clean up readme

README.md

@@ -54,16 +54,18 @@ from pydantic import BaseModel
 # Enables `response_model`
 client = instructor.patch(OpenAI())
 
+
 class UserDetail(BaseModel):
     name: str
     age: int
 
+
 user = client.chat.completions.create(
     model="gpt-3.5-turbo",
     response_model=UserDetail,
     messages=[
         {"role": "user", "content": "Extract Jason is 25 years old"},
-    ]
+    ],
 )
 
 assert isinstance(user, UserDetail)
@@ -71,23 +73,6 @@ assert user.name == "Jason"
 assert user.age == 25
 ```
 
-### Using `openai<1.0.0`
-
-If you're using `openai<1.0.0` then make sure you `pip install instructor<0.3.0`
-where you can patch a global client like so:
-
-```python hl_lines="4 8"
-import openai
-import instructor
-
-instructor.patch()
-
-user = openai.ChatCompletion.create(
-    ...,
-    response_model=UserDetail,
-)
-```
-
 ### Using async clients
 
 For async clients you must use `apatch` vs. `patch`, as shown:
@@ -99,10 +84,12 @@ from pydantic import BaseModel
 
 aclient = instructor.apatch(AsyncOpenAI())
 
+
 class UserExtract(BaseModel):
     name: str
     age: int
 
+
 model = await aclient.chat.completions.create(
     model="gpt-3.5-turbo",
     response_model=UserExtract,
@@ -121,7 +108,6 @@ First, import the required libraries and apply the `patch` function to the OpenA
 ```python
 import instructor
 from openai import OpenAI
-from pydantic import BaseModel
 
 # This enables response_model keyword
 # from client.chat.completions.create
@@ -135,6 +121,7 @@ Create a Pydantic model to define the structure of the data you want to extract.
 ```python
 from pydantic import BaseModel
 
+
 class UserDetail(BaseModel):
     name: str
     age: int
@@ -145,12 +132,13 @@ class UserDetail(BaseModel):
 Use the `client.chat.completions.create` method to send a prompt and extract the data into the Pydantic object. The `response_model` parameter specifies the Pydantic model to use for extraction. It is helpful to annotate the variable with the type of the response model which will help your IDE provide autocomplete and spell check.
 
 ```python
+
 user: UserDetail = client.chat.completions.create(
     model="gpt-3.5-turbo",
     response_model=UserDetail,
     messages=[
         {"role": "user", "content": "Extract Jason is 25 years old"},
-    ]
+    ],
 )
 
 assert user.name == "Jason"
@@ -168,13 +156,14 @@ from pydantic import BaseModel, ValidationError, BeforeValidator
 from typing_extensions import Annotated
 from instructor import llm_validator
 
+
 class QuestionAnswer(BaseModel):
     question: str
     answer: Annotated[
-        str,
-        BeforeValidator(llm_validator("don't say objectionable things"))
+        str, BeforeValidator(llm_validator("don't say objectionable things"))
     ]
 
+
 try:
     qa = QuestionAnswer(
         question="What is the meaning of life?",
@@ -182,6 +171,12 @@ try:
     )
 except ValidationError as e:
     print(e)
+    """
+    1 validation error for QuestionAnswer
+    answer
+      Assertion failed, The statement promotes objectionable behavior. [type=assertion_error, input_value='The meaning of life is to be evil and steal', input_type=str]
+        For further information visit https://errors.pydantic.dev/2.6/v/assertion_error
+    """
 ```
 
 It is important to note here that the **error message is generated by the LLM**, not the code. Thus, it is helpful for re-asking the model.
@@ -205,6 +200,7 @@ from pydantic import BaseModel, field_validator
 # Apply the patch to the OpenAI client
 client = instructor.patch(OpenAI())
 
+
 class UserDetails(BaseModel):
     name: str
     age: int
@@ -216,6 +212,7 @@ class UserDetails(BaseModel):
             raise ValueError("Name must be in uppercase.")
         return v
 
+
 model = client.chat.completions.create(
     model="gpt-3.5-turbo",
     response_model=UserDetails,