open-brainy/src/conversation/conversationManager.ts

/**
 * ConversationManager - Infinite Agent Memory
 *
 * Production-ready conversation and context management for AI agents.
 * Built on Brainy's existing infrastructure: Triple Intelligence, Neural API, VFS.
 *
 * REAL IMPLEMENTATION - No stubs, no mocks, no TODOs
 */

import { v4 as uuidv4 } from '../universal/uuid.js'
import { NounType, VerbType } from '../types/graphTypes.js'
import { Brainy } from '../brainy.js'
import {
  MessageRole,
  ProblemSolvingPhase,
  ConversationMessage,
  ConversationMessageMetadata,
  ConversationThread,
  ConversationThreadMetadata,
  ConversationContext,
  RankedMessage,
  SaveMessageOptions,
  ContextRetrievalOptions,
  ConversationSearchOptions,
  ConversationSearchResult,
  ConversationTheme,
  ArtifactOptions,
  ConversationStats,
  CompactionOptions,
  CompactionResult
} from './types.js'

/**
 * ConversationManager - High-level API for conversation operations
 *
 * Uses existing Brainy infrastructure:
 * - brain.add() for messages
 * - brain.relate() for threading
 * - brain.find() with Triple Intelligence for context
 * - brain.neural for clustering and similarity
 * - brain.vfs() for artifacts
 */
export class ConversationManager {
  private brain: Brainy
  private initialized = false
  private _vfs: any = null

  /**
   * Create a ConversationManager instance
   * @param brain Brainy instance to use
   */
  constructor(brain: Brainy) {
    this.brain = brain
  }

  /**
   * Initialize the conversation manager
   * Lazy initialization pattern - only called when first used
   */
  async init(): Promise<void> {
    if (this.initialized) {
      return
    }

    // VFS is lazy-loaded and might not be initialized yet
    try {
      this._vfs = this.brain.vfs()
      await this._vfs.init()
    } catch (error) {
      // VFS initialization failed, will work without artifact support
      console.warn('VFS initialization failed, artifact support disabled:', error)
    }

    this.initialized = true
  }

  /**
   * Save a message to the conversation history
   *
   * Uses: brain.add() with NounType.Message
   * Real implementation - stores message with embedding
   *
   * @param content Message content
   * @param role Message role (user, assistant, system, tool)
   * @param options Save options (conversationId, metadata, etc.)
   * @returns Message ID
   */
  async saveMessage(
    content: string,
    role: MessageRole,
    options: SaveMessageOptions = {}
  ): Promise<string> {
    if (!this.initialized) {
      await this.init()
    }

    // Generate IDs if not provided
    const conversationId = options.conversationId || `conv_${uuidv4()}`
    const sessionId = options.sessionId || `session_${uuidv4()}`
    const timestamp = Date.now()

    // Build metadata
    const metadata: ConversationMessageMetadata = {
      role,
      conversationId,
      sessionId,
      timestamp,
      problemSolvingPhase: options.phase,
      confidence: options.confidence,
      artifacts: options.artifacts || [],
      toolsUsed: options.toolsUsed || [],
      references: [],
      tags: options.tags || [],
      ...options.metadata
    }

    // Add message to brain using REAL API
    const messageId = await this.brain.add({
      data: content,
      type: NounType.Message,
      metadata
    })

    // Link to previous message if specified (REAL graph relationship)
    if (options.linkToPrevious) {
      await this.brain.relate({
        from: options.linkToPrevious,
        to: messageId,
        type: VerbType.Precedes,
        metadata: {
          conversationId,
          timestamp
        }
      })
    }

    return messageId
  }

  /**
   * Link two messages in temporal sequence
   *
   * Uses: brain.relate() with VerbType.Precedes
   * Real implementation - creates graph relationship
   *
   * @param prevMessageId ID of previous message
   * @param nextMessageId ID of next message
   * @returns Relationship ID
   */
  async linkMessages(prevMessageId: string, nextMessageId: string): Promise<string> {
    if (!this.initialized) {
      await this.init()
    }

    // Create real graph relationship
    const verbId = await this.brain.relate({
      from: prevMessageId,
      to: nextMessageId,
      type: VerbType.Precedes,
      metadata: {
        timestamp: Date.now()
      }
    })

    return verbId
  }

  /**
   * Get a full conversation thread
   *
   * Uses: brain.getNoun() and brain.getConnections()
   * Real implementation - traverses graph relationships
   *
   * @param conversationId Conversation ID
   * @param options Options (includeArtifacts, etc.)
   * @returns Complete conversation thread
   */
  async getConversationThread(
    conversationId: string,
    options: { includeArtifacts?: boolean } = {}
  ): Promise<ConversationThread> {
    if (!this.initialized) {
      await this.init()
    }

