> 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.
' You said your favorite food is pizza.'
# 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?")
> 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.
' Your name is Perry.'
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ConversationTokenBufferMemory
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How to add Memory to an LLMChain
 Contents
  
Initialize your VectorStore
Create your the VectorStoreRetrieverMemory
Using in a chain
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
rtdocs_stable/api.python.langchain.com/en/stable/modules/memory/types/vectorstore_retriever_memory.html