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참고/instructor-main/docs/examples/extracting_receipts.md

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+---
+title: Receipt Data Extraction with GPT-4 Vision - Expense Tracking
+description: Extract and validate receipt data from images using GPT-4 Vision and Instructor. Automate expense tracking with structured receipt parsing.
+---
+
+# Extracting Receipt Data using GPT-4 and Python
+
+This post demonstrates how to use Python's Pydantic library and OpenAI's GPT-4 model to extract receipt data from images and validate the total amount. This method is particularly useful for automating expense tracking and financial analysis tasks.
+
+## Defining the Item and Receipt Classes
+
+First, we define two Pydantic models, `Item` and `Receipt`, to structure the extracted data. The `Item` class represents individual items on the receipt, with fields for name, price, and quantity. The `Receipt` class contains a list of `Item` objects and the total amount.
+
+```python
+from pydantic import BaseModel
+
+
+class Item(BaseModel):
+    name: str
+    price: float
+    quantity: int
+
+
+class Receipt(BaseModel):
+    items: list[Item]
+    total: float
+```
+
+## Validating the Total Amount
+
+To ensure the accuracy of the extracted data, we use Pydantic's `model_validator` decorator to define a custom validation function, `check_total`. This function calculates the sum of item prices and compares it to the extracted total amount. If there's a discrepancy, it raises a `ValueError`.
+
+```python
+from pydantic import model_validator
+
+
+@model_validator(mode="after")
+def check_total(self):
+    items = self.items
+    total = self.total
+    calculated_total = sum(item.price * item.quantity for item in items)
+    if calculated_total != total:
+        raise ValueError(
+            f"Total {total} does not match the sum of item prices {calculated_total}"
+        )
+    return self
+```
+
+## Extracting Receipt Data from Images
+
+The `extract_receipt` function uses OpenAI's GPT-4 model to process an image URL and extract receipt data. We utilize the `instructor` library to configure the OpenAI client for this purpose.
+
+```python
+import instructor
+
+# <%hide%>
+from pydantic import BaseModel, model_validator
+
+
+class Item(BaseModel):
+    name: str
+    price: float
+    quantity: int
+
+
+class Receipt(BaseModel):
+    items: list[Item]
+    total: float
+
+    @model_validator(mode="after")
+    def check_total(cls, values: "Receipt"):
+        items = values.items
+        total = values.total
+        calculated_total = sum(item.price * item.quantity for item in items)
+        if calculated_total != total:
+            raise ValueError(
+                f"Total {total} does not match the sum of item prices {calculated_total}"
+            )
+        return values
+
+
+# <%hide%>
+
+client = instructor.from_provider("openai/gpt-5-nano")
+
+
+def extract(url: str) -> Receipt:
+    return client.create(
+        model="gpt-4",
+        max_tokens=4000,
+        response_model=Receipt,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": url},
+                    },
+                    {
+                        "type": "text",
+                        "text": "Analyze the image and return the items in the receipt and the total amount.",
+                    },
+                ],
+            }
+        ],
+    )
+```
+
+## Practical Examples
+
+In these examples, we apply the method to extract receipt data from two different images. The custom validation function ensures that the extracted total amount matches the sum of item prices.
+
+```python
+# <%hide%>
+from pydantic import BaseModel, model_validator
+import instructor
+
+
+class Item(BaseModel):
+    name: str
+    price: float
+    quantity: int
+
+
+class Receipt(BaseModel):
+    items: list[Item]
+    total: float
+
+    @model_validator(mode="after")
+    def check_total(cls, values: "Receipt"):
+        items = values.items
+        total = values.total
+        calculated_total = round(sum(item.price * item.quantity for item in items), 2)
+        if calculated_total != total:
+            raise ValueError(
+                f"Total {total} does not match the sum of item prices {calculated_total}"
+            )
+        return values
+
+
+client = instructor.from_provider("openai/gpt-5-nano")
+
+
+def extract(url: str) -> Receipt:
+    return client.create(
+        model="gpt-4o",
+        max_tokens=4000,
+        response_model=Receipt,
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": url},
+                    },
+                    {
+                        "type": "text",
+                        "text": "Analyze the image and return the items in the receipt and the total amount.",
+                    },
+                ],
+            }
+        ],
+    )
+
+
+# <%hide%>
+url = "https://templates.mediamodifier.com/645124ff36ed2f5227cbf871/supermarket-receipt-template.jpg"
+
+
+receipt = extract(url)
+print(receipt)
+"""
+items=[Item(name='Lorem ipsum', price=9.2, quantity=1), Item(name='Lorem ipsum dolor sit', price=19.2, quantity=1), Item(name='Lorem ipsum dolor sit amet', price=15.0, quantity=1), Item(name='Lorem ipsum', price=15.0, quantity=1), Item(name='Lorem ipsum', price=15.0, quantity=1), Item(name='Lorem ipsum dolor sit', price=15.0, quantity=1), Item(name='Lorem ipsum', price=19.2, quantity=1)] total=107.6
+"""
+```
+
+By combining the power of GPT-4 and Python's Pydantic library, we can accurately extract and validate receipt data from images, streamlining expense tracking and financial analysis tasks.