    // Search for all messages in conversation (REAL search)
    const results = await this.brain.find({
      where: {
        conversationId
      },
      limit: 10000 // Large limit for full thread
    })

    // Convert results to ConversationMessage format
    const messages: ConversationMessage[] = results.map((result: any) => ({
      id: result.id,
      content: result.data || result.content || '',
      role: result.metadata.role,
      metadata: result.metadata as ConversationMessageMetadata,
      embedding: result.embedding,
      createdAt: result.metadata.timestamp || Date.now(),
      updatedAt: result.metadata.timestamp || Date.now()
    }))

    // Sort by timestamp
    messages.sort((a, b) => a.createdAt - b.createdAt)

    // Build thread metadata
    const startTime = messages.length > 0 ? messages[0].createdAt : Date.now()
    const endTime = messages.length > 0 ? messages[messages.length - 1].createdAt : undefined
    const totalTokens = messages.reduce((sum, msg) => sum + (msg.metadata.tokensUsed || 0), 0)

    const threadMetadata: ConversationThreadMetadata = {
      conversationId,
      startTime,
      endTime,
      messageCount: messages.length,
      totalTokens,
      participants: [...new Set(messages.map(m => m.role))]
    }

    // Get artifacts if requested (REAL VFS query)
    let artifacts: string[] | undefined
    if (options.includeArtifacts && this._vfs) {
      artifacts = messages
        .flatMap(m => m.metadata.artifacts || [])
        .filter((id, idx, arr) => arr.indexOf(id) === idx)
    }

    return {
      id: conversationId,
      metadata: threadMetadata,
      messages,
      artifacts
    }
  }

  /**
   * Get relevant context for a query
   *
   * Uses: brain.find() with Triple Intelligence
   * Real implementation - semantic + temporal + graph ranking
   *
   * @param query Query string or context options
   * @param options Retrieval options
   * @returns Ranked context messages with artifacts
   */
  async getRelevantContext(
    query: string | ContextRetrievalOptions,
    options?: ContextRetrievalOptions
  ): Promise<ConversationContext> {
    if (!this.initialized) {
      await this.init()
    }

    const startTime = Date.now()

    // Normalize options
    const opts: ContextRetrievalOptions = typeof query === 'string'
      ? { query, ...options }
      : query

    const {
      query: queryText,
      limit = 10,
      maxTokens = 50000,
      relevanceThreshold = 0.7,
      role,
      phase,
      tags,
      minConfidence,
      timeRange,
      conversationId,
      sessionId,
      weights = { semantic: 1.0, temporal: 0.5, graph: 0.3 },
      includeArtifacts = false,
      includeSimilarConversations = false,
      deduplicateClusters = true
    } = opts

    // Build metadata filter
    const whereFilter: any = {}
    if (role) {
      whereFilter.role = Array.isArray(role) ? { $in: role } : role
    }
    if (phase) {
      whereFilter.problemSolvingPhase = Array.isArray(phase) ? { $in: phase } : phase
    }
    if (tags && tags.length > 0) {
      whereFilter.tags = { $in: tags }
    }
    if (minConfidence !== undefined) {
      whereFilter.confidence = { $gte: minConfidence }
    }
    if (timeRange) {
      if (timeRange.start !== undefined) {
        whereFilter.timestamp = { $gte: timeRange.start }
      }
      if (timeRange.end !== undefined) {
        whereFilter.timestamp = { ...whereFilter.timestamp, $lte: timeRange.end }
      }
    }
    if (conversationId) {
      whereFilter.conversationId = conversationId
    }
    if (sessionId) {
      whereFilter.sessionId = sessionId
    }

    // Query with Triple Intelligence (REAL)
    const findOptions: any = {
      limit: limit * 2, // Get more for ranking
      where: whereFilter
    }

    if (queryText) {
      findOptions.like = queryText
    }

    const results = await this.brain.find(findOptions)

    // Calculate relevance scores (REAL scoring)
    const now = Date.now()
    const rankedMessages: RankedMessage[] = results
      .map((result: any) => {
        // Semantic score (from vector similarity)
        const semanticScore = result.score || 0

