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Cruwys (AppyDave)","license":"MIT","homepage":"https://github.com/KybernesisAI/cortex/tree/main/packages/brain-storage-vec#readme","repository":{"url":"git+https://github.com/KybernesisAI/cortex.git","type":"git","directory":"packages/brain-storage-vec"},"description":"sqlite-vec VectorStore implementation for brain-core (1536-dim, cap 8192)","maintainers":[{"name":"ianborders","email":"ian@kybernesis.ai"},{"name":"klueless-io","email":"david@ideasmen.com.au"}],"readme":"# @kybernesis/brain-storage-vec\n\n[![npm](https://img.shields.io/npm/v/@kybernesis/brain-storage-vec)](https://www.npmjs.com/package/@kybernesis/brain-storage-vec)\n\nThe **`VectorRepository`** for [Cortex](../../README.md) — a [sqlite-vec](https://github.com/asg017/sqlite-vec)\nimplementation of the vector store. It holds the embeddings that power semantic recall.\n\n**Used with [`@kybernesis/brain-core`](../brain-core), via\n[`@kybernesis/brain-storage-sqlite`](../brain-storage-sqlite).** The SQLite storage provider\nlazy-loads this package for its vector store — you don't usually import it directly, you just\ninstall it alongside the SQLite provider.\n\n## Install\n\n```bash\npnpm add @kybernesis/brain-storage-vec @kybernesis/brain-storage-sqlite @kybernesis/brain-core\n```\n\nPeer dependencies (host-provided): `better-sqlite3` (`>= 9`) and `sqlite-vec` (`>= 0.1`).\nThis package carries the **native sqlite-vec binary**, which is why it's split out from the\nSQLite provider and loaded lazily.\n\n## How it fits\n\nYou typically never call this directly — wiring the SQLite provider is enough:\n\n```ts\nimport { createSqliteStorageProvider } from '@kybernesis/brain-storage-sqlite';\nimport { setStorageProvider, indexChunk, semanticSearch } from '@kybernesis/brain-core';\nimport { createOpenAIEmbedder } from '@kybernesis/brain-embed-openai';\nimport { setEmbeddingProvider } from '@kybernesis/brain-core';\n\nsetStorageProvider(createSqliteStorageProvider());  // lazy-loads brain-storage-vec\nsetEmbeddingProvider(createOpenAIEmbedder());\n\nawait indexChunk(tenant, 'apples are red', { ts: '2026-06-06T09:00:00Z', origin_id: 'a' });\nawait semanticSearch(tenant, 'fruit colour');\n```\n\n## Direct API (advanced)\n\nFor tooling or custom providers, the low-level handle is exported:\n\n```ts\nimport { getVectorDb, vectorCount, toFloat32Buffer, closeAllVectorDbs } from '@kybernesis/brain-storage-vec';\n```\n\nThe schema (matching KAD's `vectors.db`): a single `chunks` `vec0` virtual table\n(`embedding float[1536]`) plus a `chunk_meta` table for the searchable fields.\n\n## Constraint: 1536 dimensions\n\nThe schema is fixed at `EMBEDDING_DIM = 1536` (`text-embedding-3-small`). A different\nembedding model must emit 1536-dim vectors, or `EMBEDDING_DIM` and the schema move together\nand you re-index. Don't mix models within one populated `vectors.db` — distances across\nmodels aren't comparable.\n\n## Notes\n\n- ESM-only, TypeScript strict. Native module — needs the `sqlite-vec` binary for your platform.\n- Search is brute-force `vec_distance_l2` (port-faithful to KAD) — fine at brain scale.\n- Part of [Cortex](../../README.md) — `@kybernesis/brain-*`.\n","readmeFilename":""}