223 lines
8.7 KiB
Python
223 lines
8.7 KiB
Python
# Copyright (C) 2024 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import argparse
|
|
import asyncio
|
|
import os
|
|
from typing import Union
|
|
|
|
from comps import MegaServiceEndpoint, MicroService, ServiceOrchestrator, ServiceRoleType, ServiceType
|
|
from comps.cores.proto.api_protocol import ChatCompletionRequest, EmbeddingRequest
|
|
from comps.cores.proto.docarray import LLMParams, LLMParamsDoc, RerankedDoc, RerankerParms, RetrieverParms, TextDoc
|
|
from fastapi import Request
|
|
|
|
MEGA_SERVICE_PORT = os.getenv("MEGA_SERVICE_PORT", 8889)
|
|
EMBEDDING_SERVICE_HOST_IP = os.getenv("EMBEDDING_SERVICE_HOST_IP", "0.0.0.0")
|
|
EMBEDDING_SERVICE_PORT = os.getenv("EMBEDDING_SERVICE_PORT", 6000)
|
|
RETRIEVER_SERVICE_HOST_IP = os.getenv("RETRIEVER_SERVICE_HOST_IP", "0.0.0.0")
|
|
RETRIEVER_SERVICE_PORT = os.getenv("RETRIEVER_SERVICE_PORT", 7000)
|
|
RERANK_SERVICE_HOST_IP = os.getenv("RERANK_SERVICE_HOST_IP", "0.0.0.0")
|
|
RERANK_SERVICE_PORT = os.getenv("RERANK_SERVICE_PORT", 8000)
|
|
|
|
|
|
def align_inputs(self, inputs, cur_node, runtime_graph, llm_parameters_dict, **kwargs):
|
|
print(f"*** Inputs to {cur_node}:\n{inputs}")
|
|
print("--" * 50)
|
|
for key, value in kwargs.items():
|
|
print(f"{key}: {value}")
|
|
if self.services[cur_node].service_type == ServiceType.EMBEDDING:
|
|
inputs["input"] = inputs["text"]
|
|
del inputs["text"]
|
|
elif self.services[cur_node].service_type == ServiceType.RETRIEVER:
|
|
# input is EmbedDoc
|
|
"""Class EmbedDoc(BaseDoc):
|
|
|
|
text: Union[str, List[str]]
|
|
embedding: Union[conlist(float, min_length=0), List[conlist(float, min_length=0)]]
|
|
search_type: str = "similarity"
|
|
k: int = 4
|
|
distance_threshold: Optional[float] = None
|
|
fetch_k: int = 20
|
|
lambda_mult: float = 0.5
|
|
score_threshold: float = 0.2
|
|
constraints: Optional[Union[Dict[str, Any], List[Dict[str, Any]], None]] = None
|
|
index_name: Optional[str] = None
|
|
"""
|
|
# prepare the retriever params
|
|
retriever_parameters = kwargs.get("retriever_parameters", None)
|
|
if retriever_parameters:
|
|
inputs.update(retriever_parameters.dict())
|
|
elif self.services[cur_node].service_type == ServiceType.RERANK:
|
|
# input is SearchedDoc
|
|
"""Class SearchedDoc(BaseDoc):
|
|
|
|
retrieved_docs: DocList[TextDoc]
|
|
initial_query: str
|
|
top_n: int = 1
|
|
"""
|
|
# prepare the reranker params
|
|
reranker_parameters = kwargs.get("reranker_parameters", None)
|
|
if reranker_parameters:
|
|
inputs.update(reranker_parameters.dict())
|
|
print(f"*** Formatted Inputs to {cur_node}:\n{inputs}")
|
|
print("--" * 50)
|
|
return inputs
|
|
|
|
|
|
def align_outputs(self, data, cur_node, inputs, runtime_graph, llm_parameters_dict, **kwargs):
|
|
print(f"*** Direct Outputs from {cur_node}:\n{data}")
|
|
print("--" * 50)
|
|
|
|
if self.services[cur_node].service_type == ServiceType.EMBEDDING:
|
|
# direct output from Embedding microservice is EmbeddingResponse
|
|
"""
|
|
class EmbeddingResponse(BaseModel):
|
|
object: str = "list"
|
|
model: Optional[str] = None
|
|
data: List[EmbeddingResponseData]
|
|
usage: Optional[UsageInfo] = None
|
|
|
|
class EmbeddingResponseData(BaseModel):
|
|
index: int
|
|
object: str = "embedding"
|
|
embedding: Union[List[float], str]
|
|
"""
|
|
# turn it into EmbedDoc
|
|
assert isinstance(data["data"], list)
|
|
next_data = {"text": inputs["input"], "embedding": data["data"][0]["embedding"]} # EmbedDoc
|
|
else:
|
|
next_data = data
|
|
|
|
print(f"*** Formatted Output from {cur_node} for next node:\n", next_data)
|
|
print("--" * 50)
|
|
return next_data
|
|
|
|
|
|
class RetrievalToolService:
|
|
def __init__(self, host="0.0.0.0", port=8000):
|
|
self.host = host
|
|
self.port = port
|
|
ServiceOrchestrator.align_inputs = align_inputs
|
|
ServiceOrchestrator.align_outputs = align_outputs
|
|
self.megaservice = ServiceOrchestrator()
|
|
self.endpoint = str(MegaServiceEndpoint.RETRIEVALTOOL)
|
|
|
|
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,
|
|
)
|
|
|
|
self.megaservice.add(embedding).add(retriever).add(rerank)
|
|
self.megaservice.flow_to(embedding, retriever)
|
|
self.megaservice.flow_to(retriever, rerank)
|
|
|
|
async def handle_request(self, request: Request):
|
|
data = await request.json()
|
|
chat_request = ChatCompletionRequest.parse_obj(data)
|
|
|
|
prompt = chat_request.messages
|
|
|
|
# dummy llm params
|
|
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,
|
|
chat_template=chat_request.chat_template if chat_request.chat_template else None,
|
|
model=chat_request.model if chat_request.model 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,
|
|
)
|
|
|
|
last_node = runtime_graph.all_leaves()[-1]
|
|
response = result_dict[last_node]
|
|
return response
|
|
|
|
def start(self):
|
|
self.service = MicroService(
|
|
self.__class__.__name__,
|
|
service_role=ServiceRoleType.MEGASERVICE,
|
|
host=self.host,
|
|
port=self.port,
|
|
endpoint=self.endpoint,
|
|
input_datatype=Union[TextDoc, EmbeddingRequest, ChatCompletionRequest],
|
|
output_datatype=Union[RerankedDoc, LLMParamsDoc],
|
|
)
|
|
self.service.add_route(self.endpoint, self.handle_request, methods=["POST"])
|
|
self.service.start()
|
|
|
|
def add_remote_service_without_rerank(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,
|
|
)
|
|
|
|
self.megaservice.add(embedding).add(retriever)
|
|
self.megaservice.flow_to(embedding, retriever)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--without-rerank", action="store_true")
|
|
|
|
args = parser.parse_args()
|
|
|
|
chatqna = RetrievalToolService(port=MEGA_SERVICE_PORT)
|
|
if args.without_rerank:
|
|
chatqna.add_remote_service_without_rerank()
|
|
else:
|
|
chatqna.add_remote_service()
|
|
chatqna.start()
|