brainy/src/importers/SmartImportOrchestrator.ts

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feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
/**
* Smart Import Orchestrator
*
* Coordinates the entire smart import pipeline:
* 1. Extract entities/relationships using SmartExcelImporter
* 2. Create entities and relationships in Brainy
* 3. Organize into VFS structure using VFSStructureGenerator
*
* NO MOCKS - Production-ready implementation
*/
import { Brainy } from '../brainy.js'
import { VirtualFileSystem } from '../vfs/VirtualFileSystem.js'
import { NounType, VerbType } from '../types/graphTypes.js'
import { SmartExcelImporter, SmartExcelOptions, SmartExcelResult } from './SmartExcelImporter.js'
import { SmartPDFImporter, SmartPDFOptions, SmartPDFResult } from './SmartPDFImporter.js'
import { SmartCSVImporter, SmartCSVOptions, SmartCSVResult } from './SmartCSVImporter.js'
import { SmartJSONImporter, SmartJSONOptions, SmartJSONResult } from './SmartJSONImporter.js'
import { SmartMarkdownImporter, SmartMarkdownOptions, SmartMarkdownResult } from './SmartMarkdownImporter.js'
import { VFSStructureGenerator, VFSStructureOptions } from './VFSStructureGenerator.js'
export interface SmartImportOptions extends SmartExcelOptions {
/** Create VFS structure */
createVFSStructure?: boolean
/** VFS root path */
vfsRootPath?: string
/** VFS grouping strategy */
vfsGroupBy?: 'type' | 'sheet' | 'flat' | 'custom'
/** Create entities in Brainy */
createEntities?: boolean
/** Create relationships in Brainy */
createRelationships?: boolean
/** Source filename */
filename?: string
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
/**
* Default subtype tag for entities + relationships this importer creates when
* the extractor doesn't set one. See `ValidImportOptions.defaultSubtype`
* same semantics, same precedence (extractor > caller default > `'imported'`).
* Added 7.30.1.
*/
defaultSubtype?: string
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
export interface SmartImportProgress {
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
phase: 'parsing' | 'extracting' | 'creating' | 'relationships' | 'organizing' | 'complete'
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
message: string
processed: number
total: number
entities: number
relationships: number
}
export interface SmartImportResult {
success: boolean
/** Extraction results */
extraction: SmartExcelResult
/** Created entity IDs */
entityIds: string[]
/** Created relationship IDs */
relationshipIds: string[]
/** VFS structure created */
vfsStructure?: {
rootPath: string
directories: string[]
files: number
}
/** Overall statistics */
stats: {
rowsProcessed: number
entitiesCreated: number
relationshipsCreated: number
filesCreated: number
totalTime: number
}
/** Any errors encountered */
errors: string[]
}
/**
* SmartImportOrchestrator - Main entry point for smart imports
*/
export class SmartImportOrchestrator {
private brain: Brainy
private excelImporter: SmartExcelImporter
private pdfImporter: SmartPDFImporter
private csvImporter: SmartCSVImporter
private jsonImporter: SmartJSONImporter
private markdownImporter: SmartMarkdownImporter
private vfsGenerator: VFSStructureGenerator
constructor(brain: Brainy) {
this.brain = brain
this.excelImporter = new SmartExcelImporter(brain)
this.pdfImporter = new SmartPDFImporter(brain)
this.csvImporter = new SmartCSVImporter(brain)
this.jsonImporter = new SmartJSONImporter(brain)
this.markdownImporter = new SmartMarkdownImporter(brain)
this.vfsGenerator = new VFSStructureGenerator(brain)
}
/**
* Initialize the orchestrator
*/
async init(): Promise<void> {
await this.excelImporter.init()
await this.pdfImporter.init()
await this.csvImporter.init()
await this.jsonImporter.init()
await this.markdownImporter.init()
await this.vfsGenerator.init()
}
/**
* Import Excel file with full pipeline
*/
async importExcel(
buffer: Buffer,
options: SmartImportOptions = {},
onProgress?: (progress: SmartImportProgress) => void
): Promise<SmartImportResult> {
const startTime = Date.now()
const result: SmartImportResult = {
success: false,
// Typed boundary: populated in the extraction phase below. If extraction
// throws, the error path returns with this still null (pre-existing
// contract — consumers check `success`/`errors` before reading it).
extraction: null as unknown as SmartExcelResult,
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
entityIds: [],
relationshipIds: [],
stats: {
rowsProcessed: 0,
entitiesCreated: 0,
relationshipsCreated: 0,
filesCreated: 0,
totalTime: 0
},
errors: []
}
try {
// Phase 1: Extract entities and relationships
onProgress?.({
phase: 'extracting',
message: 'Extracting entities and relationships...',
processed: 0,
total: 0,
entities: 0,
relationships: 0
})
result.extraction = await this.excelImporter.extract(buffer, {
...options,
onProgress: (stats) => {
onProgress?.({
phase: 'extracting',
message: `Processing row ${stats.processed}/${stats.total}...`,
processed: stats.processed,
total: stats.total,
entities: stats.entities,
relationships: stats.relationships
})
}
})
result.stats.rowsProcessed = result.extraction.rowsProcessed
// Phase 2: Create entities in Brainy
if (options.createEntities !== false) {
onProgress?.({
phase: 'creating',
message: 'Creating entities in knowledge graph...',
processed: 0,
total: result.extraction.rows.length,
entities: 0,
relationships: 0
})
for (let i = 0; i < result.extraction.rows.length; i++) {
const extracted = result.extraction.rows[i]
try {
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// Create main entity. Subtype precedence: extractor-set → caller default
// → Brainy default `'imported'` (added 7.30.1).
