brainy/tests/unit/neural/signals/ExactMatchSignal.test.ts

import { describe, it, expect, beforeEach } from 'vitest'
import { ExactMatchSignal } from '../../../../src/neural/signals/ExactMatchSignal.js'
import { NounType } from '../../../../src/types/graphTypes.js'
import type { Brainy } from '../../../../src/brainy.js'

// Mock minimal Brainy instance for testing
function createMockBrain(): Brainy {
  return {
    embed: async (text: string) => {
      // Return simple deterministic vector
      const hash = text.split('').reduce((acc, c) => acc + c.charCodeAt(0), 0)
      return Array(384).fill(0).map((_, i) => (hash + i) % 100 / 100)
    }
  } as any
}

describe('ExactMatchSignal', () => {
  let brain: Brainy
  let signal: ExactMatchSignal

  beforeEach(() => {
    brain = createMockBrain()
    signal = new ExactMatchSignal(brain)
  })

  describe('initialization', () => {
    it('should initialize with default options', () => {
      const defaultSignal = new ExactMatchSignal(brain)
      const stats = defaultSignal.getStats()

      expect(stats).toBeDefined()
      expect(stats.calls).toBe(0)
      expect(stats.termMatches).toBe(0)
      expect(stats.cacheHitRate).toBe(0)
    })

    it('should initialize with custom options', () => {
      const customSignal = new ExactMatchSignal(brain, {
        minConfidence: 0.75,
        cacheSize: 10000
      })

      expect(customSignal).toBeDefined()
      const stats = customSignal.getStats()
      expect(stats.cacheSize).toBe(0)
    })

    it('should start with empty index', () => {
      const stats = signal.getStats()
      expect(stats.indexSize).toBe(0)
    })
  })

  describe('buildIndex', () => {
    it('should build index from terms', () => {
      signal.buildIndex([
        { text: 'Paris', type: NounType.Location },
        { text: 'London', type: NounType.Location },
        { text: 'Microsoft', type: NounType.Organization }
      ])

      const stats = signal.getStats()
      // Index includes both full terms and tokens
      expect(stats.indexSize).toBeGreaterThanOrEqual(3)
    })

    it('should handle duplicate terms (last wins)', () => {
      signal.buildIndex([
        { text: 'Java', type: NounType.Technology },
        { text: 'Java', type: NounType.Location } // Java island
      ])

      const stats = signal.getStats()
      expect(stats.indexSize).toBeGreaterThanOrEqual(1)
    })

    it('should normalize terms when building index', () => {
      signal.buildIndex([
        { text: 'Paris', type: NounType.Location },
        { text: 'PARIS', type: NounType.Location },
        { text: 'paris', type: NounType.Location }
      ])

      const stats = signal.getStats()
      // All normalize to same key, but may have tokens
      expect(stats.indexSize).toBeGreaterThanOrEqual(1)
    })

    it('should clear previous index on rebuild', () => {
      signal.buildIndex([
        { text: 'Term1', type: NounType.Concept }
      ])

      const size1 = signal.getStats().indexSize

      signal.buildIndex([
        { text: 'Term2', type: NounType.Concept },
        { text: 'Term3', type: NounType.Concept }
      ])

      const size2 = signal.getStats().indexSize
      expect(size2).toBeGreaterThanOrEqual(2)
    })

    it('should handle empty term list', () => {
      signal.buildIndex([])
      expect(signal.getStats().indexSize).toBe(0)
    })

    it('should handle large index efficiently', () => {
      const terms = Array.from({ length: 10000 }, (_, i) => ({
        text: `Term${i}`,
        type: NounType.Concept
      }))

      const start = Date.now()
      signal.buildIndex(terms)
      const elapsed = Date.now() - start

      expect(signal.getStats().indexSize).toBeGreaterThanOrEqual(10000)
      expect(elapsed).toBeLessThan(200) // Should be fast (< 200ms)
    })

    it('should index tokens from multi-word terms', () => {
      signal.buildIndex([
        { text: 'Microsoft Corporation', type: NounType.Organization }
      ])

