File size: 2,975 Bytes
f469bad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import os
from typing import List, Dict, Any
from openai import OpenAI
class OpenAILLM:
def __init__(self,model_chat:str = "gpt-4o",model_translate:str = "gpt-4o", model_summarize:str = "gpt-4o"):
api_key = os.environ.get("OPENAI_API_KEY2")
if not api_key:
raise RuntimeError("OPENAI_API_KEY2 is not set in environment variables.")
self.client = OpenAI(api_key=api_key)
self.model_chat = model_chat
self.model_translate = model_translate
self.model_summarize = model_summarize
def generate_ceo_message(self,meta, kpi:Dict[str,float],esg_row:List[Dict[str,Any]])->str:
prompt = (
"以下の企業情報・KPI・ESG指標をもとに、日本語で200字程度のCEOメッセージ草案を出力してください。"
"投資家に伝わる簡潔なトーンで、過度な形容表現は避けてください。\n\n"
f"企業情報: {meta.model_dump()}\n"
f"KPI: {kpi}\n"
f"ESG指標: {esg_row}\n"
)
rsp = self.client.chat.completions.create(
model=self.model_chat,
messages=[{"role": "user", "content": prompt}],
temperature=0.3,
)
return rsp.choices[0].message.content.strip()
def generate_risk_opportunity(self,meta,kpi:Dict[str,float],esg_row:List[Dict[str,Any]])->str:
prompt = (
"以下の情報から、日本語で200字程度のリスクと機会の草案を出力してください。"
"定量/定性のバランスを取り、具体的な観点を1-2点挙げてください。\n\n"
f"企業情報: {meta.model_dump()}\n"
f"KPI: {kpi}\n"
f"ESG: {esg_row}\n"
)
rsp = self.client.chat.completions.create(
model=self.model_chat,
messages=[{"role": "user", "content": prompt}],
temperature=0.3,
)
return rsp.choices[0].message.content.strip()
def translate_text(self, texts:List[str], target_language: str) -> List[str]:
if not texts:
return texts
system=(
"You are precise financial/ESG translator.Preserve numbers,units,and proper nouns."
"Keep tone concise,suitable for investor reports."
)
SEP = "\n<<<SEP>>>\n"
joined = SEP.join(texts)
prompt = f"Translate the following content to {target_language}.Keep layout minimal.\n\n{joined}"
rsp = self.client.chat.completions.create(
model=self.model_translate,
messages=[
{"role": "user", "content": system},{"role": "user", "content": prompt}
],
temperature=0.2,
)
out = rsp.choices[0].message.content
parts = [p.strip() for p in out.split("<<<SEP>>>")]
if len(parts) != len(texts):
parts = [out]+texts[1:]
parts = parts[:len(texts)]
return parts |