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OKF v0.2 (llm-wiki/2) conformance with back-compat to v0.1 (llm-wiki/1) and profile-0 bundles. Episodic fact extraction, semantic vector search, multi-agent architectures over SQLite. Bring your o","maintainers":[{"name":"equationalapplications-admin","email":"info@equationalapplications.com"}],"readme":"# @equationalapplications/core-llm-wiki\n\nPlatform-agnostic TypeScript engine for hybrid LLM memory. Features episodic fact extraction, semantic vector search, and multi-agent architectures over SQLite. Bring your own adapter.\n\n[![npm version](https://img.shields.io/npm/v/%40equationalapplications%2Fcore-llm-wiki?label=core)](https://www.npmjs.com/package/@equationalapplications/core-llm-wiki) [![npm downloads](https://img.shields.io/npm/dm/%40equationalapplications%2Fcore-llm-wiki?label=downloads)](https://www.npmjs.com/package/@equationalapplications/core-llm-wiki)\n[![TypeScript](https://img.shields.io/badge/TypeScript-5.x-3178C6?logo=typescript&logoColor=white)](https://www.typescriptlang.org/)\n[![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://github.com/equationalapplications/expo-llm-wiki/blob/main/packages/core/LICENSE)\n\n**[GitHub](https://github.com/equationalapplications/expo-llm-wiki)** · **[ScopeLab](https://equationalapplications.github.io/expo-llm-wiki/scopelab/)** · **[WikiDemo](https://equationalapplications.github.io/expo-llm-wiki/wiki-demo/)** · **[Changelog](https://github.com/equationalapplications/expo-llm-wiki/blob/main/CHANGELOG.md)** · **[Issues](https://github.com/equationalapplications/expo-llm-wiki/issues)**\n\n> Inspired by [Andrej Karpathy's LLM Wiki memory spec](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f).\n\n\n## Features\n\n- **Platform-agnostic** — Zero runtime dependencies; works with any SQLite driver via the `SQLiteAdapter` interface\n- **Semantic search** — Vector embeddings via your LLM's `embed` function, ranked by cosine similarity\n- **Keyword fallback** — [MiniSearch](https://github.com/lucaong/minisearch) in-memory index for offline/degraded scenarios when embeddings unavailable\n- **Retrieval tuning** — Per-call overrides for `maxResults`, `preFilterLimit`, `hybridWeight`, `tierWeights`, `tierFloors`, and `includeZeroWeightEntities`\n- **Multi-entity reads** — Search across multiple `entity_id` namespaces in one pass with per-entity score multipliers (`tierWeights`); `tierFloors` reserves each namespace's top-N matching results; optional `factScores` and `metadata` for explainability\n- **Immutable vs mutable facts** — Use `WikiFact.source_type` to distinguish document-sourced facts (`immutable_document`) from derived or user-provided facts (`librarian_inferred`, `user_stated`, `user_confirmed`). Immutable document facts are not rewritten by `runLibrarian()` or `runHeal()` and can only be removed by `forget()` or re-ingesting.\n- **Full-featured memory** — Facts, tasks, events, maintenance jobs (librarian, heal, reembed, prune)\n- **Type-safe** — Built with TypeScript, full type exports\n- **Interoperability:** Supports [Open Knowledge Format (OKF)](https://github.com/GoogleCloudPlatform/knowledge-catalog/tree/main/okf) v0.1 + v0.2 import and export via the [llm-wiki OKF profiles](https://github.com/equationalapplications/expo-llm-wiki/blob/main/docs/okf-profile.md) (default `llm-wiki/2`, back-compat `llm-wiki/1`).\n- **Per-entity seeded ontology** — Optional Strict, Emergent, or Off modes govern LLM graph extraction; seed taxonomies per entity and persist typed facts with inline edges.\n- **Diagnostics** — Optional `onDiagnostic` hook with typed, content-free reports of dropped chunks, facts, edges, embedding failures and background-job failures ([Diagnostics](#diagnostics))\n- **Draft review** — `excludeDrafts` on reads and traversal, plus `listDrafts` / `promoteDraft` ([Draft Review](#draft-review))\n- **Evidence grounding** — Opt-in `grounding` check; facts that don't quote their source are stored as drafts ([Grounding](#grounding))\n- **Optional classifier** — `LLMProvider.classify` types facts during ontology backfill when you opt in with `ontology.backfillClassifier: 'auto'` ([Ontology backfill](#ontology-backfill))\n\n## GraphRAG & Multi-Modal Retrieval\n\n`@equationalapplications/core-llm-wiki` exposes three complementary retrieval modes, each addressing a different shape of query:\n\n| Mode | API | Best for |\n|---|---|---|\n| **Semantic** (vector cosine) | `wiki.read(entityId, query)` with `embed` configured | Open-ended natural-language questions; \"what do I know about X\" |\n| **Keyword** (MiniSearch) | `wiki.read(entityId, query)` with `embed` absent or offline | Exact terms, identifiers, names; offline fallback |\n| **GraphRAG** (recursive CTE) | `wiki.traverseGraph(entityId, options)` + `formatGraphContext(result)` | Structural questions; \"what connects to X\", \"everything two hops from this fact\", \"summarise the people, places, and projects linked to Alice\" |\n\nThe GraphRAG path is structurally distinct: it doesn't rank by relevance to a query string, it walks `llm_wiki_edges` from a known anchor fact. The result is dense and connected — subgraphs, not loose top-K hits.\n\n### Graph traversal APIs\n\n```typescript\nimport { WikiMemory, formatGraphContext } from '@equationalapplications/core-llm-wiki';\n\nconst graph = await wikiMemory.traverseGraph('user-123', {\n  sourceId: '<anchor-fact-id>',\n  maxDepth: 2,\n  direction: 'both',          // 'inbound' | 'outbound' | 'both'\n  edgeTypes: ['reports_to'],   // optional filter\n  excludeSourceTypes: ['immutable_document'],\n  minTraversalConfidence: 'inferred',\n  maxTraversalNodes: 20,\n});\n\nconst promptContext = formatGraphContext(graph);\n// → dense text block ready for prompt injection\n```\n\n`traverseGraph` runs as a single recursive CTE in SQLite (see the root README's [\"The SQL: how traversal works in one query\"](../../README.md#the-sql-how-traversal-works-in-one-query) for the query shape). No external graph database.\n\n### Deterministic graph seeding (no LLM)\n\nFor programmatic pipelines — importing pre-classified data, building a GraphRAG corpus from a CSV, or seed-loading from a JSON file — use `upsertGraph()`. It writes nodes and edges directly under the same `(sourceRef, sourceHash)` ownership semantics as `ingestDocument()`, but skips the LLM extraction step. See [Direct Graph Write](#direct-graph-write) for the canonical signature, including the required `SQLiteAdapter` argument and transactional semantics.\n\nThis is the GraphRAG seed path: load a corpus, walk it.\n\n## Installation\n\n```bash\nnpm install @equationalapplications/core-llm-wiki\n```\n\n## Semantic Search with Embeddings\n\nProvide an `embed` function in `llmProvider` to enable vector-based retrieval:\n\n```typescript\nimport { WikiMemory } from '@equationalapplications/core-llm-wiki';\n\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: {\n    generateText: async ({ systemPrompt, userPrompt }) => {\n      // Your LLM call for extracting facts, tasks\n      return 'Model output';\n    },\n    maxOutputTokens: 4096, // optional — lets runHeal/runOntologyBackfill size their first LLM call correctly instead of discovering the ceiling via a truncated response and retrying smaller\n    embed: async (text: string) => {\n      // Your embedding service (e.g., OpenAI, Cohere, local)\n      const response = await fetch('https://your-app.example.com/api/embed', {\n        method: 'POST',\n        body: JSON.stringify({ text }),\n      });\n      const { embedding } = await response.json();\n      return embedding; // number[]\n    },\n  },\n});\n\nawait wikiMemory.setup();\n\n// Query with semantic matching\nconst memory = await wikiMemory.read('user-123', 'What should I do this weekend?');\n// Returns facts semantically similar to the query, not lexical matches\n// E.g., fact \"Saturday hiking trip\" ranks high even though no lexical overlap\n```\n\nWhen `embed` is unavailable, `read()` silently falls back to MiniSearch keyword search. If an embedding attempt throws, `read()` falls back and calls `onRetrievalFallback` if provided:\n\n```typescript\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: {\n    generateText: async () => { /* ... */ },\n    embed: undefined, // or throws on network error\n  },\n  onRetrievalFallback: (error) => {\n    console.warn('Embedding retrieval unavailable, using keyword search:', error);\n  },\n});\n\n// read() returns MiniSearch results, onRetrievalFallback not called (embed absent is expected)\n// read() returns MiniSearch results, onRetrievalFallback called (embed threw)\n```\n\n## Configuration\n\nAll `WikiConfig` fields are optional:\n\n```typescript\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: { /* ... */ },\n  config: {\n    tablePrefix: 'llm_wiki_',          // default: 'llm_wiki_'\n    maxResults: 10,                    // default: 10\n    autoLibrarianThreshold: 20,        // default: 20 — events before librarian auto-runs\n    autoHealThreshold: 100,            // default: 100 — events before heal auto-runs\n    maxChunkLength: 12000,             // default: 12000 (char count per ingestDocument chunk; exported as DEFAULT_MAX_CHUNK_LENGTH)\n    chunkOverlap: 400,                 // default: 400 (overlap between chunks in characters; exported as DEFAULT_CHUNK_OVERLAP)\n    chunkConcurrency: 1,               // default: 1 (parallel LLM calls per ingestDocument)\n    maxEmbedChars: 6000,               // default: 6000 (chars of title+body+tags sent to embed(); hard ceiling 16000; exported as DEFAULT_MAX_EMBED_CHARS)\n    pruneRetainSoftDeletedFor: 7,      // default: 7 (days before hard-deleting soft-deleted facts)\n    pruneEventsAfter: 30,              // default: 30 (days before hard-deleting old events)\n    orphanAfterDays: 30,               // default: 30 (days before runHeal flags sourceless facts; null to disable)\n    staleInferredAfterDays: 60,        // default: 60 (days before runHeal downgrades inferred facts; null to disable)\n    preFilterLimit: 50,                // default: undefined — MiniSearch pre-filter before cosine scan; recommended for >500 facts\n    hybridWeight: 0.7,                 // default: undefined — blend semantic (1.0) ↔ keyword (0.0); pure semantic when unset\n    enableOutbox: false,               // default: false — when true, entry/task mutations write to an internal SQLite outbox table for external sync (e.g. via @equationalapplications/prisma-outbox)\n    excludeDrafts: false,              // default: false — engine default for read()/traverseGraph() excludeDrafts; see Draft Review\n    grounding: { mode: 'off' },        // default: off — 'draft' checks evidence quotes; see Grounding\n    ontology: {\n      // mode, seedManifests: see Per-Entity Seeded Ontology\n      backfillClassifier: 'llm',       // default: 'llm' — 'auto' uses llmProvider.classify when present; see Ontology backfill\n      classifyMinConfidence: 0.5,      // default: 0.5 — classifier answers below this are left untyped\n    },\n\n    // Global prompt overrides — librarianSystemPrompt and healSystemPrompt apply to write() auto-runs;\n    // ingestSystemPrompt applies only to explicit ingestDocument() calls.\n    // ⚠ Overrides replace the entire default prompt, including the JSON output contract.\n    // See \"JSON Output Contracts\" in the Prompt Management & Overrides section below.\n    prompts: {\n      ingestSystemPrompt: `Extract core facts from this document: {{documentChunk}}\\n\\nReturn ONLY valid JSON: { \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }] }. No markdown.`,\n      librarianSystemPrompt: `You are an expert curator. Synthesize these thoughts:\\n{{events}}\\n\\nCurrent Facts:\\n{{currentFacts}}\\n\\nReturn ONLY valid JSON: { \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }], \"tasks\": [{ \"description\": \"string\", \"priority\": 0 }] }. No markdown.`,\n      healSystemPrompt: `Fix the memory graph based on these candidates: {{healCandidates}}\\n\\nReturn ONLY valid JSON: { \"downgraded\": [\"factId\"], \"deleted\": [\"factId\"], \"newFacts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }] }. No markdown.`,\n    },\n  },\n  // Host callbacks sit beside llmProvider, not inside config:\n  // onDiagnostic: (d) => { ... }, // see Diagnostics\n});\n```\n\n## Prompt Management & Overrides\n\nCore maintenance tasks (`ingestDocument`, `runLibrarian`, `runHeal`) use system prompts to instruct the LLM. You can customize these prompts using `{{mustache}}` style variables to inject context dynamically.\n\n> **JSON Output Contracts:** Prompt overrides replace the *entire* default system prompt, including the JSON response schema the parser depends on. Your override **must** instruct the LLM to return raw JSON — no markdown. Required shapes:\n>\n> | Operation | Required JSON shape |\n> |-----------|-------------------|\n> | `ingestDocument` | `{ \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain\\|inferred\\|tentative\" }] }` |\n> | `runLibrarian` | `{ \"facts\": [...], \"tasks\": [{ \"description\": \"string\", \"priority\": 5 }] }` — `priority` is an integer 0–10 |\n> | `runHeal` | `{ \"downgraded\": [\"factId\"], \"deleted\": [\"factId\"], \"newFacts\": [...] }` |\n>\n> **Grounding:** when [`config.grounding`](#grounding) is on, the evidence instruction is appended after your override (and after ontology context) for every writer in `grounding.writers`. Your override does not need to ask for `evidence` itself.\n\n### Global Overrides (Auto-Runs)\n\nIf your application relies on `write()` to automatically maintain the memory graph in the background (via `autoLibrarianThreshold` and `autoHealThreshold`), configure custom prompts globally at instantiation. This ensures the internal `WriteService` uses your domain-specific instructions when it triggers an auto-run.\n\n```typescript\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider,\n  config: {\n    prompts: {\n      // Override must include the JSON output contract — it replaces the entire default prompt.\n      librarianSystemPrompt: `You are an expert curator. Synthesize these thoughts:\\n{{events}}\\n\\nCurrent Facts:\\n{{currentFacts}}\\n\\nReturn ONLY a valid JSON object: { \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }], \"tasks\": [{ \"description\": \"string\", \"priority\": 0 }] }. No markdown.`,\n    },\n  },\n});\n\n// WriteService uses the global prompt whenever autoLibrarianThreshold is hit\nawait wikiMemory.write('user-123', { event_type: 'observation', summary: '...' });\n```\n\nAvailable `{{variables}}` per prompt type:\n\n| Prompt | Variables |\n|--------|-----------|\n| `ingestSystemPrompt` | `{{documentChunk}}` |\n| `librarianSystemPrompt` | `{{events}}`, `{{currentFacts}}` |\n| `healSystemPrompt` | `{{healCandidates}}`, `{{documentAnchors}}`, `{{allTasks}}`, `{{recentEvents}}` |\n\nWhen a template contains `{{variable}}` tags, the matching data is hydrated directly into `systemPrompt` and a short fixed string is used as `userPrompt`. When a template has no `{{}}` tags, the raw data is appended as `userPrompt` — backward compatible with plain-string overrides.\n\n### Runtime Overrides (Manual Execution)\n\nPass `promptOverride` per-call for one-off instructions. **Runtime overrides apply only to that single call — they do not persist for future auto-runs triggered by `write()`.**\n\n```typescript\n// Override the base default AND global config for this single execution.\n// Each override must include the JSON output contract (replaces the entire default prompt).\nawait wikiMemory.runLibrarian('user-123', {\n  promptOverride: `One-off extraction task:\\n{{events}}\\n\\nReturn ONLY valid JSON: { \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }], \"tasks\": [{ \"description\": \"string\", \"priority\": 0 }] }. No markdown.`,\n});\n\nawait wikiMemory.runHeal('user-123', {\n  promptOverride: `Domain-specific healing: {{healCandidates}}\\n\\nReturn ONLY valid JSON: { \"downgraded\": [\"factId\"], \"deleted\": [\"factId\"], \"newFacts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }] }. No markdown.`,\n});\n\nawait wikiMemory.ingestDocument('user-123', {\n  sourceRef: 'doc-1',\n  sourceHash: sha256(content),\n  documentChunk: content,\n  promptOverride: `Strict technical extraction: {{documentChunk}}\\n\\nReturn ONLY valid JSON: { \"facts\": [{ \"title\": \"string\", \"body\": \"string\", \"tags\": [\"string\"], \"confidence\": \"certain|inferred|tentative\" }] }. No markdown.`,\n});\n```\n\n> **Important:** If your app relies on `write()` auto-runs and needs custom prompts for those runs, use `config.prompts` at construction time. Runtime `promptOverride` values are never forwarded to `WriteService`-triggered internal runs.