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fix: update README.md #783

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210 changes: 120 additions & 90 deletions README.md
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Expand Up @@ -117,98 +117,8 @@ showcasing its capabilities in **information extraction**, **temporal and cross-
- **Textual Memory**: For storing and retrieving unstructured or structured text knowledge.
- **Activation Memory**: Caches key-value pairs (`KVCacheMemory`) to accelerate LLM inference and context reuse.
- **Parametric Memory**: Stores model adaptation parameters (e.g., LoRA weights).
- **Tool Memory** 🆕: Records Agent tool call trajectories and experiences to improve planning capabilities.
- **📚 Knowledge Base System** 🆕: Build multi-dimensional knowledge bases with automatic document/URL parsing, splitting, and cross-project sharing capabilities.
- **🔧 Memory Controllability** 🆕:
- **Feedback Mechanism**: Use `add_feedback` API to correct, supplement, or replace existing memories with natural language.
- **Precise Deletion**: Delete specific memories by User ID or Memory ID via API or MCP tools.
- **👁️ Multi-Modal Support** 🆕: Support for image understanding and memory, including chart parsing in documents.
- **⚡ Advanced Architecture**:
- **DB Optimization**: Enhanced connection management and batch insertion for high-concurrency scenarios.
- **Advanced Retrieval**: Custom tag and info field filtering with complex logical operations.
- **Redis Streams Scheduler**: Multi-level queue architecture with intelligent orchestration for fair multi-tenant scheduling.
- **Stream & Non-Stream Chat**: Ready-to-use streaming and non-streaming chat interfaces.
- **🔌 Extensible**: Easily extend and customize memory modules, data sources, and LLM integrations.
- **🏂 Lightweight Deployment** 🆕: Support for quick mode and complete mode deployment options.

## 🚀 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
from memos.mem_cube.general import GeneralMemCube

# Initialize a MemCube from a local directory
mem_cube = GeneralMemCube.init_from_dir("examples/data/mem_cube_2")

# Access and print all memories
print("--- Textual Memories ---")
for item in mem_cube.text_mem.get_all():
print(item)

print("\n--- Activation Memories ---")
for item in mem_cube.act_mem.get_all():
print(item)

# Save the MemCube to a new directory
mem_cube.dump("tmp/mem_cube")
```

**`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
from memos.mem_os.main import MOS


# init MOS
mos_config = MOSConfig.from_json_file("examples/data/config/simple_memos_config.json")
memory = MOS(mos_config)

# create user
user_id = "b41a34d5-5cae-4b46-8c49-d03794d206f5"
memory.create_user(user_id=user_id)

# register cube for user
memory.register_mem_cube("examples/data/mem_cube_2", user_id=user_id)

# add memory for user
memory.add(
messages=[
{"role": "user", "content": "I like playing football."},
{"role": "assistant", "content": "I like playing football too."},
],
user_id=user_id,
)

# Later, when you want to retrieve memory for user
retrieved_memories = memory.search(query="What do you like?", user_id=user_id)
# output text_memories: I like playing football, act_memories, para_memories
print(f"text_memories: {retrieved_memories['text_mem']}")
```

For more detailed examples, please check out the [`examples`](./examples) directory.

## 📦 Installation

Expand Down Expand Up @@ -259,6 +169,126 @@ To download example code, data and configurations, run the following command:
memos download_examples
```

## 🚀 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
```

### Interface SDK
#### Here is a quick example showing how to create interface SDK

This interface is used to add messages, supporting multiple types of content and batch additions. MemOS will automatically parse the messages and handle memory for reference in subsequent conversations.
```python
# Please make sure MemoS is installed (pip install MemoryOS -U)
from memos.api.client import MemOSClient

# Initialize the client using the API Key
client = MemOSClient(api_key="YOUR_API_KEY")

messages = [
{"role": "user", "content": "I have planned to travel to Guangzhou during the summer vacation. What chain hotels are available for accommodation?"},
{"role": "assistant", "content": "You can consider [7 Days, All Seasons, Hilton], and so on."},
{"role": "user", "content": "I'll choose 7 Days"},
{"role": "assistant", "content": "Okay, ask me if you have any other questions."}
]
user_id = "memos_user_123"
conversation_id = "0610"
res = client.add_message(messages=messages, user_id=user_id, conversation_id=conversation_id)

print(f"result: {res}")
```

This interface is used to retrieve the memories of a specified user, returning the memory fragments most relevant to the input query for Agent use. The recalled memory fragments include 'factual memory', 'preference memory', and 'tool memory'.
```python
# Please make sure MemoS is installed (pip install MemoryOS -U)
from memos.api.client import MemOSClient

# Initialize the client using the API Key
client = MemOSClient(api_key="YOUR_API_KEY")

query = "I want to go out to play during National Day. Can you recommend a city I haven't been to and a hotel brand I haven't stayed at?"
user_id = "memos_user_123"
conversation_id = "0928"
res = client.search_memory(query=query, user_id=user_id, conversation_id=conversation_id)

print(f"result: {res}")
```

This interface is used to delete the memory of specified users and supports batch deletion.
```python
# Please make sure MemoS is installed (pip install MemoryOS -U)
from memos.api.client import MemOSClient

# Initialize the client using the API Key
client = MemOSClient(api_key="YOUR_API_KEY")

user_ids = ["memos_user_123"]
# Replace with the memory ID
memory_ids = ["6b23b583-f4c4-4a8f-b345-58d0c48fea04"]
res = client.delete_memory(user_ids=user_ids, memory_ids=memory_ids)

print(f"result: {res}")
```

This interface is used to add feedback to messages in the current session, allowing MemOS to correct its memory based on user feedback.
```python
# Please make sure MemoS is installed (pip install MemoryOS -U)
from memos.api.client import MemOSClient

# Initialize the client using the API Key
client = MemOSClient(api_key="YOUR_API_KEY")

user_id = "memos_user_123"
conversation_id = "memos_feedback_conv"
feedback_content = "No, let's change it now to a meal allowance of 150 yuan per day and a lodging subsidy of 700 yuan per day for first-tier cities; for second- and third-tier cities, it remains the same as before."
# Replace with the knowledgebase ID
allow_knowledgebase_ids = ["basee5ec9050-c964-484f-abf1-ce3e8e2aa5b7"]

res = client.add_feedback(
user_id=user_id,
conversation_id=conversation_id,
feedback_content=feedback_content,
allow_knowledgebase_ids=allow_knowledgebase_ids
)

print(f"result: {res}")
```

This interface is used to create a knowledgebase associated with a project
```python
# Please make sure MemoS is installed (pip install MemoryOS -U)
from memos.api.client import MemOSClient

# Initialize the client using the API Key
client = MemOSClient(api_key="YOUR_API_KEY")

knowledgebase_name = "Financial Reimbursement Knowledge Base"
knowledgebase_description = "A compilation of all knowledge related to the company's financial reimbursements."

res = client.create_knowledgebase(
knowledgebase_name=knowledgebase_name,
knowledgebase_description=knowledgebase_description
)
print(f"result: {res}")
```

## 💬 Community & Support

Join our community to ask questions, share your projects, and connect with other developers.
Expand Down