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
const entityId = await this.brain.add({
data: extracted.entity.description,
type: extracted.entity.type,
subtype:
(extracted.entity as typeof extracted.entity & { subtype?: string })
.subtype ?? options.defaultSubtype ?? 'imported',
feat(8.0): reserved-field contract — one canonical location, typed prevention, unified read/write Brainy-owned field names (noun/verb, subtype, createdAt, updatedAt, confidence, weight, service, data, createdBy, _rev) now have exactly one home — top level — enforced by three layers driven from a single source of truth, src/types/reservedFields.ts (RESERVED_ENTITY_FIELDS / RESERVED_RELATION_FIELDS, exported): 1. Compile time — AddParams/UpdateParams/RelateParams/UpdateRelationParams metadata (and the transact() ops that extend them) reject a literal reserved key as a TypeScript error while keeping generic T ergonomics (typed bags, untyped brains, index-signature shapes, and a documented exemption for T-declared reserved keys). Pinned by @ts-expect-error type tests run under vitest typecheck mode on every unit run. 2. Write time — the 7.x update() remap is ported to 8.0 and extended to every write path: add/update/relate/updateRelation, their transact() mirrors, and db.with() overlays. User-settable fields lift to their dedicated param (top-level wins when both are supplied — closes the 7.x trap where update({metadata:{confidence}}) silently no-oped), and system-managed fields drop with a one-shot warning naming the right path. A remapped subtype satisfies subtype-pairing enforcement exactly like a top-level one. 3. Read time — every storage combine goes through one canonical hydration helper (hydrateNounWithMetadata / hydrateVerbWithMetadata over splitNoun/VerbMetadataRecord), so reserved fields surface ONLY top-level and entity/relation.metadata carry ONLY custom fields on live reads, batch reads, paginated listings, getRelations by source/target, streamed verbs, and historical asOf() materialization alike. Read-path echoes found and fixed (previously the full stored record — including the verb type key — leaked inside metadata): noun pagination, verb pagination, getVerbsBySource/ByTarget (adjacency + shard fallback), getVerbsBySourceBatch (which also dropped subtype/data), and the filesystem verb stream. getRelations() results now surface confidence/updatedAt top-level via verbsToRelations, updateRelation() no longer erases service/createdBy, relate() persists its top-level confidence/service params, and the dead convertHNSWVerbToGraphVerb echo path is deleted. Import paths (CLI extract, deduplicator, coordinators, neural import) write confidence through the dedicated param instead of the bag. UpdateRelationParams is now exported from the package root. Documented for consumers in docs/concepts/consistency-model.md ("Reserved fields") and RELEASES.md. Regression tests ported from the 7.x fix and extended to the full 8.0 contract (17 runtime tests + 41 type-level assertions); full unit suite 1427/1427, db-mvcc integration 24/24.
2026-06-11 13:12:50 -07:00
confidence: extracted.entity.confidence, // reserved field — dedicated param, not metadata
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
metadata: {
...extracted.entity.metadata,
name: extracted.entity.name,
importedFrom: 'smart-import'
}
})
result.entityIds.push(entityId)
result.stats.entitiesCreated++
// Update entity ID in extraction result
extracted.entity.id = entityId
onProgress?.({
phase: 'creating',
message: `Created entity: ${extracted.entity.name}`,
processed: i + 1,
total: result.extraction.rows.length,
entities: result.entityIds.length,
relationships: result.relationshipIds.length
})
} catch (error: any) {
result.errors.push(`Failed to create entity ${extracted.entity.name}: ${error.message}`)
}
}
}
// Phase 3: Create relationships
if (options.createRelationships !== false && options.createEntities !== false) {
onProgress?.({
phase: 'creating',
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
message: 'Preparing relationships...',
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
processed: 0,
total: result.extraction.rows.length,
entities: result.entityIds.length,
relationships: 0
})
// Build entity name -> ID map
const entityMap = new Map<string, string>()
for (const extracted of result.extraction.rows) {
entityMap.set(extracted.entity.name.toLowerCase(), extracted.entity.id)
}
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
// Collect all relationship parameters
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
const relationshipParams: Array<{from: string; to: string; type: VerbType; subtype?: string; metadata?: any}> = []
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
for (const extracted of result.extraction.rows) {
for (const rel of extracted.relationships) {
try {
// Find target entity ID
let toEntityId: string | undefined
// Try to find by name in our extracted entities
for (const otherExtracted of result.extraction.rows) {
if (rel.to.toLowerCase().includes(otherExtracted.entity.name.toLowerCase()) ||
otherExtracted.entity.name.toLowerCase().includes(rel.to.toLowerCase())) {
toEntityId = otherExtracted.entity.id
break
}
}
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// If not found, create a placeholder entity. `import-placeholder` marks
// these as synthetic targets so consumers can distinguish them from real
// imports and downstream dedup can consolidate (added 7.30.1).
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
if (!toEntityId) {
toEntityId = await this.brain.add({
data: rel.to,
type: NounType.Thing,
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
subtype: 'import-placeholder',
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
metadata: {
name: rel.to,
placeholder: true,
extractedFrom: extracted.entity.name
}
})
result.entityIds.push(toEntityId)
}
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// Collect relationship parameter. Subtype precedence: extractor-set rel
// subtype → caller default → Brainy default `'imported'` (added 7.30.1).