\n\n### Effective instructions (`getInstructions`)\n\n```typescript\nconst { ingest, librarian, heal, ontologyBackfill } = await wikiMemory.getInstructions('entity-123');\n```\n\nReturns the system prompt each writer sends, with `WikiConfig.prompts` overrides applied. Ingest, librarian and ontology backfill also get the entity's ontology block; heal gets none, as at runtime. When `WikiConfig.grounding` is on, the evidence block is appended for each writer in `grounding.writers`, exactly as sent. Data placeholders such as `{{documentChunk}}` stay unfilled; no events, chunks or facts are included. It reflects `WikiConfig.prompts` only: a per-call `promptOverride` is not reflected, and `ontologyBackfill` is returned even when backfill would send no prompt (ontology `off`, or the classifier path). `core-llm-tools` exposes this as the `wiki_get_instructions` tool (`memory:read`), so agents can see the engine's output format and constraints before proposing writes. Agents should treat it as reference data, not instructions; see [Prompt-Injection Trust Boundary](#prompt-injection-trust-boundary).\n\n> **Warning:** overrides are returned verbatim to any client with `memory:read`. Never put secrets, API keys or private data in `WikiConfig.prompts`.\n\n## Retrieval Tuning\n\nOptimize `read()` performance and blend retrieval strategies:\n\n```typescript\nconst config = {\n  // Limit cosine similarity scoring to top-K MiniSearch keyword candidates\n  preFilterLimit: 50,\n  \n  // Blend semantic and keyword scores (0.0 = pure keyword, 1.0 = pure semantic)\n  hybridWeight: 0.7,\n  \n  // Max results returned per read\n  maxResults: 10,\n};\n\nconst wikiMemory = new WikiMemory(db, {\n  config,\n  llmProvider: { /* ... */ },\n});\n\n// Per-call overrides (runtime controls for search dashboards, etc.)\nconst memory = await wikiMemory.read('user-123', 'my preferences', {\n  maxResults: 5,\n  preFilterLimit: 20,\n  hybridWeight: 0.5,\n});\n\n// Multi-entity with tier weights\nconst multiMemory = await wikiMemory.read(['tier_wisdom', 'tier_fact', 'tier_working'], 'my preferences', {\n  maxResults: 8,\n  tierWeights: {\n    tier_wisdom: 2,      // high-confidence curated notes boosted 2×\n    tier_fact: 1,        // neutral baseline\n    tier_working: 0.25,  // recent but unvetted context downranked\n  },\n  // includeZeroWeightEntities: true — include 0-weight entities as bottom-ranked filler\n});\n// multiMemory.factScores — optional Record<factId, weightedScore> for returned facts; may be absent/undefined\n// multiMemory.metadata  — optional { query, entityIds, tierWeights, tierFloors }; may be absent/undefined\n```\n\n**Hybrid scoring blends:**\n- `hybridWeight: 1.0` → all-semantic blend with semantic scores clamped to non-negative range (no keyword component)\n- `hybridWeight: 0.5` → balanced semantic + keyword (50/50 blend)\n- `hybridWeight: 0.0` → pure keyword ranking, skips `embed()` entirely (no LLM API cost)\n\nTrue cosine-range pure semantic ranking (including negative cosine values) is used when `hybridWeight` is left `undefined`.\n\n**Tier weights:**\n- `tierWeights` applies a per-entity multiplier after semantic/keyword scoring: `finalScore = retrievalScore × weight`\n- Missing weights default to `1.0`. Negative weights clamp to `0`. Non-finite weights default to `1.0`.\n- `tierWeights[entity] = 0` skips that entity's scored retrieval branch (no compute cost).\n- `includeZeroWeightEntities: true` includes zero-weight entities as bottom-ranked filler instead of skipping them.\n- `tierFloors: Record<string, number>` reserves that entity's top-N scored results before the global `maxResults` cut — a floor on entity `X` means at least `N` of the returned facts come from `X`. Floors apply *after* scoring (including `tierWeights`) and *after* `preFilterLimit`'s candidate selection, so a row excluded by `preFilterLimit` cannot be resurrected by a floor. An entity with fewer matching facts than its floor contributes what it has — this is not an error. Only meaningful when `entityId` is an array; ignored for single-string calls and for empty-query (\"recent facts\") reads. Throws `WikiInvalidReadOptions` when a floor cannot be satisfied by construction: floors summing above `maxResults`, a floor keyed to an entity not in `entityId`, or a floor on an entity excluded by `tierWeights: 0` when `includeZeroWeightEntities` is not set. With an external `vectorRanker`, ranker-omitted rows (typically un-embedded facts) for a floored entity are pre-reserved before the global backfill budget is spent, so an unembedded floored entity still satisfies its floor.\n- `factScores` is present for array-shaped `entityId` calls only when the query is non-empty and at least one fact is scored; empty-query (\"recent facts\") reads leave it absent even when `entityId` is an array. Plain string calls never expose it. `metadata` is present for all array-shaped calls regardless of query.\n- `maxResults` applies globally across all requested entities.\n- Tasks are capped at `min(20 × entityCount, 200)`; events at `min(10 × entityCount, 100)` for multi-entity reads.\n\n**Pre-filtering optimization:**\nWhen `preFilterLimit: 50` is set with 1000 facts, cosine similarity is computed only for the top 50 MiniSearch keyword matches, reducing O(N) scoring to O(50).\n\n## Draft Review\n\nFacts can carry `lifecycle_status: 'draft'`, for example when a host marks model output as unreviewed. Drafts stay visible by default. To keep them out of results:\n\n```ts\nawait wiki.read('user-1', 'deploy process', { excludeDrafts: true });\nawait wiki.traverseGraph('user-1', { sourceId, excludeDrafts: true });\n// or engine-wide:\ncreateWiki(db, { llmProvider, config: { excludeDrafts: true } });\n```\n\n- On every `read()` path, drafts are removed **before** `maxResults`, `tierFloors`, and pre-filter cuts, so they never take slots from reviewed facts.\n- In traversal, drafts are dead ends. The starting fact is always returned.\n- Status is read from SQLite on every call. A promotion is visible immediately, with no re-indexing.\n\nReview API:\n\n```ts\nconst { facts, nextCursor } = await wiki.listDrafts('user-1', { limit: 50 });\nawait wiki.promoteDraft(facts[0].id, 'user-1', { by: 'human:alice' }); // → stable, trustTier 'human-reviewed'\n// Reject with setLifecycleStatus(id, entityId, 'deprecated') or forget().\n```\n\n`promoteDraft` throws `WikiDraftNotFound` when no live draft with that id exists for the entity. The error is contextless by design. Promotion does not change `updated_at`, so a promoted fact keeps its recency position.\n\n## Grounding\n\nOpt-in, deterministic evidence check for LLM-authored facts. When on, writers you choose must quote the source they were shown. A fact whose quotes are missing or not found is stored as a `draft` (see [Draft Review](#draft-review)), not rejected. A fact whose quotes all check out is stored `stable` with a `process:grounding-check` verifier, so its `trustTier` is `machine-confirmed`. Default off: 7.x write behavior is unchanged.\n\n```ts\nnew WikiMemory(db, {\n  llmProvider,\n  config: {\n    grounding: {\n      mode: 'draft',            // 'off' (default) | 'draft'\n      writers: ['ingest'],      // default ['ingest']; also 'librarian', 'heal'\n      minEvidenceChars: 20,     // shorter quotes count as absent\n      maxEvidence: 3,           // quotes asked for per fact\n      maxEvidenceChars: 300,\n    },\n  },\n});\n```\n\n- **What counts as source.**\n  - Ingest: the chunk text.\n  - Librarian: the `summary` of each event in the prompt.\n  - Heal: the `summary` of each recent event in the prompt, plus the bodies of non-draft document anchors. When heal is a writer, anchors are shown with their body clipped to 800 characters.\n  - Instructions, the ontology manifest, existing facts and identifiers never count, so a model cannot ground a claim by quoting them.\n- **The check.** Both sides are normalized with NFKC, whitespace runs collapse to one space, and matching is case-sensitive. A fact with more than 10 quotes, or any quote not found, fails.\n- **Diagnostics.** `grounding_missing` (reasons `no_evidence`, `evidence_too_short`) and `grounding_failed` (reasons `quote_not_found`, `too_many_quotes`), one per fact, with the new fact's `factId`. Quotes are never included.