091b66a9bb
将角色与 Skill 从 .claude 迁入 .agent，移除 Claude CLI 运行时并接入固定 Opus 角色 profile、完整 schema、预算 deadline、raw 与回执证据链。 同步拆分 Skill 职责、复利 lesson、Gate 回放、Dashboard 人审入口、数据库登记和机械门禁；候选设计正文不包含在本提交中。
267 lines
12 KiB
Python
267 lines
12 KiB
Python
#!/usr/bin/env python3
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"""search-knowledge Skill：知识向量检索（余弦召回 + 授权过滤 + aiContext 字段裁剪）。
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合同见同目录 SKILL.md。查询嵌入与知识行同模型同维（复用 embed-knowledge 的实现）。
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"""
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import json
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import sys
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from typing import Any, Callable
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import click
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import psycopg
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from muse_embed import _session, embed_texts
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from muse_db import DSN
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TENANT = 1
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def load_ai_context(conn, *, tenant_id: int = TENANT):
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"""读库内 23 型的字段级 aiContext 细则：{target_type: {field: true/false/[用途]}}。"""
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rows = conn.execute(
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"""SELECT s.target_type, v.policy_snapshot->'fieldAiContext'
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FROM muse_meta_schema s
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JOIN muse_meta_schema_version sv ON sv.id = s.active_version_id
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JOIN muse_meta_visibility_policy v ON v.schema_version_id = sv.id
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WHERE s.tenant_id=%s AND s.deleted=FALSE""", (tenant_id,)).fetchall()
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return {t: (m or {}) for t, m in rows}
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def visible(ai_rule, purpose):
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"""aiContext 判定：true 全用途可见；false 不可见；[用途] 仅列出的可见；无规则默认可见。"""
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if ai_rule is None:
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return True
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if isinstance(ai_rule, bool):
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return ai_rule
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return purpose in ai_rule
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def _default_embedder(intent: str) -> list[float]:
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"""复用 embed-knowledge 生成单条查询向量，并统一失败语义。"""
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vectors, bad = embed_texts(_session(), [intent])
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if bad or not vectors:
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raise ValueError("查询嵌入失败")
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return vectors[0]
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def _structured_source_refs(payload: dict[str, Any], lineage: Any) -> list[dict[str, Any]]:
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"""只接收结构化来源指针；人类可读的“出处”文本不能冒充可回读引用。"""
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candidates = []
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if isinstance(lineage, dict):
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candidates.append(lineage.get("sourceRefs"))
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fields = payload.get("字段")
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if isinstance(fields, dict):
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candidates.append(fields.get("sourceRefs"))
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candidates.append(payload.get("sourceRefs"))
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for value in candidates:
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if isinstance(value, list) and all(isinstance(item, dict) for item in value):
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return value
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return []
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def _milestones(payload: dict[str, Any]) -> list[dict[str, Any]]:
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"""从卡字段中提取历史里程碑，终态摘要不会进入冻结投影。"""
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fields = payload.get("字段")
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if not isinstance(fields, dict):
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return []
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for key in ("演变历程", "演变轨迹", "milestones"):
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value = fields.get(key)
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if isinstance(value, list) and all(isinstance(item, dict) for item in value):
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return value
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return []
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def search_cards(
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intent: str,
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*,
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scope: str = "admin",
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work_id: int | None = None,
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ttype: str | None = None,
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purpose: str = "generation",
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top: int = 5,
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dsn: str = DSN,
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tenant_id: int = TENANT,
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connection_factory: Callable[..., Any] = psycopg.connect,
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embedder: Callable[[str], list[float]] = _default_embedder,
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) -> list[dict[str, Any]]:
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"""执行唯一的卡检索语义，CLI 与正文读取器共同调用本函数。
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生产作品面只读 active Canonical entity、允许状态和有效绑定；治理面
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保留原有 draft 能力，但正文生产适配器不会调用治理面。
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"""
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if scope not in {"admin", "public_pattern", "work"}:
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raise ValueError("scope 只能是 admin、public_pattern 或 work")
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if scope == "work" and not work_id:
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raise ValueError("scope=work 必须提供 work_id")
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if purpose not in {"generation", "planning", "detection", "extraction"}:
