LangMem 简介¶

这是 LangMem 的早期预览，LangMem 是由 LangChain 构建的长期记忆服务，旨在帮助您轻松构建具有个性化用户体验的 LLM。我们将逐步介绍基本功能，作为对该服务的介绍，包括

1. 创建内存类型
2. 将消息发布到服务以触发内存形成
3. 回忆内存以在您的机器人中使用。

通过将此功能融入您的聊天机器人，LangMem 将异步地帮助其了解用户的偏好和兴趣，从而提高其响应和推荐的质量。

0. 环境设置¶

我们为您创建了一个演示“快速入门”实例，供您在此笔记本中试用。

虽然 LangMem 仍处于私人 Alpha 阶段，但您需要从 LangChain 团队成员处获得对专用 LangMem 实例的访问权限，才能在此演示之外进行更个人的使用。请在 Slack 或 support@langchain.dev 上联系我们，以获取您的连接信息。

然后，您需要安装 SDK（我们在下面的示例聊天中也将使用 openai）。

在 [ ]

%pip install -U --quiet langmem openai

%pip install -U --quiet langmem openai

在 [ ]

import getpass
import os

# Update to use your instance's URL

os.environ["LANGMEM_API_URL"] = getpass.getpass("LANGMEM_API_URL")
# Update to your API Key
os.environ["LANGMEM_API_KEY"] = getpass.getpass("API_KEY")
# Used for the example chat bot later in this tutorial
os.environ["OPENAI_API_KEY"] = getpass.getpass("OPENAI_API_KEY:")

import getpass import os # 更新为使用您的实例的 URL os.environ["LANGMEM_API_URL"] = getpass.getpass("LANGMEM_API_URL") # 更新为您的 API 密钥 os.environ["LANGMEM_API_KEY"] = getpass.getpass("API_KEY") # 用于本教程后面示例聊天机器人 os.environ["OPENAI_API_KEY"] = getpass.getpass("OPENAI_API_KEY:")

API_KEY··········
OPENAI_API_KEY:··········

配置好环境后，就可以创建您的客户端了。我们将连接到 OpenAI 和 Langmem。

在 [ ]

import openai
from langmem import AsyncClient, Client

oai_client = openai.AsyncClient()
langmem_client = AsyncClient()

import openai from langmem import AsyncClient, Client oai_client = openai.AsyncClient() langmem_client = AsyncClient()

1. 创建自定义内存类型¶

内存类型允许您对应用程序域进行建模，以便您的机器人能够以适合您的用例的格式保留信息。

LangMem 支持用户级和线程级内存。

LangMem 支持 3 种主要的用户内存类型

1. 结构化的“用户状态”内存，用于推断和管理预先指定的用户信息
2. 结构化的“用户追加状态”内存，用于推断与您的应用程序上下文相关的且可以语义查询的信息
3. 非结构化用户语义记忆

非结构化语义记忆在每个部署中默认启用。我们将在下面看到更多内容。

启用结构化内存功能就像将模式发布到 LangMem 一样简单。然后，LangMem 会在每次向服务发送新消息时自动管理这些自定义配置文件。

用户状态¶

这是一个自定义用户信息。通过提供 JSON 模式或 pydantic 模型来实例化。

在 [ ]

import json
from typing import List

from pydantic import BaseModel, Field


class Person(BaseModel):
    name: str = Field(default=None, description="The name of the family member.")
    relation: str = Field(
        default=None, description="The relation of the family member to the user."
    )


class UserProfile(BaseModel):
    preferred_name: str = Field(default=None, description="The user's name.")