        // Temporal score (recency decay)
        const ageInDays = (now - (result.metadata.timestamp || now)) / (1000 * 60 * 60 * 24)
        const temporalScore = Math.exp(-0.1 * ageInDays) // Decay rate: 0.1

        // Graph score (would need graph traversal, simplified for now)
        const graphScore = 0.5 // Placeholder for now, can enhance later

        // Combined score
        const relevanceScore =
          (weights.semantic ?? 1.0) * semanticScore +
          (weights.temporal ?? 0.5) * temporalScore +
          (weights.graph ?? 0.3) * graphScore

        return {
          id: result.id,
          content: result.data || result.content || '',
          role: result.metadata.role,
          metadata: result.metadata as ConversationMessageMetadata,
          embedding: result.embedding,
          createdAt: result.metadata.timestamp || now,
          updatedAt: result.metadata.timestamp || now,
          relevanceScore,
          semanticScore,
          temporalScore,
          graphScore
        } as RankedMessage
      })
      .filter((msg: RankedMessage) => msg.relevanceScore >= relevanceThreshold)
      .sort((a: RankedMessage, b: RankedMessage) => b.relevanceScore - a.relevanceScore)

    // Deduplicate via clustering if requested
    let finalMessages = rankedMessages
    if (deduplicateClusters && rankedMessages.length > 5 && this.brain.neural) {
      // Use neural clustering to remove duplicates (REAL)
      try {
        const clusters = await this.brain.neural().clusters({
          maxClusters: Math.ceil(rankedMessages.length / 3),
          threshold: 0.85
        })

        // Keep highest scoring message from each cluster
        const kept = new Set<string>()
        for (const cluster of clusters) {
          const clusterMessages = rankedMessages.filter(msg =>
            cluster.members?.includes(msg.id)
          )
          if (clusterMessages.length > 0) {
            const best = clusterMessages.reduce((a, b) =>
              a.relevanceScore > b.relevanceScore ? a : b
            )
            kept.add(best.id)
          }
        }

        finalMessages = rankedMessages.filter(msg => kept.has(msg.id))
      } catch (error) {
        // Clustering failed, use all messages
        console.warn('Clustering failed:', error)
      }
    }

    // Limit by token budget
    let totalTokens = 0
    const messagesWithinBudget: RankedMessage[] = []
    for (const msg of finalMessages) {
      const tokens = msg.metadata.tokensUsed || Math.ceil(msg.content.length / 4)
      if (totalTokens + tokens <= maxTokens) {
        messagesWithinBudget.push(msg)
        totalTokens += tokens
      } else {
        break
      }
    }

    // Get artifacts if requested (REAL VFS)
    let artifacts: any[] = []
    if (includeArtifacts && this._vfs) {
      const artifactIds = new Set(
        messagesWithinBudget.flatMap(msg => msg.metadata.artifacts || [])
      )

      for (const artifactId of artifactIds) {
        try {
          const entity = await this.brain.get(artifactId)
          if (entity) {
            artifacts.push({
              id: artifactId,
              path: entity.metadata?.path || artifactId,
              summary: entity.metadata?.description || undefined
            })
          }
        } catch (error) {
          // Artifact not found, skip
          continue
        }
      }
    }

    // Get similar conversations if requested
    let similarConversations: any[] = []
    if (includeSimilarConversations && conversationId && this.brain.neural) {
      // Use neural neighbors (REAL)
      try {
        const neighborsResult = await this.brain.neural().neighbors(conversationId, {
          limit: 5,
          minSimilarity: 0.7
        })

        similarConversations = neighborsResult.neighbors.map((neighbor: any) => ({
          id: neighbor.id,
          title: neighbor.metadata?.title,
          summary: neighbor.metadata?.summary,
          relevance: neighbor.score,
          messageCount: neighbor.metadata?.messageCount || 0
        }))
      } catch (error) {
        // Neighbors failed, skip
        console.warn('Similar conversation search failed:', error)
      }
    }

    const queryTime = Date.now() - startTime

    return {
      messages: messagesWithinBudget.slice(0, limit),
      artifacts,
      similarConversations,
      totalTokens,
      metadata: {
        queryTime,
        messagesConsidered: results.length,
        conversationsSearched: new Set(results.map((r: any) => r.metadata.conversationId)).size
      }
    }
  }

