feat(selection): 任务自适应自动选型auto_select_model+models_catalog(本机构+owner机构候选,utility语义匹配,owner硬校验双保险);治理链govern_resolve加模型归属校验——他机构模型剔除顺延替代,显式指定越权模型报可行动错误(2026-09-07用户需求)
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@ -483,6 +483,25 @@ async def govern_resolve(org_id: str, user_id: str = '', model_name: str = '',
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return False, '__LEGACY__' # 指定模型不在新表 → 旧表兜底
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return False, '__LEGACY__' # 指定模型不在新表 → 旧表兜底
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return False, ('生效策略未配置模型：请在模型治理→组织容错策略配置主/备模型，'
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return False, ('生效策略未配置模型：请在模型治理→组织容错策略配置主/备模型，'
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'或在调用时指定模型名')
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'或在调用时指定模型名')
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# ⑤.5 模型归属硬校验（2026-09-07 用户需求）：只有平台 owner（'0'/系统级共享）
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# 或本机构的模型可调用；他机构模型从链中剔除，链内替代模型（策略备链）自然顺延。
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# 显式指定的模型被剔除且链内无替代 → 报可行动错误（禁止静默回退/越权放行）。
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from .selection import _owner_allowed
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allowed = [m for m in models if _owner_allowed(m.get('org_id'), org_id)]
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if len(allowed) != len(models):
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for m in models:
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if not _owner_allowed(m.get('org_id'), org_id):
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logger.info("pipeline_llm: 模型 %s 归属机构 %s，调用方 %s 无权使用，"
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"已从模型链剔除（顺延替代模型）",
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m.get('name'), m.get('org_id'), org_id or '(未指定)')
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if not allowed:
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if model_name:
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return False, ('模型「%s」归属其他机构（非平台 owner 共享、也非本机构模型），'
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'不允许调用。请选择本机构或平台共享模型'
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'（模型治理→模型注册可查看归属）' % model_name)
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return False, ('生效策略模型链全部归属其他机构，调用方 %s 无可调用模型——'
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'请检查机构策略配置' % (org_id or '(未指定)'))
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models = allowed
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# ⑥ 逐模型尝试（辅助 → 主 → 备容错）
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# ⑥ 逐模型尝试（辅助 → 主 → 备容错）
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last_err = ''
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last_err = ''
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chosen = None
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chosen = None
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@ -496,6 +515,8 @@ async def govern_resolve(org_id: str, user_id: str = '', model_name: str = '',
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# 本机构链全不可用 → 容错落 owner 链
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# 本机构链全不可用 → 容错落 owner 链
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if chosen is None and policy_org != '0':
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if chosen is None and policy_org != '0':
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models0, pref0 = await _model_chain(sor, '0', '', purpose)
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models0, pref0 = await _model_chain(sor, '0', '', purpose)
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# owner 容错链同样过归属校验（策略里可能配了他机构注册的模型）
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models0 = [m for m in models0 if _owner_allowed(m.get('org_id'), org_id)]
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for m in models0:
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for m in models0:
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ok, cand = await _pick_candidate(sor, m, pref0)
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ok, cand = await _pick_candidate(sor, m, pref0)
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if ok and isinstance(cand, tuple):
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if ok and isinstance(cand, tuple):
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@ -162,6 +162,156 @@ async def resolve_model_name(model_ref, org_id='', capabilities=()):
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return getattr(recs[0], 'name', '') or ''
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return getattr(recs[0], 'name', '') or ''
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def _owner_allowed(model_org, org_id):
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"""模型归属校验（2026-09-07 用户需求）：平台 owner（'0'/空 = 系统级共享）
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或本机构的模型才可调用；其他机构的模型一律拒绝（由调用方顺延替代候选）。"""
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m = (model_org or '').strip()
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return m in ('', '0') or m == (org_id or '').strip()
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async def models_catalog(org_id, capabilities=(), limit=50):
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"""本机构可用模型明细（本机构 + 平台 owner('0')/系统级共享）。
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供 agent 的 list_models 工具与 auto_select_model 候选池共用——与推理链
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（govern_resolve）同一机构可见性语义，杜绝两处 SQL 漂移。
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返回 [dict]: name/vendor_model_id/capability/description/vendor/org_id。
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"""
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db, dbname = _get_sor()
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caps = _norm_caps(capabilities)
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async with db.sqlorContext(dbname) as sor:
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sql = ("SELECT m.id, m.name, m.vendor_model_id, m.capability, m.description, "
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"m.org_id, v.name AS vendor_name "
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"FROM llm_model m LEFT JOIN llm_vendor v ON v.id=m.vendor_id "
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"WHERE m.status='active'")
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params = {}
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if org_id and org_id != '0':
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sql += " AND (m.org_id=${org}$ OR m.org_id='' OR m.org_id='0')"
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params['org'] = org_id
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if caps:
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# sqlor 的 IN 列表必须展开占位符（传 list 会崩）：${c0}$,${c1}$,...
