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.
100 lines
4.5 KiB
Python
100 lines
4.5 KiB
Python
"""Silent Knowledge Pass — recognise everything possible BEFORE asking a single question (Phase 0).
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The Advisor can say "I need 5 answers" but does not yet decide WHAT it can find out by itself. The Silent
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Pass runs first: from signals that existing scanners/parsers already produce (website, repository,
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documents, product data) it deterministically derives capabilities the company demonstrably HAS and
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product facts that drive scope — so every recognised item shrinks the delta and removes a question.
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The customer then experiences "we already recognised 11 of 17 — only these 4 remain" instead of a
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question wall. This is NOT new architecture: it is one orchestration step in front of the Advisor
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Company -> Silent Intake -> Company Profile -> Hypotheses -> Delta -> Top Questions
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All building blocks already exist. SIGNALS are INJECTED (the scanners produce them); the signal->capability
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map is curated DATA, also injected. Pure, deterministic, no I/O. Python 3.9 compatible.
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"""
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from __future__ import annotations
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from typing import Dict, List, Optional, Sequence, Set
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from pydantic import BaseModel, Field
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class IntakeSignal(BaseModel):
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"""One finding a scanner/parser produced (no LLM here — the scanners are upstream)."""
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source: str # website / repository / document / product
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signal: str # signal id, e.g. "sbom_file_found"
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detail: str = "" # optional (url, filename) for the audit trail
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class SignalMapping(BaseModel):
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"""Curated: what a signal lets us conclude. A signal yields a capability OR a product fact."""
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signal: str
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capability: Optional[str] = None # capability the signal evidences
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relationship: str = "detected" # detected (concrete artifact) / partial (indicative)
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evidence: Optional[str] = None # the artifact found (already in hand -> no upload needed)
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product_fact: Optional[str] = None # e.g. "connected_to_internet"
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fact_value: str = "true"
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class DetectedCapability(BaseModel):
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capability: str
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relationship: str = "detected"
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source: str = "" # which signal/source detected it (audit trail)
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evidence: Optional[str] = None
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class ProductFact(BaseModel):
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key: str
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value: str = "true"
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source: str = ""
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class SilentIntakeResult(BaseModel):
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detected_capabilities: List[DetectedCapability] = Field(default_factory=list)
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product_facts: List[ProductFact] = Field(default_factory=list)
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evidence_found: List[str] = Field(default_factory=list)
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summary: str = ""
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def capability_ids(self) -> List[str]:
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"""The detected capability ids — fed into the Advisor as already-present (delta-reducing)."""
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return sorted({d.capability for d in self.detected_capabilities})
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def silent_intake(
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signals: Sequence[IntakeSignal], signal_map: Sequence[SignalMapping]
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) -> SilentIntakeResult:
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"""Derive capabilities + product facts from injected scanner signals (deterministic, no questions).
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Each signal is matched to curated mappings by `signal` id; a mapping contributes either a detected
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capability (+ optional evidence already in hand) or a product fact. Deduped, deterministic order.
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"""
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by_signal: Dict[str, List[SignalMapping]] = {}
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for m in signal_map:
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by_signal.setdefault(m.signal, []).append(m)
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caps: Dict[str, DetectedCapability] = {}
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facts: Dict[str, ProductFact] = {}
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evidence: Set[str] = set()
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for s in signals:
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for m in by_signal.get(s.signal, []):
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if m.capability and m.capability not in caps:
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caps[m.capability] = DetectedCapability(
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capability=m.capability, relationship=m.relationship,
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source="%s:%s" % (s.source, s.signal), evidence=m.evidence)
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if m.evidence:
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evidence.add(m.evidence)
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if m.product_fact:
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facts[m.product_fact] = ProductFact(key=m.product_fact, value=m.fact_value, source=s.source)
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detected = [caps[k] for k in sorted(caps)]
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product_facts = [facts[k] for k in sorted(facts)]
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summary = (
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"Stille Vorbefüllung: %d Fähigkeit(en) automatisch erkannt, %d Produktfakt(en), %d Nachweis(e) bereits vorhanden."
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% (len(detected), len(product_facts), len(evidence))
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)
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return SilentIntakeResult(
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detected_capabilities=detected, product_facts=product_facts,
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evidence_found=sorted(evidence), summary=summary)
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