\n- **Duplicate titles in one ingest.** When chunks yield facts with the same title, ingest keeps one: a grounded fact beats one with missing or failed evidence, and on a tie the first one wins. The others are reported as `fact_deduplicated`. A fact already stored for the same `sourceRef` is not replaced by a later partial ingest, and that skip is reported the same way. Either way the diagnostic carries `{ sourceRef, chunkIndex, itemIndex }` for the skipped fact, whether or not grounding is enabled.\n- **`upsertGraph`** nodes are host-supplied and never grounded.\n- **Librarian and heal** synthesize across events, so their pass rates are unknown. Measure them on your own event log before opting them in.\n- Evidence quotes are not stored.\n\n## Pluggable Vector Retrieval\n\nWhen your entity corpus grows, in-process cosine similarity scoring becomes a bottleneck. The optional **`VectorRanker`** interface lets you delegate semantic ranking to [**sqlite-vec**](https://github.com/asg017/sqlite-vec), [**sqlite-vss**](https://github.com/asg017/sqlite-vss), or an external vector database while `WikiMemory` handles embedding validation, hybrid scoring, and tier-2 row hydration.\n\n### `VectorRanker` purpose\n\n`VectorRanker` provides an optional injection point for approximate nearest-neighbor (ANN) ranking:\n\n```typescript\nexport interface VectorRanker {\n  /**\n   * Return semantic scores for facts in scope, sorted by similarity.\n   * - `entityId`: restricts results to one entity\n   * - `queryVec`: the embedded query (Float32Array or number[])\n   * - `candidateIds` (optional): when set, rank only within this set (MiniSearch pre-filter mode)\n   * - `limit`: requested top-K count\n   */\n  rankBySimilarity(args: VectorRankerRankArgs): Promise<VectorRankerSemanticResult[]>;\n\n  /**\n   * Optional hook called after embedding persistence (upsert, reembed, delete).\n   * Implementations use this to keep external indexes (sqlite-vec, remote ANN) in sync.\n   */\n  onEmbeddingPersisted?(event: {\n    entityId: string;\n    factId: string;\n    vector: Float32Array | null; // null = embedding removed\n  }): void | Promise<void>;\n}\n```\n\n**When no ranker is configured**, `WikiMemory` uses built-in JS cosine similarity — the same behavior as today. When a ranker is supplied and embeddings preconditions are met (`embed` available, dimensions match, no mismatches), `WikiMemory` delegates scoring to the ranker and blends results with keyword scores.\n\n### Example: sqlite-vec adapter\n\n```typescript\nimport { WikiMemory } from '@equationalapplications/core-llm-wiki';\nimport type { VectorRanker, VectorRankerRankArgs, VectorRankerSemanticResult } from '@equationalapplications/core-llm-wiki';\n\n// Minimal sqlite-vec adapter (pseudo-code)\nconst sqliteVecRanker: VectorRanker = {\n  async rankBySimilarity(args: VectorRankerRankArgs): Promise<VectorRankerSemanticResult[]> {\n    const { entityId, queryVec, candidateIds, limit } = args;\n\n    // Build KNN query using sqlite-vec's distance functions.\n    // sqlite-vec returns cosine distance (0 = identical, 2 = opposite) ascending.\n    // Invert to semanticScore: higher = more similar, matching VectorRanker contract.\n    let sql = `SELECT id, (1.0 - distance) AS semanticScore FROM vec_facts \n              WHERE entity_id = ? AND deleted_at IS NULL`;\n    const params: any[] = [entityId];\n\n    // Apply pre-filter if provided\n    if (candidateIds) {\n      sql += ` AND id IN (${candidateIds.map(() => '?').join(',')})`;\n      params.push(...candidateIds);\n    }\n\n    // KNN search (example syntax; adjust for your sqlite-vec version)\n    sql += ` ORDER BY vec MATCH vec_neighbor(?) LIMIT ?`;\n    params.push(queryVec, limit);\n\n    const rows = await db.getAllAsync<{ id: string; semanticScore: number }>(sql, params);\n    return rows; // sorted descending by semanticScore (closest distance → highest similarity)\n  },\n\n  async onEmbeddingPersisted(event) {\n    const { entityId, factId, vector } = event;\n    if (vector) {\n      // Upsert into sqlite-vec table\n      await db.runAsync(\n        `INSERT OR REPLACE INTO vec_facts (id, entity_id, vec) VALUES (?, ?, ?)`,\n        [factId, entityId, vector]\n      );\n    } else {\n      // Delete when embedding is removed\n      await db.runAsync(`DELETE FROM vec_facts WHERE id = ?`, [factId]);\n    }\n  },\n};\n\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: { /* ... */ },\n  vectorRanker: sqliteVecRanker,\n});\n\n// read() now uses sqlite-vec for scoring instead of JS cosine\nconst memory = await wikiMemory.read('user-123', 'my preferences');\n```\n\n### Fallback policies\n\nWhen `rankBySimilarity` rejects (e.g., ANN service outage, misconfiguration), `WikiMemory` applies a recovery policy:\n\n```typescript\nexport type VectorRankerFallback =\n  | 'js-cosine'  // (default) Score candidates in-process with JS cosine — same as no ranker\n  | 'keyword'    // Skip semantic ranking; return keyword-only results\n  | 'empty'      // Semantic facts list empty for this read; tasks/events still included\n  | 'throw';     // Reject read() with the ranker error\n\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: { /* ... */ },\n  vectorRanker: sqliteVecRanker,\n  vectorRankerFallback: 'js-cosine', // default\n  onVectorRankerFallback: (info) => {\n    console.warn(\n      `Ranker failed (policy: ${info.policy}); error:`,\n      info.error\n    );\n  },\n});\n```\n\n- **`'js-cosine'` (default):** Seamless degradation; same behavior as if no ranker was configured.\n- **`'keyword'`:** Useful when semantic ranking is optional; keyword search proceeds normally.\n- **`'empty'`:** Return no facts for this query (but tasks/events still load); useful for strict consistency.\n- **`'throw'`:** Propagate the error and fail the read.\n\n### `onEmbeddingPersisted` eventual consistency\n\nIf `vectorRanker.onEmbeddingPersisted` returns a pending Promise, the hook **may resolve asynchronously**. This supports ANN indexes that rebuild on a schedule (e.g., sqlite-vec triggers on transaction commit) or external services with eventual consistency.\n\n**Best practice:**\n- If your adapter has **synchronous guarantees** (in-process sqlite-vec, same transaction), await the promise.\n- If your adapter is **eventually consistent** (remote ANN, async rebuild), document the lag and document that queries may miss recently-added facts until the index refreshes.\n- The **SQLite blob remains the source of truth**; `WikiMemory` always writes embeddings to `embedding_blob` first before calling the hook.\n\n### Hybrid scoring with ranker\n\nWhen both `vectorRanker` and `hybridWeight` are configured, `WikiMemory` still applies hybrid blending after the ranker returns scores:\n\n```typescript\nconst wikiMemory = new WikiMemory(db, {\n  config: {\n    hybridWeight: 0.7, // 70% semantic, 30% keyword\n  },\n  vectorRanker: sqliteVecRanker,\n});\n\n// ranker returns semanticScore; WikiMemory blends with MiniSearch keyword score\nconst memory = await wikiMemory.read('user-123', 'my preferences', {\n  hybridWeight: 0.5, // per-call override to 50/50 blend\n});\n```\n\nNote on semantics:\n- Leave `hybridWeight` undefined for true pure-semantic cosine-range scoring.\n- Set `hybridWeight: 1` for an all-semantic variant that clamps negative semantic scores to 0.\n\nFor details on hybrid scoring formulas and trade-offs, see [Retrieval Tuning](#retrieval-tuning) above.\n\n### Spec and issue reference\n\n- **Full spec:** [`docs/superpowers/specs/2026-05-07-pluggable-vector-retrieval.md`](https://github.com/equationalapplications/expo-llm-wiki/blob/main/docs/superpowers/specs/2026-05-07-pluggable-vector-retrieval.md)\n- **GitHub issue:** [#15](https://github.com/equationalapplications/expo-llm-wiki/issues/15)\n\n## Vector Cache\n\nParsed embedding vectors from full-scan `read()` calls are cached in memory, keyed by entity ID (max 16 entities, max 500 vectors per entity). This avoids redundant `Float32Array` parsing on repeated queries for the same entity. When the 16-entity limit is reached, the oldest-inserted entity is evicted to make room; if an entity exceeds 500 facts, its vectors are not cached at all for that read.