Benjamin Admin
9c33582412
Not the endpoint yet — the bigger knowledge lever first. The Advisor can say "I need 5 answers" but does not yet decide what it can find out by ITSELF. The Silent Knowledge Pass runs in front of the Advisor and, from signals existing scanners/parsers already produce (website, repository, documents, product data), deterministically derives capabilities the company demonstrably HAS + product facts that drive scope — so every recognised item shrinks the delta and removes a question. compliance/onboarding/silent_intake.py: silent_intake(signals, signal_map) -> detected_capabilities (+ evidence already in hand) + product_facts. The signal->conclusion map is curated DATA (knowledge/onboarding/intake_signal_map.yaml), signals are injected (scanners are upstream). Pure, deterministic, no LLM. advisor_start gains detected_capabilities (folded into the profile at HIGH confidence -> covered, not asked) and an auto_detected result + headline. The experience flips from a question wall to "we already recognised 4 capabilities, 2 product facts and have 4 pieces of evidence in hand — only these few remain". Order now: Silent Pass -> #58 endpoint/frontend -> #59 empirical loop. NOT new architecture, just an orchestration step in front. Non-runtime (no app caller) -> no deploy. 15 onboarding tests pass, mypy --strict clean, check-loc 0.
64 lines
2.6 KiB
Python
64 lines
2.6 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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auto_detected: List[str] = Field(default_factory=list) # Silent Pass: recognised w/o asking
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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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