    summary: str = Field(
        default="",
        description="A quick summary of how the user would describe themselves.",
    )
    interests: List[str] = Field(
        default_factory=list,
        description="Short (two to three word) descriptions of areas of particular interest for the user. This can be a concept, activity, or idea. Favor broad interests over specific ones.",
    )
    relationships: List[Person] = Field(
        default_factory=Person,
        description="A list of friends, family members, colleagues, and other relationships.",
    )
    other_info: List[str] = Field(
        default_factory=list,
        description="",
    )


user_profile_memory = await langmem_client.create_memory_function(
    UserProfile, target_type="user_state"
)

import json from typing import List from pydantic import BaseModel, Field class Person(BaseModel): name: str = Field(default=None, description="家庭成员的姓名。") relation: str = Field( default=None, description="家庭成员与用户的关系。") class UserProfile(BaseModel): preferred_name: str = Field(default=None, description="用户的姓名。") summary: str = Field( default="", description="用户如何简单地描述自己的摘要。", ) interests: List[str] = Field( default_factory=list, description="用户特别感兴趣的领域简短（两到三个词）描述。这可以是概念、活动或想法。 优先考虑广泛的兴趣，而不是特定的兴趣。", ) relationships: List[Person] = Field( default_factory=Person, description="朋友、家人、同事和其他关系的列表。", ) other_info: List[str] = Field( default_factory=list, description="", ) user_profile_memory = await langmem_client.create_memory_function( UserProfile, target_type="user_state" )

用户追加状态¶

顾名思义，user_append_state 是一个追加状态（这意味着配置文件永远不会被覆盖），它允许您定义模式来表示单个内存，您可以稍后以语义方式查询这些内存。

在 [ ]

class CoreBelief(BaseModel):
    belief: str = Field(
        default="",
        description="The belief the user has about the world, themselves, or anything else.",
    )
    why: str = Field(description="Why the user believes this.")
    context: str = Field(
        description="The raw context from the conversation that leads you to conclude that the user believes this."
    )


belief_function = await langmem_client.create_memory_function(
    CoreBelief, target_type="user_append_state"
)


class FormativeEvent(BaseModel):
    event: str = Field(
        default="",
        description="The event that occurred. Must be important enough to be formative for the user.",
    )
    impact: str = Field(default="", description="How this event influenced the user.")


event_function = await langmem_client.create_memory_function(
    FormativeEvent, target_type="user_append_state"
)

class CoreBelief(BaseModel): belief: str = Field( default="", description="用户对世界、自身或其他任何事物的看法。", ) why: str = Field(description="用户为什么相信这一点。") context: str = Field( description="从对话中得出用户相信这一点的原始上下文。") belief_function = await langmem_client.create_memory_function( CoreBelief, target_type="user_append_state" ) class FormativeEvent(BaseModel): event: str = Field( default="", description="发生的事件。 必须足够重要，才能对用户产生影响。", ) impact: str = Field(default="", description="此事件如何影响用户。") event_function = await langmem_client.create_memory_function( FormativeEvent, target_type="user_append_state" )

用户语义记忆¶

还有一个基于三元组的user_semantic_memory，默认启用。如果您不打算使用它，可以将其关闭。

在 [ ]

functions = langmem_client.list_memory_functions(target_type="user")
semantic_memory = None
async for func in functions:
    if func["type"] == "user_semantic_memory":
        semantic_memory = func


# Uncomment to disable the unstructured memory
# await langmem_client.update_memory_function(semantic_memory["id"], status="disabled")

functions = langmem_client.list_memory_functions(target_type="user") semantic_memory = None async for func in functions: if func["type"] == "user_semantic_memory": semantic_memory = func # 解释注释以禁用非结构化内存 # await langmem_client.update_memory_function(semantic_memory["id"], status="disabled")

线程摘要¶

LangMem 还支持线程级内存。我们将在下面创建它们。

在 [ ]

class ConversationSummary(BaseModel):
    title: str = Field(description="Distinct for the conversation.")
    summary: str = Field(description="High level summary of the interactions.")
    topic: List[str] = Field(
        description="Tags for topics discussed in this conversation."
    )


thread_summary_function = await langmem_client.create_memory_function(
    ConversationSummary, target_type="thread_summary"
)

class ConversationSummary(BaseModel): title: str = Field(description="对话的唯一标识。") summary: str = Field(description="交互的高级摘要。") topic: List[str] = Field( description="此对话中讨论的主题标签。") thread_summary_function = await langmem_client.create_memory_function( ConversationSummary, target_type="thread_summary" )

