Benjamin Admin
3ba90f49cf
The user-named "right next runtime step": stop building knowledge, start using it automatically in onboarding — no sales training, no regulation picking. compliance/onboarding/ is an ORCHESTRATOR (not a new engine) wiring Company 2A -> RS-005 -> optimization -> completeness: advisor_start(input, cert_hypotheses, target_requirements, ...) -> AdvisorResult From (company + products + certifications + target) it returns inferred_assumptions, rejected_ assumptions, next_best_questions (<=5, ranked by information_gain + leverage + unknown_high_risk + evidence_missing, each self-explaining), capability_delta, top_measures, evidence_requests, unsupported_domains, completeness_summary. apply_answer() updates the profile (delta shrinks). Welt-1 throughout: certificates REDUCE questions but satisfy nothing automatically (verification_ required); relevance(evidence,target) keeps ISO 14001 out of the CRA result. Certificate->capability hypotheses + target requirements are INJECTED (curated knowledge, outsourced; not in code). All 7 acceptance criteria pass; mypy --strict clean. First app-caller wiring the engines into a product flow — still no endpoint/persistence, so 0 runtime effect -> no deploy yet (deploys when POST /onboarding/advisor-start + frontend are wired). check-loc 0.
63 lines
2.5 KiB
Python
63 lines
2.5 KiB
Python
"""Schemas for the Smart Onboarding Advisor — the onboarding RUNTIME step.
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DTOs only. The Advisor ORCHESTRATES the existing engines (Company 2A, RS-005, optimization,
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completeness) — no new reasoning engine, no new capability registry, no new meta-model. Welt-1
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discipline: a certificate yields PROBABLE capabilities (verification required), never "erfüllt".
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Python 3.9 compatible (no `|` unions).
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"""
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from __future__ import annotations
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from typing import List, Optional
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from pydantic import BaseModel, Field
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class OnboardingInput(BaseModel):
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company: str = ""
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industry: Optional[str] = None
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products: List[str] = Field(default_factory=list)
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markets: List[str] = Field(default_factory=list)
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certifications: List[str] = Field(default_factory=list)
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known_evidence: List[str] = Field(default_factory=list)
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target: List[str] = Field(default_factory=list) # informational; the delta uses injected requirements
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class InferredAssumption(BaseModel):
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certification: str
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capabilities: List[str] = Field(default_factory=list) # RELEVANT-to-target caps the cert probably provides
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verification_required: bool = True # Welt-1: never auto-satisfied
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statement: str = ""
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class RejectedAssumption(BaseModel):
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certification: Optional[str] = None
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statement: str = ""
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reason: str = "" # e.g. "relevance(evidence, target) = 0"
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class AdvisorQuestion(BaseModel):
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capability_id: str
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question_intent: str
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why: str # every question explains itself
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information_value: float = 0.0 # deterministic rank score
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priority: str = "medium"
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class AdvisorMeasure(BaseModel):
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capability_id: str
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leverage: int = 0
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closes: List[str] = Field(default_factory=list)
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class AdvisorResult(BaseModel):
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inferred_assumptions: List[InferredAssumption] = Field(default_factory=list)
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rejected_assumptions: List[RejectedAssumption] = Field(default_factory=list)
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next_best_questions: List[AdvisorQuestion] = Field(default_factory=list) # max 5
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capability_delta: List[str] = Field(default_factory=list)
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top_measures: List[AdvisorMeasure] = Field(default_factory=list)
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evidence_requests: List[str] = Field(default_factory=list)
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unsupported_domains: List[str] = Field(default_factory=list)
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completeness_summary: str = ""
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headline: str = "" # "N erkannt, M wahrscheinlich abgedeckt, K zu klären"
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