BYOK provider embeddings (wave 6 chunk B): kill local transformers
Replace the @huggingface/transformers local embedder (~316MB ONNX cache) with an EmbeddingPort seam: graph.embeddingModel (nullable "provider:id" slug) selects an OpenRouter/OpenAI/Gemini adapter, resolved fresh per call from settings + the auth store so key rotations land immediately. - src/server/embeddings.ts rewritten: slug grammar, injected-fetch adapters (OpenRouter input_type, Gemini taskType batching, OpenAI index-ordered rows), honest resolution (null = skip-with-info, set model with missing key = honest error), embedNewNodes with model-change re-embed wipe + ghost pruning + usage steps, federated catalog (OpenRouter /api/v1/embeddings/models — VERIFIED live, the inverse path 404s; OpenAI text-embedding- filter; Gemini embedContent filter). - GET /api/models/embeddings: federated catalog, empty when no keys. - Extraction tail records stages.embedding usage into the run's money channel; EMBEDDING_PRICES overlay in core/prices.ts (outside the sync-generated block), callCost falls back to it; cost-summary folds the fourth stage; graph-context injection resolves its embedder per run, fails honestly when unconfigured, records query tokens. - Settings: GraphSettings.embeddingModel (null factory), section parser validates slug shape (id may contain colons — OpenRouter :free suffixes, live catalog fact). - Removal: @huggingface/transformers uninstalled, PATHS.modelCache and onnx tracing entries deleted, @huggingface tracing-excludes guard added, stale sharp/libvips comments updated to historical. Live-verified on port 3004: catalog federates 36 rows (33 OpenRouter + 3 OpenAI), embedNewNodes embedded 10 nodes via the free liquid/lfm-2.5 model (dim 1024, 435 prompt tokens, 1 call, $0), settings PUT rejects bad slugs with 400. Gates: typecheck, lint, 895/895 tests, build.
This commit is contained in:
amosblomqvist
@@ -0,0 +1,17 @@
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/**
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* GET /api/models/embeddings — the BYOK embedding-model picker's data
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* (wave 6, owner ruling 2026-09-23): every KEYED provider's embedding
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* catalog, federated LIVE (unlike /api/models — pi-ai's static catalog
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* carries no embedding models). Rows are provider-qualified slugs, the
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* exact shape graph.embeddingModel stores. Empty array when no provider
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* is keyed (or every catalog fetch failed — per-provider failures are
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* logged and skipped, never 500s).
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*/
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import { embeddingModelCatalog } from "@/src/server";
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export const dynamic = "force-dynamic";
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export async function GET() {
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const models = await embeddingModelCatalog();
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return Response.json(models);
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}
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@@ -139,9 +139,9 @@ export function AppearanceSection({
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}
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const LADDER: readonly {
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key: keyof GraphSettings;
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key: "enabled" | "embedding" | "clustering" | "labeling";
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label: string;
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needs: keyof GraphSettings | null;
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needs: "enabled" | "embedding" | "clustering" | "labeling" | null;
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}[] = [
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{ key: "enabled", label: "Graph", needs: null },
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{ key: "embedding", label: "Embedding", needs: "enabled" },
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@@ -26,11 +26,6 @@ export const PATHS = {
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/** Chat sessions (AI SDK UIMessage transcripts, one JSON file per chat). */
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chats: path.join(DATA_ROOT, "chats"),
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graph: path.join(DATA_ROOT, "graph"),
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/** transformers.js model cache (server-side embeddings, ~300MB).
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* null in dev = keep the package default (the model is already
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* downloaded there); under LH_DATA_DIR the packaged app must never
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* try to cache inside its own bundle. */
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modelCache: process.env.LH_DATA_DIR ? path.join(DATA_ROOT, "models") : null,
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} as const;
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// ─── Models ────────────────────────────────────────────────────────────────
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Vendored
+2
-2
@@ -1,7 +1,7 @@
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/// <reference types="next" />
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/// <reference types="next/image-types/global" />
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import "./.next/dev/types/routes.d.ts";
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import "./.next/dev/types/root-params.d.ts";
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import "./.next/types/routes.d.ts";
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import "./.next/types/root-params.d.ts";
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// NOTE: This file should not be edited
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// see https://nextjs.org/docs/app/api-reference/config/typescript for more information.
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+7
-8
@@ -12,7 +12,13 @@ const nextConfig: NextConfig = {
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// INTO the standalone bundle. A shipped desktop app would carry the
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// developer's session data inside it.
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outputFileTracingExcludes: {
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"*": ["data/**/*"],
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"*": [
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"data/**/*",
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// Wave 6 (2026-09-23): the local transformers embeddings died — any
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// @huggingface copy (or its ~300MB model cache, if one ever
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// reappears) must never sweep into the standalone bundle again.
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"node_modules/@huggingface/**/*",
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],
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},
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// Empirically verified gaps (2026-09-17 standalone smoke): the tracer
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// misses JSON loaded via `import ... with { type: "json" }` attributes —
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@@ -31,21 +37,14 @@ const nextConfig: NextConfig = {
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"node_modules/pdfjs-dist/standard_fonts/**/*",
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"node_modules/@earendil-works/pi-ai/dist/providers/data/**/*",
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"node_modules/@earendil-works/pi-agent-core/dist/**/*.json",
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// onnxruntime-node: the .node binding was traced, but its
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// sibling dylib (libonnxruntime.*.dylib, dlopen'd via @rpath)
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// was not — dlopen fails at module load.
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"node_modules/onnxruntime-node/bin/**/*",
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],
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},
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// Framework-free core lives in src/ (plain TS, imported by routes and CLI alike).
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// The pi engine must stay external: its providers are runtime-loaded Node
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// code (undici streams, catalog data) that breaks when bundled into routes.
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// transformers.js (embeddings) is runtime-loaded ONNX — same reason as
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// the pi engine: bundling onnxruntime-node breaks route builds.
