test/data/eval_result_turn_1_2024-07-10-10-48-30.xlsx)
+[dataAgent生产的完整评测结果样例地址](../../../../../sample_standard_app/intelligence/test/data/eval_result_turn_1_2024-07-10-10-48-30.xlsx)
 
 ### 综合评测报告
 根据多轮完整的评测结果，生成一份综合评测报告。
@@ -139,34 +139,34 @@ tips: 请合理配置问题集及具体评测行数，以免造成大量算力
  - Overall Avg Score: 单轮数据集中所有数据的综合分数总和 / 数据集中对应数据量（满分5分，分数范围0-5）
  - 相关性 Avg Score: 单轮数据集中所有数据的相关性分数总和 / 数据集中对应数据量（满分5分，分数范围0-5）
  - 更多维度 Avg Score 以此类推
-![data_agent_eval_report](../_picture/data_agent_eval_report.png)
+![data_agent_eval_report](../../../_picture/data_agent_eval_report.png)
 
-[dataAgent生产的综合评测报告样例地址](../../../sample_standard_app/intelligence/test/data/eval_report_2024-07-10-10-48-30.xlsx)
+[dataAgent生产的综合评测报告样例地址](../../../../../sample_standard_app/intelligence/test/data/eval_report_2024-07-10-10-48-30.xlsx)
 
 ### 对比实验
 调整aU中`demo_rag_agent`中的模型从上文生产评测报告时的**qwen1.5-7b-chat**改为**qwen1.5-72b-chat**，通过dataAgent评测后，生成的综合评测报告如下:
 
 下图为模型为qwen1.5-7b-chat时，数据自治智能体产出的综合评测报告:
-![data_agent_eval_report](../_picture/data_agent_eval_report.png)
+![data_agent_eval_report](../../../_picture/data_agent_eval_report.png)
 
 下图为模型为qwen1.5-72b-chat时，数据自治智能体产出的综合评测报告:
-![data_agent_eval_report](../_picture/data_agent_eval_report_qwen72b.png)
+![data_agent_eval_report](../../../_picture/data_agent_eval_report_qwen72b.png)
 对比两次综合评测报告，可以看到智能体模型从qwen1.5-7b-chat改为qwen1.5-72b-chat后，各维度多轮评分均存在大幅度上升，以此方法快速分辨智能体配置变更后，智能体效果差异。
 
 
 ## dataAgent详细说明
 ### data_agent
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
-- [智能体文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
+- [配置文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.yaml)
+- [智能体文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/data_agent.py)
 
 ### dataset_build_agent
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
-- [智能体文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
+- [配置文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.yaml)
+- [智能体文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_build_agent.py)
 - dataset_build_agent生产的评测数据以jsonl格式本地存储（jsonl文件名称为dataset_turn_{i}_{date}，`i`代表轮次，`date`代表生成时间）
 
 ### dataset_eval_agent
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
-- [智能体文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
-- [提示词文件](../../../sample_standard_app/intelligence/agentic/prompt/dataset_eval_agent_cn.yaml)：agentUniverse当前开放了6种产业中验证有效的智能体评估标准（MVP版本未开放**全面性维度**，当前全面性评估标准偏向金融领域，故未在开源社区中提及）
+- [配置文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.yaml)
+- [智能体文件](../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/data_agent_case/dataset_eval_agent.py)
+- [提示词文件](../../../../../sample_standard_app/intelligence/agentic/prompt/dataset_eval_agent_cn.yaml)：agentUniverse当前开放了6种产业中验证有效的智能体评估标准（MVP版本未开放**全面性维度**，当前全面性评估标准偏向金融领域，故未在开源社区中提及）
 - dataset_eval_agent生产的**完整评测结果**以excel格式本地存储（文件名称为eval_result_turn_{i}_{date}，`i`代表轮次，`date`代表生成时间）
 - dataset_eval_agent生产的**综合评测报告**以excel格式本地存储（文件名称为eval_report_{date}，date代表生成时间）
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223.md"
similarity index 99%
rename from "docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223.md"
index 15020d06..e2829699 100644
--- "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223.md"
@@ -2,7 +2,7 @@
 智能体（Agent）能够自主行动实现人类设定的目标，它具备学习、推理、决策和执行能力，通过任务拆分、工具知识使用、进度控制等步骤，实现目标并自主结束工作。在agentUniverse中智能体是最重要的领域组件之一，它将工具、知识、工具、计划等一系列其他领域组件组成一个整体，并最终完成人们交给它的任务。
 
 智能体的表现直接影响了应用服务能力的强弱，在复杂的服务场景中往往需要一个或多个非常出色的智能体去完成任务。agentUniverse根据业界与学界的成果结合实际产业落地过程中的经验拥有如下对于agent的定义，见图。
-![](../_picture/agent.jpg)
+![](../../../../_picture/agent.jpg)
 
 让我们分别来介绍一下Agent组件中各个组成部分的作用。
 
diff --git "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
similarity index 97%
rename from "docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
index a0a53691..3b769ab8 100644
--- "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\210\233\345\273\272\344\270\216\344\275\277\347\224\250.md"
@@ -1,5 +1,5 @@
 # 如何创建智能体
-您或许已经在[快速开始](1_3_快速开始.md)章节中了解如何快速创建一个智能体，亦或是在[智能体原理](2_2_1_智能体.md)掌握了智能体的重要组成部分，在本节我们会进一步详细说明如何创建智能体。
+您或许已经在[快速开始](../../../开始使用/快速开始.md)章节中了解如何快速创建一个智能体，亦或是在[智能体原理](智能体.md)掌握了智能体的重要组成部分，在本节我们会进一步详细说明如何创建智能体。
 
 根据agentUniverse领域组件的设计特性，创建一个智能体在创建过程中由2部分组成:
 * agent_xx.yaml
@@ -24,7 +24,7 @@
   * `name`: LLM 名称
   * `model_name`: LLM 模型名  
 
-  您可以选择已有或接入任意的LLM，我们在本部分不展开说明，您可以关注[模型LLM章节](2_2_2_模型.md)。
+  您可以选择已有或接入任意的LLM，我们在本部分不展开说明，您可以关注[模型LLM章节](../模型/模型.md)。
 
 ### 设置智能体计划
 **`plan` - 智能体计划**
@@ -41,7 +41,7 @@
     \- tool_name_b  
     \- tool_name_c  
 
-  您可以选择已有或接入任意的Tool，我们在本部分不展开说明，您可以关注[Tool章节](2_2_3_工具.md)。
+  您可以选择已有或接入任意的Tool，我们在本部分不展开说明，您可以关注[Tool章节](../工具/工具.md)。
 
 * `Knowledge` : 供智能体使用的知识
   * knowledge_name_list，知识名称列表, 如:  
@@ -388,7 +388,7 @@ if __name__ == '__main__':
 ```
 
 ## 方案二: 使用agent_serve服务化能力
-agentUniverse提供多套标准的web serve能力, 同时提供标准的http与rpc协议。您可以进一步关注文档[服务注册与使用](2_4_1_服务注册与使用.md)与[Web_Server](2_4_1_Web_Server.md)部分。
+agentUniverse提供多套标准的web serve能力, 同时提供标准的http与rpc协议。您可以进一步关注文档[服务注册与使用](../../技术组件/服务化/服务注册与使用.md)与[Web_Server](../../技术组件/服务化/Web_Server.md)部分。
 
 # 了解更多已有的智能体
 框架提供的智能体在`agentuniverse.agent.default`包路径下，您可以进一步查看对应代码或在我们的扩展组件介绍部分进一步了解他们。
diff --git "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 85%
rename from "docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index 6ccb4056..e7b2d2c8 100644
--- "a/docs/guidebook/zh/2_2_1_\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,5 +1,5 @@
 # 智能体与相关领域对象
-在本章节我们会重点列出智能体与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。
+在本章节我们会重点列出智能体与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## Agent基础类
 包路径：`agentuniverse.agent.agent.Agent`
diff --git "a/docs/guidebook/zh/2_2_7_\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md"
similarity index 85%
rename from "docs/guidebook/zh/2_2_7_\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md"
index 633bac80..fcc73a11 100644
--- "a/docs/guidebook/zh/2_2_7_\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\231\272\350\203\275\344\275\223/\346\231\272\350\203\275\344\275\223\346\250\241\347\211\210.md"
@@ -2,8 +2,8 @@
 智能体模版用于帮助用户快速构建智能体，当完成template模版层抽象后，使用者只需填写智能体模版里特定属性配置信息，即可执行特定编排逻辑，如agentUniverse系统内置的RAG template/ReAct template/PEER template等。
 
 ## 如何定义智能体模版
-### [智能体模版基础类定义](../../../agentuniverse/agent/template/agent_template.py)
-AgentTemplate基于[Agent基础类](../../../agentuniverse/agent/agent.py)，包含配置信息如下：
+### [智能体模版基础类定义](../../../../../../agentuniverse/agent/template/agent_template.py)
+AgentTemplate基于[Agent基础类](../../../../../../agentuniverse/agent/agent.py)，包含配置信息如下：
 
 - llm_name: LLM领域组件名称, 用户在agent yaml profile中`llm_model`配置后，模版类自动装配
 - memory_name: 记忆领域组件名称, 用户在agent yaml `memory`中配置后，模版类自动装配
@@ -55,7 +55,7 @@ template模版实现类，需继承AgentTemplate基础类，并定义当前模
 注：AgentTemplate基础类定义已足够抽象，**推荐您根据需求重写特定方法，不推荐完全复制，代码结构臃肿复杂，维护成本高**。
 
 ## 如何使用智能体模版
-以agentUniverse内置的[PEER智能体模版](../../../agentuniverse/agent/template/peer_agent_template.py)为例，可填写配置信息如下（以下配置信息均存在默认值）：
+以agentUniverse内置的[PEER智能体模版](../../../../../../agentuniverse/agent/template/peer_agent_template.py)为例，可填写配置信息如下（以下配置信息均存在默认值）：
 - planning_agent_name: plan拆解节点智能体名称，用户在agent yaml profile中配置`planning`后，模版类自动装配
 - executing_agent_name: execute执行节点智能体名称，用户在agent yaml profile中配置`executing`后，模版类自动装配
 - expressing_agent_name: express表达节点智能体名称，用户在agent yaml profile中配置`expressing`后，模版类自动装配
@@ -93,24 +93,24 @@ metadata:
 
 
 ## agentUniverse目前内置有以下智能体模版:
-### [PeerAgentTemplate](../../../agentuniverse/agent/template/peer_agent_template.py)
+### [PeerAgentTemplate](../../../../../../agentuniverse/agent/template/peer_agent_template.py)
 上文已介绍，此处省略。
 
 Peer智能体模版中四个节点智能体实例类型需为对应智能体模版类（PlanningAgentTemplate/ExecutingAgentTemplate/ExpressingAgentTemplate/ReviewingAgentTemplate）或对应自定义子类。
 
-### [PlanningAgentTemplate](../../../agentuniverse/agent/template/planning_agent_template.py)
+### [PlanningAgentTemplate](../../../../../../agentuniverse/agent/template/planning_agent_template.py)
 Peer中的Planning节点智能体模版。
 
-### [ExecutingAgentTemplate](../../../agentuniverse/agent/template/executing_agent_template.py)
+### [ExecutingAgentTemplate](../../../../../../agentuniverse/agent/template/executing_agent_template.py)
 Peer中的Executing节点智能体模版。
 
-### [ExpressingAgentTemplate](../../../agentuniverse/agent/template/expressing_agent_template.py)
+### [ExpressingAgentTemplate](../../../../../../agentuniverse/agent/template/expressing_agent_template.py)
 Peer中的Expressing节点智能体模版。
 
-### [ReviewingAgentTemplate](../../../agentuniverse/agent/template/reviewing_agent_template.py)
+### [ReviewingAgentTemplate](../../../../../../agentuniverse/agent/template/reviewing_agent_template.py)
 Peer中的Reviewing节点智能体模版。
 
-### [RagAgentTemplate](../../../agentuniverse/agent/template/rag_agent_template.py)
+### [RagAgentTemplate](../../../../../../agentuniverse/agent/template/rag_agent_template.py)
 RAG智能体模版，具体配置示例如下：
 ```yaml
 info:
@@ -133,7 +133,7 @@ metadata:
   class: 'RagAgentTemplate'
 ```
 
-### [ReActAgentTemplate](../../../agentuniverse/agent/template/react_agent_template.py)
+### [ReActAgentTemplate](../../../../../../agentuniverse/agent/template/react_agent_template.py)
 ReAct智能体模版，具体配置示例如下：
 ```yaml
 info:
@@ -160,7 +160,7 @@ metadata:
   class: 'ReActAgentTemplate'
 ```
 
-### [Nl2ApiAgentTemplate](../../../agentuniverse/agent/template/nl2api_agent_template.py)
+### [Nl2ApiAgentTemplate](../../../../../../agentuniverse/agent/template/nl2api_agent_template.py)
 Nl2Api智能体模版，具体配置示例如下：
 ```yaml
 info:
diff --git "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213.md"
similarity index 100%
rename from "docs/guidebook/zh/2_2_2_\346\250\241\345\236\213.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213.md"
diff --git "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 80%
rename from "docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index 2b2eb2c2..dad2078b 100644
--- "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,5 +1,5 @@
 # 模型与相关领域对象
-在本章节我们会重点列出LLM与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。
+在本章节我们会重点列出LLM与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## LLM基础类
 包路径：`agentuniverse.llm.llm.LLM`
diff --git "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
similarity index 98%
rename from "docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
index 42cc86bd..32f8ffdd 100644
--- "a/docs/guidebook/zh/2_2_2_\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\346\250\241\345\236\213/\346\250\241\345\236\213\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
@@ -479,7 +479,7 @@ metadata:
 
 # 如何使用模型LLM组件
 ## 在Agent中配置使用
-您可以根据[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的llm_model中设置您创建的任意LLM。
+您可以根据[智能体创建与使用](../智能体/智能体创建与使用.md)中的内容在agent的llm_model中设置您创建的任意LLM。
 
 可参考示例：`demo_multillm_agent`， 具体文件路径为 `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_multillm_agent.yaml`。
 
@@ -494,7 +494,7 @@ llm: LLM = LLMManager().get_instance_obj('llm_name')
 ```
 
 # 了解更多已有模型LLM组件
-框架提供的更多LLM组件在`agentuniverse.llm.default`包路径下，您可以进一步查看对应代码或在我们的扩展组件介绍部分进一步了解他们，更多已经接入的模型您可以在[模型列表章节](3_1_2_0_模型列表.md)中了解。
+框架提供的更多LLM组件在`agentuniverse.llm.default`包路径下，您可以进一步查看对应代码或在我们的扩展组件介绍部分进一步了解他们，更多已经接入的模型您可以在[模型列表章节](../../组件列表/模型列表/模型列表.md)中了解。
 
 # 总结
 至此您已经掌握了模型LLM的定义与使用，赶快去尝试定义与使用LLM吧。
\ No newline at end of file
diff --git a/docs/guidebook/zh/2_2_4_DocProcessor.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/DocProcessor.md"
similarity index 90%
rename from docs/guidebook/zh/2_2_4_DocProcessor.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/DocProcessor.md"
index 0434f694..a0c27ebd 100644
--- a/docs/guidebook/zh/2_2_4_DocProcessor.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/DocProcessor.md"
@@ -82,7 +82,7 @@ doc_processor = ['sample_standard_app.intelligence.agentic.knowledge.doc_process
 
 
 ## agentUniverse内置有以下DocProcessor:
-### [CharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
+### [CharacterTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
 该组件根据字符数对原始文本进行拆分。  
 组件定义文件如下：
 ```yaml
@@ -100,7 +100,7 @@ metadata:
 - chunk_overlap: 相邻切分文本重合部分的长度。
 - separators: 指定的分隔符
 
-### [TokenTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
+### [TokenTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/character_text_splitter.yaml)
 该组件根据指定的 tokenizer 对文本进行切分，按照设定的 chunk_size 和 chunk_overlap 将文本拆分为多个片段，每个片段包含指定数量的tokens。
 
 组件定义文件如下：
@@ -120,7 +120,7 @@ metadata:
 - chunk_overlap: 相邻切分文本重合部分的token数量。
 - tokenizer: 指定的tokenizer，用于将文本切分为tokens
 
-### [RecursiveCharacterTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
+### [RecursiveCharacterTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
 
 该组件根据指定的分隔符递归地对原始文本进行切分。它首先尝试使用优先级最高的分隔符进行切分，如果无法满足 chunk_size 的要求，则会递归地使用下一个分隔符进行切分，直到文本被成功分割。
 
@@ -142,7 +142,7 @@ metadata:
 - chunk_overlap: 相邻切分文本重合部分的长度。
 - separators: 指定的分隔符列表，按顺序尝试使用分隔符进行切分。如果第一个分隔符不能满足条件，则递归地使用下一个分隔符。
 
-### [JiebaKeywordExtractor](../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml)
+### [JiebaKeywordExtractor](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/jieba_keyword_extractor.yaml)
 该组件使用结巴（Jieba）分词库从文本中提取关键词。它可以根据设定的 top_k 参数提取出最重要的几个关键词，用于后续作为倒排索引。  
 组件定义文件如下：
 ```yaml
@@ -156,7 +156,7 @@ metadata:
 ```
 - top_k: 从文本中提取的关键词数量，即排名前 top_k 的关键词会被提取。
 
-### [DashscopeReranker](../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml)
+### [DashscopeReranker](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/dashscope_reranker.yaml)
 
