43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
"""大模型客户端（OpenAI 兼容接口）。
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未配置 LLM_API_KEY 时 enabled() 返回 False，调用方应走规则降级。
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"""
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from __future__ import annotations
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import requests
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import config
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def enabled() -> bool:
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return bool(config.LLM_API_KEY)
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def chat(messages, temperature: float = 0.5, max_tokens: int = 900) -> str:
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"""调用 chat/completions。失败抛异常，由上层捕获降级。"""
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if not enabled():
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raise RuntimeError("LLM 未配置")
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url = config.LLM_BASE_URL.rstrip("/") + "/chat/completions"
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headers = {"Authorization": f"Bearer {config.LLM_API_KEY}", "Content-Type": "application/json"}
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payload = {"model": config.LLM_MODEL, "messages": messages,
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"temperature": temperature, "max_tokens": max_tokens, "stream": False}
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r = requests.post(url, json=payload, headers=headers, timeout=config.LLM_TIMEOUT)
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r.raise_for_status()
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data = r.json()
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return data["choices"][0]["message"]["content"].strip()
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SYSTEM_PROMPT = (
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"你是一名专业的A股市场分析师，擅长复盘、个股诊断与题材分析。"
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"你的结论必须严格基于用户提供的结构化数据，不臆造未提供的数字。"
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"语言简洁专业，使用中文。涉及操作建议时必须同时给出风险提示，"
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"并说明这不构成投资建议。"
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)
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def ask(user_content: str, temperature: float = 0.5, max_tokens: int = 900) -> str:
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return chat([
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": user_content},
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], temperature=temperature, max_tokens=max_tokens)
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