1070 lines
59 KiB
Python
1070 lines
59 KiB
Python
from __future__ import annotations
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from typing import Any
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from .ai_prompts_catalog import __all__ as PROMPT_CATALOG_EXPORTS
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PROMPT_FAMILY_CLASSIFICATION = "classification"
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PROMPT_FAMILY_EXTRACTION = "extraction"
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PROMPT_FAMILY_PLANNING = "planning"
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PROMPT_FAMILY_RESPONSE = "response"
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PROMPT_FAMILY_GOVERNANCE = "governance"
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PROMPT_FAMILY_ALLOWED = {
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PROMPT_FAMILY_CLASSIFICATION,
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PROMPT_FAMILY_EXTRACTION,
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PROMPT_FAMILY_PLANNING,
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PROMPT_FAMILY_RESPONSE,
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PROMPT_FAMILY_GOVERNANCE,
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}
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PROMPT_FAMILY_KEYWORD_RULES: tuple[tuple[str, str], ...] = (
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("CLASSIFICATION", PROMPT_FAMILY_CLASSIFICATION),
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("EXTRACTION", PROMPT_FAMILY_EXTRACTION),
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("PREFILL", PROMPT_FAMILY_EXTRACTION),
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("INTENT", PROMPT_FAMILY_CLASSIFICATION),
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("TASK_ORCHESTRATION", PROMPT_FAMILY_PLANNING),
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("PLANNER", PROMPT_FAMILY_PLANNING),
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("GENERATE", PROMPT_FAMILY_PLANNING),
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("DECOMPOSE", PROMPT_FAMILY_PLANNING),
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("RECOMMEND", PROMPT_FAMILY_PLANNING),
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("DRAFT", PROMPT_FAMILY_PLANNING),
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("TASK_BREAKDOWN", PROMPT_FAMILY_PLANNING),
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("QA", PROMPT_FAMILY_RESPONSE),
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("REPLY", PROMPT_FAMILY_RESPONSE),
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)
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PROMPT_FAMILY_SCENARIO_RULES: tuple[tuple[str, str], ...] = (
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("分类", PROMPT_FAMILY_CLASSIFICATION),
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("抽取", PROMPT_FAMILY_EXTRACTION),
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("预填", PROMPT_FAMILY_EXTRACTION),
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("规划", PROMPT_FAMILY_PLANNING),
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("编排", PROMPT_FAMILY_PLANNING),
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("草案", PROMPT_FAMILY_PLANNING),
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("拆解", PROMPT_FAMILY_PLANNING),
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("问答", PROMPT_FAMILY_RESPONSE),
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("回复", PROMPT_FAMILY_RESPONSE),
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)
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RESPONSIBILITY_SCOPE_BY_FAMILY: dict[str, str] = {
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PROMPT_FAMILY_CLASSIFICATION: "分类层：只负责识别请求类型、归属和优先级，不生成执行方案。",
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PROMPT_FAMILY_EXTRACTION: "抽取层：只负责结构化提取实体、槽位和证据，不做规划决策。",
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PROMPT_FAMILY_PLANNING: "规划层：只负责生成可执行规划、任务和负责人建议，不承担最终对话呈现。",
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PROMPT_FAMILY_RESPONSE: "呈现层：只负责面向用户输出结论与建议，不直接做写操作决策。",
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PROMPT_FAMILY_GOVERNANCE: "治理层：只负责约束、评估、同步与风控，不承担主对话生成。",
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}
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PROMPT_PRIORITY_BY_FAMILY: dict[str, str] = {
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PROMPT_FAMILY_CLASSIFICATION: "high",
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PROMPT_FAMILY_EXTRACTION: "high",
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PROMPT_FAMILY_PLANNING: "high",
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PROMPT_FAMILY_RESPONSE: "high",
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PROMPT_FAMILY_GOVERNANCE: "medium",
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}
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PROMPT_AUDIENCE_BY_FAMILY: dict[str, str] = {
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PROMPT_FAMILY_CLASSIFICATION: "signal_router",
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PROMPT_FAMILY_EXTRACTION: "signal_ingestion",
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PROMPT_FAMILY_PLANNING: "planning_orchestrator",
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PROMPT_FAMILY_RESPONSE: "assistant_response",
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PROMPT_FAMILY_GOVERNANCE: "risk_budget_guardrail",
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}
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def _normalize_family(prompt_family: str) -> str:
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value = str(prompt_family or "").strip().lower()
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if value in PROMPT_FAMILY_ALLOWED:
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return value
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return PROMPT_FAMILY_GOVERNANCE
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def _derive_prompt_family(prompt_key: str, business_scenario: str) -> str:
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key_text = str(prompt_key or "").strip().upper()
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scenario_text = str(business_scenario or "").strip()
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for keyword, family in PROMPT_FAMILY_KEYWORD_RULES:
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if keyword in key_text:
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return family
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for keyword, family in PROMPT_FAMILY_SCENARIO_RULES:
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if keyword in scenario_text:
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return family
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return PROMPT_FAMILY_GOVERNANCE
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def _derive_responsibility_scope(prompt_family: str) -> str:
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family = _normalize_family(prompt_family)
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return RESPONSIBILITY_SCOPE_BY_FAMILY[family]
