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Exemplar Selection (in context learning) API #1055

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Why are these changes needed?

Produced for in-context learning user cases.

For example:

prompt_fn = oai.ExemplarSelector.get_few_shot_template(train_data, method="random", method_params={"k": 3}, template_params={"key_order": key_order})

messages_few_shot = [
    {
        "role": "user",
        "content": prompt_fn,
    },
]

response = oai.ChatCompletion.create(
    context = context,
    messages= messages_few_shot,
    config_list=[
        {
            "model": "gpt-3.5-turbo",
            "api_key": "your key here",
            "api_type": "open_ai",
            "api_base": "https://api.openai.com/v1",
            "api_version": None,
        },
    ],
)

Related issue number

Automate example selection in LLM few-shot learning microsoft/autogen#448

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@sonichi sonichi left a comment

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Could you add an example of user's code, in test or documentation?

key + ": " + str(context[key]) for key in key_order[:-1]
]
)
few_shot_prompt += "\n" + key_order[-1] + ": " + "\n"
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maybe use format string here?

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Are there any benefits to use format string?

}

@classmethod
def get_few_shot_template(cls, train_data, method=None, few_shot_template=None, method_params=None, template_params=None):
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@LittleLittleCloud LittleLittleCloud May 26, 2023

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Instead of passing selection method into exemplar_selector, maybe it would be easier to implement exemplar_selector with different methods?

BTW I'm not too familiar with pythonic-way of abstract class so I'm just make up pattern here.

abstract class ExemplarSelector:
   def construct_template(context) -> str;
      pass

# random_exemplar_selector
class RandomExemplarSelector(ExemplarSelector):
    def __init__(self, train_data, k):
        # initialize

    def construct_template(context):
        # select `k` examples from train_data and render template. 

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I add the abstract class to exemplar_selector.py


def test_case_existing_method_default_template(self):
# Most cases should use the default template and existing methods
prompt_fn = oai.ExemplarSelector.get_few_shot_template(self.exemplars, method="random",
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Should ExemplarSelector be under oai? It doesn't look right. What about autogen/icl?

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moved to autogen/icl

def test_case_existing_method_default_template(self):
# Most cases should use the default template and existing methods
prompt_fn = oai.ExemplarSelector.get_few_shot_template(self.exemplars, method="random",
method_params={"k": 3}, template_params={"key_order": self.key_order})
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In general, the field to predict in the context is not necessarily the last key. Also, the number of such fields is not necessarily 1. Make sure the prompt_fn does not depend on such assumptions.

@@ -1,6 +1,6 @@
import unittest
import datasets
from flaml import oai
from flaml import icl
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Suggested change
from flaml import icl
from flaml.autogen.icl import ExemplarSelector

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