长春网络建站北京网站建设制作开发

分类目录：《自然语言处理从入门到应用》总目录

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对话令牌缓冲存储器ConversationTokenBufferMemory

ConversationTokenBufferMemory在内存中保留了最近的一些对话交互，并使用标记长度来确定何时刷新交互，而不是交互数量。

from langchain.memory import ConversationTokenBufferMemory
from langchain.llms import OpenAI
llm = OpenAI()
memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})
memory.load_memory_variables({})

输出：

{‘history’: ‘Human: not much you\nAI: not much’}

我们还可以将历史记录作为消息列表获取，如果我们正在使用聊天模型，将非常有用：

memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10, return_messages=True)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})

在链式模型中的应用

让我们通过一个例子来演示如何在链式模型中使用它，同样设置verbose=True，以便我们可以看到提示信息。

from langchain.chains import ConversationChain
conversation_with_summary = ConversationChain(llm=llm, # We set a very low max_token_limit for the purposes of testing.memory=ConversationTokenBufferMemory(llm=OpenAI(), max_token_limit=60),verbose=True
)
conversation_with_summary.predict(input="Hi, what's up?")

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, what's up?
AI:> Finished chain.

输出：

" Hi there! I'm doing great, just enjoying the day. How about you?"

输入：

conversation_with_summary.predict(input="Just working on writing some documentation!")

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, what's up?
AI:  Hi there! I'm doing great, just enjoying the day. How about you?
Human: Just working on writing some documentation!
AI:> Finished chain.

输出：

    ' Sounds like a productive day! What kind of documentation are you writing?'

输入：

conversation_with_summary.predict(input="For LangChain! Have you heard of it?")

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, what's up?
AI:  Hi there! I'm doing great, just enjoying the day. How about you?
Human: Just working on writing some documentation!
AI:  Sounds like a productive day! What kind of documentation are you writing?
Human: For LangChain! Have you heard of it?
AI:> Finished chain.

输出：

    " Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?"

输入：

# 我们可以看到这里缓冲区被更新了
conversation_with_summary.predict(input="Haha nope, although a lot of people confuse it for that")

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: For LangChain! Have you heard of it?
AI:  Yes, I have heard of LangChain! It is a decentralized language-learning platform that connects native speakers and learners in real time. Is that the documentation you're writing about?
Human: Haha nope, although a lot of people confuse it for that
AI:> Finished chain.

输出：

" Oh, I see. Is there another language learning platform you're referring to?"

基于向量存储的记忆VectorStoreRetrieverMemory

VectorStoreRetrieverMemory将内存存储在VectorDB中，并在每次调用时查询最重要的前$K$个文档。与大多数其他Memory类不同，它不明确跟踪交互的顺序。在这种情况下，“文档”是先前的对话片段。这对于提及AI在对话中早些时候得知的相关信息非常有用。

from datetime import datetime
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.memory import VectorStoreRetrieverMemory
from langchain.chains import ConversationChain
from langchain.prompts import PromptTemplate

初始化VectorStore

根据我们选择的存储方式，此步骤可能会有所不同，我们可以查阅相关的VectorStore文档以获取更多详细信息。

import faissfrom langchain.docstore import InMemoryDocstore
from langchain.vectorstores import FAISSembedding_size = 1536 # Dimensions of the OpenAIEmbeddings
index = faiss.IndexFlatL2(embedding_size)
embedding_fn = OpenAIEmbeddings().embed_query
vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {})

创建VectorStoreRetrieverMemory

记忆对象是从VectorStoreRetriever实例化的。

# In actual usage, you would set `k` to be a higher value, but we use k=1 to show that the vector lookup still returns the semantically relevant information
retriever = vectorstore.as_retriever(search_kwargs=dict(k=1))
memory = VectorStoreRetrieverMemory(retriever=retriever)# When added to an agent, the memory object can save pertinent information from conversations or used tools
memory.save_context({"input": "My favorite food is pizza"}, {"output": "thats good to know"})
memory.save_context({"input": "My favorite sport is soccer"}, {"output": "..."})
memory.save_context({"input": "I don't the Celtics"}, {"output": "ok"}) # 
# Notice the first result returned is the memory pertaining to tax help, which the language model deems more semantically relevant
# to a 1099 than the other documents, despite them both containing numbers.
print(memory.load_memory_variables({"prompt": "what sport should i watch?"})["history"])

输出：

input: My favorite sport is soccer
output: ...

在对话链中使用

让我们通过一个示例来演示，在此示例中我们继续设置verbose=True以便查看提示。

llm = OpenAI(temperature=0) # Can be any valid LLM
_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.Relevant pieces of previous conversation:
{history}(You do not need to use these pieces of information if not relevant)Current conversation:
Human: {input}
AI:"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=_DEFAULT_TEMPLATE
)
conversation_with_summary = ConversationChain(llm=llm, prompt=PROMPT,# We set a very low max_token_limit for the purposes of testing.memory=memory,verbose=True
)
conversation_with_summary.predict(input="Hi, my name is Perry, what's up?")

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.Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know(You do not need to use these pieces of information if not relevant)Current conversation:
Human: Hi, my name is Perry, what's up?
AI:> Finished chain.

输出：

" Hi Perry, I'm doing well. How about you?"

输入：

# Here, the basketball related content is surfaced
conversation_with_summary.predict(input="what's my favorite sport?")

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.Relevant pieces of previous conversation:
input: My favorite sport is soccer
output: ...(You do not need to use these pieces of information if not relevant)Current conversation:
Human: what's my favorite sport?
AI:> Finished chain.

输出：

  ' You told me earlier that your favorite sport is soccer.'

输入：

# Even though the language model is stateless, since relavent memory is fetched, it can "reason" about the time.
# Timestamping memories and data is useful in general to let the agent determine temporal relevance
conversation_with_summary.predict(input="Whats my favorite food")

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.Relevant pieces of previous conversation:
input: My favorite food is pizza
output: thats good to know(You do not need to use these pieces of information if not relevant)Current conversation:
Human: Whats my favorite food
AI:> Finished chain.

输出：

  ' You said your favorite food is pizza.'

输入：

# The memories from the conversation are automatically stored,
# since this query best matches the introduction chat above,
# the agent is able to 'remember' the user's name.
conversation_with_summary.predict(input="What's my name?")

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.Relevant pieces of previous conversation:
input: Hi, my name is Perry, what's up?
response:  Hi Perry, I'm doing well. How about you?(You do not need to use these pieces of information if not relevant)Current conversation:
Human: What's my name?
AI:> Finished chain.

输出：

' Your name is Perry.'

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/