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def _derive_priority(prompt_family: str) -> str:
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family = _normalize_family(prompt_family)
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return PROMPT_PRIORITY_BY_FAMILY[family]
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def _derive_audience(prompt_family: str) -> str:
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family = _normalize_family(prompt_family)
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return PROMPT_AUDIENCE_BY_FAMILY[family]
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def _entry(
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prompt_key: str,
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*,
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module: str,
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business_scenario: str,
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input_contract: str,
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output_contract: str,
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call_sites: tuple[str, ...] = (),
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prompt_family: str | None = None,
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responsibility_scope: str | None = None,
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priority: str | None = None,
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audience: str | None = None,
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runtime_source: str = "catalog",
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editable_policy: str = "managed_release",
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risk_level: str | None = None,
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) -> dict[str, Any]:
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resolved_family = _normalize_family(prompt_family or _derive_prompt_family(prompt_key, business_scenario))
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resolved_scope = str(responsibility_scope or "").strip() or _derive_responsibility_scope(resolved_family)
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resolved_priority = str(priority or "").strip().lower() or _derive_priority(resolved_family)
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resolved_audience = str(audience or "").strip() or _derive_audience(resolved_family)
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resolved_risk = str(risk_level or "").strip().lower()
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if resolved_risk not in {"critical", "high", "medium", "low"}:
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resolved_risk = "high" if resolved_family in {PROMPT_FAMILY_CLASSIFICATION, PROMPT_FAMILY_EXTRACTION, PROMPT_FAMILY_PLANNING} else "medium"
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return {
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"prompt_key": prompt_key,
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"module": module,
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"business_scenario": business_scenario,
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"call_sites": tuple(str(site) for site in call_sites if str(site or "").strip()),
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"input_contract": input_contract,
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"output_contract": output_contract,
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"prompt_family": resolved_family,
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"responsibility_scope": resolved_scope,
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"priority": resolved_priority,
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"audience": resolved_audience,
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"runtime_source": str(runtime_source or "catalog").strip() or "catalog",
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"editable_policy": str(editable_policy or "managed_release").strip() or "managed_release",
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"risk_level": resolved_risk,
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}
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JSON_INPUT = "输入为结构化 JSON 上下文。"
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JSON_OUTPUT = "输出严格 JSON 对象，不要 Markdown。"
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JSON_LIST_OUTPUT = "输出严格 JSON 数组，不要 Markdown。"
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QA_INPUT = "输入为问题文本 + 业务上下文。"
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QA_OUTPUT = "输出自然语言答复。"
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PLAN_INPUT = "输入为信号 / 项目 / 宪章 / 成员上下文。"
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PLAN_OUTPUT = "输出可执行规划 JSON。"
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SCORE_INPUT = "输入为对比对象 + 评分上下文。"
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SCORE_OUTPUT = "输出评分或建议 JSON。"
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FRAGMENT_INPUT = "输入为 prompt 片段占位内容。"
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FRAGMENT_OUTPUT = "输出为可拼接的 prompt 片段。"
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DEEP_GUARD_CALL_SITES = ("backend/app/services/adaptive_rag_service/mixin_deep_guard_answer.py",)
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AI_BUSINESS_TASKS_CALL_SITES = ("backend/app/services/ai_service_core/mixin_business_tasks.py",)
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AI_BUSINESS_ANALYSIS_CALL_SITES = ("backend/app/services/ai_service_core/mixin_business_analysis.py",)
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FEISHU_EXTRACT_CALL_SITES = ("backend/app/services/integrations/feishu_signal_pipeline/mixin_extraction.py",)
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FEISHU_REPLY_CALL_SITES = ("backend/app/services/integrations/feishu_integration_service/mixin_reply_llm_core.py",)
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FEISHU_PIPELINE_CALL_SITES = ("backend/app/services/integrations/feishu_signal_pipeline/mixin_task_orchestration_main.py",)
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FEISHU_SIGNAL_CLASSIFY_CALL_SITES = ("backend/app/services/signal/signal_capture_service/mixin_analysis_normalize.py",)
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SIGNAL_SOURCE_CALL_SITES = ("backend/app/services/signal/signal_source_orchestration/mixin_registry_execute.py",)
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DRAFT_GENERATOR_CALL_SITES = ("backend/app/services/signal/draft_generator_service.py",)
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PROJECT_REQUIREMENT_EXTRACTION_CALL_SITES = (
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"backend/app/services/ai_steward_service/mixin_draft_generation.py",
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"backend/app/services/signal/signal_capture_service/mixin_new_project.py",
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)
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RAG_INTENT_CALL_SITES = ("backend/app/services/adaptive_rag_service/mixin_core_intent.py",)
