David Snelling
35b9d7ef43
The old config-generation subsystem (src/config/ + autoConfiguration.ts) was
superseded during the 8.0 rework and never wired into init(): it emitted settings
for a partitioning subsystem that no longer exists and probed deleted cloud env
vars. The live zero-config path is inline — recall preset → HNSW knobs, storage
auto-detect, auto persistMode, container-memory-aware cache sizing.
The storage progressive-init / cloud-detection cluster was equally dead after the
cloud adapters were dropped: isCloudStorage() is permanently false (no overriders),
scheduleBackgroundInit/runBackgroundInit were never called (the latter an empty
body), initMode was never assigned, and Brainy.isFullyInitialized()/
awaitBackgroundInit() were always-trivial with zero callers. scheduleCountPersist()
collapses to its only-ever-taken immediate write-through path.
Removed:
- src/config/{index,zeroConfig,storageAutoConfig,modelAutoConfig,sharedConfigManager}.ts
- src/utils/autoConfiguration.ts + the inert BrainyZeroConfig export
- Brainy.isFullyInitialized()/awaitBackgroundInit() (+ BrainyInterface decls)
- InitMode type, isCloudStorage/detectCloudEnvironment/resolveInitMode,
scheduleBackgroundInit/runBackgroundInit/ensureValidatedForWrite and their state
- Dead cloud env-var probes (K_SERVICE/K_REVISION/AWS_LAMBDA_FUNCTION_NAME/
FUNCTIONS_TARGET/AZURE_FUNCTIONS_ENVIRONMENT)
Kept (verified live): production-detection logging (environment.ts), container-
memory cache sizing (memoryDetection/paramValidation), on-disk hash bucketing
(sharding.ts).
Docs: scrubbed deleted-subsystem references (JS quantization knobs, cloud/OPFS
adapters, partitioning, old zero-config API) across 14 files; deleted two wholly-
obsolete feature docs (complete-feature-list, v3-features); rewrote
architecture/zero-config for 8.0.
~3,700 LOC removed. Build clean; 1392 unit + 24 db-mvcc green.
111 lines
3.3 KiB
Markdown
111 lines
3.3 KiB
Markdown
---
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title: Quick Start
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slug: getting-started/quick-start
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public: true
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category: getting-started
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template: guide
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order: 2
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description: Build your first knowledge graph in 60 seconds. Add entities, create relationships, and query with Triple Intelligence — vector + graph + metadata in one call.
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next:
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- concepts/triple-intelligence
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- api/reference
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---
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# Quick Start
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Get Brainy running in under a minute.
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## 1. Install
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```bash
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npm install @soulcraft/brainy
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```
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## 2. Initialize
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```typescript
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import { Brainy, NounType, VerbType } from '@soulcraft/brainy'
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const brain = new Brainy()
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await brain.init()
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```
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That's it. Brainy auto-configures storage, loads the embedding model, and builds the indexes.
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## 3. Add Knowledge
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```typescript
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// Text is automatically embedded into 384-dim vectors
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const reactId: string = await brain.add({
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data: 'React is a JavaScript library for building user interfaces',
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type: NounType.Concept,
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subtype: 'library', // Sub-classification within Concept
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metadata: { category: 'frontend', year: 2013 }
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})
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const nextId: string = await brain.add({
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data: 'Next.js framework for React with server-side rendering',
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type: NounType.Concept,
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subtype: 'framework',
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metadata: { category: 'framework', year: 2016 }
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})
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```
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`type` is one of Brainy's 42 stable NounTypes. `subtype` is your free-form sub-classification within that type — flat string, no hierarchy, indexed on the fast path. See **[Subtypes & Facets](./subtypes-and-facets.md)** for the full guide.
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## 4. Create Relationships
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```typescript
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// Typed graph relationships
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await brain.relate({
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from: nextId,
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to: reactId,
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type: VerbType.BuiltOn
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})
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```
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## 5. Query with Triple Intelligence
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```typescript
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import type { FindResult } from '@soulcraft/brainy'
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// All three search paradigms in one call
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const results: FindResult[] = await brain.find({
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query: 'modern frontend frameworks', // Vector similarity search
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where: { year: { greaterThan: 2015 } }, // Metadata filtering
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connected: { to: reactId, depth: 2 } // Graph traversal
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})
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console.log(results[0].data) // 'Next.js framework for React...'
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console.log(results[0].score) // 0.94
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```
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## What Just Happened
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Every entity you `add()` lives in three indexes simultaneously:
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| Index | What it stores | Query with |
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|-------|---------------|------------|
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| Vector | 384-dim embedding of `data` | `find({ query: '...' })` |
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| Metadata | All `metadata` fields | `find({ where: { ... } })` |
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| Graph | Typed relationships from `relate()` | `find({ connected: { ... } })` |
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`find()` queries all three in parallel and fuses the results.
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## Natural Language Queries
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Brainy understands 220+ natural language patterns:
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```typescript
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// These all work without any configuration
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await brain.find({ query: 'recent documents about machine learning' })
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await brain.find({ query: 'articles created this week' })
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await brain.find({ query: 'people who work at Anthropic' })
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```
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## Next Steps
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- [Triple Intelligence](/docs/concepts/triple-intelligence) — understand how the query engine works
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- [The Find System](/docs/guides/find-system) — advanced queries, operators, and graph traversal
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- [API Reference](/docs/api/reference) — complete method documentation
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- [Storage Adapters](/docs/guides/storage-adapters) — filesystem, memory
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