51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
"""Prompt templates for natural language outputs."""
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from __future__ import annotations
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from typing import Dict, TYPE_CHECKING
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if TYPE_CHECKING: # pragma: no cover
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from app.utils.config import DepartmentSettings
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from app.agents.departments import DepartmentContext
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def plan_prompt(data: Dict) -> str:
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"""Build a concise instruction prompt for the LLM."""
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_ = data
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return "你是一个投资助理，请根据提供的数据给出三条要点和两条风险提示。"
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def department_prompt(settings: "DepartmentSettings", context: "DepartmentContext") -> str:
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"""Compose a structured prompt for department-level LLM ensemble."""
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feature_lines = "\n".join(
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f"- {key}: {value}" for key, value in sorted(context.features.items())
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)
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market_lines = "\n".join(
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f"- {key}: {value}" for key, value in sorted(context.market_snapshot.items())
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)
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instructions = f"""
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部门名称：{settings.title}
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股票代码：{context.ts_code}
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交易日：{context.trade_date}
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【核心特征】
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{feature_lines or '- (无)'}
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【市场背景】
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{market_lines or '- (无)'}
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请基于以上数据给出该部门对当前股票的操作建议。输出必须是 JSON，字段如下：
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{{
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"action": "BUY|BUY_S|BUY_M|BUY_L|SELL|HOLD",
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"confidence": 0-1 之间的小数，表示信心，
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"summary": "一句话概括理由",
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"signals": ["详细要点", "..."],
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"risks": ["风险点", "..."]
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}}
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请严格返回单个 JSON 对象，不要添加额外文本。
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"""
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return instructions.strip()
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