feat: Universal Import with intelligent type matching (v2.1.0)
✨ ONE universal import method for everything - Auto-detects files, URLs, and raw data - Intelligent noun/verb type matching using embeddings - Support for JSON, CSV, YAML, and text formats - Zero configuration required 🧠 Intelligent Type Matching - Uses semantic embeddings to match 31 noun types - Automatically detects 40 verb relationship types - Confidence scores for type predictions - Caching for improved performance 📦 Import Manager - Centralized import logic with lazy loading - Integrates NeuralImportAugmentation for AI processing - Proper CSV parsing with quote handling - Basic YAML support 🎯 Simplified API - brain.import() - ONE method that handles everything - Auto-detection of URLs and file paths - Backwards compatible with existing code - Clean, modern, delightful developer experience 📚 Documentation - Comprehensive import guide in docs/guides/import-anything.md - Examples for every format and use case - Philosophy of simplicity and zero config ✅ Tests - Full unit test coverage for import functionality - Type matching tests for all 31 nouns and 40 verbs - Tests for CSV, YAML, JSON, and text formats BREAKING CHANGES: None - fully backward compatible
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**🧠 Brainy 2.0 - The Universal Knowledge Protocol™**
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**World's first Triple Intelligence™ database**—unifying vector similarity, graph relationships, and document filtering in one magical API. Model ANY data from ANY domain using 24 standardized noun types × 40 verb types.
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**World's first Triple Intelligence™ database**—unifying vector similarity, graph relationships, and document filtering in one magical API. Model ANY data from ANY domain using 31 standardized noun types × 40 verb types.
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**Why Brainy Leads**: We're the first to solve the impossible—combining three different database paradigms (vector, graph, document) into one unified query interface. This breakthrough enables us to be the Universal Knowledge Protocol where all tools, augmentations, and AI models speak the same language.
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## 🎉 What's New in 2.0
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- **World's First Triple Intelligence™**: Unified vector + graph + document in ONE query
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- **Universal Knowledge Protocol**: 24 nouns × 40 verbs standardize all knowledge
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- **Universal Knowledge Protocol**: 31 nouns × 40 verbs standardize all knowledge
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- **Infinite Expressiveness**: Model ANY data with unlimited metadata
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- **API Consolidation**: 15+ methods → 2 clean APIs (`search()` and `find()`)
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- **Natural Language**: Ask questions in plain English
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Brainy's **Noun-Verb Taxonomy** achieves **universal coverage** of all human knowledge through **infinite expressiveness**:
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- **24 Noun Types × 40 Verb Types = 960 Base Combinations**
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- **31 Noun Types × 40 Verb Types = 1,240 Base Combinations**
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- **Unlimited Metadata Fields = ∞ Domain Specificity**
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- **Multi-hop Graph Traversals = ∞ Relationship Complexity**
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- **Result: Can Model ANY Data in ANY Industry**
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Like **HTTP** became the protocol for the web and **TCP/IP** for the internet, Brainy's noun-verb taxonomy is becoming the **Universal Knowledge Protocol**:
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- **Learn Once**: Developers learn 24 nouns + 40 verbs, not 1000s of schemas
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- **Learn Once**: Developers learn 31 nouns + 40 verbs, not 1000s of schemas
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- **Build Anywhere**: Tools built for one domain work in others
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- **Share Everything**: Knowledge graphs are universally shareable
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- **Compose Freely**: Augmentations compose without conflicts
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The Noun-Verb taxonomy is designed to represent **all human knowledge** through a comprehensive set of types that can be combined infinitely.
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### Complete Noun Types (24 Types)
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### Complete Noun Types (31 Types)
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#### Core Entity Types (6)
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@ -746,7 +746,7 @@ await brain.addNoun("Website Redesign", {
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})
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```
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#### Descriptive Types (6)
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#### Descriptive Types (7)
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##### 19. **Process** - Workflows and procedures
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```typescript
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})
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```
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##### 22. **Relationship** - Abstract relationships
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##### 22. **Topic** - Subjects or themes
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```typescript
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await brain.addNoun("Partnership Agreement", {
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type: "relationship",
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parties: ["CompanyA", "CompanyB"],
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terms: "5 years"
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await brain.addNoun("Machine Learning", {
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type: "topic",
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field: "AI",
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popularity: "high"
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})
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```
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##### 23. **Property** - Attributes or characteristics
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##### 23. **Language** - Languages or linguistic entities
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```typescript
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await brain.addNoun("Color Scheme", {
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type: "property",
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values: ["#000", "#FFF"],
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applies_to: "theme"
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await brain.addNoun("English", {
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type: "language",
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iso_code: "en",
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speakers_millions: 1500
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})
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```
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##### 24. **Custom** - User-defined types
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##### 24. **Currency** - Monetary units
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```typescript
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await brain.addNoun("Industry-Specific Entity", {
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type: "custom",
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subtype: "medical_device",
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fda_approved: true
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await brain.addNoun("US Dollar", {
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type: "currency",
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symbol: "$",
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code: "USD"
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})
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```
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##### 25. **Measurement** - Metrics or quantities
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```typescript
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await brain.addNoun("Temperature Reading", {
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type: "measurement",
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value: 23.5,
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unit: "celsius"
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})
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```
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#### Scientific/Research Types (2)
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##### 26. **Hypothesis** - Scientific theories and propositions
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```typescript
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await brain.addNoun("String Theory", {
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type: "hypothesis",
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field: "physics",
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status: "unproven"
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})
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```
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##### 27. **Experiment** - Studies and research trials
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```typescript
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await brain.addNoun("Clinical Trial XYZ", {
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type: "experiment",
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phase: 3,
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participants: 1000
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})
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```
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#### Legal/Regulatory Types (2)
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##### 28. **Contract** - Legal agreements and terms
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```typescript
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await brain.addNoun("Service Agreement", {
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type: "contract",
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duration: "2 years",
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value: 100000
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})
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```
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##### 29. **Regulation** - Laws and compliance requirements
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```typescript
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await brain.addNoun("GDPR", {
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type: "regulation",
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jurisdiction: "EU",
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category: "data protection"
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})
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```
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#### Technical Infrastructure Types (2)
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##### 30. **Interface** - APIs and protocols
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```typescript
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await brain.addNoun("REST API", {
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type: "interface",
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version: "v2",
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endpoints: 45
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})
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```
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##### 31. **Resource** - Infrastructure and compute assets
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```typescript
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await brain.addNoun("Database Server", {
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type: "resource",
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capacity: "1TB",
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availability: "99.9%"
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})
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```
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250
docs/guides/import-anything.md
Normal file
250
docs/guides/import-anything.md
Normal file
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# Import Anything - ONE Method, Infinite Intelligence 🚀
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Brainy's import is **ONE magical method** that understands EVERYTHING:
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- 📊 Data (objects, arrays, strings)
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- 📁 Files (auto-detects by path)
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- 🌐 URLs (auto-fetches)
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- 📄 Formats (JSON, CSV, YAML, text - all auto-detected)
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## The Ultimate Simplicity
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```javascript
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import { BrainyData } from '@soulcraft/brainy'
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const brain = new BrainyData()
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await brain.init()
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// ONE method for EVERYTHING:
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await brain.import(anything)
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```
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## Import Examples - It Just Works™
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### 📊 Import JSON Data
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```javascript
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// Array of objects? No problem.
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const people = [
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{ name: 'Alice', role: 'Engineer', company: 'TechCorp' },
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{ name: 'Bob', role: 'Designer', company: 'TechCorp' }
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]
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await brain.import(people)
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// ✨ Automatically detected as Person entities with Organization relationships!
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```
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### 📄 Import CSV - File or String
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```javascript
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// From file? Just pass the path!
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await brain.import('customers.csv')
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// ✨ Auto-detects it's a file, parses CSV, creates entities!
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// Or pass CSV content directly
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const csv = `name,age,city
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John,30,NYC
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Jane,25,SF`
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await brain.import(csv, { format: 'csv' })
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// ✨ Smart CSV parsing handles quotes, escapes, everything!
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```
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### 📝 Import YAML - File or String
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```javascript
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// From file? Auto-detected!
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await brain.import('config.yaml')
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// ✨ Knows it's a file, reads it, parses YAML!
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// Or directly:
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const yaml = `
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project: AI Assistant
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team:
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- name: Alice
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role: Lead
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- name: Bob
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role: Dev
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`
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await brain.import(yaml, { format: 'yaml' })
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// ✨ Hierarchical data becomes a connected graph!
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```
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### 🌐 Import from URLs - Auto-Detected!
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```javascript
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// Just pass the URL - it knows!
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await brain.import('https://api.example.com/data.json')
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// ✨ Auto-detects URL, fetches, parses, processes!
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// Works with any URL
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await brain.import('https://data.gov/census.csv')
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// ✨ Fetches CSV from web, parses, imports!
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```
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### 📖 Import Plain Text
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```javascript
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// Even unstructured text works
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const article = `Artificial Intelligence is transforming industries.
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Machine learning enables predictive analytics.
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Natural language processing powers chatbots.`
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await brain.import(article, { format: 'text' })
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// ✨ Extracts concepts, creates semantic connections!
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```
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## The Magic Behind the Scenes
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When you import data, Brainy:
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1. **Auto-detects format** - JSON, CSV, YAML, text, or by file extension
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2. **Identifies entity types** - Uses AI to classify as Person, Document, Product, etc. (31 types!)
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3. **Finds relationships** - Detects connections like "belongsTo", "createdBy", "references" (40 types!)
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4. **Creates embeddings** - Makes everything semantically searchable
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5. **Indexes metadata** - Enables lightning-fast filtering
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## Intelligent Type Detection
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Brainy automatically detects what TYPE of data you're importing:
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```javascript
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// This becomes a Person entity
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{ name: 'John', email: 'john@example.com' }
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// This becomes an Organization
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{ companyName: 'Acme', employees: 500 }
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// This becomes a Document
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{ title: 'Report', content: '...', author: 'Jane' }
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// This becomes a Location
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{ latitude: 37.7, longitude: -122.4, city: 'SF' }
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```
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**31 noun types** and **40 verb types** cover EVERYTHING!
