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release: 1.14.1 (openai#1239)
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* docs(readme): assistant streaming (openai#1238)

* release: 1.14.1
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stainless-bot authored and megamanics committed Aug 14, 2024
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2 changes: 1 addition & 1 deletion .release-please-manifest.json
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{
".": "1.14.0"
".": "1.14.1"
}
8 changes: 8 additions & 0 deletions CHANGELOG.md
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# Changelog

## 1.14.1 (2024-03-15)

Full Changelog: [v1.14.0...v1.14.1](https://github.com/openai/openai-python/compare/v1.14.0...v1.14.1)

### Documentation

* **readme:** assistant streaming ([#1238](https://github.com/openai/openai-python/issues/1238)) ([0fc30a2](https://github.com/openai/openai-python/commit/0fc30a23030b4ff60f27cd2f472517926ed0f300))

## 1.14.0 (2024-03-13)

Full Changelog: [v1.13.4...v1.14.0](https://github.com/openai/openai-python/compare/v1.13.4...v1.14.0)
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161 changes: 161 additions & 0 deletions helpers.md
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# Streaming Helpers

OpenAI supports streaming responses when interacting with the [Assistant](#assistant-streaming-api) APIs.

## Assistant Streaming API

OpenAI supports streaming responses from Assistants. The SDK provides convenience wrappers around the API
so you can subscribe to the types of events you are interested in as well as receive accumulated responses.

More information can be found in the documentation: [Assistant Streaming](https://platform.openai.com/docs/assistants/overview?lang=python)

#### An example of creating a run and subscribing to some events

You can subscribe to events by creating an event handler class and overloading the relevant event handlers.

```python
from typing_extensions import override
from openai import AssistantEventHandler

# First, we create a EventHandler class to define
# how we want to handle the events in the response stream.

class EventHandler(AssistantEventHandler):
@override
def on_text_created(self, text) -> None:
print(f"\nassistant > ", end="", flush=True)

@override
def on_text_delta(self, delta, snapshot):
print(delta.value, end="", flush=True)

def on_tool_call_created(self, tool_call):
print(f"\nassistant > {tool_call.type}\n", flush=True)

def on_tool_call_delta(self, delta, snapshot):
if delta.type == 'code_interpreter':
if delta.code_interpreter.input:
print(delta.code_interpreter.input, end="", flush=True)
if delta.code_interpreter.outputs:
print(f"\n\noutput >", flush=True)
for output in delta.code_interpreter.outputs:
if output.type == "logs":
print(f"\n{output.logs}", flush=True)

# Then, we use the `create_and_stream` SDK helper
# with the `EventHandler` class to create the Run
# and stream the response.

with client.beta.threads.runs.create_and_stream(
thread_id=thread.id,
assistant_id=assistant.id,
instructions="Please address the user as Jane Doe. The user has a premium account.",
event_handler=EventHandler(),
) as stream:
stream.until_done()
```

### Assistant Events

The assistant API provides events you can subscribe to for the following events.

```python
def on_event(self, event: AssistantStreamEvent)
```

This allows you to subscribe to all the possible raw events sent by the OpenAI streaming API.
In many cases it will be more convenient to subscribe to a more specific set of events for your use case.

More information on the types of events can be found here: [Events](https://platform.openai.com/docs/api-reference/assistants-streaming/events)

```python
def on_run_step_created(self, run_step: RunStep)
def on_run_step_delta(self, delta: RunStepDelta, snapshot: RunStep)
def on_run_step_done(self, run_step: RunStep)
```

These events allow you to subscribe to the creation, delta and completion of a RunStep.

For more information on how Runs and RunSteps work see the documentation [Runs and RunSteps](https://platform.openai.com/docs/assistants/how-it-works/runs-and-run-steps)

```python
def on_message_created(self, message: Message)
def on_message_delta(self, delta: MessageDelta, snapshot: Message)
def on_message_done(self, message: Message)
```

This allows you to subscribe to Message creation, delta and completion events. Messages can contain
different types of content that can be sent from a model (and events are available for specific content types).
For convenience, the delta event includes both the incremental update and an accumulated snapshot of the content.

More information on messages can be found
on in the documentation page [Message](https://platform.openai.com/docs/api-reference/messages/object).

```python
def on_text_created(self, text: Text)
def on_text_delta(self, delta: TextDelta, snapshot: Text)
def on_text_done(self, text: Text)
```

These events allow you to subscribe to the creation, delta and completion of a Text content (a specific type of message).
For convenience, the delta event includes both the incremental update and an accumulated snapshot of the content.

```python
def on_image_file_done(self, image_file: ImageFile)
```

Image files are not sent incrementally so an event is provided for when a image file is available.

```python
def on_tool_call_created(self, tool_call: ToolCall)
def on_tool_call_delta(self, delta: ToolCallDelta, snapshot: ToolCall)
def on_tool_call_done(self, tool_call: ToolCall)
```

These events allow you to subscribe to events for the creation, delta and completion of a ToolCall.

More information on tools can be found here [Tools](https://platform.openai.com/docs/assistants/tools)

```python
def on_end(self)
```

The last event send when a stream ends.

```python
def on_timeout(self)
```

This event is triggered if the request times out.

```python
def on_exception(self, exception: Exception)
```

This event is triggered if an exception occurs during streaming.

### Assistant Methods

The assistant streaming object also provides a few methods for convenience:

```python
def current_event()
def current_run()
def current_message_snapshot()
def current_run_step_snapshot()
```

These methods are provided to allow you to access additional context from within event handlers. In many cases
the handlers should include all the information you need for processing, but if additional context is required it
can be accessed.

Note: There is not always a relevant context in certain situations (these will be undefined in those cases).

```python
def get_final_run(self)
def get_final_run_steps(self)
def get_final_messages(self)
```

These methods are provided for convenience to collect information at the end of a stream. Calling these events
will trigger consumption of the stream until completion and then return the relevant accumulated objects.
2 changes: 1 addition & 1 deletion pyproject.toml
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[project]
name = "openai"
version = "1.14.0"
version = "1.14.1"
description = "The official Python library for the openai API"
readme = "README.md"
license = "Apache-2.0"
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2 changes: 1 addition & 1 deletion src/openai/_version.py
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# File generated from our OpenAPI spec by Stainless.

__title__ = "openai"
__version__ = "1.14.0" # x-release-please-version
__version__ = "1.14.1" # x-release-please-version

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