mirror of
https://github.com/trustgraph-ai/trustgraph.git
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150 lines
4.4 KiB
Python
Executable file
150 lines
4.4 KiB
Python
Executable file
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"""
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Simple LLM service, performs text prompt completion using an Ollama service.
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Input is prompt, output is response.
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"""
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from ollama import Client
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from prometheus_client import Histogram, Info
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import os
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from .... schema import TextCompletionRequest, TextCompletionResponse, Error
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from .... schema import text_completion_request_queue
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from .... schema import text_completion_response_queue
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from .... log_level import LogLevel
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from .... base import ConsumerProducer
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from .... exceptions import TooManyRequests
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = text_completion_request_queue
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default_output_queue = text_completion_response_queue
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default_subscriber = module
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default_model = 'gemma2:9b'
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default_ollama = os.getenv("OLLAMA_HOST", 'http://localhost:11434')
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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model = params.get("model", default_model)
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ollama = params.get("ollama", default_ollama)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"model": model,
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"ollama": ollama,
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"input_schema": TextCompletionRequest,
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"output_schema": TextCompletionResponse,
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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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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__, "model_metric"):
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__class__.model_metric = Info(
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'model', 'Model information'
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)
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__class__.model_metric.info({
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"model": model,
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"ollama": ollama,
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})
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self.model = model
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self.llm = Client(host=ollama)
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async def handle(self, msg):
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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print(f"Handling prompt {id}...", flush=True)
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prompt = v.system + "\n\n" + v.prompt
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try:
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with __class__.text_completion_metric.time():
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response = self.llm.generate(self.model, prompt)
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response_text = response['response']
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print("Send response...", flush=True)
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print(response_text, flush=True)
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inputtokens = int(response['prompt_eval_count'])
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outputtokens = int(response['eval_count'])
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r = TextCompletionResponse(response=response_text, error=None, in_token=inputtokens, out_token=outputtokens, model="ollama")
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await self.send(r, properties={"id": id})
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print("Done.", flush=True)
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# SLM, presumably no rate limits
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = 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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await self.send(r, properties={"id": id})
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self.consumer.acknowledge(msg)
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@staticmethod
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def add_args(parser):
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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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parser.add_argument(
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'-m', '--model',
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default="gemma2",
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help=f'LLM model (default: {default_model})'
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)
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parser.add_argument(
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'-r', '--ollama',
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default=default_ollama,
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help=f'ollama (default: {default_ollama})'
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)
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def run():
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Processor.launch(module, __doc__)
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