AnalyticDB:AnalyticDB for MySQL + Mem0 User Guide

Prepare AnalyticDB for MySQL

1. Create a database in your AnalyticDB for MySQL cluster for this tutorial.

   CREATE DATABASE mem0;

2. If you plan to connect via the public network, enable public network access for the cluster in the AnalyticDB for MySQL console, and add the public IP address of your demo environment to the whitelist.

Install Mem0 and dependencies

1. Check your Python environment: Ensure Python is installed in your environment. This tutorial is validated with Python 3.12.2.

   # For Mac, check Python version. If not installed, install from https://www.python.org/
   python3 --version
   
   # For Linux, check Python version. If not installed, install using yum: sudo yum install python
   python --version

2. Install the Mem0 framework: Unzip the Mem0 framework file you obtained and install the necessary dependency libraries.

   # Unzip and install
   unzip mem0.zip 
   cd mem0
   pip install .
   pip install pymysql dbutils

Configure environment variables:

For security, configure the Large Language Model (LLM) API key as an environment variable.

echo "export DASHSCOPE_API_KEY='<YOUR_DASHSCOPE_API_KEY>'" >> ~/.bashrc
# Optional; defaults to Singapore
echo "export DASHSCOPE_BASE_URL='https://dashscope-intl.aliyuncs.com/compatible-mode/v1'" >> ~/.bashrc   

source ~/.bashrc

# Check if it took effect
echo $DASHSCOPE_API_KEY
echo $DASHSCOPE_BASE_URL  

Initialize a Mem0 instance:

In your Python script, configure and initialize a Memory instance.

• LLM configuration: Use the Alibaba Cloud Model Studio qwen-plus model.

• Embedder configuration: Use the Alibaba Cloud Model Studio text-embedding-v4 model, with vector dimensions of 1536.

• Vector Store configuration: Use aliyun_adb, and enter your AnalyticDB for MySQL connection information.

from mem0 import Memory

# This example must connect to an adb instance with 1536 vector dimensions specified.
config = {
    "llm": {
        "provider": "bailian",
        "config": {"model": "qwen-plus"},
    },
    "embedder": {
        "provider": "bailian",
        "config": {"model": "text-embedding-v4", "embedding_dims": 1536},
    },
    "vector_store": {
        "provider": "aliyun_adb",
        "config": {
            "host": "<adb host>",
            "port": 3306,
            "database": "mem0",
            "user": "<your user name>",
            "password": "<your password>",
            "collection_name": "adb_mem0_demo"
        }
    }
}


"""
Suppose we have two users, Mike and Julia
1、Mike is a vegetarian, who likes fruit and playing basketball
2、Julia is a carnivore, who likes steak and playing badminton
"""

m = Memory.from_config(config_dict=config)

Add and retrieve memories:

Use the m.add() method to add memories for a user, and the m.get_all() method to retrieve all memories for a user.

• Code

  # Add memories for Mike
  result = m.add("I'm a vegetarian", user_id="Mike")
  memory_id = result["results"]["id"]
  print("add a memory for Mike:" + "I'm a vegetarian" + ", result => ", result , "\n")
  
  
  result = m.add("I like durian", user_id="Mike")
  print("add a memory for Mike:" + "I like durian" + ", result => " , result , "\n")
  
  # Get all memories for a user
  result = m.get_all(user_id="Mike")
  print("Get all memories for a user:" + "Mike" + ", result => " , result , "\n")

• Result analysis
  The returned result is a list containing Mike's two memories and their metadata, such as ID, content, and creation time.
  