2. 开始对话¶

只要您的聊天机器人将消息发布到服务，就会形成内存。

只要在消息元数据中提供用户 ID，LangMem 就会自动创建一个新用户条目并开始跟踪该用户的内存。

在 [ ]

import uuid

johnny_user_id = uuid.uuid4()
jimmy_user_id = uuid.uuid4()
jimmy_username = f"jimmy-{uuid.uuid4().hex[:4]}"
johnny_username = f"johnny-{uuid.uuid4().hex[:4]}"

import uuid johnny_user_id = uuid.uuid4() jimmy_user_id = uuid.uuid4() jimmy_username = f"jimmy-{uuid.uuid4().hex[:4]}" johnny_username = f"johnny-{uuid.uuid4().hex[:4]}"

以下是 1 个或多个用户与 AI 之间的示例对话¶

在 [ ]

# Unique for a given converstaion
thread_id = uuid.uuid4()


async def completion(messages: list):
    stripped_messages = [
        {k: v for k, v in m.items() if k != "metadata"} for m in messages
    ]
    return await oai_client.chat.completions.create(
        model="gpt-3.5-turbo", messages=stripped_messages
    )


messages = [
    {"role": "system", "content": "You are a helpful AI assistant"},
    {
        "role": "user",
        # Names are optional but should be consistent with a given user id, if provided
        "name": jimmy_username,
        "content": "Hey johnny have i ever told you about my older bro steve?",
        "metadata": {
            "user_id": str(jimmy_user_id),
        },
    },
    {
        "content": "no, you didn't, but I think he was friends with my younger sister sueann",
        "role": "user",
        "name": johnny_username,
        "metadata": {
            "user_id": str(johnny_user_id),
        },
    },
    {
        "content": "yeah me and him used to play stickball down in the park before school started. I think it was in 1980",
        "role": "user",
        "name": jimmy_username,
        "metadata": {
            "user_id": str(jimmy_user_id),
        },
    },
    {
        "content": "That was totally 1979! I remember because i was stuck at home all summer.",
        "role": "user",
        "name": "Jeanne",
        # If the user ID isn't provided, we treat this as a guest message and won't assign memories to the user
    },
    {
        "content": "That was so long ago. I have gotten old and gained 200 pounds since then. I can't even remember who was president. @ai, who was the president in 1980?",
        "role": "user",
        "name": johnny_username,
        "metadata": {
            "user_id": str(johnny_user_id),
        },
    },
    {
        "content": "The president of the United States in 1980 was Jimmy Carter.",
        "role": "assistant",
    },
    {
        "content": "Wow ya i forgot that. Stickleball really impacted my life. It's how i first met Jeanne! wonder how my life would have turned out if it hadn't happened that way.",
        "role": "user",
        "name": jimmy_username,
        "metadata": {
            "user_id": str(jimmy_user_id),
        },
    },
    {
        "content": "Yeah wow. That was a big year! @ai could you remind me what else was going on that year?",
        "role": "user",
        "name": johnny_username,
        "metadata": {
            "user_id": str(johnny_user_id),
        },
    },
]

result = await completion(messages)

messages.append(result.choices[0].message)
print(result.choices[0].message.content)

