Merge pull request #532 from stanfordnlp/thomasahle-readme-patch

Update README.md

README.md

@@ -276,9 +276,32 @@ compiled_rag = teleprompter.compile(RAG(), trainset=my_rag_trainset)
 If we now use `compiled_rag`, it will invoke our LM with rich prompts with few-shot demonstrations of chain-of-thought retrieval-augmented question answering on our data.
 
 
+## 5) Pydantic Types
 
+Sometimes you need more than just string inputs/outputs.
+Assume, for example, you need to find 
 
-## 5) FAQ: Is DSPy right for me?
+```python
+from pydantic import BaseModel, Field
+
+class TravelInformation(BaseModel):
+    origin: str = Field(pattern=r"^[A-Z]{3}$")
+    destination: str = Field(pattern=r"^[A-Z]{3}$")
+    date: datetime.date
+    confidence: float = Field(gt=0, lt=1)
+
+class TravelSignature(Signature):
+    """ Extract all travel information in the given email """
+    email: str = InputField()
+    flight_information: list[TravelInformation] = OutputField()
+
+predictor = dspy.TypedPredictor(TravelSignature)
+predictor(email='...')
+```
+
+Which will output a list of `TravelInformation` objects.
+
+## 6) FAQ: Is DSPy right for me?
 
 The **DSPy** philosophy and abstraction differ significantly from other libraries and frameworks, so it's usually straightforward to decide when **DSPy** is (or isn't) the right framework for your usecase.