feat(8.0): API simplification — remove neural()/Db.search, one storage path key, integration→0

8.0 RC cleanup toward "one place per thing, zero-config, no deprecation":

- Remove the `brain.neural()` clustering namespace (ImprovedNeuralAPI + the dead
  legacy NeuralAPI + the neural CLI + neural-only types). Similarity is `find({vector})`
  / `similar({to})`; attribute grouping is the aggregation `GROUP BY` engine. The separate
  entity-extraction / smart-import feature (NeuralImport, NeuralEntityExtractor, SmartExtractor,
  NaturalLanguageProcessor, `brain.extract()`/`brain.nlp()`) is kept.
- Remove `Db.search()`; `find()` is the one query verb (accepts a bare string or FindParams).
  Fix the bundled MCP client, which called a non-existent `brain.search(query, limit)` →
  now `find({ query, limit })`.
- Storage config: collapse to one canonical top-level `path` key. The pre-8.0 aliases
  (`rootDirectory`, `options.*`, `fileSystemStorage.*`) are removed and now THROW with the
  exact rename instead of silently defaulting to `./brainy-data` on upgrade. A single resolver
  feeds createStorage, the 7.x→8.0 migration probe, and the plugin-factory handoff, so a native
  storage provider resolves the identical root (no split-brain).
- Fix `similar({ threshold })`: the min-similarity filter was silently dropped; it is now
  applied as a post-filter on `result.score` (the documented way to bound semantic results).
- Fix `vfs.rename()` on a directory: child path updates spread the entity vector into `update()`
  and failed dimension validation; they are metadata-only updates now.
- Fix `vfs.move()`: copy+delete orphaned the content-addressed content blob (the destination
  shared the source hash, then unlink removed it). `move()` now delegates to `rename()` — an
  in-place path change that preserves the blob and the entity id, for files and directories.
- Fix streaming import: the bulk fast path never flushed mid-import nor signalled queryability.
  Entity writes are now chunked by a progressive flush interval (100 → 1000 → 5000); each chunk
  flushes and emits `progress.queryable`, so imported data is queryable during the import.
- Sweep all docs, comments, and JSDoc for the removed/changed APIs.

Integration suite: 49 files / 588 passed / 0 failed. Unit: 80 files / 1456 passed, no type errors.

docs/DEVELOPER_LEARNING_PATH.md

@@ -429,23 +429,7 @@ fusionResults.forEach(r => {
   }
 })
 
-// 5. NEURAL API: Automatic clustering
-console.log('\n\n🤖 NEURAL API: Automatic Clustering')
-const neural = brain.neural()
-
-const clusters = await neural.clusters({
-  maxClusters: 3,
-  minClusterSize: 1
-})
-
-console.log(`Found ${clusters.length} semantic clusters:`)
-clusters.forEach((cluster, i) => {
-  console.log(`\n  Cluster ${i + 1}: ${cluster.label || cluster.id}`)
-  console.log(`    Members: ${cluster.members.length}`)
-  console.log(`    Centroid topics: ${cluster.metadata?.topics?.join(', ') || 'N/A'}`)
-})
-
-// 6. SIMILARITY: Find similar documents
+// 5. SIMILARITY: Find similar documents
 console.log('\n\n🔍 SIMILARITY: Find Similar Documents')
 const similarTo = await brain.similar({
   to: paper1,  // Entity ID of first AI paper
@@ -459,19 +443,6 @@ similarTo.forEach(r => {
   console.log(`  [${r.score.toFixed(3)}] ${r.entity.data?.substring(0, 50)}...`)
 })
 
-// 7. OUTLIER DETECTION
-console.log('\n\n🚨 OUTLIER DETECTION')
-const outliers = await neural.outliers({
-  method: 'statistical',
-  threshold: 2.0  // 2 standard deviations
-})
-
-console.log(`Found ${outliers.length} outlier documents:`)
-outliers.forEach(o => {
-  const entity = await brain.get(o.id)
-  console.log(`  [Anomaly score: ${o.score.toFixed(3)}] ${entity?.data?.substring(0, 50)}...`)
-})
-
 await brain.close()
 ```
 
@@ -869,7 +840,7 @@ console.log('Initializing production storage...\n')
 const brain = new Brainy({
   storage: {
     type: 'filesystem',
-    rootDirectory: '/var/lib/brainy'
+    path: '/var/lib/brainy'
   },
 
   // Performance tuning
@@ -963,26 +934,6 @@ console.log(`  Failed: ${batchResult.failed.length}`)
 console.log(`  Duration: ${duration}ms`)
 console.log(`  Throughput: ${(batchResult.successful.length / (duration / 1000)).toFixed(0)} entities/sec`)
 
-// 5. ADVANCED CLUSTERING (Large Dataset)
-console.log('\n\n🤖 Clustering 1000 entities...\n')
-
-const neural = brain.neural()
-
-// Use fast clustering for large datasets
-const clusters = await neural.clusterFast({
-  maxClusters: 5
-})
-
-console.log(`Found ${clusters.length} clusters:`)
-clusters.forEach((cluster, i) => {
-  console.log(`\n  Cluster ${i + 1}: ${cluster.label || cluster.id}`)
-  console.log(`    Size: ${cluster.members.length} members`)
-  console.log(`    Density: ${(cluster.density || 0).toFixed(3)}`)
-  if (cluster.metadata?.keywords) {
-    console.log(`    Keywords: ${cluster.metadata.keywords.slice(0, 5).join(', ')}`)
-  }
-})
-
 // 6. PRODUCTION STATISTICS
 console.log('\n\n📊 Production Statistics:\n')
 
@@ -1040,7 +991,7 @@ console.log('✅ Brain closed cleanly')
 
 console.log('\n\n🎓 Production Deployment Complete!')
 console.log('\n📚 Key Production Learnings:')
-console.log('  1. Use filesystem storage and snapshot rootDirectory off-site from your scheduler')
+console.log('  1. Use filesystem storage and snapshot the data directory off-site from your scheduler')
 console.log('  2. Batch operations = 100x faster than individual ops')
 console.log('  3. Metadata query optimization for complex filters')
 console.log('  4. Monitor query performance with explain: true')
@@ -1067,7 +1018,7 @@ console.log('  7. Stream large imports with progress callbacks')
 */15 * * * *  gsutil rsync -r /var/lib/brainy gs://my-bucket/brainy-backup
 ```
 
-Brainy itself never reaches out to an object store. Snapshot `rootDirectory` from your scheduler.
+Brainy itself never reaches out to an object store. Snapshot `path` from your scheduler.
 
 #### 3. **Performance Optimization**
 
@@ -1141,7 +1092,7 @@ const brain = new Brainy({ verbose: true })
 
 #### Before Deployment
 
-- [ ] Provision a writable `rootDirectory` on the host
+- [ ] Provision a writable `path` on the host
 - [ ] Set up an off-site snapshot job (`rclone` / `aws s3 sync` / `gsutil rsync`)
 - [ ] Configure caching
 - [ ] Test batch operations