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You give it a\n[`@adhd/sox-store-adapter`](https://www.npmjs.com/package/@adhd/sox-store-adapter)\nconnection and an embedding config; it hands back a `SemanticBackend` that embeds text, upserts\nvectors, and runs ranked semantic search over your graph's nodes — without you having to hand-wire\nthe embedding provider, the vector index, and the node join yourself.\n\nBecause it is built on `@adhd/sox-store-adapter`, it inherits that adapter's concurrency model.\nTurso — the default backend — runs in `multiprocess-wal` mode: multiple OS processes can hold\nconcurrent write connections to the *same* store file, serialized through a coordinator sidecar,\nwith no opt-out. Point several CLI invocations, MCP servers, or worker processes at one store file\nand their embeds, upserts, and searches through `SemanticBackend` are all safe to run concurrently.\n(The SQLite fallback remains genuinely single-writer, per `@adhd/sox-store-adapter`'s own\ndocumented behavior.)\n\n```bash\npnpm add @adhd/sox-semantic\n```\n\n## Optional dependencies — the injected path needs no native chain\n\n`@adhd/sox-vector-store` and `@adhd/sox-embedding-provider` are declared as\n**optionalDependencies**, not `dependencies`. Each drags a native toolchain — vector-store pulls\n`sqlite-vec` / `better-sqlite3` / `lancedb`, embedding-provider pulls `onnxruntime` / `fastembed` —\nand none of it is required when you supply your own live objects.\n\nThe package resolves those two only through **lazy, non-literal dynamic imports** taken on the\ndefault capability-probe path. If you inject both `embeddingProvider` and `vectorBackend`, neither\nspecifier is ever resolved: importing the package cannot fail with `ERR_MODULE_NOT_FOUND` for them,\nand the native chain can be omitted (`npm i --omit=optional`) or fail to build without taking your\nprocess down. If a default path needs a package that is genuinely absent, `createSemanticBackend`\nreturns the typed `not_installed` failure rather than throwing.\n\n```typescript\n// No native chain required: both live objects are injected.\nconst result = await createSemanticBackend({\n  adapter,\n  embedding: { type: 'fastembed', model: 'unused' }, // never consulted — provider injected\n  embeddingProvider: myProvider,\n  vectorBackend: myVectorBackend,\n});\n```\n\n`semanticSearchNodes` additionally needs `@adhd/sox-hybrid-search` (the reciprocal-rank fusion it\ndelegates to), which is a **mandatory** dependency. It too is loaded lazily, so it costs nothing\nuntil you actually search.\n\n## Quick start\n\n```typescript\nimport { createStoreAdapter } from '@adhd/sox-store-adapter';\nimport { createGraphBackend } from '@adhd/sox-graph-store';\nimport { createSemanticBackend } from '@adhd/sox-semantic';\n\nconst adapter = await createStoreAdapter({ dbPath: 'app.db' });\n\nconst graph = createGraphBackend(adapter);\nawait graph.applySchema();\n\nconst result = await createSemanticBackend({\n  adapter,\n  embedding: { type: 'fastembed', model: 'bge-small-en-v1.5' },\n});\nif (!result.ok) {\n  throw new Error(`semantic backend unavailable: ${result.failure.reason} — ${result.failure.detail}`);\n}\nconst backend = result.backend;\n\n// Write two nodes, then embed + index each one.\nconst id1 = await graph.writeNode('Rust is a systems language with memory safety', { kind: 'generic' });\nconst id2 = await graph.writeNode('Bananas are a good source of potassium', { kind: 'generic' });\nawait backend.upsertVector(id1, await backend.embedQuery('Rust is a systems language with memory safety'));\nawait backend.upsertVector(id2, await backend.embedQuery('Bananas are a good source of potassium'));\n\n// Semantic search: embeds the query, runs vector + text ranking, joins back to graph nodes.\nconst results = await backend.semanticSearchNodes('memory-safe programming language', { limit: 5 });\nfor (const { node, score } of results) {\n  console.log(score.toFixed(4), node.content);\n}\n\nawait adapter.close();\n```\n\n## API reference\n\n### `createSemanticBackend(config): Promise<SemanticBackendResult>`\n\nThe composition seam. **Never throws for a configurable failure** — it returns a typed result.