- Add @deprecated JSDoc tags to TypeScript definitions
- Update all documentation examples to use modern add() and relate() API
- Preserve batch operations (addNouns, addVerbs) as they remain current
- Mark deprecated methods in both source and compiled definitions
Migration guide:
- addNoun(data, type, metadata) → add(data, { nounType: type, ...metadata })
- addVerb(source, target, type, metadata) → relate(source, target, type, metadata)
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395 lines
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Markdown
# 🧠 Brainy 2.0 API Reference
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> **The definitive API documentation for Brainy 2.0**
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> Clean • Powerful • Zero-Configuration
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## Quick Start
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```typescript
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import { BrainyData } from '@soulcraft/brainy'
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const brain = new BrainyData() // Zero config!
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await brain.init()
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// Add data (text auto-embeds!)
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await brain.add('The future of AI is here', { nounType: 'content' })
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// Search with Triple Intelligence
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const results = await brain.find({
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like: 'artificial intelligence',
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where: { year: { greaterThan: 2020 } },
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connected: { via: 'references' }
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})
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```
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## Core Concepts
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### 🧬 Nouns
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Vectors with metadata - the fundamental data unit in Brainy.
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### 🔗 Verbs
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Relationships between nouns - the connections that create knowledge graphs.
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### 🧠 Triple Intelligence
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Vector search + Graph traversal + Metadata filtering in one unified query.
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---
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## API Reference
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### Data Operations
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#### Nouns (Vectors with Metadata)
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##### `addNoun(dataOrVector, metadata?)`
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Add a single noun to the database.
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- **dataOrVector**: `string | number[]` - Text (auto-embeds) or pre-computed vector
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- **metadata**: `object` - Associated metadata
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- **Returns**: `Promise<string>` - The noun's ID
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##### `getNoun(id)`
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Retrieve a noun by ID.
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- **id**: `string` - The noun's ID
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- **Returns**: `Promise<VectorDocument | null>`
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##### `updateNoun(id, dataOrVector?, metadata?)`
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Update an existing noun.
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- **id**: `string` - The noun's ID
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- **dataOrVector**: `string | number[]` - New data/vector (optional)
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- **metadata**: `object` - New metadata (optional)
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- **Returns**: `Promise<void>`
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##### `deleteNoun(id)`
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Delete a noun.
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- **id**: `string` - The noun's ID
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- **Returns**: `Promise<boolean>`
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##### `getNouns(options)`
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Get multiple nouns (unified method).
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- **options**: Can be:
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- `string[]` - Array of IDs
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- `{where: object}` - Metadata filter
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- `{limit: number, offset: number}` - Pagination
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- **Returns**: `Promise<VectorDocument[]>`
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#### Verbs (Relationships)
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##### `addVerb(source, target, type, metadata?)`
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Create a relationship between nouns.
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- **source**: `string` - Source noun ID
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- **target**: `string` - Target noun ID
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- **type**: `string` - Relationship type
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- **metadata**: `object` - Relationship metadata (optional)
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- **Returns**: `Promise<string>` - The verb's ID
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##### `getVerbsBySource(sourceId)`
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Get all outgoing relationships.
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- **sourceId**: `string` - Source noun ID
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- **Returns**: `Promise<Verb[]>`
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##### `getVerbsByTarget(targetId)`
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Get all incoming relationships.
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- **targetId**: `string` - Target noun ID
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- **Returns**: `Promise<Verb[]>`
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---
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### Search Operations
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#### `search(query, k?)`
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Simple vector similarity search.
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- **query**: `string | number[]` - Text or vector
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- **k**: `number` - Number of results (default: 10)
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- **Returns**: `Promise<SearchResult[]>`
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> 💡 This is equivalent to: `find({like: query, limit: k})`
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#### `find(query)` - Triple Intelligence 🧠
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The ultimate search method combining vector, graph, and metadata search.
