143 lines
5.9 KiB
Python
143 lines
5.9 KiB
Python
# Copyright (C) 2024 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import os
|
|
|
|
from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
|
|
from comps.cores.mega.utils import handle_message
|
|
from comps.cores.proto.api_protocol import (
|
|
ChatCompletionRequest,
|
|
ChatCompletionResponse,
|
|
ChatCompletionResponseChoice,
|
|
ChatMessage,
|
|
UsageInfo,
|
|
)
|
|
from comps.cores.proto.docarray import LLMParams, RerankerParms, RetrieverParms
|
|
from fastapi import Request
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
MEGA_SERVICE_HOST_IP = os.getenv("MEGA_SERVICE_HOST_IP", "0.0.0.0")
|
|
MEGA_SERVICE_PORT = int(os.getenv("MEGA_SERVICE_PORT", 8888))
|
|
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
|
|
EMBEDDING_SERVICE_PORT = int(os.getenv("EMBEDDING_SERVICE_PORT", 6000))
|
|
RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
|
|
RETRIEVER_SERVICE_PORT = int(os.getenv("RETRIEVER_SERVICE_PORT", 7000))
|
|
RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
|
|
RERANK_SERVICE_PORT = int(os.getenv("RERANK_SERVICE_PORT", 8000))
|
|
LLM_SERVICE_HOST_IP = os.getenv("LLM_SERVICE_HOST_IP", "0.0.0.0")
|
|
LLM_SERVICE_PORT = int(os.getenv("LLM_SERVICE_PORT", 9000))
|
|
|
|
|
|
class ChatQnAService:
|
|
def __init__(self, host="0.0.0.0", port=8000):
|
|
self.host = host
|
|
self.port = port
|
|
self.megaservice = ServiceOrchestrator()
|
|
self.endpoint = str(MegaServiceEndpoint.CHAT_QNA)
|
|
|
|
def add_remote_service(self):
|
|
embedding = MicroService(
|
|
name="embedding",
|
|
host=EMBEDDING_SERVICE_HOST_IP,
|
|
port=EMBEDDING_SERVICE_PORT,
|
|
endpoint="/v1/embeddings",
|
|
use_remote_service=True,
|
|
service_type=ServiceType.EMBEDDING,
|
|
)
|
|
retriever = MicroService(
|
|
name="retriever",
|
|
host=RETRIEVER_SERVICE_HOST_IP,
|
|
port=RETRIEVER_SERVICE_PORT,
|
|
endpoint="/v1/retrieval",
|
|
use_remote_service=True,
|
|
service_type=ServiceType.RETRIEVER,
|
|
)
|
|
rerank = MicroService(
|
|
name="rerank",
|
|
host=RERANK_SERVICE_HOST_IP,
|
|
port=RERANK_SERVICE_PORT,
|
|
endpoint="/v1/reranking",
|
|
use_remote_service=True,
|
|
service_type=ServiceType.RERANK,
|
|
)
|
|
llm = MicroService(
|
|
name="llm",
|
|
host=LLM_SERVICE_HOST_IP,
|
|
port=LLM_SERVICE_PORT,
|
|
endpoint="/v1/chat/completions",
|
|
use_remote_service=True,
|
|
service_type=ServiceType.LLM,
|
|
)
|
|
self.megaservice.add(embedding).add(retriever).add(rerank).add(llm)
|
|
self.megaservice.flow_to(embedding, retriever)
|
|
self.megaservice.flow_to(retriever, rerank)
|
|
self.megaservice.flow_to(rerank, llm)
|
|
|
|
async def handle_request(self, request: Request):
|
|
data = await request.json()
|
|
stream_opt = data.get("stream", True)
|
|
chat_request = ChatCompletionRequest.parse_obj(data)
|
|
prompt = handle_message(chat_request.messages)
|
|
parameters = LLMParams(
|
|
max_tokens=chat_request.max_tokens if chat_request.max_tokens else 1024,
|
|
top_k=chat_request.top_k if chat_request.top_k else 10,
|
|
top_p=chat_request.top_p if chat_request.top_p else 0.95,
|
|
temperature=chat_request.temperature if chat_request.temperature else 0.01,
|
|
frequency_penalty=chat_request.frequency_penalty if chat_request.frequency_penalty else 0.0,
|
|
presence_penalty=chat_request.presence_penalty if chat_request.presence_penalty else 0.0,
|
|
repetition_penalty=chat_request.repetition_penalty if chat_request.repetition_penalty else 1.03,
|
|
stream=stream_opt,
|
|
chat_template=chat_request.chat_template if chat_request.chat_template else None,
|
|
)
|
|
retriever_parameters = RetrieverParms(
|
|
search_type=chat_request.search_type if chat_request.search_type else "similarity",
|
|
k=chat_request.k if chat_request.k else 4,
|
|
distance_threshold=chat_request.distance_threshold if chat_request.distance_threshold else None,
|
|
fetch_k=chat_request.fetch_k if chat_request.fetch_k else 20,
|
|
lambda_mult=chat_request.lambda_mult if chat_request.lambda_mult else 0.5,
|
|
score_threshold=chat_request.score_threshold if chat_request.score_threshold else 0.2,
|
|
)
|
|
reranker_parameters = RerankerParms(
|
|
top_n=chat_request.top_n if chat_request.top_n else 1,
|
|
)
|
|
result_dict, runtime_graph = await self.megaservice.schedule(
|
|
initial_inputs={"text": prompt},
|
|
llm_parameters=parameters,
|
|
retriever_parameters=retriever_parameters,
|
|
reranker_parameters=reranker_parameters,
|
|
)
|
|
for node, response in result_dict.items():
|
|
if isinstance(response, StreamingResponse):
|
|
return response
|
|
last_node = runtime_graph.all_leaves()[-1]
|
|
response = result_dict[last_node]["text"]
|
|
choices = []
|
|
usage = UsageInfo()
|
|
choices.append(
|
|
ChatCompletionResponseChoice(
|
|
index=0,
|
|
message=ChatMessage(role="assistant", content=response),
|
|
finish_reason="stop",
|
|
)
|
|
)
|
|
return ChatCompletionResponse(model="chatqna", choices=choices, usage=usage)
|
|
|
|
def start(self):
|
|
self.service = MicroService(
|
|
self.__class__.__name__,
|
|
service_role=ServiceRoleType.MEGASERVICE,
|
|
host=self.host,
|
|
port=self.port,
|
|
endpoint=self.endpoint,
|
|
input_datatype=ChatCompletionRequest,
|
|
output_datatype=ChatCompletionResponse,
|
|
)
|
|
self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
|
|
self.service.start()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
chatqna = ChatQnAService(port=MEGA_SERVICE_PORT)
|
|
chatqna.add_remote_service()
|
|
chatqna.start()
|