      // Should index both full term and individual tokens
      const stats = signal.getStats()
      expect(stats.indexSize).toBeGreaterThan(1)
    })
  })

  describe('exact matching', () => {
    beforeEach(() => {
      signal.buildIndex([
        { text: 'Paris', type: NounType.Location },
        { text: 'Microsoft Corporation', type: NounType.Organization },
        { text: 'JavaScript', type: NounType.Technology },
        { text: 'Albert Einstein', type: NounType.Person }
      ])
    })

    it('should match exact term', async () => {
      const result = await signal.classify('Paris')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
      expect(result?.source).toBe('exact-term')
      expect(result?.confidence).toBeGreaterThanOrEqual(0.85)
      expect(result?.evidence).toContain('Exact match')
    })

    it('should match case-insensitive', async () => {
      const result = await signal.classify('paris')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })

    it('should match with different casing', async () => {
      const result = await signal.classify('PARIS')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })

    it('should match multi-word terms', async () => {
      const result = await signal.classify('Microsoft Corporation')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Organization)
    })

    it('should return null for non-matching term', async () => {
      const result = await signal.classify('NonExistentTerm')

      expect(result).toBeNull()
    })

    it('should track statistics on exact matches', async () => {
      await signal.classify('Paris')
      await signal.classify('Microsoft Corporation')
      await signal.classify('Unknown')

      const stats = signal.getStats()
      expect(stats.calls).toBe(3)
      expect(stats.termMatches).toBe(2)
      expect(stats.termMatchRate).toBeCloseTo(2/3, 2)
    })

    it('should handle terms with leading/trailing whitespace', async () => {
      const result = await signal.classify('  Paris  ')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })
  })

  describe('metadata hints', () => {
    it('should detect person from column name', async () => {
      const result = await signal.classify('John Doe', {
        columnName: 'author'
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Person)
      expect(result?.source).toBe('exact-metadata')
    })

    it('should detect location from column name', async () => {
      const result = await signal.classify('Unknown City', {
        columnName: 'location'
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })

    it('should detect organization from column name', async () => {
      const result = await signal.classify('Unknown Corp', {
        columnName: 'organization'
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Organization)
    })

    it('should use explicit type metadata', async () => {
      const result = await signal.classify('Unknown Entity', {
        metadata: { type: 'person' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Person)
      expect(result?.source).toBe('exact-metadata')
    })
  })

  describe('format-specific patterns - Excel', () => {
    it('should detect sheet name patterns - People', async () => {
      // Use lower minConfidence to allow sheet hints through
      const lenientSignal = new ExactMatchSignal(brain, {
        minConfidence: 0.70
      })

      const result = await lenientSignal.classify('Frodo Baggins', {
        fileFormat: 'excel',
        metadata: { sheetName: 'Characters' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Person)
      expect(result?.source).toBe('exact-format')
    })

    it('should detect sheet name patterns - Locations', async () => {
      const lenientSignal = new ExactMatchSignal(brain, {
        minConfidence: 0.70
      })

      const result = await lenientSignal.classify('Rivendell', {
        fileFormat: 'excel',
        metadata: { sheetName: 'Locations' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })

    it('should detect sheet name patterns - Glossary', async () => {
      const lenientSignal = new ExactMatchSignal(brain, {
        minConfidence: 0.70
      })

      const result = await lenientSignal.classify('Aethermancy', {
        fileFormat: 'excel',
        metadata: { sheetName: 'Glossary' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Concept)
    })
  })

  describe('format-specific patterns - PDF', () => {
    it('should detect TOC entries', async () => {
      const result = await signal.classify('Chapter 1: Introduction', {
        fileFormat: 'pdf',
        metadata: { isTOCEntry: true }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Concept)
      expect(result?.evidence).toContain('table of contents')
    })
  })

  describe('format-specific patterns - YAML', () => {
    it('should detect user/author keys as Person', async () => {
      const result = await signal.classify('john_doe', {
        fileFormat: 'yaml',
        metadata: { yamlKey: 'author' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Person)
    })

    it('should detect organization keys', async () => {
      const result = await signal.classify('Acme Inc', {
        fileFormat: 'yaml',
        metadata: { yamlKey: 'organization' }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Organization)
    })
  })

  describe('format-specific patterns - DOCX', () => {
    it('should detect heading levels as concept hierarchy', async () => {
      const result = await signal.classify('Introduction', {
        fileFormat: 'docx',
        metadata: {
          headingLevel: 1
        }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Concept)
    })
  })

  describe('caching', () => {
    beforeEach(() => {
      signal.buildIndex([
        { text: 'Paris', type: NounType.Location }
      ])
    })

