Examples in DSPy
Working in DSPy involves training sets, development sets, and test sets. This is like traditional ML, but you usually need far fewer labels (or zero labels) to use DSPy effectively.
The core data type for data in DSPy is Example. You will use Examples to represent items in your training set and test set.
DSPy Examples are similar to Python dicts but have a few useful utilities. Your DSPy modules will return values of the type Prediction, which is a special sub-class of Example.
Creating an Example
When you use DSPy, you will do a lot of evaluation and optimization runs. Your individual datapoints will be of type Example:
qa_pair = dspy.Example(question="This is a question?", answer="This is an answer.")
print(qa_pair)
print(qa_pair.question)
print(qa_pair.answer)
Output:
Example({'question': 'This is a question?', 'answer': 'This is an answer.'}) (input_keys=None)
This is a question?
This is an answer.
Examples can have any field keys and any value types, though usually values are strings.
object = Example(field1=value1, field2=value2, field3=value3, ...)
Specifying Input Keys
In traditional ML, there are separated "inputs" and "labels".
In DSPy, the Example objects have a with_inputs() method, which can mark specific fields as inputs. (The rest are just metadata or labels.)
# Single Input.
print(qa_pair.with_inputs("question"))
# Multiple Inputs; be careful about marking your labels as inputs unless you mean it.
print(qa_pair.with_inputs("question", "answer"))
This flexibility allows for customized tailoring of the Example object for different DSPy scenarios.
When you call with_inputs(), you get a new copy of the example. The original object is kept unchanged.
Element Access and Updation
Values can be accessed using the .(dot) operator. You can access the value of key name in defined object Example(name="John Doe", job="sleep") through object.name.
To access or exclude certain keys, use inputs() and labels() methods to return new Example objects containing only input or non-input keys, respectively.
article_summary = dspy.Example(article= "This is an article.", summary= "This is a summary.").with_inputs("article")
input_key_only = article_summary.inputs()
non_input_key_only = article_summary.labels()
print("Example object with Input fields only:", input_key_only)
print("Example object with Non-Input fields only:", non_input_key_only)
Output
Example object with Input fields only: Example({'article': 'This is an article.'}) (input_keys=None)
Example object with Non-Input fields only: Example({'summary': 'This is a summary.'}) (input_keys=None)
To exclude keys, use without():
article_summary = dspy.Example(context="This is an article.", question="This is a question?", answer="This is an answer.", rationale= "This is a rationale.").with_inputs("context", "question")
print("Example object without answer & rationale keys:", article_summary.without("answer", "rationale"))
Output
Example object without answer & rationale keys: Example({'context': 'This is an article.', 'question': 'This is a question?'}) (input_keys=None)
Updating values is simply assigning a new value using the . operator.
article_summary.context = "new context"
Iterating over Example
Iteration in the Example class also functions like a dictionary, supporting methods like keys(), values(), etc:
for k, v in article_summary.items():
print(f"{k} = {v}")
Output
context = This is an article.
question = This is a question?
answer = This is an answer.
rationale = This is a rationale.