Fix documentation formatting issues:

- Add spaces after comment markers in chain-of-density.md
- Fix string formatting in multimodal-gemini.md
- Standardize comment markers to '# >' in introducing-structured-outputs.md
- Update type annotation syntax for Python 3.7 compatibility in version-1.md

docs/blog/posts/chain-of-density.md

@@ -394,18 +394,18 @@ In order to prevent any contamination of data during testing, we randomly sample
 
 ```py hl_lines="2 9 11 13-21 40 43"
 from typing import List
-from chain_of_density import summarize_article #(1)!
+from chain_of_density import summarize_article  # (1)!
 import csv
 import logging
 import instructor
 from pydantic import BaseModel
 from openai import OpenAI
 
-client = instructor.from_openai(OpenAI()) # (2)!
+client = instructor.from_openai(OpenAI())  # (2)!
 
-logging.basicConfig(level=logging.INFO) #(3)!
+logging.basicConfig(level=logging.INFO)  # (3)!
 
-instructions = instructor.Instructions( #(4)!
+instructions = instructor.Instructions(  # (4)!
     name="Chain Of Density",
     finetune_format="messages",
     # log handler is used to save the data to a file
@@ -432,18 +432,17 @@ class GeneratedSummary(BaseModel):
         description="This represents the final summary generated that captures the meaning of the original article which is as concise as possible. ",
     )
 
-@instructions.distil #(4)!
+@instructions.distil  # (4)!
 def distil_summarization(text: str) -> GeneratedSummary:
     summary_chain: List[str] = summarize_article(text)
-    return GeneratedSummary(summary=summary_chain[-1]) #(5)!
+    return GeneratedSummary(summary=summary_chain[-1])  # (5)!
 
 with open("train.csv", "r") as file:
     reader = csv.reader(file)
     next(reader)  # Skip the header
     for article, summary in reader:
         # Run Distillisation to generate the values
         distil_summarization(article)
-```
 
 1.  In this example, we're using the summarize_article that we defined up above. We saved it in a local file called `chain_of_density.py`,
     hence the import
@@ -547,4 +546,4 @@ Interestingly, the model finetuned with the least examples seems to outperform t
 
 Finetuning this iterative method was 20-40x faster while improving overall performance, resulting in massive efficiency gains by finetuning and distilling capabilities into specialized models.
 
-We've seen how `Instructor` can make your life easier, from data modeling to distillation and finetuning. If you enjoy the content or want to try out `instructor` check out the [github](https://github.com/jxnl/instructor) and don't forget to give us a star!
+We've seen how `Instructor` can make your life easier, from data modeling to distillation and finetuning. If you enjoy the content or want to try out `instructor` check out the [github](https://github.com/jxnl/instructor) and don't forget to give us a star!

docs/blog/posts/introducing-structured-outputs.md

@@ -117,27 +117,24 @@ with client.beta.chat.completions.stream(
     for event in stream:
         if event.type == "content.delta":
             print(event.snapshot, flush=True, end="\n")
-            #> 
-            #> {"
-            #> {"name
-            #> {"name":"
-            #> {"name":"Jason
-            #> {"name":"Jason","
-            #> {"name":"Jason","age
-            #> {"name":"Jason","age":
-            #> {"name":"Jason","age":25
-            #> {"name":"Jason","age":25}
-            # >
-            #> {"
-            #> {"name
-            #> {"name":"
-            #> {"name":"Jason
-            #> {"name":"Jason","
-            #> {"name":"Jason","age
-            #> {"name":"Jason","age":
-            #> {"name":"Jason","age":25
-            #> {"name":"Jason","age":25}
-```
+            # > {"
+            # > {"name
+            # > {"name":"
+            # > {"name":"Jason
+            # > {"name":"Jason","
+            # > {"name":"Jason","age
+            # > {"name":"Jason","age":
+            # > {"name":"Jason","age":25
+            # > {"name":"Jason","age":25}
+            # > {"
+            # > {"name
+            # > {"name":"