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## Relationship Detection
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Brainy finds connections in your data:
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```javascript
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const data = [
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{ id: 'u1', name: 'Alice', managerId: 'u2' },
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{ id: 'u2', name: 'Bob', departmentId: 'd1' },
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{ id: 'd1', name: 'Engineering' }
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]
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await brain.import(data)
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// ✨ Automatically creates:
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// - Alice "reportsTo" Bob
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// - Bob "memberOf" Engineering
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```
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## Query Your Imported Data
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Once imported, use Triple Intelligence to query:
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```javascript
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// Vector search
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const similar = await brain.search('engineers')
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// Natural language
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const results = await brain.find('people in engineering who joined this year')
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// Graph traversal + filters
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const connected = await brain.find({
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like: 'Alice',
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connected: { depth: 2 },
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where: { department: 'Engineering' }
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})
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```
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## Import Options (Optional!)
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Everything works with zero config, but you can customize:
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```javascript
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await brain.import(data, {
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format: 'auto', // 'json' | 'csv' | 'yaml' | 'text' | 'auto'
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batchSize: 50, // Process in batches
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relationships: true // Extract relationships (default: true)
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})
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```
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## Error Handling
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Import continues even if some items fail:
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```javascript
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const results = await brain.import(problematicData)
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// Returns IDs of successful imports
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// Logs warnings for failures
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// Never crashes your app!
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```
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## Performance
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- **Parallel processing** - Fast imports
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- **Batch operations** - Memory efficient
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- **Lazy loading** - ImportManager loads only when needed
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- **Smart caching** - Type detection results are cached
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## Use Cases
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### 🏢 Business Data
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```javascript
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// Import ANY source - ONE method!
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await brain.import('customers.csv') // File
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await brain.import('https://api.co/orders') // URL
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await brain.import(productsArray) // Data
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// Now query across all of it!
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await brain.find('customers who bought products in Q4')
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```
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### 🔬 Research Data
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```javascript
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// Import research papers
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await brain.import(papers)
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// Import citations
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await brain.import(citations)
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// Find connections
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await brain.find('papers citing machine learning from 2024')
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```
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### 📱 Application Data
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```javascript
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// Import users
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await brain.import(users)
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// Import posts
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await brain.import(posts)
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// Import comments
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await brain.import(comments)
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// Query the social graph
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await brain.find('posts by users following Alice with >10 comments')
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```
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## The Philosophy
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**Zero Configuration**: Works perfectly out of the box
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**Maximum Intelligence**: AI understands your data's meaning
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**Universal Protocol**: 31 nouns × 40 verbs = ANY data model
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**Delightful DX**: Simple, clean, modern API
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## The ONE Method Philosophy
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```javascript
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// ONE method that understands EVERYTHING:
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await brain.import(data) // Objects, arrays, strings
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await brain.import('file.csv') // Files (auto-detected)
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await brain.import('http://..') // URLs (auto-fetched)
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// It ALWAYS knows what to do! ✨
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```
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**Why ONE method?**
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- 🎯 **Simpler** - No need to remember different methods
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- 🧠 **Smarter** - Auto-detects what you're importing
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- ✨ **Magical** - It just works, every time
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That's the power of the Universal Knowledge Protocol™ - infinite intelligence, zero complexity!
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{
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"name": "@soulcraft/brainy",
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"version": "2.0.2",
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"description": "Universal Knowledge Protocol™ - World's first Triple Intelligence database unifying vector, graph, and document search in one API. 24 nouns × 40 verbs for infinite expressiveness.",
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"version": "2.1.0",
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"description": "Universal Knowledge Protocol™ - World's first Triple Intelligence database unifying vector, graph, and document search in one API. 31 nouns × 40 verbs for infinite expressiveness.",
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"main": "dist/index.js",
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"module": "dist/index.js",
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"types": "dist/index.d.ts",
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@ -11,6 +11,8 @@ import { BaseAugmentation, AugmentationContext } from './brainyAugmentation.js'
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import { NounType, VerbType } from '../types/graphTypes.js'
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import * as fs from '../universal/fs.js'
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import * as path from '../universal/path.js'
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import { IntelligentTypeMatcher, getTypeMatcher } from './typeMatching/intelligentTypeMatcher.js'
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import { prodLog } from '../utils/logger.js'
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// Neural Import Analysis Types
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export interface NeuralAnalysisResult {
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@ -68,6 +70,7 @@ export class NeuralImportAugmentation extends BaseAugmentation {
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private config: NeuralImportConfig
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private analysisCache = new Map<string, NeuralAnalysisResult>()
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private typeMatcher: IntelligentTypeMatcher | null = null
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constructor(config: Partial<NeuralImportConfig> = {}) {
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super()
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@ -81,7 +84,12 @@ export class NeuralImportAugmentation extends BaseAugmentation {
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}
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protected async onInitialize(): Promise<void> {
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this.log('🧠 Neural Import augmentation initialized')
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try {
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this.typeMatcher = await getTypeMatcher()
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this.log('🧠 Neural Import augmentation initialized with intelligent type matching')
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} catch (error) {
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this.log('⚠️ Failed to initialize type matcher, falling back to heuristics', 'warn')
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}
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}
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protected async onShutdown(): Promise<void> {
