Benjamin Admin
c2c8f7e424
Not scanner stubs — the scanners exist. The Silent Pass needs only their UNIFIED output. This adds the
small common DATA FORMAT (not a new module/framework) the user asked for, exactly the Requirement-
Source / MCAP / regulation-alias pattern: many inputs, one language.
Producer A / B / C -> normalize_signals (vocabulary: id + aliases) -> canonical IntakeSignal -> Silent Pass
- ProducedSignal {signal_id, source_type, confidence, evidence, provenance} = what ANY source emits
(website scanner, repo scanner, PDF parser, tender parser, API, the user).
- knowledge/onboarding/signal_vocabulary.yaml reduces producer dialects to a canonical signal: "SBOM
present" arrives as cyclonedx_found / spdx_found / sbom_uploaded / requires_sbom (tender) — all become
`sbom_file_found`. The Silent Pass cannot tell where it came from -> no per-scanner special logic, ever.
- Unknown signals pass through (a new producer stays visible). confidence/evidence/provenance flow to
the detected capability for the audit trail.
A tender that "requires SBOM" now produces the same effect as a repo that HAS one — fits Vision V2
(Requirement Source over Regulation). Endpoint (#58) then has its final shape: POST -> Producers ->
Normalizer -> Silent Pass -> Profile -> Delta -> Questions -> Roadmap. Non-runtime -> no deploy. mypy
--strict clean, 14 onboarding tests pass, check-loc 0.
107 lines
5.1 KiB
Python
107 lines
5.1 KiB
Python
"""Silent Knowledge Pass — recognise everything possible BEFORE asking a single question (Phase 0).
|
|
|
|
The Advisor can say "I need 5 answers" but does not yet decide WHAT it can find out by itself. The Silent
|
|
Pass runs first: from signals that existing scanners/parsers already produce (website, repository,
|
|
documents, product data) it deterministically derives capabilities the company demonstrably HAS and
|
|
product facts that drive scope — so every recognised item shrinks the delta and removes a question.
|
|
|
|
The customer then experiences "we already recognised 11 of 17 — only these 4 remain" instead of a
|
|
question wall. This is NOT new architecture: it is one orchestration step in front of the Advisor
|
|
Company -> Silent Intake -> Company Profile -> Hypotheses -> Delta -> Top Questions
|
|
All building blocks already exist. SIGNALS are INJECTED (the scanners produce them); the signal->capability
|
|
map is curated DATA, also injected. Pure, deterministic, no I/O. Python 3.9 compatible.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Dict, List, Optional, Sequence, Set
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
|
|
class IntakeSignal(BaseModel):
|
|
"""A CANONICAL signal the Silent Pass consumes. Producer-agnostic: the same `signal` may have come
|
|
from a website, a repo, a PDF, a tender or the user — normalize_signals() unified them (see signals.py)."""
|
|
|
|
source: str # source_type: website / repository / document / product / tender / user
|
|
signal: str # CANONICAL signal id, e.g. "sbom_file_found"
|
|
confidence: float = 1.0 # carried from the producer
|
|
evidence: Optional[str] = None # the artifact already in hand
|
|
provenance: str = "" # where it came from (url / filename / tender clause) — audit trail
|
|
detail: str = "" # free-text (kept for back-compat)
|
|
|
|
|
|
class SignalMapping(BaseModel):
|
|
"""Curated: what a signal lets us conclude. A signal yields a capability OR a product fact."""
|
|
|
|
signal: str
|
|
capability: Optional[str] = None # capability the signal evidences
|
|
relationship: str = "detected" # detected (concrete artifact) / partial (indicative)
|
|
evidence: Optional[str] = None # the artifact found (already in hand -> no upload needed)
|
|
product_fact: Optional[str] = None # e.g. "connected_to_internet"
|
|
fact_value: str = "true"
|
|
|
|
|
|
class DetectedCapability(BaseModel):
|
|
capability: str
|
|
relationship: str = "detected"
|
|
source: str = "" # which signal/source detected it (audit trail)
|
|
evidence: Optional[str] = None
|
|
confidence: float = 1.0 # carried from the producing signal
|
|
provenance: str = "" # where the signal came from
|
|
|
|
|
|
class ProductFact(BaseModel):
|
|
key: str
|
|
value: str = "true"
|
|
source: str = ""
|
|
|
|
|
|
class SilentIntakeResult(BaseModel):
|
|
detected_capabilities: List[DetectedCapability] = Field(default_factory=list)
|
|
product_facts: List[ProductFact] = Field(default_factory=list)
|
|
evidence_found: List[str] = Field(default_factory=list)
|
|
summary: str = ""
|
|
|
|
def capability_ids(self) -> List[str]:
|
|
"""The detected capability ids — fed into the Advisor as already-present (delta-reducing)."""
|
|
return sorted({d.capability for d in self.detected_capabilities})
|
|
|
|
|
|
def silent_intake(
|
|
signals: Sequence[IntakeSignal], signal_map: Sequence[SignalMapping]
|
|
) -> SilentIntakeResult:
|
|
"""Derive capabilities + product facts from injected scanner signals (deterministic, no questions).
|
|
|
|
Each signal is matched to curated mappings by `signal` id; a mapping contributes either a detected
|
|
capability (+ optional evidence already in hand) or a product fact. Deduped, deterministic order.
|
|
"""
|
|
by_signal: Dict[str, List[SignalMapping]] = {}
|
|
for m in signal_map:
|
|
by_signal.setdefault(m.signal, []).append(m)
|
|
|
|
caps: Dict[str, DetectedCapability] = {}
|
|
facts: Dict[str, ProductFact] = {}
|
|
evidence: Set[str] = set()
|
|
for s in signals:
|
|
for m in by_signal.get(s.signal, []):
|
|
if m.capability and m.capability not in caps:
|
|
caps[m.capability] = DetectedCapability(
|
|
capability=m.capability, relationship=m.relationship,
|
|
source="%s:%s" % (s.source, s.signal), evidence=m.evidence,
|
|
confidence=s.confidence, provenance=s.provenance)
|
|
if m.evidence:
|
|
evidence.add(m.evidence)
|
|
if m.product_fact:
|
|
facts[m.product_fact] = ProductFact(key=m.product_fact, value=m.fact_value, source=s.source)
|
|
|
|
detected = [caps[k] for k in sorted(caps)]
|
|
product_facts = [facts[k] for k in sorted(facts)]
|
|
summary = (
|
|
"Stille Vorbefüllung: %d Fähigkeit(en) automatisch erkannt, %d Produktfakt(en), %d Nachweis(e) bereits vorhanden."
|
|
% (len(detected), len(product_facts), len(evidence))
|
|
)
|
|
return SilentIntakeResult(
|
|
detected_capabilities=detected, product_facts=product_facts,
|
|
evidence_found=sorted(evidence), summary=summary)
|