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RAG_DB_CALL_SITES = ("backend/app/services/adaptive_rag_service/mixin_fast_answer_semantic_p1.py",)
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SIGNAL_PLANNER_CALL_SITES = ("backend/app/services/signal/signal_planner/mixin_prompt.py", "backend/app/services/signal/signal_planner/mixin_analyze.py")
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SKILL_SCORE_CALL_SITES = ("backend/app/services/skill/skill_score_service.py",)
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WORKLOAD_CALL_SITES = ("backend/app/services/workload/ai_optimize.py",)
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RECIPE_CALL_SITES = ("backend/app/services/recipe/service.py",)
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PMO_INTENT_CALL_SITES: tuple[str, ...] = ()
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PMO_BATCH_CALL_SITES = ("backend/app/services/ai_pmo/ai_pmo_typed_executor_discovery/mixin_analysis.py",)
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def _part_01_entries() -> tuple[dict[str, Any], ...]:
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return (
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_entry(
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"PROMPT_IMPACT_ANALYSIS",
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module="part_01",
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business_scenario="需求变更影响分析",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含影响结论、风险、建议。",
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),
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_entry(
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"PROMPT_STRATEGIC_ANALYSIS_HEADER",
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module="part_01",
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business_scenario="战略分析上下文片段",
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input_contract=FRAGMENT_INPUT,
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output_contract=FRAGMENT_OUTPUT,
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),
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_entry(
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"PROMPT_RAG_CONTEXT_HEADER",
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module="part_01",
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business_scenario="RAG 上下文 header 片段",
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input_contract=FRAGMENT_INPUT,
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output_contract=FRAGMENT_OUTPUT,
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),
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_entry(
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"PROMPT_RAG_CONTEXT_ITEM",
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module="part_01",
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business_scenario="RAG 上下文 item 片段",
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input_contract=FRAGMENT_INPUT,
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output_contract=FRAGMENT_OUTPUT,
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),
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_entry(
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"PROMPT_CODE_AUDIT",
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module="part_01",
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business_scenario="代码审计",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含问题、证据、风险与修复建议。",
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),
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_entry(
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"PROMPT_CONTEXT_INSIGHTS",
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module="part_01",
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business_scenario="上下文洞察",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含关键洞察与下一步建议。",
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),
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_entry(
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"PROMPT_FEISHU_HUMANIZED_REPLY",
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module="part_01",
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business_scenario="飞书回复润色",
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input_contract=QA_INPUT,
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output_contract=QA_OUTPUT,
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call_sites=FEISHU_REPLY_CALL_SITES,
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),
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_entry(
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"PROMPT_FEISHU_QA_NATURAL_REPLY",
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module="part_01",
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business_scenario="飞书问答自然回复",
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input_contract="输入 question / answer / context_summary。",
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output_contract=QA_OUTPUT,
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call_sites=FEISHU_REPLY_CALL_SITES,
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),
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_entry(
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"PROMPT_FEISHU_QA_NATURAL_REPLY_REWRITE",
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module="part_01",
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business_scenario="飞书问答自然回复改写",
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input_contract="输入原始回答、上下文摘要和语气约束。",
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output_contract=QA_OUTPUT,
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call_sites=FEISHU_REPLY_CALL_SITES,
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),
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_entry(
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"PROMPT_FEISHU_PROJECT_SIGNAL_CLASSIFICATION",
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module="part_01",
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business_scenario="项目信号分类",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含分类、优先级与推荐动作。",
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call_sites=FEISHU_SIGNAL_CLASSIFY_CALL_SITES,
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),
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_entry(
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"PROMPT_FEISHU_LEVEL_CLASSIFICATION",
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module="part_01",
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business_scenario="飞书消息层级分类",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含层级与判断原因。",
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call_sites=FEISHU_EXTRACT_CALL_SITES,
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),
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)
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def _part_02_entries() -> tuple[dict[str, Any], ...]:
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return (
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_entry(