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serverExternalPackages: [
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"@earendil-works/pi-ai",
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"@earendil-works/pi-agent-core",
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"@huggingface/transformers",
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"pdfjs-dist", // sources v0: bundling breaks its node fake-worker path
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],
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};
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Generated
+41
-653
@@ -11,7 +11,6 @@
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"@ai-sdk/react": "^4.0.79",
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"@earendil-works/pi-agent-core": "^0.85.1",
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"@earendil-works/pi-ai": "^0.85.1",
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"@huggingface/transformers": "^4.2.0",
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"@mozilla/readability": "^0.6.0",
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"@radix-ui/react-use-controllable-state": "^1.2.6",
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"@rive-app/react-webgl2": "^4.32.1",
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@@ -2231,582 +2230,6 @@
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}
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}
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},
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"node_modules/@huggingface/jinja": {
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"version": "0.5.10",
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"resolved": "https://registry.npmjs.org/@huggingface/jinja/-/jinja-0.5.10.tgz",
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|
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"license": "MIT",
|
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"engines": {
|
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"node": ">=18"
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}
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},
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"node_modules/@huggingface/tokenizers": {
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"version": "0.1.3",
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"resolved": "https://registry.npmjs.org/@huggingface/tokenizers/-/tokenizers-0.1.3.tgz",
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"integrity": "sha512-8rF/RRT10u+kn7YuUbUg0OF30K8rjTc78aHpxT+qJ1uWSqxT1MHi8+9ltwYfkFYJzT/oS+qw3JVfHtNMGAdqyA==",
|
||||
"license": "Apache-2.0"
|
||||
},
|
||||
"node_modules/@huggingface/transformers": {
|
||||
"version": "4.2.0",
|
||||
"resolved": "https://registry.npmjs.org/@huggingface/transformers/-/transformers-4.2.0.tgz",
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"integrity": "sha512-8BRCoBMH0XsWaEIamuR0LrJGAfftgHAfb2Vrffy0VKlSAE/MnUJ5/h/zTfEP3fDIft+nk7TqB8xXEyABGitBjQ==",
|
||||
"license": "Apache-2.0",
|
||||
"dependencies": {
|
||||
"@huggingface/jinja": "^0.5.6",
|
||||
"@huggingface/tokenizers": "^0.1.3",
|
||||
"onnxruntime-node": "1.24.3",
|
||||
"onnxruntime-web": "1.26.0-dev.20260416-b7804b056c",
|
||||
"sharp": "^0.34.5"
|
||||
}
|
||||
},
|
||||
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|
||||
"version": "0.34.5",
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"cpu": [
|
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"arm64"
|
||||
],
|
||||
"license": "Apache-2.0",
|
||||
"optional": true,
|
||||
"os": [
|
||||
"darwin"
|
||||
],
|
||||
"engines": {
|
||||
"node": "^18.17.0 || ^20.3.0 || >=21.0.0"
|
||||
},
|
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"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
},
|
||||
"optionalDependencies": {
|
||||
"@img/sharp-libvips-darwin-arm64": "1.2.4"
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
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|
||||
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|
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|
||||
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|
||||
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||||
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@@ -9397,6 +8816,7 @@
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"license": "Apache-2.0",
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@@ -9406,7 +8826,9 @@
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@@ -10118,6 +9540,7 @@
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"dev": true,
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@@ -10127,6 +9550,7 @@
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"dev": true,
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"engines": {
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@@ -10211,7 +9635,9 @@
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"license": "MIT"
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"dev": true,
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"optional": true
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},
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"node_modules/esbuild": {
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"version": "0.28.2",
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@@ -10947,12 +10373,6 @@
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"node": ">=16"
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}
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},
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"node_modules/flatbuffers": {
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"license": "Apache-2.0"
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"resolved": "https://registry.npmjs.org/flatted/-/flatted-3.4.4.tgz",
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@@ -11333,7 +10753,9 @@
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"version": "3.0.0",
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"resolved": "https://registry.npmjs.org/global-agent/-/global-agent-3.0.0.tgz",
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"dev": true,
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"license": "BSD-3-Clause",
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"optional": true,
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"dependencies": {
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"boolean": "^3.0.1",
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"es6-error": "^4.1.1",
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@@ -11363,6 +10785,7 @@
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"version": "1.0.4",
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"resolved": "https://registry.npmjs.org/globalthis/-/globalthis-1.0.4.tgz",
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"integrity": "sha512-DpLKbNU4WylpxJykQujfCcwYWiV/Jhm50Goo0wrVILAv5jOr9d+H+UR3PhSCD2rCCEIg0uc+G+muBTwD54JhDQ==",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"define-properties": "^1.2.1",
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@@ -11405,6 +10828,7 @@
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"version": "1.2.0",
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"resolved": "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz",
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"integrity": "sha512-ZUKRh6/kUFoAiTAtTYPZJ3hw9wNxx+BIBOijnlG9PnrJsCcSjs1wyyD6vJpaYtgnzDrKYRSqf3OO6Rfa93xsRg==",
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"dev": true,
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"license": "MIT",
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"engines": {
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"node": ">= 0.4"
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@@ -11446,12 +10870,6 @@
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"dev": true,
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"license": "ISC"
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},
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"node_modules/guid-typescript": {
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"version": "1.0.9",
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"resolved": "https://registry.npmjs.org/guid-typescript/-/guid-typescript-1.0.9.tgz",
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"integrity": "sha512-Y8T4vYhEfwJOTbouREvG+3XDsjr8E3kIr7uf+JZ0BYloFsttiHU0WfvANVsR7TxNUJa/WpCnw/Ino/p+DeBhBQ==",
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"license": "ISC"
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},
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"node_modules/hachure-fill": {
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"version": "0.5.2",
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"resolved": "https://registry.npmjs.org/hachure-fill/-/hachure-fill-0.5.2.tgz",
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@@ -11485,6 +10903,7 @@
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"version": "1.0.2",
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"resolved": "https://registry.npmjs.org/has-property-descriptors/-/has-property-descriptors-1.0.2.tgz",
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"integrity": "sha512-55JNKuIW+vq4Ke1BjOTjM2YctQIvCT7GFzHwmfZPGo5wnrgkid0YQtnAleFSqumZm4az3n2BS+erby5ipJdgrg==",
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"dev": true,
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"license": "MIT",
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"dependencies": {
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"es-define-property": "^1.0.0"
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@@ -12708,7 +12127,9 @@
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"version": "5.0.1",
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"resolved": "https://registry.npmjs.org/json-stringify-safe/-/json-stringify-safe-5.0.1.tgz",
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"license": "ISC"
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"dev": true,
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"license": "ISC",
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"optional": true
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},
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"node_modules/json5": {
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"version": "1.0.2",
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@@ -13229,7 +12650,9 @@
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"version": "3.0.0",
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"resolved": "https://registry.npmjs.org/matcher/-/matcher-3.0.0.tgz",
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"integrity": "sha512-OkeDaAZ/bQCxeFAozM55PKcKU0yJMPGifLwV4Qgjitu+5MoAfSQN4lsLJeXZ1b8w0x+/Emda6MZgXS1jvsapng==",
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"dev": true,
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"license": "MIT",
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"optional": true,
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"dependencies": {
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"escape-string-regexp": "^4.0.0"
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@@ -13241,7 +12664,9 @@
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"version": "4.0.0",
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"resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-4.0.0.tgz",
