Migrate from Poetry to UV

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

@@ -204,16 +204,15 @@ class RewrittenSummary(BaseModel):
         ...,
         description="This is a new, denser summary of identical length which covers every entity and detail from the previous summary plus the Missing Entities. It should have the same length ( ~ 80 words ) as the previous summary and should be easily understood without the Article",
     )
-    absent: List[str] = Field(
+    absent: typing.List[str] = Field(
         ...,
         default_factory=list,
         description="this is a list of Entities found absent from the new summary that were present in the previous summary",
     )
-    missing: List[str] = Field(
+    missing: typing.List[str] = Field(
         default_factory=list,
         description="This is a list of 1-3 informative Entities from the Article that are missing from the new summary which should be included in the next generated summary.",
     )
-```
 
 !!! tip "Using Pydantic Validators with Instructor"
 
@@ -394,18 +393,18 @@ 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 +431,17 @@ 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 +545,4 @@ 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

@@ -117,38 +117,15 @@ 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}
-```
-
-### Unpredictable Latency Spikes
-
-In order to benchmark the two modes, we made 200 identical requests to OpenAI and noted the time taken for each request to complete. The results are summarized in the following table:
-
-| mode               | mean  | min   | max    | std_dev | variance |
-| ------------------ | ----- | ----- | ------ | ------- | -------- |
-| Tool Calling       | 6.84  | 6.21  | 12.84  | 0.69    | 0.47     |
-| Structured Outputs | 28.20 | 14.91 | 136.90 | 9.27    | 86.01    |
-
-Structured Outputs suffers from unpredictable latency spikes while Tool Calling maintains consistent performance. This could cause users to occasionally experience significant delays in response times, potentially impacting the overall user satisfication and retention rates.
+            # > {"name
+            # > {"name":"
+            # > {"name":"Jason
+            # > {"name":"Jason","
+            # > {"name":"Jason","age
+            # > {"name":"Jason","age":
+            # > {"name":"Jason","age":25
+            # > {"name":"Jason","age":25}
 
