Mem0¶

Mem0（发音为“mem-zero”）通过智能记忆层增强了AI助手和代理，实现了个性化的AI交互。它记住用户的偏好和特征并随时间不断更新，非常适合客户支持聊天机器人和AI助手等应用。

Mem0 提供了两种强大的方式来利用我们的技术：我们的托管平台和我们的开源解决方案。

如果您正在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%pip install llama-index llama-index-memory-mem0

%pip install llama-index llama-index-memory-mem0

使用 Mem0 平台进行设置¶

将您的 Mem0 平台 API 密钥设置为环境变量。您可以将 <your-mem0-api-key> 替换为您的实际 API 密钥

注意：您可以从 Mem0 平台获取您的 Mem0 平台 API 密钥。

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import os

os.environ["MEM0_API_KEY"] = "m0-..."

import os os.environ["MEM0_API_KEY"] = "m0-..."

使用 from_client（针对 Mem0 平台 API）

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from llama_index.memory.mem0 import Mem0Memory

context = {"user_id": "test_users_1"}
memory_from_client = Mem0Memory.from_client(
    context=context,
    api_key="m0-...",
    search_msg_limit=4,  # Default is 5
)

from llama_index.memory.mem0 import Mem0Memory context = {"user_id": "test_users_1"} memory_from_client = Mem0Memory.from_client( context=context, api_key="m0-...", search_msg_limit=4, # 默认值为 5 )

Mem0 上下文用于在 Mem0 中识别用户、代理或对话。在 Mem0Memory 构造函数中，至少需要传入其中一个字段。

search_msg_limit 是可选的，默认值为 5。它是用于从 Mem0 中检索记忆的聊天历史消息数量。更多的消息数量将导致用于检索的上下文更多，但也会增加检索时间，并可能导致一些不想要的结果。

使用 from_config（针对 Mem0 开源版）

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os.environ["OPENAI_API_KEY"] = "<your-api-key>"
config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "test_9",
            "host": "localhost",
            "port": 6333,
            "embedding_model_dims": 1536,  # Change this according to your local model's dimensions
        },
    },
    "llm": {
        "provider": "openai",
        "config": {
            "model": "gpt-4o",
            "temperature": 0.2,
            "max_tokens": 1500,
        },
    },
    "embedder": {
        "provider": "openai",
        "config": {"model": "text-embedding-3-small"},
    },
    "version": "v1.1",
}
memory_from_config = Mem0Memory.from_config(
    context=context,
    config=config,
    search_msg_limit=4,  # Default is 5
)

os.environ["OPENAI_API_KEY"] = "" config = { "vector_store": { "provider": "qdrant", "config": { "collection_name": "test_9", "host": "localhost", "port": 6333, "embedding_model_dims": 1536, # 根据您的本地模型维度更改此处 }, }, "llm": { "provider": "openai", "config": { "model": "gpt-4o", "temperature": 0.2, "max_tokens": 1500, }, }, "embedder": { "provider": "openai", "config": {"model": "text-embedding-3-small"}, }, "version": "v1.1", } memory_from_config = Mem0Memory.from_config( context=context, config=config, search_msg_limit=4, # 默认值为 5 )

初始化 LLM¶

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from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o", api_key="sk-...")

from llama_index.llms.openai import OpenAI llm = OpenAI(model="gpt-4o", api_key="sk-...")

用于函数调用代理的 Mem0¶

使用 Mem0 作为 FunctionCallingAgents 的记忆。

初始化工具¶

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def call_fn(name: str):
    """Call the provided name.
    Args:
        name: str (Name of the person)
    """
    print(f"Calling... {name}")


def email_fn(name: str):
    """Email the provided name.
    Args:
        name: str (Name of the person)
    """
    print(f"Emailing... {name}")

def call_fn(name: str): """调用提供的名字。Args: name: str（人名）""" print(f"Calling... {name}") def email_fn(name: str): """给提供的名字发送电子邮件。Args: name: str（人名）""" print(f"Emailing... {name}")

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from llama_index.core.agent.workflow import FunctionAgent

agent = FunctionAgent(
    tools=[email_fn, call_fn],
    llm=llm,
)

from llama_index.core.agent.workflow import FunctionAgent agent = FunctionAgent( tools=[email_fn, call_fn], llm=llm, )

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response = await agent.run("Hi, My name is Mayank.", memory=memory_from_client)
print(str(response))

response = await agent.run("Hi, My name is Mayank.", memory=memory_from_client) print(str(response))

/Users/loganmarkewich/Library/Caches/pypoetry/virtualenvs/llama-index-caVs7DDe-py3.10/lib/python3.10/site-packages/mem0/client/main.py:33: DeprecationWarning: output_format='v1.0' is deprecated therefore setting it to 'v1.1' by default.Check out the docs for more information: https://docs.mem0.ai/platform/quickstart#4-1-create-memories
  return func(*args, **kwargs)

Hello Mayank! How can I assist you today?

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response = await agent.run(
    "My preferred way of communication would be Email.",
    memory=memory_from_client,
)
print(str(response))

response = await agent.run( "My preferred way of communication would be Email.", memory=memory_from_client, ) print(str(response))

Got it, Mayank! Your preferred way of communication is Email. If there's anything specific you need, feel free to let me know!

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response = await agent.run(
    "Send me an update of your product.", memory=memory_from_client
)
print(str(response))

response = await agent.run( "Send me an update of your product.", memory=memory_from_client ) print(str(response))

Emailing... Mayank
Emailing... Mayank
Calling... Mayank
Emailing... Mayank

I've sent you an update on our product via email. If you have any other questions or need further assistance, feel free to ask!

用于 ReAct 代理的 Mem0¶

使用 Mem0 作为 ReActAgent 的记忆。

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from llama_index.core.agent.workflow import ReActAgent

agent = ReActAgent(
    tools=[call_fn, email_fn],
    llm=llm,
)

from llama_index.core.agent.workflow import ReActAgent agent = ReActAgent( tools=[call_fn, email_fn], llm=llm, )

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response = await agent.run("Hi, My name is Mayank.", memory=memory_from_client)
print(str(response))

response = await agent.run("Hi, My name is Mayank.", memory=memory_from_client) print(str(response))

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response = await agent.run(
    "My preferred way of communication would be Email.",
    memory=memory_from_client,
)
print(str(response))

response = await agent.run( "My preferred way of communication would be Email.", memory=memory_from_client, ) print(str(response))

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response = await agent.run(
    "Send me an update of your product.", memory=memory_from_client
)
print(str(response))

response = await agent.run( "Send me an update of your product.", memory=memory_from_client ) print(str(response))

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response = await agent.run(
    "First call me and then communicate me requirements.",
    memory=memory_from_client,
)
print(str(response))

response = await agent.run( "First call me and then communicate me requirements.", memory=memory_from_client, ) print(str(response))