import type { Message } from '../models/types.js';
import type { AgentOrchestrator } from '../backends/native/orchestrator.js';
import type { MemoryStore } from '../memory/store.js';
import { COMPACTION_SYSTEM_PROMPT, MEMORY_EXTRACTION_PROMPT, buildCompactionPrompt } from '../backends/native/prompts.js';
import { estimateMessageTokens } from './tokens.js';
import { getMessageText } from '../models/media.js';
import { selectImportantMessages } from './weighting.js';

export interface CompactionConfig {
  /** Percentage of context window that triggers compaction (default: 80). */
  thresholdPct: number;
  /** Number of recent turns (user+assistant pairs) to always keep intact. */
  keepTurns: number;
  /** Maximum tokens for the compaction summary response. */
  summaryMaxTokens: number;
  /** Preserve messages at or above this importance score from compaction. */
  importanceThreshold: number;
  /** Optional proactive context usage thresholds and actions. */
  proactive?: ProactiveCompactionConfig;
}

export interface ProactiveCompactionConfig {
  /** Enable proactive context warnings/checkpoints before hard compaction cliffs. */
  enabled: boolean;
  /** Emit warning signals when usage crosses this percentage. */
  warnPct: number;
  /** Save a checkpoint summary to memory when usage crosses this percentage. */
  checkpointPct: number;
  /** Auto-run compaction when usage crosses this percentage. */
  autoCompactPct: number;
  /** Cooldown window between checkpoint writes. */
  checkpointCooldownMs: number;
  /** Memory namespace base for proactive checkpoints. */
  memoryNamespace: string;
}

export interface CompactionResult {
  /** The compacted messages: [summary, ...recentMessages]. */
  messages: Message[];
  /** Number of messages that were compacted (removed). */
  compactedCount: number;
  /** Estimated tokens before compaction. */
  tokensBefore: number;
  /** Estimated tokens after compaction. */
  tokensAfter: number;
  /** The raw summary text produced by the compaction model (populated when compaction ran). */
  summary?: string;
}

export const DEFAULT_COMPACTION_CONFIG: CompactionConfig = {
  thresholdPct: 80,
  keepTurns: 4,
  summaryMaxTokens: 1024,
  importanceThreshold: 1,
  proactive: {
    enabled: false,
    warnPct: 75,
    checkpointPct: 85,
    autoCompactPct: 95,
    checkpointCooldownMs: 300_000,
    memoryNamespace: 'session/checkpoints',
  },
};

export async function compactHistory(opts: {
  messages: Message[];
  orchestrator: AgentOrchestrator;
  config: CompactionConfig;
  memoryStore?: MemoryStore;
  autoExtract?: boolean;
  usePersonalAssistantPrompt?: boolean;
  memoryExtractionNamespace?: string;
}): Promise<CompactionResult> {
  const { messages, orchestrator, config } = opts;

  const keepCount = config.keepTurns * 2;
  if (messages.length <= keepCount) {
    return {
      messages,
      compactedCount: 0,
      tokensBefore: estimateMessageTokens(messages),
      tokensAfter: estimateMessageTokens(messages),
    };
  }

  const toCompact = messages.slice(0, -keepCount);
  const toKeep = messages.slice(-keepCount);

  // Ensure toKeep starts with a user message to avoid assistant→assistant
  // after the compaction summary (which has role 'assistant').
  while (toKeep.length > 0 && toKeep[0].role === 'assistant') {
    const shifted = toKeep.shift();
    if (!shifted) {
      break;
    }
    toCompact.push(shifted);
  }

  const preservedImportant = selectImportantMessages(toCompact, {
    threshold: config.importanceThreshold,
    maxMessages: Math.max(1, config.keepTurns),
  });

  const preservedSet = new Set(preservedImportant.map(item => item.index));
  const toSummarize = toCompact.filter((_, index) => !preservedSet.has(index));

  const formattedConversation = toSummarize.map((msg) => `${msg.role}: ${getMessageText(msg)}`).join('\n\n');

  const preservedMessages = preservedImportant.map(item => item.message);

  if (formattedConversation.trim().length === 0) {
    const compactedMessages = [...preservedMessages, ...toKeep];
    return {
      messages: compactedMessages,
      compactedCount: messages.length - compactedMessages.length,
      tokensBefore: estimateMessageTokens(messages),
      tokensAfter: estimateMessageTokens(compactedMessages),
    };
  }

  const tier = orchestrator.getDelegationTier('compaction');

  const systemPrompt = opts.usePersonalAssistantPrompt
    ? buildCompactionPrompt({ personalAssistant: true })
    : COMPACTION_SYSTEM_PROMPT;

  const result = await orchestrator.delegate({
    task: 'compaction',
    tier,
    systemPrompt,
    message: formattedConversation,
    maxTokens: config.summaryMaxTokens,
  });

  const summaryMessage: Message = {
    role: 'assistant',
    content: '[Summary of earlier conversation]\n\n' + result.content,
  };

  // Phase 2: Extract persistent facts and append to memory (if enabled)
  if (opts.memoryStore && opts.autoExtract !== false) {
    try {
      const extractionTier = orchestrator.getDelegationTier('memory_extraction');
      const extraction = await orchestrator.delegate({
        task: 'memory_extraction',
        tier: extractionTier,
        systemPrompt: MEMORY_EXTRACTION_PROMPT,
        message: `Extract persistent facts from this conversation:\n\n${formattedConversation}`,
        maxTokens: 512,
      });

      // Only write if the extraction produced meaningful content
      const extractedContent = extraction.content.trim();
      if (extractedContent.length > 0 && !extractedContent.toLowerCase().includes('no facts')) {
        const extractionNs = opts.memoryExtractionNamespace ?? 'global';
        opts.memoryStore.write(extractionNs, extractedContent, 'append');
        console.log(`[Flynn:memory] Extracted ${extractedContent.length} chars of facts to ${extractionNs} memory`);
      }
    } catch (error) {
      // Memory extraction is best-effort — don't fail compaction if it errors
      console.warn('[Flynn:memory] Failed to extract facts during compaction:', error);
    }
  }

  return {
    messages: [...preservedMessages, summaryMessage, ...toKeep],
    compactedCount: toSummarize.length,
    tokensBefore: estimateMessageTokens(messages),
    tokensAfter: estimateMessageTokens([...preservedMessages, summaryMessage, ...toKeep]),
    summary: result.content,
  };
}