# 对于给定对话而言是唯一的 thread_id = uuid.uuid4() async def completion(messages: list): stripped_messages = [ {k: v for k, v in m.items() if k != "metadata"} for m in messages ] return await oai_client.chat.completions.create( model="gpt-3.5-turbo", messages=stripped_messages ) messages = [ {"role": "system", "content": "您是一个乐于助人的 AI 助理"}, { "role": "user", # 姓名是可选的，但应与提供的用户 ID 保持一致 "name": jimmy_username, "content": "嘿，约翰尼，我有没有告诉你我哥哥史蒂夫的事？", "metadata": { "user_id": str(jimmy_user_id), }, }, { "content": "没有，你没有，但我认为他和我妹妹苏安是朋友", "role": "user", "name": johnny_username, "metadata": { "user_id": str(johnny_user_id), }, }, { "content": "是的，我和他以前在学校开始上课之前会在公园里打棒球。 我想那是 1980 年", "role": "user", "name": jimmy_username, "metadata": { "user_id": str(jimmy_user_id), }, }, { "content": "那是 1979 年！我记得因为我整个夏天都困在家里。", "role": "user", "name": "Jeanne", # 如果未提供用户 ID，我们将视为访客消息，不会将内存分配给用户 }, { "content": "那是很久以前了。 我变老了，还胖了 200 磅。 我甚至不记得谁是总统了。@ai，1980 年的总统是谁？", "role": "user", "name": johnny_username, "metadata": { "user_id": str(johnny_user_id), }, }, { "content": "1980 年的美国总统是吉米·卡特。", "role": "assistant", }, { "content": "哇，我忘了。 打棒球确实影响了我的生活。 这就是我第一次认识 Jeanne 的方式！想知道如果没发生那样的事，我的生活会变成什么样。", "role": "user", "name": jimmy_username, "metadata": { "user_id": str(jimmy_user_id), }, }, { "content": "是的，太棒了。 那一年很重要！@ai，你能提醒我那一年还发生了什么事吗？", "role": "user", "name": johnny_username, "metadata": { "user_id": str(johnny_user_id), }, }, ] result = await completion(messages) messages.append(result.choices[0].message) print(result.choices[0].message.content)

In 1980, some other significant events included the eruption of Mount St. Helens in Washington state, the United States boycotting the Summer Olympics in Moscow, the launch of CNN (Cable News Network), and the assassination of former Beatle John Lennon. It was definitely a year filled with memorable events.

现在我们有了消息，我们可以将它们与 LangMem 共享。¶

在 [ ]

await langmem_client.add_messages(thread_id=thread_id, messages=messages)

await langmem_client.add_messages(thread_id=thread_id, messages=messages)

在 [ ]

# LangMem will automatically process memories after some delay (~60 seconds), but we can eagerly process the memories as well
await langmem_client.trigger_all_for_thread(thread_id=thread_id)
# You could also trigger for a single user if you'd like
# await langmem_client.trigger_all_for_user(user_id=jimmy_user_id)

# LangMem 会在一段时间后（约 60 秒）自动处理内存，但我们也可以急切地处理内存 await langmem_client.trigger_all_for_thread(thread_id=thread_id) # 您也可以为单个用户触发 # await langmem_client.trigger_all_for_user(user_id=jimmy_user_id)

获取消息¶

您可以通过该线程的 GET 端点获取 LangMem 线程中的所有消息。这样，LangMem 可以充当通用的聊天机器人后端。

在 [ ]

messages = langmem_client.list_messages(thread_id=thread_id)
async for message in messages:
    print(message)

messages = langmem_client.list_messages(thread_id=thread_id) async for message in messages: print(message)