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"integrity": "sha512-TtpcNJ3XAzx3Gq8sWRzJaVajRs0uVxA2YAkdb1jm2YkPz4G6egUFAyA3n5vtEIZefPk5Wa4UXbKuS5fKkJWdgA==",
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"dev": true,
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"license": "MIT",
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"optional": true,
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"engines": {
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"node": ">=10"
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},
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@@ -14870,6 +14295,7 @@
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"version": "1.1.1",
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"resolved": "https://registry.npmjs.org/object-keys/-/object-keys-1.1.1.tgz",
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"integrity": "sha512-NuAESUOUMrlIXOfHKzD6bpPu3tYt3xvjNdRIQ+FeT0lNb4K8WR70CaDxhuNguS2XG+GjkyMwOzsN5ZktImfhLA==",
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"dev": true,
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"license": "MIT",
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"engines": {
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"node": ">= 0.4"
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@@ -14992,49 +14418,6 @@
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"regex-recursion": "^6.0.2"
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}
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},
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"node_modules/onnxruntime-common": {
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"resolved": "https://registry.npmjs.org/onnxruntime-common/-/onnxruntime-common-1.24.3.tgz",
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"license": "MIT"
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},
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"node_modules/onnxruntime-node": {
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"version": "1.24.3",
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"resolved": "https://registry.npmjs.org/onnxruntime-node/-/onnxruntime-node-1.24.3.tgz",
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"integrity": "sha512-JH7+czbc8ALA819vlTgcV+Q214/+VjGeBHDjX81+ZCD0PCVCIFGFNtT0V4sXG/1JXypKPgScQcB3ij/hk3YnTg==",
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"hasInstallScript": true,
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"license": "MIT",
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"os": [
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"win32",
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"darwin",
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"linux"
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],
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"dependencies": {
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"adm-zip": "^0.5.16",
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"global-agent": "^3.0.0",
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"onnxruntime-common": "1.24.3"
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}
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},
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"node_modules/onnxruntime-web": {
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"resolved": "https://registry.npmjs.org/onnxruntime-web/-/onnxruntime-web-1.26.0-dev.20260416-b7804b056c.tgz",
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"integrity": "sha512-MD6Ss4GSpQBo6zqoJzyT9LRbKYs7x/JVN23FT24EcEvlqF4VuzPOeH6X38orZPKHQDbprn7K+SBpu0/mj2CQiw==",
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"dependencies": {
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"flatbuffers": "^25.1.24",
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"guid-typescript": "^1.0.9",
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"long": "^5.2.3",
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"onnxruntime-common": "1.24.0-dev.20251116-b39e144322",
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"platform": "^1.3.6",
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"protobufjs": "^7.2.4"
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}
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},
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"node_modules/onnxruntime-web/node_modules/onnxruntime-common": {
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"version": "1.24.0-dev.20251116-b39e144322",
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"resolved": "https://registry.npmjs.org/onnxruntime-common/-/onnxruntime-common-1.24.0-dev.20251116-b39e144322.tgz",
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"integrity": "sha512-BOoomdHYmNRL5r4iQ4bMvsl2t0/hzVQ3OM3PHD0gxeXu1PmggqBv3puZicEUVOA3AtHHYmqZtjMj9FOfGrATTw==",
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"node_modules/openai": {
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"version": "6.40.0",
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"resolved": "https://registry.npmjs.org/openai/-/openai-6.40.0.tgz",
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@@ -15336,12 +14719,6 @@
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"url": "https://paulmillr.com/funding/"
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}
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},
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"node_modules/platform": {
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"resolved": "https://registry.npmjs.org/plist/-/plist-3.1.0.tgz",
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@@ -16334,7 +15711,9 @@
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"version": "2.15.4",
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"resolved": "https://registry.npmjs.org/roarr/-/roarr-2.15.4.tgz",
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"dev": true,
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"license": "BSD-3-Clause",
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"optional": true,
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"dependencies": {
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"boolean": "^3.0.1",
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"detect-node": "^2.0.4",
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@@ -16516,6 +15895,7 @@
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"version": "7.8.5",
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"resolved": "https://registry.npmjs.org/semver/-/semver-7.8.5.tgz",
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"integrity": "sha512-Y7/KDsb8LjooZpwaqGyulO6DQlksgCncchHGk+sZIY4SBvUocMBEFH5Ur1fI4dV+Jvl0w6cjvucaIi40puRioA==",
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"devOptional": true,
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"license": "ISC",
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"bin": {
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"semver": "bin/semver.js"
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@@ -16528,13 +15908,17 @@
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"version": "1.0.0",
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"resolved": "https://registry.npmjs.org/semver-compare/-/semver-compare-1.0.0.tgz",
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"integrity": "sha512-YM3/ITh2MJ5MtzaM429anh+x2jiLVjqILF4m4oyQB18W7Ggea7BfqdH/wGMK7dDiMghv/6WG7znWMwUDzJiXow==",
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"license": "MIT"
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"dev": true,
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"license": "MIT",
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"optional": true
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},
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"node_modules/serialize-error": {
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"dev": true,
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"license": "MIT",
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"optional": true,
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"dependencies": {
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"type-fest": "^0.13.1"
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},
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@@ -16820,7 +16204,9 @@
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"version": "1.1.3",
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"resolved": "https://registry.npmjs.org/sprintf-js/-/sprintf-js-1.1.3.tgz",
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"integrity": "sha512-Oo+0REFV59/rz3gfJNKQiBlwfHaSESl1pcGyABQsnnIfWOFt6JNj5gCog2U6MLZ//IGYD+nA8nI+mTShREReaA==",
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"license": "BSD-3-Clause"
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"dev": true,
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"license": "BSD-3-Clause",
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"optional": true
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},
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"node_modules/stable-hash-x": {
|
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"version": "0.2.0",
|
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@@ -17510,7 +16896,9 @@
|
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"version": "0.13.1",
|
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"resolved": "https://registry.npmjs.org/type-fest/-/type-fest-0.13.1.tgz",
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"integrity": "sha512-34R7HTnG0XIJcBSn5XhDd7nNFPRcXYRZrBB2O2jdKqYODldSzBAqzsWoZYYvduky73toYS/ESqxPvkDf/F0XMg==",
|
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"dev": true,
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"license": "(MIT OR CC0-1.0)",
|
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"optional": true,
|
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"engines": {
|
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"node": ">=10"
|
||||
},
|
||||
|
||||
@@ -20,7 +20,6 @@
|
||||
"@ai-sdk/react": "^4.0.79",
|
||||
"@earendil-works/pi-agent-core": "^0.85.1",
|
||||
"@earendil-works/pi-ai": "^0.85.1",
|
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"@huggingface/transformers": "^4.2.0",
|
||||
"@mozilla/readability": "^0.6.0",
|
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"@radix-ui/react-use-controllable-state": "^1.2.6",
|
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"@rive-app/react-webgl2": "^4.32.1",
|
||||
|
||||
+24
-9
@@ -1,20 +1,35 @@
|
||||
/**
|
||||
* Embed the whole graph (backfill / cold start): one-time pass that
|
||||
* fills the embedding ledger for every node — the same embedNewNodes the
|
||||
* post-merge hook runs, just with an empty (or stale-model) ledger so
|
||||
* every node is "new". First run downloads the model weights (~300MB)
|
||||
* to the local HF cache; subsequent runs embed only what's missing.
|
||||
* Embed the whole graph (backfill / cold start / model change): one-time
|
||||
* pass that fills the embedding ledger for every node — the same
|
||||
* embedNewNodes the post-merge hook runs, just with an empty (or
|
||||
* stale-model) ledger so every node is "new".
|
||||
*
|
||||
* BYOK (wave 6, 2026-09-23): vectors come from the configured provider
|
||||
* (graph.embeddingModel, keys from the auth store) — this script resolves
|
||||
* the live state up front so an unconfigured machine says so before
|
||||
* anything bills. A model CHANGE re-embeds everything (ledger staleness).
