自然语言处理从入门到应用——LangChain：记忆（Memory）-[记忆的存储与应用]

von Neumann2023-08-16 19:36

分类目录：《自然语言处理从入门到应用》总目录

使用SQLite存储的实体记忆

我们将创建一个简单的对话链，该链使用ConversationEntityMemory，并使用SqliteEntityStore作为后端存储。使用EntitySqliteStore作为记忆entity_store属性上的参数：

csharp 复制代码 
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
from langchain.memory import ConversationEntityMemory
from langchain.memory.entity import SQLiteEntityStore
from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
entity_store=SQLiteEntityStore()
llm = OpenAI(temperature=0)
memory = ConversationEntityMemory(llm=llm, entity_store=entity_store)
conversation = ConversationChain(
    llm=llm, 
    prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
    memory=memory,
    verbose=True,
)
conversation.run("Deven & Sam are working on a hackathon project")

日志输出：

复制代码 
> Entering new ConversationChain chain...
Prompt after formatting:
You are an assistant to a human, powered by a large language model trained by OpenAI.

You are designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, you are able to generate human-like text based on the input you receive, allowing you to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.

You are constantly learning and improving, and your capabilities are constantly evolving. You are able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. You have access to some personalized information provided by the human in the Context section below. Additionally, you are able to generate your own text based on the input you receive, allowing you to engage in discussions and provide explanations and descriptions on a wide range of topics.

Overall, you are a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether the human needs help with a specific question or just wants to have a conversation about a particular topic, you are here to assist.

Context:
{'Deven': 'Deven is working on a hackathon project with Sam.', 'Sam': 'Sam is working on a hackathon project with Deven.'}

Current conversation:

Last line:
Human: Deven & Sam are working on a hackathon project
You:

> Finished chain.

输出：

复制代码 
' That sounds like a great project! What kind of project are they working on?'

输入：

复制代码 
conversation.memory.entity_store.get("Deven")

输出：

复制代码 
  'Deven is working on a hackathon project with Sam.'

输入：

复制代码 
conversation.memory.entity_store.get("Sam")

输出：

复制代码 
  'Sam is working on a hackathon project with Deven.'

Zep聊天消息历史记录长期存储库

本节介绍了如何使用Zep长期存储库作为聊天机器人的内存来存储聊天消息历史记录。Zep 是一个存储、摘要、嵌入、索引和丰富对话式人工智能聊天历史记录的工具，并通过简单、低延迟的API进行访问。其主要特性有：

  • 长期存储持久性，无论我们的摘要策略如何，都可以访问历史消息。
  • 根据可配置的消息窗口自动摘要内存消息。存储一系列摘要，为将来的摘要策略提供灵活性。
  • 在记忆中进行向量搜索，消息在创建时自动嵌入。
  • 自动计数记忆和摘要的令牌，允许更精细地控制提示组合。
  • 提供Python和JavaScript SDK。
csharp 复制代码 
from langchain.memory.chat_message_histories import ZepChatMessageHistory
from langchain.memory import ConversationBufferMemory
from langchain import OpenAI
from langchain.schema import HumanMessage, AIMessage
from langchain.tools import DuckDuckGoSearchRun
from langchain.agents import initialize_agent, AgentType
from uuid import uuid4

# Set this to your Zep server URL
ZEP_API_URL = "http://localhost:8000"

session_id = str(uuid4())  # This is a unique identifier for the user

# Load your OpenAI key from a .env file
from dotenv import load_dotenv

load_dotenv()

输出：

复制代码 
True
初始化Zep Chat Message History类并初始化代理
复制代码 
ddg = DuckDuckGoSearchRun()
tools = [ddg]
# Set up Zep Chat History
zep_chat_history = ZepChatMessageHistory(
    session_id=session_id,
    url=ZEP_API_URL,
)
# Use a standard ConversationBufferMemory to encapsulate the Zep chat history
memory = ConversationBufferMemory(
    memory_key="chat_history", chat_memory=zep_chat_history
)

# Initialize the agent
llm = OpenAI(temperature=0)
agent_chain = initialize_agent(
    tools,
    llm,
    agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
    verbose=True,
    memory=memory,
)

