diff --git a/openai/structure1.py b/openai/structure1.py
new file mode 100644
index 0000000..1aaf23f
--- /dev/null
+++ b/openai/structure1.py
@@ -0,0 +1,79 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from enum import Enum
+from typing import Union
+
+from pydantic import BaseModel
+
+import openai
+from openai import OpenAI
+
+
+class Table(str, Enum):
+    orders = "orders"
+    customers = "customers"
+    products = "products"
+
+
+class Column(str, Enum):
+    id = "id"
+    status = "status"
+    expected_delivery_date = "expected_delivery_date"
+    delivered_at = "delivered_at"
+    shipped_at = "shipped_at"
+    ordered_at = "ordered_at"
+    canceled_at = "canceled_at"
+
+
+class Operator(str, Enum):
+    eq = "="
+    gt = ">"
+    lt = "<"
+    le = "<="
+    ge = ">="
+    ne = "!="
+
+
+class OrderBy(str, Enum):
+    asc = "asc"
+    desc = "desc"
+
+
+class DynamicValue(BaseModel):
+    column_name: str
+
+
+class Condition(BaseModel):
+    column: str
+    operator: Operator
+    value: Union[str, int, DynamicValue]
+
+
+class Query(BaseModel):
+    table_name: Table
+    columns: list[Column]
+    conditions: list[Condition]
+    order_by: OrderBy
+
+
+client = OpenAI()
+
+completion = client.beta.chat.completions.parse(
+    model="gpt-4o-2024-08-06",
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a helpful assistant. The current date is August 6, 2024. You help users query for the data they are looking for by calling the query function.",
+        },
+        {
+            "role": "user",
+            "content": "look up all my orders in may of last year that were fulfilled but not delivered on time",
+        },
+    ],
+    tools=[
+        openai.pydantic_function_tool(Query),
+    ],
+)
+
+print(completion.choices[0].message.tool_calls[0].function.parsed_arguments)
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diff --git a/openai/structure2.py b/openai/structure2.py
new file mode 100644
index 0000000..f86ab42
--- /dev/null
+++ b/openai/structure2.py
@@ -0,0 +1,37 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+# https://openai.com/index/introducing-structured-outputs-in-the-api/
+
+from pydantic import BaseModel
+
+from openai import OpenAI
+
+
+class Step(BaseModel):
+    explanation: str
+    output: str
+
+
+class MathResponse(BaseModel):
+    steps: list[Step]
+    final_answer: str
+
+
+client = OpenAI()
+
+completion = client.beta.chat.completions.parse(
+    model="gpt-4o-2024-08-06",
+    messages=[
+        {"role": "system", "content": "You are a helpful math tutor."},
+        {"role": "user", "content": "solve 8x + 31 = 2"},
+    ],
+    response_format=MathResponse,
+)
+
+message = completion.choices[0].message
+if message.parsed:
+    print(message.parsed.steps)
+    print(message.parsed.final_answer)
+else:
+    print(message.refusal)
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