|
||||
*
|
||||
* npx tsx scripts/embed-graph.ts
|
||||
*/
|
||||
import { embedNewNodes, embeddingPort } from "../src/server/embeddings.ts";
|
||||
import { graphStore } from "../src/store/graph.ts";
|
||||
import { embedNewNodes, EMBEDDING_MODEL, EMBEDDING_DIM } from "../src/server/embeddings.ts";
|
||||
|
||||
async function main() {
|
||||
const resolved = embeddingPort();
|
||||
if (!resolved.ok) {
|
||||
console.error(`[embed] cannot run — ${resolved.reason}`);
|
||||
process.exit(1);
|
||||
}
|
||||
const t0 = Date.now();
|
||||
console.log(`[embed] model: ${EMBEDDING_MODEL} (dim ${EMBEDDING_DIM}, q8)`);
|
||||
const { embedded, total } = await embedNewNodes(graphStore);
|
||||
console.log(`[embed] embedded ${embedded} nodes → ledger holds ${total} vectors (${((Date.now() - t0) / 1000).toFixed(1)}s)`);
|
||||
console.log(`[embed] model: ${resolved.port.model} (provider embeddings, batch 32)`);
|
||||
const { embedded, total, usage } = await embedNewNodes(graphStore);
|
||||
console.log(
|
||||
`[embed] embedded ${embedded} nodes → ledger holds ${total} vectors (${((Date.now() - t0) / 1000).toFixed(1)}s)`,
|
||||
);
|
||||
if (usage) {
|
||||
const tokens = usage.steps.reduce((sum, s) => sum + (s.inputTokens ?? 0), 0);
|
||||
console.log(`[embed] embedding spend: ${tokens} prompt tokens over ${usage.steps.length} call(s)`);
|
||||
}
|
||||
}
|
||||
|
||||
main().catch((err) => {
|
||||
|
||||
@@ -505,8 +505,14 @@ export interface RunningExtraction {
|
||||
phase: ExtractionPhase;
|
||||
startedAt: number;
|
||||
/** Live per-stage usage, accumulated as steps bill (the dock's cost /
|
||||
* context-window blobs read this through /api/graph/status). */
|
||||
usage: { extractor?: ExtractionStageUsage; auditor?: ExtractionStageUsage };
|
||||
* context-window blobs read this through /api/graph/status). Wave 6:
|
||||
* the provider-embedding tail became a billable stage — recorded from
|
||||
* the embedder's reported usage when present. */
|
||||
usage: {
|
||||
extractor?: ExtractionStageUsage;
|
||||
auditor?: ExtractionStageUsage;
|
||||
embedding?: ExtractionStageUsage;
|
||||
};
|
||||
/** True-cancellation handle (the dock's abort button): one controller per
|
||||
* run, shared across phases; abort() cancels the in-flight model call. */
|
||||
controller: AbortController;
|
||||
@@ -574,9 +580,12 @@ export interface ExtractionModels {
|
||||
export interface ExtractionPipelineDeps extends GraphDeps, ExtractionModels {
|
||||
/** The post-merge embedding batch (semantic clusters run on it).
|
||||
* INJECTED by the composition root (the agent layer never imports
|
||||
* the server layer — architecture law): production passes the real
|
||||
* local-model embedder (server/embeddings.ts embedNewNodes), tests
|
||||
* fake it. Failure inside it is non-fatal by contract. */
|
||||
* the server layer — architecture law): production passes the
|
||||
* provider-embedder batch (server/embeddings.ts embedNewNodes — BYOK
|
||||
* wave 6; null model = it skips itself with an info log), tests fake
|
||||
* it. Failure inside it is non-fatal by contract. When the result
|
||||
* carries a `usage` stage (the EmbedBatchResult contract), it is
|
||||
* recorded into the run's money-channel capture. */
|
||||
embedder: (graphStore: GraphStore) => Promise<unknown>;
|
||||
}
|
||||
|
||||
@@ -664,6 +673,27 @@ export function createExtractionPipeline(deps: ExtractionPipelineDeps): Extracti
|
||||
run.usage[stage]!.steps.push(usage);
|
||||
}
|
||||
|
||||
/** The embedding tail's failure log (non-fatal by contract). */
|
||||
function recordEmbedFailure(chatId: string): (err: unknown) => undefined {
|
||||
return (err) => {
|
||||
console.error(`[extraction ${chatId}] embedding failed (non-fatal):`, err);
|
||||
return undefined;
|
||||
};
|
||||
}
|
||||
|
||||
/** Wave 6: fold the provider-embedding tail's reported usage into the
|
||||
* run's capture — one ExtractionStageUsage per run (the embedder
|
||||
* already aggregated its API calls into steps). Absent/skipped/failed
|
||||
* embeds contribute nothing (a run that embedded nothing stays
|
||||
* two-stage, like the whole local-embedder era). */
|
||||
function captureEmbedUsage(chatId: string, result: unknown): void {
|
||||
const stage = (result as { usage?: ExtractionStageUsage } | undefined)?.usage;
|
||||
if (!stage) return;
|
||||
const run = runningExtractions.get(chatId);
|
||||
if (!run) return;
|
||||
run.usage.embedding = stage;
|
||||
}
|
||||
|
||||
|
||||
async function extract(chatId: string, view: string): Promise<SessionFindings> {
|
||||
const signal = runningExtractions.get(chatId)?.controller.signal;
|
||||
@@ -739,6 +769,7 @@ export function createExtractionPipeline(deps: ExtractionPipelineDeps): Extracti
|
||||
stages: {
|
||||
extractor: run.usage.extractor ?? { model: "unknown", steps: [] },
|
||||
auditor: run.usage.auditor ?? { model: "unknown", steps: [] },
|
||||
...(run.usage.embedding ? { embedding: run.usage.embedding } : {}),
|
||||
},
|
||||
};
|
||||
if (run.kind === "notes") graphStore.appendSourceUsage(chatId, entry);
|
||||
@@ -830,9 +861,7 @@ export function createExtractionPipeline(deps: ExtractionPipelineDeps): Extracti
|
||||
// MEANING, so embed the new propositions BEFORE the settle and
|
||||
// the namer look at clusters. Failure is non-fatal — clustering
|
||||
// runs without the new nodes until the next merge retries.
|
||||
await embed(graphStore).catch((err) => {
|
||||
console.error(`[extraction ${chatId}] embedding failed (non-fatal):`, err);
|
||||
});
|
||||
await captureEmbedUsage(chatId, await embed(graphStore).catch(recordEmbedFailure(chatId)));
|
||||
// Option A: the layout ledger evolves with the merge (sync — the
|
||||
// next snapshot fetch must already reflect the new nodes).
|
||||
settleGraphLayout(graphStore);
|
||||
@@ -963,9 +992,7 @@ export function createExtractionPipeline(deps: ExtractionPipelineDeps): Extracti
|
||||
// (topics from meaning), then the settle, then the namer —
|
||||
// under their own phase, exactly like the chat path.
|
||||
setPhase(id, "embedding", "notes", input.filePath);
|
||||
await embed(graphStore).catch((err) => {
|
||||
console.error(`[extraction ${id}] embedding failed (non-fatal):`, err);
|
||||
});
|
||||
await captureEmbedUsage(id, await embed(graphStore).catch(recordEmbedFailure(id)));
|
||||
// Option A: same post-merge settle as the chat path.
|
||||
settleGraphLayout(graphStore);
|
||||
persistRunUsage(id, Math.round(performance.now() - t0), stats);
|
||||
|
||||
@@ -12,9 +12,10 @@
|
||||
* quiz/ask calls, so delivery lands mid-probing, before plan_init — where
|
||||
* prior knowledge matters most.
