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Add Long Term Memory and Feedback #80
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35db97e
fixed save and load
dillonalaird 8d3c60d
added long term memory
dillonalaird bf50f74
added dynamic re-planning
dillonalaird e7ef95d
add gpt-4o
dillonalaird fd55636
update tests
dillonalaird 13df90f
update tests
dillonalaird 3bfab15
fixed exit loop early
dillonalaird 6e09b1f
add some extra parsing for code snippets
dillonalaird 6bd0bb6
fix formatting
dillonalaird ebd4d96
fix typing error
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Can you describe what happens in this function ? Sim returns a df with embedding calculated on the given column. From what i see we build df from a working memory which contains desc and doc. Are the desc and doc are description and doc string of tools or are they something else ? because i was under the assumption working memory is the all the artifacts from a previous run.
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Oh yeah, for memory desc and doc are probably not the best terminology. In this case, desc is the subtask string and doc is the debug attempts at trying to build code for that subtask. For example, you might have:
desc: "load the file 'dog.png'"
doc: """
image = open('cat.png')
reflection: You opened the wrong image name, it should be 'dog.png'
image = open('dog.png'
"""
So this context could be saved as long term memory. Then the next time the agent encounters the question "load the file 'dog.png'" it could retrieve this context to help it.