Revert Upgrade doc style

.readthedocs.yaml

@@ -0,0 +1,19 @@
+# .readthedocs.yaml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+mkdocs:
+  configuration: mkdocs.yml
+
+# Optionally declare the Python requirements required to build your docs
+python:
+  install:
+    - requirements: docs/requirements.txt

docs/docs-en/source/1.README.md → README.md

@@ -1,4 +1,4 @@
-# Guide
+# TuGraph Analytics
 
 [![Star](https://shields.io/github/stars/tugraph-family/tugraph-analytics?logo=startrek&label=Star&color=yellow)](https://github.com/TuGraph-family/tugraph-analytics/stargazers)
 [![Fork](https://shields.io/github/forks/tugraph-family/tugraph-analytics?logo=forgejo&label=Fork&color=orange)](https://github.com/TuGraph-family/tugraph-analytics/forks)
@@ -11,13 +11,13 @@
 [![EN](https://shields.io/badge/Docs-English-blue?logo=readme)](https://tugraph-analytics.readthedocs.io/en/latest/docs-en/introduction/)
 [![Blog](https://badgen.net/static/Blog/github.io/orange?icon=rss)](https://tugraph-analytics.github.io/)
 
-> 🌐️ English | [中文](../../docs-cn/source/1.README_cn.md)
+> 🌐️ English | [中文](README_cn.md)
 
 <!--intro-start-->
 ## Introduction
 **TuGraph Analytics** (alias: GeaFlow) is the [**fastest**](https://ldbcouncil.org/benchmarks/snb-bi/) open-source OLAP graph database developed by Ant Group. It supports core capabilities such as trillion-level graph storage, hybrid graph and table processing, real-time graph computation, and interactive graph analysis. Currently, it is widely used in scenarios such as data warehousing acceleration, financial risk control, knowledge graph, and social networks.
 
-For more information about GeaFlow: [GeaFlow Introduction](2.introduction.md)
+For more information about GeaFlow: [GeaFlow Introduction](docs/docs-en/introduction.md)
 
 For GeaFlow design paper: [GeaFlow: A Graph Extended and Accelerated Dataflow System](https://dl.acm.org/doi/abs/10.1145/3589771)
 
@@ -43,21 +43,21 @@ For GeaFlow design paper: [GeaFlow: A Graph Extended and Accelerated Dataflow Sy
 3. Build Image：`./build.sh --all`
 4. Start Container：`docker run -d --name geaflow-console -p 8888:8888 geaflow-console:0.1`
 
-For more details：[Quick Start](../../docs-cn/source/3.quick_start.md)。
+For more details：[Quick Start](docs/docs-cn/quick_start.md)。
 
 ## Development Manual
 
 GeaFlow supports two sets of programming interfaces: DSL and API. You can develop streaming graph computing jobs using GeaFlow's SQL extension language SQL+ISO/GQL or use GeaFlow's high-level API programming interface to develop applications in Java.
-* DSL application development: [DSL Application Development](6.application-development/2.dsl/1.overview.md)
-* API application development: [API Application Development](6.application-development/1.api/1.overview.md)
+* DSL application development: [DSL Application Development](docs/docs-en/application-development/dsl/overview.md)
+* API application development: [API Application Development](docs/docs-en/application-development/api/overview.md)
 
 ## Real-time Capabilities
 
 Compared with traditional stream processing engines such as Flink and Storm, which use tables as their data model for real-time processing, GeaFlow's graph-based data model has significant performance advantages when handling join relationship operations, especially complex multi-hops relationship operations like those involving 3 or more hops of join and complex loop searches.
 
-[![total_time](../../static/img/vs_join_total_time_en.jpg)](8.principle/vs_join.md)
+[![total_time](./docs/static/img/vs_join_total_time_en.jpg)](./docs/docs-en/principle/vs_join.md)
 
-[Why using graphs for relational operations is more appealing than table joins?](8.principle/vs_join.md)
+[Why using graphs for relational operations is more appealing than table joins?](./docs/docs-en/principle/vs_join.md)
 
 Association Analysis Demo Based on GQL:
 
@@ -82,7 +82,7 @@ JOIN student s ON sc.srcId = s.id
 ## Contribution
 Thank you very much for contributing to GeaFlow, whether bug reporting, documentation improvement, or major feature development, we warmly welcome all contributions. 
 