|
||||
*
|
||||
* No model call, no usage: the job's only cost is one embedding pass over
|
||||
* the warm singleton (tens–~200ms) plus a brute-force cosine scan. It still
|
||||
* carries a JobRecord (kind "graph-context") so the dock shows it, and it
|
||||
* No teacher-model call: the job's only model spend is ONE embedding
|
||||
* query (the local embedder was free, provider ones are not — wave 6
|
||||
* records its prompt tokens on the job record). It still carries a
|
||||
* JobRecord (kind "graph-context") so the dock shows it, and it
|
||||
* registers its AbortController so the dock's END kills it — but an abort
|
||||
* steers NOTHING: nothing was ever promised in the lesson (the injection is
|
||||
* server-initiated scaffolding, unlike a teacher-dispatched sub-agent).
|
||||
@@ -27,6 +28,7 @@
|
||||
*/
|
||||
|
||||
import {
|
||||
accumulateJobUsage,
|
||||
buildGraphContextSteer,
|
||||
steerDelivered,
|
||||
type JobRecord,
|
||||
@@ -41,20 +43,25 @@ import type { UIMessage } from "ai";
|
||||
import { registerJobAbort, unregisterJobAbort } from "./aborts.ts";
|
||||
import type { ChatStore, GraphStore, JobStore } from "../../core/ports.ts";
|
||||
|
||||
/** The embedder as the runner needs it: the model/dim the ledger must
|
||||
* match (parity: one vector space) + the query seam. Injected so tests
|
||||
* run without the transformer model. */
|
||||
/** The embedder as the runner needs it (BYOK wave 6): the model slug the
|
||||
* ledger must match (parity: one vector space) + the query seam.
|
||||
* Resolved FRESH per run by the composition root (deps.embedder) so a
|
||||
* settings PUT or a key save between dispatch and spawn lands; null
|
||||
* model = no embedding configured — the run skips honestly. The query
|
||||
* carries its prompt-token count so the job records its spend (the
|
||||
* local embedder was free; provider ones are not). */
|
||||
export interface GraphContextEmbedder {
|
||||
model: string;
|
||||
dim: number;
|
||||
embedQuery: (text: string) => Promise<number[]>;
|
||||
model: string | null;
|
||||
embedQuery: (text: string) => Promise<{ vector: number[]; promptTokens?: number }>;
|
||||
}
|
||||
|
||||
export interface GraphContextDeps {
|
||||
chatStore: Pick<ChatStore, "getChat">;
|
||||
jobStore: Pick<JobStore, "createJob" | "getJob" | "updateJob" | "findJobByToolCallId">;
|
||||
graphStore: Pick<GraphStore, "readNodes" | "readEvents" | "readEmbeddings">;
|
||||
embedder: GraphContextEmbedder;
|
||||
/** The embedder RESOLVER (wave 6): called once at run start — fresh
|
||||
* setting + key reads each run. null = no embedding model configured. */
|
||||
embedder: () => GraphContextEmbedder | null;
|
||||
/** The wake seam — jobs.ts deliverJobResult (resolves on delivery). */
|
||||
deliver: (steer: SteerMessage) => Promise<void>;
|
||||
}
|
||||
@@ -126,7 +133,7 @@ export function startGraphContextInjection(deps: GraphContextDeps, chatId: strin
|
||||
* crash-recovery respawn seam: a job record orphaned "running" by a
|
||||
* process crash re-runs from here (the record is the only input needed). */
|
||||
export async function runGraphContextJob(deps: GraphContextDeps, jobId: string): Promise<void> {
|
||||
const { chatStore, jobStore, graphStore, embedder, deliver } = deps;
|
||||
const { chatStore, jobStore, graphStore, deliver } = deps;
|
||||
const job = jobStore.getJob(jobId);
|
||||
if (!job || job.status !== "running") return;
|
||||
|
||||
@@ -135,6 +142,13 @@ export async function runGraphContextJob(deps: GraphContextDeps, jobId: string):
|
||||
};
|
||||
const chat = chatStore.getChat(job.chatId);
|
||||
if (!chat) return fail(`chat not found: ${job.chatId}`);
|
||||
// BYOK (wave 6): fresh resolution per run; null = honestly off (the
|
||||
// dispatch gate already skips unconfigured sessions — this only fires
|
||||
// on a config change racing a respawn, and the error says exactly why).
|
||||
const embedder = deps.embedder();
|
||||
if (!embedder || embedder.model === null) {
|
||||
return fail("no embedding model configured — injection skipped (graph.embeddingModel is null)");
|
||||
}
|
||||
const topic = topicOf(chat.messages);
|
||||
if (topic === null) return fail("no injectable topic (session past its start)");
|
||||
const budget = effectiveContextBudget(chat.settings);
|
||||
@@ -146,19 +160,39 @@ export async function runGraphContextJob(deps: GraphContextDeps, jobId: string):
|
||||
try {
|
||||
jobStore.updateJob(jobId, { activity: "embedding topic" });
|
||||
const ledger = graphStore.readEmbeddings();
|
||||
if (ledger.model !== embedder.model || ledger.dim !== embedder.dim) {
|
||||
return fail("embeddings ledger is stale (model/dim mismatch) — injection skipped");
|
||||
// Staleness is the MODEL identity (wave 6): a given model serves one
|
||||
// dim by default, so model parity is space parity; the dim is checked
|
||||
// defensively against the served vector below.
|
||||
if (ledger.model !== embedder.model) {
|
||||
return fail("embeddings ledger is stale (model mismatch) — injection skipped");
|
||||
}
|
||||
if (Object.keys(ledger.vectors).length === 0) {
|
||||
return fail("the knowledge graph has no embeddings yet — nothing to inject");
|
||||
}
|
||||
const query = await embedder.embedQuery(topic);
|
||||
if (controller.signal.aborted) return;
|
||||
// The defensive dim half of the old parity check: a provider-side dim
|
||||
// change (rare) must fail the query, never silently mismatch.
|
||||
if (query.vector.length !== ledger.dim) {
|
||||
return fail(
|
||||
`embedding dim mismatch: query is ${query.vector.length}, ledger holds ${ledger.dim}`,
|
||||
);
|
||||
}
|
||||
// Money channel (wave 6): the one embedding call's prompt tokens fold
|
||||
// into the job record — priced via the EMBEDDING_PRICES overlay.
|
||||
if (query.promptTokens !== undefined) {
|
||||
jobStore.updateJob(jobId, {
|
||||
usage: accumulateJobUsage(job.usage, embedder.model, {
|
||||
inputTokens: query.promptTokens,
|
||||
outputTokens: 0,
|
||||
}),
|
||||
});
|
||||
}
|
||||
|
||||
jobStore.updateJob(jobId, { activity: "selecting propositions" });
|
||||
const data = selectGraphContext({
|
||||
chatId: job.chatId,
|
||||
query,
|
||||
query: query.vector,
|
||||
vectors: ledger.vectors,
|
||||
nodes: graphStore.readNodes(),
|
||||
events: graphStore.readEvents(),
|
||||
|
||||
@@ -205,11 +205,13 @@ function svgViewport(svg: string, opts: RenderSvgOptions): { width: number; heig
|
||||
* are an arbitrary SPLIT of one zlib stream, so merging them is the more
|
||||
* standard shape, byte-identical stream, zero re-encoding.
