e18369ba0d
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
108 lines
3.8 KiB
Python
108 lines
3.8 KiB
Python
# Copyright (c) 2024 Intel Corporation
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import os
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from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
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from comps.cores.proto.api_protocol import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseChoice,
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ChatMessage,
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UsageInfo,
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)
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from fastapi import Request
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from fastapi.responses import StreamingResponse
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MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
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LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
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LLM_SERVICE_PORT = int(os.getenv("LLM_SERVICE_PORT", 9000))
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class TranslationService:
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def __init__(self, host="0.0.0.0", port=8000):
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self.host = host
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self.port = port
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self.megaservice = ServiceOrchestrator()
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self.endpoint = str(MegaServiceEndpoint.TRANSLATION)
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def add_remote_service(self):
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llm = MicroService(
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name="llm",
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host=LLM_SERVICE_HOST_IP,
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port=LLM_SERVICE_PORT,
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endpoint="/v1/chat/completions",
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use_remote_service=True,
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service_type=ServiceType.LLM,
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)
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self.megaservice.add(llm)
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async def handle_request(self, request: Request):
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data = await request.json()
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language_from = data["language_from"]
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language_to = data["language_to"]
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source_language = data["source_language"]
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prompt_template = """
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Translate this from {language_from} to {language_to}:
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{language_from}:
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{source_language}
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{language_to}:
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"""
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prompt = prompt_template.format(
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language_from=language_from, language_to=language_to, source_language=source_language
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)
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result_dict, runtime_graph = await self.megaservice.schedule(initial_inputs={"query": prompt})
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for node, response in result_dict.items():
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# Here it suppose the last microservice in the megaservice is LLM.
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if (
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isinstance(response, StreamingResponse)
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and node == list(self.megaservice.services.keys())[-1]
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and self.megaservice.services[node].service_type == ServiceType.LLM
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):
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return response
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last_node = runtime_graph.all_leaves()[-1]
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response = result_dict[last_node]["text"]
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choices = []
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usage = UsageInfo()
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choices.append(
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ChatCompletionResponseChoice(
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index=0,
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message=ChatMessage(role="assistant", content=response),
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finish_reason="stop",
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)
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)
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return ChatCompletionResponse(model="translation", choices=choices, usage=usage)
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def start(self):
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self.service = MicroService(
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self.__class__.__name__,
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service_role=ServiceRoleType.MEGASERVICE,
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host=self.host,
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port=self.port,
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endpoint=self.endpoint,
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input_datatype=ChatCompletionRequest,
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output_datatype=ChatCompletionResponse,
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)
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self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
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self.service.start()
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if __name__ == "__main__":
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translation = TranslationService(port=MEGA_SERVICE_PORT)
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translation.add_remote_service()
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translation.start()
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