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"PROMPT_FEISHU_SIGNAL_EXTRACTION",
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module="part_02",
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business_scenario="飞书信号结构化抽取",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含标题、摘要、优先级、匹配项目和建议动作。",
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call_sites=FEISHU_EXTRACT_CALL_SITES,
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),
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_entry(
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"PROMPT_FEISHU_PROJECT_PREFILL",
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module="part_02",
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business_scenario="飞书项目预填",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含项目名、描述、负责人、时间线与置信度。",
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call_sites=FEISHU_EXTRACT_CALL_SITES,
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),
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_entry(
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"PROMPT_PROJECT_REQUIREMENT_EXTRACTION",
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module="part_02",
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business_scenario="项目需求抽取与信号源落点",
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input_contract=JSON_INPUT,
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output_contract="输出 JSON 对象，包含项目基础信息、核心痛点、MVP 功能与 requirement_items。",
|
||
call_sites=PROJECT_REQUIREMENT_EXTRACTION_CALL_SITES,
|
||
),
|
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_entry(
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"PROMPT_FEISHU_TASK_ORCHESTRATION",
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module="part_02",
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business_scenario="飞书任务编排与负责人建议",
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input_contract="输入 project_json / charter_json / signal_json / members_json。",
|
||
output_contract="输出 JSON 对象，包含任务列表、负责人建议与置信度。",
|
||
call_sites=FEISHU_PIPELINE_CALL_SITES,
|
||
),
|
||
_entry(
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||
"PROMPT_SIGNAL_SOURCE_ONBOARDING",
|
||
module="part_02",
|
||
business_scenario="信号源接入规划",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含是否接入、信号源信息、角色分工与执行指令。",
|
||
call_sites=SIGNAL_SOURCE_CALL_SITES,
|
||
),
|
||
_entry(
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||
"PROMPT_AI_WORKER_SIGNAL_SOURCE_EXECUTION",
|
||
module="part_02",
|
||
business_scenario="信号源接入 worker 执行",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含交付物、风险与下一步。",
|
||
call_sites=SIGNAL_SOURCE_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_AI_PMO_INTENT_CLASSIFICATION",
|
||
module="part_02",
|
||
business_scenario="AI PMO 意图识别",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含 family / action / intent / confidence。",
|
||
call_sites=PMO_INTENT_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_AI_PMO_SIGNAL_BATCH_ANALYSIS",
|
||
module="part_02",
|
||
business_scenario="AI PMO 批量信号分析",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含 signals 数组。",
|
||
call_sites=PMO_BATCH_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_DRAFT_GENERATOR",
|
||
module="part_02",
|
||
business_scenario="立项草案生成",
|
||
input_contract="输入 request / project / active_projects / similar_projects / charter / members / recent_signals。",
|
||
output_contract="输出 JSON 对象，包含项目草案、定位、核心痛点、MVP 分期、技术架构、商业模式、验证指标与风险。",
|
||
call_sites=DRAFT_GENERATOR_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_INTENT_CLASSIFICATION",
|
||
module="part_02",
|
||
business_scenario="RAG 意图识别",
|
||
input_contract=QA_INPUT,
|
||
output_contract="输出 JSON 对象，包含 intent / confidence / reason。",
|
||
call_sites=RAG_INTENT_CALL_SITES,
|
||
),
|
||
)
|
||
|
||
|
||
def _part_03_entries() -> tuple[dict[str, Any], ...]:
|
||
return (
|
||
_entry(
|
||
"PROMPT_RAG_ANSWER",
|
||
module="part_03",
|
||
business_scenario="RAG 问答生成",
|
||
input_contract=QA_INPUT,
|
||
output_contract=QA_OUTPUT,
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_DECISION_QA",
|
||
module="part_03",
|
||
business_scenario="决策类问答",
|
||
input_contract=QA_INPUT,
|
||
output_contract="输出自然语言结论，必要时包含取舍理由。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_DB_PROJECT_QA",
|
||
module="part_03",
|
||
business_scenario="项目知识库问答",
|
||
input_contract=JSON_INPUT,
|
||
output_contract=QA_OUTPUT,
|
||
call_sites=RAG_DB_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_GENERATE_CHARTER",
|
||
module="part_03",
|
||
business_scenario="宪章草案生成",
|
||
input_contract="输入 request / project / active_projects / similar_projects / signal_context。",
|
||
output_contract="输出 JSON 对象，包含 modules / field-level evidence / readiness / derived_artifacts。",
|
||
call_sites=("backend/app/services/ai_service_core/mixin_business_tasks.py", "backend/app/services/sstc/charter_prefill_utils.py"),
|
||
),
|
||
_entry(
|
||
"PROMPT_DECOMPOSE_TASKS",
|
||
module="part_03",
|
||
business_scenario="任务拆解",
|
||
input_contract="输入 request / project / charter / modules 的结构化上下文。",
|
||
output_contract="输出 JSON 数组，包含任务名称、描述、工时、优先级和技能标签。",
|
||
call_sites=("backend/app/services/ai_service_core/mixin_business_tasks.py", "backend/app/api/tasks.py", "backend/app/api/sstc_api/tasks_ai_breakdown.py"),
|
||
),
|
||
_entry(
|
||
"PROMPT_RECOMMEND_ASSIGNEES",
|
||
module="part_03",
|
||
business_scenario="负责人推荐",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 数组，包含候选人、匹配理由和负载信息。",
|
||
call_sites=AI_BUSINESS_TASKS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_ALIGNMENT_SCORE",
|
||
module="part_03",
|
||
business_scenario="对齐度评分",
|
||
input_contract=SCORE_INPUT,
|
||
output_contract=SCORE_OUTPUT,
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_COGNITIVE_CONFLICTS",
|
||
module="part_03",
|
||
business_scenario="认知冲突识别",
|
||
input_contract=SCORE_INPUT,
|
||
output_contract=SCORE_OUTPUT,
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_EVALUATE_MODEL_REQUIREMENT",
|
||
module="part_03",
|
||
business_scenario="模型需求评估",
|
||
input_contract=SCORE_INPUT,
|
||
output_contract=SCORE_OUTPUT,
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_ANALYZE_CAPABILITY_GAP",
|
||
module="part_03",
|
||
business_scenario="能力缺口分析",
|
||
input_contract=SCORE_INPUT,
|
||
output_contract=SCORE_OUTPUT,
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_ANALYZE_COST_ROI",
|
||
module="part_03",
|
||
business_scenario="成本收益分析",
|
||
input_contract=SCORE_INPUT,
|
||
output_contract=SCORE_OUTPUT,
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
)
|
||
|
||
|
||
def _part_04_entries() -> tuple[dict[str, Any], ...]:
|
||
return (
|
||
_entry(
|
||
"PROMPT_ANALYZE_COST_OPTIMIZATION",
|
||
module="part_04",
|
||
business_scenario="成本优化分析",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含降级、缓存、批处理与节省测算。",
|
||
call_sites=AI_BUSINESS_ANALYSIS_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_ASSESS_MODEL_UPDATE_IMPACT",
|
||
module="part_04",
|
||
business_scenario="模型升级影响评估",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含成本变化、兼容风险与建议。",
|
||