|
||||
*
|
||||
* Pure Node on purpose (no sharp): the process already loads transformers'
|
||||
* nested sharp + libvips, and importing the root sharp (Next's optional
|
||||
* dependency) put a SECOND libvips into the process — macOS's ObjC runtime
|
||||
* warns (duplicate GNotificationCenterDelegate, "mysterious crashes").
|
||||
* One native image lib per process; app code carries none. */
|
||||
* Pure Node on purpose (no sharp): historically the process already carried
|
||||
* a native image lib via @huggingface/transformers' nested sharp + libvips,
|
||||
* and importing the root sharp (Next's optional dependency) put a SECOND
|
||||
* libvips into the process — macOS's ObjC runtime warns (duplicate
|
||||
* GNotificationCenterDelegate, "mysterious crashes"). The transformers
|
||||
* embedder died with wave 6 (2026-09-23) but the rule lives on: one native
|
||||
* image lib per process; app code carries none. */
|
||||
export function providerSafePng(png: Buffer): Buffer {
|
||||
const SIG_LEN = 8;
|
||||
if (png.length < SIG_LEN + 12 || png.subarray(0, SIG_LEN).toString("hex") !== "89504e470d0a1a0a") return png;
|
||||
|
||||
@@ -108,6 +108,10 @@ export interface ExtractionRunUsage {
|
||||
stages: {
|
||||
extractor: ExtractionStageUsage;
|
||||
auditor: ExtractionStageUsage;
|
||||
/** Wave 6 (2026-09-23): the provider-embedding tail — present only
|
||||
* when an embedding model is configured and the run embedded
|
||||
* (local embeddings were free, so this post-dates them). */
|
||||
embedding?: ExtractionStageUsage;
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -55,10 +55,12 @@ export interface GraphCostSummary {
|
||||
totalDollars: number;
|
||||
/** Runs where EVERY stage was unpriced (totalDollars excludes them). */
|
||||
unpricedRuns: number;
|
||||
/** The three billable pipeline stages (merger/embedder are free). */
|
||||
/** The billable pipeline stages (wave 6: the embedder became one —
|
||||
* provider embeddings cost money; merger is the only free stage). */
|
||||
stages: {
|
||||
extraction: StageCost;
|
||||
audit: StageCost;
|
||||
embedding: StageCost;
|
||||
naming: StageCost;
|
||||
};
|
||||
/** Newest first, capped at `recentCap` (default 10). */
|
||||
@@ -113,6 +115,7 @@ function mergeTokens(into: CallUsage, steps: readonly CallUsage[]): void {
|
||||
export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
|
||||
const extraction = emptyStage();
|
||||
const audit = emptyStage();
|
||||
const embedding = emptyStage();
|
||||
const naming = emptyStage();
|
||||
const runs: RecentRun[] = [];
|
||||
|
||||
@@ -131,10 +134,25 @@ export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
|
||||
if (audDollars === null) audit.unpricedRuns++;
|
||||
else audit.dollars += audDollars;
|
||||
|
||||
// Run-level: null only when BOTH stages are unpriced.
|
||||
const priced = extDollars ?? 0;
|
||||
const runDollars = extDollars === null && audDollars === null ? null : priced + (audDollars ?? 0);
|
||||
const allSteps = [...stages.extractor.steps, ...stages.auditor.steps];
|
||||
// Wave 6: the embedding tail — present only when an embedding model
|
||||
// ran (absent stage = the run embedded nothing, never "free").
|
||||
let embDollars: number | null = null;
|
||||
let embSteps: CallUsage[] = [];
|
||||
if (stages.embedding) {
|
||||
embedding.runs++;
|
||||
embSteps = stages.embedding.steps;
|
||||
mergeTokens(embedding.tokens, embSteps);
|
||||
embDollars = stageDollars(stages.embedding.model, embSteps);
|
||||
if (embDollars === null) embedding.unpricedRuns++;
|
||||
else embedding.dollars += embDollars;
|
||||
}
|
||||
|
||||
// Run-level: null only when EVERY stage is unpriced.
|
||||
const allStages: Array<number | null> = [extDollars, audDollars, embDollars];
|
||||
const runDollars = allStages.every((d) => d === null)
|
||||
? null
|
||||
: allStages.reduce<number>((a, d) => a + (d ?? 0), 0);
|
||||
const allSteps = [...stages.extractor.steps, ...stages.auditor.steps, ...embSteps];
|
||||
runs.push({
|
||||
startedAt: run.startedAt,
|
||||
at: run.at,
|
||||
@@ -177,9 +195,9 @@ export function graphCostSummary(input: CostSummaryInput): GraphCostSummary {
|
||||
// A run is "unpriced" when it billed nothing countable at all.
|
||||
const unpricedRuns = runs.filter((r) => r.dollars === null).length;
|
||||
return {
|
||||
totalDollars: extraction.dollars + audit.dollars + naming.dollars,
|
||||
totalDollars: extraction.dollars + audit.dollars + embedding.dollars + naming.dollars,
|
||||
unpricedRuns,
|
||||
stages: { extraction, audit, naming },
|
||||
stages: { extraction, audit, embedding, naming },
|
||||
runs: capped,
|
||||
};
|
||||
}
|
||||
|
||||
@@ -243,3 +243,49 @@ export interface AuthStore {
|
||||
/** Remove one VAR if present (absent = no-op, still a silent success). */
|
||||
deleteAuthVar(name: string): void;
|
||||
}
|
||||
|
||||
// ─── EmbeddingPort — BYOK provider embeddings (wave 6, 2026-09-23) ───────────
|
||||
|
||||
/**
|
||||
* The provider-embedding seam (the local transformers embedder died with
|
||||
* this wave): vectors come from the user's keyed provider — OpenRouter,
|
||||
* OpenAI, or Google — named by graph.embeddingModel ("provider:id", null =
|
||||
* honestly off: embedding/clustering/injection skip with an info log).
|
||||
*
|
||||
* Owner decisions (2026-09-23) baked in:
|
||||
* - The adapters are resolved FRESH per call from the setting + the auth
|
||||
* store (key rotations land immediately — the search port's treatment).
|
||||
* - Vectors are stored as the provider returns them (default dims, no
|
||||
* truncation, no normalization); the ledger is self-describing on
|
||||
* model + dim, and any model change voids it (full re-embed).
|
||||
* - Usage (prompt tokens) flows back to the money channel: adapters
|
||||
* report it, the caller records it as a CallUsage step.
|
||||
*
|
||||
* Pure types only (browser-safe): the adapters are server-side
|
||||
* (src/server/embeddings.ts) and arrive as injected dependencies.
