ruff

examples/codegen-from-schema/create_fastapi_app.py

@@ -64,7 +64,7 @@ def load_json_schema(json_schema_path: str) -> dict:
 
 def generate_pydantic_model(json_schema_path: str):
     input_path = Path(json_schema_path)
-    output_path = Path(f"./models.py")
+    output_path = Path("./models.py")
     generate(
         input_=input_path, input_file_type=InputFileType.JsonSchema, output=output_path
     )

examples/codegen-from-schema/models.py

@@ -11,9 +11,9 @@ from pydantic import BaseModel
 
 
 class Type(Enum):
-    home = 'home'
-    work = 'work'
-    mobile = 'mobile'
+    home = "home"
+    work = "work"
+    mobile = "mobile"
 
 
 class PhoneNumber(BaseModel):

examples/codegen-from-schema/run.py

@@ -10,14 +10,14 @@ from jinja2 import Template
 from models import ExtractPerson
 
 import openai
-import instructor 
+import instructor
 
 instructor.patch()
 
 app = FastAPI()
 
-class TemplateVariables(BaseModel):
 
+class TemplateVariables(BaseModel):
     biography: str
 
 
@@ -26,7 +26,11 @@ class RequestSchema(BaseModel):
     model: str
     temperature: int
 
-PROMPT_TEMPLATE = Template("""Extract the person from the following: {{biography}}""".strip())
+
+PROMPT_TEMPLATE = Template(
+    """Extract the person from the following: {{biography}}""".strip()
+)
+
 
 @app.post("/api/v1/extract_person", response_model=ExtractPerson)
 async def extract_person(input: RequestSchema) -> ExtractPerson:
@@ -35,7 +39,5 @@ async def extract_person(input: RequestSchema) -> ExtractPerson:
         model=input.model,
         temperature=input.temperature,
         response_model=ExtractPerson,
-        messages=[
-            {"role": "user", "content": rendered_prompt}
-        ]
-    ) # type: ignore
+        messages=[{"role": "user", "content": rendered_prompt}],
+    )  # type: ignore