PHP API

Motivation

The PHP surface is the native integration point for jobs, controllers, policies, and application services. It should remain the thinnest adapter because ReviewEngine owns orchestration.

Theory

Evidence risk review treats an answer as a set of claims C={c1,c2,...,cn}C = \{c_1, c_2, ..., c_n\} and sources S={s1,s2,...,sm}S = \{s_1, s_2, ..., s_m\}. Each source receives a tier rank r(s)∈[0,100]r(s) \in [0,100]. A claim with assertiveness a(c)a(c) has a profile-specific minimum rank t(a,c)t(a,c). A deterministic gap is:

g(c)=max⁡(0,t(a,c)−max⁡s∈Scr(s)) g(c) = \max(0, t(a,c) - \max_{s \in S_c} r(s))

The package then combines evidence gaps, profile checks, and verdict precedence into a stable ReviewResult.

Design + Diagram

flowchart LR A[Artifact] --> B[Evidence tier labeling] B --> C[Risk sweep] C --> D[Verdict reduction] D --> E[ReviewResult] C --> F[(Review log)] G[Domain profile] --> C H[Budget meter] --> C

Data Model / Contract

EvidenceRiskReview::review, reviewArray, labelTier, listProfiles, and taxonomy are stable public entry points.

Field Meaning
artifact_id Host-defined stable identifier.
claims Discrete claims extracted by the host.
sources Cited evidence with optional tier hints.
profile_key Risk profile used for thresholds and checks.
findings Structured review findings emitted by checks.

ADR

Problem: adapters tend to grow business logic

Decision: keep PHP facade, Artisan, HTTP, and MCP as thin adapters over ReviewEngine.

Consequences: there is one behavior to test, but adapters must validate input carefully.

Problem: external review can become expensive

Decision: run cheap deterministic checks first and spend LLM budget only when enabled and useful.

Consequences: default installs have zero token cost, but hosts must bind an LLM contract for semantic review.

Worked Example

use Padosoft\EvidenceRiskReview\Facades\EvidenceRiskReview;

$result = EvidenceRiskReview::reviewArray($payload);
$profiles = EvidenceRiskReview::listProfiles();

Gotcha / Limits

The package reviews evidence strength and risk boundaries; it does not retrieve sources, extract claims automatically, or replace human review for regulated decisions.

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