  

  add a memory for Mike:I'm a vegetarian, result =>  {'results': [{'id': '32ac2932-10da-422b-beba-bb5a2b4666aa', 'memory': "I'm a vegetarian", 'event': 'ADD'}]} 
  
  add a memory for Mike:I like durian, result =>  {'results': [{'id': '40098c00-9f96-4486-af8b-5c79612fea10', 'memory': 'I like durian', 'event': 'ADD'}]} 
  
  Get all memories for a user:Mike, result =>  {'results': [{'id': '40098c00-9f96-4486-af8b-5c79612fea10', 'memory': 'I like durian', 'hash': 'dfd43e3d0a6ddc82cda440e0a1d39e7f', 'metadata': None, 'created_at': '2025-10-29T20:52:54.156926-07:00', 'updated_at': None, 'user_id': 'Mike'}, {'id': '32ac2932-10da-422b-beba-bb5a2b4666aa', 'memory': "I'm a vegetarian", 'hash': '27f3478f622dd27dd71969f131bd886f', 'metadata': None, 'created_at': '2025-10-29T20:52:51.126628-07:00', 'updated_at': None, 'user_id': 'Mike'}]} 

Update and delete memories:

Mem0 supports manual deletion by memory ID or automatic conflict resolution when adding new memories.

• Code

  result = m.delete(memory_id=memory_id)
  print("Manual Delete a memory:" + "I like durian" + ", result => " , result , "\n")
  
  result = m.add("I hate things that taste like durian", user_id="Mike")
  print("Automatic Delete a contradictory historical memory for Mike:" + "I like durian" + ", result => " , result , "\n")
  
  # Get all memories for a user
  result = m.get_all(user_id="Mike")
  print("Get all memories for a user:" + "Mike" + ", result => " , result , "\n")

• Result analysis

  Manual Delete a memory:I like durian, result =>  {'message': 'Memory deleted successfully!'} 
  
  Automatic Delete a contradictory historical memory for Mike:I like durian, result =>  {'results': [{'id': 'dc308443-ae8c-4cb3-851f-060adfc39eaf', 'memory': 'I like durian', 'event': 'DELETE'}]} 
  
  Get all memories for a user:Mike, result =>  {'results': []} 

  • After adding a conflicting memory, the return result of the add method includes a DELETE event, indicating that the old, conflicting memory ("I like durian") was automatically removed.

  • When calling get_all again, you will find that the old memory no longer exists, demonstrating Mem0's adaptive and self-consistent capabilities.

Retrieve and search memories:

Mem0 supports precise filtered retrieval using metadata and similarity search using semantics.

1. Prepare data: Add memories with agent_id and metadata for Mike and Julia.

   • Code

     # Mike Memory
     result = m.add("I'm a vegetarian", user_id="Mike", agent_id="diet-assistant")
     print("add a memory for Mike:" + "I'm a vegetarian" + ", result => ", result , "\n")
     
     result = m.add("I like durian, watermelon and strawberries", user_id="Mike", metadata={"category": "hobbies"})
     print("add a memory for Mike with meta:" + "I like durian, watermelon and strawberries" + ", result => " , result , "\n")
     
     result = m.add("I like playing basketball", user_id="Mike", agent_id="fitness-assistant")
     print("add a memory for Mike:" + "I like playing basketball" + ", result => " , result , "\n")
     
     
     # Julia Memory
     result = m.add("I'm a carnivore", user_id="Julia", agent_id="diet-assistant")
     print("add a memory for Julia:" + "I'm a carnivore" + ", result => " , result , "\n")
     
     result = m.add("I like steak", user_id="Julia", metadata={"category": "hobbies"})
     print("add a memory for Julia:" + "I like steak" + ", result => " , result , "\n")
     
     result = m.add("I like playing badminton", user_id="Julia", agent_id="fitness-assistant")
     print("add a memory for Julia:" + "I like playing badminton" + ", result => " , result , "\n")
     