call_sites=("backend/app/services/model/model_capability/service.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_EXPERIMENT_SYNC_DECISION",
|
||
module="part_04",
|
||
business_scenario="实验结果同步决策",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含置信度、是否自动同步与原因。",
|
||
call_sites=("backend/app/services/ai_pmo/ai_pmo_cadence/mixin_builders.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_SKILL_SCORE_UPDATE",
|
||
module="part_04",
|
||
business_scenario="技能评分更新",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含技能增量列表。",
|
||
call_sites=SKILL_SCORE_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_WORKLOAD_OPTIMIZATION",
|
||
module="part_04",
|
||
business_scenario="任务负载优化",
|
||
input_contract=JSON_INPUT,
|
||
output_contract="输出 JSON 对象，包含重分配建议。",
|
||
call_sites=WORKLOAD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_ANALYZE_FOOD_CALORIES",
|
||
module="part_04",
|
||
business_scenario="食物热量估算",
|
||
input_contract=QA_INPUT,
|
||
output_contract="输出 JSON 对象，包含食物名、热量、营养和不确定因素。",
|
||
call_sites=RECIPE_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_SIGNAL_PLANNER",
|
||
module="part_04",
|
||
business_scenario="信号规划 / 任务编排建议",
|
||
input_contract="输入 signal / project / charter_json / recent_signals / member_profiles。",
|
||
output_contract="输出 JSON 对象，包含 summary、priority、负责人建议、执行计划和风险。",
|
||
call_sites=SIGNAL_PLANNER_CALL_SITES,
|
||
),
|
||
)
|
||
|
||
|
||
def _scene_entries() -> tuple[dict[str, Any], ...]:
|
||
return (
|
||
_entry(
|
||
"PROMPT_FEISHU_DIRECT_QA",
|
||
module="part_scenes_01",
|
||
business_scenario="飞书直接问答",
|
||
input_contract="输入 question / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract=QA_OUTPUT,
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_FEISHU_MIXED_QA",
|
||
module="part_scenes_02",
|
||
business_scenario="飞书混合问答",
|
||
input_contract="输入 project_id / message / question_part / signal_part / system_snapshot / context_blocks。",
|
||
output_contract="输出先结论后入库说明的自然语言答复。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_PROJECT_STATUS_QA",
|
||
module="part_scenes_03",
|
||
business_scenario="项目状态问答",
|
||
input_contract="输入 question / project_id / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract="输出项目状态结论、进度与下一步建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_PROJECT_WEEKLY_REPORT_JSON",
|
||
module="part_scenes_03",
|
||
business_scenario="项目周报结构化输出",
|
||
input_contract="输入 question / asks_risk_or_blockers / project_snapshot / internal_findings / external_updates。",
|
||
output_contract="输出 JSON 对象，包含 summary / stage_judgement / evidence_summary / core_gap / external_impact / actions。",
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_fast_answer_project_main.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_MEMBER_WORKLOAD_QA",
|
||
module="part_scenes_04",
|
||
business_scenario="成员负载问答",
|
||
input_contract="输入 question / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract="输出成员负载与推荐建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_AI_PMO_QA",
|
||
module="part_scenes_05",
|
||
business_scenario="AI PMO 问答",
|
||
input_contract="输入 question / project_id / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract="输出 AI PMO 视角的结论与建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_AI_PMO_REQUIREMENT_PLAN_QA",
|
||
module="part_scenes_05",
|
||
business_scenario="AI PMO 需求收敛问答",
|
||
input_contract="输入 question / project_id / world_model / conversation / context_blocks。",
|
||
output_contract="输出需求收敛、选项澄清与下一步建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_TREND_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="外部趋势问答",
|
||
input_contract="输入 question / project_id / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract="输出行业/市场趋势判断与建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_INDUSTRY_TREND_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="行业趋势问答",
|
||
input_contract="输入 question / project_id / system_snapshot / user_snapshot / context_blocks。",
|
||
output_contract="输出行业趋势判断与建议。",
|
||
call_sites=DEEP_GUARD_CALL_SITES,
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_DIRECT_FACT_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="外部事实直答",
|
||
input_contract="输入 question / plan_summary / evidence_blocks / source_summaries / time_range / uncertainties。",
|
||
output_contract="输出仅基于外部证据的事实结论。",
|
||
call_sites=("backend/app/services/external_query_service/presenter.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_SUMMARY_WITH_SOURCES_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="外部事实总结",
|
||
input_contract="输入 question / plan_summary / evidence_blocks / source_summaries / time_range / uncertainties。",
|
||
output_contract="输出仅基于外部证据的总结性回答。",
|
||
call_sites=("backend/app/services/external_query_service/presenter.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_COMPARE_WITH_SOURCES_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="外部事实对比",
|
||
input_contract="输入 question / plan_summary / evidence_blocks / source_summaries / time_range / uncertainties。",
|
||
output_contract="输出仅基于外部证据的对比回答。",
|
||
call_sites=("backend/app/services/external_query_service/presenter.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_QA",
|
||
module="part_scenes_06",
|
||
business_scenario="外部信号源推荐问答",
|
||
input_contract="输入 question / plan_summary / fit_brief_summary / recommendation_goal / project_object_anchors / signal_source_objectives / retrieval_status。",
|
||
output_contract=QA_OUTPUT,
|
||
call_sites=("backend/app/services/external_query_service/presenter.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_SIGNAL_SOURCE_RECOMMENDATION_PACKET",
|
||
module="part_scenes_06",
|
||
business_scenario="外部信号源推荐候选包",
|
||
input_contract="输入 question / plan_summary / fit_brief_summary / recommendation_goal / project_object_anchors / signal_source_objectives / retrieval_status。",
|
||
output_contract="输出 JSON 对象，包含 candidates 和 caveat。",
|
||
call_sites=("backend/app/services/external_query_service/presenter.py",),
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_LAUNCH_DRAFT",
|
||
module="part_scenes_06",
|
||
business_scenario="PMFlow 新项目启动草案",
|
||
input_contract="输入用户创意文本与附件摘要。",
|
||
output_contract="输出 JSON 对象，包含待确认项目草案、需求、任务、风险、决策和证据。",
|
||
call_sites=("backend/app/services/pmflow_launch_service.py", "backend/app/services/space_saved_idea_service.py"),
|
||
prompt_family=PROMPT_FAMILY_PLANNING,
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_CHANGE_REVIEW_EXTRACTION",
|
||
module="part_scenes_06",
|
||
business_scenario="项目主链变更抽取",
|
||
input_contract="输入项目线程消息与附件摘要。",
|
||
output_contract="输出 JSON 对象，包含 change_summary、proposed_items、evidence、missing_info、warnings 和 confidence。",
|
||
call_sites=("backend/app/services/pmflow_change_review_service.py",),
|
||
prompt_family=PROMPT_FAMILY_EXTRACTION,
|
||
),
|
||
_entry(
|
||
"PROMPT_SAVED_IDEA_ENRICHMENT",
|
||
module="part_scenes_06",
|
||
business_scenario="暂存想法结构化整理",
|
||
input_contract="输入用户保存的想法文本；需要识别问候、测试词、无意义短语等非想法输入。",
|
||
output_contract="输出 JSON 对象，包含完整度、缺口、readiness_score 和状态。",
|
||
call_sites=("backend/app/services/space_saved_idea_service.py",),
|
||
prompt_family=PROMPT_FAMILY_EXTRACTION,
|
||
),
|
||
)
|
||
|
||
|
||
def _runtime_guardrail_entries() -> tuple[dict[str, Any], ...]:
|
||
return (
