Lesson2 - Understanding the Purpose of 'save context' in LangChain's Memory Module

Short Course Q&A LangChain for LLM Application Development
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Hi everyone,

I’m currently going through the course on LangChain for LLM Application Development , specifically in the section about memory, and I’ve come across the save_context method in the ConversationSummaryBufferMemory class. However, I’m a bit confused about its exact purpose.

Here’s the code snippet from my notebook:

python

memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=100)

memory.save_context({"input": "Hello"}, {"output": "What's up"})
memory.save_context({"input": "Not much, just hanging"}, {"output": "Cool"})
memory.save_context({"input": "What is on the schedule today?"}, {"output": f"{schedule}"})

memory.load_memory_variables({})

From my understanding, save_context is used to store the input-output pairs from the conversation. But I’m not entirely sure what the main goal of saving this context is. How does it affect the behavior of the language model during a conversation? Does it help the AI generate more relevant responses based on past interactions?

I’d really appreciate it if someone could clarify the purpose of save_context and how it influences the conversation flow in LangChain.

Thanks in advance for your help!