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ConversationSummaryBufferMemory
 Contents 
Using in a chain
ConversationSummaryBufferMemory#
ConversationSummaryBufferMemory combines the last 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. Unlike the previous implementation though, 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
from langchain.memory import ConversationSummaryBufferMemory
from langchain.llms 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(
rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/types/summary_buffer.html