Fixed ruff errors

docs/examples/extracting_receipts.md

@@ -12,6 +12,9 @@ This post demonstrates how to use Python's Pydantic library and OpenAI's GPT-4 m
 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
@@ -29,15 +32,15 @@ To ensure the accuracy of the extracted data, we use Pydantic's `model_validator
 
 ```python
 @model_validator(mode="after")
-def check_total(cls, values: "Receipt"):
-    items = values.items
-    total = values.total
+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 values
+    return self
 ```
 
 ## Extracting Receipt Data from Images
@@ -48,10 +51,35 @@ The `extract_receipt` function uses OpenAI's GPT-4 model to process an image URL
 import instructor
 from openai import OpenAI
 
-client = instructor.from_openai(
-    client=OpenAI(),
-    mode=instructor.Mode.TOOLS,
-)
+# <%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_openai(OpenAI())
 
 
 def extract(url: str) -> Receipt:
@@ -82,14 +110,66 @@ def extract(url: str) -> Receipt:
 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
-urls = [
-    "https://templates.mediamodifier.com/645124ff36ed2f5227cbf871/supermarket-receipt-template.jpg",
-    "https://ocr.space/Content/Images/receipt-ocr-original.jpg",
-]
-
-for url in urls:
-    receipt = extract(url)
-    print(receipt)
+# <%hide%>
+from pydantic import BaseModel, model_validator
+import instructor
+from openai import OpenAI
+
+
+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
+
+
+client = instructor.from_openai(OpenAI())
+
+
+def extract(url: str) -> Receipt:
+    return client.chat.completions.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)
 ```
 
 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.

docs/examples/local_classification.md

@@ -39,37 +39,40 @@ CMAKE_ARGS="-DGGML_CUDA=on" pip install llama-cpp-python
 
 Here's an example of how to implement a system for handling confidential document queries using local models:
 
-```python hl_lines="7-12 14-16 40-46"
-from llama_cpp import Llama
+```python hl_lines="7-12 14-16 43-52"
+from llama_cpp import Llama  # type: ignore
 import instructor
 from pydantic import BaseModel
 from enum import Enum
 from typing import Optional
 
-llm = Llama.from_pretrained(
-    repo_id="TheBloke/Mistral-7B-Instruct-v0.2-GGUF", # (1)!
+llm = Llama.from_pretrained(  # type: ignore
+    repo_id="TheBloke/Mistral-7B-Instruct-v0.2-GGUF",  # (1)!
     filename="*Q4_K_M.gguf",
-    verbose=False, # (2)!
-    n_gpu_layers=-1, # (3)!
+    verbose=False,  # (2)!
+    n_gpu_layers=-1,  # (3)!
 )
 
 create = instructor.patch(
-    create=llm.create_chat_completion_openai_v1, #(4)!
+    create=llm.create_chat_completion_openai_v1,  # type: ignore  # (4)!
 )
 
+
 # Define query types for document-related inquiries
 class QueryType(str, Enum):
     DOCUMENT_CONTENT = "document_content"
     LAST_MODIFIED = "last_modified"
     ACCESS_PERMISSIONS = "access_permissions"
     RELATED_DOCUMENTS = "related_documents"
 
+
 # Define the structure for query responses
 class QueryResponse(BaseModel):
     query_type: QueryType
     response: str
     additional_info: Optional[str] = None
 
+
 def process_confidential_query(query: str) -> QueryResponse:
     prompt = f"""Analyze the following confidential document query and provide an appropriate response:
     Query: {query}
@@ -80,9 +83,12 @@ def process_confidential_query(query: str) -> QueryResponse:
     """
 
     return create(
-        response_model=QueryResponse, #(5)!
+        response_model=QueryResponse,  # (5)!
         messages=[
-            {"role": "system", "content": "You are a secure AI assistant trained to handle confidential document queries."},
+            {
+                "role": "system",
+                "content": "You are a secure AI assistant trained to handle confidential document queries.",
+            },
             {"role": "user", "content": prompt},
         ],
     )
@@ -98,8 +104,8 @@ confidential_queries = [
 ]
 
 # Process each query and print the results
-for i, query in enumerate(confidential_queries, 1):
-    response:QueryResponse = process_confidential_query(query)
+for query in confidential_queries:
+    response: QueryResponse = process_confidential_query(query)
     print(f"{query} : {response.query_type}")
     """
     #> What are the key findings in the Q4 financial report? : document_content

docs/examples/mistral.md

@@ -27,7 +27,7 @@ An example:
 ```python
 import os
 from pydantic import BaseModel
-from mistralai.client import MistralClient
+from mistralai import Mistral
 from instructor import from_mistral, Mode
 
 
@@ -37,7 +37,7 @@ class UserDetails(BaseModel):
 
 
 # enables `response_model` in chat call
-client = MistralClient(api_key=os.environ.get("MISTRAL_API_KEY"))
+client = Mistral(api_key=os.environ.get("MISTRAL_API_KEY"))
 
 instructor_client = from_mistral(
     client=client,
@@ -53,6 +53,7 @@ resp = instructor_client.messages.create(
 )
 
 print(resp)
+#> name='Jason' age=10
 
 # output: UserDetails(name='Jason', age=10)
 ```

docs/examples/sqlmodel.md

@@ -35,6 +35,8 @@ class Hero(SQLModel, instructor.OpenAISchema, table=True):
 The `create_hero` function will query `OpenAI` for a `Hero` record
 
 ```python
+import instructor
+
 client = instructor.from_openai(OpenAI())
 
 
@@ -56,9 +58,9 @@ SQLModel.metadata.create_all(engine)
 
 hero = create_hero()
 print(hero.model_dump())
-    """
-    {'name': 'SuperNova', 'secret_name': 'Mia Thompson', 'age': 28, 'id': None}
-    """
+"""
+{'name': 'SuperNova', 'secret_name': 'Mia Thompson', 'age': 28, 'id': None}
+"""
 
 with Session(engine) as session:
     session.add(hero)

docs/hub/knowledge_graph.md

@@ -45,7 +45,7 @@ client = instructor.from_openai(OpenAI())
 def generate_graph(input_text: str) -> KnowledgeGraph:
     """Generates a knowledge graph from the input text."""
     return client.chat.completions.create(
-        model="gpt-3.5-turbo",
+        model="gpt-4o-mini",
         messages=[
             {
                 "role": "user",