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
relationshipParams.push({
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
from: extracted.entity.id,
to: toEntityId,
type: rel.type,
subtype:
(rel as typeof rel & { subtype?: string }).subtype ??
options.defaultSubtype ?? 'imported',
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
metadata: {
confidence: rel.confidence,
evidence: rel.evidence
}
})
} catch (error: any) {
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
result.errors.push(`Failed to prepare relationship: ${error.message}`)
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
}
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
// Batch create all relationships with progress
if (relationshipParams.length > 0) {
onProgress?.({
phase: 'relationships',
message: 'Building relationships...',
processed: 0,
total: relationshipParams.length,
entities: result.entityIds.length,
relationships: 0
})
try {
const relationshipIds = await this.brain.relateMany({
items: relationshipParams,
parallel: true,
chunkSize: 100,
continueOnError: true,
onProgress: (done, total) => {
onProgress?.({
phase: 'relationships',
message: `Building relationships: ${done}/${total}`,
processed: done,
total: total,
entities: result.entityIds.length,
relationships: done
})
}
})
result.relationshipIds = relationshipIds
result.stats.relationshipsCreated = relationshipIds.length
} catch (error: any) {
result.errors.push(`Failed to create relationships: ${error.message}`)
}
}
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
// Phase 4: Create VFS structure
if (options.createVFSStructure !== false) {
onProgress?.({
phase: 'organizing',
message: 'Organizing into file structure...',
processed: 0,
total: result.extraction.rows.length,
entities: result.entityIds.length,
relationships: result.relationshipIds.length
})
const vfsOptions: VFSStructureOptions = {
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
groupBy: options.vfsGroupBy || 'type',
preserveSource: true,
sourceBuffer: buffer,
sourceFilename: options.filename || 'import.xlsx',
createRelationshipFile: true,
createMetadataFile: true
}
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
result.vfsStructure = {
rootPath: vfsResult.rootPath,
directories: vfsResult.directories,
files: vfsResult.files.length
}
result.stats.filesCreated = vfsResult.files.length
}
// Complete
result.success = result.errors.length === 0
result.stats.totalTime = Date.now() - startTime
onProgress?.({
phase: 'complete',
message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`,
processed: result.extraction.rows.length,
total: result.extraction.rows.length,
entities: result.stats.entitiesCreated,
relationships: result.stats.relationshipsCreated
})
} catch (error: any) {
result.errors.push(`Import failed: ${error.message}`)
result.success = false
}
return result
}
/**
* Import PDF file with full pipeline
*/
async importPDF(
buffer: Buffer,
options: SmartImportOptions & SmartPDFOptions = {},
onProgress?: (progress: SmartImportProgress) => void
): Promise<SmartImportResult> {
const startTime = Date.now()
const result: SmartImportResult = {
success: false,
// Typed boundary: populated after extraction (see importExcel).
extraction: null as unknown as SmartExcelResult,
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
entityIds: [],
relationshipIds: [],
stats: {
rowsProcessed: 0,
entitiesCreated: 0,
relationshipsCreated: 0,
filesCreated: 0,
totalTime: 0
},
errors: []
}
try {
// Phase 1: Extract from PDF
onProgress?.({ phase: 'extracting', message: 'Extracting from PDF...', processed: 0, total: 0, entities: 0, relationships: 0 })
const pdfResult = await this.pdfImporter.extract(buffer, options)
// Convert PDF result to Excel-like format for processing
result.extraction = this.convertPDFToExcelFormat(pdfResult)
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
result.stats.rowsProcessed = pdfResult.sectionsProcessed
// Phase 2 & 3: Create entities and relationships
await this.createEntitiesAndRelationships(result, options, onProgress)
// Phase 4: Create VFS structure
if (options.createVFSStructure !== false) {
const vfsOptions: VFSStructureOptions = {
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
groupBy: options.vfsGroupBy || 'type',
preserveSource: true,
sourceBuffer: buffer,
sourceFilename: options.filename || 'import.pdf',
createRelationshipFile: true,
createMetadataFile: true
}
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
result.stats.filesCreated = vfsResult.files.length
}
result.success = result.errors.length === 0
result.stats.totalTime = Date.now() - startTime
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
} catch (error: any) {
result.errors.push(`PDF import failed: ${error.message}`)
result.success = false
}
return result
}
/**
* Import CSV file with full pipeline
*/
async importCSV(
buffer: Buffer,
options: SmartImportOptions & SmartCSVOptions = {},
onProgress?: (progress: SmartImportProgress) => void
): Promise<SmartImportResult> {
// CSV is very similar to Excel, can reuse importExcel logic
return this.importExcel(buffer, options, onProgress)
}
/**
* Import JSON data with full pipeline
*/
async importJSON(
data: any,
options: SmartImportOptions & SmartJSONOptions = {},
onProgress?: (progress: SmartImportProgress) => void
): Promise<SmartImportResult> {
const startTime = Date.now()
const result: SmartImportResult = {
success: false,
// Typed boundary: populated after extraction (see importExcel).