 该组件使用 DashScope API 对文本进行重新排序（rerank），对Store召回的内容按照Query内容进行相关性排序。
 
@@ -171,7 +171,7 @@ metadata:
 ```
 该组件需要在环境变量中配置`DASHSCOPE_API_KEY`。
 
-### [HierarchicalRegexTextSplitter](../../../agentuniverse/agent/action/knowledge/doc_processor/hierarchical_regex_text_spliter.py)
+### [HierarchicalRegexTextSplitter](../../../../../../agentuniverse/agent/action/knowledge/doc_processor/hierarchical_regex_text_spliter.py)
 
 该组件使用通过指定的正则规则对原始文本进行多层级的拆分，形成树状的文档结构。
 该组件需要用户自行创建定义文件，一个示例定义文件如下：
diff --git a/docs/guidebook/zh/2_2_4_QueryParaphraser.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/QueryParaphraser.md"
similarity index 96%
rename from docs/guidebook/zh/2_2_4_QueryParaphraser.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/QueryParaphraser.md"
index 2e5d12b9..05a71eb5 100644
--- a/docs/guidebook/zh/2_2_4_QueryParaphraser.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/QueryParaphraser.md"
@@ -69,7 +69,7 @@ query_paraphraser = ['sample_standard_app.intelligence.agentic.knowledge.query_p
 ```
 
 ## agentUniverse目前内置有以下QueryParaphraser:
-### [query_keyword_extractor](../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml)
+### [query_keyword_extractor](../../../../../../agentuniverse/agent/action/knowledge/query_paraphraser/query_keyword_extractor.yaml)
 该组件会根据Query中的原始文本提取关键词，并保存至Query的keywords字段
 组件定义文件如下：
 ```yaml
diff --git a/docs/guidebook/zh/2_2_4_RagRouter.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/RagRouter.md"
similarity index 93%
rename from docs/guidebook/zh/2_2_4_RagRouter.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/RagRouter.md"
index 265d432e..c276e9aa 100644
--- a/docs/guidebook/zh/2_2_4_RagRouter.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/RagRouter.md"
@@ -49,7 +49,7 @@ rag_router = ['sample_standard_app.intelligence.agentic.knowledge.rag_router']
 
 
 ## agentUniverse目前内置有以下RagRouter:
-### [base_router](../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml)
+### [base_router](../../../../../../agentuniverse/agent/action/knowledge/rag_router/base_router.yaml)
 该组件的的主要功能是将查询传递给系统中的所有存储库，以确保查询能够在所有可能的资源中找到答案，它也是Knowledge中默认的RagRouter。
 ```yaml
 name: 'base_router'
@@ -60,7 +60,7 @@ metadata:
   class: 'BaseRouter'
 ```
 
-### [nlu_rag_router](../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py)
+### [nlu_rag_router](../../../../../../agentuniverse/agent/action/knowledge/rag_router/nlu_rag_router.py)
 该组件的通过配置的llm分析所有store的描述信息与原始查询文本的关联程度，从所有Store中选出最相关的N个进行查询。
 
 该组件需要用户自己编写yaml文件，格式可参考如下：
diff --git a/docs/guidebook/zh/2_2_4_Reader.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Reader.md"
similarity index 68%
rename from docs/guidebook/zh/2_2_4_Reader.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Reader.md"
index 5bfe24e0..d664bfcb 100644
--- a/docs/guidebook/zh/2_2_4_Reader.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Reader.md"
@@ -48,9 +48,9 @@ reader = ['sample_standard_app.intelligence.agentic.knowledge.reader']
 ```
 
 ## agentUniverse目前内置有以下Reader:
-- [default_docx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml)：读取本地Docx文件中的文本内容
-- [default_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)：读取本地Pdf文件中的文本内容
-- [default_pptx_reader](../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml)：读取本地Pptx文件中的文本内容
-- [default_txt_reader](../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml)：读取txt文件中的文本内容
-- [default_web_pdf_reader](../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml)：读取本地Docx文件中的文本内容
-- [default_markdown_reader](../../../agentuniverse/agent/action/knowledge/reader/file/markdown_reader.yaml)：读取本地Markdown文件中的文本内容
\ No newline at end of file
+- [default_docx_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/docx_reader.yaml)：读取本地Docx文件中的文本内容
+- [default_pdf_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)：读取本地Pdf文件中的文本内容
+- [default_pptx_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/pptx_reader.yaml)：读取本地Pptx文件中的文本内容
+- [default_txt_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/txt_reader.yaml)：读取txt文件中的文本内容
+- [default_web_pdf_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/web_pdf_reader.yaml)：读取本地Docx文件中的文本内容
+- [default_markdown_reader](../../../../../../agentuniverse/agent/action/knowledge/reader/file/markdown_reader.yaml)：读取本地Markdown文件中的文本内容
\ No newline at end of file
diff --git a/docs/guidebook/zh/2_2_4_Store.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Store.md"
similarity index 90%
rename from docs/guidebook/zh/2_2_4_Store.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Store.md"
index 5d24782c..d466faf7 100644
--- a/docs/guidebook/zh/2_2_4_Store.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/Store.md"
@@ -52,7 +52,7 @@ class Store(ComponentBase):
     def update_document(self, documents: List[Document], **kwargs):
         raise NotImplementedError
 ```
-- `_new_client`和`_new_async_client`用于创建数据库链接，在组件注册阶段会被添加到[post_fork](2_4_1_Web_Server.md)执行列表中，保证创建的数据库连接在Gunicorn模式下的子进程中是独立的。
+- `_new_client`和`_new_async_client`用于创建数据库链接，在组件注册阶段会被添加到[post_fork](../../技术组件/服务化/Web_Server.md)执行列表中，保证创建的数据库连接在Gunicorn模式下的子进程中是独立的。
 - `query`函数是知识组件在查询时调用的函数，负责根据传入的Query实例在store中查找相关的内容并以document的形式返回
 - `Store`的还包括对`Docuemnt`类型数据的增删改查，作为知识存储的管理接口。
 
@@ -77,6 +77,6 @@ store = ['sample_standard_app.intelligence.agentic.knowledge.store']
 ```
 
 ## agentUniverse目前内置Store：
-- [Chroma](3_3_2_ChromaDB.md)
-- [Milvus](3_3_1_Milvus.md)
-- [Sqlite](3_3_3_Sqlite.md)
\ No newline at end of file
+- [Chroma](../../技术组件/存储/ChromaDB.md)
+- [Milvus](../../技术组件/存储/Milvus.md)
+- [Sqlite](../../技术组件/存储/Sqlite.md)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206.md"
similarity index 98%
rename from "docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206.md"
index e02d5b26..ee6c30ba 100644
--- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206.md"
@@ -9,7 +9,7 @@ agentUniverse定义了标准的知识定义，其包含了各类知识数据的
 
 ## 知识架构
 agentUniverse中，知识的整体架构图如下所示：
-![agentUniverse知识架构](../_picture/knowledge_structure.png)
+![agentUniverse知识架构](../../../../_picture/knowledge_structure.png)
 其中上半部分从Reader到Store为知识的注入流程，而下半部分为知识的查询流程。
 
 ### 知识注入流程
diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 71%
rename from "docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index 6e40ae23..120c32cb 100644
--- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -3,24 +3,24 @@
 
 ### Knowledge
 包路径：`agentuniverse.agent.action.knowledge.knowledge.Knowledge`  
-文档链接：[Knowledge](2_2_4_知识定义与使用.md)
+文档链接：[Knowledge](知识定义与使用.md)
 
 ### Reader
 包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-文档链接：[Reader](2_2_4_Reader.md)
+文档链接：[Reader](Reader.md)
 
 ### DocProcessor
 包路径：`agentuniverse.agent.action.knowledge.doc_processor.doc_processor.DocProcessor`  
-文档链接：[DocProcessor](2_2_4_DocProcessor.md)
+文档链接：[DocProcessor](DocProcessor.md)
 
 ### Store
 包路径：`agentuniverse.agent.action.knowledge.store.store.Store`  
-文档链接：[Store](2_2_4_Store.md)
+文档链接：[Store](Store.md)
 
 ### QueryParaphraser
 包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-文档链接：[QueryParaphraser](2_2_4_QueryParaphraser.md)
+文档链接：[QueryParaphraser](QueryParaphraser.md)
 
 ### RagRouter
 包路径：`agentuniverse.agent.action.knowledge.reader.reader.Reader`  
-文档链接：[RagRouter](2_2_4_RagRouter.md)
+文档链接：[RagRouter](RagRouter.md)
diff --git "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
similarity index 93%
rename from "docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
index 6d6ae43a..d8833050 100644
--- "a/docs/guidebook/zh/2_2_4_\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\347\237\245\350\257\206/\347\237\245\350\257\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
@@ -39,7 +39,7 @@ metadata:
 ## 创建Knowledge领域行为定义 - knowledge_xx.py
 agentUniverse提供了一个标准的Knowledge类，您可以直接在yaml定义文件中使用该类或是继承它并改写其中的部分方法。  
 
-### [Knowledge类的定义:](../../../agentuniverse/agent/action/knowledge/knowledge.py)
+### [Knowledge类的定义:](../../../../../../agentuniverse/agent/action/knowledge/knowledge.py)
 
 - _load_data(self, *args: Any, **kwargs: Any) -> List[Document]
 : 加载数据源，根据数据源的类型（文件或URL）选择合适的`Reader`，并加载文档数据。
@@ -80,7 +80,7 @@ knowledge = ['sample_standard_app.intelligence.agentic.knowledge']
 
 # 如何使用知识Knowledge组件
 ## 在Agent中配置使用
-您可以根据[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的action下的knowledge中设置您创建的任意知识。
+您可以根据[智能体创建与使用](../智能体/智能体创建与使用.md)中的内容在agent的action下的knowledge中设置您创建的任意知识。
 
 ## 使用Knowledge管理器
 通过Knowledge管理器中的`.get_instance_obj(xx_knowledge_name)` 方法可以获取对应名称的Knowledge实例, 使用`query_knowledge`方法进行调用。
@@ -94,4 +94,4 @@ knowledge.query_knowledge()
 ```
 
 ## 了解更多已有知识Knowledge
-[Knowledge法律咨询案例](7_1_1_法律咨询案例.md)
\ No newline at end of file
+[Knowledge法律咨询案例](../../../样例文档/法律咨询案例.md)
diff --git "a/docs/guidebook/zh/2_2_6_\350\256\241\345\210\222.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222.md"
similarity index 100%
rename from "docs/guidebook/zh/2_2_6_\350\256\241\345\210\222.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222.md"
diff --git "a/docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 78%
rename from "docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index 7ba0484a..cf2c2c9a 100644
--- "a/docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,5 +1,5 @@
 # 计划与相关领域对象
-在本章节我们会重点列出Planner与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](4_1_API参考.md)。
+在本章节我们会重点列出Planner与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## Planner基础类
 包路径：`agentuniverse.agent.plan.planner.planner.Planner`
diff --git "a/docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
similarity index 98%
rename from "docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
index 772a596e..93695f87 100644
--- "a/docs/guidebook/zh/2_2_6_\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\241\345\210\222/\350\256\241\345\210\222\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
@@ -183,7 +183,7 @@ planner = ['sample_standard_app.app.core.planner']
 
 # 如何使用计划Planner组件
 ## 在Agent中配置使用
-您可以根据[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的planner中设置您创建的任意计划组件。
+您可以根据[智能体创建与使用](../智能体/智能体创建与使用.md)中的内容在agent的planner中设置您创建的任意计划组件。
 
 可参考示例：`demo_rag_agent`， 具体文件路径为 `sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/demo_rag_agent.yaml`。
 
diff --git a/docs/guidebook/zh/2_2_5_MemoryCompressor.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryCompressor.md"
similarity index 93%
rename from docs/guidebook/zh/2_2_5_MemoryCompressor.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryCompressor.md"
index f1aafdba..81f9dc16 100644
--- a/docs/guidebook/zh/2_2_5_MemoryCompressor.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryCompressor.md"
@@ -40,7 +40,7 @@ metadata:
 agentUniverse提供了一个MemoryCompressor基础类，并提供了默认的记忆压缩方法，您可以直接在yaml定义文件中使用该类或是继承它并改写`compress_memory`
 函数。
 
-#### [MemoryCompressor类的定义:](../../../agentuniverse/agent/memory/memory_compressor/memory_compressor.py)
+#### [MemoryCompressor类的定义:](../../../../../../agentuniverse/agent/memory/memory_compressor/memory_compressor.py)
 
 - compress_memory(self, new_memories: List[Message], max_tokens: int = 500, existing_memory: str = '', **kwargs) -> str:
   : 记忆信息压缩，将待压缩的记忆信息结合已有的记忆信息，压缩为满足压缩后的记忆信息最大token数限制的最终memory。
@@ -70,7 +70,7 @@ memory_compressor = ['sample_standard_app.intelligence.agentic.memory.memory_com
 
 ## agentUniverse目前内置有以下MemoryCompressor:
 
-### [default_memory_compressor](../../../agentuniverse/agent/memory/memory_compressor/default_memory_compressor.yaml)
+### [default_memory_compressor](../../../../../../agentuniverse/agent/memory/memory_compressor/default_memory_compressor.yaml)
 
 组件配置文件如下：
 
diff --git a/docs/guidebook/zh/2_2_5_MemoryStorage.md "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryStorage.md"
similarity index 89%
rename from docs/guidebook/zh/2_2_5_MemoryStorage.md
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryStorage.md"
index a54f2261..92ed7fd7 100644
--- a/docs/guidebook/zh/2_2_5_MemoryStorage.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/MemoryStorage.md"
@@ -41,7 +41,7 @@ metadata:
 
 agentUniverse提供了一个MemoryStorage基础类，您需要继承它并重写add/delete/get函数，完成对记忆信息的存储/删除/获取。
 
-#### [MemoryStorage类的定义:](../../../agentuniverse/agent/memory/memory_storage/memory_storage.py)
+#### [MemoryStorage类的定义:](../../../../../../agentuniverse/agent/memory/memory_storage/memory_storage.py)
 
 - add(self, message_list: List[Message], session_id: str = '', agent_id: str = '', **kwargs) -> None:
   : 记忆信息存储，将记忆的消息列表、智能体id（agent_id）、会话id（session_id）、source（记忆来源）等信息，在特定存储仓库中完成存储。
@@ -79,7 +79,7 @@ memory_storage = ['sample_standard_app.intelligence.agentic.memory.memory_storag
 
 ## agentUniverse目前内置有以下MemoryStorage组件:
 
-### [local_memory_storage](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)
+### [local_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)
 
 本地内存记忆存储器，系统内置组件配置文件如下：
 
@@ -92,7 +92,7 @@ metadata:
   class: 'LocalMemoryStorage'
 ```
 
-### [chroma_memory_storage](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
+### [chroma_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
 
 ChromaDB记忆存储器，记忆获取时包含向量检索和条件检索两种方式，sample工程中**示例**组件配置文件如下：
 
@@ -111,9 +111,9 @@ metadata:
 其中`collection_name`为chroma的collection名称，`persist_path`为chroma的持久化路径，`embedding_model`
 为用于向量检索的embedding领域组件名称。
 
-### [sql_alchemy_memory_storage](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
+### [sql_alchemy_memory_storage](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)
 
-SqlAlchemy记忆存储器，内部成员包含aU另一领域组件[SQLDB_WRAPPER](2_3_1_SQLDB_WRAPPER.md)，sample工程中**示例**组件配置文件如下：
+SqlAlchemy记忆存储器，内部成员包含aU另一领域组件[SQLDB_WRAPPER](../../技术组件/存储/SQLDB_WRAPPER.md)，sample工程中**示例**组件配置文件如下：
 
 ```yaml
 name: 'mysql_memory_storage'
diff --git "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206.md"
similarity index 80%
rename from "docs/guidebook/zh/2_2_5_\350\256\260\345\277\206.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206.md"
index 38f066f7..1ec82014 100644
--- "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206.md"
@@ -11,7 +11,7 @@
 
 ## 记忆架构
 agentUniverse中，记忆的整体架构图如下所示：
-![agentUniverse记忆架构](../_picture/memory.jpg)
+![agentUniverse记忆架构](../../../../_picture/memory.jpg)
 包含多智能体记忆传递、添加记忆、检索记忆、裁剪压缩记忆流程。
 
 ### 多智能体记忆传递流程
@@ -19,7 +19,7 @@ agentUniverse中，记忆的整体架构图如下所示：
 在多智能体记忆传递流程中，以`chat_history`参数为入口，将智能体A的特定记忆信息携带source（来源Agent A）标签，传递给智能体B，智能体B参照aU planner中的`handle_memory`方法，读取`chat_history`携带的记忆信息，并添加到智能体B对应的记忆中。
 
 #### 多智能体记忆传递代码示例
-在智能体A运行时，传递特定记忆信息给到智能体B，具体应用案例可参照aU sample工程中的[多轮多Agent讨论小组](6_2_1_讨论组.md)。
+在智能体A运行时，传递特定记忆信息给到智能体B，具体应用案例可参照aU sample工程中的[多轮多Agent讨论小组](../../../样例文档/讨论组.md)。
 ```python
 from typing import List
 
@@ -36,8 +36,8 @@ agent_b.run({'chat_history': memory_messages})
 ### 添加记忆流程
 在添加记忆流程中，获取记忆实例中配置的多个`memory_storage`（记忆存储）领域组件，将记忆的消息列表、智能体id（agent_id）、会话id（session_id）、source（记忆来源）等信息，进行多路存储。
 
-目前agentUniverse内置[ChromaDB](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
-、[SqlAlchemy](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)、[本地内存](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)三种记忆存储方式，用户可根据实际需求选择合适的记忆存储方式。
+目前agentUniverse内置[ChromaDB](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)
+、[SqlAlchemy](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)、[本地内存](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)三种记忆存储方式，用户可根据实际需求选择合适的记忆存储方式。
 