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ph = []
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for i, c in enumerate(caps):
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k = 'c%d' % i
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ph.append('${%s}$' % k)
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params[k] = c
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sql += (" AND COALESCE(NULLIF(m.capability,''),'t2t') IN (%s)"
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% ','.join(ph))
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sql += " ORDER BY m.capability, m.name LIMIT %d" % int(limit)
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recs = await sor.sqlExe(sql, params)
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await sor.sqlExe("COMMIT", {})
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out = []
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for r in (recs or []):
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out.append({
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'name': getattr(r, 'name', '') or '',
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'vendor_model_id': getattr(r, 'vendor_model_id', '') or '',
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'capability': (getattr(r, 'capability', '') or 't2t').strip().lower(),
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'description': (getattr(r, 'description', '') or '').strip(),
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'vendor': getattr(r, 'vendor_name', '') or '',
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'org_id': getattr(r, 'org_id', '') or '',
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})
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# owner 双保险（SQL 已过滤，防未来改动破坏不变量）
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return [c for c in out if _owner_allowed(c['org_id'], org_id)]
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async def auto_select_model(org_id, user_input, user_id='', capabilities='chat'):
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"""根据用户输入自动匹配最合适的模型（任务自适应路由）。
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候选池 = models_catalog（本机构 + 平台 owner('0')/系统级共享，与推理链
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可见性一致），按能力白名单过滤（缺省 'chat' = CHAT_CAPS 对话能力；
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传 '' = 全能力，供 invoke_model 生成类任务自动选型）。
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匹配 = utility 廉价模型语义选择（purpose='utility' 现成治理链，temperature=0），
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依据 = 模型名 + 能力类型 + description（模型注册时填写的擅长说明）。
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Owner 硬校验：LLM 返回的模型名逐个对照候选池（池外名字 = 幻觉，跳过），
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命中候选即天然满足 owner ∈ ('', '0', 本机构)——池已过滤 + 此处双保险。
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全部无效 → 兜底机构策略缺省模型（inference._pick_default_model_name）。
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返回 (model_name, reason)；无候选/匹配失败返回 ('', 原因)——调用方沿用
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原有回退链（策略主备），不因此报错。
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"""
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candidates = await models_catalog(org_id, capabilities=capabilities)
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if not candidates:
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return '', '无可用模型候选（本机构+平台owner机构均无该能力的启用模型）'
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if len(candidates) == 1:
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# 唯一候选：跳过 LLM 匹配（省一次 utility 调用）
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return candidates[0]['name'], '唯一候选'
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# ── utility 模型语义匹配 ──
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catalog = '\n'.join(
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'- %s [%s]%s%s' % (
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c['name'], c['capability'],
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(' ' + c['vendor']) if c['vendor'] else '',
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('：' + c['description'][:150]) if c['description'] else '')
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for c in candidates)
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task = (user_input or '').strip()[:600]
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prompt = (
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'你是平台模型调度器。以下是本机构当前可用的模型目录'
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'（格式：模型名 [能力类型] 供应商：描述）：\n%s\n\n'
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'能力类型含义：t2t=文本对话 i2t=图像理解 m2t=多媒体理解 t2i=文生图 '
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'i2v=图生视频 t2v=文生视频 r2v=参考生视频 tts=语音合成 asr=语音识别 '
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'embedding=向量化 rerank=重排序\n\n'
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'用户任务：\n%s\n\n'
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'请根据任务的内容领域、语言、复杂度，从目录中选出最适合完成该任务的模型'
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'（只能选目录中的模型名，按适合度从高到低排序，最多 3 个）。'
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'只返回 JSON 对象：{"models": ["模型名1", "模型名2"], "reason": "一句话理由"}。'
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'没有适合的模型时 models 返回空数组。' % (catalog, task))
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from .inference import chat_inference
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try:
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data = await chat_inference(
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org_id or '0', user_id or '',