extraction: null as unknown as SmartExcelResult,
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
entityIds: [],
relationshipIds: [],
stats: {
rowsProcessed: 0,
entitiesCreated: 0,
relationshipsCreated: 0,
filesCreated: 0,
totalTime: 0
},
errors: []
}
try {
onProgress?.({ phase: 'extracting', message: 'Extracting from JSON...', processed: 0, total: 0, entities: 0, relationships: 0 })
const jsonResult = await this.jsonImporter.extract(data, options)
result.extraction = this.convertJSONToExcelFormat(jsonResult)
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
result.stats.rowsProcessed = jsonResult.nodesProcessed
await this.createEntitiesAndRelationships(result, options, onProgress)
if (options.createVFSStructure !== false) {
const sourceBuffer = Buffer.from(typeof data === 'string' ? data : JSON.stringify(data, null, 2))
const vfsOptions: VFSStructureOptions = {
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
groupBy: options.vfsGroupBy || 'type',
preserveSource: true,
sourceBuffer,
sourceFilename: options.filename || 'import.json',
createRelationshipFile: true,
createMetadataFile: true
}
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
result.stats.filesCreated = vfsResult.files.length
}
result.success = result.errors.length === 0
result.stats.totalTime = Date.now() - startTime
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
} catch (error: any) {
result.errors.push(`JSON import failed: ${error.message}`)
result.success = false
}
return result
}
/**
* Import Markdown content with full pipeline
*/
async importMarkdown(
markdown: string,
options: SmartImportOptions & SmartMarkdownOptions = {},
onProgress?: (progress: SmartImportProgress) => void
): Promise<SmartImportResult> {
const startTime = Date.now()
const result: SmartImportResult = {
success: false,
// Typed boundary: populated after extraction (see importExcel).
extraction: null as unknown as SmartExcelResult,
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
entityIds: [],
relationshipIds: [],
stats: {
rowsProcessed: 0,
entitiesCreated: 0,
relationshipsCreated: 0,
filesCreated: 0,
totalTime: 0
},
errors: []
}
try {
onProgress?.({ phase: 'extracting', message: 'Extracting from Markdown...', processed: 0, total: 0, entities: 0, relationships: 0 })
const mdResult = await this.markdownImporter.extract(markdown, options)
result.extraction = this.convertMarkdownToExcelFormat(mdResult)
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
result.stats.rowsProcessed = mdResult.sectionsProcessed
await this.createEntitiesAndRelationships(result, options, onProgress)
if (options.createVFSStructure !== false) {
const sourceBuffer = Buffer.from(markdown, 'utf-8')
const vfsOptions: VFSStructureOptions = {
rootPath: options.vfsRootPath || '/imports/' + (options.filename || 'import'),
groupBy: options.vfsGroupBy || 'type',
preserveSource: true,
sourceBuffer,
sourceFilename: options.filename || 'import.md',
createRelationshipFile: true,
createMetadataFile: true
}
const vfsResult = await this.vfsGenerator.generate(result.extraction, vfsOptions)
result.vfsStructure = { rootPath: vfsResult.rootPath, directories: vfsResult.directories, files: vfsResult.files.length }
result.stats.filesCreated = vfsResult.files.length
}
result.success = result.errors.length === 0
result.stats.totalTime = Date.now() - startTime
onProgress?.({ phase: 'complete', message: `Import complete: ${result.stats.entitiesCreated} entities, ${result.stats.relationshipsCreated} relationships`, processed: result.stats.rowsProcessed, total: result.stats.rowsProcessed, entities: result.stats.entitiesCreated, relationships: result.stats.relationshipsCreated })
} catch (error: any) {
result.errors.push(`Markdown import failed: ${error.message}`)
result.success = false
}
return result
}
/**
* Helper: Create entities and relationships from extraction result
*/
private async createEntitiesAndRelationships(
result: SmartImportResult,
options: SmartImportOptions,
onProgress?: (progress: SmartImportProgress) => void
): Promise<void> {
if (options.createEntities !== false) {
onProgress?.({ phase: 'creating', message: 'Creating entities in knowledge graph...', processed: 0, total: result.extraction.rows.length, entities: 0, relationships: 0 })
for (let i = 0; i < result.extraction.rows.length; i++) {
const extracted = result.extraction.rows[i]
try {
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// Subtype precedence: extractor → caller default → `'imported'` (7.30.1).
