Add pydantic example

docs/griptape-framework/structures/rulesets.md

@@ -47,6 +47,25 @@ This is particularly useful when you need the LLM to return well-formed data, su
                              }
 ```
 
+Although Griptape leverages the `schema` library, you're free to use any JSON schema generation library to define your schema!
+
+For example, using `pydantic`:
+
+```python
+--8<-- "docs/griptape-framework/structures/src/json_schema_rule_pydantic.py"
+```
+
+```
+[09/11/24 09:45:58] INFO     PromptTask eae43f52829c4289a6cca9ee7950e075
+                             Input: What is the sentiment of this message?: 'I am so happy!'
+                    INFO     PromptTask eae43f52829c4289a6cca9ee7950e075
+                             Output: {
+                               "answer": "The sentiment of the message is positive.",
+                               "relevant_emojis": ["😊", "😄"]
+                             }
+answer='The sentiment of the message is positive.' relevant_emojis=['😊', '😄']
+```
+
 ## Structure
 
 ### Rulesets

docs/griptape-framework/structures/src/json_schema_rule.py

@@ -1,3 +1,5 @@
+import json
+
 import schema
 
 from griptape.rules.json_schema_rule import JsonSchemaRule
@@ -11,4 +13,6 @@
     ]
 )
 
-agent.run("What is the sentiment of this message?: 'I am so happy!'")
+output = agent.run("What is the sentiment of this message?: 'I am so happy!'").output
+
+print(json.dumps(json.loads(output.value), indent=2))

docs/griptape-framework/structures/src/json_schema_rule_pydantic.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+import pydantic
+
+from griptape.rules.json_schema_rule import JsonSchemaRule
+from griptape.structures import Agent
+
+
+class SentimentModel(pydantic.BaseModel):
+    answer: str
+    relevant_emojis: list[str]
+
+
+agent = Agent(rules=[JsonSchemaRule(SentimentModel.model_json_schema())])
+
+output = agent.run("What is the sentiment of this message?: 'I am so happy!'").output
+
+sentiment_analysis = SentimentModel.model_validate_json(output.value)
+
+print(sentiment_analysis.answer)
+print(sentiment_analysis.relevant_emojis)