Benjamin Admin
7df15010ff
Per the user's decision (2026-06-28): observations are CALIBRATION data for the knowledge base, NOT
business data and NOT product-DB data. So they live with the other versioned knowledge artifacts as an
append-only JSONL log under knowledge/observations/ — NO migration, NO DB. (A real persistence layer is
only warranted once thousands of onboardings exist; not before.)
- ObservationRecord = Observation + log metadata (observation_id, timestamp [caller-stamped, no hidden
clock], customer_archetype [anonymised — NEVER a real name], evidence, provenance, knowledge_version).
- append_observation() writes one JSON line; append-only, lines are never rewritten. A later review is a
NEW line with the same observation_id; load_observations(reconcile=True) keeps the latest per id.
- load_observations() reads a single .jsonl or a directory of monthly .jsonl files.
- aggregate_by_hypothesis() (59c) -> per-hypothesis distribution + confidence, COMPUTED from the log
(computed-not-stored); the review gate (reviewed-only) is enforced in empirical_distribution/confidence.
- review_queue() -> the unreviewed worklist. Observation -> Review -> Accepted -> recompute, never
Observation -> confidence++. Nothing is ever written back to a hypothesis.
You can `rm` the log and recompute, `git diff` it over months, or rebuild confidence under a new policy —
fully consistent with computed-not-stored and the product/knowledge data separation.
Non-runtime (module + tests only, no endpoint) -> origin/main, NO dev deploy. 5 new tests (append-only,
review supersession, review-gate statistics, queue, monthly-file load); 27 onboarding tests pass, mypy
--strict clean (9 modules), check-loc 0. 59d (surface computed confidence at runtime) stays a later step.
87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
"""Smart Onboarding Advisor — the onboarding runtime step (orchestration over existing engines).
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Turns (company + products + certifications + target) into inferred assumptions, the next best questions
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(<=5, each self-explaining), the capability delta, top measures, evidence requests and completeness —
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with NO sales interpretation and NO regulation picking. Orchestrator only: no new engine/registry/
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meta-model; certificate->capability hypotheses and target requirements are INJECTED.
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"""
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from __future__ import annotations
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from .engine import advisor_start, apply_answer
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from .hypotheses import (
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CapabilityHypothesis,
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inferred_hypotheses,
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resolve_for_certifications,
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)
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from .observations import (
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Observation,
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ObservationType,
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empirical_confidence,
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empirical_distribution,
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reviewed,
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)
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from .observation_log import (
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HypothesisStats,
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ObservationRecord,
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aggregate_by_hypothesis,
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append_observation,
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load_observations,
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review_queue,
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)
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from .signals import (
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ProducedSignal,
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SignalVocabularyEntry,
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normalize_signals,
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)
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from .silent_intake import (
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DetectedCapability,
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IntakeSignal,
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ProductFact,
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SignalMapping,
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SilentIntakeResult,
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silent_intake,
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)
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from .schemas import (
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AdvisorMeasure,
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AdvisorQuestion,
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AdvisorResult,
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InferredAssumption,
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OnboardingInput,
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RejectedAssumption,
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)
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__all__ = [
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"advisor_start",
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"apply_answer",
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"OnboardingInput",
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"AdvisorResult",
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"AdvisorQuestion",
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"AdvisorMeasure",
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"InferredAssumption",
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"RejectedAssumption",
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"CapabilityHypothesis",
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"inferred_hypotheses",
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"resolve_for_certifications",
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"Observation",
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"ObservationType",
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"empirical_distribution",
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"empirical_confidence",
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"reviewed",
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"silent_intake",
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"IntakeSignal",
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"SignalMapping",
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"DetectedCapability",
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"ProductFact",
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"SilentIntakeResult",
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"ProducedSignal",
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"SignalVocabularyEntry",
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"normalize_signals",
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"ObservationRecord",
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"HypothesisStats",
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"append_observation",
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"load_observations",
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"aggregate_by_hypothesis",
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"review_queue",
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]
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