add streaming examples

README.md

@@ -147,14 +147,15 @@ transcript = '''
 [00:04:30] Customer: But I think the pricing for additional users is a bit steep compared to other solutions we looked at.
 '''
 
-# Analyze the feedback
-result = await analyze_call_feedback(
-    CallFeedbackInput(
-        transcript=transcript,
-        call_date=date(2024, 1, 15)
-    )
+# Create input
+feedback_input = CallFeedbackInput(
+    transcript=transcript,
+    call_date=date(2024, 1, 15)
 )
 
+# Analyze the feedback
+result = await analyze_call_feedback(feedback_input)
+
 # Print the analysis
 print("\nPositive Points:")
 for point in result.positive_points:
@@ -212,7 +213,7 @@ more flexible than changing the function parameters when running in production.
 
 ```python
 @workflowai.agent(deployment="production") # or simply @workflowai.agent()
-async def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[Run[CallFeedbackOutput]]:
+def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[Run[CallFeedbackOutput]]:
     ...
 ```
 
@@ -230,7 +231,7 @@ async def analyze_call_feedback(input: CallFeedbackInput) -> Run[CallFeedbackOut
     ...
 
 
-run = await say_hello(Input(name="John"))
+run = await analyze_call_feedback(feedback_input)
 print(run.output) # the output, as before
 print(run.model) # the model used for the run
 print(run.cost_usd) # the cost of the run in USD
@@ -241,16 +242,55 @@ print(run.duration_seconds) # the duration of the inference in seconds
 
 You can configure the agent function to stream by changing the type annotation to an AsyncIterator.
 
+#### Streaming the output only
+
+Use `AsyncIterator[Output]` to get the **output** as it is generated.
+
 ```python
+from collections.abc import AsyncIterator
+
 # Stream the output, the output is filled as it is generated
 @workflowai.agent()
-async def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[CallFeedbackOutput]:
+def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[CallFeedbackOutput]:
     ...
 
+async for chunk in analyze_call_feedback(feedback_input):
+    # Just get the output as it's generated
+    print(chunk.output)
+```
+
+> Note: no need to mark the agent as async here ! It is already asynchronous since it returns an AsyncIterator.
+> The type checkers some times get confused since they consider that an async function that returns an AsyncIterator is
+> async twice.
+> For example, a function with the signature `async def foo() -> AsyncIterator[int]` may be called
+> `async for c in await foo():...` in certain cases...
+
+
+#### Streaming the run object
+
+Use `AsyncIterator[Run[Output]]` to get the **run** object as it is generated, which allows you, for the **last chunk**, to access the cost and duration of the run.
+
+```python
+import workflowai
+from workflowai import Run
+from collections.abc import AsyncIterator
+
 # Stream the run object, the output is filled as it is generated
 @workflowai.agent()
-async def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[Run[CallFeedbackOutput]]:
+def analyze_call_feedback(input: CallFeedbackInput) -> AsyncIterator[Run[CallFeedbackOutput]]:
     ...
+
+last_chunk = None
+
+async for chunk in analyze_call_feedback(feedback_input):
+    # Show output as it's generated
+    print(chunk.output)
+    last_chunk = chunk
+
+if last_chunk:
+    # Cost and duration are only available on the last chunk
+    print(f"\nCost: ${last_chunk.cost_usd}")
+    print(f"Latency: {last_chunk.duration_seconds:.2f}s")
 ```
 
 ### Images

examples/streaming_summary.py

@@ -0,0 +1,87 @@
+import asyncio
+from collections.abc import AsyncIterator
+
+from pydantic import BaseModel, Field  # pyright: ignore [reportUnknownVariableType]
+
+import workflowai
+from workflowai import Model, Run
+
+
+class TranslationInput(BaseModel):
+    """Input for text translation."""
+
+    text: str = Field(description="The French text to translate.")
+
+
+class TranslationOutput(BaseModel):
+    """Output containing the translated text."""
+
+    translation: str = Field(
+        default="",
+        description="The text translated into English.",
+    )
+
+
+@workflowai.agent(id="french-translator", model=Model.CLAUDE_3_5_SONNET_LATEST)
+def translate_to_english(_: TranslationInput) -> AsyncIterator[Run[TranslationOutput]]:
+    """
+    Translate French text into natural, fluent English.
+
+    Guidelines:
+    - Maintain the original tone and style
+    - Ensure accurate translation of idioms and expressions
+    - Preserve formatting and paragraph structure
+    - Focus on clarity and readability in English
+    """
+    ...
+
+
+async def main():
+    # Example French text
+    french_text = """
+    Cher journal,
+
+    Aujourd'hui, j'ai fait une magnifique randonnée dans les Alpes.
+    Le temps était parfait, avec un ciel bleu éclatant et une légère brise.
+    Les montagnes étaient encore couvertes de neige, créant un paysage à couper le souffle.
+    Les sommets majestueux se dressaient devant moi comme des géants silencieux.
+
+    En chemin, j'ai rencontré des randonneurs sympathiques qui m'ont partagé leur pique-nique.
+    Nous avons échangé des histoires et des conseils sur les meilleurs sentiers de la région.
+
+    Cette expérience restera gravée dans ma mémoire. La nature a vraiment le pouvoir de nous
+    ressourcer et de nous faire oublier le stress du quotidien.
+
+    À bientôt,
+    Pierre
+    """
+
+    print("Starting translation...\n")
+
+    # Keep track of the last chunk to get cost info
+    last_chunk = None
+    chunk_num = 1
+
+    # Stream the translation with run information
+    # We set use_cache='never' to prevent caching the response
+    # This ensures we can see the streaming effect in the example
+    # Otherwise, subsequent runs would return the cached result instantly,
+    # making it hard to observe the incremental streaming behavior
+    async for chunk in translate_to_english(TranslationInput(text=french_text), use_cache="never"):
+        print(f"--- Translation Progress (Chunk {chunk_num}) ---")
+        print(chunk.output.translation)
+        print("-" * 50)
+        chunk_num += 1
+        last_chunk = chunk
+
+    if last_chunk:
+        # Cost and duration are only available on the last streaming chunk
+        # since they represent the final totals for the complete run.
+        # We store the last chunk in last_chunk to access these values
+        # after streaming completes.
+        print(f"\nCost: ${last_chunk.cost_usd}")
+        print(f"Latency: {last_chunk.duration_seconds:.2f}s")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())