 ## Why use `instructor`
 

docs/blog/posts/multimodal-gemini.md

@@ -87,74 +87,93 @@ 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=" ".join([
+            "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=" ".join([
+            "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.',
-                location='Hida Region, Gifu Prefecture'
+                name="Takayama",
+                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(
-                name='Miyagawa Morning Market',
-                description="The Miyagawa Morning Market, or the Miyagawa Asai-chi in Japanese, is a market that
-    has existed officially since the Edo Period, more than 100 years ago. It's open every single day, rain or
-    shine, from 7am to noon.",
-                location='Hida Takayama'
+                name="Miyagawa Morning Market",
+                description=(
+                    "The Miyagawa Morning Market, or the Miyagawa Asai-chi in Japanese, is a market that "
+                    "has existed officially since the Edo Period, more than 100 years ago. It's open every "
+                    "single day, rain or shine, from 7am to noon."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Nakaya - Handmade Hida Sarubobo',
-                description='The Nakaya shop sells handcrafted Sarubobo good luck charms.',
-                location='Hida Takayama'
+                name="Nakaya - Handmade Hida Sarubobo",
+                description="The Nakaya shop sells handcrafted Sarubobo good luck charms.",
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Koma Coffee',
-                description="Koma Coffee is a shop that has been in business for about 50 or 60 years, and they
-    serve coffee in a cookie cup. They've been serving coffee for about 10 years.",
-                location='Hida Takayama'
+                name="Koma Coffee",
+                description=(
+                    "Koma Coffee is a shop that has been in business for about 50 or 60 years, and they "
+                    "serve coffee in a cookie cup. They've been serving coffee for about 10 years."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Kissako Katsure',
-                description='Kissako Katsure is a traditional Japanese style cafe, called Kissako, and the name
-    means would you like to have some tea. They have a variety of teas and sweets.',
-                location='Hida Takayama'
+                name="Kissako Katsure",
+                description=(
+                    "Kissako Katsure is a traditional Japanese style cafe, called Kissako, and the name "
+                    "means would you like to have some tea. They have a variety of teas and sweets."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Sanmachi Historic District',
-                description='Sanmachi Dori is a Historic Merchant District in Takayama, all of the buildings here
-    have been preserved to look as they did in the Edo Period.',
-                location='Hida Takayama'
+                name="Sanmachi Historic District",
+                description=(
+                    "Sanmachi Dori is a Historic Merchant District in Takayama, all of the buildings here "
+                    "have been preserved to look as they did in the Edo Period."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Suwa Orchard',
-                description='The Suwa Orchard has been in business for more than 50 years.',
-                location='Hida Takayama'
+                name="Suwa Orchard",
+                description="The Suwa Orchard has been in business for more than 50 years.",
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Kitchen HIDA',
-                description='Kitchen HIDA is a restaurant with a 50 year history, known for their Hida Beef dishes
-    and for using a lot of local ingredients.',
-                location='Hida Takayama'
+                name="Kitchen HIDA",
+                description=(
+                    "Kitchen HIDA is a restaurant with a 50 year history, known for their Hida Beef dishes "
+                    "and for using a lot of local ingredients."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Kin no Kotte Ushi',
-                description='Kin no Kotte Ushi is a shop known for selling Beef Sushi, especially Hida Wagyu Beef
-    Sushi. Their sushi is medium rare.',
-                location='Hida Takayama'
+                name="Kin no Kotte Ushi",
+                description=(
+                    "Kin no Kotte Ushi is a shop known for selling Beef Sushi, especially Hida Wagyu Beef "
+                    "Sushi. Their sushi is medium rare."
+                ),
+                location="Hida Takayama"
             ),
             TouristDestination(
-                name='Shirakawa-go',
-                description='Shirakawa-go is a World Heritage Site in Gifu Prefecture.',
-                location='Gifu Prefecture'
+                name="Shirakawa-go",
+                description="Shirakawa-go is a World Heritage Site in Gifu Prefecture.",
+                location="Gifu Prefecture"
             )
         ]
     )

docs/blog/posts/version-1.md

@@ -62,11 +62,12 @@ Now, whenever you call `client.chat.completions.create` the `model` and `tempera
 When I first started working on this project, my goal was to ensure that we weren't introducing any new standards. Instead, our focus was on maintaining compatibility with existing ones. By creating our own client, we can seamlessly proxy OpenAI's `chat.completions.create` and Anthropic's `messages.create` methods. This approach allows us to provide a smooth upgrade path for your client, enabling support for all the latest models and features as they become available. Additionally, this strategy safeguards us against potential downstream changes.
 
 ```python
+from __future__ import annotations  # Required for Python 3.7
 import openai
 import anthropic
 import litellm
 import instructor
-from typing import TypeVar
+from typing import TypeVar, Type, Any
 
 T = TypeVar("T")
 
@@ -77,10 +78,11 @@ 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
-```
+
+
+def create(model: str, response_model: 'Any') -> 'T': ...  # type: ignore
+def chat_completions_create(model: str, response_model: 'Any') -> 'T': ...  # type: ignore
+def messages_create(model: str, response_model: 'Any') -> 'T': ...  # type: ignore
 
 ## Type are infered correctly
 
@@ -204,25 +206,25 @@ user_stream = client.chat.completions.create_partial(
 
 for user in user_stream:
     print(user)
-    #> name=None age=None
-    #> name=None age=None
-    #> name=None age=None
-    #> name=None age=25
-    #> name=None age=25
-    #> name=None age=25
-    #> name='' age=25
-    #> 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=None age=None
+    # > name=None age=None
+    # > name=None age=25
+    # > name=None age=25
+    # > name=None age=25
+    # > name='' age=25
+    # > 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
+
+# Note: The return type is Generator[User, None, None]
 ```
 
-Notice now that the type infered is `Generator[User, None]`
-
 ![generator](./img/generator.png)
 
 ### Streaming Iterables: `create_iterable`