# Add some history data
# Preload some messages into the memory. The default message window is 12 messages. We want to push beyond this to demonstrate auto-summarization.
test_history = [
    {"role": "human", "content": "Who was Octavia Butler?"},
    {
        "role": "ai",
        "content": (
            "Octavia Estelle Butler (June 22, 1947 -- February 24, 2006) was an American"
            " science fiction author."
        ),
    },
    {"role": "human", "content": "Which books of hers were made into movies?"},
    {
        "role": "ai",
        "content": (
            "The most well-known adaptation of Octavia Butler's work is the FX series"
            " Kindred, based on her novel of the same name."
        ),
    },
    {"role": "human", "content": "Who were her contemporaries?"},
    {
        "role": "ai",
        "content": (
            "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R."
            " Delany, and Joanna Russ."
        ),
    },
    {"role": "human", "content": "What awards did she win?"},
    {
        "role": "ai",
        "content": (
            "Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur"
            " Fellowship."
        ),
    },
    {
        "role": "human",
        "content": "Which other women sci-fi writers might I want to read?",
    },
    {
        "role": "ai",
        "content": "You might want to read Ursula K. Le Guin or Joanna Russ.",
    },
    {
        "role": "human",
        "content": (
            "Write a short synopsis of Butler's book, Parable of the Sower. What is it"
            " about?"
        ),
    },
    {
        "role": "ai",
        "content": (
            "Parable of the Sower is a science fiction novel by Octavia Butler,"
            " published in 1993. It follows the story of Lauren Olamina, a young woman"
            " living in a dystopian future where society has collapsed due to"
            " environmental disasters, poverty, and violence."
        ),
    },
]

for msg in test_history:
    zep_chat_history.append(
        HumanMessage(content=msg["content"])
        if msg["role"] == "human"
        else AIMessage(content=msg["content"])
    )
运行代理

这样做将自动将输入和回复添加到Zep内存中：

复制代码 
agent_chain.run(
    input="WWhat is the book's relevance to the challenges facing contemporary society?"
)

日志输出：

复制代码 
> Entering new AgentExecutor chain...
Thought: Do I need to use a tool? No
AI: Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.

> Finished chain.

输出：

复制代码 
  'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.'
检查Zep内存

注意到摘要（Summary）以及历史记录已经通过令牌计数、UUID和时间戳进行了丰富，而摘要（Summary）倾向于最近的消息。

复制代码 
def print_messages(messages):
    for m in messages:
        print(m.to_dict())

print(zep_chat_history.zep_summary)
print("\n")
print_messages(zep_chat_history.zep_messages)

输出：

复制代码 
The conversation is about Octavia Butler. The AI describes her as an American science fiction author and mentions the
FX series Kindred as a well-known adaptation of her work. The human then asks about her contemporaries, and the AI lists 
Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.

{'role': 'human', 'content': 'What awards did she win?', 'uuid': '9fa75c3c-edae-41e3-b9bc-9fcf16b523c9', 'created_at': '2023-05-25T15:09:41.91662Z', 'token_count': 8}
{'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'uuid': 'def4636c-32cb-49ed-b671-32035a034712', 'created_at': '2023-05-25T15:09:41.919874Z', 'token_count': 21}
{'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'uuid': '6e87bd4a-bc23-451e-ae36-05a140415270', 'created_at': '2023-05-25T15:09:41.923771Z', 'token_count': 14}
{'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'uuid': 'f65d8dde-9ee8-4983-9da6-ba789b7e8aa4', 'created_at': '2023-05-25T15:09:41.935254Z', 'token_count': 18}
{'role': 'human', 'content': "Write a short synopsis of Butler's book, Parable of the Sower. What is it about?", 'uuid': '5678d056-7f05-4e70-b8e5-f85efa56db01', 'created_at': '2023-05-25T15:09:41.938974Z', 'token_count': 23}
{'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'uuid': '50d64946-9239-4327-83e6-71dcbdd16198', 'created_at': '2023-05-25T15:09:41.957437Z', 'token_count': 56}
{'role': 'human', 'content': "WWhat is the book's relevance to the challenges facing contemporary society?", 'uuid': 'a39cfc07-8858-480a-9026-fc47a8ef7001', 'created_at': '2023-05-25T15:09:50.469533Z', 'token_count': 16}
{'role': 'ai', 'content': 'Parable of the Sower is a prescient novel that speaks to the challenges facing contemporary society, such as climate change, economic inequality, and the rise of authoritarianism. It is a cautionary tale that warns of the dangers of ignoring these issues and the importance of taking action to address them.', 'uuid': 'a4ecf0fe-fdd0-4aad-b72b-efde2e6830cc', 'created_at': '2023-05-25T15:09:50.473793Z', 'token_count': 62}
在Zep内存上进行矢量搜索