   • Result analysis

     add a memory for Mike:I'm a vegetarian, result =>  {'results': [{'id': 'a0fadb7e-c6b7-46b2-b0b8-6b036d879015', 'memory': "I'm a vegetarian", 'event': 'ADD'}]} 
     
     add a memory for Mike with meta:I like durian, watermelon and strawberries, result =>  {'results': [{'id': 'efec6c25-c2a7-454f-8344-367018c5b607', 'memory': 'I like durian, watermelon and strawberries', 'event': 'ADD'}]} 
     
     add a memory for Mike:I like playing basketball, result =>  {'results': [{'id': '75da451c-dbf7-45aa-b9ab-72aab54df7b8', 'memory': 'I like playing basketball', 'event': 'ADD'}]} 
     
     add a memory for Julia:I'm a carnivore, result =>  {'results': [{'id': '93a03639-195e-40ef-9f5f-55d366b76333', 'memory': "I'm a carnivore", 'event': 'ADD'}]} 
     
     add a memory for Julia:I like steak, result =>  {'results': [{'id': 'e30faceb-ba71-4b27-b052-28863f26abf9', 'memory': 'I like steak', 'event': 'ADD'}]} 
     
     add a memory for Julia:I like playing badminton, result =>  {'results': [{'id': '90d45867-b198-415f-99e8-7375b4c6c35b', 'memory': 'I like playing badminton', 'event': 'ADD'}]} 

2. Precise filtered retrieval: Use the get_all method and pass filter conditions.

   • Code

     # Get all memories for a specific agent belonging to a user
     result = m.get_all(user_id="Mike", agent_id="fitness-assistant")
     print("Get all memories for a specific agent belonging to a user:" + "Mike" + ", result => " , result , "\n")
     
     
     # Get all memories belonging to a user with filter
     result = m.get_all(user_id="Julia", filters={"category": "hobbies"})
     print("Get all memories belonging to a user with filter:" + "Julia" + ", result => " , result , "\n")

   • Result analysis

     Get all memories for a specific agent belonging to a user:Mike, result =>  {'results': [{'id': '75da451c-dbf7-45aa-b9ab-72aab54df7b8', 'memory': 'I like playing basketball', 'hash': '000b24fa8e8f76134286d2c4626c036d', 'metadata': None, 'created_at': '2025-10-30T00:13:08.390949-07:00', 'updated_at': None, 'user_id': 'Mike', 'agent_id': 'fitness-assistant'}]} 
     
     Get all memories belonging to a user with filter:Julia, result =>  {'results': [{'id': 'e30faceb-ba71-4b27-b052-28863f26abf9', 'memory': 'I like steak', 'hash': '6e7129fc2010e93e94c943dbf6f69246', 'metadata': {'category': 'hobbies'}, 'created_at': '2025-10-30T00:13:14.631531-07:00', 'updated_at': None, 'user_id': 'Julia'}]} 

3. Similarity search: Use the search method for semantic search. The results are sorted by similarity score (score) in descending order.

   • Code

     # Search memories for a user
     result = m.search("Who likes playing badminton better", agent_id="fitness-assistant")
     print("Search memories:" + "Who likes playing badminton better" + ", result => " , result , "\n")
     
     # Search memories for a specific agent belonging to a user
     result = m.search("Who will celebrate on International Vegetarian Day", agent_id="diet-assistant")
     print("Search memories:" + "Who will celebrate on International Vegetarian Day" + ", result => " , result , "\n")
     
     # Search memories for a user
     result = m.search("what kind of fruit do you like", user_id="Mike")
     print("Search memories:" + "what kind of fruit do you like." + ", result => " , result , "\n")
     
     # Search memories for a user
     result = m.search("what sport do you like", user_id="Julia", agent_id="fitness-assistant")
     print("Search memories:" + "what sport do you like." + ", result => " , result , "\n")
     

   • Result analysis

     Search memories:Who likes playing badminton better, result =>  {'results': [{'id': '90d45867-b198-415f-99e8-7375b4c6c35b', 'memory': 'I like playing badminton', 'hash': '304d03421834255d1650b46877b3785b', 'metadata': None, 'score': 0.82532454, 'created_at': '2025-10-30T00:13:18.218251-07:00', 'updated_at': None, 'user_id': 'Julia', 'agent_id': 'fitness-assistant'}, {'id': '75da451c-dbf7-45aa-b9ab-72aab54df7b8', 'memory': 'I like playing basketball', 'hash': '000b24fa8e8f76134286d2c4626c036d', 'metadata': None, 'score': 0.56634325, 'created_at': '2025-10-30T00:13:08.390949-07:00', 'updated_at': None, 'user_id': 'Mike', 'agent_id': 'fitness-assistant'}]} 
     
