import { SQLiteMemoryCore, Agent, Memory, Knowledge, Context, Relationship, Event, Preference, Cache, Conversation, Tool, Workflow, Performance } from './sqlite-memory-core';

/**
 * @class MemoryIntelligence
 * @description Builds intelligent memory management with semantic search, optimization, and context-aware retrieval.
 */
export class MemoryIntelligence {
  private dbCore: SQLiteMemoryCore;

  constructor(dbCore: SQLiteMemoryCore) {
    this.dbCore = dbCore;
  }

  /**
   * Performs a semantic search across specified memory tables.
   * This is a placeholder for actual semantic search implementation, which would involve
   * vector embeddings and similarity comparisons.
   * @param {string} query The search query.
   * @param {string[]} tables The tables to search (e.g., ['knowledge', 'memories', 'conversations']).
   * @param {number} limit The maximum number of results to return.
   * @returns {Promise<any[]>} A list of relevant memory entries.
   */
  public async semanticSearch(query: string, tables: string[], limit: number = 10): Promise<any[]> {
    console.log(`Performing semantic search for: "${query}" in tables: ${tables.join(', ')}`);
    const results: any[] = [];

    // This is a simplified simulation. Real semantic search would involve:
    // 1. Generating an embedding for the query.
    // 2. Retrieving embeddings from relevant tables.
    // 3. Performing vector similarity search.
    // 4. Returning the original data based on similarity scores.

    if (tables.includes('knowledge')) {
      const knowledgeEntries = await this.dbCore.allQuery('SELECT * FROM knowledge WHERE content LIKE ? LIMIT ?', [`%${query}%`, limit]);
      results.push(...knowledgeEntries.map(entry => ({ type: 'knowledge', ...entry })));
    }
    if (tables.includes('memories')) {
      const memoryEntries = await this.dbCore.allQuery('SELECT * FROM memories WHERE value LIKE ? OR key LIKE ? LIMIT ?', [`%${query}%`, `%${query}%`, limit]);
      results.push(...memoryEntries.map(entry => ({ type: 'memory', ...entry })));
    }
    // Add more table searches as needed

    // Simulate relevance scoring and sorting
    return results.sort((a, b) => Math.random() - 0.5).slice(0, limit); // Random sort for simulation
  }

  /**
   * Automatically consolidates and optimizes memory entries.
   * This could involve merging similar knowledge, archiving old conversations, etc.
   * @returns {Promise<void>}
   */
  public async optimizeMemory(): Promise<void> {
    console.log('Running memory optimization and consolidation...');
    // Placeholder for complex optimization logic:
    // - Identify duplicate knowledge entries and merge.
    // - Archive old conversations based on policies.
    // - Re-index semantic embeddings.
    // - Clean up expired cache entries.

    // Example: Clean up expired cache entries
    const now = Date.now();
    await this.dbCore.runQuery('DELETE FROM cache WHERE expires_at < ?', [now]);
    console.log('Expired cache entries cleaned.');

    // Example: Archive old conversations (e.g., older than 30 days)
    const thirtyDaysAgo = now - (30 * 24 * 60 * 60 * 1000);
    await this.dbCore.runQuery('UPDATE conversations SET status = ? WHERE last_activity < ? AND status = ?', ['archived', thirtyDaysAgo, 'active']);
    console.log('Old conversations archived.');

    console.log('Memory optimization complete.');
  }

  /**
   * Retrieves context-aware information based on current interaction.
   * This would typically use the current conversation, agent state, etc., to narrow down search.
   * @param {string} agentId The ID of the agent.
   * @param {string} sessionId The current session ID.
   * @param {string} currentQuery The current query or context.
   * @param {number} limit The maximum number of results.
   * @returns {Promise<any[]>} A list of contextually relevant memories.
   */
  public async getContextAwareMemories(agentId: string, sessionId: string, currentQuery: string, limit: number = 5): Promise<any[]> {
    console.log(`Retrieving context-aware memories for agent ${agentId} in session ${sessionId} with query: "${currentQuery}"`);
    // This is a placeholder. Real implementation would:
    // 1. Get current context from 'contexts' table for agent/session.
    // 2. Use semantic search with currentQuery and context data.
    // 3. Prioritize memories related to the agent's capabilities or recent activities.

    const relevantMemories = await this.semanticSearch(currentQuery, ['knowledge', 'memories', 'conversations'], limit * 2);
    // Further filter/score based on agentId, sessionId, and recent activity
    return relevantMemories.slice(0, limit);
  }

  /**
   * Manages the lifecycle of memory entries (e.g., archival, cleanup).
   * @param {string} memoryId The ID of the memory entry.
   * @param {'archive' | 'delete' | 'update'} action The action to perform.
   * @param {Partial<Memory>} [updates] Optional updates for the memory.
   * @returns {Promise<void>}
   */
  public async manageMemoryLifecycle(memoryId: string, action: 'archive' | 'delete' | 'update', updates?: Partial<Memory>): Promise<void> {
    console.log(`Managing memory lifecycle for ${memoryId}: ${action}`);
    switch (action) {
      case 'archive':
        // Implement archival logic (e.g., move to an archived_memories table or set a flag)
        await this.dbCore.updateMemory(memoryId, { namespace: 'archived' });
        break;
      case 'delete':
        await this.dbCore.deleteMemory(memoryId);
        break;
      case 'update':
        if (updates) {
          await this.dbCore.updateMemory(memoryId, updates);
        }
        break;
      default:
        throw new Error(`Unknown memory lifecycle action: ${action}`);
    }
    console.log(`Memory lifecycle action "${action}" completed for ${memoryId}.`);
  }

  /**
   * Analyzes cross-table relationships to provide insights.
   * @returns {Promise<any>} Insights derived from relationships.
   */
  public async analyzeRelationships(): Promise<any> {
    console.log('Analyzing cross-table relationships for insights...');
    // Placeholder for complex relationship analysis:
    // - Find agents frequently interacting with certain tools.
    // - Identify knowledge gaps based on conversation topics and available knowledge.
    // - Discover patterns in workflow failures and related events.

    const agents = await this.dbCore.allQuery('SELECT id, name FROM agents');
    const relationships = await this.dbCore.allQuery('SELECT * FROM relationships');

    const insights = {
      agentCount: agents.length,
      relationshipCount: relationships.length,
      // More complex insights would be generated here
      exampleInsight: 'Agents A and B frequently collaborate on tasks related to X.'
    };
    console.log('Relationship analysis complete.', insights);
    return insights;
  }

  /**
   * Prioritizes memory retrieval based on performance metrics.
   * This could involve caching frequently accessed memories or pre-loading relevant knowledge.
   * @param {string[]} memoryIds The IDs of memories to prioritize.
   * @returns {Promise<void>}
   */
  public async prioritizeMemories(memoryIds: string[]): Promise<void> {
    console.log(`Prioritizing memories: ${memoryIds.join(', ')}`);
    // Placeholder for performance-based prioritization:
    // - Update cache entries for these memories.
    // - Mark them for faster retrieval in future searches.
    // - Potentially pre-load into an in-memory cache.

    for (const id of memoryIds) {
      // Simulate updating cache hits/misses or refreshing cache
      const cacheEntry = await this.dbCore.getQuery('SELECT * FROM cache WHERE key = ?', [id]);
      if (cacheEntry) {
        await this.dbCore.runQuery('UPDATE cache SET hits = hits + 1, expires_at = ? WHERE key = ?', [Date.now() + (24 * 60 * 60 * 1000), id]); // Extend expiry
      }
    }
    console.log('Memory prioritization complete.');
  }
}