自然语言处理从入门到应用——LangChain：记忆（Memory）-[自定义对话记忆与自定义记忆类]-Toy模板网

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LangChain系列文章：

基础知识
快速入门
- 安装与环境配置
- 链（Chains）、代理（Agent:）和记忆（Memory）
- 快速开发聊天模型
模型（Models）
- 基础知识
- 大型语言模型（LLMs）
  - 基础知识
  - LLM的异步API、自定义LLM包装器、虚假LLM和人类输入LLM（Human Input LLM）
  - 缓存LLM的调用结果
  - 加载与保存LLM类、流式传输LLM与Chat Model响应和跟踪tokens使用情况
- 聊天模型（Chat Models）
  - 基础知识
  - 使用少量示例和响应流式传输
- 文本嵌入模型
  - Aleph Alpha、Amazon Bedrock、Azure OpenAI、Cohere等
  - Embaas、Fake Embeddings、Google Vertex AI PaLM等
提示（Prompts）
- 基础知识
- 提示模板
  - 基础知识
  - 连接到特征存储
  - 创建自定义提示模板和含有Few-Shot示例的提示模板
  - 部分填充的提示模板和提示合成
  - 序列化提示信息
- 示例选择器（Example Selectors）
- 输出解析器（Output Parsers）
记忆（Memory）
- 基础知识
- 记忆的类型
  - 会话缓存记忆、会话缓存窗口记忆和实体记忆
  - 对话知识图谱记忆、对话摘要记忆和会话摘要缓冲记忆
  - 对话令牌缓冲存储器和基于向量存储的记忆
- 将记忆添加到LangChain组件中
- 自定义对话记忆与自定义记忆类
- 聊天消息记录
- 记忆的存储与应用
索引（Indexes）
- 基础知识
- 文档加载器（Document Loaders）
- 文本分割器（Text Splitters）
- 向量存储器（Vectorstores）
- 检索器（Retrievers）
链（Chains）
- 基础知识
- 通用功能
  - 自定义Chain和Chain的异步API
  - LLMChain和RouterChain
  - SequentialChain和TransformationChain
  - 链的保存（序列化）与加载（反序列化）
- 链与索引
  - 文档分析和基于文档的聊天
  - 问答的基础知识
  - 图问答（Graph QA）和带来源的问答（Q&A with Sources）
  - 检索式问答
  - 文本摘要（Summarization）、HyDE和向量数据库的文本生成
代理（Agents）
- 基础知识
- 代理类型
- 自定义代理（Custom Agent）
- 自定义MRKL代理
- 带有ChatModel的LLM聊天自定义代理和自定义多操作代理（Custom MultiAction Agent）
- 工具
  - 基础知识
  - 自定义工具（Custom Tools）
  - 多输入工具和工具输入模式
  - 人工确认工具验证和Tools作为OpenAI函数
- 工具包（Toolkit）
- 代理执行器（Agent Executor）
  - 结合使用Agent和VectorStore
  - 使用Agents的异步API和创建ChatGPT克隆
  - 处理解析错误、访问中间步骤和限制最大迭代次数
  - 为代理程序设置超时时间和限制最大迭代次数和为代理程序和其工具添加共享内存
- 计划与执行
回调函数（Callbacks）

自定义对话记忆

本节介绍了几种自定义对话记忆的方法：

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

llm = OpenAI(temperature=0)

AI前缀

第一种方法是通过更改对话摘要中的AI前缀来实现。默认情况下，它设置为AI，但你可以将其设置为任何你想要的内容。需要注意的是，如果我们更改了这个前缀，我们还应该相应地更改链条中使用的提示来反映这个命名更改。让我们通过下面的示例来演示这个过程。

# Here it is by default set to "AI"
conversation = ConversationChain(
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")

日志输出：

> 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 ConversationChain chain.

输出：

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

输入：

conversation.predict(input="What's the weather?")

日志输出：

> 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: What's the weather?
AI:

> Finished ConversationChain chain.

输出：

' The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the next few days is sunny with temperatures in the mid-70s.'

输入：

# Now we can override it and set it to "AI Assistant"
from langchain.prompts.prompt import PromptTemplate

template = """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:
{history}
Human: {input}
AI Assistant:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=template
)
conversation = ConversationChain(
    prompt=PROMPT,
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory(ai_prefix="AI Assistant")
)
conversation.predict(input="Hi there!")

日志输出：

> 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 Assistant:

> Finished ConversationChain chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="What's the weather?")
> 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 Assistant:  Hi there! It's nice to meet you. How can I help you today?
Human: What's the weather?
AI Assistant:

> Finished ConversationChain chain.

输出：

The current weather is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is sunny with a high of 78 degrees and a low of 65 degrees.'

