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Conversation Summary Buffer | Документация для разработчиков

Conversation Summary Buffer

Обновлено 4 марта 2024

ConversationSummaryBufferMemory combines the two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. It uses token length rather than number of interactions to determine when to flush interactions.

Let's first walk through how to use the utilities

Using memory with LLM

from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import OpenAI

llm = OpenAI()
memory = ConversationSummaryBufferMemory(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({})
    {'history': 'System: \nThe human says "hi", and the AI responds with "whats up".\nHuman: not much you\nAI: not much'}

We can also get the history as a list of messages (this is useful if you are using this with a chat model).

memory = ConversationSummaryBufferMemory(
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"})

We can also utilize the predict_new_summary method directly.

messages = memory.chat_memory.messages
previous_summary = ""
memory.predict_new_summary(messages, previous_summary)
    '\nThe human and AI state that they are not doing much.'

Using in a chain

Let's walk through an example, again setting verbose=True so we can see the prompt.

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=ConversationSummaryBufferMemory(llm=OpenAI(), max_token_limit=40),
verbose=True,
)
conversation_with_summary.predict(input="Hi, what's up?")
    

> 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.
    " Hi there! I'm doing great. I'm learning about the latest advances in artificial intelligence. What about you?"
conversation_with_summary.predict(input="Just working on writing some documentation!")
    

> 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. I'm spending some time learning about the latest developments in AI technology. How about you?
Human: Just working on writing some documentation!
AI:

> Finished chain.
    ' That sounds like a great use of your time. Do you have experience with writing documentation?'
# We can see here that there is a summary of the conversation and then some previous interactions
conversation_with_summary.predict(input="For LangChain! Have you heard of it?")
    

> 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:
System:
The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology.
Human: Just working on writing some documentation!
AI: That sounds like a great use of your time. Do you have experience with writing documentation?
Human: For LangChain! Have you heard of it?
AI:

> Finished chain.
    " No, I haven't heard of LangChain. Can you tell me more about it?"
# We can see here that the summary and the buffer are updated
conversation_with_summary.predict(
input="Haha nope, although a lot of people confuse it for that"
)
    

> 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:
System:
The human asked the AI what it was up to and the AI responded that it was learning about the latest developments in AI technology. The human then mentioned they were writing documentation, to which the AI responded that it sounded like a great use of their time and asked if they had experience with writing documentation.
Human: For LangChain! Have you heard of it?
AI: No, I haven't heard of LangChain. Can you tell me more about it?
Human: Haha nope, although a lot of people confuse it for that
AI:

> Finished chain.
    ' Oh, okay. What is LangChain?'
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