DSPy example with chain of thought.

dspy_chain_of_thought_example.py

# install DSPy: pip install dspy
import dspy


# This sets up the language model for DSPy in this case we are using GPT-3.5-turbo
turbo = dspy.OpenAI(model='gpt-3.5-turbo')

# This sets the language model for DSPy. This must be set or you get an error that is not helpful:
# --> temperature = lm.kwargs['temperature'] if temperature is None else temperature
# --> AttributeError: 'NoneType' object has no attribute 'kwargs'
dspy.settings.configure(lm=turbo)

# This is not required but it helps to understand what is happening
my_example = {
    "question": "What was the first commercially successful video game released by Atari in 1972?",
    "answer": "Pong",
}

# This is the signature for the predictor. It is a simple question and answer model.
class BasicQA(dspy.Signature):
    """Answer questions with short factoid answers."""

    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

# Define the predictor. This time it is Chain of Thought.
generate_answer = dspy.ChainOfThought(BasicQA)

# Call the predictor on a particular input.
pred = generate_answer(question=my_example['question'])

# Print the whole answer to see the 'Thought'...profit :)
print(pred)