内存记忆 ( Memory )

默认情况下，链式模型和代理模型都是无状态的，这意味着它们将每个传入的查询独立处理（就像底层的 LLMs 和聊天模型本身一样）。在某些应用程序中，比如聊天机器人，记住先前的交互是至关重要的。无论是短期还是长期，都要记住先前的交互。Memory 类正是做到了这一点。 LangChain 提供了两种形式的记忆组件。首先，LangChain 提供了用于管理和操作以前的聊天消息的辅助工具。这些工具被设计成模块化的，无论如何使用都很有用。其次，LangChain 提供了将这些工具轻松整合到链式模型中的方法。

入门

记忆涉及在用户与语言模型的交互过程中始终保留状态的概念。用户与语言模型的交互被捕获在 ChatMessages 的概念中，因此这归结为在一系列聊天消息中摄取、捕获、转换和提取知识。有许多不同的方法可以做到这一点，每种方法都作为自己的记忆类型存在。 通常情况下，对于每种类型的记忆，有两种理解和使用记忆的方式。一种是独立的函数，从一系列消息中提取信息，然后是您可以在链式模型中使用此类型的记忆的方式。 记忆可以返回多个信息片段（例如，最近的 N 条消息和所有先前消息的摘要）。返回的信息可以是字符串或消息列表。

我们将介绍最简单的存储形式：“缓冲”存储，它只涉及保留所有先前的消息的缓冲区。我们将展示如何在这里使用模块化实用函数，然后展示它如何在链中使用（返回字符串以及消息列表）。

聊天消息历史 (ChatMessageHistory)

大多数（如果不是全部）内存模块的核心实用类之一是 ChatMessageHistory 类。这是一个超轻量级的包装器，它公开了方便的方法来保存人类消息、AI 消息，然后获取它们全部。

如果您在链外管理内存，可能需要直接使用此类。

from langchain.memory import ChatMessageHistory

history = ChatMessageHistory()

history.add_user_message("hi!")

history.add_ai_message("whats up?")

history.messages

    [HumanMessage(content='hi!', additional_kwargs={}),
     AIMessage(content='whats up?', additional_kwargs={})]

ConversationBufferMemory

现在我们展示如何在链中使用这个简单的概念。我们首先展示 ConversationBufferMemory，它只是 ChatMessageHistory 的一个包装器，可以提取变量中的消息。

我们可以首先将其提取为字符串。

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")

memory.load_memory_variables({})

    {'history': 'Human: hi!\nAI: whats up?'}

我们还可以将历史记录作为消息列表获取

memory = ConversationBufferMemory(return_messages=True)
memory.chat_memory.add_user_message("hi!")
memory.chat_memory.add_ai_message("whats up?")

memory.load_memory_variables({})

    {'history': [HumanMessage(content='hi!', additional_kwargs={}),
      AIMessage(content='whats up?', additional_kwargs={})]}

Using in a chain

Finally, let's take a look at using this in a chain (setting verbose=True so we can see the prompt).

from langchain.llms import OpenAI
from langchain.chains import ConversationChain


llm = OpenAI(temperature=0)
conversation = ConversationChain(
    llm=llm,
    verbose=True,
    memory=ConversationBufferMemory()
)

conversation.predict(input="Hi there!")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:

    Human: Hi there!
    AI:

    > Finished chain.





    " Hi there! It's nice to meet you. How can I help you today?"

conversation.predict(input="I'm doing well! Just having a conversation with an AI.")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi there!
    AI:  Hi there! It's nice to meet you. How can I help you today?
    Human: I'm doing well! Just having a conversation with an AI.
    AI:

    > Finished chain.





    " That's great! It's always nice to have a conversation with someone new. What would you like to talk about?"

conversation.predict(input="Tell me about yourself.")

    > Entering new ConversationChain chain...
    Prompt after formatting:
    The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.

    Current conversation:
    Human: Hi there!
    AI:  Hi there! It's nice to meet you. How can I help you today?
    Human: I'm doing well! Just having a conversation with an AI.
    AI:  That's great! It's always nice to have a conversation with someone new. What would you like to talk about?
    Human: Tell me about yourself.
    AI:

    > Finished chain.





    " Sure! I'm an AI created to help people with their everyday tasks. I'm programmed to understand natural language and provide helpful information. I'm also constantly learning and updating my knowledge base so I can provide more accurate and helpful answers."

保存消息历史

您经常需要保存消息，然后加载它们以便再次使用。这可以通过先将消息转换为普通的 Python 字典，保存这些字典（如 json 或其他格式），然后加载它们来轻松完成。以下是一个示例。

import json

from langchain.memory import ChatMessageHistory
from langchain.schema import messages_from_dict, messages_to_dict

history = ChatMessageHistory()

history.add_user_message("hi!")

history.add_ai_message("whats up?")

dicts = messages_to_dict(history.messages)

dicts

    [{'type': 'human', 'data': {'content': 'hi!', 'additional_kwargs': {}}},
     {'type': 'ai', 'data': {'content': 'whats up?', 'additional_kwargs': {}}}]

new_messages = messages_from_dict(dicts)

new_messages

    [HumanMessage(content='hi!', additional_kwargs={}),
     AIMessage(content='whats up?', additional_kwargs={})]

这就是入门的全部内容！有许多不同类型的内存，请查看我们的示例以了解全部内容