RedditBot/src/services/conversation-learning-extractor.ts

import type { LearningConfig } from '../config/index.js';
import type { AttributionConfidence } from '../database/conversation-event-repository.js';
import {
  stableLearningId,
  type ConversationLearningRepository,
  type UpsertLearningDecision,
} from '../database/conversation-learning-repository.js';
import type { SentMessageRecord } from '../database/repositories.js';
import { containsMediaRequest } from './conversation-boundaries.js';
import type { ConversationState } from './conversation-state.js';
import type {
  AttributedConversationMessage,
  LearningCreditClass,
  LearningStage,
  LearningTrajectoryStatus,
  LearningTurn,
} from './conversation-learning-types.js';
import { redactLearningText } from './conversation-learning-redactor.js';

const CONFIDENCE_RANK: Record<AttributionConfidence, number> = { inferred: 1, fingerprint: 2, exact: 3 };

export interface LearningExtractionInput {
  conversationId: string;
  messages: readonly AttributedConversationMessage[];
  opener: SentMessageRecord;
  state: ConversationState | undefined;
  canonicalOutcome: 'engaged' | 'declined' | 'boundary' | 'upsell' | 'unknown' | undefined;
  now?: number;
}

/** Derives revocable decision examples from an authoritative attributed timeline. */
export function extractConversationLearning(
  input: LearningExtractionInput,
  repository: ConversationLearningRepository,
  config: LearningConfig,
): number {
  if (!config.captureEnabled) return 0;
  const now = input.now ?? Date.now();
  const negative = input.canonicalOutcome && ['declined', 'boundary', 'upsell'].includes(input.canonicalOutcome);
  const inboundImages = input.messages.filter((message) =>
    message.actor === 'recipient' && message.kind === 'inbound-media' && message.mediaCount > 0);
  let extracted = 0;

  for (const image of inboundImages) {
    const timeline = input.messages.filter((message) => message.timestamp <= image.timestamp);
    const humans = timeline.filter((message) => message.actor === 'human');
    const asks = timeline.filter((message) => message.isSelf && containsMediaRequest(message.text));
    const directHumanAsk = [...humans].reverse().find((message) => containsMediaRequest(message.text));
    const priorAiAsk = asks.find((message) => message.actor === 'ai');
    const confidence = minimumConfidence(timeline);
    const creditClass = classifyCredit(humans.length > 0, directHumanAsk !== undefined, priorAiAsk !== undefined, asks.length > 0);
    const maturityAt = image.timestamp + config.maturityHours * 60 * 60 * 1_000;
    const status: LearningTrajectoryStatus = negative
      ? 'invalidated'
      : CONFIDENCE_RANK[confidence] < CONFIDENCE_RANK[config.minimumAttributionConfidence]
        ? 'needs-review'
        : now < maturityAt ? 'provisional' : 'eligible';
    const trajectoryId = stableLearningId(input.conversationId, image.eventId);
    repository.upsertTrajectory({
      id: trajectoryId,
      conversationId: input.conversationId,
      sentMessageId: input.opener.id,
      subreddit: input.opener.subreddit ?? null,
      sourceType: input.opener.sourceType ?? null,
      policyMode: input.state?.automation.policyMode ?? null,
      firstHumanEventId: humans[0]?.eventId ?? null,
      outcomeEventId: image.eventId,
      outcomeType: 'image-received',
      creditClass,
      attributionConfidence: confidence,
      status,
      maturityAt,
      invalidatedAt: negative ? now : null,
      invalidationReason: negative ? `Canonical outcome changed to ${input.canonicalOutcome}` : null,
      now,
    });

    const decisionMessages = humans.filter((message) => message.kind === 'manual-text');
    const decisions = decisionMessages.map((decision): UpsertLearningDecision => ({
      id: stableLearningId(trajectoryId, decision.eventId),
      trajectoryId,
      decisionEventId: decision.eventId,
      stage: inferStage(decision.text),
      context: buildContext(timeline, decision, config.maxTurnsPerExample),
      chosenResponse: redactLearningText(decision.text),
      turnsUntilOutcome: timeline.filter((message) => message.timestamp > decision.timestamp).length,
      isDirectMediaInvitation: containsMediaRequest(decision.text),
      qualityStatus: status === 'provisional' ? 'eligible' : status,
      now,
    }));
    repository.replaceDecisions(trajectoryId, decisions);
    extracted += 1;
  }
  return extracted;
}

function classifyCredit(hasHuman: boolean, directHumanAsk: boolean, priorAiAsk: boolean, anyAsk: boolean): LearningCreditClass {
  if (!hasHuman) return anyAsk ? 'autonomous-success' : 'recipient-initiated-media';
  if (directHumanAsk) return priorAiAsk ? 'human-improved-success' : 'human-created-success';
  return 'human-completed-ai-trajectory';
}

function minimumConfidence(messages: readonly AttributedConversationMessage[]): AttributionConfidence {
  return messages.reduce<AttributionConfidence>((lowest, message) =>
    CONFIDENCE_RANK[message.confidence] < CONFIDENCE_RANK[lowest] ? message.confidence : lowest, 'exact');
}

function inferStage(text: string): LearningStage {
  if (containsMediaRequest(text)) return 'media-invitation';
  if (/\b(sorry|understand|no pressure|comfortable|boundary)\b/i.test(text)) return 'handoff';
  if (/\?/.test(text)) return 'answer';
  return 'rapport';
}

function buildContext(
  timeline: readonly AttributedConversationMessage[],
  decision: AttributedConversationMessage,
  maxTurns: number,
): LearningTurn[] {
  return timeline
    .filter((message) => message.timestamp < decision.timestamp && message.kind !== 'inbound-media')
    .slice(-maxTurns)
    .flatMap((message): LearningTurn[] => {
      if (!['inbound-text', 'opener', 'automation-reply', 'manual-text', 'library-reciprocal'].includes(message.kind)) return [];
      return [{
        eventId: message.eventId,
        timestamp: message.timestamp,
        role: message.actor === 'recipient' ? 'recipient' : 'operator',
        actor: message.actor === 'unknown' ? 'ai' : message.actor,
        kind: message.kind as LearningTurn['kind'],
        text: redactLearningText(message.text),
      }];
    });
}