chatqna_wrapper.py

# 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()