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raise ValueError("purpose 非法")
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if isinstance(top, bool) or not isinstance(top, int) or top <= 0:
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raise ValueError("top 必须是正整数")
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qvec = json.dumps(embedder(intent))
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with connection_factory(dsn) as conn:
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ai_rules = load_ai_context(conn, tenant_id=tenant_id)
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if scope == "admin":
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sql = """SELECT 'draft' AS src, d.id, d.draft_payload AS payload, d.status,
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1 - (e.embedding <=> %s::vector) AS score,
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d.revision, d.current_canonical_snapshot AS lineage,
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NULL::varchar AS binding_status, d.source_status
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FROM example_knowledge_embedding e
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JOIN muse_knowledge_draft d ON d.id = e.draft_id
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WHERE e.tenant_id=%s AND e.deleted=FALSE AND d.deleted=FALSE"""
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args = [qvec, tenant_id]
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elif scope == "public_pattern":
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# WHY: 公共范式仍处于 draft 双轨，不能走作品 entity/binding 面；专用查询必须在
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# SQL 层同时锁住全局作品号、公共目标库、全局库存在性和来源资格，不能复用会
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# 召回同租户全部治理草稿的 admin 面。当前 draft schema 没有 kb_id/scope 列，
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# 所以库归属按已登记的数据合同由 work_id + 目标库绑定，scope 缺省按 global。
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sql = """SELECT 'draft' AS src, d.id, d.draft_payload AS payload, d.status,
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1 - (e.embedding <=> %s::vector) AS score,
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d.revision, d.current_canonical_snapshot AS lineage,
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NULL::varchar AS binding_status, d.source_status
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FROM example_knowledge_embedding e
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JOIN muse_knowledge_draft d ON d.id = e.draft_id
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WHERE e.tenant_id=%s AND d.tenant_id=%s
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AND e.deleted=FALSE AND d.deleted=FALSE
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AND d.work_id=0
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AND d.draft_payload->>'目标库'='公共范式库'
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AND COALESCE(d.draft_payload->>'scope', 'global')='global'
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AND d.status IN ('pending','confirmed')
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AND COALESCE(d.source_status, 'active') IN ('active','authorized')
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AND COALESCE(d.source_action_policy, 'allowed')='allowed'
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AND EXISTS (
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SELECT 1 FROM muse_knowledge_base kb
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WHERE kb.tenant_id=%s AND kb.deleted=FALSE
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AND kb.kb_type='global' AND kb.status='active'
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)"""
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args = [qvec, tenant_id, tenant_id, tenant_id]
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else:
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sql = """SELECT 'entity' AS src, en.id,
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jsonb_build_object('型', en.entity_type, '名称', en.normalized_name,
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'一句话摘要', en.description, '字段', en.attributes) AS payload,
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en.status, 1 - (e.embedding <=> %s::vector) AS score,
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en.revision, en.lineage_payload AS lineage,
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b.binding_status, en.source_status
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FROM example_knowledge_embedding e
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JOIN muse_knowledge_entity en ON en.id = e.entity_id
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JOIN muse_knowledge_binding b ON b.kb_id = en.kb_id AND b.work_id = %s
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AND b.binding_status='active' AND b.deleted=FALSE AND b.tenant_id=%s
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WHERE e.tenant_id=%s AND e.deleted=FALSE AND en.deleted=FALSE
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AND en.status='active' AND en.source_status IN ('active','authorized')
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AND en.source_action_policy='allowed'"""
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args = [qvec, work_id, tenant_id, tenant_id]
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if ttype:
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sql += (
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" AND d.draft_payload->>'型' = %s"
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if scope in {"admin", "public_pattern"}
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else " AND en.entity_type = %s"
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)
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args.append(ttype)
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draft_scope = scope in {"admin", "public_pattern"}
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id_column = "d.id" if draft_scope else "en.id"
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revision_column = "d.revision" if draft_scope else "en.revision"
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sql += f" ORDER BY score DESC, {revision_column}::text ASC, {id_column}::text ASC LIMIT %s"
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args.append(top)
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rows = conn.execute(sql, args).fetchall()
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results = []
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for src, row_id, raw_payload, status, score, revision, lineage, binding_status, source_status in rows:
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payload = raw_payload or {}
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card_type = payload.get("型") or payload.get("type") or "?"
