docs: clean up docs

docs/hub/action_items.md

@@ -1,4 +1,4 @@
-# Example: Extracting Action Items from Meeting Transcripts
+# Extracting Action Items from Meeting Transcripts
 
 In this guide, we'll walk through how to extract action items from meeting transcripts using OpenAI's API and Pydantic. This use case is essential for automating project management tasks, such as task assignment and priority setting.
 
@@ -10,7 +10,6 @@ instructor hub pull --slug action_items --py > action_items.py
 
 For multi-label classification, we introduce a new enum class and a different Pydantic model to handle multiple labels.
 
-
 !!! tips "Motivation"
 
     Significant amount of time is dedicated to meetings, where action items are generated as the actionable outcomes of these discussions. Automating the extraction of action items can save time and guarantee that no critical tasks are overlooked.
@@ -27,7 +26,6 @@ To extract action items from a meeting transcript, we use the **`generate`** fun
 
 To test the **`generate`** function, we provide it with a sample transcript, and then print the JSON representation of the extracted action items.
 
-
 ```python
 import instructor
 from openai import OpenAI

docs/hub/partial_streaming.md

@@ -1,3 +1,9 @@
+# Streaming Partial Responses
+
+Field level streaming provides incremental snapshots of the current state of the response model that are immediately useable. This approach is particularly relevant in contexts like rendering UI components.
+
+Instructor supports this pattern by making use of `Partial[T]`. This lets us dynamically create a new class that treats all of the original model's fields as `Optional`.
+
 If you want to try outs via `instructor hub`, you can pull it by running
 
 ```bash
@@ -65,4 +71,4 @@ for extraction in extraction_stream:
     obj = extraction.model_dump()
     console.clear()
     console.print(obj)
-```
+```