{'id': 'c478e641-dacb-4e3a-8cac-7d9574ed8056', 'content': 'You are a helpful AI assistant', 'timestamp': '2024-03-28T17:10:25.937750', 'user': {'user_id': None, 'user_name': None, 'role': 'system'}}
{'id': 'b0776988-8113-4e2d-b62d-f827eee98867', 'content': 'Hey johnny have i ever told you about my older bro steve?', 'timestamp': '2024-03-28T17:10:25.937784', 'user': {'user_id': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6', 'user_name': 'jimmy-56c5', 'role': 'user'}}
{'id': '1cd7a340-cebc-495f-8a0d-fb5965c57d40', 'content': "no, you didn't, but I think he was friends with my younger sister sueann", 'timestamp': '2024-03-28T17:10:25.937813', 'user': {'user_id': 'ceab7620-9bd2-45b6-b2e8-88848c0c3965', 'user_name': 'johnny-0fd1', 'role': 'user'}}
{'id': '03c4975c-2656-457e-9851-dd8ee1496216', 'content': 'yeah me and him used to play stickball down in the park before school started. I think it was in 1980', 'timestamp': '2024-03-28T17:10:25.937834', 'user': {'user_id': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6', 'user_name': 'jimmy-56c5', 'role': 'user'}}
{'id': '56a87add-1ef3-44f5-9d5c-b3a4f5796063', 'content': 'That was totally 1979! I remember because i was stuck at home all summer.', 'timestamp': '2024-03-28T17:10:25.937855', 'user': {'user_id': None, 'user_name': None, 'role': 'user'}}
{'id': '339ad2ad-10be-4f19-91bd-4595044d1cdf', 'content': "That was so long ago. I have gotten old and gained 200 pounds since then. I can't even remember who was president. @ai, who was the president in 1980?", 'timestamp': '2024-03-28T17:10:25.941504', 'user': {'user_id': 'ceab7620-9bd2-45b6-b2e8-88848c0c3965', 'user_name': 'johnny-0fd1', 'role': 'user'}}
{'id': '8dddbcaf-33b8-426e-a580-801d3f37823b', 'content': 'The president of the United States in 1980 was Jimmy Carter.', 'timestamp': '2024-03-28T17:10:25.941557', 'user': {'user_id': None, 'user_name': None, 'role': 'ai'}}
{'id': '8d85bf30-10bb-46b0-9d75-c4afa9506398', 'content': "Wow ya i forgot that. Stickleball really impacted my life. It's how i first met Jeanne! wonder how my life would have turned out if it hadn't happened that way.", 'timestamp': '2024-03-28T17:10:25.941578', 'user': {'user_id': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6', 'user_name': 'jimmy-56c5', 'role': 'user'}}
{'id': '3fa420f7-8a5c-46a2-a1fb-d9a4c12bdbbb', 'content': 'Yeah wow. That was a big year! @ai could you remind me what else was going on that year?', 'timestamp': '2024-03-28T17:10:25.941596', 'user': {'user_id': 'ceab7620-9bd2-45b6-b2e8-88848c0c3965', 'user_name': 'johnny-0fd1', 'role': 'user'}}
{'id': 'd9854557-f966-465a-9f6b-f8a5d047c56b', 'content': 'In 1980, some other significant events included the eruption of Mount St. Helens in Washington state, the United States boycotting the Summer Olympics in Moscow, the launch of CNN (Cable News Network), and the assassination of former Beatle John Lennon. It was definitely a year filled with memorable events.', 'timestamp': '2024-03-28T17:10:25.941615', 'user': {'user_id': None, 'user_name': None, 'role': 'ai'}}

3. 查询内存¶

您还可以查询用户内存，一旦它更新。这可能需要几分钟 - 请耐心等待 😊

要查询非结构化语义记忆，您可以提供查询文本和要返回的内存数量。

在 [ ]

# Wait a few moments for the memories to process. If this is empty, you'll likely have to wait a bit longer
mems = None
while not mems:
    mem_response = await langmem_client.query_user_memory(
        user_id=jimmy_user_id, text="stickleball", k=3
    )
    mems = mem_response["memories"]
mems

# 等待几分钟，让内存处理。 如果为空，您可能需要等待更长时间 mems = None while not mems: mem_response = await langmem_client.query_user_memory( user_id=jimmy_user_id, text="棒球", k=3 ) mems = mem_response["memories"] mems

Out[ ]