+            # > {"name":"Jason
+            # > {"name":"Jason","
+            # > {"name":"Jason","age
+            # > {"name":"Jason","age":
+            # > {"name":"Jason","age":25
+            # > {"name":"Jason","age":25}
 
 ### Unpredictable Latency Spikes
 

docs/blog/posts/multimodal-gemini.md

@@ -87,21 +87,24 @@ print(resp)
 
     ```python
     Recomendations(
-        chain_of_thought='The video recommends visiting Takayama city, in the Hida Region, Gifu Prefecture. The
-    video suggests visiting the Miyagawa Morning Market, to try the Sarubobo good luck charms, and to enjoy the
-    cookie cup espresso, made by Koma Coffee. Then, the video suggests visiting a traditional Japanese Cafe,
-    called Kissako Katsure, and try their matcha and sweets. Afterwards, the video suggests to visit the Sanmachi
-    Historic District, where you can find local crafts and delicious foods. The video recommends trying Hida Wagyu
-    beef, at the Kin no Kotte Ushi shop, or to have a sit-down meal at the Kitchen Hida. Finally, the video
-    recommends visiting Shirakawa-go, a World Heritage Site in Gifu Prefecture.',
-        description='This video recommends a number of places to visit in Takayama city, in the Hida Region, Gifu
-    Prefecture. It shows some of the local street food and highlights some of the unique shops and restaurants in
-    the area.',
+        chain_of_thought=(
+            "The video recommends visiting Takayama city, in the Hida Region, Gifu Prefecture. "
+            "The video suggests visiting the Miyagawa Morning Market, to try the Sarubobo good luck charms, "
+            "and to enjoy the cookie cup espresso, made by Koma Coffee. Then, the video suggests visiting "
+            "a traditional Japanese Cafe, called Kissako Katsure, and try their matcha and sweets. "
+            "Afterwards, the video suggests to visit the Sanmachi Historic District, where you can find "
+            "local crafts and delicious foods. The video recommends trying Hida Wagyu beef, at the Kin "
+            "no Kotte Ushi shop, or to have a sit-down meal at the Kitchen Hida. Finally, the video "
+            "recommends visiting Shirakawa-go, a World Heritage Site in Gifu Prefecture."
+        ),
+        description='This video recommends a number of places to visit in Takayama city, in the Hida Region, Gifu '
+    'Prefecture. It shows some of the local street food and highlights some of the unique shops and restaurants in '
+    'the area.',
         destinations=[
             TouristDestination(
                 name='Takayama',
-                description='Takayama is a city at the base of the Japan Alps, located in the Hida Region of
-    Gifu.',
+                description='Takayama is a city at the base of the Japan Alps, located in the Hida Region of '
+    'Gifu.',
                 location='Hida Region, Gifu Prefecture'
             ),
             TouristDestination(

docs/blog/posts/version-1.md

@@ -77,10 +77,9 @@ client = instructor.from_litellm(litellm.completion)
 
 # all of these will route to the same underlying create function
 # allow you to add instructor to try it out, while easily removing it
-client.create(model="gpt-4", response_model=type[T]) -> T
-client.chat.completions.create(model="gpt-4", response_model=type[T]) -> T
-client.messages.create(model="gpt-4", response_model=type[T]) -> T
-```
+client.create(model="gpt-4", response_model=Type[T]) -> T
+client.chat.completions.create(model="gpt-4", response_model=Type[T]) -> T
+client.messages.create(model="gpt-4", response_model=Type[T]) -> T
 
 ## Type are infered correctly
 
@@ -214,12 +213,11 @@ for user in user_stream:
     #> name='John' age=25
     #> name='John Smith' age=25
     #> name='John Smith' age=25
-    # name=None age=None
-    # name='' age=None
-    # name='John' age=None
-    # name='John Doe' age=None
-    # name='John Doe' age=30
-```
+    #> name=None age=None
+    #> name='' age=None
+    #> name='John' age=None
+    #> name='John Doe' age=None
+    #> name='John Doe' age=30
 
 Notice now that the type infered is `Generator[User, None]`