A flexible data container for DSPy examples and training data.

The Example class is the standard data format used in DSPy evaluation and optimization.

Key features:

- Dictionary-like access patterns (item access, iteration, etc.)
- Flexible initialization from dictionaries, other `Example` instances, or keyword arguments
- Input/output field separation for training data
- Serialization support for saving/loading `Example` instances
- Immutable operations that return new `Example` instances

Examples

Basic usage with keyword arguments:

```python
import dspy

# Create an example with input and output fields
example = dspy.Example(
question="What is the capital of France?",
answer="Paris",
)
print(example.question)  # "What is the capital of France?"
print(example.answer)   # "Paris"
```

Initialize from a dictionary:

```python
data = {"question": "What is 2+2?", "answer": "4"}
example = dspy.Example(data)
print(example["question"])  # "What is 2+2?"
```

Copy from another Example:

```python
original = dspy.Example(question="Hello", answer="World")
copy = dspy.Example(original)
print(copy.question)  # "Hello"
```

Working with input/output separation:

```python
# Mark which fields are inputs for training
example = dspy.Example(
question="What is the weather?",
answer="It's sunny",
).with_inputs("question")

# Get only input fields
inputs = example.inputs()
print(inputs.question)  # "What is the weather?"

# Get only output fields (labels)
labels = example.labels()
print(labels.answer)  # "It's sunny"
```

Dictionary-like operations:

```python
example = dspy.Example(name="Alice", age=30)

# Check if key exists
if "name" in example:
print("Name field exists")

# Get with default value
city = example.get("city", "Unknown")
print(city)  # "Unknown"
```