[{'id': 'b6a742c8-736d-40d4-8109-b704bc472c67',
  'created_at': '2024-03-28T16:52:39.364396Z',
  'last_accessed': '2024-03-28T17:10:43.774991Z',
  'text': 'jimmy-56c5 met Jeanne through stickball impacted life significantly',
  'content': {'subject': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6',
   'predicate': 'met Jeanne through stickball',
   'object': 'impacted life significantly'},
  'scores': {'recency': 0.9998505211700639,
   'importance': 1.0,
   'relevance': 0.6725992391294546}},
 {'id': 'dac7b7fb-e63a-4763-af4e-3669f25ad76f',
  'created_at': '2024-03-28T16:38:03.900300Z',
  'last_accessed': '2024-03-28T17:10:43.774991Z',
  'text': 'jimmy-56c5 played stickball in the park before school 1979',
  'content': {'subject': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6',
   'predicate': 'played stickball in the park before school',
   'object': '1979'},
  'scores': {'recency': 0.9998109278725811,
   'importance': 0.5714285714285714,
   'relevance': 0.9951020385149173}},
 {'id': 'e4400a4d-5df7-4741-abe4-30f85c752a5f',
  'created_at': '2024-03-28T16:38:03.900300Z',
  'last_accessed': '2024-03-28T17:10:43.774991Z',
  'text': 'jimmy-56c5 has older brother Steve',
  'content': {'subject': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6',
   'predicate': 'has older brother',
   'object': 'Steve'},
  'scores': {'recency': 0.9997723259170942,
   'importance': 0.8571428571428571,
   'relevance': 0.1416941375483881}}]

在 [ ]

# In a similar way, you can include
# different `user_append_state` memory results
# in the ranked response
mems = await langmem_client.query_user_memory(
    user_id=jimmy_user_id,
    text="stickleball",
    k=3,
    memory_function_ids=[belief_function["id"], event_function["id"]],
)
mems

# 以类似的方式，您可以包含 # 不同的 `user_append_state` 内存结果 # 在排名响应中 mems = await langmem_client.query_user_memory( user_id=jimmy_user_id, text="棒球", k=3, memory_function_ids=[belief_function["id"], event_function["id"]], ) mems

Out[ ]

{'user_id': 'c1fd3d58-0e67-43ec-b688-1c59f41f79c6',
 'memories': [{'id': '16ad398b-2c3e-461e-9688-ca6cebfa4704',
   'created_at': '2024-03-28T17:10:28.376135Z',
   'last_accessed': '2024-03-28T17:10:44.920281Z',
   'text': '',
   'content': {'event': 'Playing stickball in the park before school in 1980',
    'impact': 'This activity was significant for the user as it was how they first met Jeanne, suggesting it had a profound impact on their life.'},
   'scores': {'recency': 1.0,
    'importance': 0.5,
    'relevance': 0.8382047058016829}},
  {'id': 'a87a5e04-f628-45f0-a6cc-41e35061f013',
   'created_at': '2024-03-28T17:09:46.501123Z',
   'last_accessed': '2024-03-28T17:10:25.875205Z',
   'text': '',
   'content': {'event': 'played stickball in the park with older brother Steve before school started',
    'impact': 'This memory is likely a cherished one, reflecting a close relationship with his brother and a fondness for simpler times.'},
   'scores': {'recency': 0.0, 'importance': 0.5, 'relevance': 1.0}},
  {'id': '24dfeaeb-e2f4-4591-9bc9-f00a63465379',
   'created_at': '2024-03-28T17:09:46.488723Z',
   'last_accessed': '2024-03-28T17:10:44.920281Z',
   'text': '',
   'content': {'belief': 'I have an older brother named Steve',
    'why': 'The user explicitly mentioned having an older brother named Steve.',
    'context': 'Hey johnny have i ever told you about my older bro steve?'},
   'scores': {'recency': 0.9999455514820141,
    'importance': 0.5,
    'relevance': 0.0}}],
 'state': [],
 'distilled': None}

在 [ ]

# For user state (profile) memories, you can make a faster and simple get request
user_state = None
while not user_state:
    user_state = await langmem_client.get_user_memory(
        user_id=jimmy_user_id, memory_function_id=user_profile_memory["id"]
    )
user_state

# 对于用户状态（配置文件）内存，您可以发出更快的简单 GET 请求 user_state = None while not user_state: user_state = await langmem_client.get_user_memory( user_id=jimmy_user_id, memory_function_id=user_profile_memory["id"] ) user_state

Out[ ]

{'other_info': ['played stickball in the park before school around 1980',
  "Stickleball really impacted my life. It's how i first met Jeanne"],
 'relationships': [{'name': 'Steve', 'relation': 'older brother'}],
 'preferred_name': 'Johnny'}

