144 lines
5.7 KiB
Python
144 lines
5.7 KiB
Python
# Copyright (C) 2024 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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.mega.utils import handle_message
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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 comps.cores.proto.docarray import LLMParams
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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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EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
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EMBEDDING_SERVICE_PORT = int(os.getenv("EMBEDDING_SERVICE_PORT", 6000))
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WEB_RETRIEVER_SERVICE_HOST_IP = os.getenv("WEB_RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
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WEB_RETRIEVER_SERVICE_PORT = int(os.getenv("WEB_RETRIEVER_SERVICE_PORT", 7000))
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RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
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RERANK_SERVICE_PORT = int(os.getenv("RERANK_SERVICE_PORT", 8000))
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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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def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
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next_data = {}
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if self.services[cur_node].service_type == ServiceType.EMBEDDING:
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next_data = {"text": inputs["input"], "embedding": data["data"][0]["embedding"], "k": 1}
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return next_data
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else:
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return data
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class SearchQnAService:
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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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ServiceOrchestrator.align_outputs = align_outputs
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self.megaservice = ServiceOrchestrator()
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self.endpoint = str(MegaServiceEndpoint.SEARCH_QNA)
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def add_remote_service(self):
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embedding = MicroService(
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name="embedding",
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host=EMBEDDING_SERVICE_HOST_IP,
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port=EMBEDDING_SERVICE_PORT,
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endpoint="/v1/embeddings",
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use_remote_service=True,
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service_type=ServiceType.EMBEDDING,
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)
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web_retriever = MicroService(
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name="web_retriever",
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host=WEB_RETRIEVER_SERVICE_HOST_IP,
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port=WEB_RETRIEVER_SERVICE_PORT,
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endpoint="/v1/web_retrieval",
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use_remote_service=True,
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service_type=ServiceType.WEB_RETRIEVER,
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)
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rerank = MicroService(
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name="rerank",
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host=RERANK_SERVICE_HOST_IP,
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port=RERANK_SERVICE_PORT,
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endpoint="/v1/reranking",
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use_remote_service=True,
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service_type=ServiceType.RERANK,
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)
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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(embedding).add(web_retriever).add(rerank).add(llm)
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self.megaservice.flow_to(embedding, web_retriever)
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self.megaservice.flow_to(web_retriever, rerank)
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self.megaservice.flow_to(rerank, llm)
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async def handle_request(self, request: Request):
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data = await request.json()
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stream_opt = data.get("stream", True)
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chat_request = ChatCompletionRequest.parse_obj(data)
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prompt = handle_message(chat_request.messages)
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parameters = LLMParams(
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max_tokens=chat_request.max_tokens if chat_request.max_tokens else 1024,
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top_k=chat_request.top_k if chat_request.top_k else 10,
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top_p=chat_request.top_p if chat_request.top_p else 0.95,
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temperature=chat_request.temperature if chat_request.temperature else 0.01,
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frequency_penalty=chat_request.frequency_penalty if chat_request.frequency_penalty else 0.0,
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presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
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repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
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stream=stream_opt,
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)
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result_dict, runtime_graph = await self.megaservice.schedule(
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initial_inputs={"input": prompt}, llm_parameters=parameters
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)
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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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print(f"================= result: {result_dict[last_node]}")
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response = result_dict[last_node]["choices"][0]["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="searchqna", 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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searchqna = SearchQnAService(port=MEGA_SERVICE_PORT)
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searchqna.add_remote_service()
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searchqna.start()
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