 特别提醒：在agent基类的`pre_parse_input`方法中，aU会自动读取当前智能体配置的`name`，设置为`agent_input`中的`agent_id`；`session_id`需用户执行智能体时传入。 记忆流程可以使用`agent_input`解析的参数作为入参。
 ```python
@@ -55,8 +55,8 @@ def pre_parse_input(self, input_object) -> dict:
 
 在检索记忆流程中，获取记忆实例中配置的`memory_retrieval_storage`领域组件（若用户未配置，默认使用`memory_storages`中的第一个记忆存储组件），根据智能体id（agent_id）、会话id（session_id）、source（记忆来源），进行记忆检索。
 
-[ChromaDB](../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)可根据query进行向量检索，
-[SqlAlchemy](../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)、[本地内存](../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)根据时间顺序条件过滤，检索出指定的记忆信息。
+[ChromaDB](../../../../../../agentuniverse/agent/memory/memory_storage/chroma_memory_storage.py)可根据query进行向量检索，
+[SqlAlchemy](../../../../../../agentuniverse/agent/memory/memory_storage/sql_alchemy_memory_storage.py)、[本地内存](../../../../../../agentuniverse/agent/memory/memory_storage/local_memory_storage.py)根据时间顺序条件过滤，检索出指定的记忆信息。
 ### 裁剪压缩记忆流程
 
 在裁剪压缩记忆流程中，获取记忆实例中的`max_tokens`参数（prompt中记忆的最大token数），若检索记忆流程中获取的记忆信息超过最大token数，则进行裁剪压缩。
diff --git "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
similarity index 79%
rename from "docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
index b00986b0..ec5f01b7 100644
--- "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\217\212\347\233\270\345\205\263\351\242\206\345\237\237\345\257\271\350\261\241.md"
@@ -1,10 +1,10 @@
 # 记忆与相关领域对象
-在本章节我们会重点列出Memory与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请根据包路径阅读对应的源码。
+在本章节我们会重点列出Memory与相关的核心领域对象, 我们省略了其对应的代码部分，若您希望进一步了解请阅读对应的[API参考部分](../../技术组件/其他/API参考.md)。
 
 ## Memory基础类
 包路径：`agentuniverse.agent.memory.memory.Memory`
 
-文档链接：[Memory](2_2_5_记忆定义与使用.md)
+文档链接：[Memory](记忆定义与使用.md)
 
 ## Memory管理器
 包路径：`agentuniverse.agent.memory.memory_manager.MemoryManager`
@@ -15,7 +15,7 @@
 ## Memory存储器基础类
 包路径：`agentuniverse.agent.memory.memory_storage.memory_storage.MemoryStorage`
 
-文档链接：[MemoryStorage](2_2_5_MemoryStorage.md)
+文档链接：[MemoryStorage](MemoryStorage.md)
 
 ## Memory存储器对应管理器
 包路径：`agentuniverse.agent.memory.memory_storage.memory_storage_manager.MemoryStorageManager`
@@ -23,7 +23,7 @@
 ## Memory压缩器基础类
 包路径：`agentuniverse.agent.memory.memory_compressor.memory_compressor.MemoryCompressor`
 
-文档链接：[MemoryCompressor](2_2_5_MemoryCompressor.md)
+文档链接：[MemoryCompressor](MemoryCompressor.md)
 
 ## Memory压缩器对应管理器
 包路径：`agentuniverse.agent.memory.memory_compressor.memory_compressor_manager.MemoryCompressorManager`
diff --git "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
similarity index 73%
rename from "docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
index f07e5e24..8038540f 100644
--- "a/docs/guidebook/zh/2_2_5_\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\350\256\260\345\277\206/\350\256\260\345\277\206\345\256\232\344\271\211\344\270\216\344\275\277\347\224\250.md"
@@ -40,15 +40,15 @@ metadata:
 
 aU sample工程中包含两种记忆配置样例:
 
-1. [demo_memory_with_chroma_storage](../../../sample_standard_app/intelligence/agentic/memory/demo_memory_a.yaml) 挂载chroma存储器的记忆示例
-2. [demo_memory_with_local_storage](../../../sample_standard_app/intelligence/agentic/memory/demo_memory_b.yaml) 挂载本地内存存储器的记忆示例
+1. [demo_memory_with_chroma_storage](../../../../../../sample_standard_app/intelligence/agentic/memory/demo_memory_a.yaml) 挂载chroma存储器的记忆示例
+2. [demo_memory_with_local_storage](../../../../../../sample_standard_app/intelligence/agentic/memory/demo_memory_b.yaml) 挂载本地内存存储器的记忆示例
 
 
 ## 创建Memory领域行为定义 - xx_memory.py
 
 agentUniverse提供了一个标准的Memory类，您可以直接在yaml定义文件中使用该类或是继承它并改写其中的部分方法。
 
-### [Memory类的定义:](../../../agentuniverse/agent/memory/memory.py)
+### [Memory类的定义:](../../../../../../agentuniverse/agent/memory/memory.py)
 
 - add(self, message_list: List[Message], session_id: str = '', agent_id: str = '', **kwargs) -> None:
   : 添加记忆，获取记忆实例中配置的多个`memory_storage`
@@ -67,7 +67,7 @@ agentUniverse提供了一个标准的Memory类，您可以直接在yaml定义文
 
 ## 在Agent中配置使用
 
-您可以根据[智能体创建与使用](2_2_1_智能体创建与使用.md)中的内容在agent的memory中设置您的memory实例。如上文中创建的`demo_memory`实例，在agent中您可以这样设置：
+您可以根据[智能体创建与使用](../智能体/智能体创建与使用.md)中的内容在agent的memory中设置您的memory实例。如上文中创建的`demo_memory`实例，在agent中您可以这样设置：
 
 ```yaml
 info:
@@ -80,13 +80,13 @@ memory:
 ```
 
 ### 案例
-例如在PEER工作模式下，将aU sample工程中的[demo_planning_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)/[demo_expressing_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)/[demo_peer_agent](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_peer_agent.yaml)三个智能体配置上文中创建的`demo_memory`实例，执行一次peer调用时，传入的`session_id`为`peer_1`，调用完成后查看memory的存储内容如下：
+例如在PEER工作模式下，将aU sample工程中的[demo_planning_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)/[demo_expressing_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)/[demo_peer_agent](../../../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_peer_agent.yaml)三个智能体配置上文中创建的`demo_memory`实例，执行一次peer调用时，传入的`session_id`为`peer_1`，调用完成后查看memory的存储内容如下：
 
 #### mysql_memory_storage
-![mysql_memory](../_picture/mysql_memory.png)
+![mysql_memory](../../../../_picture/mysql_memory.png)
 
 #### chroma_memory_storage
-![chroma_memory](../_picture/chroma_memory.png)
+![chroma_memory](../../../../_picture/chroma_memory.png)
 
 ## 使用Memory管理器
 
@@ -101,6 +101,6 @@ memory = MemoryManager().get_instance_obj(component_instance_name=memory_name)
 
 # 总结
 
-至此您已经掌握了记忆Memory的基本使用方法，具体`memory_compressor`和`memory_storage`领域组件内容可查阅[MemoryCompressor文档](2_2_5_MemoryCompressor.md)和[MemoryStorage文档](2_2_5_MemoryStorage.md)。
+至此您已经掌握了记忆Memory的基本使用方法，具体`memory_compressor`和`memory_storage`领域组件内容可查阅[MemoryCompressor文档](MemoryCompressor.md)和[MemoryStorage文档](MemoryStorage.md)。
 
 赶快去尝试使用Memory吧。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_2_\351\242\206\345\237\237\347\273\204\344\273\266\345\216\237\347\220\206.md" "b/docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\351\242\206\345\237\237\347\273\204\344\273\266\345\216\237\347\220\206.md"
similarity index 100%
rename from "docs/guidebook/zh/2_2_\351\242\206\345\237\237\347\273\204\344\273\266\345\216\237\347\220\206.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\345\216\237\347\220\206\344\273\213\347\273\215/\351\242\206\345\237\237\347\273\204\344\273\266\345\216\237\347\220\206.md"
diff --git "a/docs/guidebook/zh/4_1_API\345\217\202\350\200\203.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\205\266\344\273\226/API\345\217\202\350\200\203.md"
similarity index 100%
rename from "docs/guidebook/zh/4_1_API\345\217\202\350\200\203.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\205\266\344\273\226/API\345\217\202\350\200\203.md"
diff --git "a/docs/guidebook/zh/2_7_\346\241\206\346\236\266\344\270\212\344\270\213\346\226\207.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\205\266\344\273\226/\346\241\206\346\236\266\344\270\212\344\270\213\346\226\207.md"
similarity index 100%
rename from "docs/guidebook/zh/2_7_\346\241\206\346\236\266\344\270\212\344\270\213\346\226\207.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\205\266\344\273\226/\346\241\206\346\236\266\344\270\212\344\270\213\346\226\207.md"
diff --git a/docs/guidebook/zh/3_3_2_ChromaDB.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/ChromaDB.md"
similarity index 89%
rename from docs/guidebook/zh/3_3_2_ChromaDB.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/ChromaDB.md"
index 33c6b0a2..14c0b82e 100644
--- a/docs/guidebook/zh/3_3_2_ChromaDB.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/ChromaDB.md"
@@ -19,7 +19,7 @@ metadata:
 - embedding_model: 用于生成嵌入向量的模型名称，这里指定为 dashscope_embedding。
 - similarity_top_k: 在相似度搜索中返回最相似结果的数量。
 
-### [ChromaHierarchicalStore](../../../agentuniverse/agent/action/knowledge/store/chroma_hierarchical_store.py)
+### [ChromaHierarchicalStore](../../../../../../agentuniverse/agent/action/knowledge/store/chroma_hierarchical_store.py)
 ChromaHierarchicalStore是ChromaStore的子类，用于存储包含层级信息的文档内容。  
 一个ChromaHierarchicalStore的定义文件示例如下：
 ```yaml
@@ -41,4 +41,4 @@ metadata:
 
 
 ### 使用方式
-[知识定义与使用](2_2_4_知识定义与使用.md)
+[知识定义与使用](../../原理介绍/知识/知识定义与使用.md)
\ No newline at end of file
diff --git a/docs/guidebook/zh/3_3_1_Milvus.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Milvus.md"
similarity index 95%
rename from docs/guidebook/zh/3_3_1_Milvus.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Milvus.md"
index fdc584c8..26696a88 100644
--- a/docs/guidebook/zh/3_3_1_Milvus.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Milvus.md"
@@ -49,5 +49,5 @@ metadata:
 - similarity_top_k: 在相似度搜索中返回最相似结果的数量。
 
 ### 使用方式
-[知识定义与使用](2_2_4_知识定义与使用.md)
+[知识定义与使用](../../原理介绍/知识/知识定义与使用.md)
 
diff --git a/docs/guidebook/zh/2_3_1_SQLDB_WRAPPER.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/SQLDB_WRAPPER.md"
similarity index 100%
rename from docs/guidebook/zh/2_3_1_SQLDB_WRAPPER.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/SQLDB_WRAPPER.md"
diff --git a/docs/guidebook/zh/3_3_3_Sqlite.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Sqlite.md"
similarity index 80%
rename from docs/guidebook/zh/3_3_3_Sqlite.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Sqlite.md"
index cf5d9506..e7fc3e80 100644
--- a/docs/guidebook/zh/3_3_3_Sqlite.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\345\255\230\345\202\250/Sqlite.md"
@@ -4,7 +4,7 @@ Store组件中的sqlite用于保存`Document`中的文本内容，并基于Jieba
 
 ### 如何配置Sqlite组件
 
-您可以在[知识组件](2_2_4_知识.md)中使用ChromaDB来存储和查询知识，你可以使用以下方式来创建一个使用ChromaDB的存储组件:
+您可以在[知识组件](../../原理介绍/知识/知识.md)中使用ChromaDB来存储和查询知识，你可以使用以下方式来创建一个使用ChromaDB的存储组件:
 ```yaml
 name: 'sqlite_store'
 description: 'a store based on sqlite'
@@ -25,4 +25,4 @@ metadata:
 - similarity_top_k: 根据BM25分数返回的最相关的top k。
 
 ### 使用方式
-[知识定义与使用](2_2_4_知识定义与使用.md)
\ No newline at end of file
+[知识定义与使用](../../原理介绍/知识/知识定义与使用.md)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_4_4_\346\227\245\345\277\227\347\273\204\344\273\266.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\346\227\245\345\277\227\347\273\204\344\273\266.md"
similarity index 97%
rename from "docs/guidebook/zh/2_4_4_\346\227\245\345\277\227\347\273\204\344\273\266.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\346\227\245\345\277\227\347\273\204\344\273\266.md"
index 5077429d..83df0e9b 100644
--- "a/docs/guidebook/zh/2_4_4_\346\227\245\345\277\227\347\273\204\344\273\266.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\346\227\245\345\277\227\347\273\204\344\273\266.md"
@@ -46,7 +46,7 @@ log_format: str = ("<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green> "
                        ":<cyan>{line}</cyan> "
                        "| <level>{message}</level>")
 ```
-其中`{extra[context_prefix]}`默认为`default`, 当框架上下文中存在`LOG_CONTEXT`时，则会内容替换为`LOG_CONTEXT`的内容。关于更多框架上下文相关的内容，请参考[框架上下文](2_7_框架上下文.md)。
+其中`{extra[context_prefix]}`默认为`default`, 当框架上下文中存在`LOG_CONTEXT`时，则会内容替换为`LOG_CONTEXT`的内容。关于更多框架上下文相关的内容，请参考[框架上下文](../其他/框架上下文.md)。
 
 ## 全局日志组件
 AgentUniverse提供一个可直接使用的日志组件`logging_util.Logger`,您可以在项目中通过如下方式引入该组件：
@@ -73,4 +73,4 @@ new_logger = get_module_logger("new_module")
 
 ## 外部日志服务对接
 
-关于外部日志服务对接相关的内容您可以参考[拓展日志组件](3_2_4_阿里云SLS.md)。
\ No newline at end of file
+关于外部日志服务对接相关的内容您可以参考[拓展日志组件](阿里云SLS.md)。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\347\233\221\346\216\247\346\250\241\345\235\227.md"
similarity index 100%
rename from "docs/guidebook/zh/2_5_1_\347\233\221\346\216\247\346\250\241\345\235\227.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\347\233\221\346\216\247\346\250\241\345\235\227.md"
diff --git "a/docs/guidebook/zh/3_2_4_\351\230\277\351\207\214\344\272\221SLS.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\351\230\277\351\207\214\344\272\221SLS.md"
similarity index 100%
rename from "docs/guidebook/zh/3_2_4_\351\230\277\351\207\214\344\272\221SLS.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\227\245\345\277\227\347\233\221\346\216\247/\351\230\277\351\207\214\344\272\221SLS.md"
diff --git a/docs/guidebook/zh/2_4_1_Web_Server.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/Web_Server.md"
similarity index 100%
rename from docs/guidebook/zh/2_4_1_Web_Server.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/Web_Server.md"
diff --git a/docs/guidebook/zh/3_2_1_gRPC.md "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/gRPC.md"
similarity index 98%
rename from docs/guidebook/zh/3_2_1_gRPC.md
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/gRPC.md"
index f8759e56..a7e6d589 100644
--- a/docs/guidebook/zh/3_2_1_gRPC.md
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/gRPC.md"
@@ -56,7 +56,7 @@ message AgentResultRequest {
 }
 ```
 \
-与[Web API](2_4_1_服务Api.md)类似，gRPC服务包含三个接口：
+与[Web API](服务Api.md)类似，gRPC服务包含三个接口：
 ```text
 service AgentUniverseService {
   rpc service_run(AgentServiceRequest) returns (AgentServiceResponse);
diff --git "a/docs/guidebook/zh/2_4_1_\346\234\215\345\212\241Api.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241Api.md"
similarity index 100%
rename from "docs/guidebook/zh/2_4_1_\346\234\215\345\212\241Api.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241Api.md"
diff --git "a/docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md"
similarity index 93%
rename from "docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md"
index e217bac7..6132c6ad 100644
--- "a/docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\344\277\241\346\201\257\345\255\230\345\202\250.md"
@@ -12,7 +12,7 @@ system_db_uri = ''
 ```
 需注意该uri应当符合`SQLAlchemy`中的uri格式规范。  
 当该值为空的时候，会在项目根目录创建一个DB文件夹，并在文件夹中创建一个名为`agent_universe.db`的sqlite DB文件作为默认系统数据库。
-如果您希望获取更多关于系统数据库的相关使用方式，您可以参考[SQLDB_WRAPPER](2_3_1_SQLDB_WRAPPER.md)章节，系统数据库的名字注册为`__system_db__`。
+如果您希望获取更多关于系统数据库的相关使用方式，您可以参考[SQLDB_WRAPPER](../存储/SQLDB_WRAPPER.md)章节，系统数据库的名字注册为`__system_db__`。
 
 ## 服务信息表格式
 agentUniverse中使用如下ORM存储请求信息：
diff --git "a/docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md"
similarity index 96%
rename from "docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md"
index b6ac0b5a..62a4ec7a 100644
--- "a/docs/guidebook/zh/2_4_1_\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\346\234\215\345\212\241\345\214\226/\346\234\215\345\212\241\346\263\250\345\206\214\344\270\216\344\275\277\347\224\250.md"
@@ -28,4 +28,4 @@ metadata:
 - **`metadata`**:表示该配置是一个Service配置，无需改动。
 
 ## 使用方式
-请参考[Web API](2_4_1_服务Api.md).
\ No newline at end of file
+请参考[Web API](服务Api.md).
\ No newline at end of file
diff --git "a/docs/guidebook/zh/5_1_1_Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md"
similarity index 94%
rename from "docs/guidebook/zh/5_1_1_Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md"
index 1b5d5f24..39b0c336 100644
--- "a/docs/guidebook/zh/5_1_1_Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/Docker\345\256\271\345\231\250\345\214\226\351\203\250\347\275\262.md"
@@ -3,7 +3,7 @@
 AgentUniverse提供标准的工作环境镜像用于容器化部署AgentUniverse工程。本文档将介绍如何基于工作环境镜像部署您自己的工程项目。镜像tag列表可以在[这里获取](https://cr.console.aliyun.com/repository/cn-hangzhou/agent_universe/agent_universe/images)。
 
 ## 准备工作
-1.  按照AgentUniverse的标准结构目录搭建自己的项目，具体结构参考[应用工程结构及说明](1_4_应用工程结构及说明.md)。为方便说明，在本文档中假设项目名称和工程目录为`sample_standard_app`。
+1.  按照AgentUniverse的标准结构目录搭建自己的项目，具体结构参考[应用工程结构及说明](../../../开始使用/应用工程结构及说明.md)。为方便说明，在本文档中假设项目名称和工程目录为`sample_standard_app`。
 2.  获取所需版本的镜像:
 ```shell
 docker pull registry.cn-hangzhou.aliyuncs.com/agent_universe/agent_universe:0.0.9_centos8
diff --git "a/docs/guidebook/zh/5_1_2_K8S\351\203\250\347\275\262.md" "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/K8S\351\203\250\347\275\262.md"
similarity index 87%
rename from "docs/guidebook/zh/5_1_2_K8S\351\203\250\347\275\262.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/K8S\351\203\250\347\275\262.md"
index 20a07020..c04c4718 100644
--- "a/docs/guidebook/zh/5_1_2_K8S\351\203\250\347\275\262.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\346\212\200\346\234\257\347\273\204\344\273\266/\351\203\250\347\275\262\350\277\220\347\273\264/K8S\351\203\250\347\275\262.md"
@@ -36,7 +36,7 @@ spec:
         ports:
         - containerPort: 8888
         command: ["/bin/bash", "-c"]
-        args: ["git clone git@github.com:alipay/agentUniverse.git; mv agentUniverse/sample_standard_app /usr/local/etc/workspace/project; /bin/bash --login /usr/local/etc/workspace/shell/start.sh"]
+        args: ["git clone git@github.com:antgroup/agentUniverse.git; mv agentUniverse/sample_standard_app /usr/local/etc/workspace/project; /bin/bash --login /usr/local/etc/workspace/shell/start.sh"]
         # Uncomment and replace "XXX" with your key to configure the agent
         # env:
         # - name: OPENAI_API_KEY
@@ -64,7 +64,7 @@ spec:
 
