Fixed the system prompt for JSON mode, enabling the use of Pydantic nested models (#249)

Co-authored-by: Jason Liu <jason@jxnl.co>
Co-authored-by: Jason Liu <jxnl@users.noreply.github.com>

instructor/patch.py

@@ -92,11 +92,13 @@ def handle_response_model(
         elif mode == Mode.JSON or mode == Mode.MD_JSON:
             if mode == Mode.JSON:
                 new_kwargs["response_format"] = {"type": "json_object"}
-                # check that the first message is a system message
-                # if it is not, add a system message to the beginning
                 message = f"""Make sure that your response to any message matches the json_schema below,
-                        do not deviate at all: \n{response_model.model_json_schema()['properties']}
-                        """
+                            do not deviate at all: \n{response_model.model_json_schema()['properties']}
+                            """
+                # Check for nested models
+                if '$defs' in response_model.model_json_schema():
+                    message += f"\nHere are some more definitions to adhere too:\n{response_model.model_json_schema()['$defs']}"
+                    
             else:
                 message = f"""
                     As a genius expert, your task is to understand the content and provide 
@@ -110,6 +112,8 @@ def handle_response_model(
                     },
                 )
                 new_kwargs["stop"] = "```"
+            # check that the first message is a system message
+            # if it is not, add a system message to the beginning
             if new_kwargs["messages"][0]["role"] != "system":
                 new_kwargs["messages"].insert(
                     0,

tests/openai/evals/test_nested_structures.py

@@ -0,0 +1,50 @@
+from typing import Iterable
+from openai import OpenAI
+from pydantic import BaseModel, Field
+from typing import List
+
+import pytest
+
+import instructor
+from instructor.function_calls import Mode
+
+
+class Item(BaseModel):
+    name: str
+    price: float
+
+
+class Order(BaseModel):
+    items: List[Item] = Field(..., default_factory=list)
+    customer: str
+
+
+@pytest.mark.parametrize("mode", [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS, Mode.MD_JSON])
+def test_nested(mode):
+    client = instructor.patch(OpenAI(), mode=mode)
+
+    content = """
+    Order Details:
+    Customer: Jason
+    Items:
+
+    Name: Apple, Price: 0.50
+    Name: Bread, Price: 2.00
+    Name: Milk, Price: 1.50
+    """
+
+    resp = client.chat.completions.create(
+        model="gpt-3.5-turbo-1106",
+        response_model=Order,
+        messages=[
+            {
+                "role": "user",
+                "content": content,
+            },
+        ],
+    )
+
+    assert len(resp.items) == 3
+    assert {x.name.lower() for x in resp.items} == {"apple", "bread", "milk"}
+    assert {x.price for x in resp.items} == {0.5, 2.0, 1.5}
+    assert resp.customer.lower() == "jason"