open-brainy/src/demo.ts

/**
 * Demo-specific entry point for browser environments
 * This excludes all Node.js-specific functionality to avoid import issues
 */

// Import only browser-compatible modules
import { MemoryStorage } from './storage/adapters/memoryStorage.js'
import { OPFSStorage } from './storage/adapters/opfsStorage.js'
import { TransformerEmbedding } from './utils/embedding.js'
import { cosineDistance, euclideanDistance } from './utils/distance.js'
import { isBrowser } from './utils/environment.js'

// Core types we need for the demo
export interface Vector extends Array<number> {}

export interface SearchResult {
  id: string
  score: number
  metadata: any
  text?: string
}

export interface VerbData {
  id: string
  source: string
  target: string
  verb: string
  metadata: any
  timestamp: number
}

/**
 * Simplified BrainyData class for demo purposes
 * Only includes browser-compatible functionality
 */
export class DemoBrainyData {
  private storage: MemoryStorage | OPFSStorage
  private embedder: TransformerEmbedding | null = null
  private initialized = false
  private vectors = new Map<string, Vector>()
  private metadata = new Map<string, any>()
  private verbs = new Map<string, VerbData[]>()

  constructor() {
    // Always use memory storage for demo simplicity
    this.storage = new MemoryStorage()
  }

  /**
   * Initialize the database
   */
  async init(): Promise<void> {
    if (this.initialized) return

    try {
      await this.storage.init()
      
      // Initialize the embedder
      this.embedder = new TransformerEmbedding({ verbose: false })
      await this.embedder.init()
      
      this.initialized = true
      console.log('✅ Demo BrainyData initialized successfully')
    } catch (error) {
      console.error('Failed to initialize demo BrainyData:', error)
      throw error
    }
  }

  /**
   * Add a document to the database
   */
  async add(text: string, metadata: any = {}): Promise<string> {
    if (!this.initialized || !this.embedder) {
      throw new Error('Database not initialized')
    }

    const id = this.generateId()
    
    try {
      // Generate embedding
      const vector = await this.embedder.embed(text)
      
      // Store data
      this.vectors.set(id, vector)
      this.metadata.set(id, { text, ...metadata, timestamp: Date.now() })
      
      return id
    } catch (error) {
      console.error('Failed to add document:', error)
      throw error
    }
  }

  /**
   * Search for similar documents
   */
  async searchText(query: string, limit: number = 10): Promise<SearchResult[]> {
    if (!this.initialized || !this.embedder) {
      throw new Error('Database not initialized')
    }

    try {
      // Generate query embedding
      const queryVector = await this.embedder.embed(query)
      
      // Calculate similarities
      const results: SearchResult[] = []
      
      for (const [id, vector] of this.vectors.entries()) {
        const score = 1 - cosineDistance(queryVector, vector) // Convert distance to similarity
        const metadata = this.metadata.get(id)
        
        results.push({
          id,
          score,
          metadata,
          text: metadata?.text
        })
      }
      
      // Sort by score (highest first) and limit
      return results
        .sort((a, b) => b.score - a.score)
        .slice(0, limit)
        
    } catch (error) {
      console.error('Search failed:', error)
      throw error
    }
  }

  /**
   * Add a relationship between two documents
   */
  async addVerb(sourceId: string, targetId: string, verb: string, metadata: any = {}): Promise<string> {
    const verbId = this.generateId()
    const verbData: VerbData = {
      id: verbId,
      source: sourceId,
      target: targetId,
      verb,
      metadata,
      timestamp: Date.now()
    }

    if (!this.verbs.has(sourceId)) {
      this.verbs.set(sourceId, [])
    }
    this.verbs.get(sourceId)!.push(verbData)

    return verbId
  }

  /**
   * Get relationships from a source document
   */
  async getVerbsBySource(sourceId: string): Promise<VerbData[]> {
    return this.verbs.get(sourceId) || []
  }

  /**
   * Get a document by ID
   */
  async get(id: string): Promise<any | null> {
    const metadata = this.metadata.get(id)
    const vector = this.vectors.get(id)
    
    if (!metadata || !vector) return null
    
    return {
      id,
      vector,
      ...metadata
    }
  }

  /**
   * Delete a document
   */
  async delete(id: string): Promise<boolean> {
    const deleted = this.vectors.delete(id) && this.metadata.delete(id)
    this.verbs.delete(id)
    return deleted
  }

  /**
   * Update document metadata
   */
  async updateMetadata(id: string, newMetadata: any): Promise<boolean> {
    const metadata = this.metadata.get(id)
    if (!metadata) return false
    
    this.metadata.set(id, { ...metadata, ...newMetadata })
    return true
  }

  /**
   * Get the number of documents
   */
  size(): number {
    return this.vectors.size
  }

  /**
   * Generate a random ID
   */
  private generateId(): string {
    return 'id-' + Math.random().toString(36).substr(2, 9) + '-' + Date.now()
  }

  /**
   * Get storage info
   */
  getStorage(): MemoryStorage | OPFSStorage {
    return this.storage
  }
}

// Export noun and verb types for compatibility
export const NounType = {
  Person: 'Person',
  Organization: 'Organization', 
  Location: 'Location',
  Thing: 'Thing',
  Concept: 'Concept',
  Event: 'Event',
  Document: 'Document',
  Media: 'Media',
  File: 'File',
  Message: 'Message',
  Content: 'Content'
} as const

export const VerbType = {
  RelatedTo: 'related_to',
  Contains: 'contains',
  PartOf: 'part_of',
  LocatedAt: 'located_at',
  References: 'references',
  Owns: 'owns',
  CreatedBy: 'created_by',
  BelongsTo: 'belongs_to',
  Likes: 'likes',
  Follows: 'follows'
} as const

// Export the main class as BrainyData for compatibility
export { DemoBrainyData as BrainyData }

// Default export
export default DemoBrainyData