my name is Perry, 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.    Relevant pieces of previous conversation:    input: My favorite food is pizza    output: that's 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.    " Hi Perry, I'm doing well. How about you?"# Here, the basketball related content is surfacedconversation_with_summary.predict(input="what's my favorite sport?")    > 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.    ' You told me earlier that your favorite sport is soccer.'# Even though the language model is stateless, since relevant memory is fetched, it can "reason" about the time.# Timestamping memories and data is useful in general to let the agent determine temporal relevanceconversation_with_summary.predict(input="Whats my favorite food")    > 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.
VectorStoreRetrieverMemory stores memories in a vector store and queries the top-K most "salient" docs every time it is called.
VectorStoreRetrieverMemory stores memories in a vector store and queries the top-K most "salient" docs every time it is called. ->: my name is Perry, 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.    Relevant pieces of previous conversation:    input: My favorite food is pizza    output: that's 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.    " Hi Perry, I'm doing well. How about you?"# Here, the basketball related content is surfacedconversation_with_summary.predict(input="what's my favorite sport?")    > 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.    ' You told me earlier that your favorite sport is soccer.'# Even though the language model is stateless, since relevant memory is fetched, it can "reason" about the time.# Timestamping memories and data is useful in general to let the agent determine temporal relevanceconversation_with_summary.predict(input="Whats my favorite food")    > 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.