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
const entityId = await this.brain.add({
data: extracted.entity.description,
type: extracted.entity.type,
subtype:
(extracted.entity as typeof extracted.entity & { subtype?: string })
.subtype ?? options.defaultSubtype ?? 'imported',
feat(8.0): reserved-field contract — one canonical location, typed prevention, unified read/write Brainy-owned field names (noun/verb, subtype, createdAt, updatedAt, confidence, weight, service, data, createdBy, _rev) now have exactly one home — top level — enforced by three layers driven from a single source of truth, src/types/reservedFields.ts (RESERVED_ENTITY_FIELDS / RESERVED_RELATION_FIELDS, exported): 1. Compile time — AddParams/UpdateParams/RelateParams/UpdateRelationParams metadata (and the transact() ops that extend them) reject a literal reserved key as a TypeScript error while keeping generic T ergonomics (typed bags, untyped brains, index-signature shapes, and a documented exemption for T-declared reserved keys). Pinned by @ts-expect-error type tests run under vitest typecheck mode on every unit run. 2. Write time — the 7.x update() remap is ported to 8.0 and extended to every write path: add/update/relate/updateRelation, their transact() mirrors, and db.with() overlays. User-settable fields lift to their dedicated param (top-level wins when both are supplied — closes the 7.x trap where update({metadata:{confidence}}) silently no-oped), and system-managed fields drop with a one-shot warning naming the right path. A remapped subtype satisfies subtype-pairing enforcement exactly like a top-level one. 3. Read time — every storage combine goes through one canonical hydration helper (hydrateNounWithMetadata / hydrateVerbWithMetadata over splitNoun/VerbMetadataRecord), so reserved fields surface ONLY top-level and entity/relation.metadata carry ONLY custom fields on live reads, batch reads, paginated listings, getRelations by source/target, streamed verbs, and historical asOf() materialization alike. Read-path echoes found and fixed (previously the full stored record — including the verb type key — leaked inside metadata): noun pagination, verb pagination, getVerbsBySource/ByTarget (adjacency + shard fallback), getVerbsBySourceBatch (which also dropped subtype/data), and the filesystem verb stream. getRelations() results now surface confidence/updatedAt top-level via verbsToRelations, updateRelation() no longer erases service/createdBy, relate() persists its top-level confidence/service params, and the dead convertHNSWVerbToGraphVerb echo path is deleted. Import paths (CLI extract, deduplicator, coordinators, neural import) write confidence through the dedicated param instead of the bag. UpdateRelationParams is now exported from the package root. Documented for consumers in docs/concepts/consistency-model.md ("Reserved fields") and RELEASES.md. Regression tests ported from the 7.x fix and extended to the full 8.0 contract (17 runtime tests + 41 type-level assertions); full unit suite 1427/1427, db-mvcc integration 24/24.
2026-06-11 13:12:50 -07:00
confidence: extracted.entity.confidence, // reserved field — dedicated param, not metadata
metadata: { ...extracted.entity.metadata, name: extracted.entity.name, importedFrom: 'smart-import' }
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
})
result.entityIds.push(entityId)
result.stats.entitiesCreated++
extracted.entity.id = entityId
} catch (error: any) {
result.errors.push(`Failed to create entity ${extracted.entity.name}: ${error.message}`)
}
}
}
if (options.createRelationships !== false && options.createEntities !== false) {
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
onProgress?.({ phase: 'creating', message: 'Preparing relationships...', processed: 0, total: result.extraction.rows.length, entities: result.entityIds.length, relationships: 0 })
// Collect all relationship parameters
feat(8.0): reserved-field contract — one canonical location, typed prevention, unified read/write Brainy-owned field names (noun/verb, subtype, createdAt, updatedAt, confidence, weight, service, data, createdBy, _rev) now have exactly one home — top level — enforced by three layers driven from a single source of truth, src/types/reservedFields.ts (RESERVED_ENTITY_FIELDS / RESERVED_RELATION_FIELDS, exported): 1. Compile time — AddParams/UpdateParams/RelateParams/UpdateRelationParams metadata (and the transact() ops that extend them) reject a literal reserved key as a TypeScript error while keeping generic T ergonomics (typed bags, untyped brains, index-signature shapes, and a documented exemption for T-declared reserved keys). Pinned by @ts-expect-error type tests run under vitest typecheck mode on every unit run. 2. Write time — the 7.x update() remap is ported to 8.0 and extended to every write path: add/update/relate/updateRelation, their transact() mirrors, and db.with() overlays. User-settable fields lift to their dedicated param (top-level wins when both are supplied — closes the 7.x trap where update({metadata:{confidence}}) silently no-oped), and system-managed fields drop with a one-shot warning naming the right path. A remapped subtype satisfies subtype-pairing enforcement exactly like a top-level one. 3. Read time — every storage combine goes through one canonical hydration helper (hydrateNounWithMetadata / hydrateVerbWithMetadata over splitNoun/VerbMetadataRecord), so reserved fields surface ONLY top-level and entity/relation.metadata carry ONLY custom fields on live reads, batch reads, paginated listings, getRelations by source/target, streamed verbs, and historical asOf() materialization alike. Read-path echoes found and fixed (previously the full stored record — including the verb type key — leaked inside metadata): noun pagination, verb pagination, getVerbsBySource/ByTarget (adjacency + shard fallback), getVerbsBySourceBatch (which also dropped subtype/data), and the filesystem verb stream. getRelations() results now surface confidence/updatedAt top-level via verbsToRelations, updateRelation() no longer erases service/createdBy, relate() persists its top-level confidence/service params, and the dead convertHNSWVerbToGraphVerb echo path is deleted. Import paths (CLI extract, deduplicator, coordinators, neural import) write confidence through the dedicated param instead of the bag. UpdateRelationParams is now exported from the package root. Documented for consumers in docs/concepts/consistency-model.md ("Reserved fields") and RELEASES.md. Regression tests ported from the 7.x fix and extended to the full 8.0 contract (17 runtime tests + 41 type-level assertions); full unit suite 1427/1427, db-mvcc integration 24/24.
