doc: Update readme

Author: Nyakult

README.md

@@ -81,6 +81,27 @@ showcasing its capabilities in **information extraction**, **temporal and cross-
 
 ## 🚀 Getting Started
 
+### ⭐️ MemOS online API
+The easiest way to use MemOS. Equip your agent with memory **in minutes**!
+
+Sign up and get started on[`MemOS dashboard`](https://memos-dashboard.openmem.net/cn/quickstart/?source=landing).
+
+
+### Self-Hosted Server
+1. Get the repository.
+```bash
+git clone https://github.com/MemTensor/MemOS.git
+cd MemOS
+pip install -r ./docker/requirements.txt
+```
+
+2. Configure `docker/.env.example` and copy to `MemOS/.env`
+3. Start the service.
+```bash
+uvicorn memos.api.server_api:app --host 0.0.0.0 --port 8001 --workers 8
+```
+
+### Local SDK
 Here's a quick example of how to create a **`MemCube`**, load it from a directory, access its memories, and save it.
 
 ```python
@@ -102,7 +123,7 @@ for item in mem_cube.act_mem.get_all():
 mem_cube.dump("tmp/mem_cube")
 ```
 
-What about **`MOS`** (Memory Operating System)? It's a higher-level orchestration layer that manages multiple MemCubes and provides a unified API for memory operations. Here's a quick example of how to use MOS:
+**`MOS`** (Memory Operating System) is a higher-level orchestration layer that manages multiple MemCubes and provides a unified API for memory operations. Here's a quick example of how to use MOS:
 
 ```python
 from memos.configs.mem_os import MOSConfig

evaluation/.env-example

@@ -22,26 +22,3 @@ SUPERMEMORY_API_KEY="sm_xxx"
 MEMOBASE_API_KEY="xxx"
 MEMOBASE_PROJECT_URL="http://***.***.***.***:8019"
 
-# eval settings
-PRE_SPLIT_CHUNK=false
-
-# Configuration Only For Scheduler
-# RabbitMQ Configuration
-MEMSCHEDULER_RABBITMQ_HOST_NAME=rabbitmq-cn-***.cn-***.amqp-32.net.mq.amqp.aliyuncs.com
-MEMSCHEDULER_RABBITMQ_USER_NAME=***
-MEMSCHEDULER_RABBITMQ_PASSWORD=***
-MEMSCHEDULER_RABBITMQ_VIRTUAL_HOST=memos
-MEMSCHEDULER_RABBITMQ_ERASE_ON_CONNECT=true
-MEMSCHEDULER_RABBITMQ_PORT=5672
-
-# OpenAI Configuration
-MEMSCHEDULER_OPENAI_API_KEY=sk-***
-MEMSCHEDULER_OPENAI_BASE_URL=http://***.***.***.***:3000/v1
-MEMSCHEDULER_OPENAI_DEFAULT_MODEL=gpt-4o-mini
-
-# Graph DB Configuration
-MEMSCHEDULER_GRAPHDBAUTH_URI=bolt://localhost:7687
-MEMSCHEDULER_GRAPHDBAUTH_USER=neo4j
-MEMSCHEDULER_GRAPHDBAUTH_PASSWORD=***
-MEMSCHEDULER_GRAPHDBAUTH_DB_NAME=neo4j
-MEMSCHEDULER_GRAPHDBAUTH_AUTO_CREATE=true

evaluation/README.md

@@ -16,10 +16,7 @@ This repository provides tools and scripts for evaluating the `LoCoMo`, `LongMem
    ```
 
 ## Configuration
-
-1. Copy the `.env-example` file to `.env`, and fill in the required environment variables according to your environment and API keys.
-
-2. Copy the `configs-example/` directory to a new directory named `configs/`, and modify the configuration files inside it as needed. This directory contains model and API-specific settings.
+Copy the `.env-example` file to `.env`, and fill in the required environment variables according to your environment and API keys.
 
 ## Setup MemOS
 ### local server

evaluation/configs-example/mem_cube_config.json

@@ -187,7 +187,7 @@ def close(self):
     },
     "handlers": {
         "console": {
-            "level": "DEBUG",
+            "level": selected_log_level,
             "class": "logging.StreamHandler",
             "stream": stdout,
             "formatter": "no_datetime",