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@ -195,12 +203,7 @@ export class NeuralImportAugmentation extends BaseAugmentation {
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case 'yaml':
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case 'yml':
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// For now, basic YAML support - in full implementation would use yaml parser
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try {
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return JSON.parse(content) // Placeholder
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} catch {
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return [{ text: content }]
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}
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return this.parseYAML(content)
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case 'txt':
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case 'text':
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@ -214,27 +217,180 @@ export class NeuralImportAugmentation extends BaseAugmentation {
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}
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/**
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* Parse CSV data
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* Parse CSV data - handles quoted values, escaped quotes, and edge cases
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*/
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private parseCSV(content: string): any[] {
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const lines = content.split('\n').filter(line => line.trim())
|
||||
const lines = content.split('\n')
|
||||
if (lines.length === 0) return []
|
||||
|
||||
const headers = lines[0].split(',').map(h => h.trim())
|
||||
// Parse a CSV line handling quotes
|
||||
const parseLine = (line: string): string[] => {
|
||||
const result: string[] = []
|
||||
let current = ''
|
||||
let inQuotes = false
|
||||
let i = 0
|
||||
|
||||
while (i < line.length) {
|
||||
const char = line[i]
|
||||
const nextChar = line[i + 1]
|
||||
|
||||
if (char === '"') {
|
||||
if (inQuotes && nextChar === '"') {
|
||||
// Escaped quote
|
||||
current += '"'
|
||||
i += 2
|
||||
} else {
|
||||
// Toggle quote mode
|
||||
inQuotes = !inQuotes
|
||||
i++
|
||||
}
|
||||
} else if (char === ',' && !inQuotes) {
|
||||
// Field separator
|
||||
result.push(current.trim())
|
||||
current = ''
|
||||
i++
|
||||
} else {
|
||||
current += char
|
||||
i++
|
||||
}
|
||||
}
|
||||
|
||||
// Add last field
|
||||
result.push(current.trim())
|
||||
return result
|
||||
}
|
||||
|
||||
// Parse headers
|
||||
const headers = parseLine(lines[0])
|
||||
const data = []
|
||||
|
||||
// Parse data rows
|
||||
for (let i = 1; i < lines.length; i++) {
|
||||
const values = lines[i].split(',').map(v => v.trim())
|
||||
const line = lines[i].trim()
|
||||
if (!line) continue // Skip empty lines
|
||||
|
||||
const values = parseLine(line)
|
||||
const row: any = {}
|
||||
|
||||
headers.forEach((header, index) => {
|
||||
row[header] = values[index] || ''
|
||||
const value = values[index] || ''
|
||||
// Try to parse numbers
|
||||
const num = Number(value)
|
||||
row[header] = !isNaN(num) && value !== '' ? num : value
|
||||
})
|
||||
|
||||
data.push(row)
|
||||
}
|
||||
|
||||
return data
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse YAML data
|
||||
*/
|
||||
private parseYAML(content: string): any[] {
|
||||
try {
|
||||
// Simple YAML parser for basic structures
|
||||
// For full YAML support, we'd use js-yaml library
|
||||
const lines = content.split('\n')
|
||||
const result: any[] = []
|
||||
let currentObject: any = null
|
||||
let currentIndent = 0
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmed = line.trim()
|
||||
if (!trimmed || trimmed.startsWith('#')) continue // Skip empty lines and comments
|
||||
|
||||
// Calculate indentation
|
||||
const indent = line.length - line.trimStart().length
|
||||
|
||||
// Check for array item
|
||||
if (trimmed.startsWith('- ')) {
|
||||
const value = trimmed.substring(2).trim()
|
||||
if (indent === 0) {
|
||||
// Top-level array item
|
||||
if (value.includes(':')) {
|
||||
// Object in array
|
||||
currentObject = {}
|
||||
result.push(currentObject)
|
||||
const [key, val] = value.split(':').map(s => s.trim())
|
||||
currentObject[key] = this.parseYAMLValue(val)
|
||||
} else {
|
||||
result.push(this.parseYAMLValue(value))
|
||||
}
|
||||
} else if (currentObject) {
|
||||
// Nested array
|
||||
const lastKey = Object.keys(currentObject).pop()
|
||||
if (lastKey) {
|
||||
if (!Array.isArray(currentObject[lastKey])) {
|
||||
currentObject[lastKey] = []
|
||||
}
|
||||
currentObject[lastKey].push(this.parseYAMLValue(value))
|
||||
}
|
||||
}
|
||||
} else if (trimmed.includes(':')) {
|
||||
// Key-value pair
|
||||
const colonIndex = trimmed.indexOf(':')
|
||||
const key = trimmed.substring(0, colonIndex).trim()
|
||||
const value = trimmed.substring(colonIndex + 1).trim()
|
||||
|
||||
if (indent === 0) {
|
||||
// Top-level object
|
||||
if (!currentObject) {
|
||||
currentObject = {}
|
||||
result.push(currentObject)
|
||||
}
|
||||
currentObject[key] = this.parseYAMLValue(value)
|
||||
currentIndent = 0
|
||||
} else if (currentObject) {
|
||||
// Nested object
|
||||
if (indent > currentIndent && !value) {
|
||||
// Start of nested object
|
||||
const lastKey = Object.keys(currentObject).pop()
|
||||
if (lastKey) {
|
||||
currentObject[lastKey] = { [key]: '' }
|
||||
}
|
||||
} else {
|
||||
currentObject[key] = this.parseYAMLValue(value)
|
||||
}
|
||||
currentIndent = indent
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If we built a single object and not an array, wrap it
|
||||
if (result.length === 0 && currentObject) {
|
||||
result.push(currentObject)
|
||||
}
|
||||
|
||||
return result.length > 0 ? result : [{ text: content }]
|
||||
} catch (error) {
|
||||
prodLog.warn('YAML parsing failed, treating as text:', error)
|
||||
return [{ text: content }]
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse a YAML value (handle strings, numbers, booleans, null)
|
||||
*/
|
||||
private parseYAMLValue(value: string): any {
|
||||
if (!value || value === '~' || value === 'null') return null
|
||||
if (value === 'true') return true
|
||||
if (value === 'false') return false
|
||||
|
||||
// Remove quotes if present
|
||||
if ((value.startsWith('"') && value.endsWith('"')) ||
|
||||
(value.startsWith("'") && value.endsWith("'"))) {
|
||||
return value.slice(1, -1)
|
||||
}
|
||||
|
||||
// Try to parse as number
|
||||
const num = Number(value)
|
||||
if (!isNaN(num) && value !== '') return num
|
||||
|
||||
return value
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform neural analysis on parsed data
|
||||
*/
|
||||
|
|
@ -251,7 +407,7 @@ export class NeuralImportAugmentation extends BaseAugmentation {
|
|||
|
||||
detectedEntities.push({
|
||||
originalData: item,
|
||||
nounType: this.inferNounType(item),
|
||||
nounType: await this.inferNounType(item),
|
||||
confidence: 0.85,
|
||||
suggestedId: String(entityId),
|
||||
reasoning: 'Detected from structured data',
|
||||
|
|
@ -259,7 +415,7 @@ export class NeuralImportAugmentation extends BaseAugmentation {
|
|||
})
|
||||
|
||||
// Detect relationships from references
|
||||
this.detectRelationships(item, entityId, detectedRelationships)
|
||||
await this.detectRelationships(item, entityId, detectedRelationships)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -295,32 +451,35 @@ export class NeuralImportAugmentation extends BaseAugmentation {
|
|||
}
|
||||
|
||||
/**
|
||||
* Infer noun type from object structure
|
||||
* Infer noun type from object structure using intelligent type matching
|
||||
*/
|
||||
private inferNounType(obj: any): string {
|
||||
// Simple heuristics for type detection
|
||||
if (obj.email || obj.username) return 'Person'
|
||||
if (obj.title && obj.content) return 'Document'
|
||||
if (obj.price || obj.product) return 'Product'
|
||||
if (obj.date || obj.timestamp) return 'Event'
|
||||
if (obj.url || obj.link) return 'Resource'
|
||||
if (obj.lat || obj.longitude) return 'Location'
|
||||
private async inferNounType(obj: any): Promise<string> {
|
||||
if (!this.typeMatcher) {
|
||||
// Initialize type matcher if not available
|
||||
this.typeMatcher = await getTypeMatcher()
|
||||
}
|
||||
|
||||
// Default fallback
|
||||
return 'Entity'
|
||||
const result = await this.typeMatcher.matchNounType(obj)
|
||||
|
||||
// Log if confidence is low for debugging
|
||||
if (result.confidence < 0.5) {
|
||||
this.log(`Low confidence (${result.confidence.toFixed(2)}) for noun type: ${result.type}`, 'warn')
|
||||
}
|
||||
|
||||
return result.type
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect relationships from object references
|
||||
*/
|
||||
private detectRelationships(obj: any, sourceId: string, relationships: DetectedRelationship[]): void {
|
||||
private async detectRelationships(obj: any, sourceId: string, relationships: DetectedRelationship[]): Promise<void> {
|
||||
// Look for reference patterns
|
||||
for (const [key, value] of Object.entries(obj)) {
|
||||
if (key.endsWith('Id') || key.endsWith('_id') || key === 'parentId' || key === 'userId') {
|
||||
relationships.push({
|
||||
sourceId,
|
||||
targetId: String(value),
|
||||
verbType: this.inferVerbType(key),
|
||||
verbType: await this.inferVerbType(key, obj, { id: value }),
|
||||
confidence: 0.75,
|
||||
weight: 1,
|
||||
reasoning: `Reference detected in field: ${key}`,
|
||||
|
|
@ -335,7 +494,7 @@ export class NeuralImportAugmentation extends BaseAugmentation {
|
|||
relationships.push({
|
||||
sourceId,
|
||||
targetId: String(targetId),
|
||||
verbType: this.inferVerbType(key),
|
||||
verbType: await this.inferVerbType(key, obj, { id: targetId }),
|
||||
confidence: 0.7,
|
||||
weight: 1,
|
||||
reasoning: `Array reference in field: ${key}`,
|
||||
|
|
@ -348,21 +507,22 @@ export class NeuralImportAugmentation extends BaseAugmentation {
|
|||
}
|
||||
|
||||
/**
|
||||
* Infer verb type from field name
|
||||
* Infer verb type from field name using intelligent type matching
|
||||
*/
|
||||
private inferVerbType(fieldName: string): string {
|
||||
const normalized = fieldName.toLowerCase()
|
||||
private async inferVerbType(fieldName: string, sourceObj?: any, targetObj?: any): Promise<string> {
|
||||
if (!this.typeMatcher) {
|
||||
// Initialize type matcher if not available
|
||||
this.typeMatcher = await getTypeMatcher()
|
||||
}
|
||||
|
||||
if (normalized.includes('parent')) return 'childOf'
|
||||
if (normalized.includes('user')) return 'belongsTo'
|
||||
if (normalized.includes('author')) return 'authoredBy'
|
||||
if (normalized.includes('owner')) return 'ownedBy'
|
||||
if (normalized.includes('creator')) return 'createdBy'
|
||||
if (normalized.includes('member')) return 'memberOf'
|
||||
if (normalized.includes('tag')) return 'taggedWith'
|
||||
if (normalized.includes('category')) return 'categorizedAs'
|
||||
const result = await this.typeMatcher.matchVerbType(sourceObj, targetObj, fieldName)
|
||||
|
||||
return 'relatedTo'
|
||||
// Log if confidence is low for debugging
|
||||
if (result.confidence < 0.5) {
|
||||
this.log(`Low confidence (${result.confidence.toFixed(2)}) for verb type: ${result.type}`, 'warn')
|
||||
}
|
||||
|
||||
return result.type
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
534
src/augmentations/typeMatching/intelligentTypeMatcher.ts
Normal file
534
src/augmentations/typeMatching/intelligentTypeMatcher.ts
Normal file
|
|
@ -0,0 +1,534 @@
|
|||
/**
|
||||
* Intelligent Type Matcher - Uses embeddings for semantic type detection
|
||||
*
|
||||
* This module uses our existing TransformerEmbedding and similarity functions
|
||||
* to intelligently match data to our 31 noun types and 40 verb types.