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```typescript
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find({
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// Vector similarity
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like: 'text query' | vector | {id: 'noun-id'},
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// Metadata filtering (Brainy operators)
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where: {
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field: value, // Exact match
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field: {
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equals: value,
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greaterThan: value,
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lessThan: value,
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greaterEqual: value,
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lessEqual: value,
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oneOf: [val1, val2], // In array
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notOneOf: [val1, val2], // Not in array
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contains: value, // Array/string contains
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startsWith: value,
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endsWith: value,
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matches: /pattern/, // Pattern match
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between: [min, max]
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}
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},
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// Graph traversal
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connected: {
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to: 'noun-id', // Target noun
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from: 'noun-id', // Source noun
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via: 'relationship-type', // Relationship type
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depth: 2 // Traversal depth
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},
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// Control
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limit: 10, // Max results
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offset: 0, // Skip results
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explain: false // Include explanation
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})
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```
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---
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### Neural API
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Access advanced AI features via `brain.neural`:
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#### `brain.neural.similar(a, b)`
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Calculate semantic similarity between two items.
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- **Returns**: `Promise<number>` - Similarity score (0-1)
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#### `brain.neural.clusters(options?)`
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Automatically cluster nouns.
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- **Returns**: `Promise<Cluster[]>` - Generated clusters
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#### `brain.neural.hierarchy(id)`
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Build semantic hierarchy from a noun.
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- **Returns**: `Promise<HierarchyTree>` - Hierarchy structure
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#### `brain.neural.neighbors(id, k?)`
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Find k-nearest neighbors.
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- **Returns**: `Promise<Noun[]>` - Nearest neighbors
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#### `brain.neural.outliers(threshold?)`
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Detect outlier nouns.
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- **Returns**: `Promise<string[]>` - Outlier IDs
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#### `brain.neural.visualize(options?)`
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Generate visualization data for external tools.
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```typescript
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visualize({
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maxNodes: 100,
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dimensions: 2 | 3,
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algorithm: 'force' | 'hierarchical' | 'radial',
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includeEdges: true
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})
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// Returns format for D3, Cytoscape, or GraphML
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```
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---
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### Import & Export
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#### `neuralImport(data, options?)`
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AI-powered smart import that auto-detects format.
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- **data**: `any` - Data to import
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- **options**: Import configuration
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- `confidenceThreshold`: Minimum confidence (0-1)
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- `autoApply`: Automatically add to database
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- `skipDuplicates`: Skip existing entities
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- **Returns**: Detected entities and relationships
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#### `backup()`
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Create a full backup.
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- **Returns**: `Promise<BackupData>`
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#### `restore(backup)`
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Restore from backup.
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- **backup**: `BackupData` - Previous backup
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- **Returns**: `Promise<void>`
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---
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### Intelligence Features
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#### Verb Scoring
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Train the relationship scoring model:
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- `provideFeedbackForVerbScoring(feedback)` - Train model
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- `getVerbScoringStats()` - Get statistics
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- `exportVerbScoringLearningData()` - Export training
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- `importVerbScoringLearningData(data)` - Import training
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#### Embeddings
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- `embed(text)` - Generate embedding vector
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- `calculateSimilarity(a, b, metric?)` - Calculate similarity
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---
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### Configuration & Management
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#### Operational Modes
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- `setReadOnly(bool)` - Toggle read-only mode
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- `setWriteOnly(bool)` - Toggle write-only mode
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- `setFrozen(bool)` - Freeze all modifications
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#### Cache & Performance
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- `getCacheStats()` - Get cache statistics
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- `clearCache()` - Clear search cache
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- `size()` - Get total noun count
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- `getStatistics()` - Get full statistics
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#### Data Management
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- `clear(options?)` - Clear all data
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- `clearNouns()` - Clear nouns only
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- `clearVerbs()` - Clear verbs only
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- `rebuildMetadataIndex()` - Rebuild index
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---
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### Lifecycle
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#### Initialization
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```typescript
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const brain = new BrainyData({
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storage: 'auto', // auto | memory | filesystem | s3
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dimensions: 384, // Vector dimensions
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cache: true, // Enable caching
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index: true // Enable indexing
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})
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await brain.init() // Required before use!
