[improvement][docs]update README and architecture diagram

README.md

@@ -2,7 +2,7 @@ English | [中文](README_CN.md)
 
 # SuperSonic (超音数)
 
-**SuperSonic is an out-of-the-box yet highly extensible framework for building a data chatbot**. SuperSonic provides a chat interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of metrics/dimensions/entities, along with their meaning, context and relationships) on top of physical data models, and no data modification or copying is required. Meanwhile, SuperSonic is designed to be pluggable, allowing new functionalities to be added through plugins and core components to be integrated with other systems.
+**SuperSonic is an out-of-the-box yet highly extensible framework for building a data chatbot**. SuperSonic provides a chat interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of metrics/dimensions/entities, along with their meaning, context and relationships) on top of physical data models, and no data modification or copying is required. Meanwhile, SuperSonic is designed to be pluggable, allowing new tools to be added through plugins.
 
 <img src="./docs/images/supersonic_demo.gif" height="100%" width="100%" align="center"/>
 
@@ -10,19 +10,20 @@ English | [中文](README_CN.md)
 
 The emergence of Large Language Model (LLM) like ChatGPT is reshaping the way information is retrieved. In the field of data analytics, both academia and industry are primarily focused on leveraging LLM to convert natural language queries into SQL queries. While some works show promising results, they are still not applicable to real-world scenarios.
 
-From our perspective, the key to filling the real-world gap lies in two aspects: 
-1. Utilize a combination of rule-based and model-based semantic parsers to deal with different scenarios.
+From our perspective, the key to filling the real-world gap lies in three aspects: 
+1. Utilize a combination of rule-based and LLM-based semantic parsers to deal with different scenarios.
 2. Introduce a semantic model layer encapsulating the underlying data complexity(joins, formulas, etc) to simplify semantic parsing.
+3. Integrate third-party plugins to augment semantic parsing capabilities or complement custom functionalities.
 
 With these ideas in mind, we develop SuperSonic as a practical reference implementation and use it to power our real-world products. Additionally, to facilitate further development of data chatbot, we decide to open source SuperSonic as an extensible framework.
 
 ## Out-of-the-box Features
 
-- Built-in graphical interface for business users to enter data queries 
-- Built-in graphical interface for analytics engineers to manage semantic models
-- Support input auto-completion as well as query recommendation
-- Support multi-turn conversation and history context management 
-- Support three-level permission control: domain-level, column-level and row-level 
+- Built-in chat UI for business users to enter natural language queries and answer results with appropriate visualization charts. 
+- Built-in modelling UI for analytics engineers to manage semantic models. The configurations related to access permission and chat conversation can also be set on the UI.
+- Support input auto-completion as well as query recommendation.
+- Support multi-turn conversation and history context management. 
+- Support four-level permission control: domain-level, model-level, column-level and row-level.
 
 ## Extensible Components
 
@@ -30,15 +31,13 @@ The high-level architecture and main process flow is shown in below diagram:
 
 <img src="./docs/images/supersonic_components.png" height="70%" width="70%" align="center"/> 
 
-- **Chat Interface:** accepts natural language queries and answer results with appropriate visualization charts. It supports input auto-completion as well as multi-turn conversation.
-
-- **Modeling Interface:** empowers analytics engineers to visually define and maintain semantic models. The configurations related to access permission and chat conversation can also be set on the UI.
-
 - **Schema Mapper Chain:** identifies references to schema elements(metrics/dimensions/entities/values) in user queries. It matches the query text against a knowledge base constructed from the semantic models.
 
-- **Semantic Parser Chain:** understands user queries and extract semantic information. It consists of a combination of rule-based and model-based parsers, each of which deals with specific scenarios.
+- **Semantic Parser Chain:** understands user queries and extract semantic information. It consists of a combination of rule-based and LLM-based parsers, each of which deals with specific scenarios.
 
-- **Semantic Query:** performs execution according to extracted semantic information. It generates SQL queries and executes them against physical data models.
+- **Semantic Layer:** performs execution according to extracted semantic information. It generates SQL queries and executes them against physical data models.
+
+- **Chat Plugins**: perform custom execution given the results of schema mapping and semantic parsing. It would optionally resort to semantic layer to query semantic models.
 
 ## Quick Demo
 

README_CN.md

@@ -8,19 +8,20 @@
 
 大型语言模型（LLMs）如ChatGPT的出现正在重塑信息检索的方式。在数据分析领域，学术界和工业界主要关注利用深度学习模型将自然语言查询转换为SQL查询。虽然一些工作显示出有前景的结果，但它们还并不适用于实际场景。
 
-在我们看来，为了在实际场景发挥价值，有两个关键点：
+在我们看来，为了在实际场景发挥价值，有三个关键点：
 1. 将基于规则和基于模型的语义解析器相结合，发挥各自优势，以便处理不同的场景。
 2. 引入语义模型层来封装数据底层的复杂性（关联、公式等），从而简化语义解析的求解空间。
+3. 整合第三方插件用于增强语义解析能力，或者扩充自定义功能。
 
 为了验证上述想法，我们开发了超音数项目，并将其应用在实际的内部产品中。与此同时，我们将超音数作为一个可扩展的框架开源，希望能够促进数据问答对话领域的进一步发展。
 
 ## 开箱即用的特性
 
-- 内置图形界面以便业务用户输入数据查询。
-- 内置图形界面以便分析工程师管理语义模型。
+- 内置的对话图形界面，使用户能够通过自然语言问询，并最终选择合适的可视化图表呈现结果。
+- 内置的建模图形界面，使分析工程师能够通过可视化方式定义和维护语义模型，与访问权限和聊天对话相关的配置也可以在用户界面上设置。
 - 支持文本输入的联想和查询问题的推荐。
 - 支持多轮对话，根据语境自动切换上下文。
-- 支持三级权限控制：主题域级、列级、行级。
+- 支持四级权限控制：主题域级、模型级、列级及行级。
 
 ## 易于扩展的组件
 
@@ -28,15 +29,13 @@
 
 <img src="./docs/images/supersonic_components.png" height="70%" width="70%" align="center"/> 
 
-- **问答对话界面(chat interface)**：接受用户查询并选择合适的可视化图表呈现结果，支持输入联想和多轮对话。
-
-- **语义建模界面(modeling interface)**：使分析工程师能够通过可视化方式定义和维护语义模型，与访问权限和聊天对话相关的配置也可以在用户界面上设置。
-
 - **模式映射器(schema mapper chain)**：基于语义模型构建知识库，然后将自然语言文本在知识库中进行匹配，为后续的语义解析提供相关信息。
 
 - **语义解析器(semantic parser chain)**：理解用户查询并抽取语义信息，其由一组基于规则和基于模型的解析器组成，每个解析器可应对不同的特定场景。
 
-- **语义查询(semantic query)**: 根据语义信息生成物理SQL执行查询。
+- **语义层(semantic layer)**: 根据语义信息生成物理SQL执行查询。
+
+- **问答插件(chat plugins)**：基于模式映射和语义解析的结果，执行自定义的操作，同时可以选择利用语义层来查询语义模型。
 
 ## 快速体验