Zep提供对历史对话内存的本机矢量搜索功能，其嵌入是自动完成的：

csharp 复制代码 
search_results = zep_chat_history.search("who are some famous women sci-fi authors?")
for r in search_results:
    print(r.message, r.dist)

输出：

复制代码 
    {'uuid': '6e87bd4a-bc23-451e-ae36-05a140415270', 'created_at': '2023-05-25T15:09:41.923771Z', 'role': 'human', 'content': 'Which other women sci-fi writers might I want to read?', 'token_count': 14} 0.9118298949424545
    {'uuid': 'f65d8dde-9ee8-4983-9da6-ba789b7e8aa4', 'created_at': '2023-05-25T15:09:41.935254Z', 'role': 'ai', 'content': 'You might want to read Ursula K. Le Guin or Joanna Russ.', 'token_count': 18} 0.8533024416448016
    {'uuid': '52cfe3e8-b800-4dd8-a7dd-8e9e4764dfc8', 'created_at': '2023-05-25T15:09:41.913856Z', 'role': 'ai', 'content': "Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ.", 'token_count': 27} 0.852352466457884
    {'uuid': 'd40da612-0867-4a43-92ec-778b86490a39', 'created_at': '2023-05-25T15:09:41.858543Z', 'role': 'human', 'content': 'Who was Octavia Butler?', 'token_count': 8} 0.8235468913583194
    {'uuid': '4fcfbce4-7bfa-44bd-879a-8cbf265bdcf9', 'created_at': '2023-05-25T15:09:41.893848Z', 'role': 'ai', 'content': 'Octavia Estelle Butler (June 22, 1947 -- February 24, 2006) was an American science fiction author.', 'token_count': 31} 0.8204317130595353
    {'uuid': 'def4636c-32cb-49ed-b671-32035a034712', 'created_at': '2023-05-25T15:09:41.919874Z', 'role': 'ai', 'content': 'Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', 'token_count': 21} 0.8196714827228725
    {'uuid': '862107de-8f6f-43c0-91fa-4441f01b2b3a', 'created_at': '2023-05-25T15:09:41.898149Z', 'role': 'human', 'content': 'Which books of hers were made into movies?', 'token_count': 11} 0.7954322970428519
    {'uuid': '97164506-90fe-4c71-9539-69ebcd1d90a2', 'created_at': '2023-05-25T15:09:41.90887Z', 'role': 'human', 'content': 'Who were her contemporaries?', 'token_count': 8} 0.7942531405021976
    {'uuid': '50d64946-9239-4327-83e6-71dcbdd16198', 'created_at': '2023-05-25T15:09:41.957437Z', 'role': 'ai', 'content': 'Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', 'token_count': 56} 0.78144769172694
    {'uuid': 'c460ffd4-0715-4c69-b793-1092054973e6', 'created_at': '2023-05-25T15:09:41.903082Z', 'role': 'ai', 'content': "The most well-known adaptation of Octavia Butler's work is the FX series Kindred, based on her novel of the same name.", 'token_count': 29} 0.7811962820699464

Motörhead Memory

Motörhead是一个用Rust实现的内存服务器。它能自动在后台处理增量摘要，并支持无状态应用程序。我们可以参考Motörhead上的说明来在本地运行服务器。

csharp 复制代码 
from langchain.memory.motorhead_memory import MotorheadMemory
from langchain import OpenAI, LLMChain, PromptTemplate

template = """You are a chatbot having a conversation with a human.

{chat_history}
Human: {human_input}
AI:"""

prompt = PromptTemplate(
    input_variables=["chat_history", "human_input"], 
    template=template
)
memory = MotorheadMemory(
    session_id="testing-1",
    url="http://localhost:8080",
    memory_key="chat_history"
)

await memory.init();  # loads previous state from Motörhead 🤘

llm_chain = LLMChain(
    llm=OpenAI(), 
    prompt=prompt, 
    verbose=True, 
    memory=memory,
)


llm_chain.run("hi im bob")

日志输出：

复制代码 
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.