|
||
_entry(
|
||
"PMFLOW_SEMANTIC_CONTEXT_DECISION_PROMPT",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 上下文与主链边界决策",
|
||
input_contract="输入 semantic_context_packet、conversation_scope、显式 reply/thread/work_item 锚点与 active workspace 摘要。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/ai_pmo/semantic_context/semantic_decision_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：判断 topic、workflow、draft follow-up、interaction boundary 与对象锚点，不执行检索、写入或最终回答。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_QUERY_SEMANTICS",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 问答语义与来源策略规划",
|
||
input_contract="输入用户消息、workflow bundle、业务对象绑定、上下文与 source policy hint。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_core_intent.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：把 query.answer 只读问题结构化为目标对象、答案形态和来源策略，不执行检索或答案生成。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_ROUTE_PLANNER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 用户侧旧路由规划（已从 Space 主链下线）",
|
||
input_contract="仅供历史 replay/迁移参考；Space 用户侧运行时不再消费该 prompt 做主控。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=(),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="legacy：旧 route_plan/source_strategy/allowed_executors 合同已由 TurnState + Action Loop 取代，不执行运行时路由、不生成最终答案。",
|
||
risk_level="critical",
|
||
editable_policy="deprecated",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_GOAL_FRAME",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 证据读取目标合同（非主控）",
|
||
input_contract="输入用户问题、conversation_context、客户安全 surface_context 与工具合同；作为 Operating Runtime 的证据读取适配合同。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_goal_frame_runtime.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：只为旧 AgentRun 证据读取适配生成 GoalFrame；Space 主控必须先进入 OperatingMission，不得再把 GoalFrame 当用户侧 mission 主控。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_OPERATING_MISSION",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Operating Mission 生成",
|
||
input_contract="输入用户问题、conversation_context、客户安全 surface_context 与 specialist capability contracts。",
|
||
output_contract="输出符合 PMFlowAIOperatingMission schema 的 JSON，包含经营目标、业务结果、对象范围、专业 agent、确认边界与下一轮策略。",
|
||
call_sites=("backend/app/services/pmflow_ai_operating_runtime/mission_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="主控层：把用户输入升级为可经营 mission，决定对象、产物方向、专业 agent 和下一轮策略；不生成最终答案、不执行写入、不调用具体工具。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_SPECIALIST_WORK_PRODUCT",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Specialist Agent 工作产物生成",
|
||
input_contract="输入 agent capability contract、OperatingMission、ProjectWorldState 与 LearningLedger。",
|
||
output_contract="输出 specialist work product JSON，包含 claims、recommendations、prepared_actions、blocked_actions、evidence_refs 与 verification。",
|
||
call_sites=("backend/app/services/pmflow_ai_operating_runtime/specialist_agents.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="专业层：每个 specialist 只产出本角色 artifact，不写最终答案、不越权执行；证据不足时输出边界和下一步研究任务。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_OPERATING_ANSWER_COMPOSER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Operating Runtime 终局答案表达",
|
||
input_contract="输入用户问题、OperatingMission、客户安全证据、specialist work products、Council/Autonomy 结果、可承接动作和早期证据答案。",
|
||
output_contract="输出客户可见中文自然答案，必须消费专业产物并面向 PMFlow 从创意到变现执行闭环组织内容。",
|
||
call_sites=("backend/app/services/pmflow_ai_operating_runtime/run_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层：把 Operating Runtime 的产品/研究/MVP/变现/执行承接产物合成最终用户答案；不做路由、不执行写入、不暴露内部运行时。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_OPERATING_COUNCIL",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Council 会审与产物投影",
|
||
input_contract="输入 OperatingMission、ProjectWorldState、specialist work products 与 LearningLedger。",
|
||
output_contract="输出 council review / operating decision JSON，包含冲突、风险否决、优先级、产品对象投影、证据边界和下一轮触发。",
|
||
call_sites=("backend/app/services/pmflow_ai_operating_runtime/council.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="会审层：检查目标覆盖、证据、产物化、可执行下一步、人类确认和越权风险；不生成自然答案、不执行写入。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_STEP_PLANNER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 下一步规划",
|
||
input_contract="输入用户问题、含 GoalFrame/ledger 的 agent_workspace、conversation_context 与客户安全 surface_context。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_step_planner.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：只规划一个下一步 runtime action 或只读工具，不生成最终答案、不执行写入、不按业务场景枚举路由。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_EVIDENCE_NORMALIZER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 通用证据归一化",
|
||
input_contract="输入用户问题、GoalFrame 和单条 raw observation。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_evidence_normalizer.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：把所有工具观察归一成 source_quality、usable claims、affordances、action_candidates、boundaries 与 gaps；raw observation 不直接进入最终答案。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_STATE_CRITIC",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 状态充分性检查",
|
||
input_contract="输入用户问题、含 GoalFrame/Claim Ledger/Boundary Ledger 的 agent_workspace、planner proposal 与 latest observation。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_state_critic.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：只判断 ledger 状态是否足以进入答案生成，或需要继续读取/请求用户补充；必须声明 retry 是否会改变状态，不生成最终正文。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_ANSWER_COMPOSER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 终局答案表达",
|
||
input_contract="输入用户问题与客户安全 terminal_answer_state。",
|
||
output_contract="输出客户可见中文自然答案，不暴露内部运行时术语、prompt、trace、函数名、表字段或供应商信息。",
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_answer_runtime.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层：只消费 terminal ledgers、action_candidates、DeliverablePlan 与 AnswerIntentPlan 写自然答案，不做目标识别、来源选择、对象绑定、工具选择或写操作。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_AGENT_ANSWER_REVISER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI Agent Runtime 答案质量修订",
|
||
input_contract="输入用户问题、客户安全 terminal_answer_state、原答案与质量检查 findings。",
|
||
output_contract="输出修订后的客户可见中文自然答案；保留原答案中有价值的安全内容，只修正质量问题。",
|
||
call_sites=("backend/app/services/pmflow_ai_agent_runtime/agent_answer_runtime.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层：根据质量门禁反馈修订答案，不做目标识别、来源选择、对象绑定、工具选择或写操作；修订失败后由代码 fail-closed。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_CONTEXT_MEMORY_COMPACTOR",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 长上下文语义记忆压缩",
|
||
input_contract="输入最新用户/助手回合、已有分层长期记忆和客户安全 context_pack。",
|
||
output_contract="输出 thread/user/workspace/project 四层语义记忆 JSON，包含 summary、durable_facts、open_loops、decisions、preferences 和 object_refs。",
|
||
call_sites=("backend/app/services/pmflow_ai_context_memory_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：只把对话压缩为客户安全长期记忆，不做路由、不生成最终答案、不执行写入项目记忆或外部动作。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_TURN_GOAL_STATE_ASSESSOR",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI TurnState 目标评估",
|
||