2026-06-11 13:12:50 -07:00
const relationshipParams: Array<{from: string; to: string; type: VerbType; subtype?: string; confidence?: number; metadata?: any}> = []
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
for (const extracted of result.extraction.rows) {
for (const rel of extracted.relationships) {
try {
let toEntityId: string | undefined
for (const otherExtracted of result.extraction.rows) {
if (rel.to.toLowerCase().includes(otherExtracted.entity.name.toLowerCase()) || otherExtracted.entity.name.toLowerCase().includes(rel.to.toLowerCase())) {
toEntityId = otherExtracted.entity.id
break
}
}
if (!toEntityId) {
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// Subtype `import-placeholder` marks synthetic targets (7.30.1).
toEntityId = await this.brain.add({ data: rel.to, type: NounType.Thing, subtype: 'import-placeholder', metadata: { name: rel.to, placeholder: true, extractedFrom: extracted.entity.name } })
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
result.entityIds.push(toEntityId)
}
fix: internal subtype consistency + brain.audit() diagnostic + improved enforcement errors Brainy 7.30 shipped opt-in subtype enforcement; SDK 3.20.0 then registered SDK_CORE_VOCABULARY on every consumer's brain (Event, Collection, Message, Contract, Media, Document NounTypes). On 2026-06-08 Venue's /book flow went 500 because their brain.add({ type: NounType.Event, ... }) call sites lacked subtype. An audit of Brainy's OWN source revealed 14 HIGH-risk internal write paths that also omit subtype — any consumer running the same vocabulary would have hit Brainy's infrastructure paths next. 7.30.1 closes both gaps before 8.0 makes strict mode the default. Additive across the board. Zero behavior change for consumers not using strict mode. Every change is JS-side — Cortex needs no work for 7.30.1. NEW — brain.audit() diagnostic - Read-only method walking storage.getNouns() / getVerbs() pagination - Returns { entitiesWithoutSubtype: { type: count }, relationshipsWithoutSubtype, total, scanned, recommendation } - VFS infrastructure entities excluded by default (they bypass enforcement via isVFSEntity marker); pass { includeVFS: true } to surface them - The companion to migrateField (7.x) and fillSubtypes (8.0): tells consumers exactly what would break under strict enforcement, deterministically NEW — Improved enforcement error messages - Caller's source location extracted from Error().stack so users see their own call site, not a Brainy internal frame - Specific guidance branches: registered vocabulary → "Pass one of: a, b, c"; brain-wide strict mode → mentions the except clause; otherwise → registration recipe via brain.requireSubtype() - Documentation link to the canonical migration recipe - Same shape for noun and verb enforcement NEW — CLI --subtype flag - brainy add and brainy relate gain -s/--subtype <value> - Defaults to 'cli-add' / 'cli-relate' so the CLI works against strict-mode brains without the user needing to know the vocabulary in advance INTERNAL — every Brainy write path now sets subtype - VFS Contains edges (5 sites at lines 503/905/1694/1772/1886) → 'vfs-contains' - VFS symlink entity → 'vfs-symlink' (NEW — distinct from 'vfs-file') - VFS copy-file → preserves source subtype, falls back to 'vfs-file' - VFS symlink also adopts the isVFSEntity infrastructure marker so it bypasses enforcement in strict mode - Aggregation materializer (Measurement entities) → 'materialized-aggregate' - ImportCoordinator (3 sites): document → 'import-source'; entities → options.defaultSubtype ?? 'imported'; placeholder → 'import-placeholder' - SmartImportOrchestrator (4 entity sites + 2 batch relate sites): same precedence (extractor → options.defaultSubtype → 'imported') - EntityDeduplicator → candidate.subtype ?? 'imported' - UniversalImportAPI → extractor → 'extracted' for both entities and relations - NeuralImport → adds defaultSubtype to NeuralImportOptions; precedence same - GoogleSheetsIntegration → request body 'subtype' ?? 'imported-from-sheets' - ODataIntegration → request body 'Subtype' ?? 'imported-from-odata' - MCP client message storage → 'mcp-message' (also fixes pre-existing missing data field and missing type by aliasing from the prior text field) Side-effect fix: storage.getNouns() paginated now surfaces subtype to top-level - Single-noun getNoun() already did this in 7.30; the paginated path was missed - Without this fix brain.audit() saw missing subtype on entities that actually had one (caught by the strict-mode self-test before release) NEW — tests/integration/strict-mode-self-test.test.ts (13 tests) - Creates a brain under the exact SDK_CORE_VOCABULARY shape Venue hit + brain- wide strict mode - Exercises every internal Brainy path: VFS root + mkdir + writeFile + cp + mv + ln + symlink; aggregation engine; audit diagnostic with includeVFS toggle - Validates error message UX: caller location, vocabulary guidance, brain-wide strict mode guidance, off-vocabulary value reporting Docs - New "Strict mode in practice" section in docs/guides/subtypes-and-facets.md covering the SDK_CORE_VOCABULARY pattern, 4-step migration recipe (audit → migrateField → hand-fix → re-audit), the Brainy-internal label reference table, and an 8.0 forward-look on fillSubtypes() - docs/api/README.md: new audit() entry, strict-mode tips on add() and relate() - RELEASES.md: full 7.30.1 entry Cortex parity (forward-looking, not blocking 7.30.1) - 6th open question added to .strategy/BRAINY-8.0-SUBTYPE-CONTRACT.md: native fast path for audit() and fillSubtypes() via column-store null-subtype bitmap for billion-scale brains - Cortex should add a parity test mirroring strict-mode-self-test.test.ts against their native paths to catch any latent bug where native writes bypass JS validation - Brainy-internal subtype labels become a documented part of the 8.0 contract (useful for Cortex telemetry surfacing Brainy-managed infrastructure %) Verification - npx tsc --noEmit: clean - npm test: 1468/1468 unit - 7.29 noun integration suite: 26/26 (no regression) - 7.30 verb subtype + enforcement integration suite: 30/30 (no regression) - New strict-mode-self-test integration suite: 13/13 - npm run build: clean - Closed-source product reference audit: clean Addresses VE-SUBTYPE-MIGRATION (Venue's reported request) and ships internal labels Venue did NOT ask for but that would have broken them next under their own vocabulary registration.
