59 lines
2.2 KiB
Python
59 lines
2.2 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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from .templates import TemplateRegistry
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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(
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settings: "DepartmentSettings",
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context: "DepartmentContext",
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supplements: str = "",
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) -> str:
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"""Compose a structured prompt for department-level LLM ensemble."""
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# Format data for template
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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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scope_lines = "\n".join(f"- {item}" for item in settings.data_scope)
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role_description = settings.description.strip()
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role_instruction = settings.prompt.strip()
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# Determine template ID based on department settings
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template_id = f"{settings.code.lower()}_dept"
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if not TemplateRegistry.get(template_id):
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template_id = "department_base"
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# Prepare template variables
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template_vars = {
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"title": settings.title,
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"ts_code": context.ts_code,
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"trade_date": context.trade_date,
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"description": role_description or "未配置，默认沿用部门名称所代表的研究职责。",
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"instruction": role_instruction or "在保持部门风格的前提下，结合可用数据做出审慎判断。",
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"data_scope": scope_lines or "- 使用系统提供的全部上下文，必要时指出仍需的额外数据。",
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"features": feature_lines or "- (无)",
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"market_snapshot": market_lines or "- (无)",
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"supplements": supplements.strip() or "- 当前无追加数据"
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}
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# Get template and format prompt
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template = TemplateRegistry.get(template_id)
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return template.format(template_vars)
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