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Mem0 的 CRUD 到底是如何实现的？我们来看下源码。

使用

先来看下，如何使用 Mem0

import os
os.environ["OPENAI_API_KEY"] = "sk-xxx"from mem0 import Memorym = Memory()# 1. Add: Store a memory from any unstructured text
result = m.add("I am working on improving my tennis skills. Suggest some online courses.", user_id="alice", metadata={"category": "hobbies"})# Created memory --> 'Improving her tennis skills.' and 'Looking for online suggestions.'# 2. Update: update the memory
result = m.update(memory_id=<memory_id_1>, data="Likes to play tennis on weekends")# Updated memory --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'# 3. Search: search related memories
related_memories = m.search(query="What are Alice's hobbies?", user_id="alice")# Retrieved memory --> 'Likes to play tennis on weekends'# 4. Get all memories
all_memories = m.get_all()
memory_id = all_memories[0]["id"] # get a memory_id# All memory items --> 'Likes to play tennis on weekends.' and 'Looking for online suggestions.'# 5. Get memory history for a particular memory_id
history = m.history(memory_id=<memory_id_1>)# Logs corresponding to memory_id_1 --> {'prev_value': 'Working on improving tennis skills and interested in online courses for tennis.', 'new_value': 'Likes to play tennis on weekends' }

MemoryBase

MemoryBase 是一个抽象类，定义了一些接口方法

• get
• get_all
• update
• delete
• history

class MemoryBase(ABC):@abstractmethoddef get(self, memory_id):"""Retrieve a memory by ID.Args:memory_id (str): ID of the memory to retrieve.Returns:dict: Retrieved memory."""pass@abstractmethoddef get_all(self):"""List all memories.Returns:list: List of all memories."""pass@abstractmethoddef update(self, memory_id, data):"""Update a memory by ID.Args:memory_id (str): ID of the memory to update.data (dict): Data to update the memory with.Returns:dict: Updated memory."""pass@abstractmethoddef delete(self, memory_id):"""Delete a memory by ID.Args:memory_id (str): ID of the memory to delete."""pass@abstractmethoddef history(self, memory_id):"""Get the history of changes for a memory by ID.Args:memory_id (str): ID of the memory to get history for.Returns:list: List of changes for the memory."""pass

Memory

Memory 实现 MemoryBase 接口

class Memory(MemoryBase):

init

    def __init__(self, config: MemoryConfig = MemoryConfig()):self.config = configself.embedding_model = EmbedderFactory.create(self.config.embedder.provider)# Initialize the appropriate vector store based on the configurationvector_store_config = self.config.vector_store.configif self.config.vector_store.provider == "qdrant":self.vector_store = Qdrant(host=vector_store_config.host,port=vector_store_config.port,path=vector_store_config.path,url=vector_store_config.url,api_key=vector_store_config.api_key,)else:raise ValueError(f"Unsupported vector store type: {self.config.vector_store_type}")self.llm = LlmFactory.create(self.config.llm.provider, self.config.llm.config)self.db = SQLiteManager(self.config.history_db_path)self.collection_name = self.config.collection_nameself.vector_store.create_col(name=self.collection_name, vector_size=self.embedding_model.dims)self.vector_store.create_col(name=self.collection_name, vector_size=self.embedding_model.dims)capture_event("mem0.init", self)

初始化 embedding_model, vector_store(这里只能是 Qdrant), llm, db, collection_name

add

    def add(self,data,user_id=None,agent_id=None,run_id=None,metadata=None,filters=None,prompt=None,):"""Create a new memory.Args:data (str): Data to store in the memory.user_id (str, optional): ID of the user creating the memory. Defaults to None.agent_id (str, optional): ID of the agent creating the memory. Defaults to None.run_id (str, optional): ID of the run creating the memory. Defaults to None.metadata (dict, optional): Metadata to store with the memory. Defaults to None.filters (dict, optional): Filters to apply to the search. Defaults to None.Returns:str: ID of the created memory."""

• 将用户 data 发给 llm ，得到 extracted_memories
• 将用户 data 转成 embeddings
• vector_store 根据 embeddings search 得到 existing_memories
• 将新，老 memory 发给 llm 来 merge
• 调用函数 _create_memory_tool 进行实际操作
  • vector_store insert
  • db add_history

get

    def get(self, memory_id):"""Retrieve a memory by ID.Args:memory_id (str): ID of the memory to retrieve.Returns:dict: Retrieved memory."""

• vector_store 根据 memory_id 去 get

get_all

    def get_all(self, user_id=None, agent_id=None, run_id=None, limit=100):"""List all memories.Returns:list: List of all memories."""

• vector_store 根据 collection_name, filters, limit 调用 list 接口

search

    def search(self, query, user_id=None, agent_id=None, run_id=None, limit=100, filters=None):"""Search for memories.Args:query (str): Query to search for.user_id (str, optional): ID of the user to search for. Defaults to None.agent_id (str, optional): ID of the agent to search for. Defaults to None.run_id (str, optional): ID of the run to search for. Defaults to None.limit (int, optional): Limit the number of results. Defaults to 100.filters (dict, optional): Filters to apply to the search. Defaults to None.Returns:list: List of search results."""

