docs(readme): assistant streaming

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.