-For more information: [Contribution](9.contribution.md).
+For more information: [Contribution](docs/docs-en/contribution.md).
 
 ## Partners
 <table cellspacing="0" cellpadding="0">
@@ -101,7 +101,7 @@ For more information: [Contribution](9.contribution.md).
 ## Contact Us
 You can contact us through the following methods:
 
-![contacts](../../static/img/contacts-en.png)
+![contacts](docs/static/img/contacts-en.png)
 
 **If you are interested in GeaFlow, please give our project a [ ⭐️ ](https://github.com/TuGraph-family/tugraph-analytics).**
 

docs/docs-cn/source/1.README_cn.md → README_cn.md

@@ -1,4 +1,4 @@
-# Geaflow 文档地图
+# TuGraph Analytics
 
 [![Star](https://shields.io/github/stars/tugraph-family/tugraph-analytics?logo=startrek&label=Star&color=yellow)](https://github.com/TuGraph-family/tugraph-analytics/stargazers)
 [![Fork](https://shields.io/github/forks/tugraph-family/tugraph-analytics?logo=forgejo&label=Fork&color=orange)](https://github.com/TuGraph-family/tugraph-analytics/forks)
@@ -11,13 +11,13 @@
 [![EN](https://shields.io/badge/Docs-English-blue?logo=readme)](https://tugraph-analytics.readthedocs.io/en/latest/docs-en/introduction/)
 [![Blog](https://badgen.net/static/Blog/github.io/orange?icon=rss)](https://tugraph-analytics.github.io/)
 
-> 🌐️ 中文 | [English](../../docs-en/source/1.README.md)
+> 🌐️ 中文 | [English](README.md)
 
 <!--intro-start-->
 ## 介绍
 **TuGraph Analytics** (别名：GeaFlow) 是蚂蚁集团开源的[**性能世界一流**](https://ldbcouncil.org/benchmarks/snb-bi/)的OLAP图数据库，支持万亿级图存储、图表混合处理、实时图计算、交互式图分析等核心能力，目前广泛应用于数仓加速、金融风控、知识图谱以及社交网络等场景。
 
-关于GeaFlow更多介绍请参考：[GeaFlow介绍文档](2.introduction.md)
+关于GeaFlow更多介绍请参考：[GeaFlow介绍文档](docs/docs-cn/introduction.md)
 
 GeaFlow设计论文参考：[GeaFlow: A Graph Extended and Accelerated Dataflow System](https://dl.acm.org/doi/abs/10.1145/3589771)
 
@@ -43,21 +43,21 @@ GeaFlow设计论文参考：[GeaFlow: A Graph Extended and Accelerated Dataflow
 3. 构建镜像：`./build.sh --all`
 4. 启动容器：`docker run -d --name geaflow-console -p 8888:8888 geaflow-console:0.1`
 
-更多详细内容请参考：[快速上手文档](3.quick_start.md)。
+更多详细内容请参考：[快速上手文档](docs/docs-cn/quick_start.md)。
 
 ## 开发手册
 
 GeaFlow支持DSL和API两套编程接口，您既可以通过GeaFlow提供的类SQL扩展语言SQL+ISO/GQL进行流图计算作业的开发，也可以通过GeaFlow的高阶API编程接口通过Java语言进行应用开发。
-* DSL应用开发：[DSL开发文档](6.application-development/2.dsl/1.overview.md)
-* API应用开发：[API开发文档](6.application-development/1.api/2.guid.md)
+* DSL应用开发：[DSL开发文档](docs/docs-cn/application-development/dsl/overview.md)
+* API应用开发：[API开发文档](docs/docs-cn/application-development/api/guid.md)
 
 ## 实时能力
 
 相比传统的流式计算引擎比如Flink、Storm这些以表为模型的实时处理系统而言，GeaFlow以图为数据模型，在处理Join关系运算，尤其是复杂多跳的关系运算如3跳以上的Join、复杂环路查找上具备极大的性能优势。
 