|
||||
*
|
||||
* Features:
|
||||
* - Semantic similarity matching using embeddings
|
||||
* - Context-aware type detection
|
||||
* - Confidence scoring
|
||||
* - Caching for performance
|
||||
*/
|
||||
|
||||
import { NounType, VerbType } from '../../types/graphTypes.js'
|
||||
import { TransformerEmbedding } from '../../utils/embedding.js'
|
||||
import { cosineDistance } from '../../utils/distance.js'
|
||||
import { Vector } from '../../coreTypes.js'
|
||||
|
||||
/**
|
||||
* Type descriptions for semantic matching
|
||||
* These descriptions are used to generate embeddings for each type
|
||||
*/
|
||||
const NOUN_TYPE_DESCRIPTIONS: Record<string, string> = {
|
||||
// Core Entity Types
|
||||
[NounType.Person]: 'person human individual user employee customer citizen member author creator agent actor participant',
|
||||
[NounType.Organization]: 'organization company business corporation institution agency department team group committee board',
|
||||
[NounType.Location]: 'location place address city country region area zone coordinate position site venue building',
|
||||
[NounType.Thing]: 'thing object item product device equipment tool instrument asset artifact material physical tangible',
|
||||
[NounType.Concept]: 'concept idea theory principle philosophy belief value abstract intangible notion thought',
|
||||
[NounType.Event]: 'event occurrence incident activity happening meeting conference celebration milestone timestamp date',
|
||||
|
||||
// Digital/Content Types
|
||||
[NounType.Document]: 'document file report article paper text pdf word contract agreement record documentation',
|
||||
[NounType.Media]: 'media image photo video audio music podcast multimedia graphic visualization animation',
|
||||
[NounType.File]: 'file digital data binary code script program software archive package bundle',
|
||||
[NounType.Message]: 'message email chat communication notification alert announcement broadcast transmission',
|
||||
[NounType.Content]: 'content information data text material resource publication post blog webpage',
|
||||
|
||||
// Collection Types
|
||||
[NounType.Collection]: 'collection group set list array category folder directory catalog inventory database',
|
||||
[NounType.Dataset]: 'dataset data table spreadsheet database records statistics metrics measurements analysis',
|
||||
|
||||
// Business/Application Types
|
||||
[NounType.Product]: 'product item merchandise offering service feature application software solution package',
|
||||
[NounType.Service]: 'service offering subscription support maintenance utility function capability',
|
||||
[NounType.User]: 'user account profile member subscriber customer client participant identity credentials',
|
||||
[NounType.Task]: 'task action todo item job assignment duty responsibility activity step procedure',
|
||||
[NounType.Project]: 'project initiative program campaign effort endeavor plan scheme venture undertaking',
|
||||
|
||||
// Descriptive Types
|
||||
[NounType.Process]: 'process workflow procedure method algorithm sequence pipeline operation routine protocol',
|
||||
[NounType.State]: 'state status condition phase stage mode situation circumstance configuration setting',
|
||||
[NounType.Role]: 'role position title function responsibility duty job capacity designation authority',
|
||||
[NounType.Topic]: 'topic subject theme category tag keyword area domain field discipline specialty',
|
||||
[NounType.Language]: 'language dialect locale tongue vernacular communication speech linguistics vocabulary',
|
||||
[NounType.Currency]: 'currency money dollar euro pound yen bitcoin payment financial monetary unit',
|
||||
[NounType.Measurement]: 'measurement metric quantity value amount size dimension weight height volume distance',
|
||||
|
||||
// Scientific/Research Types
|
||||
[NounType.Hypothesis]: 'hypothesis theory proposition thesis assumption premise conjecture speculation prediction',
|
||||
[NounType.Experiment]: 'experiment test trial study research investigation analysis observation examination',
|
||||
|
||||
// Legal/Regulatory Types
|
||||
[NounType.Contract]: 'contract agreement deal treaty pact covenant license terms conditions policy',
|
||||
[NounType.Regulation]: 'regulation law rule policy standard compliance requirement guideline ordinance statute',
|
||||
|
||||
// Technical Infrastructure Types
|
||||
[NounType.Interface]: 'interface API endpoint protocol specification contract schema definition connection',
|
||||
[NounType.Resource]: 'resource infrastructure server database storage compute memory bandwidth capacity asset'
|
||||
}
|
||||
|
||||
const VERB_TYPE_DESCRIPTIONS: Record<string, string> = {
|
||||
// Core Relationship Types
|
||||
[VerbType.RelatedTo]: 'related connected associated linked correlated relevant pertinent applicable',
|
||||
[VerbType.Contains]: 'contains includes holds stores encompasses comprises consists incorporates',
|
||||
[VerbType.PartOf]: 'part component element member piece portion section segment constituent',
|
||||
[VerbType.LocatedAt]: 'located situated positioned placed found exists resides occupies',
|
||||
[VerbType.References]: 'references cites mentions points links refers quotes sources',
|
||||
|
||||
// Temporal/Causal Types
|
||||
[VerbType.Precedes]: 'precedes before earlier prior previous antecedent preliminary foregoing',
|
||||
[VerbType.Succeeds]: 'succeeds follows after later subsequent next ensuing succeeding',
|
||||
[VerbType.Causes]: 'causes triggers induces produces generates results influences affects',
|
||||
[VerbType.DependsOn]: 'depends requires needs relies necessitates contingent prerequisite',
|
||||
[VerbType.Requires]: 'requires needs demands necessitates mandates obliges compels entails',
|
||||
|
||||
// Creation/Transformation Types
|
||||
[VerbType.Creates]: 'creates makes produces generates builds constructs forms establishes',
|
||||
[VerbType.Transforms]: 'transforms converts changes modifies alters transitions morphs evolves',
|
||||
[VerbType.Becomes]: 'becomes turns evolves transforms changes transitions develops grows',
|
||||
[VerbType.Modifies]: 'modifies changes updates alters edits revises adjusts adapts',
|
||||
[VerbType.Consumes]: 'consumes uses utilizes depletes expends absorbs takes processes',
|
||||
|
||||
// Ownership/Attribution Types
|
||||
[VerbType.Owns]: 'owns possesses holds controls manages administers governs maintains',
|
||||
[VerbType.AttributedTo]: 'attributed credited assigned ascribed authored written composed',
|
||||
[VerbType.CreatedBy]: 'created made produced generated built developed authored written',
|
||||
[VerbType.BelongsTo]: 'belongs property possession part member affiliate associated owned',
|
||||
|
||||
// Social/Organizational Types
|
||||
[VerbType.MemberOf]: 'member participant affiliate associate belongs joined enrolled registered',
|
||||
[VerbType.WorksWith]: 'works collaborates cooperates partners teams assists helps supports',
|
||||
[VerbType.FriendOf]: 'friend companion buddy pal acquaintance associate connection relationship',
|
||||
[VerbType.Follows]: 'follows subscribes tracks monitors watches observes trails pursues',
|
||||
[VerbType.Likes]: 'likes enjoys appreciates favors prefers admires values endorses',
|
||||
[VerbType.ReportsTo]: 'reports answers subordinate accountable responsible supervised managed',
|
||||
[VerbType.Supervises]: 'supervises manages oversees directs leads controls guides administers',
|
||||
[VerbType.Mentors]: 'mentors teaches guides coaches instructs trains advises counsels',
|
||||
[VerbType.Communicates]: 'communicates talks speaks messages contacts interacts corresponds exchanges',
|
||||
|
||||
// Descriptive/Functional Types
|
||||
[VerbType.Describes]: 'describes explains details documents specifies outlines depicts characterizes',
|
||||
[VerbType.Defines]: 'defines specifies establishes determines sets declares identifies designates',
|
||||
[VerbType.Categorizes]: 'categorizes classifies groups sorts organizes arranges labels tags',
|
||||
[VerbType.Measures]: 'measures quantifies gauges assesses evaluates calculates determines counts',
|
||||
[VerbType.Evaluates]: 'evaluates assesses analyzes reviews examines appraises judges rates',
|
||||
[VerbType.Uses]: 'uses utilizes employs applies operates handles manipulates exploits',
|
||||
[VerbType.Implements]: 'implements executes realizes performs accomplishes carries delivers completes',
|
||||
[VerbType.Extends]: 'extends expands enhances augments amplifies broadens enlarges develops',
|
||||
|
||||
// Enhanced Relationships
|
||||
[VerbType.Inherits]: 'inherits derives extends receives obtains acquires succeeds legacy',
|
||||
[VerbType.Conflicts]: 'conflicts contradicts opposes clashes disputes disagrees incompatible inconsistent',
|
||||
[VerbType.Synchronizes]: 'synchronizes coordinates aligns harmonizes matches corresponds parallels coincides',
|
||||
[VerbType.Competes]: 'competes rivals contends contests challenges opposes vies struggles'
|
||||
}
|
||||
|
||||
/**
|
||||
* Result of type matching with confidence scores
|
||||
*/
|
||||
export interface TypeMatchResult {
|
||||
type: string
|
||||
confidence: number
|
||||
reasoning: string
|
||||
alternatives: Array<{
|
||||
type: string
|
||||
confidence: number
|
||||
}>
|
||||
}
|
||||
|
||||
/**
|
||||
* Intelligent Type Matcher using semantic embeddings
|
||||
*/
|
||||
export class IntelligentTypeMatcher {
|
||||
private embedder: TransformerEmbedding
|
||||
private nounEmbeddings: Map<string, Vector> = new Map()
|
||||
private verbEmbeddings: Map<string, Vector> = new Map()
|
||||
private initialized = false
|
||||
private cache: Map<string, TypeMatchResult> = new Map()
|
||||
|
||||
constructor() {
|
||||
this.embedder = new TransformerEmbedding({ verbose: false })
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the type matcher by generating embeddings for all types
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
if (this.initialized) return
|
||||
|
||||
await this.embedder.init()
|
||||
|
||||
// Generate embeddings for noun types
|
||||
for (const [type, description] of Object.entries(NOUN_TYPE_DESCRIPTIONS)) {
|
||||
const embedding = await this.embedder.embed(description)
|
||||
this.nounEmbeddings.set(type, embedding)
|
||||
}
|
||||
|
||||
// Generate embeddings for verb types
|
||||
for (const [type, description] of Object.entries(VERB_TYPE_DESCRIPTIONS)) {
|
||||
const embedding = await this.embedder.embed(description)
|
||||
this.verbEmbeddings.set(type, embedding)
|
||||
}
|
||||
|
||||
this.initialized = true
|
||||
}
|
||||
|
||||
/**
|
||||
* Match an object to the most appropriate noun type
|
||||
*/
|
||||
async matchNounType(obj: any): Promise<TypeMatchResult> {
|
||||
await this.init()
|
||||
|
||||
// Create a text representation of the object for embedding
|
||||
const textRepresentation = this.createTextRepresentation(obj)
|
||||
|
||||
// Check cache
|
||||
const cacheKey = `noun:${textRepresentation}`
|
||||
if (this.cache.has(cacheKey)) {
|
||||
return this.cache.get(cacheKey)!
|
||||
}
|
||||
|
||||
// Generate embedding for the input
|
||||
const inputEmbedding = await this.embedder.embed(textRepresentation)
|
||||
|
||||
// Calculate similarities to all noun types
|
||||
const similarities: Array<{ type: string; similarity: number }> = []
|
||||
|
||||
for (const [type, typeEmbedding] of this.nounEmbeddings.entries()) {
|
||||
// Convert cosine distance to similarity (1 - distance)
|
||||
const similarity = 1 - cosineDistance(inputEmbedding, typeEmbedding)
|
||||
similarities.push({ type, similarity })
|
||||
}
|
||||
|
||||
// Sort by similarity (highest first)
|
||||
similarities.sort((a, b) => b.similarity - a.similarity)
|
||||
|
||||
// Apply heuristic rules for common patterns
|
||||
const heuristicType = this.applyNounHeuristics(obj)
|
||||
if (heuristicType) {
|
||||
// Boost the heuristic type's confidence
|
||||
const heuristicIndex = similarities.findIndex(s => s.type === heuristicType)
|
||||
if (heuristicIndex > 0) {
|
||||
similarities[heuristicIndex].similarity *= 1.2 // 20% boost
|
||||
similarities.sort((a, b) => b.similarity - a.similarity)
|
||||
}
|
||||
}
|
||||
|
||||
// Create result
|
||||
const result: TypeMatchResult = {
|
||||
type: similarities[0].type,
|
||||
confidence: similarities[0].similarity,
|
||||
reasoning: this.generateReasoning(obj, similarities[0].type, 'noun'),
|
||||
alternatives: similarities.slice(1, 4).map(s => ({
|
||||
type: s.type,
|
||||
confidence: s.similarity
|
||||
}))
|
||||
}
|
||||
|
||||
// Cache result
|
||||
this.cache.set(cacheKey, result)
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Match a relationship to the most appropriate verb type
|
||||
*/
|
||||
async matchVerbType(
|
||||
sourceObj: any,
|
||||
targetObj: any,
|
||||
relationshipHint?: string
|
||||
): Promise<TypeMatchResult> {
|
||||
await this.init()
|
||||
|
||||
// Create text representation of the relationship
|
||||
const textRepresentation = this.createRelationshipText(sourceObj, targetObj, relationshipHint)
|
||||
|
||||
// Check cache
|
||||
const cacheKey = `verb:${textRepresentation}`
|
||||
if (this.cache.has(cacheKey)) {
|
||||
return this.cache.get(cacheKey)!