 #### 方式2
 
-请参考配置文件开始部分的描述: [快速开始指南](https://github.com/alipay/agentUniverse/blob/master/docs/guidebook/zh/1_3_快速开始.md)
+请参考配置文件开始部分的描述: [快速开始指南](../../../开始使用/快速开始.md)
 
 ## 2. 构建资源
 
@@ -81,7 +81,7 @@ kubectl apply -f agentuniverse.yaml
 ```
 kubectl get all -n agent-namespace
 ```
-![资源部署情况](../_picture/k8s_resource.png)
+![资源部署情况](../../../../_picture/k8s_resource.png)
 ## 4. 从集群内部访问AgentUniverse 服务
 
 要从集群内部访问 AgentUniverse 服务，请使用以下命令行示例：
@@ -98,11 +98,11 @@ kubectl exec -it [Pod名称] -n agent-namespace -- curl http://agentuniverse-ser
 kubectl exec -it agentuniverse-deployment-55cfd778d-g7d9d -n agent-namespace -- curl http://agentuniverse-service:9999/echo
 ```
 
-![连通性测试](../_picture/k8s_hello.png)
+![连通性测试](../../../../_picture/k8s_hello.png)
 #### 4.1.2 问答测试
 
 ```
 kubectl exec -it agentuniverse-deployment-55cfd778d-g7d9d -n agent-namespace -- curl -X POST -H "Content-Type: application/json" -d '{"service_id":"demo_service","params":{"input":"(18+3-5)/2*4=?"}}' http://agentuniverse-service:9999/service_run
 ```
 
-![问答测试](../_picture/k8s_question.png)
\ No newline at end of file
+![问答测试](../../../../_picture/k8s_question.png)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md"
similarity index 92%
rename from "docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md"
index 95e18e27..be91ef15 100644
--- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220LangChain\345\267\245\345\205\267.md"
@@ -9,7 +9,7 @@
 注意，如果你想直接使用LangChain中的description，在配置文件中description必须要配置为空
 
 一个工具初始化示例：
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/human_input_run.yaml)
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/human_input_run.yaml)
 ```yaml
 name: 'human_input_run'
 description: ''
@@ -39,7 +39,7 @@ metadata:
 该工具可以直接使用，无需任何keys
 
 ## 1. 集成LangChain中的DuckDuckGo工具
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/duckduckgo_search.yaml)
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/langchain_tool/duckduckgo_search.yaml)
 ```yaml
 name: 'duckduckgo_search'
 description: 'DuckDuckGo Search tool'
diff --git "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md"
similarity index 90%
rename from "docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md"
index 9bb8b651..86017d6a 100644
--- "a/docs/guidebook/zh/2_2_3_\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md"
+++ "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\345\267\245\345\205\267\345\210\227\350\241\250/\351\233\206\346\210\220\347\232\204\345\267\245\345\205\267.md"
@@ -5,7 +5,7 @@
 ## 1. 搜索工具
 
 ### 1.1 谷歌搜索
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)  
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)  
 详细配置信息：
 
 ```yaml
@@ -39,7 +39,7 @@ SERPER_API_KEY="xxxx"
 
 ### 1.2 Bing(必应)搜索
 当前集成了Bing官方的搜索
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/bing_search_tool.yaml)  
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/bing_search_tool.yaml)  
 工具配置：
 ```yaml
 name: 'bing_search_tool'
@@ -69,8 +69,8 @@ BING_SUBSCRIPTION_KEY="xxxx"
 
 ### 1.3 Search API
 支持多种搜索工具，例如：   
-- [百度搜索](../../../sample_standard_app/intelligence/agentic/tool/search_api_baidu_tool.yaml)
-- [Bing搜索](../../../sample_standard_app/intelligence/agentic/tool/search_api_bing_tool.yaml)  
+- [百度搜索](../../../../../../sample_standard_app/intelligence/agentic/tool/search_api_baidu_tool.yaml)
+- [Bing搜索](../../../../../../sample_standard_app/intelligence/agentic/tool/search_api_bing_tool.yaml)  
 其他搜索引擎还包括: 谷歌搜索、亚马逊搜索、YouTube搜索等,详细信息可以参考:https://www.searchapi.io/
 工具配置：
 ```yaml
@@ -109,7 +109,7 @@ SEARCHAPI_API_KEY="xxxxxx"
 ## 2. 代码工具
 
 ### 2.1 PythonRepl
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)  
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)  
 该工具可以执行一段Python代码,工具的配置信息:  
 ```yaml
 name: 'python_runner'
@@ -136,7 +136,7 @@ metadata:
 该工具可以直接使用，无需任何key，但是为了系统安全，请不要在生产环境使用该工具
 
 ## 3.HTTP 工具
-[工具地址](../../../sample_standard_app/intelligence/agentic/tool/request_get_tool.yaml)
+[工具地址](../../../../../../sample_standard_app/intelligence/agentic/tool/request_get_tool.yaml)
 该工具可以发送一个GET请求，工具的配置信息 :  
 ```yaml
 name: 'requests_get'
diff --git "a/docs/guidebook/zh/3_1_2_\347\231\276\345\267\235\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/BaiChuan\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_\347\231\276\345\267\235\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/BaiChuan\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_Claude\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Claude\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_Claude\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Claude\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/DeepSeek\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_DeepSeek\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/DeepSeek\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_Kimi\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Kimi\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_Kimi\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Kimi\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_Ollama\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Ollama\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_Ollama\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Ollama\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/OpenAIStyleLLM\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_OpenAIStyleLLM\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/OpenAIStyleLLM\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_OpenAI\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/OpenAI\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_OpenAI\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/OpenAI\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_Qwen\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Qwen\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_Qwen\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/Qwen\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_\346\226\207\345\277\203\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\226\207\345\277\203\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_\346\226\207\345\277\203\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\226\207\345\277\203\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/3_1_2_\346\231\272\350\260\261GLM\344\275\277\347\224\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\231\272\350\260\261GLM\344\275\277\347\224\250.md"
similarity index 100%
rename from "docs/guidebook/zh/3_1_2_\346\231\272\350\260\261GLM\344\275\277\347\224\250.md"
rename to "docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\231\272\350\260\261GLM\344\275\277\347\224\250.md"
diff --git "a/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250.md" "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250.md"
new file mode 100644
index 00000000..29ecd8fb
--- /dev/null
+++ "b/docs/guidebook/zh/In-Depth_Guides/\347\273\204\344\273\266\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250/\346\250\241\345\236\213\345\210\227\350\241\250.md"
@@ -0,0 +1,20 @@
+# 模型（LLM）列表
+目前平台已经接入了如下模型列表，详细使用方式请关注本章节各LLM的说明。
+
+| 模型LLM(系列)名称                     |
+|---------------------------------|
+| [OpenAI](OpenAI使用.md)     |
+| [通义千问](Qwen使用.md)         |
+| [文心一言](文心使用.md)           |
+| [Kimi](Kimi使用.md)         |
+| [百川](BaiChuan使用.md)             |
+| [Claude](Claude使用.md)     |
+| [ollama](Ollama使用.md)     |
+| [DeepSeek](DeepSeek使用.md) |
+| [智谱GLM](智谱GLM使用.md)    |
+
+为方便用户接入OpenAI风格协议的模型，提供基于OpenAI通用协议接入包装对象 [OpenAIStyleLLM](OpenAIStyleLLM使用.md)。
+
+未在列表中的模型您可以根据[模型定义](../../原理介绍/模型/模型定义与使用.md)中的步骤自定义接入任何模型。
+
+同时我们正在添加Gemini、Llama、ZhipuAI等常见LLM的接入方式，如果您在工程中使用了这些LLM并创建了属于自己的实例，欢迎您提交pr成为agentUniverse的contributor。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/react_chat_bot_\345\277\253\351\200\237\345\274\200\345\247\213.md" "b/docs/guidebook/zh/react_chat_bot_\345\277\253\351\200\237\345\274\200\345\247\213.md"
deleted file mode 100644
index d44f5a3c..00000000
--- "a/docs/guidebook/zh/react_chat_bot_\345\277\253\351\200\237\345\274\200\345\247\213.md"
+++ /dev/null
@@ -1,146 +0,0 @@
-# react chat bot 快速开始
-为了帮助大家快速体验aU,我们提供了一个react chat bot的快速开始教程。
-
-## 1. 环境准备
-使用 git clone aU 项目,并进入到样例工程目录
-```shell
-git clone https://github.com/alipay/agentUniverse.git
-cd agentUniverse/sample_standard_app
-```
-### 1.1 无有效Python环境
-对于 Mac & Linux 系统, 可以直接使用提供的脚本quick_start.sh安装python环境,脚本会自动安装python环境与依赖包。
-```shell
-bash quick_start.sh
-```
-
-对于 Windows 系统, 可以使用 quick_start.ps1 安装一个python环境。使用之前请先安装conda。
-```shell
-quick_start.ps1
-```
-运行成功后，会启动一个服务，服务地址为：http://127.0.0.1:8888/  
-
-### 1.2 已有python环境
-注意：如果你已经有了一个有效的python环境，且python版本为3.10，可以直接安装agentuniverse依赖。
-#### 1.2.1 针对 Mac & Linux 系统
-运行命令
-```shell
-pip install agentuniverse==0.0.12
-export PYTHONPATH=$PWD/../..
-cd app/bootstrap
-python -u bootstrap.py
-```
-
-#### 1.2.2 针对 Windows 系统
-运行命令
-```shell
-pip install agentuniverse==0.0.12
-set PYTHONPATH=%CD%\..\..\..\..\
-python -u bootstrap.py
-```
-
-### 1.3 vscode 配置
-如果想要使用vscode进行调试，请先使用vscode打开au项目，安装python插件，创建一个名为.vscode的文件夹，并在文件夹下创建一个名为launch.json的文件，内容如下：
-```json
-{
-    "version": "0.2.0",
-    "configurations": [
-        {
-            "name": "Python 调试程序: 当前文件",
-            "type": "debugpy",
-            "request": "launch",
-            "program": "${file}",
-            "cwd": "${fileDirname}",
-            "justMyCode": true,
-            "subProcess": true,
-            "python": "${command:python.interpreterPath}",
-            "env":{
-                 "PYTHONPATH": "${workspaceFolder}"
-            },
-            "purpose": [
-					"debug-test"
-			]
-        }
-    ]
-}
-```
-使用命令command + shift + P命令或者打开一个py文件，右下角选择你想要使用的python环境。在vscode的终端当中激活python环境，并在vscode的命令行当中安装依赖：
-```shell
-pip install agentuniverse==0.0.12
-```  
-最后，使用vscode调试，打开sample_standard_app/bootstrap/intelligence/server_application.py,点击右上角中的调试按钮，即可进行启动进行调试。
-
-### 1.4 Pycharm 配置
-如果使用pycharm，请使用Pycharm打开agentUniverse项目，点击右上角选择Python解释器，选择你想使用的python环境。选择好python环境之后，在terminal中运行命令：
-```shell
-pip install agentuniverse==0.0.12
-```
-命令执行完成之后，双击打开sample_standard_app/bootstrap/intelligence/server_application.py，点击右上角中的调试或者运行按钮，即可进行启动进行调试。
-
-## 2. 项目配置
-复制一份config目录custom_key.toml.sample下的配置模板，重命名为custom_key.yaml
-
-```shell
-cp config/custom_key.yaml.sample config/custom_key.yaml
-```
-
-配置文件在config.yaml中，将`#custom_key_path = './custom_key.toml'` 的注释删除,配置结果如下:     
-  
-```toml
-[SUB_CONFIG_PATH]
-# Log config file path, an absolute path or a relative path based on the dir where the current config file is located.
-log_config_path = './log_config.toml'
-# Custom key file path, use to save your own secret key like open ai or sth else. REMEMBER TO ADD IT TO .gitignore.
-custom_key_path = './custom_key.toml'
-```  
-  
-注意: 你已经使用过 quick_start.sh 脚本安装python环境,这里可以直接跳过。
-
-## 3. 运行案例
-当前运行案例是基于千问大模型，在使用案例之前，必须在config/custom_key.toml中配置好DASHSCOPE_API_KEY：
-```toml
-DASHSCOPE_API_KEY='sk-*****'
-```
-key的获取方式请参考：[千问](https://dashscope.console.aliyun.com/apiKey)  
-进入到案例目录,对于 Mac & Linux 系统，运行如下命令：
-
-```shell
-source ~/.bashrc
-cd intelligence/test
-bash example_start.sh
-```
-或者
-
-```shell
-cd intelligence/test
-export PYTHONPATH=$PWD/../../../..
-python react_chat_bot.py
-```
-
-对于 Windows 系统，运行如下命令：
-```shell
-cd intelligence/test
-set PYTHONPATH=%CD%\..\..\..\..\
-example_start.ps1
-```
-  
-或者
-  
-```shell
-cd intelligence/test
-set PYTHONPATH=%CD%\..\..\..\..\
-python react_chat_bot.py
-```
-
-在 vscode或者pycharm中可以直接通过打开想要运行的文件，点击右上角中的调试或者运行按钮，即可进行启动进行调试。
-
-## 4.为agent添加工具
-当前bot当中已经配置了一个google工具，可以通过修改agent配置来修改或添加工具。针对当前的ragchatbot所绑定的agent，修改agent配置文件config/agent_config.yaml,  
-其配置文件为：agentUniverse/sample_standard_app/intelligence/agentic/agent/agent_instance/react_agent_case/demo_react_agent.yaml，新增一个human_input_run工具,可以让模型询问用户问题。
-![新增工具](../_picture/react_chat_bot_tool.png)
-重新测试，修改问题为： 我想知道我的公历生日对应的农历生日是哪一天？
-```shell
-cd intelligence/test
-export PYTHONPATH=$PWD/../../../..
-python react_chat_bot.py --question "我可以购买好医保么，你可以询问我的健康状况，但是每次只能询问一个问题"
-```
-![执行效果](../_picture/react_input_tool.png)
diff --git "a/docs/guidebook/zh/1_2_\345\256\211\350\243\205.md" "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\256\211\350\243\205.md"
similarity index 92%
rename from "docs/guidebook/zh/1_2_\345\256\211\350\243\205.md"
rename to "docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\256\211\350\243\205.md"
index 00b59366..e24b1790 100644
--- "a/docs/guidebook/zh/1_2_\345\256\211\350\243\205.md"
+++ "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\256\211\350\243\205.md"
@@ -24,7 +24,7 @@ poetry add agentUniverse
 [tool.poetry.dependencies]
 agentUniverse = "^0.0.3"
 ```
-一个标准工程的`pyproject.toml`可参考[这里](../../../sample_standard_app/pyproject.toml) 。
+一个标准工程的`pyproject.toml`可参考[这里](../../../../sample_standard_app/pyproject.toml) 。
 
 使用poetry update命令更新依赖：
 ```shell
@@ -37,4 +37,4 @@ pip list | grep agentUniverse
 ```
 若您看到了`agentUniverse`及其版本号，则说明安装成功。
 
-![image](../_picture/1_2_Installation_0.png)
\ No newline at end of file
+![image](../../_picture/1_2_Installation_0.png)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/1_4_\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md" "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md"
similarity index 64%
rename from "docs/guidebook/zh/1_4_\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md"
rename to "docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md"
index be4d3d15..bf2b17dd 100644
--- "a/docs/guidebook/zh/1_4_\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md"
+++ "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\272\224\347\224\250\345\267\245\347\250\213\347\273\223\346\236\204\345\217\212\350\257\264\346\230\216.md"
@@ -5,80 +5,65 @@
 下列目录结构为agentUniverse官方推荐结构：
 
