address comments left by @jxnl on #682 (Anthropic streaming support);

docs/concepts/partial.md

@@ -1,47 +1,50 @@
-# Streaming Partial Responses
+# Streaming Partial Responses (Anthropics Support)
+
 
 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.
 
+Anthropics now supports streaming with the `create_partial` function. This allows for streaming of partial responses with Anthropics as a provider. The examples in this document have been updated to reflect this change.
+
 Instructor supports this pattern by making use of `create_partial`. This lets us dynamically create a new class that treats all of the original model's fields as `Optional`.
 
 ## Understanding Partial Responses
 
 Consider what happens whene we define a response model:
 
-```python
+\`\`\`python
 from pydantic import BaseModel
 
 
 class User(BaseModel):
     name: str
     age: int
-```
+\`\`\`
 
 If we streamed json out from OpenAI, we would only be able to parse when the object is completed returned!
 
-```
+\`\`\`
 {"name": "Jo
 {"name": "John", "ag
 {"name": "John", "age":
 {"name": "John", "age": 25} # Completed
-```
+\`\`\`
 
 When specifying a `create_partial` and setting `stream=True`, the response from `instructor` becomes a `Generator[T]`. As the generator yields results, you can iterate over these incremental updates. The last value yielded by the generator represents the completed extraction!
 
-```
+\`\`\`
 {"name": "Jo                 => User(name="Jo", age=None)
 {"name": "John", "ag         => User(name="John", age=None)
 {"name": "John", "age:       => User(name="John", age=None)
 {"name": "John", "age": 25}  => User(name="John", age=25)
-```
+\`\`\`
 
 !!! warning "Limited Validator Support"
 
     Due to the streaming nature of the response model, we do not support validators since they would not be able to be applied to the streaming response.
 
 Let's look at an example of streaming an extraction of conference information, that would be used to stream in an react component.
 
-```python
+\`\`\`python
 import instructor
 from openai import OpenAI
 from pydantic import BaseModel
@@ -50,6 +53,8 @@ from rich.console import Console
 
 client = instructor.from_openai(OpenAI())
 
+client = instructor.from_anthropics(Anthropics())
+
 text_block = """
 In our recent online meeting, participants from various backgrounds joined to discuss the upcoming tech conference. The names and contact details of the participants were as follows:
 
@@ -65,12 +70,14 @@ A follow-up meetingis scheduled for January 25th at 3 PM GMT to finalize the age
 """
 
 
+
 class User(BaseModel):
     name: str
     email: str
     twitter: str
 
 
+
 class MeetingInfo(BaseModel):
     users: List[User]
     date: str
@@ -79,8 +86,9 @@ class MeetingInfo(BaseModel):
     deadline: str
 
 
+
 extraction_stream = client.chat.completions.create_partial(
-    model="gpt-4",
+    model="claude-3-haiku-20240307",
     response_model=MeetingInfo,
     messages=[
         {
@@ -92,6 +100,7 @@ extraction_stream = client.chat.completions.create_partial(
 )
 
 
+
 console = Console()
 
 for extraction in extraction_stream:
@@ -100,6 +109,7 @@ for extraction in extraction_stream:
     console.print(obj)
 
 print(extraction.model_dump_json(indent=2))
+
 """
 {
   "users": [
@@ -125,7 +135,8 @@ print(extraction.model_dump_json(indent=2))
   "deadline": "February 20th"
 }
 """
-```
+
+\`\`\`
 
 This will output the following:
 
@@ -135,22 +146,23 @@ This will output the following:
 
 I also just want to call out in this example that `instructor` also supports asynchronous streaming. This is useful when you want to stream a response model and process the results as they come in, but you'll need to use the `async for` syntax to iterate over the results.
 
-```python
+
 import instructor
-from openai import AsyncOpenAI
+from anthropics import AsyncAnthropics
 from pydantic import BaseModel
 
-client = instructor.from_openai(AsyncOpenAI())
+client = instructor.from_anthropics(AsyncAnthropics())
 
 
 class User(BaseModel):
     name: str
     age: int
 
 
+
 async def print_partial_results():
     user = client.chat.completions.create_partial(
-        model="gpt-4-turbo-preview",
+        model="claude-3-haiku-20240307",
         response_model=User,
         max_retries=2,
         stream=True,
@@ -172,7 +184,7 @@ async def print_partial_results():
         #> name='Jason' age=12
 
 
+
 import asyncio
 
-asyncio.run(print_partial_results())
-```
+asyncio.run(print_partial_results())