     Search memories:Who will celebrate on International Vegetarian Day, result =>  {'results': [{'id': 'a0fadb7e-c6b7-46b2-b0b8-6b036d879015', 'memory': "I'm a vegetarian", 'hash': '27f3478f622dd27dd71969f131bd886f', 'metadata': None, 'score': 0.5640734, 'created_at': '2025-10-30T00:13:01.011810-07:00', 'updated_at': None, 'user_id': 'Mike', 'agent_id': 'diet-assistant'}, {'id': '93a03639-195e-40ef-9f5f-55d366b76333', 'memory': "I'm a carnivore", 'hash': '5f81d3e6f06baba2e10ec6c155d012e3', 'metadata': None, 'score': 0.41749594, 'created_at': '2025-10-30T00:13:11.462159-07:00', 'updated_at': None, 'user_id': 'Julia', 'agent_id': 'diet-assistant'}]} 
     
     Search memories:what kind of fruit do you like., result =>  {'results': [{'id': 'efec6c25-c2a7-454f-8344-367018c5b607', 'memory': 'I like durian, watermelon and strawberries', 'hash': '07cc049e2beaa043bb359b50a33664ac', 'metadata': {'category': 'hobbies'}, 'score': 0.6519712, 'created_at': '2025-10-30T00:13:04.981195-07:00', 'updated_at': None, 'user_id': 'Mike'}, {'id': '75da451c-dbf7-45aa-b9ab-72aab54df7b8', 'memory': 'I like playing basketball', 'hash': '000b24fa8e8f76134286d2c4626c036d', 'metadata': None, 'score': 0.3975049, 'created_at': '2025-10-30T00:13:08.390949-07:00', 'updated_at': None, 'user_id': 'Mike', 'agent_id': 'fitness-assistant'}, {'id': 'a0fadb7e-c6b7-46b2-b0b8-6b036d879015', 'memory': "I'm a vegetarian", 'hash': '27f3478f622dd27dd71969f131bd886f', 'metadata': None, 'score': 0.3382097, 'created_at': '2025-10-30T00:13:01.011810-07:00', 'updated_at': None, 'user_id': 'Mike', 'agent_id': 'diet-assistant'}]} 
     
     Search memories:what sport do you like., result =>  {'results': [{'id': '90d45867-b198-415f-99e8-7375b4c6c35b', 'memory': 'I like playing badminton', 'hash': '304d03421834255d1650b46877b3785b', 'metadata': None, 'score': 0.61615115, 'created_at': '2025-10-30T00:13:18.218251-07:00', 'updated_at': None, 'user_id': 'Julia', 'agent_id': 'fitness-assistant'}]} 

     • Search for badminton: The agent_id filters out non-sports-related memories. "I like playing badminton" has the highest score and ranks first in the results.

     • Search for Vegetarian Day: The agent_id filters out non-diet-related memories. "I'm a vegetarian" has the highest score and ranks first in the results.

     • Search for Mike's fruit preferences: The user_id filters out Julia's memories. "I like durian, watermelon and strawberries" has the highest score.

     • Search for Julia's sports preferences: The user_id and agent_id provide a double filter, precisely returning "I like playing badminton".

Delete memories:

Use the delete_all method to delete all memories for a specified user or agent.

• Code

  # Delete all memories for a specific agent belonging to a user
  m.delete_all(user_id="Julia")
  result = m.get_all(user_id="Julia")
  print(result)
  
  # Delete all memories for a user
  m.delete_all(user_id="Mike")
  result = m.get_all(user_id="Mike")
  print(result)

• Result analysis

  Resetting collection mem0_adb_demo...
  {'results': []}
  Resetting collection mem0_adb_demo...
  {'results': []}