docs highlights

docs/index.md

@@ -87,10 +87,9 @@ The patch introduces 3 features to the `ChatCompletion` class:
 
 First, import the required libraries and apply the patch function to the OpenAI module. This exposes new functionality with the response_model parameter.
 
-```python
+```python hl_lines="6"
 import instructor
 from openai import OpenAI
-from pydantic import BaseModel
 
 # This enables response_model keyword
 # from client.chat.completions.create
@@ -114,7 +113,7 @@ 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. Its helpful to annotate the variable with the type of the response model.
 which will help your IDE provide autocomplete and spell check.
 
-```python
+```python hl_lines="3"
 user: UserDetail = client.chat.completions.create(
     model="gpt-3.5-turbo",
     response_model=UserDetail,
@@ -127,7 +126,7 @@ assert user.name == "Jason"
 assert user.age == 25
 ```
 
-## Pydantic Validation
+## Advanced: Pydantic Validation
 
 Validation can also be plugged into the same Pydantic model. Here, if the answer attribute contains content that violates the rule "don't say objectionable things," Pydantic will raise a validation error.
 
@@ -154,7 +153,7 @@ except ValidationError as e:
 
 Its important to not here that the error message is generated by the LLM, not the code, so it'll be helpful for re asking the model.
 
-```plaintext
+```plaintext hl_lines="3"
 1 validation error for QuestionAnswer
 answer
    Assertion failed, The statement is objectionable. (type=assertion_error)
@@ -164,10 +163,10 @@ answer
 
 Here, the `UserDetails` model is passed as the `response_model`, and `max_retries` is set to 2.
 
-```python
-from openai import OpenAI
+```python hl_lines="15-18 22 23 29"
 import instructor
 
+from openai import OpenAI
 from pydantic import BaseModel, field_validator
 
 # Apply the patch to the OpenAI client