\n\nAfter heavy read workloads or on memory-constrained runtimes, you can release the entire cache explicitly:\n\n```typescript\n// Release all cached embedding vectors\nwikiMemory.clearVectorCache();\n```\n\nThe cache is also automatically invalidated on any mutation (`runLibrarian`, `runHeal`, `runPrune`, `runReembed`, `ingestDocument`, `importDump`, `forget`).\n\n## Re-Embedding & Retry Behavior\n\n`runReembed(entityId?, opts?)` returns `{ embedded, skipped, failed, deferred, permanentlyFailed }`.\n\n- `failed` — attempted this sweep and failed.\n- `deferred` — previously failed and still inside its exponential backoff window (60s doubling, capped at 24h). Not an error; a later sweep will retry.\n- `permanentlyFailed` — excluded for good: a `float32_overflow` failure, or 5 failed attempts. Pass `{ force: true }` to retry these anyway.\n\nConvergence loops should test `failed`, never `deferred` — deferred rows clear themselves once their backoff elapses.\n\nTwo caveats on that guidance:\n\n- A `storage_error` failure counts in `failed` but is never marked for backoff — a failure marker is itself a DB write, which is exactly what is broken. While DB writes fail, every sweep re-attempts every row, so a loop testing `failed` can spin without converging. Treat repeated `storage_error` failures as an infrastructure signal to fix, not a retry window to wait out.\n- `{ force: true }` bypasses more than `permanentlyFailed`: it also skips the backoff window, so rows that would otherwise be `deferred` are re-attempted immediately.\n\n## Entity Status\n\n`WikiMemory` exposes the in-flight job state for a single entity through two complementary APIs.\n\n### `getEntityStatus(entityId)`\n\nSynchronous point-in-time snapshot:\n\n```typescript\nconst status = wikiMemory.getEntityStatus('user-42');\n// { ingesting: boolean, librarian: boolean, heal: boolean }\n```\n\nUse this when you only need the current value (e.g. inside a request handler).\n\n### `subscribeEntityStatus(entityId, callback)`\n\nPush-based change notification — the callback fires synchronously once with the current status, then again on every transition where any of the three booleans flips. There is no polling and no duplicate snapshots.\n\n```typescript\nconst unsubscribe = wikiMemory.subscribeEntityStatus('user-42', (status) => {\n  console.log(status); // { ingesting, librarian, heal }\n});\n\n// Later:\nunsubscribe(); // idempotent — safe to call more than once\n```\n\nNotes:\n\n- The first invocation happens **before** `subscribeEntityStatus` returns. Treat it as the initial render value.\n- Each emission may be a fresh object literal. Do not rely on referential equality between callbacks; equality of the three booleans is the contract.\n- A throwing callback is caught (logged via `console.error`) and does not block other subscribers or the underlying job.\n- Subscriptions are scoped to a single `entityId`. There is no wildcard or \"all entities\" form.\n\n## Diagnostics\n\nPass `onDiagnostic` to receive typed, content-free reports of events core used to drop silently or only log: failed chunks, rejected facts and tasks, dedupe drops, dropped edges, embedding and host-hook failures, heal skips, and background-job failures.\n\n```ts\nconst wiki = createWiki(db, {\n  llmProvider,\n  onDiagnostic: (d) => {\n    // d.code, d.severity ('info' | 'warn' | 'error'), d.operation, d.trigger ('call' | 'auto'),\n    // d.entityId, d.at, d.message, d.detail?: { factId, sourceRef, chunkIndex, itemIndex,\n    // edgeType, sourceNodeType, targetNodeType, reason, count, chunkIndexes }\n    telemetry.record(d);\n  },\n});\n```\n\n- **Content-free.** Diagnostics carry IDs, indexes, counts, ontology slugs and reason slugs only. They never carry titles, bodies, LLM output, provider error messages, or hashes of content.\n- **Isolated.** The hook is called synchronously. A throwing or rejecting hook never affects the operation, and its failure is logged with `console.warn`.\n- **After commit.** Transactional diagnostics are delivered after the operation's transaction commits. An operation that throws delivers none; the exception is the signal. `upsertGraph` runs in your transaction, so its diagnostics are delivered when it resolves. Disregard them if you roll back.\n- **`trigger`.** `'auto'` marks work started by `autoLibrarianThreshold` / `autoHealThreshold`. A failed background job is reported as `background_job_failed` with `operation` set to the job.\n- **Forward-compatible.** New codes may be added in minor releases; ignore codes you don't recognize.\n- **Console output is unchanged** when a hook is set and succeeds: every existing core line is identical. A throwing or rejecting hook adds only its own `console.warn`, and a non-function `onDiagnostic` warns once.\n\n## Per-Entity Seeded Ontology\n\nControl how librarian and ingest passes classify facts and extract graph relationships. The system defaults to **`off`** so existing deployments behave unchanged.\n\n### The Three Modes\n\n| Mode | Behavior |\n|------|----------|\n| **`off`** (default) | No ontology guidance. LLM output and persistence match pre-ontology behavior: `okf_type` stays `null` on LLM-created facts; maintenance passes do not create edges. OKF import still populates `okf_type` and edges independently. |\n| **`strict`** | The LLM must use only `node_types` and `edge_types` from the entity manifest. Invalid `okf_type` falls back to an untyped fact with no edges; invalid individual edges are dropped while a valid `okf_type` and matching edges are kept. |\n| **`emergent`** | Same validation as Strict, plus the LLM may return `ontology_updates` with new node/edge types. Updates are append-only (deduped by `type` string) and take effect before facts from the same response are validated. |\n\nMode resolution per entity: persisted DB row `mode` (when present) → `seedManifests[entityId].mode` (when no row but a seed exists) → `WikiConfig.ontology.mode` → `'off'`.\n\n### WikiConfig\n\nSet a global default mode and bootstrap manifests for known entities at construction time:\n\n```typescript\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider,\n  config: {\n    ontology: {\n      mode: 'strict', // global default when an entity has no per-entity override\n      seedManifests: {\n        'team-alpha': {\n          mode: 'emergent', // optional per-entity override\n          manifest: {\n            node_types: [\n              { type: 'person', description: 'An individual or user.' },\n              { type: 'project', description: 'An ongoing initiative.' },\n            ],\n            edge_types: [\n              {\n                type: 'contributes_to',\n                source_type: 'person',\n                target_type: 'project',\n                description: 'Person working on a project.',\n              },\n            ],\n          },\n        },\n      },\n    },\n  },\n});\n```\n\n`seedManifests` entries are written to SQLite the first time an entity's ontology is\nresolved *inside a transaction* (ingest, heal) and no row exists for it. A read outside a\ntransaction — including `getOntologyManifest` — resolves the seed without persisting it, so\na configured entity can report a manifest while still having no row in `entity_manifests`.\nThat distinction matters for `ifAbsent` below.\n\n### Public API\n\nRead or seed an entity's ontology at runtime:\n\n```typescript\n// Read effective mode + manifest (DB row, then seedManifests fallback)\nconst ontology = await wikiMemory.getOntologyManifest('team-alpha');\n// { mode: 'emergent', manifest: { node_types: [...], edge_types: [...] } }\n// null when no row and no seed entry\n\n// Seed or replace manifest; optional per-entity mode override\nawait wikiMemory.setOntologyManifest('team-alpha', {\n  node_types: [{ type: 'person', description: 'An individual.' }],\n  edge_types: [{\n    type: 'reports_to',\n    source_type: 'person',\n    target_type: 'person',\n    description: 'Reporting hierarchy.',\n  }],\n}, { mode: 'strict' });\n```\n\nSeed several entities atomically — all manifests are written in one transaction,\nso a failure partway through leaves none of them behind:\n\n```typescript\nconst { written, skipped } = await wikiMemory.setOntologyManifests(\n  [\n    { entityId: 'tier_fact', manifest, mode: 'strict' },\n    { entityId: 'tier_wisdom', manifest, mode: 'strict' },\n  ],\n  { ifAbsent: true },\n);\n// written: entities whose manifest this call wrote\n// skipped: entities that already had a persisted manifest (only under `ifAbsent`)\n```\n\n`ifAbsent` makes each write create-if-absent rather than an upsert, so a\nconcurrent initializer loses the race by writing nothing instead of overwriting\na manifest it never read. Omit it for replace-on-conflict, which is what\n`setOntologyManifest` does.