2026-06-08 11:31:47 -07:00
// Relationship subtype precedence: extractor → caller default → `'imported'` (7.30.1).
feat(8.0): reserved-field contract — one canonical location, typed prevention, unified read/write Brainy-owned field names (noun/verb, subtype, createdAt, updatedAt, confidence, weight, service, data, createdBy, _rev) now have exactly one home — top level — enforced by three layers driven from a single source of truth, src/types/reservedFields.ts (RESERVED_ENTITY_FIELDS / RESERVED_RELATION_FIELDS, exported): 1. Compile time — AddParams/UpdateParams/RelateParams/UpdateRelationParams metadata (and the transact() ops that extend them) reject a literal reserved key as a TypeScript error while keeping generic T ergonomics (typed bags, untyped brains, index-signature shapes, and a documented exemption for T-declared reserved keys). Pinned by @ts-expect-error type tests run under vitest typecheck mode on every unit run. 2. Write time — the 7.x update() remap is ported to 8.0 and extended to every write path: add/update/relate/updateRelation, their transact() mirrors, and db.with() overlays. User-settable fields lift to their dedicated param (top-level wins when both are supplied — closes the 7.x trap where update({metadata:{confidence}}) silently no-oped), and system-managed fields drop with a one-shot warning naming the right path. A remapped subtype satisfies subtype-pairing enforcement exactly like a top-level one. 3. Read time — every storage combine goes through one canonical hydration helper (hydrateNounWithMetadata / hydrateVerbWithMetadata over splitNoun/VerbMetadataRecord), so reserved fields surface ONLY top-level and entity/relation.metadata carry ONLY custom fields on live reads, batch reads, paginated listings, getRelations by source/target, streamed verbs, and historical asOf() materialization alike. Read-path echoes found and fixed (previously the full stored record — including the verb type key — leaked inside metadata): noun pagination, verb pagination, getVerbsBySource/ByTarget (adjacency + shard fallback), getVerbsBySourceBatch (which also dropped subtype/data), and the filesystem verb stream. getRelations() results now surface confidence/updatedAt top-level via verbsToRelations, updateRelation() no longer erases service/createdBy, relate() persists its top-level confidence/service params, and the dead convertHNSWVerbToGraphVerb echo path is deleted. Import paths (CLI extract, deduplicator, coordinators, neural import) write confidence through the dedicated param instead of the bag. UpdateRelationParams is now exported from the package root. Documented for consumers in docs/concepts/consistency-model.md ("Reserved fields") and RELEASES.md. Regression tests ported from the 7.x fix and extended to the full 8.0 contract (17 runtime tests + 41 type-level assertions); full unit suite 1427/1427, db-mvcc integration 24/24.
2026-06-11 13:12:50 -07:00
// `confidence` is a reserved top-level field — dedicated relate() param, not metadata
relationshipParams.push({ from: extracted.entity.id, to: toEntityId, type: rel.type, subtype: (rel as typeof rel & { subtype?: string }).subtype ?? options.defaultSubtype ?? 'imported', confidence: rel.confidence, metadata: { evidence: rel.evidence } })
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
} catch (error: any) {
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
result.errors.push(`Failed to prepare relationship: ${error.message}`)
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
}
feat: add real-time progress callbacks for relationship building phase Extends the import progress callback system to provide real-time updates during the relationship building phase, eliminating the 1-2 minute silent period for large imports. New Features: - Progress callbacks now fire during relationship building (brain.relateMany) - New 'phase' field distinguishes 'extraction' vs 'relationships' phases - Chunk-based progress emission (<0.01% overhead for 573 relationships) - Works across all import paths: ImportCoordinator, SmartImportOrchestrator, UniversalImportAPI API Enhancements: - ImportProgress: Added 'phase' and 'current' fields - SmartImportProgress: Added 'relationships' phase - NeuralImportProgress: New interface for UniversalImportAPI - Refactored to use brain.relateMany() for batch operations Examples: - NEW: examples/import-with-progress.ts - Complete demo with progress bars and ETA - UPDATED: examples/complete-import-demo.ts - Shows both extraction and relationship phases Performance: - Minimal overhead: 6 callbacks for 573 relationships = 0.6ms / 5730ms = 0.01% - Chunk size: 100 relationships per batch (configurable) - Storage agnostic: Works with all adapters (FileSystem, S3, R2, GCS, Memory, OPFS, TypeAware) Backward Compatible: - All new fields are optional - Existing code continues to work unchanged - Zero breaking changes This addresses the UX issue where users couldn't tell if imports were frozen during the relationship building phase for large datasets. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-16 12:08:46 -07:00
// Batch create all relationships with progress
if (relationshipParams.length > 0) {
onProgress?.({ phase: 'relationships', message: 'Building relationships...', processed: 0, total: relationshipParams.length, entities: result.entityIds.length, relationships: 0 })
try {
const relationshipIds = await this.brain.relateMany({
items: relationshipParams,
parallel: true,
chunkSize: 100,
continueOnError: true,
onProgress: (done, total) => {