 ```
+/
+├── bootstrap/
+│   ├── intelligence/
+│   │   └── server_application.py
+│   ├── platform/
+│   │   └── product_application.py
 ├── intelligence/
-│   ├── service/
-│   │   └── agent_service/
-│   │   └── classic_service/
-│   └── dal
-│   └── integration
-│   └── utils
-│   └── agentic/ 
-│   │   └── agent/ 
-│   │   │   └── agent_template/ 
-│   │   │   └── agent_instances 
-│   │   └── knowledge/ 
+│   ├── agentic/
+│   │   ├── agent
+│   │   │   └── agent_instance
+│   │   │   └── agent_template
+│   │   ├── knowledge
 │   │   │   └── store/
 │   │   │   └── rag_router/
 │   │   │   └── doc_processor/
-│   │   │   └── knowledge_instances 
-│   │   └── memory/ 
-│   │   │   └── memory_storage/
-│   │   │   └── memory_compressor/
-│   │   │   └── memory_instances 
-│   │   └── llm/ 
-│   │   │   └── llm_instances 
-│   │   └── tool/ 
-│   │   │   └── tool_instances 
-│   │   └── prompt/ 
-│   │   └── work_pattern/ 
-│   └── test
+│   │   ├── llm
+│   │   ├── prompt
+│   │   ├── memory
+│   │   ├── tool
+│   │   └── work_pattern
+│   ├── service/
+│   │   └── agent_service
+│   │   └── classic_service
+│   ├── dal/
+│   ├── integration/
+│   ├── utils/
+│   └── test/
 ├── platform/
-│   ├── difizen/
-│   │   └── product/
-│   │   └── workflow/
-│   └── chat
-├── boostrap/
-│   └── intelligence/
-│   │       └── service_application.py
-│   └── platform/
-│           └── product_application.py
-├── config/
-│   └── config.toml
-│   └── custom_key.toml
-│   └── log_config.toml
+├── config
 ├── pyproject.toml
 └── other project files...
 ```
 
 每个层级包目录具体含义如下：
-* intelligence - 智能工程
+* bootstrap - webserve启动入口层
+  * intelligence - 智能应用webserve启动入口层
+  * platform - 产品化服务webserve启动入口层
+* intelligence - 智能工程层：用于智能体构建，组件拓展定制以及服务化实现
+  * agentic - 智能领域层，放置智能体相关的agentUniverse领域组件
+    * agent - 智能体层，对应agentUniverse的agent领域组件，可构建智能体模版、智能体实例等
+      * agent_template - 智能体模版层，基于业务理解抽象出的智能体模版，用于帮助用户快速构建智能体。完成模版构建后，使用者只需基于模版配置特定属性，即可执行对应的编排逻辑
+      * agent_instance - 智能体实例层
+    * knowledge - 知识层，对应agentUniverse的knowledge领域组件，包含知识注入，查询能力。
+      * store - 知识库存储，对应agentUniverse的store领域组件
+      * rag_router - 知识库rag路由，对应agentUniverse的rag_router领域组件
+      * doc_processor - 知识库文档处理器，对应agentUniverse的doc_processor领域组件
+    * llm - llm层，对应agentUniverse的llm领域组件，提供大模型服务，如chatgpt/qwen等
+    * prompt - prompt层，对应agentUniverse的prompt领域组件，提供提示词模板、提示词生成等能力
+    * memory - 记忆层，对应agentUniverse的memory领域组件，提供agent的记忆能力，包含记忆压缩和记忆存储
+    * tool - 工具层，对应agentUniverse的tool领域组件，提供辅助工具，如api服务
+    * work_pattern - 工作模式层，提供典型的单智能体和多智能体间的工作协同范式，包括PEER/DOE/MAP等。
   * service - 智能工程服务层
-    * agent_service - 智能体服务层，包含agentUniverse的service领域组件，关联智能体实例，对外提供服务
-    * classic_service - 传统服务层，包含非智能体服务，如文件服务、会话服务等
-  * dal - 智能工程数据访问层，提供数据访问机制，抽象数据访问细节
-  * integration - 智能工程集成层，用于在不同系统或服务间传递消息和数据。
-  * utils - 智能工程工具层
-  * agentic - 智能化工程，放置智能化相关的agentUniverse领域组件
-    * agent - agent层，包含agentUniverse的agent领域组件，如智能体模版、智能体实例等
-    * knowledge - knowledge层，包含agentUniverse的knowledge领域组件
-      * doc_processor - 知识库文档处理器，包含agentUniverse的doc_processor领域组件
-      * rag_router - 知识库rag路由，包含agentUniverse的rag_router领域组件
-      * store - 知识库存储，包含agentUniverse的store领域组件
-    * llm - llm层，包含agentUniverse的llm领域组件，提供大语言模型服务，如chatgpt/qwen等
-    * tool - 工具层，包含agentUniverse的tool领域组件，提供智能体相关工具服务，如api服务等
-    * prompt - prompt层，包含agentUniverse的prompt领域组件，提供提示词模板、提示词生成等能力
-    * work_pattern - 工作模式层，包含agentUniverse的work_pattern领域组件
-    * memory - 记忆层，包含agentUniverse的memory领域组件，提供智能体记忆能力
-      * memory_compressor - 记忆压缩器，包含agentUniverse的memory_compressor领域组件
-      * memory_storage - 记忆存储器，包含agentUniverse的memory_storage领域组件
-  * test - 测试目录
-* platform - 平台工程，智能工程以外的平台能力
-  * difizen - difizen x agentUniverse产品化低代码平台
-    * product - 产品化平台产品信息存储
-    * workflow - 产品化平台工作流配置存储
-* boostrap - 启动目录
-  * intelligence - 智能工程启动目录
-  * platform - 平台工程启动目录
+    * agent_service - 智能服务层，对应agentUniverse的service领域组件，关联智能体实例，对外提供服务
+    * classic_service - 传统服务层，承接非智能体服务，如文件服务、会话服务等
+  * dal - 数据访问层，提供数据源访问机制，抽象数据访问接口
+  * integration - 集成层，用于连接和协调不同系统、服务及组件
+  * utils - 基础工具层，提供小型，通用的辅助函数
+  * test - 单元测试
+* platform - 平台工程，承接智能工程以外的平台能力
 * config - 系统级配置目录
 
 
@@ -150,4 +135,4 @@ workflow = ['sample_standard_app.platform.difizen.workflow']
 ```
 包定义的格式遵从标准的python包路径格式，框架在启动时会根据定义的各类组件package路径进行注册扫描与统一管理。
 
-**Tips**: package路径以您的项目根目录下一级开始，如sample_standard_app示例中的package路径由于在agentUniverse层级下，所以由sample_standard_app开始, 若您以sample_standard_app为项目模版则路径应以intelligence.xxx 开头。
\ No newline at end of file
+**Tips**: package路径以您的项目根目录下一级开始，如sample_standard_app示例中的package路径由于在agentUniverse层级下，所以由sample_standard_app开始, 若您以sample_standard_app为项目模版则路径应以intelligence.xxx 开头。
diff --git "a/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md" "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\274\200\345\247\213\344\275\277\347\224\250.md"
similarity index 98%
rename from "docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md"
rename to "docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\274\200\345\247\213\344\275\277\347\224\250.md"
index eb8a3a39..0f2dbb28 100644
--- "a/docs/guidebook/zh/1_3_\345\277\253\351\200\237\345\274\200\345\247\213.md"
+++ "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\274\200\345\247\213\344\275\277\347\224\250.md"
@@ -1,4 +1,4 @@
-# 快速开始
+# 开始使用
 在本部分我们将向您展示如何：
 * 进行环境与应用工程准备
 * 构建一个简单的agent
@@ -11,7 +11,7 @@
 - python 3.10+
 
 ### 应用工程准备
-我们提供了一个标准的工程模板，您可以到 [这里](../../../sample_standard_app) 查看并进行下载。
+我们提供了一个标准的工程模板，您可以到 [这里](../../../../sample_standard_app) 查看并进行下载。
 
 sample_standard_app文件夹包含了一个标准的工程模板，您可以根据自己的需求进行修改。您也可以将sample_standard_app文件夹复制作为您的应用工程根目录。
 
@@ -224,7 +224,7 @@ python server_application.py
 ```
 
 当出现如下命令行说明监听成功，默认监听127.0.0.1地址8000端口，worker数为5，可在`config/gunicorn_config.toml`中修改配置。（注意windows系统与直接使用flask启动的目前默认监听8888端口）
-![image](../_picture/1_3_Quick%20Start_0.png)
+![image](../../_picture/1_3_Quick%20Start_0.png)
 
 ### 服务访问
 #### 本机或局域网访问
diff --git "a/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\277\253\351\200\237\345\274\200\345\247\213.md" "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\277\253\351\200\237\345\274\200\345\247\213.md"
new file mode 100644
index 00000000..c57237e7
--- /dev/null
+++ "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\345\277\253\351\200\237\345\274\200\345\247\213.md"
@@ -0,0 +1,164 @@
+# 快速开始-运行第一个教程案例
+通过本文档，您将了解到如何快速运行agentUniverse的示例，并亲身体验其效果。
+
+## 1.准备工作
+在正式运行示例案例之前，需要确保您安装了必要的运行环境与工程准备。
+
+### 1.1 环境准备
+agentUniverse支持 `macos`、`linux`、`windows`3个系统平台，不同的系统平台环境配置细节会略有不同，这里我们不展开罗列，可以根据文档提供的必要步骤检查是否满足所有，并自行根据系统平台搜索对应软件配置教程。
+
+#### 1.1.1 python环境准备
+使用 **python 3.10** 版本
+
+Tips: 如果您的电脑已经安装其他python版本环境，强烈建议您使用conda等python环境管理工具隔离出不同的多版本python环境。
+
+#### 1.1.2 开发工具准备
+agentUniverse是面向标准容器化交付的，包含标准镜像，您可以通过shell运行和修改源代码用于研发阶段，但是这种方式效率不高。
+
+尽管本部分不是必须的，但是我们强烈建议您安装一个面向python的IDE工具。结合python的研发体验我们推荐使用pycharm，其次是vscode（vs面向python研发配置会麻烦些，不建议python研发初学者使用）。
+
+不要忘了在对应的IDE工具配置python interpreter。
+
+### 1.2 工程准备
+#### 1.2.1 样例工程准备
+您可以在项目门户网页中或使用`git clone`下载agentUniverse源代码。
+
+完成本部分操作您会获得如下的工程结构，其中源码中的`sample_standard_app`文件夹为样例工程代码，该文件夹位于根目录下一级，根目录为`agentUniverse`。
+
+```text
+➜  agentUniverse ✗ tree -L 1
+.
+├── CHANGELOG.md
+├── CHANGELOG_zh.md
+├── LICENSE
+├── README.md
+├── README_PYPI.md
+├── README_jp.md
+├── README_zh.md
+├── agentuniverse
+├── agentuniverse_connector
+├── agentuniverse_extension
+├── agentuniverse_product
+├── dataset
+├── dist
+├── docs
+├── poetry.toml
+├── pyproject.toml
+├── sample_standard_app
+└── tests
+```
+
+Tips: 这里我们为了简化设置，请先不要修改任何工程目录与重命名文件夹。agentUniverse支持任意工程目录结构，这需要一些额外的配置，我们在其他章节会说明其用法。
+
+#### 1.2.2 安装对应的框架包
+```shell
+# 框架包
+pip install agentUniverse
+
+# 产品可视化界面包(按需安装)
+pip install magent-ui ruamel.yaml
+```
+
+#### 1.2.3 密钥配置
+本框架支持通过环境变量、文件配置等方式注入密钥，我们在密钥设置的单独章节会说明完整用法，这里我们展示文件配置密钥的步骤。请严格按顺序检查下列步骤是否满足：
+
+##### step1. 确认config文件开启自动读取外置密钥文件
+找到项目工程中的主配置文件（路径为：agentUniverse/sample_standard_app/config/config.toml）；找到 SUB_CONFIG_PATH 配置项下的 custom_key_path 配置项，将其注释放开如下图:
+![config_ak_path](../../_picture/config_ak_path.png)
+
+这一步将开启项目自动读取配置文件批量加载密钥的能力，我们可以看到 custom_key_path 配置项已经默认包含了1个路径，为了简化教程在这里我们暂不修改路径。
+
+##### step2. 根据模版创建外置密钥文件
+外置密钥文件模版在config的同层级路径（具体路径为sample_standard_app/config/custom_key.toml.sample），我们将 custom_key.toml.sample 拷贝并且创建一个新文件，结合step1中custom_key_path的默认配置命名为 custom_key.toml，如下图：
+![config_ak_create_file](../../_picture/config_ak_create_file.png)
+
+Tips: 外置密钥文件一般包含了您的所有ak，这是非常私密且需要严格保护的，这个文件是绝对不允许被泄漏或者被git这类代码平台管理的。在实际的生产项目中，我们通常会将该文件剥离出项目外，并且加上一个系统级别的强权限管控，本框架的密钥配置之所以会有这些步骤更多的是出于生产安全性的考虑。
+
+##### step3. 在外置密钥文件中配置您的常用模型ak
+密钥文件已包含了数十种常见的模型服务ak格式，你可以根据自身需求填写自己的密钥，别忘了放开注释。
+
+后续的教程中，我们将以千问与GPT模型作为教程agent使用的llm，所以这里我们以千问、GPT为例配置对应的ak如下图：
+![config_ak_demo_key](../../_picture/config_ak_demo_key.png)
+
+## 2. 运行第一个案例
+agentUniverse目前包含5个官方教程案例，路径在 agentUniverse/sample_standard_app/app/examples
+
+在本节我们来运行第一个案例，我们选取 law_chat_bot 作为第一个教程案例。
+
+### 2.1 确定example背后使用的agent与其配置
+以 law_chat_bot 为例（路径：agentUniverse/sample_standard_app/app/examples/law_chat_bot.py），我们先在脚本中找到对应的agent_name, 以law_chat_bot为例law_rag_agent。
+![demo_case_law_rag_agent](../../_picture/demo_case_law_rag_agent.png)
+
+确定example使用的agent后，我们到项目agent目录（目录路径为：agentUniverse/sample_standard_app/app/core/agent）下找到对应的agent配置 law_rag_agent.yaml，注意agent配置中的name项即为law_chat_bot中的agent名称
+![demo_case_law_rag_agent_yaml](../../_picture/demo_case_law_rag_agent_yaml.png)
+
+我们进一步查看 law_rag_agent.yaml 中的其他配置详情，在这里请进一步关注 llm_model 这一个配置项，这一项为配置选择agent所使用的llm，law_rag_agent 中默认使用了 qwen系列的qwen-max 作为模型内核，若您在密钥配置阶段配置的为其他系列模型类型需要在这里替换，对应常用模型替换参考如下。默认复制即可，model_name有需要可以自行按照服务商官方模型code替换。
+
+千问系列
+```text
+  llm_model:
+    name: 'qwen_llm'
+    model_name: 'qwen-max'
+```
+
+gpt系列
+```text
+  llm_model:
+    name: 'demo_llm'
+    model_name: 'gpt-4o'
+```
+
+文心系列
+```text
+  llm_model:
+    name: 'wenxin_llm'
+    model_name: 'ERNIE-3.5-8K'
+```
+
+kimi系列
+```text
+  llm_model:
+    name: 'kimi_llm'
+    model_name: 'moonshot-v1-8k'
+```
+
+百川系列
+```text
+  llm_model:
+    name: 'baichuan_llm'
+    model_name: 'Baichuan2-Turbo'
+```
+
+DeepSeek系列
+```text
+  llm_model:
+    name: 'deep_seek_llm'
+    model_name: 'deepseek-chat'
+```
+
+Tips：为简化配置过程，这里我们只列举了部分常用模型服务，此外除了模型服务商、本地部署模型均可配置，我们在这个章节不演示，有这部分需求的用户可以进一步关注llm配置相关的章节。
+
+
+### 2.2 运行案例
+通过上述的步骤，您已经完成了所有前置工作，让我们来直接运行看一看效果。找到agentUniverse/sample_standard_app/app/examples/law_chat_bot.py文件，在ide或在shell中运行。
+
+![demo_case_law_rag_agent_run](../../_picture/demo_case_law_rag_agent_run.png)
+
+当你看到如下结果时说明案例已经跑通了。
+
+![demo_case_law_rag_agent_answer](../../_picture/demo_case_law_rag_agent_answer.png)
+
+
+## 3. 其他案例的运行
+至此你已经完成了第一个案例的运行，在其他官方案例中我们使用了 google_search_tool 这个工具，这将给您的agent提供网络检索能力，为了体验其他用例我们建议您申请serper并提前在密钥部分内置搜索工具的ak，serper官方已经提供上千次的免费检索额度用于您的体验。
+
+你需要在serper官网申请SERPER_API_KEY并配置，官网地址： https://serper.dev
+
+申请完成后，在密钥配置文件 custom_key.toml 中找到对应的key填写，并放开注释如下：
+
+```toml
+#Google search
+SERPER_API_KEY='xxxxxx'
+```
+
+按照第2部分的步骤执行其他案例即可，现在您可以运行所有官方案例了。
diff --git "a/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\347\256\200\344\273\213.md" "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\347\256\200\344\273\213.md"
new file mode 100644
index 00000000..93276485
--- /dev/null
+++ "b/docs/guidebook/zh/\345\274\200\345\247\213\344\275\277\347\224\250/\347\256\200\344\273\213.md"
@@ -0,0 +1,135 @@
+## agentUniverse是什么？
+
+**agentUniverse 是一个基于大型语言模型的多智能体框架。** agentUniverse为您提供灵活易拓展的单智能体构建能力；agentUniverse核心拥有丰富的多智能体协同模式组件（可视为一个协同模式工厂Pattern Factory），它能让智能体们各司其职在解决不同领域问题时发挥最大的能力；同时agentUniverse专注于领域经验的融合，帮助您轻松将领域经验融入到智能体的工作中。🎉🎉🎉
+
+**🌈🌈🌈agentUniverse帮助开发者、企业轻松构建出领域专家级别的强大智能体协同为您工作。**
+
+![](../../_picture/agent_universe_framework_resize.jpg)
+
+我们期待您通过社区对不同领域的Pattern进行实践与交流共享，框架也预置有若干已在真实产业中验证有效的多智能体协作模式组件，并在未来持续丰富。目前即将开放的模式组件包括：
+* PEER 模式组件： 该pattern通过计划（Plan）、执行（Execute）、表达（Express）、评价（Review）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。典型适用场景：事件解读、行业分析
+* DOE 模式组件： 该pattern通过数据精制（Data-fining）、观点注入（Opinion-inject）、表达（Express）三个智能体，实现对数据密集、高计算精度、融合专家观点的生成任务的效果提升。典型适用场景：财报生成
+
+更多模式组件持续推出中...
+
+****************************************
+
+## 目录
+* [快速开始](#快速开始)  
+|  &nbsp; [快速安装](#快速安装) &nbsp; |
+&nbsp; [运行案例](#运行第一个教程案例) &nbsp; |
+* [如何搭建一个智能体应用](#如何搭建一个智能体应用)  
+| &nbsp; [工程化搭建](#工程化搭建) &nbsp; |
+&nbsp; [产品化搭建](#产品化搭建) &nbsp; |
+* [为什么使用agentUniverse](#为什么使用agentUniverse)  
+| &nbsp; [设计思路](#设计思路) &nbsp; | 
+&nbsp; [协同机制](#多智能体协同机制) &nbsp; | 
+&nbsp; [研究文献](#文献) &nbsp; | 
+&nbsp; [核心特性](#核心特性) &nbsp; |
+* [案例与样例工程](#案例与样例工程)  
+| &nbsp; [官方案例](#官方案例) &nbsp; | 
+&nbsp; [样例工程](#示例工程) &nbsp; | 
+&nbsp; [典型产品](#使用aU构建的典型产品) &nbsp; |
+* [用户指南手册](#用户指南手册)
+* [更多](#更多)  
+| &nbsp; Roadmap &nbsp; | 
+&nbsp; [API说明](#API参考) &nbsp; | 
+&nbsp; [项目支持](#支持) &nbsp; |
+&nbsp;&nbsp; [鸣谢](#鸣谢)  &nbsp;&nbsp; |
+
+****************************************
+
+## 快速开始
+### 快速安装
+使用pip：
+```shell
+pip install agentUniverse
+```
+
+### 运行第一个教程案例
+
+运行您的第一个案例，您可以通过教程快速体验agentUniverse构建出的智能体(组)运行效果。 
+
+详细步骤请阅读文档: [运行第一个教程案例](../开始使用/快速开始.md) 。
+
+****************************************
+
+## 如何搭建一个智能体应用
+
+### 工程化搭建
+
+#### 智能体创建与使用
+您可以通过 [智能体介绍](../In-Depth_Guides/原理介绍/智能体/智能体.md) 了解智能体的重要组成部分，通过 [智能体创建与使用](../In-Depth_Guides/原理介绍/智能体/智能体创建与使用.md) 了解创建智能体的详细过程。您也可以结合官方案例，如 [Python代码生成与执行Agent](../样例文档/Python自动执行案例.md) 进一步理解智能体创建与使用过程。 
+
+#### 知识库构建与使用  
+在智能体应用构建中，知识库构建与召回是不可缺少的，agentUniverse框架以[RAG技术](../In-Depth_Guides/原理介绍/RAG.md)为基础提供了高效的知识库构建和RAG检索召回环节的标准作业流程。您可以通过[知识介绍](../In-Depth_Guides/原理介绍/知识/知识.md)与[知识定义与使用](../In-Depth_Guides/原理介绍/知识/知识定义与使用.md)了解其使用方式，进一步通过[如何构建RAG智能体](../How-to/如何构建RAG智能体.md)掌握如何快速构建知识库与构建具有召回能力的智能体。
+
+#### 工具自定义与使用  
+在智能体应用构建中，智能体需要连接各种各样的工具，您需要为其指定可使用的工具范围列表。您可以通过[工具创建与使用](../In-Depth_Guides/原理介绍/工具/工具创建与使用.md)将各类自有API与服务以工具插件形式接入。目前框架已经集成了LangChain与部分第三方工具包，详细使用法可阅读[集成LangChain工具](../In-Depth_Guides/组件列表/工具列表/集成LangChain工具.md)、[现有集成的工具](../In-Depth_Guides/组件列表/工具列表/集成LangChain工具.md)。
+
+#### 效果评估  
+智能体的效果评估一方面可以通过人工专家评定，另一个方面也可以依托智能体的评估能力。agentUniverse推出了DataAgent（Minimum Viable Product版本）, DataAgent旨在使用智能体能力让您的Agent拥有自我评价与演进的能力，您也可以在其中自定义评估标准。详情见文档: [DataAgent - 数据自治智能体](../In-Depth_Guides/原理介绍/数据自治智能体.md)。
+
+#### 服务化
+agentUniverse提供多套标准的web serve能力, 同时提供标准的http与rpc协议。您可以进一步关注文档[服务注册与使用](../In-Depth_Guides/技术组件/服务化/服务注册与使用.md)与[Web_Server](../In-Depth_Guides/技术组件/服务化/Web_Server.md)部分。
+
+### 平台化搭建
+
+agentUniverse提供基于本地的可视化平台搭建能力，请按照如下步骤快速启动。
+
+**通过pip安装**
+```shell
+pip install magent-ui ruamel.yaml
+```
+
+**一键运行**
+
+运行sample_standard_app/app/boostrap下的[product_application.py](../../../../sample_standard_app/boostrap/platform/product_application.py)文件，一键启动。
+
+更多详情参考 [产品化平台快速开始](../How-to/画布式研发平台快速开始.md)
+与 [产品化平台进阶指南](../How-to/画布式研发平台进阶指南.md) 。本功能由 🔗[difizen](https://github.com/difizen/magent) 项目组 X agentUniverse 项目组联合推出。
+
+****************************************
+
+## 为什么使用agentUniverse
+### 设计思路
+
+![](../../_picture/agentuniverse_structure.png)
+
+agentUniverse核心提供了搭建单一智能体的全部关键组件、多智能体之间的协作机制、以及专家经验的注入机制，可以帮助开发者轻松构建具备专业KnowHow的智能应用。
+
+### 多智能体协同机制
+
+agentUniverse提供了若干已在真实产业中验证有效的多智能体协作模式组件，其中，“PEER”是最具特色的模式之一。
+
+PEER模式通过计划（Planning）、执行（Executing）、表达（Expressing）、评价（Reviewing）四个不同职责的智能体，实现对复杂问题的多步拆解、分步执行，并基于评价反馈进行自主迭代，最终提升推理分析类任务表现。这一模式显著适用于需要多步拆解、深度分析的场景，比如对于事件的解读、宏中观经济分析、商业方案的可行性分析等。
+
+PEER模式取得了令人兴奋的效果，最新的研究成果与实验结果我们可以在下列文献中阅读。
+
+### 文献
+
+BibTeX formatted
+```text
+@misc{wang2024peerexpertizingdomainspecifictasks,
+      title={PEER: Expertizing Domain-Specific Tasks with a Multi-Agent Framework and Tuning Methods}, 
+      author={Yiying Wang and Xiaojing Li and Binzhu Wang and Yueyang Zhou and Han Ji and Hong Chen and Jinshi Zhang and Fei Yu and Zewei Zhao and Song Jin and Renji Gong and Wanqing Xu},
+      year={2024},
+      eprint={2407.06985},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/2407.06985}, 
+}
+```
+文献简介：该文献详细介绍了介绍了PEER多智能体框架的机制原理，同时在实验部分分别从**完整性、相关性、紧凑性、事实性、逻辑性、结构性和全面性七个维度进行打分（各纬度满分为5分）**，PEER模式在每个测评维度的平均分数均高于BabyAGI，且在**完整性、相关性、逻辑性、结构性和全面性五个纬度有显著优势**；同时PEER模式在 GPT-3.5 turbo (16k) 模型下相较于 BabyAGI 的择优胜率达到 83%，在 GPT-4o 模型下择优胜率达到 81%，更多详情请阅读文献。
+🔗https://arxiv.org/pdf/2407.06985
+
+### 核心特性
+通过上述的介绍我们将其归纳总结，agentUniverse包含如下主要特点：
+
+灵活易拓的智能体构建能力： 提供智能体构建所必须的全部关键组件，所有组件均可支持定制供用户增强专属智能体；
+
+丰富有效的多智能体协同模式： 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，支持用户自定义编排新模式，让多个智能体有机合作；
+
+轻松融入领域经验： 提供领域prompt、知识构建与管理的能力，支持领域级SOP编排与注入，将智能体对齐至领域专家级别；
+
+💡 更多特点见[agentUniverse核心特性](../设计理念/核心特性.md)部分。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/7_1_1_Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md" "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md"
similarity index 85%
rename from "docs/guidebook/zh/7_1_1_Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md"
rename to "docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md"
index eceff224..dc08b59f 100644
--- "a/docs/guidebook/zh/7_1_1_Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md"
+++ "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/Python\350\207\252\345\212\250\346\211\247\350\241\214\346\241\210\344\276\213.md"
@@ -40,13 +40,13 @@ metadata:
 ```
 