|
||||
*/
|
||||
|
||||
/** Which side of retrieval the text is — providers bill identically but
|
||||
* quality-optimize differently (OpenRouter's input_type, Gemini's
|
||||
* taskType); every input in one call shares the task. */
|
||||
export type EmbeddingTask = "document" | "query";
|
||||
|
||||
/** One embedding batch's result: vectors in input order, the model's
|
||||
* identity + dim (the ledger stamps), and the provider-reported prompt
|
||||
* token count (undefined = provider didn't report; cost stays honest
|
||||
* "—" via the existing unpriced path). */
|
||||
export interface EmbeddingResult {
|
||||
vectors: number[][];
|
||||
/** The full "provider:id" slug — ledger/cost keys use it verbatim. */
|
||||
model: string;
|
||||
dim: number;
|
||||
promptTokens?: number;
|
||||
}
|
||||
|
||||
/** A live provider embedding adapter. `embed` throws on failure (adapters
|
||||
* map HTTP errors to clear Error messages); callers treat failure as
|
||||
* non-fatal per the extraction contract. */
|
||||
export interface EmbeddingPort {
|
||||
model: string;
|
||||
embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult>;
|
||||
}
|
||||
|
||||
+26
-1
@@ -2752,6 +2752,30 @@ export const CONTEXT_LIMITS: Record<string, number> = {
|
||||
|
||||
// ─── Pure helpers (folds + cost inference) ───────────────────────────────────
|
||||
|
||||
// ─── Embedding prices (wave 6, 2026-09-23) ─────────────────────────────────
|
||||
|
||||
/** Hand-maintained BYOK embedding prices, USD per 1M INPUT tokens — the
|
||||
* sync script never touches this block (pi-ai's catalogs carry no
|
||||
* embedding models, so the generated PRICES table has none of these).
|
||||
* VERIFIED against provider pricing pages 2026-09-23: OpenAI's model
|
||||
* docs ($0.02 3-small / $0.13 3-large / $0.10 ada-002), Google's pricing
|
||||
* page ($0.15 gemini-embedding-001 / $0.20 Gemini Embedding 2 text),
|
||||
* OpenRouter passes provider pricing through. output: 0 — embeddings
|
||||
* emit no output tokens. Unlisted models stay unpriced ("—", never
|
||||
* zero); tokens still get captured. */
|
||||
export const EMBEDDING_PRICES: Record<string, ModelPrice> = {
|
||||
"openai:text-embedding-3-small": { input: 0.02, output: 0 },
|
||||
"openai:text-embedding-3-large": { input: 0.13, output: 0 },
|
||||
"openai:text-embedding-ada-002": { input: 0.1, output: 0 },
|
||||
"google:gemini-embedding-001": { input: 0.15, output: 0 },
|
||||
"google:gemini-embedding-2": { input: 0.2, output: 0 },
|
||||
"openrouter:openai/text-embedding-3-small": { input: 0.02, output: 0 },
|
||||
"openrouter:openai/text-embedding-3-large": { input: 0.13, output: 0 },
|
||||
"openrouter:openai/text-embedding-ada-002": { input: 0.1, output: 0 },
|
||||
"openrouter:google/gemini-embedding-001": { input: 0.15, output: 0 },
|
||||
};
|
||||
|
||||
|
||||
/** Sum a list of per-step usages into one CallUsage. A field stays
|
||||
* undefined when EVERY step left it unreported — absent is not zero. */
|
||||
export function sumSteps(steps: readonly CallUsage[]): CallUsage {
|
||||
@@ -2773,7 +2797,8 @@ export function callCost(
|
||||
usage: { inputTokens?: number; outputTokens?: number; cacheReadTokens?: number; cacheWriteTokens?: number },
|
||||
model: string,
|
||||
): number | null {
|
||||
const price = PRICES[model];
|
||||
// Wave 6: the hand-maintained embedding overlay backs the lookup too.
|
||||
const price = PRICES[model] ?? EMBEDDING_PRICES[model];
|
||||
if (!price) return null;
|
||||
if (usage.inputTokens === undefined || usage.outputTokens === undefined) return null;
|
||||
const cacheRead = usage.cacheReadTokens ?? 0;
|
||||
|
||||
@@ -201,6 +201,9 @@ export function resolveSettings(record: UserSettings | null): EffectiveSettings
|
||||
embedding: graph.embedding ?? FACTORY_SETTINGS.graph.embedding,
|
||||
clustering: graph.clustering ?? FACTORY_SETTINGS.graph.clustering,
|
||||
labeling: graph.labeling ?? FACTORY_SETTINGS.graph.labeling,
|
||||
// BYOK (wave 6): null is the factory floor and a VALID resolved
|
||||
// value — ?? only fills the absent case, null passes through.
|
||||
embeddingModel: graph.embeddingModel ?? FACTORY_SETTINGS.graph.embeddingModel,
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
+16
-1
@@ -251,6 +251,13 @@ export interface GraphSettings {
|
||||
clustering?: boolean;
|
||||
/** The LLM cluster-namer (needs clustering). */
|
||||
labeling?: boolean;
|
||||
/** BYOK embeddings (wave 6, 2026-09-23): the provider embedding model
|
||||
* as a "provider:id" slug (openrouter:…, openai:…, google:…). null =
|
||||
* no model — embedding/clustering/injection skip honestly (info log,
|
||||
* graph stays textual); no factory floor exists. Keys live in the auth
|
||||
* store, resolved fresh per call. A model CHANGE re-embeds everything
|
||||
* (the ledger's staleness semantics). */
|
||||
embeddingModel?: string | null;
|
||||
}
|
||||
|
||||
/** THE record. Every section optional (delta); unknown top-level keys are
|
||||
@@ -303,7 +310,7 @@ export const FACTORY_SETTINGS: FactorySettings = {
|
||||
sessionNameSerif: true,
|
||||
mdRendering: { headingWeight: 900, boldColor: null, italicColor: null },
|
||||
},
|
||||
graph: { enabled: true, embedding: true, clustering: true, labeling: true },
|
||||
graph: { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null },
|
||||
};
|
||||
|
||||
// ─── Sanitizer ─────────────────────────────────────────────────────────────
|
||||
@@ -453,6 +460,14 @@ function sanitizeGraph(raw: Record<string, unknown>): GraphSettings {
|
||||
if (clustering !== undefined) out.clustering = clustering;
|
||||
const labeling = boolOrDrop(raw.labeling);
|
||||
if (labeling !== undefined) out.labeling = labeling;
|
||||
// BYOK embeddings (wave 6): null or a provider-qualified slug; anything
|
||||
// else (bad type, bad shape) DROPS to absent — an invalid write never
|
||||
// becomes a live model (the PUT parser 400s it first; this is the
|
||||
// read-side belt-and-suspenders for hand-edited files).
|
||||
if (raw.embeddingModel === null) out.embeddingModel = null;
|
||||
else if (typeof raw.embeddingModel === "string" && /^\S+:\S+$/.test(raw.embeddingModel)) {
|
||||
out.embeddingModel = raw.embeddingModel;
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
|
||||
@@ -5,7 +5,7 @@ import { piEngineForSpec, type PiEngine } from "../agent/models.ts";
|
||||
import { createJobRunner } from "../agent/subagents/runner.ts";
|
||||
import { createExtractionPipeline } from "../agent/subagents/extraction.ts";
|
||||
import { runGraphContextJob, type GraphContextDeps } from "../agent/subagents/graph-context.ts";
|
||||
import { EMBEDDING_DIM, EMBEDDING_MODEL, embedNewNodes, embedQuery } from "./embeddings.ts";
|
||||
import { embedNewNodes, embeddingPort } from "./embeddings.ts";
|
||||
import type { ModelRole } from "../../config.ts";
|
||||
import { claimSteers, deliverJobResult, setContextRespawn, setJobRespawn, setTurnRunner } from "./jobs.ts";
|
||||
import { recordToPiMessages } from "./pi/record-adapter.ts";
|
||||
@@ -63,8 +63,8 @@ const jobRunner = createJobRunner({
|
||||
* (/api/graph/import) hit the same single-flight registry. */
|
||||
export const extraction = createExtractionPipeline({
|
||||
...stores,
|
||||
// The REAL embedder (local transformer model): post-merge embedding
|
||||
// batch — semantic clusters are computed and persisted here.
