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from abc import ABC, abstractmethod
from typing import Callable, Awaitable, Optional
from aiohttp import ClientResponse
from pydantic import ConfigDict, BaseModel
from starlette.responses import StreamingResponse as _StreamingResponse
from starlette.types import Send, Receive, Scope
from .synapse import Synapse
[docs]
class BTStreamingResponseModel(BaseModel):
"""
:func:`BTStreamingResponseModel` is a Pydantic model that encapsulates the token streamer callable for Pydantic validation.
It is used within the :func:`StreamingSynapse` class to create a :func:`BTStreamingResponse` object, which is responsible for handling
the streaming of tokens.
The token streamer is a callable that takes a send function and returns an awaitable. It is responsible for generating
the content of the streaming response, typically by processing tokens and sending them to the client.
This model ensures that the token streamer conforms to the expected signature and provides a clear interface for
passing the token streamer to the BTStreamingResponse class.
Attributes:
token_streamer: Callable[[Send], Awaitable[None]] The token streamer callable, which takes a send function (provided by the ASGI server) and returns an awaitable. It is responsible for generating the content of the streaming response.
"""
token_streamer: Callable[[Send], Awaitable[None]]
[docs]
class StreamingSynapse(Synapse, ABC):
"""
The :func:`StreamingSynapse` class is designed to be subclassed for handling streaming responses in the Bittensor network.
It provides abstract methods that must be implemented by the subclass to deserialize, process streaming responses,
and extract JSON data. It also includes a method to create a streaming response object.
"""
model_config = ConfigDict(validate_assignment=True)
[docs]
class BTStreamingResponse(_StreamingResponse):
"""
:func:`BTStreamingResponse` is a specialized subclass of the Starlette StreamingResponse designed to handle the streaming
of tokens within the Bittensor network. It is used internally by the StreamingSynapse class to manage the response
streaming process, including sending headers and calling the token streamer provided by the subclass.
This class is not intended to be directly instantiated or modified by developers subclassing StreamingSynapse.
Instead, it is used by the :func:`create_streaming_response` method to create a response object based on the token streamer
provided by the subclass.
"""
def __init__(
self,
model: "BTStreamingResponseModel",
*,
synapse: "Optional[StreamingSynapse]" = None,
**kwargs,
):
"""
Initializes the BTStreamingResponse with the given token streamer model.
Args:
model (bittensor.core.stream.BTStreamingResponseModel): A BTStreamingResponseModel instance containing the token streamer callable, which is responsible for generating the content of the response.
synapse (bittensor.core.stream.StreamingSynapse): The response Synapse to be used to update the response headers etc.
**kwargs: Additional keyword arguments passed to the parent StreamingResponse class.
"""
super().__init__(content=iter(()), **kwargs)
self.token_streamer = model.token_streamer
self.synapse = synapse
[docs]
async def stream_response(self, send: "Send"):
"""
Asynchronously streams the response by sending headers and calling the token streamer.
This method is responsible for initiating the response by sending the appropriate headers, including the content type for event-streaming. It then calls the token streamer to generate the content and sends the response body to the client.
Args:
send (starlette.types.Send): A callable to send the response, provided by the ASGI server.
"""
headers = [(b"content-type", b"text/event-stream")] + self.raw_headers
await send(
{"type": "http.response.start", "status": 200, "headers": headers}
)
await self.token_streamer(send)
await send({"type": "http.response.body", "body": b"", "more_body": False})
[docs]
async def __call__(self, scope: "Scope", receive: "Receive", send: "Send"):
"""
Asynchronously calls the :func:`stream_response method`, allowing the :func:`BTStreamingResponse` object to be used as an ASGI application.
This method is part of the ASGI interface and is called by the ASGI server to handle the request and send the response. It delegates to the :func:`stream_response` method to perform the actual streaming process.
Args:
scope (starlette.types.Scope): The scope of the request, containing information about the client, server, and request itself.
receive (starlette.types.Receive): A callable to receive the request, provided by the ASGI server.
send (starlette.types.Send): A callable to send the response, provided by the ASGI server.
"""
await self.stream_response(send)
[docs]
@abstractmethod
async def process_streaming_response(self, response: "ClientResponse"):
"""
Abstract method that must be implemented by the subclass.
This method should provide logic to handle the streaming response, such as parsing and accumulating data.
It is called as the response is being streamed from the network, and should be implemented to handle the specific streaming data format and requirements of the subclass.
Args:
response (aiohttp.ClientResponse): The response object to be processed, typically containing chunks of data.
"""
...
[docs]
def create_streaming_response(
self, token_streamer: Callable[[Send], Awaitable[None]]
) -> "BTStreamingResponse":
"""
Creates a streaming response using the provided token streamer.
This method can be used by the subclass to create a response object that can be sent back to the client.
The token streamer should be implemented to generate the content of the response according to the specific requirements of the subclass.
Args:
token_streamer (Callable[[starlette.types.Send], Awaitable[None]]): A callable that takes a send function and returns an awaitable. It's responsible for generating the content of the response.
Returns:
BTStreamingResponse (bittensor.core.stream.StreamingSynapse.BTStreamingResponse): The streaming response object, ready to be sent to the client.
"""
model_instance = BTStreamingResponseModel(token_streamer=token_streamer)
return self.BTStreamingResponse(model_instance, synapse=self)