onProgress?.({ phase: 'relationships', message: `Building relationships: ${done}/${total}`, processed: done, total: total, entities: result.entityIds.length, relationships: done })
}
})
result.relationshipIds = relationshipIds
result.stats.relationshipsCreated = relationshipIds.length
} catch (error: any) {
result.errors.push(`Failed to create relationships: ${error.message}`)
}
}
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
/**
* Helper: Convert PDF result to Excel-like format
*/
private convertPDFToExcelFormat(pdfResult: SmartPDFResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
const rows = pdfResult.sections.flatMap(section =>
section.entities.map(entity => ({
entity,
relatedEntities: [],
relationships: section.relationships.filter(r => r.from === entity.id),
concepts: section.concepts
}))
)
return {
rowsProcessed: pdfResult.sectionsProcessed,
entitiesExtracted: pdfResult.entitiesExtracted,
relationshipsInferred: pdfResult.relationshipsInferred,
rows,
entityMap: pdfResult.entityMap,
processingTime: pdfResult.processingTime,
stats: pdfResult.stats
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
/**
* Helper: Convert JSON result to Excel-like format
*/
private convertJSONToExcelFormat(jsonResult: SmartJSONResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
const rows = jsonResult.entities.map(entity => ({
entity,
relatedEntities: [],
relationships: jsonResult.relationships.filter(r => r.from === entity.id),
concepts: entity.metadata.concepts || []
}))
return {
rowsProcessed: jsonResult.nodesProcessed,
entitiesExtracted: jsonResult.entitiesExtracted,
relationshipsInferred: jsonResult.relationshipsInferred,
rows,
entityMap: jsonResult.entityMap,
processingTime: jsonResult.processingTime,
stats: jsonResult.stats
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
/**
* Helper: Convert Markdown result to Excel-like format
*/
private convertMarkdownToExcelFormat(mdResult: SmartMarkdownResult): Omit<SmartExcelResult, 'rows'> & { rows: any[] } {
const rows = mdResult.sections.flatMap(section =>
section.entities.map(entity => ({
entity,
relatedEntities: [],
relationships: section.relationships.filter(r => r.from === entity.id),
concepts: section.concepts
}))
)
return {
rowsProcessed: mdResult.sectionsProcessed,
entitiesExtracted: mdResult.entitiesExtracted,
relationshipsInferred: mdResult.relationshipsInferred,
rows,
entityMap: mdResult.entityMap,
processingTime: mdResult.processingTime,
stats: mdResult.stats
feat: add unified import system with auto-detection and dual storage Implemented a comprehensive unified import system that revolutionizes how data flows into Brainy: ## Core Features (Phase 1) - Auto-detection of file formats (Excel, PDF, CSV, JSON, Markdown) via magic bytes and content analysis - Dual storage architecture: creates both VFS files AND knowledge graph entities - Single unified API: brain.import() handles all formats automatically - Format-specific importers for optimal extraction from each file type - VFS structure generation with configurable grouping (by type, sheet, or flat) ## Entity Deduplication (Phase 2) - Embedding-based similarity matching to detect duplicate entities across imports - Intelligent merging with provenance tracking (records which imports contributed) - Fuzzy name matching using Levenshtein distance - Confidence score merging with weighted averages - Cross-import shared knowledge: same entity referenced in multiple datasets gets merged ## Streaming Support (Phase 3) - Chunked processing for memory-efficient handling of large datasets - Configurable chunk size for optimal performance - Progress tracking with real-time callbacks - Scales to millions of entities without memory issues ## Import History & Rollback (Phase 4) - Complete tracking of all imports with full metadata - Rollback capability to undo any import completely - Statistics and analytics across all imports - Persistent history stored in VFS ## Architecture - ImportCoordinator: orchestrates the entire import pipeline - FormatDetector: auto-detects file formats with high confidence - EntityDeduplicator: prevents duplicate entities across imports - ImportHistory: tracks and enables rollback of imports - Format-specific importers: SmartExcelImporter, SmartPDFImporter, etc. - VFSStructureGenerator: creates organized file hierarchies ## Usage ```typescript const result = await brain.import('/path/to/file.xlsx', { vfsPath: '/imports/data', groupBy: 'type', enableDeduplication: true, onProgress: (progress) => console.log(progress) }) ``` ## Production Ready - 5,500+ lines of production code - All integration tests passing - No mocks, stubs, or TODOs - Full TypeScript type safety - Comprehensive error handling - Memory efficient and scalable Closes requirements for unified data ingestion pipeline.
2025-10-08 16:55:30 -07:00
}
}
/**
* Get import statistics
*/
async getImportStatistics(vfsRootPath: string): Promise<{
entitiesInGraph: number
relationshipsInGraph: number
filesInVFS: number
lastImport?: Date
}> {
// Read metadata file
const vfs = new VirtualFileSystem(this.brain)
await vfs.init()
const metadataPath = `${vfsRootPath}/_metadata.json`
try {
const metadataBuffer = await vfs.readFile(metadataPath)
const metadata = JSON.parse(metadataBuffer.toString('utf-8'))
return {
entitiesInGraph: metadata.import.stats.entitiesExtracted,
relationshipsInGraph: metadata.import.stats.relationshipsInferred,
filesInVFS: metadata.structure.fileCount,
lastImport: new Date(metadata.import.timestamp)
}
} catch (error) {
return {
entitiesInGraph: 0,
relationshipsInGraph: 0,
filesInVFS: 0
}
}
}
}