\n\n**`ifAbsent` tests for a persisted row, not for an effective manifest.** An entity\nwhose manifest comes from `WikiConfig.ontology.seedManifests` has no row until an\ningest materializes one, so `ifAbsent` writes over the configured seed and reports\nthe entity in `written`; after an ingest has run for that entity the same call\nreports it in `skipped`. Don't mix the two seeding routes for one entity: seed it\nthrough the config, or through this method, not both.\n\nThe method takes data, never a transaction handle: `WikiMemory` serializes\ntransactions on the adapter it is given, so a transaction opened on the adapter\nyou passed to `createWiki` would not participate in that serialization.\n\n### Fact Shape Extensions\n\nIn **Strict** and **Emergent** modes, librarian and ingest JSON may include typed facts with inline edges:\n\n```json\n{\n  \"facts\": [{\n    \"title\": \"Jane reports to Bob\",\n    \"body\": \"Jane reports to Bob Smith.\",\n    \"tags\": [],\n    \"confidence\": \"certain\",\n    \"okf_type\": \"person\",\n    \"edges\": [{ \"edge_type\": \"reports_to\", \"target_title\": \"Bob Smith\" }]\n  }]\n}\n```\n\n- `okf_type` maps to a `node_types[].type` entry (case-insensitive lookup; canonical manifest casing is persisted).\n- `edges` are resolved by `target_title` within the same maintenance transaction and persisted via `EdgeRepository`.\n- Invalid `okf_type` falls back to `null` with no edges for that fact. Invalid individual edges are dropped; valid `okf_type` and matching edges are still persisted.\n\nSee the design spec: [`docs/superpowers/specs/2026-06-23-per-entity-seeded-ontology-design.md`](https://github.com/equationalapplications/expo-llm-wiki/blob/main/docs/superpowers/specs/2026-06-23-per-entity-seeded-ontology-design.md).\n\n### Ontology type inheritance\n\n`OntologyNodeType` accepts an optional `parent_type` field for **one-level type inheritance** — the building block for polymorphic queries like \"all CreativeWorks\" without deep, multi-level hierarchies.\n\n```ts\n// packages/core/src/types.ts\nexport interface OntologyNodeType {\n  type: string;\n  description: string;\n  /** Optional parent type slug. One level only — the parent must exist in the\n   *  same manifest and must not itself declare a `parent_type`. */\n  parent_type?: string;\n}\n```\n\nA concrete type declares `parent_type: '<parent-slug>'`; the parent must be a top-level node in the same manifest. A `design_spec` node with `parent_type: 'creativework'` is treated as both itself *and* a `creativework` during edge matching.\n\n#### Inheritance rules (strictly enforced)\n\n`validateManifest` enforces the **one-level invariant** at every persist and read (`entity_manifests.manifest_json` is `JSON.parse`d on every read, so validation runs on the read path too). Violations throw:\n\n| Condition | Error |\n|-----------|-------|\n| `parent_type` references an unknown slug | `Parent type not found: <slug>` |\n| `parent_type` equals the node's own slug | `Self-parent: <type>` |\n| The referenced parent also declares a `parent_type` | `Parent chain too deep: <a> → <b> → <grandparent>` |\n| `parent_type` is present but blank (`''` / whitespace) or non-string (`number`, `null`, `object`) | `Ontology parent_type must be a non-empty string when present: <type>` |\n\nTwo consequences worth knowing:\n\n- The depth check rejects **2-cycles** (`a → b → a`) which a self-parent check alone would miss.\n- A *present* `parent_type` key is required to be a usable string; an *absent* key (`undefined`) is fine and means \"no parent\". This is the same rule core applies to blank slugs elsewhere (`Ontology node type slug must be non-empty`).\n\nParent types are **instantiable** — a fact may be classified as bare `creativework`, and `resolveNodeType('creativework')` returns `'creativework'` normally. There is no `abstract` flag; if you want a parent to stay abstract, write the description to steer classification toward the concrete children.\n\n#### Edge matching (symmetric, exact-first)\n\nEdge matching is parent-aware on **both** sides and routes through a single primitive, `typeSatisfies(declaredType, concreteType, manifest)`:\n\n- `declaredType === concreteType` (case-insensitive) → **exact match**, short-circuits before the node lookup. Manifests with no `parent_type` behave bit-for-bit as before.\n- Otherwise: looks up `concreteType`'s definition in the manifest and returns `true` if its `parent_type` equals `declaredType`. One hop only — `typeSatisfies` never recurses.\n\n`typeSatisfies` is used at four gates — `validateInlineEdges` (source), `OntologyService.resolveEdges` (source and target, via two-pass), `IngestionService.upsertGraph` (source) — so a parent-satisfied edge that validates will also persist.\n\n**Targets resolve exact-first.** When resolving an edge against a target fact, `OntologyService.resolveEdges` runs two passes over candidate defs:\n\n```ts\n// Packages/core/src/services/OntologyService.ts (abridged)\nconst targetType = (target.okf_type ?? '').trim().toLowerCase();\nconst def = candidates.find(d => d.target_type.trim().toLowerCase() === targetType)\n  ?? candidates.find(d => typeSatisfies(d.target_type, targetType, manifest));\n```\n\nThe exact pass runs first, so a `design_spec` target with both `about creativework → creativework` and `about creativework → design_spec` declared resolves to the **narrower** row — array order never decides which pass wins. Ties within a single pass are immaterial: only `def.type` is read off the winning def, and `validateManifest` already rejects a manifest whose triples spell one edge name with different casing, so every candidate within a pass yields a byte-identical edge row.\n\n> **Accepted cost:** declaring `→ creativework` now admits every child of `creativework`, and there is no way to say \"the parent type only.\" A type that needs an exact-only target must not be given children.\n\n#### Emergent prompt schema\n\nIn `emergent` mode the LLM may propose new types via `ontology_updates`. The prompt schema (`EMERGENT_EXTRA` in `packages/core/src/prompts/ontology.ts`) advertises the optional `parent_type` so emergent proposals can suggest children of an existing type:\n\n```json\n\"ontology_updates\": {\n  \"node_types\": [{ \"type\": \"slug\", \"description\": \"...\", \"parent_type\": \"optional existing slug\" }],\n  \"edge_types\": [{ \"type\": \"slug\", \"source_type\": \"...\", \"target_type\": \"...\", \"description\": \"...\" }]\n}\n```\n\nThe rest of the manifest reaches the LLM unchanged — `buildPromptContext` does `JSON.stringify(manifest, null, 2)`, so an established manifest's `parent_type` fields appear verbatim alongside `type` and `description`.\n\nEmergent proposals are **untrusted input**: `mergeOntologyUpdates` drops any `parent_type` (rather than throwing) when it is a non-string, blank, unresolvable, self-referential, or whose referenced parent already declares its own parent. The lenient merge contract keeps malformed LLM proposals from aborting an ingest transaction. Changing an established type's parent is a `setOntologyManifest` operation.\n\n#### Backwards compatibility\n\n`parent_type` is optional and manifests persist as a whole JSON blob in `entity_manifests.manifest_json`. Existing manifests without the field validate identically; no SQLite migration is needed. `setOntologyManifest` rejects a two-level chain at the public API boundary, so callers cannot accidentally introduce an unenforced chain through a typo.\n\n`typeSatisfies` is intentionally **not** re-exported from the package's public surface (`packages/core/src/index.ts`). No host needs the primitive to author or validate a manifest; publishing it would freeze an internal matching rule into the package's public surface.\n\nSee the design spec: [`docs/superpowers/specs/2026-08-28-ontology-parent-field-spec.md`](https://github.com/equationalapplications/expo-llm-wiki/blob/main/docs/superpowers/specs/2026-08-28-ontology-parent-field-spec.md).