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fix(vfs): prevent race condition in bulkWrite by ordering operations

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- Process mkdir operations sequentially first (sorted by path depth)
- Then process write/delete/update operations in parallel batches
- Prevents duplicate directory entities when mkdir and write for
  related paths are in the same batch
- Add comprehensive tests for bulkWrite race condition scenarios
- Update API documentation for accuracy

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
David Snelling 2025-12-11 13:26:07 -08:00
parent ec6fe0c039
commit c8eb813a15
7 changed files with 906 additions and 340 deletions
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51
docs/guides/neural-api.md
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@ -4,7 +4,7 @@
## Overview
The Neural API provides advanced AI-powered features for understanding relationships and patterns in your data. Access it through `brain.neural` after initializing Brainy.
The Neural API provides advanced AI-powered features for understanding relationships and patterns in your data. Access it through `brain.neural()` (method call) after initializing Brainy.
## Quick Start
@ -14,8 +14,8 @@ import { Brainy } from '@soulcraft/brainy'
const brain = new Brainy()
await brain.init()
// Access Neural API
const neural = brain.neural
// Access Neural API (note: neural() is a method call)
const neural = brain.neural()
// Find similar items
const similarity = await neural.similar('text1', 'text2')
@ -235,49 +235,64 @@ const sameTopicArticles = currentTopic.members
### Customer Feedback Analysis
```javascript
import { NounType } from '@soulcraft/brainy'
// Add feedback with metadata
const feedbackIds = []
for (const feedback of customerFeedback) {
const id = await brain.add(feedback.text, {
rating: feedback.rating,
date: feedback.date,
product: feedback.product
const id = await brain.add({
data: feedback.text,
type: NounType.Document,
metadata: {
rating: feedback.rating,
date: feedback.date,
product: feedback.product
}
})
feedbackIds.push(id)
}
// Cluster to find themes
const neural = brain.neural()
const themes = await neural.clusters(feedbackIds)
// Analyze each theme
for (const theme of themes) {
const items = await brain.getNouns(theme.members)
const avgRating = items.reduce((sum, item) =>
sum + item.metadata.rating, 0) / items.length
// Get items using Promise.all with brain.get()
const items = await Promise.all(
theme.members.map(id => brain.get(id))
)
const avgRating = items.reduce((sum, item) =>
sum + (item?.metadata?.rating || 0), 0) / items.length
console.log(`Theme with ${theme.members.length} items`)
console.log(`Average rating: ${avgRating}`)
// Find representative feedback for this theme
const centroidId = theme.members[0] // Closest to center
const example = await brain.getNoun(centroidId)
console.log(`Example: "${example.data}"`)
const example = await brain.get(centroidId)
console.log(`Example: "${example?.data}"`)
}
```
### Knowledge Base Organization
```javascript
// Analyze existing knowledge base
const allDocs = await brain.getNouns({ type: 'document' })
import { NounType } from '@soulcraft/brainy'
// Analyze existing knowledge base - use find() to get documents
const allDocs = await brain.find({ type: NounType.Document, limit: 1000 })
// Access neural API
const neural = brain.neural()
// Find duplicate or highly similar content
const duplicates = []
for (let i = 0; i < allDocs.length; i++) {
for (let j = i + 1; j < allDocs.length; j++) {
const similarity = await neural.similar(
allDocs[i].id,
allDocs[i].id,
allDocs[j].id
)
if (similarity > 0.95) {