\n\n```typescript\ninterface SemanticBackendConfig {\n  /** The shared connection for graph + vector tables. */\n  adapter: StoreAdapter;\n  /** Embedding provider config ({ type, model, options }). */\n  embedding: EmbeddingProviderConfig;\n  /** Vector space; defaults to provider.metadata (modelId + dimensions). */\n  space?: { modelId: string; dim: number };\n  /** Optional injected provider; default: createEmbeddingProvider(embedding). */\n  embeddingProvider?: EmbeddingProvider;\n  /** Optional injected vector backend; default: capability-probed from adapter.capabilities.nativeVectors. */\n  vectorBackend?: VectorBackend | AsyncVectorBackend;\n}\n\ntype SemanticBackendResult =\n  | { ok: true; backend: SemanticBackend }\n  | { ok: false; failure: SemanticFailure };\n\ntype SemanticFailure =\n  | { reason: 'not_installed'; detail: string }\n  | { reason: 'provider_failed'; detail: string }\n  | { reason: 'unsupported_adapter'; detail: string }\n  | { reason: 'vector_store_failed'; detail: string };\n```\n\n### `SemanticBackend`\n\n```typescript\ninterface SemanticBackend {\n  readonly modelId: string;\n  readonly dim: number;\n\n  embedQuery(text: string): Promise<Float32Array>;\n  embedDocuments(texts: string[]): AsyncIterable<EmbedDocumentResult>;\n\n  upsertVector(nodeId: number, vec: Float32Array): Promise<void>;\n  upsertVectors(items: Array<{ nodeId: number; vec: Float32Array }>): Promise<void>;\n  deleteVector(nodeId: number): Promise<void>;\n\n  semanticSearchNodes(\n    query: string,\n    opts?: SemanticSearchOpts,\n  ): Promise<Array<{ node: NodeRecord; score: number }>>;\n\n  health(): EmbeddingHealth;\n}\n\ninterface SemanticSearchOpts {\n  nodeFilter?: NodeFilter;  // scope the search — see @adhd/sox-graph-store's NodeFilter\n  limit?: number;           // default 10\n  offset?: number;          // default 0\n  liveOnly?: boolean;       // default true\n}\n\ninterface EmbedDocumentResult {\n  index: number;        // position in the input texts array\n  vec?: Float32Array;   // present on success\n  error?: string;       // present on per-document failure (never both)\n}\n```\n\n`semanticSearchNodes` embeds the query, ranks candidate nodes by both text relevance\n(`graph.searchNodes`) and vector similarity (`knn`), fuses the two rankings with reciprocal-rank\nfusion, and returns nodes joined back from the graph — so a title/body text match surfaces even\nwhen its vector isn't the nearest neighbor.\n\n## Batch embedding — per-item error containment\n\n`embedDocuments` never lets one bad document abort the batch, and it never fabricates a\nplaceholder vector for a failed item:\n\n```typescript\nfor await (const result of backend.embedDocuments(['first doc', 'second doc', 'third doc'])) {\n  if (result.error) {\n    console.error(`document ${result.index} failed to embed: ${result.error}`);\n    continue;\n  }\n  await backend.upsertVector(nodeIdForIndex(result.index), result.vec!);\n}\n```\n\n## The embedding-lifecycle observer\n\n`createEmbeddingObserver` wires embedding into your graph's write path: embed-on-write,\ndelete-on-invalidate. Register it on your own graph backend (not the facade's internal one):\n\n```typescript\nimport { createGraphBackend } from '@adhd/sox-graph-store';\nimport { createEmbeddingObserver } from '@adhd/sox-semantic';\n\nconst observer = createEmbeddingObserver(backend);\nconst observedGraph = createGraphBackend(adapter, { observers: [observer] });\n\n// Every writeNode() through observedGraph now embeds + upserts a vector automatically.\nawait observedGraph.writeNode('a new fact', { kind: 'generic' });\n```\n\nA failed embed degrades the write — it never corrupts it. No placeholder vectors are ever written,\nand the original node write is never rolled back; a reconciliation pass can re-embed the gap later.\n`onNodeInvalidated` deletes the corresponding vector, tolerating an already-missing vector as a\nno-op rather than an error.\n\n## Invariants\n\n- `createSemanticBackend` returns a typed result, never throws, for any configurable failure\n  (`not_installed` / `provider_failed` / `unsupported_adapter` / `vector_store_failed`).\n- The DI-injected path resolves **neither** optional native-chain package\n  (`@adhd/sox-vector-store`, `@adhd/sox-embedding-provider`). Loading the module with both injected\n  cannot throw `ERR_MODULE_NOT_FOUND` for either — asserted by a resolve-hook guard against the\n  built artifact in `src/optional-loadability.spec.ts`.\n- A failed embed degrades the write, never corrupts it — no placeholder vectors, no rolled-back\n  node write.\n- `score` from `semanticSearchNodes` is a reciprocal-rank-fusion magnitude across the text and\n  vector channels, not a raw cosine similarity — don't assume it's bounded to `[0, 1]`.\n- Live objects (the open DB connection, the provider instance, the vector backend) cross into this\n  package via dependency injection over your `StoreAdapter` — this package never opens a second,\n  duplicate connection to your store.\n","readmeFilename":"README.md"}