\n\n### Ontology backfill\n\nFacts that enter the store without passing through the librarian (synced-down\nremote facts via `importDump`, or facts created before the ontology feature)\nhave `okf_type = NULL` and no edges, so `traverseGraph` cannot reach them.\n`runOntologyBackfill` types them in place:\n\n```ts\nconst result = await wiki.runOntologyBackfill(entityId);\n// { scanned, typed, failedValidation, edgesAdded, remaining, deferred, skipped }\n```\n\n- **When to call it:** you own the trigger — the library provides the operation,\n  not a scheduler (same as `runLibrarian`/`runHeal`). A good default is after\n  each sync/import completes. `WikiBusyError` under concurrency is safe to\n  swallow; the next trigger retries.\n- **Cost model:** one or more LLM calls only when eligible untyped facts exist;\n  otherwise one SELECT. Each run scans at most 25 facts (override via\n  `options.batchSize`), oldest first. Facts are sent to the LLM in bounded\n  sub-batches — sized from `llmProvider.maxOutputTokens` when supplied,\n  otherwise a conservative default — with the full serialized prompt\n  (system + user) capped at 40k chars. A sub-batch whose response is truncated\n  or fails to parse is halved and retried automatically; a single fact that\n  still fails alone is counted in `skipped` rather than aborting the run.\n- **Convergence:** loop `while (result.remaining > 0)` to drain a backlog.\n  `remaining` counts only *eligible* untyped facts, so the loop terminates even\n  when unclassifiable facts exist; those are cooldown-stamped and retried after\n  7 days (`deferred` reports them). Because of that weekly retry, host\n  dashboards may see small recurring bumps in `scanned`/`failedValidation` as\n  the unclassifiable backlog is re-tested — expected, not a regression.\n- **Strictly additive:** never creates, deletes, or rewrites facts, and never\n  overwrites an existing `okf_type` (guarded in SQL, not just convention).\n  `updated_at` is never touched, so sync merge resolution is unaffected.\n- **Prompt customization:** per-call `options.promptOverride`, or global\n  `config.prompts.ontologyBackfillSystemPrompt` (template may use `{{facts}}`\n  and the ontology placeholders).\n\n#### Classifier mode (optional)\n\nA System-One classifier (for example TypeSafe's Jev, an OpenJev-style open model, or a local ONNX classifier) can type facts during backfill without generating text. Add `classify` to your provider and opt in:\n\n```ts\nconst wiki = createWiki(db, {\n  llmProvider: { generateText, classify },\n  config: { ontology: { backfillClassifier: 'auto', classifyMinConfidence: 0.5 } },\n});\nawait wiki.runOntologyBackfill('user-1');                        // uses classify\nawait wiki.runOntologyBackfill('user-1', { classifier: 'llm' }); // force the generative path\n```\n\n- Providing `classify` changes nothing by itself. The default is `'llm'`.\n- Each untyped fact gets one `choice` question over the entity manifest's node types. Answers below `classifyMinConfidence` (default 0.5) are left untyped and retried after the cooldown.\n- **No edges are proposed in classifier mode** (`edgesAdded: 0`): a classifier cannot extract edge targets. Run with `classifier: 'llm'` when you want edges.\n- Manifests with more than 255 node types, or providers without `classify`, use the generative path.\n- Answers are validated as untrusted. Off-list choices and out-of-range probabilities count toward `failedValidation`. A thrown `classify` counts toward `skipped` and is retried on the next pass.\n\nExample adapter for Cloudflare Workers AI's `typesafe/jev`. This is illustrative, not a supported package; check the provider's current API reference before use.\n\n```ts\nconst classify: LLMProvider['classify'] = async ({ state, questions }) => {\n  const jevQuestions = Object.fromEntries(Object.entries(questions).map(([key, q]) => [key,\n    q.kind === 'choice' ? { type: 'choice', instructions: q.instructions, criteria: Object.fromEntries(q.options.map((o) => [o, o])) }\n    : q.kind === 'score' ? { type: 'score', instructions: q.instructions, criteria: q.levels }\n    : { type: 'noul', instructions: q.instructions },\n  ]));\n  const res = await env.AI.run('typesafe/jev', { state, questions: jevQuestions });\n  const answers = Object.fromEntries(Object.entries(res.answers).map(([key, a]: [string, any]) => [key,\n    a.type === 'choice' ? { kind: 'choice', choice: a.choice, confidence: a.confidence, probabilities: a.probabilities }\n    : a.type === 'score' ? {\n        kind: 'score', score: a.score, confidence: a.confidence,\n        probabilities: Object.keys(a.probabilities).sort((x, y) => Number(x) - Number(y)).map((k) => a.probabilities[k]),\n      }\n    : { kind: 'binary', probability: a.noul },\n  ]));\n  return { answers };\n};\n```\n\n## OKF Import/Export\n\nThe core package integrates with `@equationalapplications/core-okf` to seamlessly adapt wiki data dumps to and from Open Knowledge Format (OKF) bundles (v0.1 and v0.2; `formatOkfBundle` defaults to the v0.2 / `llm-wiki/2` profile).\n\n### Exporting an OKF Bundle\n\nConvert an existing wiki dump into a flat array of OKF files, ready to be written to disk or zipped:\n\n```typescript\nimport { formatOkfBundle } from '@equationalapplications/core-llm-wiki';\n\nconst dump = await wiki.exportDump(['entity-123']);\nconst { files } = formatOkfBundle(dump);\n\n// files: Array<{ path: string; content: string }>\n// e.g., [{ path: 'entities/entity-123/facts/fact_abc.md', content: '---\\n...' }]\n```\n\n### Importing an OKF Bundle\n\nParse raw OKF files back into a `MemoryDump` that the wiki can ingest:\n\n```typescript\nimport { parseOkfBundle } from '@equationalapplications/core-llm-wiki';\n\n// Assuming you read OKF files for this entity (e.g. under `entities/entity-123/`) from disk/zip into OkfFile[] shape\nconst dump = parseOkfBundle('entity-123', files, {\n  defaultSchema: 'fact',\n  typeMapping: {\n    'custom_type': 'fact',\n    'archived': 'ignore', // Skips these concepts\n  },\n});\n\nawait wiki.importDump(dump, { merge: true });\n```\n\n**Routing Precedence:** Concepts are routed into either the `entries` (facts) or `tasks` tables based on a three-step fallback:\n\n1. `OkfImportOptions.typeMapping` explicitly mapping an OKF `type` to `'fact'`, `'task'`, or `'ignore'`.\n2. Directory convention (e.g., files in `/facts/` become facts, `/tasks/` become tasks).\n3. The `OkfImportOptions.defaultSchema` (defaults to `'fact'`).\n\n### WikiEdge and the `## Related` Section\n\nA `WikiEdge` represents a directed link between two concepts (`source_id`, `target_id`, `edge_type`). In profile llm-wiki/1, edges are serialized in a trailing `## Related` section on each concept file. `parseOkfBundle()` extracts them into `WikiEdge` rows and strips the section from stored bodies; `formatOkfBundle()` emits the section from the `edges` array in the dump.\n\nSee [`docs/okf-profile.md`](https://github.com/equationalapplications/expo-llm-wiki/blob/main/docs/okf-profile.md) for the full normative spec and [`packages/okf/fixtures/`](https://github.com/equationalapplications/expo-llm-wiki/tree/main/packages/okf/fixtures) for conformance bundles.\n\n`importDump` persists an imported entity summary (profile ≥ 1 bundles) and\n`exportDump` re-emits it, so the profile §4 round-trip holds across storage.\nRead it directly with `wiki.getEntitySummary(entityId)`. The value lives in the\n`{prefix}meta` table under `entity_summary:{entity_id}` and is removed by\n`forget(entityId, { clearAll: true })`. See the\n[design spec](../../docs/superpowers/specs/2026-07-05-okf-summary-persistence-design.md)\nfor import/merge semantics and wipe-path cleanup.\n\n### The `okf_type` Field\n\nFacts and tasks include a nullable `okf_type` column. This preserves the literal OKF `type` string from an imported bundle frontmatter, independent of whether the item was routed to the `entries` or `tasks` table. When `formatOkfBundle` runs, it restores this specific string, falling back to `'fact'` or `'task'` if the field is null (ensuring non-imported rows export cleanly).\n\n## Security\n\n`@equationalapplications/core-llm-wiki` enforces multiple security layers:\n\n### VectorRanker Adapter Security\n\nIf implementing a custom `VectorRanker`:\n\n- **SQL Injection**: ALWAYS use parameterized queries for `entityId`, `factId`, `candidateIds`. Never concatenate into SQL strings.