 这里使用了google_search_tool与python_runner两个工具，相关工具代码链接如下：
-- [google_search_tool](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)
-- [python_runner](../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)
+- [google_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.yaml)
+- [python_runner](../../../../sample_standard_app/intelligence/agentic/tool/python_repl_tool.yaml)
 
 
 ### 代码运行
 1. 测试用例运行
-直接使用测试代码运行[test_case](../../../sample_standard_app/intelligence/test/test_react_agent.py)
+直接使用测试代码运行[test_case](../../../../sample_standard_app/intelligence/test/test_react_agent.py)
 2. 接口运行
 配置好相关key之后，启动web服务，使用下面的curl进行测试
 ```shell
@@ -63,7 +63,7 @@ curl --location --request POST 'http://localhost:8888/service_run' \
 ```
 
 ### 执行结果
-![test_case](../_picture/react_demo_step.png)
+![test_case](../../_picture/react_demo_step.png)
 
 图片中，react一共执行了三步：  
     第一步，模型根据问题给出了一段python代码，并交给python_runner工具执行，但是执行失败了，失败原因是没有使用print打印执行结果。  
diff --git "a/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md" "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md"
new file mode 100644
index 00000000..a2efc8a6
--- /dev/null
+++ "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\346\263\225\345\276\213\345\222\250\350\257\242\346\241\210\344\276\213.md"
@@ -0,0 +1,88 @@
+# 法律咨询案例
+## 案例说明
+本案例基于RagPlanner，搭建了一个简单的法律咨询智能体，通过检索民法典和刑法中的相关条例并结合案件背景给出相关的法律建议。
+
+该案例基于千问大模型和DashScope的embedding和rerank功能，使用前需要您在环境变量中配置`DASHSCOPE_API_KEY`。
+
+## 构成
+### 法律知识库
+法律知识库基于agentUniverse中的[知识组件](../In-Depth_Guides/原理介绍/知识/知识.md)，通过将法律条款原文存储至ChromaDB和Sqlite中，构建方便智能体查阅检索的知识库。  
+法律书籍原本：
+- [民法典.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
+- [刑法.pdf](../../../../sample_standard_app/intelligence/agentic/knowledge/raw_knowledge_file/民法典.pdf)
+
+### [知识定义](../../../../sample_standard_app/intelligence/agentic/knowledge/law_knowledge.yaml)
+```yaml
+name: "law_knowledge"
+description: "中国民法与刑法相关的知识库"
+stores:
+    - "civil_law_chroma_store"
+    - "criminal_law_chroma_store"
+    - "civil_law_sqlite_store"
+    - "criminal_law_sqlite_store"
+query_paraphrasers:
+    - "custom_query_keyword_extractor"
+insert_processors:
+    - "recursive_character_text_splitter"
+rag_router: "nlu_rag_router"
+post_processors:
+    - "dashscope_reranker"
+readers:
+    pdf: "default_pdf_reader"
+
+metadata:
+  type: 'KNOWLEDGE'
+  module: 'sample_standard_app.app.core.knowledge.law_knowledge'
+  class: 'LawKnowledge'
+```
+
+### Reader组件
+- [default_pdf_reader](../../../../agentuniverse/agent/action/knowledge/reader/file/pdf_reader.yaml)
+
+### DocProcessor组件
+- [custom_query_keyword_extractor](../../../../sample_standard_app/intelligence/agentic/knowledge/doc_processor/query_keyword_extractor.yaml)
+- [recursive_character_text_splitter](../../../../agentuniverse/agent/action/knowledge/doc_processor/recursive_character_text_splitter.yaml)
+
+### QueryParaphraser组件
+- [custom_query_keyword_extractor](../../../../sample_standard_app/intelligence/agentic/knowledge/query_paraphraser/custom_query_keyword_extractor.yaml)
+
+### RagRouter组件
+- [nlu_rag_router](../../../../sample_standard_app/intelligence/agentic/knowledge/rag_router/nlu_rag_router.yaml)
+
+### Store组件
+- [civil_law_chroma_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_chroma_store.yaml)
+- [criminal_law_chroma_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_chroma_store.yaml)
+- [civil_law_sqlite_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/civil_law_sqlite_store.yaml)
+- [criminal_law_sqlite_store](../../../../sample_standard_app/intelligence/agentic/knowledge/store/criminal_law_sqlite_store.yaml)
+
+为了方便您使用，我们已经将存储有相关信息的数据库文件放在[该目录下](../../../../sample_standard_app/db)，如果您想从头构建知识库的话，您可以运行如下代码：
+```python
+from agentuniverse.base.agentuniverse import AgentUniverse
+from agentuniverse.agent.action.knowledge.knowledge_manager import KnowledgeManager
+
+
+if __name__ == '__main__':
+    AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
+    civil_store_list = ["civil_law_sqlite_store", "civil_law_chroma_store"]
+    criminal_store_list = ["criminal_law_sqlite_store", "criminal_law_chroma_store"]
+    law_knowledge = KnowledgeManager().get_instance_obj("law_knowledge")
+    law_knowledge.insert_knowledge(
+        source_path="../resources/刑法.pdf",
+        stores=criminal_store_list
+    )
+    law_knowledge.insert_knowledge(
+        source_path="../resources/民法典.pdf",
+        stores=civil_store_list
+    )
+```
+
+### 法律智能体
+该智能体涉及如下配置：
+- [law_rag_agent.yaml](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/rag_agent_case/law_rag_agent.yaml): 定义了智能体的相关提示词
+
+
+### 演示代码
+[代码链接](../../../../sample_standard_app/intelligence/test/law_chat_bot.py)
+
+## 效果演示
+![演示图片](../../_picture/law_agent_demo.png)
\ No newline at end of file
diff --git "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md" "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\347\277\273\350\257\221\346\241\210\344\276\213.md"
similarity index 52%
rename from "docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md"
rename to "docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\347\277\273\350\257\221\346\241\210\344\276\213.md"
index 34679247..ff141058 100644
--- "a/docs/guidebook/zh/7_1_1_\347\277\273\350\257\221\346\241\210\344\276\213.md"
+++ "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\347\277\273\350\257\221\346\241\210\344\276\213.md"
@@ -8,50 +8,50 @@
 ## 多智能体的协同工作过程
 翻译过程中，优先判断模型的长度是否超过了模型所能承受的最大token,针对超过模型长度的情况,先对文本进行切块，再对分块后的文本进行翻译，
 但是无论长文本还是短文本，翻译过程都是基于初始翻译->反思->修改的流程进行的。
-![多智能体协同工作流程](../_picture/translation_flow_graph.png)
+![多智能体协同工作流程](../../_picture/translation_flow_graph.png)
 
 ## 在aU中的实现
 在aU中实现该翻译的过程主要分为以下几步：
 1. 定义翻译相关的prompt，短文本翻译三个，长文本翻译三个，相关文件如下:  
-[短文本翻译init prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_init_en.yaml)  
-[短文本翻译reflection prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_reflection_en.yaml)  
-[短文本翻译improve prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_improve_en.yaml)  
-[长文本翻译init prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_init_en.yaml)  
-[长文本翻译reflection prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
-[长文本翻译improve prmpt](../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
+[短文本翻译init prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_init_en.yaml)  
+[短文本翻译reflection prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_reflection_en.yaml)  
+[短文本翻译improve prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/translation_improve_en.yaml)  
+[长文本翻译init prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_init_en.yaml)  
+[长文本翻译reflection prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
+[长文本翻译improve prmpt](../../../../sample_standard_app/intelligence/agentic/prompt/translation/multi_translation_improve_en.yaml)  
 在aU中,可以很容易的通过配置文件定义prompt，并统一由PromptManager管理。
   
 
 2. 定义三个智能体  
-[短文本翻译智能体](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_work_agent.yaml)  
-[短文本翻译智能体](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_reflection_agent.yaml)  
-[短文本翻译智能体](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_improve_agent.yaml)  
-该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[代码实现](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.py)  
+[短文本翻译智能体](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_work_agent.yaml)  
+[短文本翻译智能体](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_reflection_agent.yaml)  
+[短文本翻译智能体](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_improve_agent.yaml)  
+该智能体当中，需要根据执行的时长文本翻译还是短文本翻译切换对应的prompt，具体逻辑可参考[代码实现](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.py)  
 由于定义的prompt全部由PromptManager进行管理，这里可以很容易的实现prompt的切换
     
 
 3. 定义三个智能体的协同工作过程  
 协同过程可参考前面介绍的多智能体协同工作过程的流程图,具体的实现逻辑如图：    
-![协同工作](../_picture/translation_execute_flow.png)    
-更细的流程可以参考[代码文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_by_token_agent.py)  
-协同智能体的[配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.yaml)
+![协同工作](../../_picture/translation_execute_flow.png)    
+更细的流程可以参考[代码文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_by_token_agent.py)  
+协同智能体的[配置文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/translation_agent_case/translation_agent.yaml)
 
 ### 演示代码
-[代码链接](../../../sample_standard_app/intelligence/test/test_translation_agent.py)
+[代码链接](../../../../sample_standard_app/intelligence/test/test_translation_agent.py)
 
-[长翻译文本](../../../sample_standard_app/intelligence/test/translation_data/long_text.txt)  
-[短翻译文本](../../../sample_standard_app/intelligence/test/translation_data/short_text.txt)  
+[长翻译文本](../../../../sample_standard_app/intelligence/test/translation_data/long_text.txt)  
+[短翻译文本](../../../../sample_standard_app/intelligence/test/translation_data/short_text.txt)  
 
