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April 28, 2024 04:41
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DSPy Chain of Thought - Query transformation
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| import dspy | |
| from dspy.teleprompt import BootstrapFewShot | |
| class CoT(dspy.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.prog = dspy.ChainOfThought("question -> answer") | |
| def forward(self, question): | |
| return self.prog(question=question) | |
| def run_cot(): | |
| # Set up the LM | |
| turbo = dspy.OpenAI(model='gpt-3.5-turbo', max_tokens=250) | |
| dspy.settings.configure(lm=turbo) | |
| trainset = [ | |
| dspy.Example({ | |
| "question": "How did revenue change between Q4 2023 and year before that?", | |
| "gold_reasoning": "Revenue is a financial metric hence metric is revenue. The question is asking about the change in revenue between Q4 2023 and the year before that hence the years are 2023 and 2022 and the quarters are 4 and 4. Hence the answer is {'years': [2023, 2022], 'quarters': [4, 4]}", | |
| "answer": "{'years': [2023, 2022], 'quarters': [4, 4]}" | |
| }), | |
| dspy.Example({ | |
| "question": "What was the gross profit in Q3 2023 and Q4 2023?", | |
| "gold_reasoning": "Gross profit is a financial metric hence metric is gross profit. The question is asking about the gross profit in Q3 2023 and Q4 2023 hence the year is 2023 and the quarters are 3 and 4. Hence the answer is {'years': [2023], 'quarters': [3, 4]}", | |
| "answer": "{'years': [2023], 'quarters': [3, 4]}" | |
| }) | |
| ] | |
| devset = [ | |
| dspy.Example({ | |
| "question": "How did revenue change between Q4 2023 and year before that?", | |
| "gold_reasoning": "Revenue is a financial metric hence metric is revenue. The question is asking about the change in revenue between Q4 2023 and the year before that hence the years are 2023 and 2022 and the quarters are 4 and 4. Hence the answer is {'years': [2023, 2022], 'quarters': [4, 4]}", | |
| "answer": "{'years': [2023, 2022], 'quarters': [4, 4]}" | |
| }), | |
| dspy.Example({ | |
| "question": "What was the gross profit in Q3 2023 and Q4 2023?", | |
| "gold_reasoning": "Gross profit is a financial metric hence metric is gross profit. The question is asking about the gross profit in Q3 2023 and Q4 2023 hence the year is 2023 and the quarters are 3 and 4. Hence the answer is {'years': [2023], 'quarters': [3, 4]}", | |
| "answer": "{'years': [2023], 'quarters': [3, 4]}" | |
| }) | |
| ] | |
| # Set up the optimizer: we want to "bootstrap" (i.e., self-generate) 2-shot examples of our CoT program. | |
| config = dict(max_bootstrapped_demos=2, max_labeled_demos=2) | |
| # Optimize! Use the `gsm8k_metric` here. In general, the metric is going to tell the optimizer how well it's doing. | |
| teleprompter = BootstrapFewShot(**config) | |
| cot = CoT() | |
| optimized_cot = teleprompter.compile( | |
| cot, trainset=trainset, valset=devset) | |
| answer = optimized_cot( | |
| question='How did revenue change between Q2 2019 and year after that?') | |
| answer = optimized_cot( | |
| question='What were the stock price changes between Q3 2018 and 7 quarters before that across 3 years?') | |
| print(answer) | |
| turbo.inspect_history(n=1) | |
| run_cot() |
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