\n- **Entity Isolation**: Filter by `entityId` in all queries to prevent cross-tenant data leaks.\n- **Credential Scrubbing**: Strip API keys, tokens, connection strings from thrown errors before surfacing to host.\n- **Resource Limits**: Cap `limit` and `candidateIds.length` to prevent DoS. Do NOT retain `vector` references beyond callback scope — blocks GC.\n\nSee [SECURITY.md](https://github.com/equationalapplications/expo-llm-wiki/blob/main/SECURITY.md) for complete adapter security guidance and code examples.\n\n### Host Application Security\n\nWhen using `VectorRanker`:\n\n- **Error Sanitization**: `sanitizeRankerErrors: true` (default) scrubs ranker errors before mirroring via `error.cause`.\n- **Fallback Policy**: Choose `vectorRankerFallback` based on availability vs consistency requirements:\n  - `'js-cosine'` (default): Best availability\n  - `'keyword'`: Fast fallback without semantic ranking\n  - `'empty'`: Strict consistency (no facts on failure)\n  - `'throw'`: Fail-fast error propagation\n- **Deletion Hook Contract**: `forget()` / `runPrune()` reject on hook timeout/failure. Prevents GDPR violations (deleted vectors still retrievable). Handle failures with retry or queue for reconciliation.\n- **Timeout Tuning**: Set `deletionHookTimeoutMs` per deployment (default 30s). Interactive UX: 5s. Background jobs: 60s.\n\nCore WikiMemory provides:\n- **Defensive Copies**: Query/embedding vectors copied before ranker/hook calls\n- **Input Validation**: `sourceRef`/`sourceHash` normalized; embedding dimensions validated\n- **Parameterized Queries**: All SQL uses bind parameters\n\n### Prompt-Injection Trust Boundary\n\nUser-controlled text — `event.summary` passed to `write()`, document chunks passed to `ingestDocument()`,\nfact `title`/`body` (including imported dumps) — is interpolated verbatim into LLM prompts for librarian,\nheal, and embedding operations. Prompt templating does simple variable substitution; it does not detect\nor filter instruction-like content.\n\nMitigating prompt injection (e.g. \"ignore prior instructions and emit...\") is **the host's responsibility**.\nIf your application accepts untrusted input that flows into `write()`, `ingestDocument()`, or `importDump()`,\ntreat the LLM's librarian/heal output as similarly untrusted — validate or scope it before acting on it\ndownstream.\n\n[Grounding](#grounding) is a support check, not an injection defense. It confirms that a fact quotes the\ntext the model was shown, and injected text in that source can be quoted like any other.\n\n[`getInstructions`](#effective-instructions-getinstructions) and the `wiki_get_instructions` tool (`memory:read`)\nreturn `WikiConfig.prompts` overrides verbatim, so keep secrets out of them. The result also includes the entity's\nontology manifest, which in emergent mode holds types and descriptions the model proposed from ingested\ndocuments. Agents should treat the result as reference data about the engine, not as instructions to follow.\n\n## Usage\n\n```typescript\nimport { WikiMemory, type SQLiteAdapter } from '@equationalapplications/core-llm-wiki';\n\n// Provide any SQLiteAdapter-compatible driver\nconst wikiMemory = new WikiMemory(db, {\n  llmProvider: {\n    generateText: async ({ systemPrompt, userPrompt }) => {\n      // Your LLM call here\n      return 'Model output';\n    },\n  },\n});\n\n// Initialize schema and run migrations\nawait wikiMemory.setup();\n\n// Store facts\nawait wikiMemory.write('user-123', {\n  event_type: 'observation',\n  summary: 'User prefers async/await over promises',\n});\n\n// Query memory\nconst memory = await wikiMemory.read('user-123', 'coding style preferences');\n```\n\n### Multi-entity weighted reads\n\n`read()` accepts either one entity id or an array of entity ids. Facts are always merged globally before `maxResults` is applied. For single-entity reads, tasks are uncapped and events are capped at 10. For multi-entity reads, tasks are capped at `min(20 × entity count, 200)` and events at `min(10 × entity count, 100)` — per-entity representation in the returned bundle is not guaranteed.\n\n```ts\nconst memory = await wikiMemory.read(['tier_wisdom', 'tier_fact', 'tier_working'], 'Which source should I trust?', {\n  maxResults: 8,\n  tierWeights: {\n    tier_wisdom: 2,\n    tier_fact: 1,\n    tier_working: 0.25,\n  },\n});\n\nconsole.log(memory.metadata);\nconsole.log(memory.factScores);\n```\n\n### Librarian prompt override contract\n\nCore exports prompt utilities for weighted retrieval-based synthesis. Use `mapLibrarianOptionsToReadOptions()` to map `entityWeights` to `tierWeights`, then hydrate a prompt with `query`, `context`, and `tasks`.\n\n```ts\nimport {\n  DEFAULT_LIBRARIAN_SYNTHESIS_PROMPT,\n  formatContext,\n  hydrateLibrarianPrompt,\n  mapLibrarianOptionsToReadOptions,\n  validateLibrarianPromptTemplate,\n} from '@equationalapplications/core-llm-wiki';\n\nconst options = {\n  entityWeights: { tier_wisdom: 2, tier_fact: 1, tier_working: 0.25 },\n  systemPrompt: `You are a strict fact checker.\nQuestion:\n{{query}}\n\nRetrieved context:\n{{context}}\n\n{{tasks}}`,\n};\n\nconst query = 'Which source should I trust for recent project decisions?';\n\nconst memory = await wikiMemory.read(['tier_wisdom', 'tier_fact', 'tier_working'], query, {\n  ...mapLibrarianOptionsToReadOptions(options),\n  maxResults: 8,\n});\n\nconst template = options.systemPrompt ?? DEFAULT_LIBRARIAN_SYNTHESIS_PROMPT;\nconst warnings = validateLibrarianPromptTemplate(template, {\n  custom: options.systemPrompt != null,\n  taskCount: memory.tasks.length,\n});\n\nfor (const warning of warnings) console.warn(warning);\n\nconst finalPrompt = hydrateLibrarianPrompt(template, {\n  query,\n  context: formatContext(memory, { includeEntityIds: true, includeFactScores: true }),\n  tasks: formatContext({ facts: [], tasks: memory.tasks, events: [] }, { format: 'plain' }),\n});\n```\n\n## Platform Random Source\n\nWiki record IDs must be cryptographically random. The core engine resolves a random source in this order:\n\n1. `crypto.randomUUID()` (Web / Node 19+)\n2. `crypto.getRandomValues()` (Web / Node / polyfilled global)\n3. A source injected via `configureRandomSource()` (e.g. `expo-crypto` on Hermes/React Native)\n\nWeb and Node are unchanged — global `crypto` wins when present. React Native / Hermes typically has no `crypto` global; use a platform package or inject your own implementation:\n\n```typescript\nimport { configureRandomSource } from '@equationalapplications/core-llm-wiki';\nimport { getRandomValues } from 'expo-crypto';\n\n// Call once at module load, before any wiki writes\nconfigureRandomSource(getRandomValues);\n```\n\n`@equationalapplications/expo-llm-wiki` does this automatically on import (main entry and `/factory` subpath). If you use `@equationalapplications/core-llm-wiki` directly on React Native without the expo package, you must call `configureRandomSource()` yourself or polyfill `globalThis.crypto.getRandomValues`.\n\n## Entity Enumeration\n\nList all entities that have stored data in the wiki:\n\n```typescript\nconst entityIds = await wikiMemory.listEntityIds();\n// Returns all entity_ids with at least one row (including soft-deleted-only entities)\n// Optional prefix filter: await wikiMemory.listEntityIds({ prefix: 'tier_' });\n```\n\nUse this for maintenance scheduling, multi-entity operations, or discovering which namespaces exist. Includes entities with only soft-deleted rows so `runPrune()` can reclaim orphaned storage.\n\n## Source Reference Enumeration\n\nList all documents currently stored for an entity:\n\n```typescript\nconst sourceRefs = await wikiMemory.listSourceRefs('user-123');\n// One row per live sourceRef (soft-deleted rows are excluded):\n// Array<{ sourceRef: string; sourceHash: string | null; factCount: number; lastIngestedAt: number }>\n// factCount — number of live facts under that sourceRef\n// lastIngestedAt — Unix timestamp in ms from the most recently updated live entry\n```\n\nUse this to audit stored documents, validate external sync state, or preview the blast radius before `forget()` operations.\n\n## Direct Graph Write\n\nWrite structured graph data directly without LLM extraction — ","readmeFilename":"README.md"}