52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
"""LLM 어댑터 — openai SDK 직접 호출 (langchain 미사용).
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provider 로 OpenAI / Azure OpenAI 분기. 무거운 import 는 호출 시점에 한다.
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chat_complete: messages → 응답 텍스트. json_mode 면 JSON 객체 강제(response_format).
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"""
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import json
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from typing import List, Optional
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from negotiation.profiling.config import LlmCredentials
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def _client(creds: LlmCredentials):
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if creds.provider == "azure":
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from openai import AzureOpenAI
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return AzureOpenAI(api_key=creds.api_key, azure_endpoint=creds.azure_endpoint,
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api_version=creds.api_version or "2024-06-01")
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from openai import OpenAI
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kwargs = {"api_key": creds.api_key}
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if creds.base_url:
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kwargs["base_url"] = creds.base_url
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return OpenAI(**kwargs)
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def chat_complete(messages: List[dict], creds: Optional[LlmCredentials] = None,
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json_mode: bool = False, temperature: float = 0.7, max_tokens: int = 1024) -> str:
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"""OpenAI(또는 Azure) chat completion 호출 → 응답 텍스트.
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Args:
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messages: [{"role": "system"|"user"|"assistant", "content": "..."}]
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creds: 자격증명(없으면 OpenAIConfig 전역값).
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json_mode: True 면 JSON 객체 응답 강제.
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"""
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if creds is None:
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creds = LlmCredentials.from_config()
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if not creds.is_configured():
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raise RuntimeError("LLM 미설정 (OpenAIConfig.api_key/model 확인)")
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client = _client(creds)
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kwargs = {"model": creds.model, "messages": messages,
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"temperature": temperature, "max_tokens": max_tokens}
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if json_mode:
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kwargs["response_format"] = {"type": "json_object"}
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resp = client.chat.completions.create(**kwargs)
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return resp.choices[0].message.content or ""
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def chat_json(messages: List[dict], creds: Optional[LlmCredentials] = None, **kw) -> dict:
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"""JSON 응답을 파싱해 dict 로 반환."""
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text = chat_complete(messages, creds=creds, json_mode=True, **kw)
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return json.loads(text)
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