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rules = ai_rules.get(card_type, {})
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fields = payload.get("字段") or {}
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visible_fields = {key: item for key, item in fields.items() if visible(rules.get(key), purpose)}
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source_version = f"{src}-revision:{revision or 0}"
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source_id = f"canonical-entity:{row_id}" if src == "entity" else f"draft:{row_id}"
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results.append(
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{
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"cardId": str(row_id),
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"type": card_type,
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"name": payload.get("名称"),
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"score": float(score),
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"summary": payload.get("一句话摘要"),
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"visibleFields": visible_fields,
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"omittedFields": sorted(set(fields) - set(visible_fields)),
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"sourceId": source_id,
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"sourceVersion": source_version,
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"sourceOffset": 0,
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"sourceRefs": _structured_source_refs(payload, lineage),
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"milestones": _milestones(payload),
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"sourceKind": "canonical_entity" if src == "entity" else "draft",
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"sourceStatus": source_status or status,
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"bindingStatus": binding_status,
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"retrievalScope": scope,
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"productionRetrievalEligible": (
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scope == "public_pattern"
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or (src == "entity" and status == "active" and binding_status == "active")
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),
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}
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)
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return results
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@click.command()
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@click.argument("intent")
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@click.option("--scope", type=click.Choice(["admin", "public_pattern", "work"]), default="admin", show_default=True,
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help="admin=治理面; public_pattern=公共范式草稿; work=作品面")
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@click.option("--work-id", type=int, help="scope=work 时必填")
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@click.option("--type", "ttype", help="限定型（如 craft/combat/emotion/scene_pattern/trope）")
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@click.option("--purpose", default="generation", show_default=True,
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type=click.Choice(["generation", "planning", "detection", "extraction"]))
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@click.option("--top", default=5, show_default=True)
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@click.option("--json", "as_json", is_flag=True)
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def main(intent, scope, work_id, ttype, purpose, top, as_json):
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try:
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cards = search_cards(
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intent,
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scope=scope,
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work_id=work_id,
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ttype=ttype,
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purpose=purpose,
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top=top,
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)
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except ValueError as error:
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raise click.ClickException(str(error)) from error
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results = [
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{
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"来源": item["sourceId"],
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"型": item["type"],
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"名称": item["name"],
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"状态": item["sourceStatus"],
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"相似度": round(item["score"], 4),
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"一句话摘要": item["summary"],
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"可见字段": item["visibleFields"],
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"出处": item["sourceRefs"],
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"裁剪回显": item["omittedFields"],
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}
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for item in cards
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]
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if as_json:
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click.echo(json.dumps(results, ensure_ascii=False, indent=1))
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return
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for i, r in enumerate(results, 1):
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click.echo(f"── {i}. [{r['相似度']}] {r['型']} · {r['名称']}（{r['状态']}，{r['来源']}）")
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click.echo(f" 摘要: {r['一句话摘要']}")
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for k, v in (r["可见字段"] or {}).items():
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click.echo(f" {k}: {str(v)[:120]}")
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if r["出处"]:
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click.echo(f" 出处: {r['出处']}")
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if r["裁剪回显"]:
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click.echo(f" [裁剪回显·{purpose} 不可见] {','.join(r['裁剪回显'])}")
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if not results:
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click.echo("（无召回）")
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if __name__ == "__main__":
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try:
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main()
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except psycopg.Error as e:
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click.echo(f"[db错误] {type(e).__name__}: {e}", err=True)
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sys.exit(1)
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