• embedding_model 将 query 转 embeddings
• vector_store 根据 embeddings search

update

    def update(self, memory_id, data):"""Update a memory by ID.Args:memory_id (str): ID of the memory to update.data (dict): Data to update the memory with.Returns:dict: Updated memory."""

• 调用 _update_memory_tool
  • existing_memory = self.vector_store.get
  • embeddings = self.embedding_model.embed(data)
  • self.vector_store.update
  • self.db.add_history

delete

    def delete(self, memory_id):"""Delete a memory by ID.Args:memory_id (str): ID of the memory to delete."""

• 调用 _delete_memory_tool
  • existing_memory = self.vector_store.get
  • self.vector_store.delete
  • self.db.add_history

delete_all

    def delete_all(self, user_id=None, agent_id=None, run_id=None):"""Delete all memories.Args:user_id (str, optional): ID of the user to delete memories for. Defaults to None.agent_id (str, optional): ID of the agent to delete memories for. Defaults to None.run_id (str, optional): ID of the run to delete memories for. Defaults to None."""

• memories = self.vector_store.list
• foreach memories
  • _delete_memory_tool

history

    def history(self, memory_id):"""Get the history of changes for a memory by ID.Args:memory_id (str): ID of the memory to get history for.Returns:list: List of changes for the memory."""

• self.db.get_history

reset

    def reset(self):"""Reset the memory store."""

• self.vector_store.delete_col
• self.db.reset()

AnonymousTelemetry

• capture_event 收集信息
• telemetry 用的是 Posthog(https://us.i.posthog.com)

SQLiteManager

• db 用的是 sqlite3
• 一个记录历史的表

CREATE TABLE IF NOT EXISTS history (id TEXT PRIMARY KEY,memory_id TEXT,prev_value TEXT,new_value TEXT,event TEXT,timestamp DATETIME,is_deleted INTEGER
)

MemoryClient

class MemoryClient:"""Client for interacting with the Mem0 API.This class provides methods to create, retrieve, search, and delete memoriesusing the Mem0 API.Attributes:api_key (str): The API key for authenticating with the Mem0 API.host (str): The base URL for the Mem0 API.client (httpx.Client): The HTTP client used for making API requests."""

• 主要用于跟平台(https://api.mem0.ai/v1)交互
• 接口
  • add
  • get
  • get_all
  • search
  • delete
  • delete_all
  • history
  • reset

Embedding

class EmbeddingBase(ABC):@abstractmethoddef embed(self, text):"""Get the embedding for the given text.Args:text (str): The text to embed.Returns:list: The embedding vector."""pass

• HuggingFaceEmbedding(model_name=“multi-qa-MiniLM-L6-cos-v1”)
• Ollama(model=“nomic-embed-text”)
• OpenAI(model=“text-embedding-3-small”)

LLM

class LLMBase(ABC):def __init__(self, config: Optional[BaseLlmConfig] = None):"""Initialize a base LLM class:param config: LLM configuration option class, defaults to None:type config: Optional[BaseLlmConfig], optional"""if config is None:self.config = BaseLlmConfig()else:self.config = config@abstractmethoddef generate_response(self, messages):"""Generate a response based on the given messages.Args:messages (list): List of message dicts containing 'role' and 'content'.Returns:str: The generated response."""pass

• AWSBedrockLLM(anthropic.claude-3-5-sonnet-20240620-v1:0)
• GroqLLM(llama3-70b-8192)
• LiteLLM(gpt-4o)
• OllamaLLM(llama3)
• OpenAILLM(gpt-4o)
• TogetherLLM(mistralai/Mixtral-8x7B-Instruct-v0.1)

VectorStore

class VectorStoreBase(ABC):@abstractmethoddef create_col(self, name, vector_size, distance):"""Create a new collection."""pass@abstractmethoddef insert(self, name, vectors, payloads=None, ids=None):"""Insert vectors into a collection."""pass@abstractmethoddef search(self, name, query, limit=5, filters=None):"""Search for similar vectors."""pass@abstractmethoddef delete(self, name, vector_id):"""Delete a vector by ID."""pass@abstractmethoddef update(self, name, vector_id, vector=None, payload=None):"""Update a vector and its payload."""pass@abstractmethoddef get(self, name, vector_id):"""Retrieve a vector by ID."""pass@abstractmethoddef list_cols(self):"""List all collections."""pass@abstractmethoddef delete_col(self, name):"""Delete a collection."""pass@abstractmethoddef col_info(self, name):"""Get information about a collection."""pass

• 只有 Qdrant 一个实现

总结

• 核心就是 Memory 类，实现了 MemoryBase 接口
• 通过 embedding_model 来处理文本
• 通过 vector_store 存储 embedding
• 通过 llm 处理数据
• 通过 db 记录 Memory 的历史

---

• github
• doc
• AI 博客 - 从零开始学AI
• 公众号 - 从零开始学AI