-[![total_time](../../static/img/vs_join_total_time_cn.jpg)](8.principle/vs_join.md)
+[![total_time](./docs/static/img/vs_join_total_time_cn.jpg)](./docs/docs-cn/principle/vs_join.md)
 
-[为什么使用图进行关联运算比表Join更具吸引力？](8.principle/vs_join.md)
+[为什么使用图进行关联运算比表Join更具吸引力？](./docs/docs-cn/principle/vs_join.md)
 
 基于GQL的关联分析Demo：
 
@@ -82,7 +82,7 @@ JOIN student s ON sc.srcId = s.id
 ## 参与贡献
 非常感谢您参与到GeaFlow的贡献中来，无论是Bug反馈还是文档完善，或者是大的功能点贡献，我们都表示热烈的欢迎。
 
-具体请参考：[参与贡献文档](9.contribution.md)。
+具体请参考：[参与贡献文档](docs/docs-cn/contribution.md)。
 
 **如果您对GeaFlow感兴趣，欢迎给我们项目一颗[ ⭐️ ](https://github.com/TuGraph-family/tugraph-analytics)。**
 
@@ -103,7 +103,7 @@ JOIN student s ON sc.srcId = s.id
 ## 联系我们
 您可以通过以下方式联系我们。
 
-![contacts](../../static/img/contacts.png)
+![contacts](docs/static/img/contacts.png)
 
 ## 致谢
 GeaFlow开发过程中部分模块参考了一些业界优秀的开源项目，包括Apache Flink、Apache Spark以及Apache Calcite等, 这里表示特别的感谢。

docs/docs-cn/application-development/api/guid.md

@@ -0,0 +1,8 @@
+* [API概览](overview.md)
+* Stream API
+  * [Source API](stream/source.md)
+  * [Process API](stream/process.md)
+  * [Sink API](stream/sink.md)
+* Graph API
+  * [Compute API](graph/compute.md)
+  * [Traversal API](graph/traversal.md)

docs/docs-cn/source/6.application-development/1.api/1.overview.md → docs/docs-cn/application-development/api/overview.md

@@ -3,7 +3,7 @@ GeaFlow API是对高阶用户提供的开发接口，其支持Graph API和Stream
 * Graph API：Graph是GeaFlow框架的一等公民，当前GeaFlow框架提供了一套基于GraphView的图计算编程接口，包含图构建、图计算及遍历。在GeaFlow中支持Static Graph和Dynamic Graph两种类型。
     * Static Graph API：静态图计算API，基于该类API可以进行全量的图计算或图遍历。
     * Dynamic Graph API：动态图计算API，GeaFlow中GraphView是动态图的数据抽象，基于GraphView之上，可以进行动态图计算或图遍历。同时支持对Graphview生成Snapshot快照，基于Snapshot可以提供和Static Graph API一样的接口能力。
-      ![api_arch](../../../../static/img/api_arch.jpeg)
+      ![api_arch](../../../static/img/api_arch.jpeg)
 * Stream API：GeaFlow提供了一套通用计算的编程接口，包括source构建、流批计算及sink输出。在GeaFlow中支持Batch和Stream两种类型。
     * Batch API：批计算API，基于该类API可以进行批量计算。
     * Stream API：流计算API，GeaFlow中StreamView是动态流的数据抽象，基于StreamView之上，可以进行流计算。
@@ -13,7 +13,7 @@ GeaFlow API是对高阶用户提供的开发接口，其支持Graph API和Stream
 * 对于批或静态图API来说，Window将采用AllWindow模式，一个窗口将读取全量数据，从而实现全量的计算。
 
 
-## Maven依赖
+# Maven依赖
 开发GeaFlow API应用需要添加一下maven依赖：
 ```xml
 <dependency>
@@ -47,8 +47,8 @@ GeaFlow API是对高阶用户提供的开发接口，其支持Graph API和Stream
 </dependency>
 ```
 
-## 功能概览
-### Graph API
+# 功能概览
+## Graph API
 Graph API是GeaFlow中的一等公民，其提供了一套基于GraphView的图计算编程接口，包含图构建、图计算及遍历。具体的API说明如下表格所示：
 <table>
 	<tr>
@@ -109,7 +109,7 @@ Graph API是GeaFlow中的一等公民，其提供了一套基于GraphView的图
 </table>
 
