doc: Update readme

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

docker/.env.example

@@ -1,29 +1,60 @@
 # MemOS Environment Variables Configuration
+TZ=Asia/Shanghai
 
-# Path to memory storage (e.g. /tmp/data_test)
-MOS_CUBE_PATH=
+MOS_CUBE_PATH="/tmp/data_test"            # Path to memory storage (e.g. /tmp/data_test)
+MOS_ENABLE_DEFAULT_CUBE_CONFIG="true"     # Enable default cube config (true/false)
 
 # OpenAI Configuration
-OPENAI_API_KEY=            # Your OpenAI API key
-OPENAI_API_BASE=           # OpenAI API base URL (default: https://api.openai.com/v1)
+OPENAI_API_KEY="sk-xxx"                   # Your OpenAI API key
+OPENAI_API_BASE="http://xxx"              # OpenAI API base URL (default: https://api.openai.com/v1)
 
-# MemOS Feature Toggles
-MOS_ENABLE_DEFAULT_CUBE_CONFIG=   # Enable default cube config (true/false)
-MOS_ENABLE_SCHEDULER=             # Enable background scheduler (true/false)
+# MemOS Chat Model Configuration
+MOS_CHAT_MODEL=gpt-4o-mini
+MOS_CHAT_TEMPERATURE=0.8
+MOS_MAX_TOKENS=8000
+MOS_TOP_P=0.9
+MOS_TOP_K=50
+MOS_CHAT_MODEL_PROVIDER=openai
 
-# Neo4j Configuration
-NEO4J_URI=               # Neo4j connection URI (e.g. bolt://localhost:7687)
-NEO4J_USER=              # Neo4j username
-NEO4J_PASSWORD=          # Neo4j password
-MOS_NEO4J_SHARED_DB=     # Shared Neo4j database name (if using multi-db)
+# graph db
+# neo4j
+NEO4J_BACKEND=xxx
+NEO4J_URI=bolt://xxx
+NEO4J_USER=xxx
+NEO4J_PASSWORD=xxx
+MOS_NEO4J_SHARED_DB=xxx
+NEO4J_DB_NAME=xxx
+
+# tetxmem reog
+MOS_ENABLE_REORGANIZE=false
 
 # MemOS User Configuration
-MOS_USER_ID=             # Unique user ID
-MOS_SESSION_ID=          # Session ID for current chat
-MOS_MAX_TURNS_WINDOW=    # Max number of turns to keep in memory
+MOS_USER_ID=root
+MOS_SESSION_ID=default_session
+MOS_MAX_TURNS_WINDOW=20
+
+# MemRader Configuration
+MEMRADER_MODEL=gpt-4o-mini
+MEMRADER_API_KEY=sk-xxx
+MEMRADER_API_BASE=http://xxx:3000/v1
+MEMRADER_MAX_TOKENS=5000
+
+#embedding & rerank
+EMBEDDING_DIMENSION=1024
+MOS_EMBEDDER_BACKEND=universal_api
+MOS_EMBEDDER_MODEL=bge-m3
+MOS_EMBEDDER_API_BASE=http://xxx
+MOS_EMBEDDER_API_KEY=EMPTY
+MOS_RERANKER_BACKEND=http_bge
+MOS_RERANKER_URL=http://xxx
+# Ollama Configuration (for embeddings)
+#OLLAMA_API_BASE=http://xxx
 
-# Ollama Configuration (for local embedding models)
-OLLAMA_API_BASE=         # Ollama API base URL (e.g. http://localhost:11434)
+# milvus for pref mem
+MILVUS_URI=http://xxx
+MILVUS_USER_NAME=xxx
+MILVUS_PASSWORD=xxx
 
-# Embedding Configuration
-MOS_EMBEDDER_BACKEND=    # Embedding backend: openai, ollama, etc.
+# pref mem
+ENABLE_PREFERENCE_MEMORY=true
+RETURN_ORIGINAL_PREF_MEM=true

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/scripts/utils/client.py

@@ -250,33 +250,23 @@ def search(self, query, user_id, top_k):
                 preference_note = json.loads(response.text)["data"]["preference_note"]
                 for i in text_mem_res:
                     i.update({"memory": i.pop("memory_value")})
+                explicit_pref_string = "Explicit Preference:"
+                implicit_pref_string = "\n\nImplicit Preference:"
+                explicit_idx = 0
+                implicit_idx = 0
+                for pref in pref_mem_res:
+                    if pref["preference_type"] == "explicit_preference":
+                        explicit_pref_string += f"\n{explicit_idx + 1}. {pref['preference']}"
+                        explicit_idx += 1
+                    if pref["preference_type"] == "implicit_preference":
+                        implicit_pref_string += f"\n{implicit_idx + 1}. {pref['preference']}"
+                        implicit_idx += 1
+
+                return {
+                    "text_mem": [{"memories": text_mem_res}],
+                    "pref_string": explicit_pref_string + implicit_pref_string + preference_note,
+                }
 
-                explicit_prefs = [
-                    p["preference"]
-                    for p in pref_mem_res
-                    if p.get("preference_type", "") == "explicit_preference"
-                ]
-                implicit_prefs = [
-                    p["preference"]
-                    for p in pref_mem_res
-                    if p.get("preference_type", "") == "implicit_preference"
-                ]
-
-                pref_parts = []
-                if explicit_prefs:
-                    pref_parts.append(
-                        "Explicit Preference:\n"
-                        + "\n".join(f"{i + 1}. {p}" for i, p in enumerate(explicit_prefs))
-                    )
-                if implicit_prefs:
-                    pref_parts.append(
-                        "Implicit Preference:\n"
-                        + "\n".join(f"{i + 1}. {p}" for i, p in enumerate(implicit_prefs))
-                    )
-
-                pref_string = "\n".join(pref_parts) + preference_note
-
-                return {"text_mem": [{"memories": text_mem_res}], "pref_string": pref_string}
             except Exception as e:
                 if attempt < max_retries - 1:
                     time.sleep(2**attempt)

src/memos/log.py

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