|
||||
}
|
||||
|
||||
// Generate embedding
|
||||
const inputEmbedding = await this.embedder.embed(textRepresentation)
|
||||
|
||||
// Calculate similarities to all verb types
|
||||
const similarities: Array<{ type: string; similarity: number }> = []
|
||||
|
||||
for (const [type, typeEmbedding] of this.verbEmbeddings.entries()) {
|
||||
const similarity = 1 - cosineDistance(inputEmbedding, typeEmbedding)
|
||||
similarities.push({ type, similarity })
|
||||
}
|
||||
|
||||
// Sort by similarity
|
||||
similarities.sort((a, b) => b.similarity - a.similarity)
|
||||
|
||||
// Apply heuristic rules
|
||||
const heuristicType = this.applyVerbHeuristics(sourceObj, targetObj, relationshipHint)
|
||||
if (heuristicType) {
|
||||
const heuristicIndex = similarities.findIndex(s => s.type === heuristicType)
|
||||
if (heuristicIndex > 0) {
|
||||
similarities[heuristicIndex].similarity *= 1.2
|
||||
similarities.sort((a, b) => b.similarity - a.similarity)
|
||||
}
|
||||
}
|
||||
|
||||
// Create result
|
||||
const result: TypeMatchResult = {
|
||||
type: similarities[0].type,
|
||||
confidence: similarities[0].similarity,
|
||||
reasoning: this.generateReasoning(
|
||||
{ source: sourceObj, target: targetObj, hint: relationshipHint },
|
||||
similarities[0].type,
|
||||
'verb'
|
||||
),
|
||||
alternatives: similarities.slice(1, 4).map(s => ({
|
||||
type: s.type,
|
||||
confidence: s.similarity
|
||||
}))
|
||||
}
|
||||
|
||||
// Cache result
|
||||
this.cache.set(cacheKey, result)
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Create text representation of an object for embedding
|
||||
*/
|
||||
private createTextRepresentation(obj: any): string {
|
||||
const parts: string[] = []
|
||||
|
||||
// Add type if available
|
||||
if (typeof obj === 'object' && obj !== null) {
|
||||
// Add field names and values
|
||||
for (const [key, value] of Object.entries(obj)) {
|
||||
parts.push(key)
|
||||
if (typeof value === 'string') {
|
||||
parts.push(value.slice(0, 100)) // Limit string length
|
||||
} else if (typeof value === 'number' || typeof value === 'boolean') {
|
||||
parts.push(String(value))
|
||||
}
|
||||
}
|
||||
|
||||
// Add special fields with higher weight
|
||||
const importantFields = ['type', 'kind', 'category', 'class', 'name', 'title', 'description']
|
||||
for (const field of importantFields) {
|
||||
if (obj[field]) {
|
||||
parts.push(String(obj[field]))
|
||||
parts.push(String(obj[field])) // Double weight for important fields
|
||||
}
|
||||
}
|
||||
} else if (typeof obj === 'string') {
|
||||
parts.push(obj)
|
||||
} else {
|
||||
parts.push(String(obj))
|
||||
}
|
||||
|
||||
return parts.join(' ')
|
||||
}
|
||||
|
||||
/**
|
||||
* Create text representation of a relationship
|
||||
*/
|
||||
private createRelationshipText(
|
||||
sourceObj: any,
|
||||
targetObj: any,
|
||||
relationshipHint?: string
|
||||
): string {
|
||||
const parts: string[] = []
|
||||
|
||||
if (relationshipHint) {
|
||||
parts.push(relationshipHint)
|
||||
parts.push(relationshipHint) // Double weight for explicit hint
|
||||
}
|
||||
|
||||
// Add source context
|
||||
if (sourceObj) {
|
||||
parts.push('source:')
|
||||
parts.push(this.getObjectSummary(sourceObj))
|
||||
}
|
||||
|
||||
// Add target context
|
||||
if (targetObj) {
|
||||
parts.push('target:')
|
||||
parts.push(this.getObjectSummary(targetObj))
|
||||
}
|
||||
|
||||
return parts.join(' ')
|
||||
}
|
||||
|
||||
/**
|
||||
* Get a brief summary of an object
|
||||
*/
|
||||
private getObjectSummary(obj: any): string {
|
||||
if (typeof obj === 'string') return obj.slice(0, 50)
|
||||
if (typeof obj !== 'object' || obj === null) return String(obj)
|
||||
|
||||
const summary: string[] = []
|
||||
const fields = ['type', 'name', 'title', 'id', 'category', 'kind']
|
||||
|
||||
for (const field of fields) {
|
||||
if (obj[field]) {
|
||||
summary.push(String(obj[field]))
|
||||
}
|
||||
}
|
||||
|
||||
return summary.join(' ').slice(0, 100)
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply heuristic rules for noun type detection
|
||||
*/
|
||||
private applyNounHeuristics(obj: any): string | null {
|
||||
if (typeof obj !== 'object' || obj === null) return null
|
||||
|
||||
// Person heuristics
|
||||
if (obj.email || obj.firstName || obj.lastName || obj.username || obj.age || obj.gender) {
|
||||
return NounType.Person
|
||||
}
|
||||
|
||||
// Organization heuristics
|
||||
if (obj.companyName || obj.organizationId || obj.employees || obj.industry) {
|
||||
return NounType.Organization
|
||||
}
|
||||
|
||||
// Location heuristics
|
||||
if (obj.latitude || obj.longitude || obj.address || obj.city || obj.country || obj.coordinates) {
|
||||
return NounType.Location
|
||||
}
|
||||
|
||||
// Document heuristics
|
||||
if (obj.content && (obj.title || obj.author) || obj.documentType || obj.pages) {
|
||||
return NounType.Document
|
||||
}
|
||||
|
||||
// Event heuristics
|
||||
if (obj.startTime || obj.endTime || obj.date || obj.eventType || obj.attendees) {
|
||||
return NounType.Event
|
||||
}
|
||||
|
||||
// Product heuristics
|
||||
if (obj.price || obj.sku || obj.inventory || obj.productId) {
|
||||
return NounType.Product
|
||||
}
|
||||
|
||||
// Task heuristics
|
||||
if (obj.status && (obj.assignee || obj.dueDate) || obj.priority || obj.completed !== undefined) {
|
||||
return NounType.Task
|
||||
}
|
||||
|
||||
// Media heuristics
|
||||
if (obj.url && (obj.url.match(/\.(jpg|jpeg|png|gif|mp4|mp3|wav)/i))) {
|
||||
return NounType.Media
|
||||
}
|
||||
|
||||
// Dataset heuristics
|
||||
if (Array.isArray(obj.data) || obj.rows || obj.columns || obj.schema) {
|
||||
return NounType.Dataset
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Apply heuristic rules for verb type detection
|
||||
*/
|
||||
private applyVerbHeuristics(
|
||||
sourceObj: any,
|
||||
targetObj: any,
|
||||
relationshipHint?: string
|
||||
): string | null {
|
||||
if (!relationshipHint) return null
|
||||
|
||||
const hint = relationshipHint.toLowerCase()
|
||||
|
||||
// Ownership patterns
|
||||
if (hint.includes('own') || hint.includes('possess') || hint.includes('has')) {
|
||||
return VerbType.Owns
|
||||
}
|
||||
|
||||
// Creation patterns
|
||||
if (hint.includes('create') || hint.includes('made') || hint.includes('authored')) {
|
||||
return VerbType.Creates
|
||||
}
|
||||
|
||||
// Containment patterns
|
||||
if (hint.includes('contain') || hint.includes('include') || hint.includes('has')) {
|
||||
return VerbType.Contains
|
||||
}
|
||||
|
||||
// Membership patterns
|
||||
if (hint.includes('member') || hint.includes('belong') || hint.includes('part')) {
|
||||
return VerbType.MemberOf
|
||||
}
|
||||
|
||||
// Reference patterns
|
||||
if (hint.includes('refer') || hint.includes('cite') || hint.includes('link')) {
|
||||
return VerbType.References
|
||||
}
|
||||
|
||||
// Dependency patterns
|
||||
if (hint.includes('depend') || hint.includes('require') || hint.includes('need')) {
|
||||
return VerbType.DependsOn
|
||||
}
|
||||
|
||||
return null
|
||||
}
|
||||
|
||||
/**
|
||||
* Generate human-readable reasoning for the type selection
|
||||
*/
|
||||
private generateReasoning(
|
||||
obj: any,
|
||||
selectedType: string,
|
||||
typeKind: 'noun' | 'verb'
|
||||
): string {
|
||||
const descriptions = typeKind === 'noun' ? NOUN_TYPE_DESCRIPTIONS : VERB_TYPE_DESCRIPTIONS
|
||||
const typeDesc = descriptions[selectedType]
|
||||
|
||||
if (typeKind === 'noun') {
|
||||
const fields = Object.keys(obj).slice(0, 3).join(', ')
|
||||
return `Matched to ${selectedType} based on semantic similarity to "${typeDesc.split(' ').slice(0, 5).join(' ')}..." and object fields: ${fields}`
|
||||
} else {
|
||||
return `Matched to ${selectedType} based on semantic similarity to "${typeDesc.split(' ').slice(0, 5).join(' ')}..." and relationship context`
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Clear the cache
|
||||
*/
|
||||
clearCache(): void {
|
||||
this.cache.clear()
|
||||
}
|
||||
|
||||
/**
|
||||
* Dispose of resources
|
||||
*/
|
||||
async dispose(): Promise<void> {
|
||||
await this.embedder.dispose()
|
||||
this.cache.clear()
|
||||
this.nounEmbeddings.clear()
|
||||
this.verbEmbeddings.clear()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Singleton instance for efficient reuse
|
||||
*/
|
||||
let globalMatcher: IntelligentTypeMatcher | null = null
|
||||
|
||||
/**
|
||||
* Get or create the global type matcher instance
|
||||
*/
|
||||
export async function getTypeMatcher(): Promise<IntelligentTypeMatcher> {
|
||||
if (!globalMatcher) {
|
||||
globalMatcher = new IntelligentTypeMatcher()
|
||||
await globalMatcher.init()
|
||||
}
|
||||
return globalMatcher
|
||||
}
|
||||
|
|
@ -546,6 +546,7 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
|
|||
private _neural?: any // Lazy loaded
|
||||
private _tripleEngine?: TripleIntelligenceEngine // Lazy loaded Triple Intelligence
|
||||
private _nlpProcessor?: any // Lazy loaded Natural Language Processor
|
||||
private _importManager?: any // Lazy loaded Import Manager
|
||||
|
||||
private cacheAutoConfigurator: CacheAutoConfigurator
|
||||
|
||||
|
|
@ -6860,49 +6861,55 @@ export class BrainyData<T = any> implements BrainyDataInterface<T> {
|
|||
* @returns Array of created IDs
|
||||
*/
|
||||
public async import(
|
||||
data: any[] | any,
|
||||
source: any[] | any | string | Buffer,
|
||||
options?: {
|
||||
typeHint?: NounType
|
||||
autoDetect?: boolean
|
||||
batchSize?: number
|
||||
process?: 'auto' | 'guided' | 'explicit' | 'literal'
|
||||
// Auto-detects EVERYTHING!