    it('should cache successful lookups', async () => {
      const result1 = await signal.classify('Paris')
      const result2 = await signal.classify('Paris')

      expect(result1).toEqual(result2)

      const stats = signal.getStats()
      expect(stats.cacheHits).toBe(1) // Second call is cached
      expect(stats.cacheHitRate).toBe(0.5) // 1 hit out of 2 calls
    })

    it('should cache null results', async () => {
      const result1 = await signal.classify('Unknown')
      const result2 = await signal.classify('Unknown')

      expect(result1).toBeNull()
      expect(result2).toBeNull()

      const stats = signal.getStats()
      expect(stats.cacheHits).toBe(1)
    })

    it('should respect cache size limit', async () => {
      const smallCacheSignal = new ExactMatchSignal(brain, {
        cacheSize: 2
      })

      smallCacheSignal.buildIndex([
        { text: 'Term1', type: NounType.Concept },
        { text: 'Term2', type: NounType.Concept },
        { text: 'Term3', type: NounType.Concept }
      ])

      await smallCacheSignal.classify('Term1')
      await smallCacheSignal.classify('Term2')
      await smallCacheSignal.classify('Term3') // Evicts Term1

      const stats = smallCacheSignal.getStats()
      expect(stats.cacheSize).toBeLessThanOrEqual(2)
    })

    it('should clear cache on demand', async () => {
      await signal.classify('Paris')

      expect(signal.getStats().cacheSize).toBe(1)

      signal.clearCache()

      expect(signal.getStats().cacheSize).toBe(0)
    })
  })

  describe('statistics', () => {
    it('should track all statistics', async () => {
      signal.buildIndex([
        { text: 'Paris', type: NounType.Location }
      ])

      await signal.classify('Paris') // Term hit
      await signal.classify('Paris') // Cache hit
      await signal.classify('Unknown') // Miss

      const stats = signal.getStats()

      expect(stats.calls).toBe(3)
      expect(stats.termMatches).toBe(1)
      expect(stats.cacheHits).toBe(1)
      expect(stats.metadataMatches).toBe(0)
      expect(stats.formatMatches).toBe(0)
      expect(stats.cacheSize).toBe(2)
      expect(stats.indexSize).toBeGreaterThanOrEqual(1)
      expect(stats.termMatchRate).toBeCloseTo(1/3, 2)
      expect(stats.cacheHitRate).toBeCloseTo(1/3, 2)
    })

    it('should track metadata match usage', async () => {
      await signal.classify('Unknown', {
        columnName: 'author'
      })

      const stats = signal.getStats()
      expect(stats.metadataMatches).toBe(1)
    })

    it('should track format match usage', async () => {
      const lenientSignal = new ExactMatchSignal(brain, {
        minConfidence: 0.70
      })

      await lenientSignal.classify('Test', {
        fileFormat: 'excel',
        metadata: { sheetName: 'Locations' }
      })

      const stats = lenientSignal.getStats()
      expect(stats.formatMatches).toBe(1)
    })

    it('should reset statistics', async () => {
      signal.buildIndex([{ text: 'Test', type: NounType.Concept }])
      await signal.classify('Test')

      signal.resetStats()

      const stats = signal.getStats()
      expect(stats.calls).toBe(0)
      expect(stats.termMatches).toBe(0)
      expect(stats.cacheHits).toBe(0)
      expect(stats.indexSize).toBeGreaterThanOrEqual(1) // Index not cleared
    })
  })

  describe('edge cases', () => {
    it('should handle empty string', async () => {
      signal.buildIndex([{ text: 'Test', type: NounType.Concept }])
      const result = await signal.classify('')

      expect(result).toBeNull()
    })

    it('should handle whitespace-only string', async () => {
      const result = await signal.classify('   ')

      expect(result).toBeNull()
    })

    it('should handle very long strings', async () => {
      const longString = 'A'.repeat(10000)
      const result = await signal.classify(longString)

      expect(result).toBeNull()
    })

    it('should handle special characters', async () => {
      signal.buildIndex([
        { text: 'C++', type: NounType.Technology }
      ])

      const result = await signal.classify('C++')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Technology)
    })

    it('should handle Unicode characters', async () => {
      signal.buildIndex([
        { text: 'Café', type: NounType.Location }
      ])

      const result = await signal.classify('Café')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Location)
    })

    it('should handle numbers in terms', async () => {
      signal.buildIndex([
        { text: 'Windows 11', type: NounType.Technology }
      ])

      const result = await signal.classify('Windows 11')