input_contract="输入用户问题、project_id、conversation_context、tenant_project_context 与 visible_context_objects。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/pmflow_ai_turn_runtime_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：只把单轮用户目标、对象锚点、证据缺口和披露边界结构化为 TurnState 候选，不执行检索、不写入、不生成最终答案。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_PMFLOW_AI_TURN_STATE_ANSWER_SYNTHESIZER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI TurnState 受控答案合成",
|
||
input_contract="输入用户问题、final TurnState、Answer Runtime 计划、evidence_findings 和 project_operating_answer_verification。",
|
||
output_contract="输出客户可见中文 Markdown 答案，不暴露内部运行时术语、prompt key、函数名、trace、表字段或供应商信息。",
|
||
call_sites=("backend/app/services/pmflow_ai_turn_runtime_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层：只消费 final TurnState 与客户安全证据合成最终答案，不做目标识别、来源选择、对象绑定、写操作或检索决策。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_CUSTOMER_ANSWER_COMPOSER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 客户答案表达生成",
|
||
input_contract="输入 customer_evidence_packet 与用户问题，只允许使用 display_label / display_summary / business_values 等客户安全字段。",
|
||
output_contract="输出客户可见中文答案文本，不暴露 raw source、trace、prompt key、函数名、表字段或供应商信息。",
|
||
call_sites=("backend/app/services/customer_answer_presentation_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层：只消费 customer-safe evidence packet 生成用户答案，不执行检索、路由拍板或写操作。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_SPACE_AI_INTERACTION_OPTION_PLANNER",
|
||
module="ai_prompts",
|
||
business_scenario="Space AI 追问选项规划",
|
||
input_contract="输入 customer_evidence_packet、用户原始问题、已生成客户答案和 fallback interaction_request。",
|
||
output_contract="输出 JSON interaction_request 片段，包含逐题 questions、客户安全 options、supplement 和 submit_label。",
|
||
call_sites=("backend/app/services/customer_answer_presentation_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示交互层：只为已判定需要追问的问题生成客户可见候选选项，不做路由、检索、写操作或事实确认。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_CUSTOMER_ANSWER_VERIFIER",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 客户答案质检",
|
||
input_contract="输入 customer_evidence_packet、用户问题与候选答案。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/customer_answer_presentation_service.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="展示层 guardrail：检查答案是否泄露内部术语、未回答问题、越过证据或过度声称；只输出质检 JSON。",
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_FOLLOWUP_ANCHOR_SEMANTICS",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 续问锚点归位",
|
||
input_contract="输入当前问题、上一轮 QA/session state、对象锚点候选和 conversation scope。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_followup_anchor.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：判断“它/这个/刚才那个”等续问应回到哪个业务对象或澄清项，不生成最终答案。",
|
||
risk_level="high",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_PENDING_CLARIFICATION_SELECTION_SEMANTICS",
|
||
module="ai_prompts",
|
||
business_scenario="PMFlow AI 待澄清项选择",
|
||
input_contract="输入当前回复、pending clarifications、会话上下文和对象绑定候选。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_followup_anchor.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
responsibility_scope="治理层：把用户补充绑定到正确 pending clarification，不做业务写入或答案生成。",
|
||
risk_level="high",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_INTERNAL_TARGET_RESOLUTION",
|
||
module="ai_prompts",
|
||
business_scenario="内部问答目标对象解析",
|
||
input_contract=JSON_INPUT,
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_target_resolution.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_EXTERNAL_QUERY_PLAN",
|
||
module="ai_prompts",
|
||
business_scenario="外部实时查询计划",
|
||
input_contract="输入 question / route decision / source policy。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/external_query_service/planner.py",),
|
||
prompt_family=PROMPT_FAMILY_PLANNING,
|
||
risk_level="high",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PROMPT_RAG_ANSWER_SCENE_SEMANTICS",
|
||
module="ai_prompts",
|
||
business_scenario="RAG answer 场景选择",
|
||
input_contract=JSON_INPUT,
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/adaptive_rag_service/mixin_deep_guard_answer.py",),
|
||
prompt_family=PROMPT_FAMILY_GOVERNANCE,
|
||
risk_level="high",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PMFLOW_REQUIREMENT_PLAN_ROUND_PROMPT",
|
||
module="ai_prompts",
|
||
business_scenario="需求收敛追问轮次",
|
||
input_contract="输入 requirement_plan_packet。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/ai_pmo/ai_pmo_typed_executor_execute/mixin_execute_query_project.py",),
|
||
prompt_family=PROMPT_FAMILY_PLANNING,
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
_entry(
|
||
"PMFLOW_REQUIREMENT_PLAN_DRAFT_PROMPT",
|
||
module="ai_prompts",
|
||
business_scenario="需求草稿生成",
|
||
input_contract="输入 requirement_plan_draft_packet。",
|
||
output_contract=JSON_OUTPUT,
|
||
call_sites=("backend/app/services/ai_pmo/ai_pmo_typed_executor_execute/mixin_execute_query_project.py",),
|
||
prompt_family=PROMPT_FAMILY_PLANNING,
|
||
risk_level="critical",
|
||
editable_policy="guarded_release",
|
||
),
|
||
)
|
||
|
||
|
||
def _semantic_fact_entries() -> tuple[dict[str, Any], ...]:
|
||
common = {
|
||
"module": "ai_prompts",
|
||
"input_contract": "输入对象快照、证据文本与抽取范围。",
|
||
"output_contract": "输出 semantic facts JSON，不要 Markdown。",
|
||
"call_sites": ("backend/app/services/semantic_fact_service/shared.py", "backend/app/services/semantic_fact_service/mixin_extract.py"),
|
||
"prompt_family": PROMPT_FAMILY_EXTRACTION,
|
||
"risk_level": "high",
|
||
"editable_policy": "guarded_release",
|
||
}
|
||
return tuple(
|
||
_entry(prompt_key, business_scenario=f"语义事实抽取：{label}", **common)
|
||
for prompt_key, label in (
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_PROJECT", "项目"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_TASK", "任务"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_SIGNAL", "信号"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_DOCUMENT", "文档"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_USER", "用户"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_REQUIREMENT", "需求"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_RELEASE", "发布"),
|
||
("PROMPT_SEMANTIC_FACT_EXTRACT_REVIEW", "复盘"),
|
||
)
|
||
)
|
||
|
||
|
||
def _inline_discovered_entries() -> tuple[dict[str, Any], ...]:
|
||
common = {
|
||
"runtime_source": "inline_discovered",
|
||
"editable_policy": "needs_migration",
|
||
"risk_level": "medium",
|
||
}
|
||
return (
|
||