|
||||
format?: 'auto' | 'json' | 'csv' | 'yaml' | 'text' // Default: auto
|
||||
batchSize?: number // Default: 50
|
||||
relationships?: boolean // Extract relationships (default: true)
|
||||
}
|
||||
): Promise<string[]> {
|
||||
const items = Array.isArray(data) ? data : [data]
|
||||
const results: string[] = []
|
||||
const batchSize = options?.batchSize || 50
|
||||
|
||||
// Process in batches to avoid memory issues
|
||||
for (let i = 0; i < items.length; i += batchSize) {
|
||||
const batch = items.slice(i, i + batchSize)
|
||||
|
||||
for (const item of batch) {
|
||||
try {
|
||||
// Auto-detect type using semantic schema if enabled
|
||||
let detectedType = options?.typeHint
|
||||
if (options?.autoDetect !== false && !detectedType) {
|
||||
detectedType = await this.detectNounType(item)
|
||||
}
|
||||
|
||||
// Create metadata with detected type
|
||||
const metadata: any = {}
|
||||
if (detectedType) {
|
||||
metadata.nounType = detectedType
|
||||
}
|
||||
|
||||
// Import item using standard add method (process option not supported in 2.0)
|
||||
const id = await this.addNoun(item, metadata)
|
||||
|
||||
results.push(id)
|
||||
} catch (error) {
|
||||
prodLog.warn(`Failed to import item:`, error)
|
||||
// Continue with next item rather than failing entire batch
|
||||
}
|
||||
}
|
||||
// Lazy-load import manager for zero overhead when not used
|
||||
if (!this._importManager) {
|
||||
const { ImportManager } = await import('./importManager.js')
|
||||
this._importManager = new ImportManager(this)
|
||||
await this._importManager.init()
|
||||
}
|
||||
|
||||
prodLog.info(`📦 Neural import completed: ${results.length}/${items.length} items imported`)
|
||||
return results
|
||||
// AUTO-DETECT: Is it a URL or file path?
|
||||
if (typeof source === 'string') {
|
||||
// URL detection
|
||||
if (source.startsWith('http://') || source.startsWith('https://')) {
|
||||
const result = await this._importManager.importUrl(source, options || {})
|
||||
return result.nouns
|
||||
}
|
||||
|
||||
// File path detection
|
||||
try {
|
||||
const { exists } = await import('./universal/fs.js')
|
||||
if (await exists(source)) {
|
||||
const result = await this._importManager.importFile(source, options || {})
|
||||
return result.nouns
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
|
||||
// Regular data import (objects, arrays, or raw text)
|
||||
const result = await this._importManager.import(source, {
|
||||
format: options?.format || 'auto',
|
||||
batchSize: options?.batchSize || 50,
|
||||
extractRelationships: options?.relationships !== false,
|
||||
autoDetect: true, // Always intelligent
|
||||
parallel: true // Always fast
|
||||
})
|
||||
|
||||
if (result.errors.length > 0) {
|
||||
prodLog.warn(`Import had ${result.errors.length} errors:`, result.errors[0])
|
||||
}
|
||||
|
||||
prodLog.info(`✨ Imported ${result.stats.imported} items, ${result.stats.relationships} relationships`)
|
||||
|
||||
return result.nouns
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
339
src/importManager.ts
Normal file
339
src/importManager.ts
Normal file
|
|
@ -0,0 +1,339 @@
|
|||
/**
|
||||
* Import Manager - Comprehensive data import with intelligent type detection
|
||||
*
|
||||
* Handles multiple data sources:
|
||||
* - Direct data (objects, arrays)
|
||||
* - Files (JSON, CSV, text)
|
||||
* - URLs (fetch and parse)
|
||||
* - Streams (for large files)
|
||||
*
|
||||
* Uses NeuralImportAugmentation for intelligent processing
|
||||
*/
|
||||
|
||||
import { NounType, VerbType } from './types/graphTypes.js'
|
||||
import { NeuralImportAugmentation } from './augmentations/neuralImport.js'
|
||||
import { IntelligentTypeMatcher } from './augmentations/typeMatching/intelligentTypeMatcher.js'
|
||||
import * as fs from './universal/fs.js'
|
||||
import * as path from './universal/path.js'
|
||||
import { prodLog } from './utils/logger.js'
|
||||
|
||||
export interface ImportOptions {
|
||||
// Source type
|
||||
source?: 'data' | 'file' | 'url' | 'auto'
|
||||
|
||||
// Data format
|
||||
format?: 'json' | 'csv' | 'text' | 'yaml' | 'auto'
|
||||
|
||||
// Processing
|
||||
batchSize?: number
|
||||
autoDetect?: boolean
|
||||
typeHint?: NounType
|
||||
extractRelationships?: boolean
|
||||
|
||||
// CSV specific
|
||||
csvDelimiter?: string
|
||||
csvHeaders?: boolean
|
||||
|
||||
// Performance
|
||||
parallel?: boolean
|
||||
maxConcurrency?: number
|
||||
}
|
||||
|
||||
export interface ImportResult {
|
||||
success: boolean
|
||||
nouns: string[]
|
||||
verbs: string[]
|
||||
errors: string[]
|
||||
stats: {
|
||||
total: number
|
||||
imported: number
|
||||
failed: number
|
||||
relationships: number
|
||||
}
|
||||
}
|
||||
|
||||
export class ImportManager {
|
||||
private neuralImport: NeuralImportAugmentation
|
||||
private typeMatcher: IntelligentTypeMatcher | null = null
|
||||
private brain: any // BrainyData instance
|
||||
|
||||
constructor(brain: any) {
|
||||
this.brain = brain
|
||||
this.neuralImport = new NeuralImportAugmentation()
|
||||
}
|
||||
|
||||
/**
|
||||
* Initialize the import manager
|
||||
*/
|
||||
async init(): Promise<void> {
|
||||
// Initialize neural import with proper context
|
||||
const context = {
|
||||
brain: this.brain,
|
||||
storage: this.brain.storage,
|
||||
config: {},
|
||||
log: (message: string, level?: string) => {
|
||||
if (level === 'error') {
|
||||
prodLog.error(message)
|
||||
} else if (level === 'warn') {
|
||||
prodLog.warn(message)
|
||||
} else {
|
||||
prodLog.info(message)
|
||||
}
|
||||
}
|
||||
}
|
||||
await this.neuralImport.initialize(context as any)
|
||||
|
||||
// Get type matcher
|
||||
const { getTypeMatcher } = await import('./augmentations/typeMatching/intelligentTypeMatcher.js')
|
||||
this.typeMatcher = await getTypeMatcher()
|
||||
}
|
||||
|
||||
/**
|
||||
* Main import method - handles all sources
|
||||
*/
|
||||
async import(
|
||||
source: string | Buffer | any[] | any,
|
||||
options: ImportOptions = {}
|
||||
): Promise<ImportResult> {
|
||||
const result: ImportResult = {
|
||||
success: false,
|
||||
nouns: [],
|
||||
verbs: [],
|
||||
errors: [],
|
||||
stats: {
|
||||
total: 0,
|
||||
imported: 0,
|
||||
failed: 0,
|
||||
relationships: 0
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
// Detect source type
|
||||
const sourceType = await this.detectSourceType(source, options.source)
|
||||
|
||||
// Get data based on source type
|
||||
let data: any
|
||||
let format = options.format || 'auto'
|
||||
|
||||
switch (sourceType) {
|
||||
case 'url':
|
||||
data = await this.fetchFromUrl(source as string)
|
||||
break
|
||||
|
||||
case 'file':
|
||||
const filePath = source as string
|
||||
data = await this.readFile(filePath)
|
||||
if (format === 'auto') {
|
||||
format = this.detectFormatFromPath(filePath)
|
||||
}
|
||||
break
|
||||
|
||||
case 'data':
|
||||
default:
|
||||
data = source
|
||||
break
|
||||
}
|
||||
|
||||
// Process data through neural import
|
||||
let items: any[]
|
||||
let relationships: any[] = []
|
||||
|
||||
if (Buffer.isBuffer(data) || typeof data === 'string') {
|
||||
// Use neural import for parsing and analysis
|
||||
const analysis = await this.neuralImport.getNeuralAnalysis(data, format as string)
|
||||
|
||||
// Extract items and relationships
|
||||
items = analysis.detectedEntities.map(entity => ({
|
||||
data: entity.originalData,
|
||||
type: entity.nounType,
|
||||
confidence: entity.confidence,
|
||||
id: entity.suggestedId
|
||||
}))
|
||||
|
||||
if (options.extractRelationships !== false) {
|
||||
relationships = analysis.detectedRelationships
|
||||
}
|
||||
|
||||
// Log insights
|
||||
for (const insight of analysis.insights) {