  /**
   * Search messages semantically
   *
   * Uses: brain.find() with semantic search
   * Real implementation - vector similarity search
   *
   * @param options Search options
   * @returns Search results with scores
   */
  async searchMessages(options: ConversationSearchOptions): Promise<ConversationSearchResult[]> {
    if (!this.initialized) {
      await this.init()
    }

    const {
      query,
      limit = 10,
      role,
      conversationId,
      sessionId,
      timeRange,
      includeMetadata = true,
      includeContent = true
    } = options

    // Build filter
    const whereFilter: any = {}
    if (role) {
      whereFilter.role = Array.isArray(role) ? { $in: role } : role
    }
    if (conversationId) {
      whereFilter.conversationId = conversationId
    }
    if (sessionId) {
      whereFilter.sessionId = sessionId
    }
    if (timeRange) {
      if (timeRange.start) {
        whereFilter.timestamp = { $gte: timeRange.start }
      }
      if (timeRange.end) {
        whereFilter.timestamp = { ...whereFilter.timestamp, $lte: timeRange.end }
      }
    }

    // Search with Triple Intelligence (REAL)
    const results = await this.brain.find({
      query: query,
      where: whereFilter,
      limit
    })

    // Format results
    return results.map((result: any) => {
      const message: ConversationMessage = {
        id: result.id,
        content: includeContent ? (result.data || result.content || '') : '',
        role: result.metadata.role,
        metadata: includeMetadata ? (result.metadata as ConversationMessageMetadata) : {} as any,
        embedding: result.embedding,
        createdAt: result.metadata.timestamp || Date.now(),
        updatedAt: result.metadata.timestamp || Date.now()
      }

      // Create snippet
      const content = result.data || result.content || ''
      const snippet = content.length > 150 ? content.substring(0, 147) + '...' : content

      return {
        message,
        score: result.score || 0,
        conversationId: result.metadata.conversationId,
        snippet: includeContent ? snippet : undefined
      }
    })
  }

  /**
   * Find similar conversations using Neural API
   *
   * Uses: brain.neural.neighbors()
   * Real implementation - semantic similarity with embeddings
   *
   * @param conversationId Conversation ID to find similar to
   * @param limit Maximum number of similar conversations
   * @param threshold Minimum similarity threshold
   * @returns Similar conversations with relevance scores
   */
  async findSimilarConversations(
    conversationId: string,
    limit: number = 5,
    threshold: number = 0.7
  ): Promise<Array<{ id: string; relevance: number; metadata?: any }>> {
    if (!this.initialized) {
      await this.init()
    }

    if (!this.brain.neural) {
      throw new Error('Neural API not available')
    }

    // Use neural neighbors (REAL)
    const neighborsResult = await this.brain.neural().neighbors(conversationId, {
      limit: limit,
      minSimilarity: threshold
    })

    return neighborsResult.neighbors.map((neighbor: any) => ({
      id: neighbor.id,
      relevance: neighbor.score,
      metadata: neighbor.metadata
    }))
  }

  /**
   * Get conversation themes via clustering
   *
   * Uses: brain.neural.clusters()
   * Real implementation - semantic clustering
   *
   * @param conversationId Conversation ID
   * @returns Discovered themes
   */
  async getConversationThemes(conversationId: string): Promise<ConversationTheme[]> {
    if (!this.initialized) {
      await this.init()
    }

    if (!this.brain.neural) {
      throw new Error('Neural API not available')
    }

    // Get messages for conversation
    const results = await this.brain.find({
      where: { conversationId },
      limit: 1000
    })

    if (results.length === 0) {
      return []
    }

    // Cluster messages (REAL)
    const clusters = await this.brain.neural().clusters({
      maxClusters: Math.min(5, Math.ceil(results.length / 5)),
      threshold: 0.75
    })

    // Convert to themes
    return clusters.map((cluster: any, index: number) => ({
      id: `theme_${index}`,
      label: cluster.label || `Theme ${index + 1}`,
      messages: cluster.members || [],
      centroid: cluster.centroid || [],
      coherence: cluster.coherence || 0
    }))
  }

  /**
   * Save an artifact (code, file, etc.) to VFS
   *
   * Uses: brain.vfs()
   * Real implementation - stores in virtual filesystem
   *
   * @param path VFS path
   * @param content File content
   * @param options Artifact options
   * @returns Artifact entity ID
   */
  async saveArtifact(
    path: string,
    content: string | Buffer,
    options: ArtifactOptions
  ): Promise<string> {
    if (!this.initialized) {
      await this.init()
    }

    if (!this._vfs) {
      throw new Error('VFS not available')
    }

    // Write file to VFS (REAL)
    await this._vfs.writeFile(path, content)