人类前缀

第二种方法是通过更改对话摘要中的人类前缀来实现。默认情况下，它设置为Human，但我们可以将其设置为任何我们想要的内容。需要注意的是，如果我们更改了这个前缀，我们还应该相应地更改链条中使用的提示来反映这个命名更改。让我们通过下面的示例来演示这个过程。

# Now we can override it and set it to "Friend"
from langchain.prompts.prompt import PromptTemplate

template = """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:
{history}
Friend: {input}
AI:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input"], template=template
)
conversation = ConversationChain(
    prompt=PROMPT,
    llm=llm, 
    verbose=True, 
    memory=ConversationBufferMemory(human_prefix="Friend")
)
conversation.predict(input="Hi there!")

日志输出：

> 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:

Friend: Hi there!
AI:

> Finished ConversationChain chain.
" Hi there! It's nice to meet you. How can I help you today?"
conversation.predict(input="What's the weather?")
> 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:

Friend: Hi there!
AI:  Hi there! It's nice to meet you. How can I help you today?
Friend: What's the weather?
AI:

> Finished ConversationChain chain.

输出：

  ' The weather right now is sunny and warm with a temperature of 75 degrees Fahrenheit. The forecast for the rest of the day is mostly sunny with a high of 82 degrees.'

创建自定义记忆类

尽管在LangChain中有几种预定义的记忆类型，但我们很可能希望添加自己的记忆类型，以使其适用于我们的应用程序。在本节中，我们将向ConversationChain添加一个自定义的记忆类型。为了添加自定义的记忆类，我们需要导入基本的记忆类并对其进行子类化。

from langchain import OpenAI, ConversationChain
from langchain.schema import BaseMemory
from pydantic import BaseModel
from typing import List, Dict, Any

在这个示例中，我们将编写一个自定义的记忆类，使用spacy提取实体并将有关它们的信息保存在一个简单的哈希表中。然后，在对话过程中，我们将查看输入文本，提取任何实体，并将关于它们的任何信息放入上下文中。需要注意的是，这种实现相当简单且脆弱，可能在生产环境中不太有用。它的目的是展示我们可以添加自定义的记忆实现。为此，我们需要首先安装spacy。

# !pip install spacy
# !python -m spacy download en_core_web_lg
import spacy
nlp = spacy.load('en_core_web_lg')
class SpacyEntityMemory(BaseMemory, BaseModel):
    """Memory class for storing information about entities."""

    # Define dictionary to store information about entities.
    entities: dict = {}
    # Define key to pass information about entities into prompt.
    memory_key: str = "entities"
        
    def clear(self):
        self.entities = {}

    @property
    def memory_variables(self) -> List[str]:
        """Define the variables we are providing to the prompt."""
        return [self.memory_key]

    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
        """Load the memory variables, in this case the entity key."""
        # Get the input text and run through spacy
        doc = nlp(inputs[list(inputs.keys())[0]])
        # Extract known information about entities, if they exist.
        entities = [self.entities[str(ent)] for ent in doc.ents if str(ent) in self.entities]
        # Return combined information about entities to put into context.
        return {self.memory_key: "\n".join(entities)}

    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
        """Save context from this conversation to buffer."""
        # Get the input text and run through spacy
        text = inputs[list(inputs.keys())[0]]
        doc = nlp(text)
        # For each entity that was mentioned, save this information to the dictionary.
        for ent in doc.ents:
            ent_str = str(ent)
            if ent_str in self.entities:
                self.entities[ent_str] += f"\n{text}"
            else:
                self.entities[ent_str] = text

我们现在定义一个提示，其中包含有关实体的信息以及用户的输入：

from langchain.prompts.prompt import PromptTemplate

template = """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. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:
{entities}

Conversation:
Human: {input}
AI:"""
prompt = PromptTemplate(
    input_variables=["entities", "input"], template=template
)

现在，我们把它们整合起来：

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

在第一个例子中，由于对Harrison没有先前的了解，"Relevant entity information"部分是空的：

conversation.predict(input="Harrison likes machine learning")

日志输出：

> 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. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:


Conversation:
Human: Harrison likes machine learning
AI:

> Finished ConversationChain chain.

输出：

" That's great to hear! Machine learning is a fascinating field of study. It involves using algorithms to analyze data and make predictions. Have you ever studied machine learning, Harrison?"

现在在第二个例子中，我们可以看到它提取了关于Harrison的信息。

conversation.predict(input="What do you think Harrison's favorite subject in college was?")

日志输出：

> 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. You are provided with information about entities the Human mentions, if relevant.

Relevant entity information:
Harrison likes machine learning

Conversation:
Human: What do you think Harrison's favorite subject in college was?
AI:

> Finished ConversationChain chain.

输出：

' From what I know about Harrison, I believe his favorite subject in college was machine learning. He has expressed a strong interest in the subject and has mentioned it often.'

这个实现方式相对简单且容易出错，可能在实际生产环境中没有太大的用途，但它展示了我们可以添加自定义的内存实现方式。

参考文献：
[1] LangChain官方网站：https://www.langchain.com/
[2] LangChain 🦜️🔗 中文网，跟着LangChain一起学LLM/GPT开发：https://www.langchain.com.cn/
[3] LangChain中文网 - LangChain 是一个用于开发由语言模型驱动的应用程序的框架：http://www.cnlangchain.com/文章来源地址https://www.toymoban.com/news/detail-648286.html

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