在 [ ]

# You can list all the thread summary memories for a given thread as well!
await langmem_client.list_thread_memory(thread_id)

# 您还可以列出给定线程的所有线程摘要内存！ await langmem_client.list_thread_memory(thread_id)

Out[ ]

[{'memory_function_id': 'ddcf937c-b201-4cc1-9779-987e2af683ef',
  'memory_function_name': 'ConversationSummary',
  'memory_function_type': 'thread_summary',
  'target': '/threads/33c69599-c86c-4f8a-a117-a88b153ee9de',
  'output': {'title': 'Reminiscing About the Past and Historical Events of 1980',
   'topic': ['Personal Memories', 'Historical Events', '1980'],
   'summary': "Two users reminisce about the past, specifically playing stickball in the park around 1979 or 1980. One user mentions gaining weight over the years and asks the AI who was the president in 1980, to which the AI responds with Jimmy Carter. The conversation touches on how stickball impacted one user's life, leading to meeting someone named Jeanne. The other user asks for a reminder of significant events from 1980, and the AI lists the eruption of Mount St. Helens, the U.S. boycotting the Summer Olympics, the launch of CNN, and the assassination of John Lennon."}}]

4. 在后续对话中使用¶

如您所见，我们已经从之前的对话中提取了一些有用的信息。我们假设您会在以后的对话中获取这些信息，以便为您的机器人提供有关用户的其他有用上下文信息。

在 [ ]

async def completion_with_memory(messages: list, user_id: uuid.UUID):
    memories = await langmem_client.query_user_memory(
        user_id=user_id, text=messages[-1]["content"], k=3
    )
    facts = "\n".join([mem["text"] for mem in memories["memories"]])
    system_prompt = {
        "role": "system",
        "content": f"You are a helpful assistant. You know the following facts about the user with which you are conversing.\n\n{facts}",
    }
    return await completion([system_prompt] + messages)


messages = [
    {
        "role": "user",
        "name": jimmy_username,
        "content": "Hi there! I'm curious what you remember. What's my brother's name?",
        "metadata": {"user_id": jimmy_user_id},
    }
]
res = await completion_with_memory(messages, user_id=jimmy_user_id)
print(res.choices[0].message.content)

async def completion_with_memory(messages: list, user_id: uuid.UUID): memories = await langmem_client.query_user_memory( user_id=user_id, text=messages[-1]["content"], k=3 ) facts = "\n".join([mem["text"] for mem in memories["memories"]]) system_prompt = { "role": "system", "content": f"你是一个乐于助人的助手。你了解以下关于你正在交谈的用户的信息。\n\n{facts}", } return await completion([system_prompt] + messages) messages = [ { "role": "user", "name": jimmy_username, "content": "嗨！我很好奇你记得什么。我兄弟叫什么名字？", "metadata": {"user_id": jimmy_user_id}, } ] res = await completion_with_memory(messages, user_id=jimmy_user_id) print(res.choices[0].message.content)

Your older brother's name is Steve.

结论¶

在本教程中，你为两个用户保存了记忆，以跟踪他们的兴趣和其他属性。你只需将聊天信息发送到 LangMem 服务即可实现。然后，你自动触发更新以存储三种形式的长期记忆

1. 用户状态“配置文件”，遵循你的自定义模式
2. 用户追加状态，存储遵循你的自定义模式的原子记忆，并且可以语义查询。
3. 通用知识作为语义三元组

你还可以跟踪线程范围内的摘要记忆，以帮助你组织对话线程。

最后，让我们清理工作！此演示是一个共享的工作区 :)

在 [ ]

## Cleanup

functions = langmem_client.list_memory_functions()

async for func in functions:
    if func["type"] == "user_semantic_memory":
        continue
    await langmem_client.delete_memory_function(func["id"])

## 清理 functions = langmem_client.list_memory_functions() async for func in functions: if func["type"] == "user_semantic_memory": continue await langmem_client.delete_memory_function(func["id"])