Tiancheng: update logo, intro and main features in readme

README.md

@@ -2,6 +2,56 @@ English | [**中文**](./README_ZH.md)
 
 # MemoryScope
 
+<p align="left">
+  <img src="docs/images/logo_1.png" width="700px" alt="MemoryScope Logo">
+</p>
+
+Equip your LLM chatbot with a powerful and flexible long term memory system.
+
+----
+## News
+
+- **[2024-07-29]** We release MemoryScope v0.1.0.2 now, which is also available in [PyPI](https://pypi.org/simple)!
+----
+## What is MemoryScope
+
+MemoryScope is a powerful and flexible long term memory system for LLM chatbots. It consists 
+of a memory database and three customizable system operations, which can be flexibly combined to provide 
+robust long term memory services for your LLM chatbot.
+
+💾 Memory Database:
+- MemoryScope comes with an *ElasticSearch (ES)* vector database to store all the 
+memory pieces recorded in the system.
+
+🛠️ System operations:
+- Memory Retrieval: Upon arrival of a user query, this operation returns the semantically related memory pieces 
+and/or those from the corresponding time if the query involves reference to time.
+- Memory Consolidation: This operation takes in a batch of user queries and returns important user information
+extracted from the queries as consolidated *observations* to be stored in the memory database.
+- Reflection and Re-consolidation: At regular intervals, this operation performs reflection upon newly recorded *observations*
+to form and update *insights*. Then, memory re-consolidation is performed to ensure contradictions and repetitions
+among memory pieces are properly handled.
+
+### Main Features
+
+⚡ Low response-time (RT) for the user:
+- Backend operations (Memory Consolidation, Reflection and Re-consolidation) are decoupled from the frontend operation
+ (Memory Retrieval) in the system.
+- While backend operations are usually (and are recommended to be) queued or executed at regular intervals, the 
+system's response time (RT) for the user depends solely on the frontend operation, which is only ~500ms.
+
+🌲 Hierarchical and coherent memory:
+- The memory pieces stored in the system are in a hierarchical structure, with *insights* being the high level information
+from the aggregation of similarly-themed *observations*.
+- Contradictions and repetitions among memory pieces are handled periodically to ensure coherence of memory.
+- Fictitious contents from the user are filtered out to avoid hallucinations by the LLM.
+
+⏰ Time awareness:
+- The system is time sensitive when performing both Memory Retrieval and Memory Consolidation. Therefore, it can retrieve
+accurate relevant information when the query involves reference to time.
+
+
+### Example Usages
 
 # 🚀 Installation
 

README_ZH.md

@@ -1,16 +1,47 @@
 [**English**](./README.md) | 中文
 
-# ModelScope
+# MemoryScope
 
-## 概念解释
+<p align="left">
+  <img src="docs/images/logo_1.png" width="700px" alt="MemoryScope Logo">
+</p>
 
-- service: 在顶层的交互对象，用于定义operation的使用范围
+为您的大语言模型聊天机器人配备强大且灵活的长期记忆系统。
 
-- operation: 读写记忆等，对于记忆的操作方法，是worker的有序组合（workflow）
+----
+## 新闻
 
-- workflow: 在operation中组合worker的方式
+- **[2024-07-26]** 我们现在发布了 MemoryScope v0.1.0.2，该版本也可以在 [PyPI](https://pypi.org/simple) 上获取！
+----
+## MemoryScope 是什么
 
-- worker: 框架中的基本工作模块
+MemoryScope 是一个为LLM聊天机器人服务的强大且灵活的长期记忆系统。它由一个记忆数据库和三个可定制的系统操作组成，这些操作可以灵活组合，
+为您的LLM聊天机器人提供强大的长期记忆服务。
+
+💾 记忆数据库:
+- MemoryScope 配备了一个 *ElasticSearch (ES)* 向量数据库，用于存储系统中记录的所有记忆片段。
+
+🛠️ 系统操作:
+- 记忆检索：当用户输入对话，此操作返回语义相关的记忆片段。如果输入对话包含对时间的指涉，则同时返回相应时间中的记忆片段。
+- 记忆巩固：此操作接收一批用户的输入对话，并从对话中提取重要的用户信息，将其作为 *observation* 形式的记忆片段存储在记忆数据库中。
+- 反思和再巩固：每隔一段时间，此操作对新记录的 *observations* 进行反思，以形成和更新 *insight* 形式的记忆片段。然后执行记忆再巩固，
+以确保记忆片段之间的矛盾和重复得到妥善处理。
+
+### 主要特点
+
+⚡ 极低的用户时延（RT）:
+- 系统中后端操作（记忆巩固、反思和再巩固）与前端操作（记忆检索）相互独立。
+- 由于后端操作通常（并且推荐）通过队列或每隔固定间隔执行，系统的用户时延（RT）完全取决于前端操作，仅为约500毫秒。
+
+🌲 记忆存储的层次结构和内容的连贯一致性:
+- 系统中存储的记忆片段采用分层结构，通过汇总主题相似的 *observations* 生成高层次的 *insights* 信息。
+- 定期处理记忆片段之间的矛盾和重复，以保证记忆内容的连贯一致性。
+- 过滤掉用户输入的虚构内容，以避免LLM产生幻觉。
+
+⏰ 时间敏感性:
+- T系统在执行记忆检索和记忆巩固时具备时间敏感性，因此在输入对话包含对时间的指涉时，可以检索到准确的相关信息。
+
+### 用法示例
 
 
 # 💡 代码贡献