This PR fixes the VideoQnA example. Fixes Issues #1476 #1478 #1477 Signed-off-by: zhanmyz <yazhan.ma@intel.com> Signed-off-by: Lacewell, Chaunte W <chaunte.w.lacewell@intel.com>
151 lines
6.1 KiB
Python
151 lines
6.1 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, TextDoc
|
|
from fastapi import Request
|
|
from fastapi.responses import StreamingResponse
|
|
|
|
MEGA_SERVICE_PORT = int(os.getenv("BACKEND_PORT", 8888))
|
|
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
|
|
EMBEDDING_SERVICE_PORT = int(os.getenv("EMBEDDER_PORT", 6990))
|
|
RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
|
|
RETRIEVER_SERVICE_PORT = int(os.getenv("RETRIEVER_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))
|
|
LVM_SERVICE_HOST_IP = os.getenv("LVM_SERVICE_HOST_IP", "0.0.0.0")
|
|
LVM_SERVICE_PORT = int(os.getenv("LVM_PORT", 9399))
|
|
|
|
|
|
def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
|
|
service_type = self.services[cur_node].service_type
|
|
if service_type == ServiceType.EMBEDDING:
|
|
if "input" in inputs:
|
|
input_text = inputs["input"]["text"] if isinstance(inputs["input"], dict) else inputs["input"]
|
|
inputs = TextDoc(text=input_text).model_dump()
|
|
return inputs
|
|
|
|
|
|
def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
|
|
if self.services[cur_node].service_type == ServiceType.EMBEDDING:
|
|
return {"text": inputs["text"], "embedding": data["embedding"]}
|
|
else:
|
|
return data
|
|
|
|
|
|
class VideoQnAService:
|
|
def __init__(self, host="0.0.0.0", port=8888):
|
|
self.host = host
|
|
self.port = port
|
|
ServiceOrchestrator.align_inputs = align_inputs
|
|
ServiceOrchestrator.align_outputs = align_outputs
|
|
self.megaservice = ServiceOrchestrator()
|
|
self.endpoint = str(MegaServiceEndpoint.VIDEO_RAG_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,
|
|
)
|
|
lvm = MicroService(
|
|
name="lvm",
|
|
host=LVM_SERVICE_HOST_IP,
|
|
port=LVM_SERVICE_PORT,
|
|
endpoint="/v1/lvm",
|
|
use_remote_service=True,
|
|
service_type=ServiceType.LVM,
|
|
)
|
|
self.megaservice.add(embedding).add(retriever).add(rerank).add(lvm)
|
|
self.megaservice.flow_to(embedding, retriever)
|
|
self.megaservice.flow_to(retriever, rerank)
|
|
self.megaservice.flow_to(rerank, lvm)
|
|
|
|
async def handle_request(self, request: Request):
|
|
data = await request.json()
|
|
stream_opt = bool(data.get("stream", False))
|
|
chat_request = ChatCompletionRequest.model_validate(data)
|
|
prompt = handle_message(chat_request.messages)
|
|
parameters = LLMParams(
|
|
max_new_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,
|
|
)
|
|
result_dict, runtime_graph = await self.megaservice.schedule(
|
|
initial_inputs={"input": prompt}, llm_parameters=parameters
|
|
)
|
|
for node, response in result_dict.items():
|
|
# Here it suppose the last microservice in the megaservice is LVM.
|
|
if (
|
|
isinstance(response, StreamingResponse)
|
|
and node == list(self.megaservice.services.keys())[-1]
|
|
and self.megaservice.services[node].service_type == ServiceType.LVM
|
|
):
|
|
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="videoqna", 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__":
|
|
videoqna = VideoQnAService(port=MEGA_SERVICE_PORT)
|
|
videoqna.add_remote_service()
|
|
videoqna.start()
|