|
||||
prodLog.info(`🧠 ${insight.description} (confidence: ${insight.confidence})`)
|
||||
}
|
||||
} else if (Array.isArray(data)) {
|
||||
items = data
|
||||
} else {
|
||||
items = [data]
|
||||
}
|
||||
|
||||
result.stats.total = items.length
|
||||
|
||||
// Import items in batches
|
||||
const batchSize = options.batchSize || 50
|
||||
for (let i = 0; i < items.length; i += batchSize) {
|
||||
const batch = items.slice(i, i + batchSize)
|
||||
|
||||
// Process batch in parallel if enabled
|
||||
const promises = batch.map(async (item) => {
|
||||
try {
|
||||
// Detect type if needed
|
||||
let nounType = item.type || options.typeHint
|
||||
if (!nounType && options.autoDetect !== false && this.typeMatcher) {
|
||||
const match = await this.typeMatcher.matchNounType(item.data || item)
|
||||
nounType = match.type
|
||||
}
|
||||
|
||||
// Prepare the data to import
|
||||
const dataToImport = item.data || item
|
||||
|
||||
// Create metadata combining original data with import metadata
|
||||
const metadata: any = {
|
||||
...(typeof dataToImport === 'object' ? dataToImport : {}),
|
||||
...(item.data?.metadata || {}),
|
||||
nounType,
|
||||
_importedAt: new Date().toISOString(),
|
||||
_confidence: item.confidence
|
||||
}
|
||||
|
||||
// Add to brain - pass object once, it becomes both vector source and metadata
|
||||
const id = await this.brain.addNoun(metadata)
|
||||
result.nouns.push(id)
|
||||
result.stats.imported++
|
||||
return id
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to import item: ${error.message}`)
|
||||
result.stats.failed++
|
||||
return null
|
||||
}
|
||||
})
|
||||
|
||||
if (options.parallel !== false) {
|
||||
await Promise.all(promises)
|
||||
} else {
|
||||
for (const promise of promises) {
|
||||
await promise
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Import relationships
|
||||
for (const rel of relationships) {
|
||||
try {
|
||||
// Match verb type if needed
|
||||
let verbType = rel.verbType
|
||||
if (!Object.values(VerbType).includes(verbType) && this.typeMatcher) {
|
||||
const match = await this.typeMatcher.matchVerbType(
|
||||
{ id: rel.sourceId },
|
||||
{ id: rel.targetId },
|
||||
rel.verbType
|
||||
)
|
||||
verbType = match.type
|
||||
}
|
||||
|
||||
const verbId = await this.brain.addVerb(
|
||||
rel.sourceId,
|
||||
rel.targetId,
|
||||
verbType as VerbType,
|
||||
rel.metadata,
|
||||
rel.weight
|
||||
)
|
||||
|
||||
result.verbs.push(verbId)
|
||||
result.stats.relationships++
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Failed to create relationship: ${error.message}`)
|
||||
}
|
||||
}
|
||||
|
||||
result.success = result.stats.imported > 0
|
||||
|
||||
prodLog.info(`✨ Import complete: ${result.stats.imported}/${result.stats.total} items, ${result.stats.relationships} relationships`)
|
||||
|
||||
} catch (error: any) {
|
||||
result.errors.push(`Import failed: ${error.message}`)
|
||||
prodLog.error('Import failed:', error)
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
/**
|
||||
* Import from file
|
||||
*/
|
||||
async importFile(filePath: string, options: ImportOptions = {}): Promise<ImportResult> {
|
||||
return this.import(filePath, { ...options, source: 'file' })
|
||||
}
|
||||
|
||||
/**
|
||||
* Import from URL
|
||||
*/
|
||||
async importUrl(url: string, options: ImportOptions = {}): Promise<ImportResult> {
|
||||
return this.import(url, { ...options, source: 'url' })
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect source type
|
||||
*/
|
||||
private async detectSourceType(source: any, hint?: string): Promise<'url' | 'file' | 'data'> {
|
||||
if (hint && hint !== 'auto') {
|
||||
return hint as any
|
||||
}
|
||||
|
||||
if (typeof source === 'string') {
|
||||
// Check if URL
|
||||
if (source.startsWith('http://') || source.startsWith('https://')) {
|
||||
return 'url'
|
||||
}
|
||||
|
||||
// Check if file path exists
|
||||
try {
|
||||
if (await fs.exists(source)) {
|
||||
return 'file'
|
||||
}
|
||||
} catch {}
|
||||
}
|
||||
|
||||
return 'data'
|
||||
}
|
||||
|
||||
/**
|
||||
* Detect format from file path
|
||||
*/
|
||||
private detectFormatFromPath(filePath: string): 'json' | 'csv' | 'text' | 'yaml' | 'auto' {
|
||||
const ext = path.extname(filePath).toLowerCase()
|
||||
switch (ext) {
|
||||
case '.json': return 'json'
|
||||
case '.csv': return 'csv'
|
||||
case '.txt': return 'text'
|
||||
case '.md': return 'text'
|
||||
case '.yaml':
|
||||
case '.yml': return 'yaml'
|
||||
default: return 'auto'
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Read file
|
||||
*/
|
||||
private async readFile(filePath: string): Promise<Buffer> {
|
||||
const content = await fs.readFile(filePath, 'utf8')
|
||||
return Buffer.from(content, 'utf8')
|
||||
}
|
||||
|
||||
/**
|
||||
* Fetch from URL
|
||||
*/
|
||||
private async fetchFromUrl(url: string): Promise<string> {
|
||||
const response = await fetch(url)
|
||||
if (!response.ok) {
|
||||
throw new Error(`Failed to fetch ${url}: ${response.statusText}`)
|
||||
}
|
||||
return response.text()
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Create an import manager instance
|
||||
*/
|
||||
export function createImportManager(brain: any): ImportManager {
|
||||
return new ImportManager(brain)
|
||||
}
|
||||
160
tests/unit/import.unit.test.ts
Normal file
160
tests/unit/import.unit.test.ts
Normal file
|
|
@ -0,0 +1,160 @@
|
|||
/**
|
||||
* Tests for the Universal Import functionality
|
||||
*/
|
||||
|
||||
import { describe, it, expect, beforeAll, afterAll } from 'vitest'
|
||||
import { BrainyData } from '../../src/brainyData.js'
|
||||
import * as fs from 'fs/promises'
|
||||
|
||||
describe('Universal Import', () => {
|
||||
let brain: BrainyData
|
||||
|
||||
beforeAll(async () => {
|
||||
// Use mock embedding for speed and in-memory storage
|
||||
brain = new BrainyData({
|
||||
embeddingFunction: async () => new Array(384).fill(0.1),
|
||||
storage: { forceMemoryStorage: true }
|
||||
})
|
||||
await brain.init()
|
||||
})
|
||||
|
||||
afterAll(async () => {
|
||||
// Clean up any test files
|
||||
await fs.unlink('test-data.csv').catch(() => {})
|
||||
await fs.unlink('test-data.yaml').catch(() => {})
|
||||
})
|
||||
|
||||
describe('Import API', () => {
|
||||
it('should have ONE universal import method', () => {
|
||||
expect(typeof brain.import).toBe('function')
|
||||
})
|
||||
|
||||
it('should NOT have separate importFile method', () => {
|
||||
expect(brain.importFile).toBeUndefined()
|
||||
})
|
||||
|
||||
it('should NOT have separate importUrl method', () => {
|
||||
expect(brain.importUrl).toBeUndefined()
|
||||
})
|
||||
})
|
||||
|
||||
describe('Data Import', () => {
|
||||
it('should import array of objects', async () => {
|
||||
const data = [
|
||||
{ name: 'Alice', type: 'person' },
|
||||
{ name: 'Bob', type: 'person' }
|
||||
]
|
||||
|
||||
const ids = await brain.import(data)
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(Array.isArray(ids)).toBe(true)
|
||||
expect(ids.length).toBe(2)
|
||||
})
|
||||
|
||||
it('should import single object', async () => {
|
||||
const data = { name: 'Test Item', value: 123 }
|
||||
|
||||
const ids = await brain.import(data)
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(Array.isArray(ids)).toBe(true)
|
||||
expect(ids.length).toBe(1)
|
||||
})
|
||||
|
||||
it('should import CSV format', async () => {
|
||||
const csv = `name,age,role
|
||||
John,30,Engineer
|
||||
Jane,25,Designer`
|
||||
|
||||
const ids = await brain.import(csv, { format: 'csv' })
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBe(2)
|
||||
})
|
||||
|
||||
it('should import text format', async () => {
|
||||
const text = 'This is a test sentence.'
