2f36e57236
Signed-off-by: zehao-intel <zehao.huang@intel.com> Co-authored-by: lvliang-intel <liang1.lv@intel.com> Co-authored-by: chensuyue <suyue.chen@intel.com>
54 lines
1.9 KiB
Python
54 lines
1.9 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 DocSumGateway, MicroService, ServiceOrchestrator, ServiceType
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SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
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class DocSumService:
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def __init__(self, port=8000):
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self.port = port
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self.megaservice = ServiceOrchestrator()
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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=SERVICE_HOST_IP,
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port=9000,
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endpoint="/v1/chat/docsum",
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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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self.gateway = DocSumGateway(megaservice=self.megaservice, host="0.0.0.0", port=self.port)
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async def schedule(self):
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await self.megaservice.schedule(
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initial_inputs={
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"text": "Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding, Ember, GTE and E5."
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}
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)
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result_dict = self.megaservice.result_dict
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print(result_dict)
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if __name__ == "__main__":
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docsum = DocSumService(port=8888)
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docsum.add_remote_service()
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asyncio.run(docsum.schedule())
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