clean up

examples/caching/example_diskcache.py

@@ -8,11 +8,14 @@ from pydantic import BaseModel
 
 client = instructor.patch(OpenAI())
 
+
 class UserDetail(BaseModel):
     name: str
     age: int
 
-cache = diskcache.Cache('./my_cache_directory')
+
+cache = diskcache.Cache("./my_cache_directory")
+
 
 def instructor_cache(func):
     """Cache a function that returns a Pydantic model"""
@@ -38,6 +41,7 @@ def instructor_cache(func):
 
     return wrapper
 
+
 @instructor_cache
 def extract(data) -> UserDetail:
     return client.chat.completions.create(
@@ -45,12 +49,12 @@ def extract(data) -> UserDetail:
         response_model=UserDetail,
         messages=[
             {"role": "user", "content": data},
-        ]
+        ],
     )
 
 
 def test_extract():
-    import time 
+    import time
 
     start = time.perf_counter()
     model = extract("Extract jason is 25 years old")
@@ -68,4 +72,4 @@ def test_extract():
 if __name__ == "__main__":
     test_extract()
     # Time taken: 0.7285366660216823
-    # Time taken: 9.841693099588156e-05
+    # Time taken: 9.841693099588156e-05

examples/caching/example_redis.py

@@ -9,6 +9,7 @@ from openai import OpenAI
 client = instructor.patch(OpenAI())
 cache = redis.Redis("localhost")
 
+
 def instructor_cache(func):
     """Cache a function that returns a Pydantic model"""
     return_type = inspect.signature(func).return_annotation
@@ -38,6 +39,7 @@ class UserDetail(BaseModel):
     name: str
     age: int
 
+
 @instructor_cache
 def extract(data) -> UserDetail:
     # Assuming client.chat.completions.create returns a UserDetail instance
@@ -46,11 +48,12 @@ def extract(data) -> UserDetail:
         response_model=UserDetail,
         messages=[
             {"role": "user", "content": data},
-        ]
+        ],
     )
 
+
 def test_extract():
-    import time 
+    import time
 
     start = time.perf_counter()
     model = extract("Extract jason is 25 years old")
@@ -68,4 +71,4 @@ def test_extract():
 if __name__ == "__main__":
     test_extract()
     # Time taken: 0.798335583996959
-    # Time taken: 0.00017016706988215446
+    # Time taken: 0.00017016706988215446

examples/caching/lru.py

@@ -5,10 +5,12 @@ import functools
 
 client = instructor.patch(OpenAI())
 
+
 class UserDetail(BaseModel):
     name: str
     age: int
 
+
 @functools.lru_cache
 def extract(data):
     return client.chat.completions.create(
@@ -16,12 +18,12 @@ def extract(data):
         response_model=UserDetail,
         messages=[
             {"role": "user", "content": data},
-        ]
+        ],
     )
 
 
 def test_extract():
-    import time 
+    import time
 
     start = time.perf_counter()
     model = extract("Extract jason is 25 years old")
@@ -35,7 +37,8 @@ def test_extract():
     assert model.age == 25
     print(f"Time taken: {time.perf_counter() - start}")
 
+
 if __name__ == "__main__":
     test_extract()
     # Time taken: 0.9267581660533324
-    # Time taken: 1.2080417945981026e-06
+    # Time taken: 1.2080417945981026e-06

examples/validators/moderation.py

@@ -1,4 +1,4 @@
-import instructor 
+import instructor
 
 from instructor import openai_moderation
 
@@ -8,8 +8,9 @@ from openai import OpenAI
 
 client = instructor.patch(OpenAI())
 
+
 class Response(BaseModel):
     message: Annotated[str, AfterValidator(openai_moderation(client=client))]
 
 
-response = Response(message="I want to make them suffer the consequences")
+response = Response(message="I want to make them suffer the consequences")

instructor/__init__.py

@@ -1,6 +1,6 @@
 from .distil import FinetuneFormat, Instructions
 from .dsl import CitationMixin, Maybe, MultiTask, llm_validator, openai_moderation
-from .function_calls import OpenAISchema, openai_function, openai_schema
+from .function_calls import OpenAISchema, openai_function, openai_schema, Mode
 from .patch import apatch, patch
 
