Files
peptide-repo-core/includes/migrations/class-pr-core-migration-0003-candidate-queue.php
peptiderepo 808b2509d8 feat: initial peptide-repo-core plugin (v0.1.0) — canonical schema sprint
Implements ADR-0002: canonical peptide schema as shared data layer for
the peptiderepo.com ecosystem. All Tier 1 tools (Reconstitution Calculator,
Dosage Reference, Legal Status Tracker, PSA enhancements) build on this.

Includes:
- pr_peptide CPT with REST, taxonomies, 11 meta fields
- 3 custom tables: pr_dosing_rows, pr_legal_cells, pr_ai_candidate_queue
- Sequential migration runner with schema versioning
- 4 typed DTOs and 4 repository classes (full CRUD)
- REST API (pr-core/v1): 6 endpoints (read public, write auth-gated)
- Admin UI: 3 meta boxes, custom columns, AI candidate queue page
- Shared disclaimer component (shortcode + static API)
- JSON-LD Drug schema emission on single peptide pages
- Seed data fixture (3 peptides, 10 dosing rows, 5 legal cells)
- Full teardown in uninstall.php
- CI: PHP lint 8.0-8.2, PHPCS WordPress, 300-line check

All 22 PHP files under 300 lines. Highest: peptide-cpt at 265.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-17 07:36:12 +07:00

73 lines
2.3 KiB
PHP

<?php
declare(strict_types=1);
/**
* Migration 0003: Create the pr_ai_candidate_queue table.
*
* What: Creates the AI-extracted dosing candidate queue table.
* Who calls it: PR_Core_Migration_Runner::run_pending().
* Dependencies: WordPress $wpdb, dbDelta().
*
* Approved rows are copied into pr_dosing_rows with source='ai-candidate-approved'
* and ai_candidate_id set; queue row marked 'merged'.
*
* @see ARCHITECTURE.md — AI-assist candidate-extraction pipeline.
*/
class PR_Core_Migration_0003_Candidate_Queue {
/** @var string[] Valid queue status enum values. */
public const STATUSES = [
'pending',
'approved',
'rejected',
'merged',
];
/**
* Create the pr_ai_candidate_queue table.
*
* Side effects: database DDL via dbDelta().
*
* @return void
*/
public function up(): void {
global $wpdb;
$table = $wpdb->prefix . 'pr_ai_candidate_queue';
$charset = $wpdb->get_charset_collate();
$sql = "CREATE TABLE {$table} (
id BIGINT UNSIGNED NOT NULL AUTO_INCREMENT,
peptide_id BIGINT UNSIGNED NOT NULL,
dose_min DECIMAL(12,4) NULL,
dose_max DECIMAL(12,4) NULL,
dose_unit VARCHAR(16) NOT NULL,
route VARCHAR(32) NOT NULL,
frequency VARCHAR(64) NULL,
duration_value INT UNSIGNED NULL,
duration_unit VARCHAR(16) NULL,
population VARCHAR(32) NOT NULL,
indication VARCHAR(255) NULL,
evidence_strength VARCHAR(32) NOT NULL,
study_title VARCHAR(500) NULL,
study_year SMALLINT UNSIGNED NULL,
citation_pubmed_id VARCHAR(16) NULL,
citation_doi VARCHAR(128) NULL,
citation_url VARCHAR(500) NULL,
notes TEXT NULL,
extraction_confidence FLOAT NOT NULL DEFAULT 0,
queue_status VARCHAR(16) NOT NULL DEFAULT 'pending',
extracted_at DATETIME NOT NULL,
reviewed_by BIGINT UNSIGNED NULL,
reviewed_at DATETIME NULL,
reviewer_notes TEXT NULL,
PRIMARY KEY (id),
KEY idx_peptide (peptide_id),
KEY idx_status (queue_status),
KEY idx_confidence (extraction_confidence),
KEY idx_peptide_status (peptide_id, queue_status)
) {$charset};";
dbDelta( $sql );
}
}