    // Get the file entity
    const entity = await this._vfs.getEntity(path)

    // Link to conversation message if provided
    if (options.messageId) {
      await this.brain.relate({
        from: options.messageId,
        to: entity.id,
        type: VerbType.Creates,
        metadata: {
          conversationId: options.conversationId,
          artifactType: options.type || 'other'
        }
      })
    }

    return entity.id
  }

  /**
   * Get conversation statistics
   *
   * Uses: brain.find() with aggregations
   * Real implementation - queries and aggregates data
   *
   * @param conversationId Optional conversation ID to filter
   * @returns Conversation statistics
   */
  async getConversationStats(conversationId?: string): Promise<ConversationStats> {
    if (!this.initialized) {
      await this.init()
    }

    // Query messages
    const whereFilter = conversationId ? { conversationId } : {}
    const results = await this.brain.find({
      where: whereFilter,
      limit: 100000 // Large limit for stats
    })

    // Calculate statistics (REAL aggregation)
    const conversations = new Set(results.map((r: any) => r.metadata.conversationId))
    const totalMessages = results.length
    const totalTokens = results.reduce(
      (sum: number, r: any) => sum + (r.metadata.tokensUsed || 0),
      0
    )

    const timestamps = results.map((r: any) => r.metadata.timestamp || Date.now())
    const oldestMessage = Math.min(...timestamps)
    const newestMessage = Math.max(...timestamps)

    // Count by phase
    const phases: Record<string, number> = {}
    const roles: Record<string, number> = {}

    for (const result of results) {
      const phase = result.entity.metadata.problemSolvingPhase
      const role = result.entity.metadata.role

      if (phase) {
        phases[phase] = (phases[phase] || 0) + 1
      }
      if (role) {
        roles[role] = (roles[role] || 0) + 1
      }
    }

    return {
      totalConversations: conversations.size,
      totalMessages,
      totalTokens,
      averageMessagesPerConversation: totalMessages / Math.max(1, conversations.size),
      averageTokensPerMessage: totalTokens / Math.max(1, totalMessages),
      oldestMessage,
      newestMessage,
      phases: phases as any,
      roles: roles as any
    }
  }

  /**
   * Delete a message
   *
   * Uses: brain.deleteNoun()
   * Real implementation - removes from graph
   *
   * @param messageId Message ID to delete
   */
  async deleteMessage(messageId: string): Promise<void> {
    if (!this.initialized) {
      await this.init()
    }

    await this.brain.delete(messageId)
  }

  /**
   * Export conversation to JSON
   *
   * Uses: getConversationThread()
   * Real implementation - serializes conversation
   *
   * @param conversationId Conversation ID
   * @returns JSON-serializable conversation object
   */
  async exportConversation(conversationId: string): Promise<any> {
    if (!this.initialized) {
      await this.init()
    }

    const thread = await this.getConversationThread(conversationId, {
      includeArtifacts: true
    })

    return {
      version: '1.0',
      exportedAt: Date.now(),
      conversation: thread
    }
  }

  /**
   * Import conversation from JSON
   *
   * Uses: saveMessage() and linkMessages()
   * Real implementation - recreates conversation
   *
   * @param data Exported conversation data
   * @returns New conversation ID
   */
  async importConversation(data: any): Promise<string> {
    if (!this.initialized) {
      await this.init()
    }

    const newConversationId = `conv_${uuidv4()}`
    const conversation = data.conversation

    if (!conversation || !conversation.messages) {
      throw new Error('Invalid conversation data')
    }

    // Import messages in order
    const messageIdMap = new Map<string, string>()

    for (let i = 0; i < conversation.messages.length; i++) {
      const msg = conversation.messages[i]
      const prevMessageId = i > 0 ? messageIdMap.get(conversation.messages[i - 1].id) : undefined

      const newMessageId = await this.saveMessage(msg.content, msg.role, {
        conversationId: newConversationId,
        sessionId: conversation.metadata.sessionId,
        phase: msg.metadata.problemSolvingPhase,
        confidence: msg.metadata.confidence,
        tags: msg.metadata.tags,
        linkToPrevious: prevMessageId,
        metadata: msg.metadata
      })

      messageIdMap.set(msg.id, newMessageId)
    }

    return newConversationId
  }
}

/**
 * Create a ConversationManager instance
 *
 * @param brain Brainy instance
 * @returns ConversationManager instance
 */
export function createConversationManager(brain: Brainy): ConversationManager {
  return new ConversationManager(brain)
}