_entry("PROMPT_SIGNAL_WEBPAGE_VALUE_ANALYSIS", module="inline_discovered", business_scenario="网页信号价值分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/signal/signal_capture_service/mixin_source_analyzers.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_SIGNAL_GITHUB_EVENT_VALUE_ANALYSIS", module="inline_discovered", business_scenario="GitHub 事件价值分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/signal/signal_capture_service/mixin_source_analyzers.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_SIGNAL_HACKERNEWS_TREND_ANALYSIS", module="inline_discovered", business_scenario="HackerNews 趋势信号分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/signal/signal_capture_service/mixin_source_analyzers.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_AUDIT_FUNCTIONALITY_REVIEW", module="inline_discovered", business_scenario="代码功能正确性审计", input_contract="输入任务、文件、diff 与可选完整文件内容。", output_contract=JSON_OUTPUT, call_sites=("backend/app/services/auditor_core/mixin_ai_score.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_AUDIT_ARCHITECTURE_REVIEW", module="inline_discovered", business_scenario="代码架构审计", input_contract="输入任务、提交信息、diff 和变更文件。", output_contract=JSON_OUTPUT, call_sites=("backend/app/services/auditor_core/mixin_ai_score.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_BLOCKER_CODE_ANALYSIS", module="inline_discovered", business_scenario="代码阻塞分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/blocker_detection_core/mixin_ai.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_BLOCKER_TASK_ANALYSIS", module="inline_discovered", business_scenario="任务阻塞分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/blocker_detection_core/mixin_ai.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_COGNITIVE_TASK_CHARTER_MISMATCH", module="inline_discovered", business_scenario="任务与宪章一致性检查", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/cognitive_conflict_core/mixin_detection.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_COGNITIVE_DEFINITION_CONFLICTS", module="inline_discovered", business_scenario="项目定义隐性冲突分析", input_contract=JSON_INPUT, output_contract=JSON_LIST_OUTPUT, call_sites=("backend/app/services/cognitive_conflict_core/mixin_detection.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_COGNITIVE_CONFLICT_RESOLUTION", module="inline_discovered", business_scenario="认知冲突解决方案", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/cognitive_conflict_core/mixin_resolution.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_COGNITIVE_CHARTER_CONFLICTS", module="inline_discovered", business_scenario="宪章潜在冲突分析", input_contract=JSON_INPUT, output_contract=JSON_LIST_OUTPUT, call_sites=("backend/app/services/cognitive_conflict_core/mixin_storage.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_CODEGEN_FULL_CODE_DRAFT", module="inline_discovered", business_scenario="代码生成草案", input_contract="输入需求、战略分析和代码上下文。", output_contract=JSON_OUTPUT, call_sites=("backend/app/services/codegen_core/mixin_prompt_llm.py",), prompt_family=PROMPT_FAMILY_PLANNING, risk_level="high", **{k: v for k, v in common.items() if k != "risk_level"}),
|
||
_entry("PROMPT_DAILY_BRIEFING_PERSONAL_SUMMARY", module="inline_discovered", business_scenario="每日个人工作摘要", input_contract=JSON_INPUT, output_contract=QA_OUTPUT, call_sites=("backend/app/services/daily_briefing_core/mixin_helpers_summary.py",), prompt_family=PROMPT_FAMILY_RESPONSE, **common),
|
||
_entry("PROMPT_EVENT_SIGNAL_IMPACT_ASSESSMENT", module="inline_discovered", business_scenario="信号对项目影响评估", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/event_workflow_core/mixin_helpers.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_EVENT_BLOCKER_WORKAROUND", module="inline_discovered", business_scenario="阻塞绕过方案生成", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/event_workflow_core/mixin_handlers_primary.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_EVENT_COMPETITOR_IMPACT", module="inline_discovered", business_scenario="竞品动态战略影响分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/event_workflow_core/mixin_handlers_secondary.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, **common),
|
||
_entry("PROMPT_AI_STEWARD_SIGNAL_IMPORTANCE", module="inline_discovered", business_scenario="手动信号重要性分析", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/api/ai_steward_api/routes_capture.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_AI_STEWARD_CODE_BLOCKER_SOLUTION", module="inline_discovered", business_scenario="代码问题解决方案", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/api/ai_steward_api/routes_brief_blockers.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_AI_STEWARD_COMPETITIVE_SIGNAL_DRAFT", module="inline_discovered", business_scenario="竞品分析信号草稿", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/ai_steward_service/mixin_draft_generation.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_AI_STEWARD_CHARTER_DRAFT", module="inline_discovered", business_scenario="项目宪章草稿", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/ai_steward_service/mixin_draft_generation.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_AI_PMO_SIGNAL_TITLE_REFINE", module="inline_discovered", business_scenario="信号标题提炼", input_contract=JSON_INPUT, output_contract="仅输出标题文本。", call_sites=("backend/app/services/ai_pmo/ai_pmo_typed_executor_execute/mixin_helpers.py",), prompt_family=PROMPT_FAMILY_RESPONSE, **common),
|
||
_entry("PROMPT_LLM_TASK_BREAKDOWN_HELPER", module="inline_discovered", business_scenario="LLM helper 任务拆解", input_contract=QA_INPUT, output_contract=JSON_LIST_OUTPUT, call_sites=("backend/app/services/llm_service/mixin_task_helpers.py",), prompt_family=PROMPT_FAMILY_PLANNING, **common),
|
||
_entry("PROMPT_LLM_CONTEXT_INSIGHTS_HELPER", module="inline_discovered", business_scenario="LLM helper 上下文洞察", input_contract=QA_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/llm_service/mixin_task_helpers.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
_entry("PROMPT_LLM_STRUCTURED_JSON_REPAIR", module="inline_discovered", business_scenario="结构化 JSON 修复", input_contract="输入原始任务 prompt、schema hint 和模型输出。", output_contract=JSON_OUTPUT, call_sites=("backend/app/services/llm_service/mixin_generate_db_structured.py",), prompt_family=PROMPT_FAMILY_GOVERNANCE, risk_level="high", **{k: v for k, v in common.items() if k != "risk_level"}),
|
||
_entry("PROMPT_LLM_ENGINE_CONNECTION_TEST", module="inline_discovered", business_scenario="供应商连接测试", input_contract="输入测试 prompt。", output_contract="默认只返回 OK。", call_sites=("backend/app/services/llm_service/mixin_engine_admin.py", "backend/app/core/startup/model_probe.py"), prompt_family=PROMPT_FAMILY_GOVERNANCE, risk_level="low", **{k: v for k, v in common.items() if k != "risk_level"}),
|
||
_entry("PROMPT_SSTC_CHARTER_SEED_LOCALIZE_ZH", module="inline_discovered", business_scenario="SSTC charter seed 中文本地化", input_contract=JSON_INPUT, output_contract=JSON_OUTPUT, call_sites=("backend/app/services/sstc/charter_seed_utils.py",), prompt_family=PROMPT_FAMILY_EXTRACTION, **common),
|
||
)
|
||
|
||
|
||
PROMPT_REGISTRY: tuple[dict[str, Any], ...] = (
|
||
*_part_01_entries(),
|
||
*_part_02_entries(),
|
||
*_part_03_entries(),
|
||
*_part_04_entries(),
|
||
*_scene_entries(),
|
||
*_runtime_guardrail_entries(),
|
||
*_semantic_fact_entries(),
|
||
*_inline_discovered_entries(),
|
||
)
|
||
|
||
PROMPT_REGISTRY_MAP: dict[str, dict[str, Any]] = {
|
||
entry["prompt_key"]: entry for entry in PROMPT_REGISTRY
|
||
}
|
||
|
||
|
||
def get_prompt_registry() -> list[dict[str, Any]]:
|
||
return [dict(entry) for entry in PROMPT_REGISTRY]
|
||
|
||
|
||
def get_prompt_registry_entry(prompt_key: str) -> dict[str, Any] | None:
|
||
key = str(prompt_key or "").strip()
|
||
if not key:
|
||
return None
|
||
entry = PROMPT_REGISTRY_MAP.get(key)
|
||
return dict(entry) if entry is not None else None
|
||
|
||
|
||
def iter_prompt_registry_keys() -> tuple[str, ...]:
|
||
return tuple(entry["prompt_key"] for entry in PROMPT_REGISTRY)
|
||
|
||
|
||
__all__ = [
|
||
"PROMPT_CATALOG_EXPORTS",
|
||
"PROMPT_REGISTRY",
|
||
"PROMPT_REGISTRY_MAP",
|
||
"get_prompt_registry",
|
||
"get_prompt_registry_entry",
|
||
"iter_prompt_registry_keys",
|
||
]
|