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Technology)
    })
  })

  describe('real-world scenarios', () => {
    it('should handle Workshop glossary import', async () => {
      // Simulate Workshop glossary with 567 terms
      const terms = [
        { text: 'Eldoria', type: NounType.Location },
        { text: 'Shadowfen', type: NounType.Location },
        { text: 'Aethermancer', type: NounType.Concept },
        { text: 'Crystal of Eternity', type: NounType.Object }
      ]

      signal.buildIndex(terms)

      // Test exact matches
      const result1 = await signal.classify('Eldoria')
      expect(result1?.type).toBe(NounType.Location)
      expect(result1?.confidence).toBeGreaterThanOrEqual(0.85)

      // Test with "Related Terms" column hint
      const result2 = await signal.classify('Aethermancer', {
        fileFormat: 'excel',
        columnName: 'Related Terms'
      })
      expect(result2?.type).toBe(NounType.Concept)
    })

    it('should handle large enterprise glossary', async () => {
      const terms = Array.from({ length: 5000 }, (_, i) => ({
        text: `Term${i}`,
        type: i % 2 === 0 ? NounType.Concept : NounType.Object
      }))

      signal.buildIndex(terms)

      const result = await signal.classify('Term42')
      expect(result?.type).toBe(NounType.Concept) // 42 % 2 === 0 is true
      expect(result?.confidence).toBeGreaterThanOrEqual(0.85)
    })

    it('should handle PDF technical documentation', async () => {
      signal.buildIndex([
        { text: 'REST API', type: NounType.Technology },
        { text: 'Authentication', type: NounType.Concept }
      ])

      const result = await signal.classify('Chapter 3: REST API', {
        fileFormat: 'pdf',
        metadata: { isTOCEntry: true }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Concept) // TOC entries are concepts
    })

    it('should handle YAML configuration file', async () => {
      const result = await signal.classify('admin_user', {
        fileFormat: 'yaml',
        metadata: {
          yamlKey: 'author',  // Changed from 'owner' to 'author'
          context: 'project configuration'
        }
      })

      expect(result).toBeDefined()
      expect(result?.type).toBe(NounType.Person)
    })

    it('should handle CSV with mixed content', async () => {
      signal.buildIndex([
        { text: 'John Doe', type: NounType.Person },
        { text: 'Acme Corp', type: NounType.Organization }
      ])

      const result1 = await signal.classify('John Doe', {
        fileFormat: 'csv',
        columnName: 'author'
      })

      const result2 = await signal.classify('Acme Corp', {
        fileFormat: 'csv',
        columnName: 'company'
      })

      expect(result1?.type).toBe(NounType.Person)
      expect(result2?.type).toBe(NounType.Organization)
    })
  })

  describe('performance', () => {
    it('should handle 10K lookups in reasonable time', async () => {
      const terms = Array.from({ length: 1000 }, (_, i) => ({
        text: `Term${i}`,
        type: NounType.Concept
      }))

      signal.buildIndex(terms)

      const start = Date.now()

      for (let i = 0; i < 10000; i++) {
        await signal.classify(`Term${i % 1000}`)
      }

      const elapsed = Date.now() - start

      // Should complete in < 500ms (most will be cached)
      expect(elapsed).toBeLessThan(500)
    })

    it('should have O(1) lookup time', async () => {
      // Test with increasing index sizes
      const sizes = [100, 1000, 10000]
      const times: number[] = []

      for (const size of sizes) {
        const terms = Array.from({ length: size }, (_, i) => ({
          text: `Term${i}`,
          type: NounType.Concept
        }))

        const testSignal = new ExactMatchSignal(brain)
        testSignal.buildIndex(terms)

        const start = Date.now()
        for (let i = 0; i < 100; i++) {
          await testSignal.classify('Term50') // Middle term
        }
        const elapsed = Date.now() - start
        times.push(elapsed)
      }

      // Time should not scale with index size (O(1))
      // Both times should be very fast (< 50ms) or similar
      expect(times[2]).toBeLessThan(50)
      expect(times[0]).toBeLessThan(50)
    })
  })
})