|
||||
|
||||
const ids = await brain.import(text, { format: 'text' })
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBeGreaterThan(0)
|
||||
})
|
||||
|
||||
it('should handle CSV with quoted values', async () => {
|
||||
const csv = `name,description
|
||||
"Smith, John","A person with a comma in name"
|
||||
"Regular Name","Normal description"`
|
||||
|
||||
const ids = await brain.import(csv, { format: 'csv' })
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBe(2)
|
||||
})
|
||||
|
||||
it('should import YAML format', async () => {
|
||||
const yaml = `name: TestProject
|
||||
version: 1.0
|
||||
active: true`
|
||||
|
||||
const ids = await brain.import(yaml, { format: 'yaml' })
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBeGreaterThan(0)
|
||||
})
|
||||
})
|
||||
|
||||
describe('File Import', () => {
|
||||
it('should import from CSV file', async () => {
|
||||
// Create test file
|
||||
const csvContent = `product,price
|
||||
Widget,19.99
|
||||
Gadget,29.99`
|
||||
|
||||
await fs.writeFile('test-data.csv', csvContent)
|
||||
|
||||
const ids = await brain.import('test-data.csv')
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBe(2)
|
||||
|
||||
// Clean up
|
||||
await fs.unlink('test-data.csv').catch(() => {})
|
||||
})
|
||||
})
|
||||
|
||||
describe('Import Options', () => {
|
||||
it('should respect batch size option', async () => {
|
||||
const data = Array(10).fill(null).map((_, i) => ({ id: i, name: `Item ${i}` }))
|
||||
|
||||
const ids = await brain.import(data, { batchSize: 5 })
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBe(10)
|
||||
})
|
||||
|
||||
it('should auto-detect format when not specified', async () => {
|
||||
const jsonData = [{ test: 'data' }]
|
||||
|
||||
const ids = await brain.import(jsonData)
|
||||
|
||||
expect(ids).toBeDefined()
|
||||
expect(ids.length).toBe(1)
|
||||
})
|
||||
})
|
||||
|
||||
describe('Data Retrieval', () => {
|
||||
it('should store imported data with metadata', async () => {
|
||||
const data = { name: 'TestItem', category: 'test' }
|
||||
|
||||
const [id] = await brain.import(data)
|
||||
const noun = await brain.getNoun(id)
|
||||
|
||||
expect(noun).toBeDefined()
|
||||
expect(noun?.metadata).toBeDefined()
|
||||
expect(noun?.metadata?.name).toBe('TestItem')
|
||||
expect(noun?.metadata?.category).toBe('test')
|
||||
})
|
||||
})
|
||||
})
|
||||
212
tests/unit/type-matching.unit.test.ts
Normal file
212
tests/unit/type-matching.unit.test.ts
Normal file
|
|
@ -0,0 +1,212 @@
|
|||
/**
|
||||
* Tests for Intelligent Type Matching with embeddings
|
||||
*/
|
||||
|
||||
import { describe, it, expect, beforeAll, afterAll } from 'vitest'
|
||||
import { IntelligentTypeMatcher } from '../../src/augmentations/typeMatching/intelligentTypeMatcher.js'
|
||||
import { NounType, VerbType } from '../../src/types/graphTypes.js'
|
||||
|
||||
describe('Intelligent Type Matching', () => {
|
||||
let matcher: IntelligentTypeMatcher
|
||||
|
||||
beforeAll(async () => {
|
||||
matcher = new IntelligentTypeMatcher()
|
||||
await matcher.init()
|
||||
})
|
||||
|
||||
afterAll(async () => {
|
||||
await matcher.dispose()
|
||||
})
|
||||
|
||||
describe('Noun Type Detection', () => {
|
||||
it('should detect Person type from user data', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
name: 'John Doe',
|
||||
email: 'john@example.com',
|
||||
age: 30
|
||||
})
|
||||
|
||||
expect(result.type).toBeDefined()
|
||||
expect(result.confidence).toBeGreaterThan(0)
|
||||
expect(result.alternatives).toBeDefined()
|
||||
expect(result.alternatives.length).toBeGreaterThanOrEqual(0)
|
||||
})
|
||||
|
||||
it('should detect Organization type from company data', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
companyName: 'Acme Corp',
|
||||
employees: 500,
|
||||
industry: 'Technology'
|
||||
})
|
||||
|
||||
expect(result.type).toBe(NounType.Organization)
|
||||
expect(result.confidence).toBeGreaterThan(0.3)
|
||||
})
|
||||
|
||||
it('should detect Location type from geographic data', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
latitude: 37.7749,
|
||||
longitude: -122.4194,
|
||||
city: 'San Francisco'
|
||||
})
|
||||
|
||||
expect(result.type).toBe(NounType.Location)
|
||||
expect(result.confidence).toBeGreaterThan(0.3)
|
||||
})
|
||||
|
||||
it('should detect Document type from text content', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
title: 'Research Paper',
|
||||
content: 'Abstract: This paper discusses...',
|
||||
author: 'Dr. Smith',
|
||||
pages: 20
|
||||
})
|
||||
|
||||
// Could be Document or Content
|
||||
expect([NounType.Document, NounType.Content]).toContain(result.type)
|
||||
})
|
||||
|
||||
it('should detect Product type from commercial data', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
price: 99.99,
|
||||
sku: 'PROD-123',
|
||||
inventory: 50,
|
||||
productId: 'abc-123'
|
||||
})
|
||||
|
||||
expect(result.type).toBe(NounType.Product)
|
||||
expect(result.confidence).toBeGreaterThan(0.25)
|
||||
})
|
||||
|
||||
it('should detect Event type from temporal data', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
startTime: '2024-01-01',
|
||||
endTime: '2024-01-02',
|
||||
attendees: 100,
|
||||
eventType: 'conference'
|
||||
})
|
||||
|
||||
expect(result.type).toBe(NounType.Event)
|
||||
expect(result.confidence).toBeGreaterThan(0.4)
|
||||
})
|
||||
|
||||
it('should handle ambiguous data with alternatives', async () => {
|
||||
const result = await matcher.matchNounType({
|
||||
value: 100,
|
||||
type: 'unknown',
|
||||
data: 'mixed'
|
||||
})
|
||||
|
||||
expect(result.type).toBeDefined()
|
||||
expect(result.alternatives).toBeDefined()
|
||||
expect(result.alternatives.length).toBeGreaterThan(0)
|
||||
})
|
||||
})
|
||||
|
||||
describe('Verb Type Detection', () => {
|
||||
it('should detect MemberOf relationship', async () => {
|
||||
const result = await matcher.matchVerbType(
|
||||
{ id: 'user1', type: 'person' },
|
||||
{ id: 'org1', type: 'organization' },
|
||||
'memberOf'
|
||||
)
|
||||
|
||||
expect(result.type).toBe(VerbType.MemberOf)
|
||||
expect(result.confidence).toBeGreaterThan(0.3)
|
||||
})
|
||||
|
||||
it('should detect CreatedBy relationship', async () => {
|
||||
const result = await matcher.matchVerbType(
|
||||
{ id: 'doc1', type: 'document' },
|
||||
{ id: 'author1', type: 'person' },
|
||||
'created by'
|
||||
)
|
||||
|
||||
expect(result.type).toBe(VerbType.CreatedBy)
|
||||
expect(result.confidence).toBeGreaterThan(0.5)
|
||||
})
|
||||
|
||||
it('should detect Contains relationship', async () => {
|
||||
const result = await matcher.matchVerbType(
|
||||
{ id: 'folder1', type: 'collection' },
|
||||
{ id: 'file1', type: 'file' },
|
||||
'contains'
|
||||
)
|
||||
|
||||
expect(result.type).toBe(VerbType.Contains)
|
||||
expect(result.confidence).toBeGreaterThan(0.4)
|
||||
})
|
||||
|
||||
it('should handle unknown relationships with defaults', async () => {
|
||||
const result = await matcher.matchVerbType(
|
||||
{ id: 'a' },
|
||||
{ id: 'b' },
|
||||
'somehow connected'
|
||||
)
|
||||
|
||||
expect(result.type).toBeDefined()
|
||||
expect(Object.values(VerbType)).toContain(result.type)
|
||||
})
|
||||
})
|
||||
|
||||
describe('Type Coverage', () => {
|
||||
it('should have embeddings for all 31 noun types', async () => {
|
||||
const nounTypes = Object.values(NounType)
|
||||
expect(nounTypes.length).toBe(31)
|
||||
|
||||
// Test that each type can be matched
|
||||
for (const nounType of nounTypes) {
|
||||
const result = await matcher.matchNounType({
|
||||
type: nounType,
|
||||
test: 'coverage check'
|
||||
})
|
||||
expect(result.type).toBeDefined()
|
||||
}
|
||||
})
|
||||
|
||||
it('should have embeddings for all 40 verb types', async () => {
|
||||
const verbTypes = Object.values(VerbType)
|
||||
expect(verbTypes.length).toBe(40)
|
||||
|
||||
// Test that each type can be matched
|
||||
for (const verbType of verbTypes) {
|
||||
const result = await matcher.matchVerbType(
|
||||
{ id: 'source' },
|
||||
{ id: 'target' },
|
||||
verbType
|
||||
)
|
||||
expect(result.type).toBeDefined()
|
||||
}
|
||||
})
|
||||
})
|
||||
|
||||
describe('Cache Performance', () => {
|
||||
it('should cache repeated type matches', async () => {
|
||||
const testData = { name: 'Cache Test', value: 123 }
|
||||
|
||||
const start1 = Date.now()
|
||||
const result1 = await matcher.matchNounType(testData)
|
||||
const time1 = Date.now() - start1
|
||||
|
||||
const start2 = Date.now()
|
||||
const result2 = await matcher.matchNounType(testData)
|
||||
const time2 = Date.now() - start2
|
||||
|
||||
expect(result1.type).toBe(result2.type)
|
||||
expect(result1.confidence).toBe(result2.confidence)
|
||||
// Second call should be faster due to cache
|
||||
expect(time2).toBeLessThanOrEqual(time1)
|
||||
})
|
||||
|
||||
it('should clear cache when requested', async () => {
|
||||
const testData = { name: 'Clear Cache Test' }
|
||||
|
||||
await matcher.matchNounType(testData) // Populate cache
|
||||
matcher.clearCache()
|
||||
|
||||
// Should still work after cache clear
|
||||
const result = await matcher.matchNounType(testData)
|
||||
expect(result.type).toBeDefined()
|
||||
})
|
||||
})
|
||||
})
|
||||
Loading…
Add table
Add a link
Reference in a new issue