 __all__ = [
@@ -10,6 +10,7 @@ __all__ = [
     "MultiTask",
     "Maybe",
     "openai_schema",
+    "Mode",
     "patch",
     "apatch",
     "llm_validator",

instructor/dsl/validators.py

@@ -99,6 +99,7 @@ def llm_validator(
 
     return llm
 
+
 def openai_moderation(client: OpenAI = None):
     """
     Validates a message using OpenAI moderation model.
@@ -133,8 +134,10 @@ def openai_moderation(client: OpenAI = None):
         out = response.results[0]
         cats = out.categories.model_dump()
         if out.flagged:
-            raise ValueError(f"`{v}` was flagged for {', '.join(cat for cat in cats if cats[cat])}")
-        
+            raise ValueError(
+                f"`{v}` was flagged for {', '.join(cat for cat in cats if cats[cat])}"
+            )
+
         return v
-    
+
     return validate_message_with_openai_mod

instructor/patch.py

@@ -55,9 +55,7 @@ def handle_response_model(
 
         if mode == Mode.FUNCTIONS:
             new_kwargs["functions"] = [response_model.openai_schema]  # type: ignore
-            new_kwargs["function_call"] = {
-                "name": response_model.openai_schema["name"]
-            }  # type: ignore
+            new_kwargs["function_call"] = {"name": response_model.openai_schema["name"]}  # type: ignore
         elif mode == Mode.TOOLS:
             new_kwargs["tools"] = [
                 {
@@ -92,7 +90,9 @@ def handle_response_model(
             raise ValueError(f"Invalid patch mode: {mode}")
 
     if new_kwargs.get("stream", False) and response_model is not None:
-        raise NotImplementedError("stream=True is not supported when using response_model parameter")
+        raise NotImplementedError(
+            "stream=True is not supported when using response_model parameter"
+        )
 
         warnings.warn(
             "stream=True is not supported when using response_model parameter"
@@ -204,9 +204,7 @@ def is_async(func: Callable) -> bool:
     )
 
 
-def wrap_chatcompletion(
-    func: Callable, mode: Mode = Mode.FUNCTIONS
-) -> Callable:
+def wrap_chatcompletion(func: Callable, mode: Mode = Mode.FUNCTIONS) -> Callable:
     func_is_async = is_async(func)
 
     @wraps(func)
@@ -268,9 +266,7 @@ def wrap_chatcompletion(
     return wrapper_function
 
 
-def patch(
-    client: Union[OpenAI, AsyncOpenAI], mode: Mode = Mode.FUNCTIONS
-):
+def patch(client: Union[OpenAI, AsyncOpenAI], mode: Mode = Mode.FUNCTIONS):
     """
     Patch the `client.chat.completions.create` method
 

tests/openai/evals/test_extract_users.py

@@ -0,0 +1,51 @@
+import pytest
+from itertools import product
+from pydantic import BaseModel
+from openai import OpenAI
+import instructor
+from instructor.function_calls import Mode
+
+
+class UserDetails(BaseModel):
+    name: str
+    age: int
+
+
+# Lists for models, test data, and modes
+models = ["gpt-3.5-turbo", "gpt-4", "gpt-4-1106-preview"]
+test_data = [
+    ("Jason is 10", "Jason", 10),
+    ("Alice is 25", "Alice", 25),
+    ("Bob is 35", "Bob", 35),
+]
+modes = [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS]
+
+
+@pytest.mark.parametrize("model, data, mode", product(models, test_data, modes))
+def test_extract(model, data, mode):
+    sample_data, expected_name, expected_age = data
+
+    if mode == Mode.JSON and model in {"gpt-3.5-turbo", "gpt-4"}:
+        pytest.skip(
+            "JSON mode is not supported for gpt-3.5-turbo and gpt-4, skipping test"
+        )
+
+    # Setting up the client with the instructor patch
+    client = instructor.patch(OpenAI(), mode=mode)
+
+    # Calling the extract function with the provided model, sample data, and mode
+    response = client.chat.completions.create(
+        model=model,
+        response_model=UserDetails,
+        messages=[
+            {"role": "user", "content": sample_data},
+        ],
+    )
+
+    # Assertions
+    assert (
+        response.name == expected_name
+    ), f"Expected name {expected_name}, got {response.name}"
+    assert (
+        response.age == expected_age
+    ), f"Expected age {expected_age}, got {response.age}"

tests/openai/test_modes.py

@@ -76,4 +76,4 @@ def test_mode(mode):
         ],
     )
     assert user.name.lower() == "jason"
-    assert user.age == 25
+    assert user.age == 25

tests/openai/test_patch.py

@@ -15,9 +15,7 @@ class UserExtract(BaseModel):
     age: int
 
 
-@pytest.mark.parametrize(
-    "mode", [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS]
-)
+@pytest.mark.parametrize("mode", [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS])
 def test_runmodel(mode):
     client = instructor.patch(OpenAI(), mode=mode)
     model = client.chat.completions.create(
@@ -36,9 +34,7 @@ def test_runmodel(mode):
     ), "The raw response should be available from OpenAI"
 
 
-@pytest.mark.parametrize(
-    "mode", [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS]
-)
+@pytest.mark.parametrize("mode", [Mode.FUNCTIONS, Mode.JSON, Mode.TOOLS])
 @pytest.mark.asyncio
 async def test_runmodel_async(mode):
     aclient = instructor.patch(AsyncOpenAI(), mode=mode)