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{'messages': [{'role': 'user', 'content': prompt}],
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'temperature': 0, '_purpose': 'utility'})
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content = ((data.get('choices') or [{}])[0].get('message') or {}).get('content') or ''
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except Exception as e:
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logger.warning("auto_select_model 匹配调用失败（走策略链兜底）: %s", e)
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return '', '匹配调用失败: %s' % str(e)[:120]
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# 解析 + 逐候选 owner/存在性校验
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import json as _json
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import re as _re
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picked = []
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reason = ''
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m = _re.search(r'\{[\s\S]*\}', content or '')
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if m:
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try:
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obj = _json.loads(m.group(0))
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if isinstance(obj, dict):
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picked = [str(x).strip() for x in (obj.get('models') or []) if str(x or '').strip()]
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reason = str(obj.get('reason') or '')[:120]
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except Exception:
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picked = []
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if not picked:
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# 兜底：整体 JSON 解析失败时按候选名逐个在文本中找（顺序 = 目录序）
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for c in candidates:
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if c['name'] and c['name'] in (content or ''):
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picked.append(c['name'])
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break
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by_name = {}
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for c in candidates:
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by_name[c['name']] = c
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if c['vendor_model_id']:
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by_name.setdefault(c['vendor_model_id'], c)
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for name in picked:
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c = by_name.get(name)
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if c and _owner_allowed(c['org_id'], org_id):
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return c['name'], (reason or '语义匹配')
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if name:
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logger.warning("auto_select_model: 跳过无效/越权候选 %s", name)
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# LLM 结果全无效 → 机构策略缺省模型兜底（治理语义一致）
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try:
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from .inference import _pick_default_model_name
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_caps = _norm_caps(capabilities)
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cap0 = _caps[0] if _caps else 't2t'
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fallback = await _pick_default_model_name(org_id or '0', cap0)
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if fallback:
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return fallback, '匹配无效，回退机构策略缺省模型'
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except Exception as e:
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logger.debug("auto_select_model 策略兜底失败: %s", e)
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return '', '未能匹配到合适模型（沿用原回退链）'
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async def org_available_models(org_id, capability=''):
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async def org_available_models(org_id, capability=''):
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"""机构可用模型注册名列表（本机构 + 系统级共享）。空 = 机构未配置模型。"""
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"""机构可用模型注册名列表（本机构 + 系统级共享）。空 = 机构未配置模型。"""
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db, dbname = _get_sor()
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db, dbname = _get_sor()
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@ -185,4 +335,6 @@ def load_selection():
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env.llm_model_options = model_options
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env.llm_model_options = model_options
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env.llm_resolve_model_name = resolve_model_name
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env.llm_resolve_model_name = resolve_model_name
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env.llm_org_available_models = org_available_models
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env.llm_org_available_models = org_available_models
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env.llm_models_catalog = models_catalog
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env.llm_auto_select_model = auto_select_model
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logger.info("[pipeline_llm] selection loaded")
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logger.info("[pipeline_llm] selection loaded")
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