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```
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#### Cleanup
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```typescript
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await brain.shutdown() // Graceful shutdown
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```
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#### Static Methods
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- `BrainyData.preloadModel()` - Preload ML model
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- `BrainyData.warmup()` - Warmup system
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---
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## Query Operators Reference
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Brainy uses its own clean, readable operators:
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| Brainy Operator | Description | Example |
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| `equals` | Exact match | `{age: {equals: 25}}` |
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| `greaterThan` | Greater than | `{age: {greaterThan: 18}}` |
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| `lessThan` | Less than | `{price: {lessThan: 100}}` |
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| `greaterEqual` | Greater or equal | `{score: {greaterEqual: 90}}` |
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| `lessEqual` | Less or equal | `{rating: {lessEqual: 3}}` |
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| `oneOf` | In array | `{color: {oneOf: ['red', 'blue']}}` |
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| `notOneOf` | Not in array | `{status: {notOneOf: ['deleted']}}` |
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| `contains` | Contains value | `{tags: {contains: 'ai'}}` |
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| `startsWith` | String prefix | `{name: {startsWith: 'John'}}` |
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| `endsWith` | String suffix | `{email: {endsWith: '@gmail.com'}}` |
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| `matches` | Pattern match | `{text: {matches: /^[A-Z]/}}` |
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| `between` | Range | `{year: {between: [2020, 2024]}}` |
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---
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## Examples
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### Basic Usage
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```typescript
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// Add data
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const id = await brain.add('Quantum computing breakthrough', {
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category: 'technology',
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year: 2024,
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importance: 'high'
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})
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// Simple search
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const results = await brain.search('quantum physics', 5)
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// Complex query with Triple Intelligence
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const articles = await brain.find({
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like: 'quantum computing',
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where: {
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year: { greaterThan: 2022 },
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importance: { oneOf: ['high', 'critical'] }
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},
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connected: {
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via: 'references',
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depth: 2
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},
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limit: 10
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})
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```
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### Creating Knowledge Graphs
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```typescript
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// Add entities
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const ai = await brain.add('Artificial Intelligence', { nounType: 'concept' })
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const ml = await brain.add('Machine Learning', { nounType: 'concept' })
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const dl = await brain.add('Deep Learning', { nounType: 'concept' })
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// Create relationships
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await brain.relate(ml, ai, 'subset_of')
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await brain.relate(dl, ml, 'subset_of')
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await brain.relate(dl, ai, 'enables')
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// Traverse the graph
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const aiEcosystem = await brain.find({
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connected: { from: ai, depth: 3 }
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})
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```
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### Using Neural Features
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```typescript
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// Find similar concepts
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const similarity = await brain.neural.similar(
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'renewable energy',
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'sustainable power'
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)
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// Auto-cluster documents
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const clusters = await brain.neural.clusters({
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method: 'kmeans',
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k: 5
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})
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// Generate visualization
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const vizData = await brain.neural.visualize({
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maxNodes: 200,
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algorithm: 'force',
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dimensions: 3
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})
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// Use vizData with D3.js, Cytoscape, etc.
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```
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---
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## Key Features
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### ✨ Zero Configuration
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Works instantly with sensible defaults. No setup required.
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### 🧠 Triple Intelligence
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Combines vector search, graph traversal, and metadata filtering in one query.
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### 🚀 Auto-Embedding
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Text automatically converts to vectors - no manual embedding needed.
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### 📊 Built-in Visualization
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Export data formatted for popular visualization libraries.
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### 🔒 Clean Operators
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Readable, intuitive operators - no cryptic symbols.
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### 🎯 Everything Included
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All features in the MIT licensed package - no premium tiers.
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---
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## Support
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- **GitHub**: [github.com/soulcraft/brainy](https://github.com/soulcraft/brainy)
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- **Documentation**: [docs.soulcraft.com/brainy](https://docs.soulcraft.com/brainy)
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- **License**: MIT
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---
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*Brainy 2.0 - Intelligence for Everyone* |