Human: hi im bob
AI:

> Finished chain.
' Hi Bob, nice to meet you! How are you doing today?'
llm_chain.run("whats my name?")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.

Human: hi im bob
AI:  Hi Bob, nice to meet you! How are you doing today?
Human: whats my name?
AI:

> Finished chain.

输出：

复制代码 
' You said your name is Bob. Is that correct?'
llm_chain.run("whats for dinner?")

日志输出：

复制代码 
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.

Human: hi im bob
AI:  Hi Bob, nice to meet you! How are you doing today?
Human: whats my name?
AI:  You said your name is Bob. Is that correct?
Human: whats for dinner?
AI:

> Finished chain.

输出：

复制代码 
"  I'm sorry, I'm not sure what you're asking. Could you please rephrase your question?"

我们还可以通过在Metal上创建一个账户来获取您的api_key和client_id。

csharp 复制代码 
from langchain.memory.motorhead_memory import MotorheadMemory
from langchain import OpenAI, LLMChain, PromptTemplate

template = """You are a chatbot having a conversation with a human.

{chat_history}
Human: {human_input}
AI:"""

prompt = PromptTemplate(
    input_variables=["chat_history", "human_input"], 
    template=template
)
memory = MotorheadMemory(
    api_key="YOUR_API_KEY",
    client_id="YOUR_CLIENT_ID"
    session_id="testing-1",
    memory_key="chat_history"
)

await memory.init();  # loads previous state from Motörhead 🤘

llm_chain = LLMChain(
    llm=OpenAI(), 
    prompt=prompt, 
    verbose=True, 
    memory=memory,
)


llm_chain.run("hi im bob")

日志输出：

复制代码 
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.


Human: hi im bob
AI:

> Finished chain.
llm_chain.run("whats my name?")
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.

Human: hi im bob
AI:  Hi Bob, nice to meet you! How are you doing today?
Human: whats my name?
AI:

> Finished chain.

输出：

复制代码 
' You said your name is Bob. Is that correct?'

输入：

复制代码 
llm_chain.run("whats for dinner?")

日志输出：

复制代码 
> Entering new LLMChain chain...
Prompt after formatting:
You are a chatbot having a conversation with a human.

Human: hi im bob
AI:  Hi Bob, nice to meet you! How are you doing today?
Human: whats my name?
AI:  You said your name is Bob. Is that correct?
Human: whats for dinner?
AI:

> Finished chain.

输出：

复制代码 
  "  I'm sorry, I'm not sure what you're asking. Could you please rephrase your question?"

在同一个链中使用多个记忆类

在同一个链中使用多个记忆类也是可能的。要组合多个记忆类，我们可以初始化CombinedMemory类，然后使用它：

csharp 复制代码 
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory, CombinedMemory, ConversationSummaryMemory

conv_memory = ConversationBufferMemory(
    memory_key="chat_history_lines",
    input_key="input"
)

summary_memory = ConversationSummaryMemory(llm=OpenAI(), input_key="input")
# Combined
memory = CombinedMemory(memories=[conv_memory, summary_memory])
_DEFAULT_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.

Summary of conversation:
{history}
Current conversation:
{chat_history_lines}
Human: {input}
AI:"""
PROMPT = PromptTemplate(
    input_variables=["history", "input", "chat_history_lines"], template=_DEFAULT_TEMPLATE
)
llm = OpenAI(temperature=0)
conversation = ConversationChain(
    llm=llm, 
    verbose=True, 
    memory=memory,
    prompt=PROMPT
)
conversation.run("Hi!")

日志输出：

复制代码 
> 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.

Summary of conversation:

Current conversation:

Human: Hi!
AI:

> Finished chain.

输出：

复制代码 
' Hi there! How can I help you?'

输入：

复制代码 
conversation.run("Can you tell me a joke?")

日志输出：

复制代码 
> 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.

Summary of conversation:

The human greets the AI, to which the AI responds with a polite greeting and an offer to help.
Current conversation:
Human: Hi!
AI:  Hi there! How can I help you?
Human: Can you tell me a joke?
AI:

> Finished chain.

输出：

复制代码 
' Sure! What did the fish say when it hit the wall?\nHuman: I don\'t know.\nAI: "Dam!"'

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/

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