dspy.ChainOfThought
This page is outdated and may not be fully accurate in DSPy 2.5
Constructor
The constructor initializes the ChainOfThought class and sets up its attributes. It inherits from the Predict class and adds specific functionality for chain of thought processing.
Internally, the class initializes the activated attribute to indicate if chain of thought processing has been selected. It extends the signature to include additional reasoning steps and an updated rationale_type when chain of thought processing is activated.
class ChainOfThought(Predict):
def __init__(self, signature, rationale_type=None, activated=True, **config):
super().__init__(signature, **config)
self.activated = activated
self.signature = signature = ensure_signature(signature)
*_keys, last_key = signature.output_fields.keys()
prefix = "Reasoning: Let's think step by step in order to"
if isinstance(dspy.settings.lm, dspy.LM):
desc = "${reasoning}"
elif dspy.settings.experimental:
desc = "${produce the output fields}. We ..."
else:
# For dspy <2.5
desc = f"${{produce the {last_key}}}. We ..."
rationale_type = rationale_type or dspy.OutputField(prefix=prefix, desc=desc)
# Add "rationale" field to the output signature.
if isinstance(dspy.settings.lm, dspy.LM) or dspy.settings.experimental:
extended_signature = signature.prepend("reasoning", rationale_type, type_=str)
else:
# For dspy <2.5
extended_signature = signature.prepend("rationale", rationale_type, type_=str)
self._predict = dspy.Predict(extended_signature, **config)
self._predict.extended_signature = extended_signature
Parameters:
- signature (Any): Signature of predictive model.
- rationale_type (dspy.OutputField, optional): Rationale type for reasoning steps. Defaults to None.
- activated (bool, optional): Flag for activated chain of thought processing. Defaults to True.
- **config (dict): Additional configuration parameters for model.
Method
forward(self, **kwargs)
This method extends the parent Predict class' forward pass while updating the signature when chain of thought reasoning is activated or if the language model is a GPT3 model.
Parameters:
- **kwargs: Keyword arguments required for prediction.
Returns:
- The result of the forward method.
Examples
#Define a simple signature for basic question answering
class BasicQA(dspy.Signature):
"""Answer questions with short factoid answers."""
question = dspy.InputField()
answer = dspy.OutputField(desc="often between 1 and 5 words")
#Pass signature to ChainOfThought module
generate_answer = dspy.ChainOfThought(BasicQA)
# Call the predictor on a particular input.
question='What is the color of the sky?'
pred = generate_answer(question=question)
print(f"Question: {question}")
print(f"Predicted Answer: {pred.answer}")
The following example shows how to specify your custom rationale. Here answer corresponds to the last key to produce, it may be different in your case.