-[短翻译结果](../../../sample_standard_app/intelligence/test/translation_data/short_text_result.txt)  
-[长翻译结果](../../../sample_standard_app/intelligence/test/translation_data/long_text_result.txt)  
+[短翻译结果](../../../../sample_standard_app/intelligence/test/translation_data/short_text_result.txt)  
+[长翻译结果](../../../../sample_standard_app/intelligence/test/translation_data/long_text_result.txt)  
 
 ### 演示结果
 可以看到，使用aU与原本的translation_agent原工程翻译结果保持一致，复刻成功。  
 aU的执行结果:  
-![aU执行长文本结果](../_picture/long_translation_au.png)    
+![aU执行长文本结果](../../_picture/long_translation_au.png)    
   
 Translation_agent的执行结果:  
-![translation执行长文本结果](../_picture/long_translation_wu.png)  
+![translation执行长文本结果](../../_picture/long_translation_wu.png)  
 
 ### 其他
 1. 在参考吴恩达教授的代码当中，处理分块翻译时，为了保证翻译的连贯性，翻译每一个分块时，会携带相应的上下文。但是在他的代码当中将所有的翻译内容都放在的上下文当中，会使切分失去原本的意义，某些情况下会导致token超过模型所能接受的最大token，针对此问题我在对应的仓库地址提问了相关[issue](https://github.com/andrewyng/translation-agent/issues/28)
diff --git "a/docs/guidebook/zh/6_2_1_\350\256\250\350\256\272\347\273\204.md" "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\350\256\250\350\256\272\347\273\204.md"
similarity index 97%
rename from "docs/guidebook/zh/6_2_1_\350\256\250\350\256\272\347\273\204.md"
rename to "docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\350\256\250\350\256\272\347\273\204.md"
index d6473269..144fa810 100644
--- "a/docs/guidebook/zh/6_2_1_\350\256\250\350\256\272\347\273\204.md"
+++ "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\350\256\250\350\256\272\347\273\204.md"
@@ -41,11 +41,11 @@ if __name__ == '__main__':
 ### 效果演示
 甜粽子好吃还是咸粽子好吃：
 
-![演示图片](../_picture/sweet_or_salted_zongzi.png)
+![演示图片](../../_picture/sweet_or_salted_zongzi.png)
 
 中医好还是西医好：
 
-![演示图片](../_picture/chinese_or_western_medicine.png)
+![演示图片](../../_picture/chinese_or_western_medicine.png)
 
 ## 更多细节
 ### 智能体配置
diff --git "a/docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md" "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md"
similarity index 53%
rename from "docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md"
rename to "docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md"
index 6706a78f..59613a08 100644
--- "a/docs/guidebook/zh/6_4_1_\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md"
+++ "b/docs/guidebook/zh/\346\240\267\344\276\213\346\226\207\346\241\243/\351\207\221\350\236\215\344\272\213\344\273\266\345\210\206\346\236\220\346\241\210\344\276\213.md"
@@ -7,41 +7,41 @@
 ## Agents
 ### Planning Agent 
 原始代码文件可参考
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)
-- [提示词](../../../sample_standard_app/intelligence/agentic/prompt/planning_agent_cn.yaml)  
+- [配置文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_planning_agent.yaml)
+- [提示词](../../../../sample_standard_app/intelligence/agentic/prompt/planning_agent_cn.yaml)  
 
 Planning Agent负责将原始的金融问题拆分为多个可被单独解决的子问题，提供给后续的Executing Agent执行。在这个案例中，原始问题“分析下巴菲特减持比亚迪的原因”可以被拆解为下图中的数个子问题：  
-![planning_result](../_picture/6_4_1_planning_result.png)
-您可以在[测试文件](../../../sample_standard_app/intelligence/test/test_planning_agent.py)中单独调试Planning Agent。
+![planning_result](../../_picture/6_4_1_planning_result.png)
+您可以在[测试文件](../../../../sample_standard_app/intelligence/test/test_planning_agent.py)中单独调试Planning Agent。
 
 ### Executing Agent 
 原始代码文件可参考
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_executing_agent.yaml)
-- [提示词](../../../sample_standard_app/intelligence/agentic/prompt/executing_agent_cn.yaml)
+- [配置文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_executing_agent.yaml)
+- [提示词](../../../../sample_standard_app/intelligence/agentic/prompt/executing_agent_cn.yaml)
 
-在这个Agent中，我们提供了一个用于在google上搜索信息的工具[google_search_tool](../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.py)，该工具的使用需要在环境信息中配置`SERPER_API_KEY`。为了方便您进行简单的尝试，当环境配置中没有`SERPER_API_KEY`时，该工具会返回一段预设好的关于本案例问题的查询结果，具体内容您可以在[mock_search_tool](../../../sample_standard_app/intelligence/agentic/tool/mock_search_tool.py)中查看。
+在这个Agent中，我们提供了一个用于在google上搜索信息的工具[google_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/google_search_tool.py)，该工具的使用需要在环境信息中配置`SERPER_API_KEY`。为了方便您进行简单的尝试，当环境配置中没有`SERPER_API_KEY`时，该工具会返回一段预设好的关于本案例问题的查询结果，具体内容您可以在[mock_search_tool](../../../../sample_standard_app/intelligence/agentic/tool/mock_search_tool.py)中查看。
 
 
 Executing Agent负责解决Planning Agent拆分出的子问题。在本案例中，Executing Agent对拆解问题的执行结果如下：  
-![executing_result](../_picture/6_4_1_executing_result.png)
-结果较长，这里只展示了前两问的执行结果。您可以在[测试文件](../../../sample_standard_app/intelligence/test/test_executing_agent.py)中单独调试Executing Agent获得完整的结果。
+![executing_result](../../_picture/6_4_1_executing_result.png)
+结果较长，这里只展示了前两问的执行结果。您可以在[测试文件](../../../../sample_standard_app/intelligence/test/test_executing_agent.py)中单独调试Executing Agent获得完整的结果。
 
 ### Expressing Agent 
 原始代码文件可参考
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)
-- [提示词](../../../sample_standard_app/intelligence/agentic/prompt/expressing_agent_cn.yaml)  
+- [配置文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_expressing_agent.yaml)
+- [提示词](../../../../sample_standard_app/intelligence/agentic/prompt/expressing_agent_cn.yaml)  
 
 Expressing Agent负责将Executing Agent输出的所有结果进行汇总，并根据提示词中的要求总结表达为对原始问题的回答，在该案例中，Expressing Agent的输出结果如下：  
-![expressing_result](../_picture/6_4_1_expressing_result.png)
-您可以在[测试文件](../../../sample_standard_app/intelligence/test/test_expressing_agent.py)中单独调试Expressing Agent。
+![expressing_result](../../_picture/6_4_1_expressing_result.png)
+您可以在[测试文件](../../../../sample_standard_app/intelligence/test/test_expressing_agent.py)中单独调试Expressing Agent。
 
 ### Reviewing Agent 
 原始代码文件可参考
-- [配置文件](../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_reviewing_agent.yaml)
+- [配置文件](../../../../sample_standard_app/intelligence/agentic/agent/agent_instance/peer_agent_case/demo_reviewing_agent.yaml)
 
 Reviewing Agent负责对Expressing Agent产出的结果进行评价，看是否对于原问题是有效的回答，在本案例中，Reviewing Agent接受了Expressing Agent的答案：  
-![reviewing_result](../_picture/6_4_1_reviewing_result.png)
-您可以在[测试文件](../../../sample_standard_app/intelligence/test/test_reviewing_agent.py)中单独调试Reviewing Agent。
+![reviewing_result](../../_picture/6_4_1_reviewing_result.png)
+您可以在[测试文件](../../../../sample_standard_app/intelligence/test/test_reviewing_agent.py)中单独调试Reviewing Agent。
 