 
-### Stream API
+## Stream API
 Stream API提供了一套通用计算的编程接口，包括source构建、流批计算及sink输出。具体的API说明如下表格所示：
 <table>
 	<tr>
@@ -182,14 +182,14 @@ Stream API提供了一套通用计算的编程接口，包括source构建、流
 </table>
 
 
-## 典型示例
-### PageRank动态图计算示例介绍
-#### PageRank的定义
+# 典型示例
+## PageRank动态图计算示例介绍
+### PageRank的定义
 PageRank算法最初作为互联网网页重要度的计算方法，1996年由Page和Brin提出，并用于谷歌搜索引擎的网页排序。事实上，PageRank 可以定义在任意有向图上，后来被应用到社会影响力分析、文本摘要等多个问题。
 假设互联网是一个有向图，在其基础上定义随机游走模型，即一阶马尔可夫链，表示网页浏览者在互联网上随机浏览网页的过程。假设浏览者在每个网页依照连接出去的超链接以等概率跳转到下一个网页，并在网上持续不断进行这样的随机跳转，这个过程形成一阶马尔可夫链。PageRank表示这个马尔可夫链的平稳分布。每个网页的PageRank值就是平稳概率。
 算法实现思路：1.假设图中每个点的初始影响值相同；2.计算每个点对其他点的跳转概率，并更新点的影响值；3.进行n次迭代计算，直到各点影响值不再变化，即收敛状态。
 
-#### 实例代码
+### 实例代码
 
 ```java
 
@@ -415,8 +415,8 @@ public class IncrGraphCompute {
 
 ```
 
-### PageRank静态图计算示例介绍
-#### 实例代码
+## PageRank静态图计算示例介绍
+### 实例代码
 
 ```java
 
@@ -551,8 +551,8 @@ public class PageRank {
 
 ```
 
-### WordCount批计算示例介绍
-#### 实例代码
+## WordCount批计算示例介绍
+### 实例代码
 
 ```java
 
@@ -654,8 +654,8 @@ public class WordCountStream {
 ```
 
 
-### KeyAgg流计算示例介绍
-#### 实例代码
+## KeyAgg流计算示例介绍
+### 实例代码
 
 ```java
 

docs/docs-cn/source/6.application-development/2.dsl/2.build-in/aggregate.md → docs/docs-cn/application-development/dsl/build-in/aggregate.md

@@ -1,13 +1,11 @@
-# Aggregate
-
 GeaFlow支持以下聚合函数：
-* [count](#count)
-* [max](#max)
-* [min](#min)
-* [sum](#sum)
-* [avg](#avg)
+* [COUNT](#COUNT)
+* [MAX](#MAX)
+* [MIN](#MIN)
+* [SUM](#SUM)
+* [AVG](#AVG)
 
-## count
+# COUNT
 **Syntax**
 
 ```sql
@@ -24,7 +22,7 @@ select count(distinct id) from user;
 select count(1) from user;
 ```
 
-## max
+# MAX
 **Syntax**
 
 ```sql
@@ -43,7 +41,7 @@ select id, max(age) from user group by id;
 select max(name) from user;
 ```
 
-## min
+# MIN
 **Syntax**
 
 ```sql
@@ -62,7 +60,7 @@ select id, min(age) from user group by id;
 select min(name) from user;
 ```
 
-## sum
+# SUM
 **Syntax**
 
 ```sql
@@ -81,7 +79,7 @@ select sum(DISTINCT age) from user;
 select sum(1) from user;
 ```
 
-## avg
+# AVG
 **Syntax**
 
 ```sql

docs/docs-cn/source/6.application-development/2.dsl/2.build-in/condition.md → docs/docs-cn/application-development/dsl/build-in/condition.md

@@ -1,10 +1,8 @@
-# Condition
-
 GeaFlow支持**Case**和**If**条件函数。
 * [Case](#Case)
 * [If](#If)
 
-## Case
+# Case
 **Syntax**
 
 ```sql
@@ -44,7 +42,7 @@ CASE WHEN a = 1 THEN '1'
 END
 ```
 
-## If
+# If
 **Syntax**
 
 ```sql