Add human-led conversation learning loop

tests/conversation-learning.test.ts

@@ -0,0 +1,83 @@
+import Database from 'better-sqlite3';
+import { afterEach, beforeEach, describe, expect, it } from 'vitest';
+import { runMigrations } from '../src/database/client.js';
+import { ConversationEventRepository } from '../src/database/conversation-event-repository.js';
+import { ConversationLearningRepository } from '../src/database/conversation-learning-repository.js';
+import { MessagesRepository } from '../src/database/repositories.js';
+import { RedditAccountsRepository } from '../src/database/account-repositories.js';
+import { attributeConversationMessages } from '../src/services/conversation-attribution.js';
+import { extractConversationLearning } from '../src/services/conversation-learning-extractor.js';
+import { redactLearningText } from '../src/services/conversation-learning-redactor.js';
+import { runConversationLearningEvaluation } from '../src/services/conversation-learning-evaluator.js';
+import { RepositoryConversationLearningProvider } from '../src/services/conversation-learning-provider.js';
+import type { LearningConfig } from '../src/config/index.js';
+
+const learningConfig: LearningConfig = {
+  captureEnabled: true, generationEnabled: false, shadowMode: true, maturityHours: 12,
+  maxContextExamples: 3, maxTurnsPerExample: 8, humanExampleSlots: 2, autonomousExampleSlots: 1,
+  minimumAttributionConfidence: 'exact', policyVersion: 'test-v1',
+};
+
+describe('conversation learning loop', () => {
+  let db: Database.Database;
+  beforeEach(() => {
+    db = new Database(':memory:');
+    db.pragma('foreign_keys = ON');
+    runMigrations(db);
+    new RedditAccountsRepository(db).upsertVerified({
+      accountId: 'account-a', redditUsername: 'account-a', matrixUserId: 'account-a',
+      cookiesJson: '[]', matrixCredentialsJson: '{}', now: 1,
+    });
+  });
+  afterEach(() => db.close());
+
+  it('credits a durable manual media invitation instead of the AI opener', () => {
+    const messages = new MessagesRepository(db, 'account-a');
+    const openerId = messages.insertChat({
+      recipient: 'recipient', subject: 'recipient', message: 'hey', sentAt: 1_000,
+      chatRoomId: 'room-a', matrixEventId: 'open-1',
+    });
+    const opener = messages.getByChatRoomId('room-a')!;
+    expect(opener.id).toBe(openerId);
+    const events = new ConversationEventRepository(db, 'account-a');
+    events.upsert({
+      conversationId: 'room-a', eventId: 'manual-1', eventTimestamp: 3_000,
+      direction: 'outbound', actor: 'human', eventKind: 'manual-text', text: 'send a selfie if comfortable',
+      sourceReference: 'dashboard:manual-reply', attributionConfidence: 'exact',
+    });
+    const attributed = attributeConversationMessages({
+      conversationId: 'room-a', opener, claims: [], mediaSends: [], messages: [
+        { eventId: 'open-1', sender: 'Me', text: 'hey', timestamp: 1_000, isSelf: true },
+        { eventId: 'reply-1', sender: 'Them', text: 'hi', timestamp: 2_000, isSelf: false },
+        { eventId: 'manual-1', sender: 'Me', text: 'send a selfie if comfortable', timestamp: 3_000, isSelf: true },
+        { eventId: 'image-1', sender: 'Them', text: '[Image]', timestamp: 4_000, isSelf: false, media: [{ originalUrl: 'x', localPath: 'x', fileName: 'x' }] },
+      ],
+    }, events);
+    const learning = new ConversationLearningRepository(db, 'account-a');
+    expect(extractConversationLearning({
+      conversationId: 'room-a', messages: attributed, opener, state: undefined,
+      canonicalOutcome: 'engaged', now: 4_000 + 13 * 60 * 60 * 1_000,
+    }, learning, learningConfig)).toBe(1);
+    const trajectory = learning.listTrajectories()[0];
+    expect(trajectory).toMatchObject({ creditClass: 'human-created-success', status: 'eligible', attributionConfidence: 'exact' });
+    expect(learning.getTrajectory(trajectory.id)?.decisions).toHaveLength(1);
+    const provider = new RepositoryConversationLearningProvider(learning, learningConfig);
+    const query = {
+      accountId: 'account-a', conversationId: 'room-b', subreddit: null, policyMode: 'legacy' as const,
+      stage: 'media-invitation' as const, purpose: 'media-ask' as const, incomingEventId: 'incoming-b',
+    };
+    const examples = provider.getExamples(query);
+    expect(examples).toHaveLength(1);
+    provider.recordGeneration({ query, mode: 'shadow', policyVersion: 'test-v1', exampleIds: [examples[0].decisionId] });
+    expect((db.prepare('SELECT COUNT(*) AS count FROM learning_generation_context').get() as { count: number }).count).toBe(1);
+  });
+
+  it('redacts direct identifiers and passes the offline hard gates', () => {
+    expect(redactLearningText('email me at a@example.com or https://example.com u/person'))
+      .toBe('email me at [email] or [url] [username]');
+    const learning = new ConversationLearningRepository(db);
+    const run = runConversationLearningEvaluation(learning, 'test-v1', null, 10);
+    expect(run).toMatchObject({ status: 'passed', hardGateFailures: 0, totalCases: 0 });
+    expect(learning.listEvaluationRuns()).toHaveLength(1);
+  });
+});