 ### PEER Agent 
 ```yaml
@@ -71,5 +71,5 @@ metadata:
 - expressing：负责Express部分的Agent名称
 - reviewing：负责Review部分的Agent名称
 
-您可以在[示例文件](../../../sample_standard_app/intelligence/test/peer_chat_bot.py)中完整运行本案例。
+您可以在[示例文件](../../../../sample_standard_app/intelligence/test/peer_chat_bot.py)中完整运行本案例。
 
diff --git "a/docs/guidebook/zh/\347\233\256\345\275\225.md" "b/docs/guidebook/zh/\347\233\256\345\275\225.md"
new file mode 100644
index 00000000..7ea008fc
--- /dev/null
+++ "b/docs/guidebook/zh/\347\233\256\345\275\225.md"
@@ -0,0 +1,114 @@
+# 使用指南
+************************************************
+## 目录
+
+**1.开始使用**
+
+* 1.1 [简介](开始使用/简介.md)
+
+* 1.2 [安装](开始使用/安装.md)
+
+* 1.3 [快速开始](开始使用/快速开始.md)
+
+* 1.4 [应用结构说明](开始使用/应用工程结构及说明.md)
+
+**2.原理介绍**
+
+* 2.1 框架原理
+* 2.2 [领域组件](In-Depth_Guides/原理介绍/领域组件原理.md)
+  * 2.2.1 [智能体(Agent)](In-Depth_Guides/原理介绍/智能体/智能体.md)
+    * 2.2.1.1 [智能体创建与使用](In-Depth_Guides/原理介绍/智能体/智能体创建与使用.md)
+    * 2.2.1.2 [智能体及相关领域对象](In-Depth_Guides/原理介绍/智能体/智能体及相关领域对象.md)
+  * 2.2.2 [模型(LLM)](In-Depth_Guides/原理介绍/智能体/模型.md)
+    * 2.2.2.1 [模型定义与使用](In-Depth_Guides/原理介绍/模型/模型定义与使用.md)
+    * 2.2.2.2 [模型及相关领域对象](In-Depth_Guides/原理介绍/模型/模型及相关领域对象.md)
+  * 2.2.3 [工具(Tool)](In-Depth_Guides/原理介绍/工具/工具.md)
+    * 2.2.3.1 [工具创建与使用](In-Depth_Guides/原理介绍/工具/工具创建与使用.md)
+    * 2.2.3.2 [工具及相关领域对象](In-Depth_Guides/原理介绍/工具/工具及相关领域对象.md)
+  * 2.2.4 [知识(Knowledge)](In-Depth_Guides/原理介绍/知识/知识.md)
+    * 2.2.4.1 [知识定义与使用](In-Depth_Guides/原理介绍/知识/知识定义与使用.md)
+    * 2.2.4.2 [知识及相关领域对象](In-Depth_Guides/原理介绍/知识/知识及相关领域对象.md)
+  * 2.2.5 [记忆(Memory)](In-Depth_Guides/原理介绍/记忆/记忆.md)
+    * 2.2.5.1 [记忆定义与使用](In-Depth_Guides/原理介绍/记忆/记忆定义与使用.md)
+    * 2.2.5.2 [记忆及相关领域对象](In-Depth_Guides/原理介绍/记忆/记忆及相关领域对象.md)
+  * 2.2.6 [计划(Planner)](In-Depth_Guides/原理介绍/计划/计划.md)
+    * 2.2.6.1 [计划定义与使用](In-Depth_Guides/原理介绍/计划/计划定义与使用.md)
+    * 2.2.6.2 [计划及相关领域对象](In-Depth_Guides/原理介绍/计划/计划及相关领域对象.md)
+* 2.3 技术组件
+  * 2.3.1 [RAG](In-Depth_Guides/原理介绍/RAG.md)
+    * 2.3.1.1 [如何构建RAG智能体](How-to/如何构建RAG智能体.md)
+* 2.4 其他
+  * 2.4.1 服务化
+    * 2.4.1.1 [注册与使用](In-Depth_Guides/技术组件/服务化/服务注册与使用.md)
+    * 2.4.1.2 [Web Server](In-Depth_Guides/技术组件/服务化/Web_Server.md)
+    * 2.4.1.3 [服务API](In-Depth_Guides/技术组件/服务化/服务Api.md)
+    * 2.4.1.4 [服务信息存储](In-Depth_Guides/技术组件/服务化/服务信息存储.md)
+  * 2.4.2 prompt管理
+  * 2.4.3 多轮会话
+  * 2.4.4 日志体系
+    * 2.4.4.1 [日志组件](In-Depth_Guides/技术组件/日志监控/日志组件.md)
+  * 2.4.5 数据采集
+    * 2.4.5.1 [monitor模块](In-Depth_Guides/技术组件/日志监控/监控模块.md)
+  * 2.4.6 数据自治
+    * 2.4.6.1 [数据自治智能体](In-Depth_Guides/原理介绍/数据自治智能体.md)
+
+**3.组件参考手册**
+* 3.1 领域组件
+  * 3.1.1 智能体列表(Agent)
+  * 3.1.2 [模型列表(LLM)](In-Depth_Guides/组件列表/模型列表/模型列表.md)
+    * 3.1.2.1 [OpenAI使用说明](In-Depth_Guides/组件列表/模型列表/OpenAI使用.md)
+    * 3.1.2.2 [通义千问使用说明](In-Depth_Guides/组件列表/模型列表/Qwen使用.md)
+    * 3.1.2.3 [文心一言使用说明](In-Depth_Guides/组件列表/模型列表/文心使用.md)
+    * 3.1.2.4 [Kimi使用说明](In-Depth_Guides/组件列表/模型列表/Kimi使用.md)
+    * 3.1.2.5 [百川使用说明](In-Depth_Guides/组件列表/模型列表/BaiChuan使用.md)
+    * 3.1.2.6 [Claude使用说明](In-Depth_Guides/组件列表/模型列表/Claude使用.md)
+    * 3.1.2.7 [Ollama使用说明](In-Depth_Guides/组件列表/模型列表/Ollama使用.md)
+    * 3.1.2.8 [DeepSeek使用说明](In-Depth_Guides/组件列表/模型列表/DeepSeek使用.md)
+    * 3.1.2.9 [智谱GLM使用说明](In-Depth_Guides/组件列表/模型列表/智谱GLM使用.md)
+    * 3.1.2.10 [通用OpenAI协议风格包装类型](In-Depth_Guides/组件列表/模型列表/OpenAIStyleLLM使用.md)
+  * 3.1.3 工具列表(Tool)
+    * 3.1.3.1 [集成工具列表](In-Depth_Guides/组件列表/工具列表/集成的工具.md)
+    * 3.1.3.2 [LangChain工具包装类型](In-Depth_Guides/组件列表/工具列表/集成LangChain工具.md)
+  * 3.1.4 知识列表(Knowledge)
+  * 3.1.5 记忆列表(Memory)
+  * 3.1.6 计划列表(Planner)
+* 3.2 技术组件
+  * 3.2.1 RPC
+    * 3.2.1.1 [gRPC](In-Depth_Guides/技术组件/服务化/gRPC.md)
+  * 3.2.2 存储 (Store)
+    * 3.2.2.1 [SQLDBWrapper](In-Depth_Guides/技术组件/存储/SQLDB_WRAPPER.md)
+    * 3.2.2.2 [Milvus](In-Depth_Guides/技术组件/存储/Milvus.md)
+    * 3.2.2.3 [ChromaDB](In-Depth_Guides/技术组件/存储/ChromaDB.md)
+    * 3.2.2.4 [Sqlite](In-Depth_Guides/技术组件/存储/Sqlite.md)
+  * 3.2.3 消息(Msg)
+  * 3.2.4 日志(Logging)
+    * 3.2.4.1 [阿里云SLS](In-Depth_Guides/技术组件/日志监控/阿里云SLS.md)
+
+**[4.API参考手册](In-Depth_Guides/技术组件/其他/API参考.md)**
+
+**5.最佳实践**
+* 5.1 运维部署
+  * 5.1.1 [Docker容器化方案](In-Depth_Guides/技术组件/部署运维/Docker容器化部署.md)
+  * 5.1.2 [K8S部署方案](In-Depth_Guides/技术组件/部署运维/K8S部署.md)
+
+**6.使用案例**
+* 6.1 RAG类Agent案例
+  * 6.1.1 [法律咨询Agent_v2](样例文档/法律咨询案例.md)
+* 6.2 ReAct类Agent案例
+  * 6.2.1 [Python代码生成与执行Agent](样例文档/Python自动执行案例.md)
+* 6.3 [基于多轮多Agent的讨论小组](样例文档/讨论组.md)
+* 6.4 PEER多Agent协作案例
+  * 6.4.1 [金融事件分析案例](样例文档/金融事件分析案例.md)
+* 6.5 [吴恩达反思工作流翻译智能体复刻](样例文档/翻译案例.md)
+
+**7.画布式研发平台**
+* 7.1 [快速使用](How-to/画布式研发平台快速开始.md)
+* 7.2 [进阶指南](How-to/画布式研发平台进阶指南.md)
+
+**8.系列文章**
+
+**9.常见问题FAQ**
+
+**[10.研究文献](设计理念/研究文献_PEER.md)**
+
+**[11.联系我们](联系我们.md)**
diff --git "a/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md" "b/docs/guidebook/zh/\350\201\224\347\263\273\346\210\221\344\273\254.md"
similarity index 81%
rename from "docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md"
rename to "docs/guidebook/zh/\350\201\224\347\263\273\346\210\221\344\273\254.md"
index f15bbfc4..802c5eb8 100644
--- "a/docs/guidebook/zh/6_1_\350\201\224\347\263\273\346\210\221\344\273\254.md"
+++ "b/docs/guidebook/zh/\350\201\224\347\263\273\346\210\221\344\273\254.md"
@@ -1,6 +1,6 @@
 ### 支持
 #### 通过github issue提交疑问
-😊 我们建议您使用[github issue](https://github.com/alipay/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。
+😊 我们建议您使用[github issue](https://github.com/antgroup/agentUniverse/issues) 提交您的疑问, 我们通常会在2日内回复。
 
 #### 通过Discord联系我们
 😊 加入我们的 [Discord频道](https://discord.gg/DHFcdkWAhn) 与我们进行交流。
diff --git "a/docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md" "b/docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md"
new file mode 100644
index 00000000..42daea39
--- /dev/null
+++ "b/docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\344\270\272\344\273\200\344\271\210\351\200\211\346\213\251agentUniverse.md"
@@ -0,0 +1,22 @@
+# 为什么选择agentUniverse
+agentUniverse是一个多智能体框架，面向实际业务场景中的复杂任务优化设计。agentUniverse不仅提供搭建单一智能体的全部关键组件，还提供了多智能体之间协作模式的编排组件（类似于Pattern Factory），开发者可以通过自定义协同模式，让智能体各司其职，更好的解决自己领域的专业问题。同时，agentUniverse提供专家经验的注入机制，帮助开发者和企业轻松构建具备专业KnowHow的智能应用。
+
+## 架构大图
+![au_architectural_diagram](../../_picture/au_architectural_diagram.png)
+
+agentUniverse基础能力包含单Agent能力，提供多智能体协同模式编排组件，同时给开发者提供知识注入的手段与机制。
+
+在这之中通过框架标准化连接，用户可以将任意自有的模型、知识与工具接入框架中供不同的智能体使用。
+
+通过上述能力您可以构建出专属的智能应用。框架将进一步提供大模型应用关键能力组件与应用解决方案，其中大模型应用关键能力组件将针对大模型应用难点，如RAG、工具调用、安全严谨等方向提供开箱即用的增强能力帮助开发者克服必经难关；应用解决方案将针对知识问答、智能搜索、分析解读等大模型应用典型场景提供端到端解决方案以提升应用构建效率。
+
+## 核心特性
+通过上述的介绍我们将其归纳总结，agentUniverse包含如下特点：
+
+* **丰富的多智能体协同模式：** 提供PEER（Plan/Execute/Express/Review）、DOE（Data-fining/Opinion-inject/Express）等产业中验证有效的协同模式，同时支持用户自定义编排新模式，让多个智能体有机合作；
+
+* **所有组件均可定制：** LLM、知识、工具、记忆等所有框架组件均提供自定义能力，供用户来增强专属智能体；
+
+* **轻松融入领域经验：** 提供领域prompt、知识构建与管理的能力，同时支持领域级SOP编排与注入，将智能体对齐至领域专家级别；
+
+💡 更多特点见[agentUniverse核心特性](核心特性.md)部分。
\ No newline at end of file
diff --git "a/docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md" "b/docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\346\240\270\345\277\203\347\211\271\346\200\247.md"
similarity index 100%
rename from "docs/guidebook/zh/1_\346\240\270\345\277\203\347\211\271\346\200\247.md"
rename to "docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\346\240\270\345\277\203\347\211\271\346\200\247.md"
diff --git "a/docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md" "b/docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\347\240\224\347\251\266\346\226\207\347\214\256_PEER.md"
similarity index 100%
rename from "docs/guidebook/zh/9_1_\347\240\224\347\251\266\346\226\207\347\214\256.md"
rename to "docs/guidebook/zh/\350\256\276\350\256\241\347\220\206\345\277\265/\347\240\224\347\251\266\346\226\207\347\214\256_PEER.md"
diff --git a/sample_standard_app/README.md b/sample_standard_app/README.md
index 6fc3eff0..2669cd4a 100644
--- a/sample_standard_app/README.md
+++ b/sample_standard_app/README.md
@@ -1,20 +1,10 @@
 # agentUniverse- Sample Standard App
-*****************************************************
-Language version: [English](./README.md) | [中文](./README_zh.md)
-*****************************************************
-
 ## Introduction
 This project is a sample project for the agentUniverse.
 
 ## Quick Start
-We will show you how to:
-* Prepare the environment and application project
-* Build a simple agent
-* Use pattern components to complete multi-agent collaboration
-* Test and optimize the performance of the agent
-* Quickly serve the agent
-For details, please read [Quick Start](../docs/guidebook/en/1_3_Quick_Start.md).
+You can run this project based on the [Quick Start](https://github.com/antgroup/agentUniverse/blob/master/docs/guidebook/en/Get_Start/Quick_Start.md).
 
 ## Guidebook
-For more detailed information, please refer to the [Guidebook](../docs/guidebook/en/0_index.md).
+For more detailed information, please refer to the [Guidebook](https://github.com/antgroup/agentUniverse/blob/master/docs/guidebook/en/Contents.md).
 
diff --git a/sample_standard_app/README_zh.md b/sample_standard_app/README_zh.md
index c3c52e82..8385ed44 100644
--- a/sample_standard_app/README_zh.md
+++ b/sample_standard_app/README_zh.md
@@ -1,20 +1,9 @@
-# agentUniverse- 示例工程
-*****************************************************
-语言版本: [English](./README.md) | [中文](./README_zh.md)
-*****************************************************
-
+# agentUniverse- 标准应用工程
 ## 介绍
-本项目是agentUniverse的示例工程。
+本项目是agentUniverse的标准应用工程。
 
 ## 快速开始
-我们将向您展示如何：
-* 进行环境与应用工程准备
-* 构建一个简单的agent
-* 使用模式组件完成多agent协同
-* 对agent执行效果进行测试调优
-* 对agent进行快速服务化
-
-详情请阅读[快速开始](../docs/guidebook/zh/1_3_快速开始.md)。
+您可以基于 [快速开始](https://github.com/antgroup/agentUniverse/blob/master/docs/guidebook/zh/%E5%BC%80%E5%A7%8B%E4%BD%BF%E7%94%A8/%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B.md) 运行第一个案例。
 
 ## 用户指南
-更多详细信息，请参阅[指南](../docs/guidebook/zh/0_目录.md)。
\ No newline at end of file
+更多详细信息，请参阅 [用户指南](https://github.com/antgroup/agentUniverse/blob/master/docs/guidebook/zh/%E7%9B%AE%E5%BD%95.md) 。
\ No newline at end of file
diff --git a/sample_standard_app/boostrap/__init__.py b/sample_standard_app/bootstrap/__init__.py
similarity index 100%
rename from sample_standard_app/boostrap/__init__.py
rename to sample_standard_app/bootstrap/__init__.py
diff --git a/sample_standard_app/boostrap/intelligence/__init__.py b/sample_standard_app/bootstrap/intelligence/__init__.py
similarity index 100%
rename from sample_standard_app/boostrap/intelligence/__init__.py
rename to sample_standard_app/bootstrap/intelligence/__init__.py
diff --git a/sample_standard_app/boostrap/intelligence/server_application.py b/sample_standard_app/bootstrap/intelligence/server_application.py
similarity index 100%
rename from sample_standard_app/boostrap/intelligence/server_application.py
rename to sample_standard_app/bootstrap/intelligence/server_application.py
diff --git a/sample_standard_app/boostrap/platform/__init__.py b/sample_standard_app/bootstrap/platform/__init__.py
similarity index 100%
rename from sample_standard_app/boostrap/platform/__init__.py
rename to sample_standard_app/bootstrap/platform/__init__.py
diff --git a/sample_standard_app/boostrap/platform/product_application.py b/sample_standard_app/bootstrap/platform/product_application.py
similarity index 100%
rename from sample_standard_app/boostrap/platform/product_application.py
rename to sample_standard_app/bootstrap/platform/product_application.py


Test patch:
diff --git a/sample_standard_app/intelligence/test/example_start.ps1 b/sample_standard_app/intelligence/test/example_start.ps1
deleted file mode 100644
index a5cfab5f..00000000
--- a/sample_standard_app/intelligence/test/example_start.ps1
+++ /dev/null
@@ -1,96 +0,0 @@
-# 忽略管道错误，PowerShell中一般不会出现类似的问题，所以可以忽略
-
-# 判断conda命令是否存在，如果不存在，则检查%USERPROFILE%\miniconda3\bin是否存在，如果存在则将conda添加到路径中
-if (-not (Get-Command conda -ErrorAction SilentlyContinue)) {
-    if (Test-Path "$env:USERPROFILE\miniconda3\Scripts\conda.exe") {
-        Write-Output "conda already installed. Adding $env:USERPROFILE\miniconda3\Scripts to PATH..."
-        $env:PATH = "$env:USERPROFILE\miniconda3\Scripts;$env:PATH"
-    } else {
-        Write-Output "conda is not installed..."
-    }
-} else {
-    Write-Output "conda is already installed."
-}
-
-# 定义安装conda的函数
-function Install-Conda {
-    if (-not (Get-Command conda -ErrorAction SilentlyContinue)) {
-        Write-Output "conda is not installed. Please install conda first, and try again"
-        New-Item -ItemType Directory -Force -Path "$env:USERPROFILE\miniconda3"
-
-        curl  https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_24.3.0-0-Windows-x86_64.exe -o miniconda.exe
-        ./miniconda.exe
-        exit
-
-        # # 静默安装Miniconda
-        # Start-Process -Wait -FilePath "$env:USERPROFILE\miniconda3\miniconda.exe" -ArgumentList "/InstallationType=JustMe", "/RegisterPython=0", "/AddToPath=0", "/S", "/D=$env:USERPROFILE\miniconda3"
-        # $env:PATH = "$env:USERPROFILE\miniconda3\Scripts;$env:USERPROFILE\miniconda3;$env:PATH"
-        # Remove-Item "$env:USERPROFILE\miniconda3\miniconda.exe"
-        
-        # # 配置清华源
-        # conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
-        # conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
-        # conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
-        # conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
-        # Write-Output "conda is installed."
-    }
-}
-
-# 检查Python是否安装，如果没有安装则安装它，或者Python的版本小于3.10
-if (-not (Get-Command python -ErrorAction SilentlyContinue)) {
-    Write-Output "Python is not installed. Installing Python..."
-    Install-Conda
-}
-# 获取当前的Python版本
-$pythonVersion = & python --version 2>&1
-
-# 如果Python版本不是3.10，则安装Python 3.10
-if ($pythonVersion -notmatch "3\.10") {
-    if (-not (Get-Command conda -ErrorAction SilentlyContinue)) {
-        Write-Output "conda is not installed. Please install conda and try again."
-        Install-Conda
-    }
-    
-    # 检查是否已经有名为python_au3.10的环境
-    if (-not (& conda env list | Select-String -Pattern "python_au3.10")) {
-        Write-Output "Python 3.10 environment is not installed. Installing Python 3.10 environment..."
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
-        conda create -n python_au3.10 python=3.10 -y
-    }
-    
-    # 获取Conda的基础路径并设置PATH变量
-    $CONDA_BASE = & conda info --base
-    $env:PATH = "$CONDA_BASE\envs\python_au3.10\Scripts;$CONDA_BASE\envs\python_au3.10;$env:PATH"
-}
-
-# 切换到Python 3.10的环境
-conda activate python_au3.10
-
-python --version
-
-# 判断pip是否已经安装了agentUniverse
-if (-not (& pip list | Select-String -Pattern "agentUniverse")) {
-    pip install agentUniverse -i https://pypi.tuna.tsinghua.edu.cn/simple
-}
-
-# 检查 custom_key 文件是否存在，如果不存在，从样本复制一份
-if (-not (Test-Path "..\..\config\custom_key.toml")) {
-    Copy-Item "..\..\config\custom_key.toml.sample" "..\..\config\custom_key.toml"
-}
-
-# 替换 config.toml 中的 #custom_key_path 为 custom_key_path
-# 确保设置 custom_key_path 正确引用 config 目录下的文件
-(Get-Content -Path "..\..\config\config.toml") -replace '^#custom_key_path = ''\.\/custom_key\.toml''', 'custom_key_path = ''custom_key.toml''' | Set-Content -Path "..\..\config\config.toml"
-
-# 设置PYTHONPATH为当前工作目录的上级目录
-# 获取当前目录的上两级目录的绝对路径
-$upperTwoLevels = Resolve-Path (Join-Path (Get-Location) "..\..\")
-
-# 设置 PYTHONPATH 环境变量
-$env:PYTHONPATH = $upperTwoLevels.Path
-
-# 启动服务
-python -u .\react_chat_bot.py
diff --git a/sample_standard_app/intelligence/test/example_start.sh b/sample_standard_app/intelligence/test/example_start.sh
deleted file mode 100644
index 46f60aa9..00000000
--- a/sample_standard_app/intelligence/test/example_start.sh
+++ /dev/null
@@ -1,98 +0,0 @@
-#!/bin/sh
-
-# Ignore broken pipe errors
-trap '' PIPE
-# 判断conda 命令是否存在,如果不存在，则判断$HOME/miniconda3/bin是否存在，如果存在则export 一下conda
-if ! command -v conda &> /dev/null; then
-    # Check if $HOME/miniconda3/bin exists
-    if [ -d "$HOME/miniconda3/bin" ]; then
-        # Add $HOME/miniconda3/bin to PATH
-        echo "conda already installed Adding $HOME/miniconda3/bin to PATH..."
-        export PATH="$HOME/miniconda3/bin:$PATH"
-    else
-        echo "conda is not installed...."
-    fi
-#否则echo conda已在环境变量当中
-else
-    echo "conda is already installed."
-fi
-
-function install_conda() {
-     if ! command -v conda &> /dev/null; then
-        echo "conda is not installed. Installing conda..."
-        mkdir -p ~/miniconda3
-        # If on macOS
-        # 根据cpu类型与系统类型mac,linux，x86_64,arm，下载
-        if [[ "$(uname)" == "Darwin" ]]; then
-            if [[ "$(uname -m)" == "x86_64" ]]; then
-                curl -o ~/miniconda3/miniconda.sh https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_24.3.0-0-MacOSX-x86_64.sh
-            elif [[ "$(uname -m)" == "arm64" ]];then
-                curl -o ~/miniconda3/miniconda.sh https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_24.3.0-0-MacOSX-arm64.sh
-            fi
-        # If on Linux
-        elif [[ "$(uname)" == "Linux" ]]; then
-            if [[ "$(uname -m)" == "x86_64" ]]; then
-                curl -o ~/miniconda3/miniconda.sh https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_24.3.0-0-Linux-x86_64.sh
-            fi
-            if [[ "$(uname -m)" == "aarch64" ]];then
-                    curl -o ~/miniconda3/miniconda.sh https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-py39_24.3.0-0-Linux-aarch64.sh
-            fi
-        fi
-        bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
-        $HOME/miniconda3/bin/conda init bash
-        $HOME/miniconda3/bin/conda init sh
-        # 配置conda环境变量
-        echo 'export PATH="$HOME/miniconda3/bin:$PATH"' >> ~/.bashrc
-        source ~/.bashrc
-        export PATH="$HOME/miniconda3/bin:$PATH"
-        rm -rf ~/miniconda3/miniconda.sh
-        # Configure Tsinghua mirrors
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
-        conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
-        echo "conda is installed."
-    fi
-}
-
-# Check if Python is installed, if not, install it ，或者python的版本小于3.10
-if ! command -v python &> /dev/null; then
-    echo "Python is not installed. Installing Python..."
-    # Check if conda is installed
-    install_conda
-fi
-
-if ! python --version | grep -q "3.10"; then
-    # 判断是否存在conda 命令
-    if ! command -v conda &> /dev/null; then
-        echo "conda is not installed. Please install conda and try again."
-        install_conda
-    fi
-    # 判断conda中是否存在python_au3.10 环境
-    if ! conda env list | grep -q "python_au3.10"; then
-        echo "Python 3.10 environment is not installed. Installing Python 3.10 environment..."
-        conda create -n python_au3.10 python=3.10 -y
-    fi
-    # conda info --base
-    #    使用conda info --base 获取conda的安装路径,并拼接envs/python_au3.10/bin到环境变量
-    export PATH="$(conda info --base)/envs/python_au3.10/bin:$PATH"
-fi
-
-# 判断 pip list 是否已安装 agentUniverse
-if ! pip list | grep -q "agentUniverse"; then
-   pip install agentUniverse -i https://pypi.tuna.tsinghua.edu.cn/simple
-fi
-
-# Check if custom_key file exists, if not, copy from sample
-if [ ! -f "../../config/custom_key.toml" ]; then
-    cp ../../config/custom_key.toml.sample ../../config/custom_key.toml
-fi
-
-sed -i '' 's/^#custom_key_path = '\''\.\/custom_key\.toml'\''/custom_key_path = '\''\.\/custom_key\.toml'\''/' ../../config/config.toml
-# Set PYTHONPATH to the parent directory of the current working directory
-export PYTHONPATH=$PWD/../../../..
-
-# Start the service
-cd app/bootstrap
-python -u react_chat_bot.py
-
diff --git a/sample_standard_app/intelligence/test/react_chat_bot.py b/sample_standard_app/intelligence/test/react_chat_bot.py
deleted file mode 100644
index 6146f940..00000000
--- a/sample_standard_app/intelligence/test/react_chat_bot.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# !/usr/bin/env python3
-# -*- coding:utf-8 -*-
-import argparse
-
-# @Time    : 2024/5/8 11:44
-# @Author  : wangchongshi
-# @Email   : wangchongshi.wcs@antgroup.com
-# @FileName: rag_chat_bot.py
-from agentuniverse.agent.output_object import OutputObject
-from agentuniverse.agent.agent import Agent
-from agentuniverse.agent.agent_manager import AgentManager
-from agentuniverse.base.agentuniverse import AgentUniverse
-
-AgentUniverse().start(config_path='../../config/config.toml', core_mode=True)
-
-
-def chat(question: str):
-    """ Rag agent example.
-
-    The rag agent in agentUniverse becomes a chatbot and can ask questions to get the answer.
-    """
-
-    instance: Agent = AgentManager().get_instance_obj('demo_react_agent')
-    output_object: OutputObject = instance.run(input=question)
-    res_info = f"\nReact chat bot execution result is :\n"
-    res_info += output_object.get_data('output')
-    print(res_info)
-
-
-if __name__ == '__main__':
-    # 获取python 命令直接执行时的 --question 参数
-    parser = argparse.ArgumentParser(description="Chat bot for answering questions about birthdays.")
-    parser.add_argument("--question", type=str, help="The question to ask.")
-    args = parser.parse_args()
-    if args.question:
-        chat(args.question)
-    else:
-        chat("请给我一段可以计算三数之和的python代码")