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더 빠르게, 더 많이 처리하기 #1 #30

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더 빠르게, 더 많이 처리하기 #1 #30

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25 changes: 19 additions & 6 deletions src/Python.FastApi/common/llmroute/openaiclient.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,7 @@
from common.llmroute.baseclient import BaseClient
from common.models.simpleDTO import SimpleDTO as ResponseDTO
import json
from concurrent.futures import ThreadPoolExecutor

class OpenAIClient(BaseClient):
"""
Expand Down Expand Up @@ -63,6 +64,11 @@ class OpenAIClient(BaseClient):
'''
currency_rates: float

'''
ThreadPoolExecutor
'''
thread_pool: ThreadPoolExecutor


def __init__(self, model_name: str, options: dict):
self.client = OpenAI(
Expand Down Expand Up @@ -93,6 +99,8 @@ def __init__(self, model_name: str, options: dict):
self.currency_rates = 1500.0
print("LOG-ERROR: 환율 조회 실패, 1500원으로 가정합니다!!")

self.thread_pool = ThreadPoolExecutor(max_workers=10)


def generate(self, prompt: str, options: dict) -> ResponseDTO:
"""
Expand Down Expand Up @@ -165,11 +173,16 @@ def embed(self, input: str, options: dict, operation: str = None) -> ResponseDTO
raise ValueError(f"지원되지 않는 모델입니다: {self.model_name}")

try:
response = self.client.embeddings.create(
model = self.model_name,
input = input
)
return ResponseDTO(status=100, message='Success', data=response.data[0].embedding)
if operation == 'batch':
futures = [self.thread_pool.submit(self.client.embeddings.create, model=self.model_name, input=inp) for inp in input]
embeddings = [future.result().data[0].embedding for future in futures]
return ResponseDTO(status=100, message='Success', data=embeddings)
else:
response = self.client.embeddings.create(
model = self.model_name,
input = input
)
return ResponseDTO(status=100, message='Success', data=response.data[0].embedding)

except Exception as e:
return ResponseDTO(status=900, message=f"Internal Server Error - {str(e)}", data=None)
Expand Down Expand Up @@ -209,4 +222,4 @@ def get_cost_per_token(self) -> float:
return self.cost_per_token

def get_chunk_size(self) -> int:
return self.chunk_size
return self.chunk_size