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License: MIT License
embeddings + raw features with options to turn off embeddings and define which features you want to embed
If we make all the base classes (ABC'
s) in base chain also inherit from pydantic's BaseModel
we will be able to catch any errors in the parameter passing types
Some logging can be done via the chain callback run manager feature
rest of logging (e.g. warning when response_validator` not set) might need to be logged outside of the chain callback but logging implementation needs to be revisited
So that it is clear that no more training is happening
essentially when there is no response_validator this means that there is no learning, could just be a function called remove_response_validator
or invalidate_response_validator
or something like that
examples:
chain.run(prefix = rl_chain.Embed(['action1', 'action2']))
chain.run(this_general_variable = "some other text")
{this_general_variable} to be used, for example, by user in prompt but ignored by VW in e.g. slateschain.run(selected = rl_chain.ToSelectFrom(rl_chain.Embed(['action1', 'action2']), based_on = rl_chain.BasedOn("Tom"), a_prompt_value = "Another Tom")))
and make some of the interfaces enforceable (e.g. ResponseValidator
)
there exists something in slates, move to base chain
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