mirror of
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168 lines
4.7 KiB
Python
168 lines
4.7 KiB
Python
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"""
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LLM text completion base class
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"""
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import time
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import logging
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from prometheus_client import Histogram, Info
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from .. schema import TextCompletionRequest, TextCompletionResponse, Error
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from .. exceptions import TooManyRequests
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from .. base import FlowProcessor, ConsumerSpec, ProducerSpec, ParameterSpec
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# Module logger
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logger = logging.getLogger(__name__)
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default_ident = "text-completion"
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default_concurrency = 1
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class LlmResult:
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def __init__(
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self, text = None, in_token = None, out_token = None,
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model = None,
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):
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self.text = text
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self.in_token = in_token
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self.out_token = out_token
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self.model = model
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__slots__ = ["text", "in_token", "out_token", "model"]
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class LlmService(FlowProcessor):
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def __init__(self, **params):
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id = params.get("id")
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concurrency = params.get("concurrency", 1)
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super(LlmService, self).__init__(**params | {
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"id": id,
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"concurrency": concurrency,
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})
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self.register_specification(
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ConsumerSpec(
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name = "request",
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schema = TextCompletionRequest,
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handler = self.on_request,
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concurrency = concurrency,
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)
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)
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self.register_specification(
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ProducerSpec(
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name = "response",
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schema = TextCompletionResponse
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)
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)
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self.register_specification(
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ParameterSpec(
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name = "model",
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)
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)
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self.register_specification(
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ParameterSpec(
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name = "temperature",
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)
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)
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if not hasattr(__class__, "text_completion_metric"):
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__class__.text_completion_metric = Histogram(
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'text_completion_duration',
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'Text completion duration (seconds)',
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["id", "flow"],
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buckets=[
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0.25, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0,
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8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
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17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
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30.0, 35.0, 40.0, 45.0, 50.0, 60.0, 80.0, 100.0,
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120.0
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]
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)
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if not hasattr(__class__, "text_completion_model_metric"):
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__class__.text_completion_model_metric = Info(
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'text_completion_model',
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'Text completion model',
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["processor", "flow"]
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)
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async def on_request(self, msg, consumer, flow):
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try:
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request = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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with __class__.text_completion_metric.labels(
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id=self.id,
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flow=f"{flow.name}-{consumer.name}",
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).time():
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model = flow("model")
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temperature = flow("temperature")
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response = await self.generate_content(
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request.system, request.prompt, model, temperature
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)
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await __class__.text_completion_model_metric.labels(
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processor = self.id, flow = flow.name
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).info({
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"model": model,
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"temperature": temperature,
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})
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await flow("response").send(
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TextCompletionResponse(
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error=None,
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response=response.text,
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in_token=response.in_token,
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out_token=response.out_token,
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model=response.model
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),
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properties={"id": id}
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)
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except TooManyRequests as e:
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raise e
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except Exception as e:
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# Apart from rate limits, treat all exceptions as unrecoverable
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logger.error(f"LLM service exception: {e}", exc_info=True)
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logger.debug("Sending error response...")
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await flow.producer["response"].send(
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TextCompletionResponse(
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error=Error(
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type = "llm-error",
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message = str(e),
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),
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response=None,
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in_token=None,
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out_token=None,
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model=None,
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),
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properties={"id": id}
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)
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@staticmethod
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def add_args(parser):
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parser.add_argument(
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'-c', '--concurrency',
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type=int,
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default=default_concurrency,
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help=f'Concurrent processing threads (default: {default_concurrency})'
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)
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FlowProcessor.add_args(parser)
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