|
||||
// The provider-embedder batch (BYOK wave 6): post-merge embedding —
|
||||
// resolves the model fresh per call; null model = info-skip inside.
|
||||
embedder: embedNewNodes,
|
||||
// BYOK (owner, 2026-09-22): the extraction stages resolve the chain —
|
||||
// role override → slot → NOTHING — read FRESH per stage call; an empty
|
||||
@@ -75,14 +75,27 @@ export const extraction = createExtractionPipeline({
|
||||
});
|
||||
|
||||
/** Graph context injection (vision-context-injection.md): the runner's
|
||||
* deps — same storage ports, the REAL embedder as the query seam (same
|
||||
* warm singleton as extraction, so vector-space parity holds by
|
||||
* construction), and the wake pipeline as the delivery seam. */
|
||||
* deps — same storage ports, the provider-embedder RESOLVER as the query
|
||||
* seam (BYOK wave 6: fresh setting + key reads per run, so vector-space
|
||||
* parity with the ledger is checked against the CURRENT model; a key or
|
||||
* settings PUT between dispatch and spawn lands), and the wake pipeline
|
||||
* as the delivery seam. */
|
||||
export const graphContext: GraphContextDeps = {
|
||||
chatStore: stores.chatStore,
|
||||
jobStore: stores.jobStore,
|
||||
graphStore: stores.graphStore,
|
||||
embedder: { model: EMBEDDING_MODEL, dim: EMBEDDING_DIM, embedQuery },
|
||||
embedder: () => {
|
||||
const resolved = embeddingPort();
|
||||
if (!resolved.ok) return null;
|
||||
const port = resolved.port;
|
||||
return {
|
||||
model: port.model,
|
||||
embedQuery: async (text: string) => {
|
||||
const result = await port.embed([text], "query");
|
||||
return { vector: result.vectors[0]!, promptTokens: result.promptTokens };
|
||||
},
|
||||
};
|
||||
},
|
||||
deliver: deliverJobResult,
|
||||
};
|
||||
|
||||
@@ -221,7 +234,10 @@ export async function handleChatRequest(
|
||||
// GRAPH GATES (settings-storage wave, 2026-09-19): the read channel
|
||||
// needs BOTH the master switch and the embedding rung (injection is a
|
||||
// vector query); with graph off there is no context job at all.
|
||||
if (graphContextEnabled(effective.graph)) {
|
||||
// BYOK (wave 6): an unconfigured embedding model means no vectors to
|
||||
// query — the dispatch skips honestly (the runner's own resolution is
|
||||
// the belt-and-suspenders for respawn races).
|
||||
if (graphContextEnabled(effective.graph) && effective.graph.embeddingModel !== null) {
|
||||
triggerGraphContext(graphContext, chatId);
|
||||
}
|
||||
|
||||
|
||||
+391
-87
@@ -1,94 +1,293 @@
|
||||
/**
|
||||
* THE EMBEDDER (DECIDED 2026-09-13): semantic topic clustering needs
|
||||
* vectors, and vectors need a model running ON the user's machine — no
|
||||
* external services, no API bills, no network dependency for re-embedding.
|
||||
* BYOK PROVIDER EMBEDDINGS (wave 6, 2026-09-23): the local transformers
|
||||
* embedder is dead — vectors now come from the user's keyed provider
|
||||
* (OpenRouter / OpenAI / Google), named by graph.embeddingModel
|
||||
* ("provider:id", null = honestly off; skip with an info log, the graph
|
||||
* stays textual — owner ruling 2026-09-23).
|
||||
*
|
||||
* Runtime: in-process transformers.js (@huggingface/transformers v4 over
|
||||
* onnxruntime-node — native Apple Silicon acceleration). The model
|
||||
* weights download ONCE to the local HF cache on first run (~300MB).
|
||||
* Owner decisions baked in:
|
||||
* - NO factory model (BYOK, like the model slots): null is the floor.
|
||||
* - Adapters resolve FRESH per call from the setting + the auth store
|
||||
* (the search port's treatment — key rotations land immediately); a
|
||||
* key missing for a SET model is an honest error, never a fallback.
|
||||
* - Vectors stored as the provider returns them (default dims, no
|
||||
* truncation, no normalization). The ledger is self-describing on
|
||||
* model + dim; a model change voids it (full re-embed, minutes at
|
||||
* current scale — the local embedder's same staleness rule).
|
||||
* - Usage (prompt tokens) flows to the money channel: every API call
|
||||
* reports one CallUsage step (outputTokens: 0 — embeddings emit no
|
||||
* output; priced via the hand-maintained EMBEDDING_PRICES overlay).
|
||||
*
|
||||
* Model: EmbeddingGemma-300M (q8) — best quality-per-megabyte small
|
||||
* embedding model as of late 2025 (MTEB-en 68.36), built for on-device
|
||||
* use, with MRL truncation so we store 512 of its 768 dims. Every input
|
||||
* uses the model card's document format ("title: none | text: …"),
|
||||
* consistently — mixing prefixed and unprefixed inputs degrades quality.
|
||||
*
|
||||
* Model swaps re-embed EVERYTHING: vectors from different models live in
|
||||
* incompatible spaces. The ledger records model + dim; a mismatch marks
|
||||
* the whole ledger stale and this module re-embeds from scratch (minutes
|
||||
* at current scale).
|
||||
* The adapters are injected-fetch dependencies so tests never touch the
|
||||
* network; wire protocols VERIFIED against provider docs 2026-09-23:
|
||||
* - OpenRouter: POST /api/v1/embeddings (Bearer), input_type
|
||||
* search_document|search_query; catalog GET /api/v1/embeddings/models
|
||||
* (VERIFIED live 2026-09-23 — the inverse path /api/v1/models/
|
||||
* embeddings 404s).
|
||||
* - OpenAI: POST /v1/embeddings (Bearer); catalog GET /v1/models.
|
||||
* - Google: POST /v1beta/models/{m}:batchEmbedContents (x-goog-api-key),
|
||||
* taskType RETRIEVAL_DOCUMENT|RETRIEVAL_QUERY; catalog GET /v1beta/models
|
||||
* filtered on supportedGenerationMethods ∋ embedContent.
|
||||
*/
|
||||
import * as fs from "node:fs";
|
||||
import { env, pipeline, type FeatureExtractionPipeline } from "@huggingface/transformers";
|
||||
import { PATHS } from "../../config.ts";
|
||||
import type { EmbeddingsLedger, GraphNode } from "../core/contracts.ts";
|
||||
|
||||
import type { CallUsage, EmbeddingsLedger, GraphNode } from "../core/contracts.ts";
|
||||
import type { ExtractionStageUsage } from "../core/contracts/chat.ts";
|
||||
import type { EmbeddingPort, EmbeddingResult, EmbeddingTask } from "../core/ports.ts";
|
||||
import { semanticClusters, SEMANTIC_SIM_THRESHOLD } from "../core/semantic-clusters.ts";
|
||||
import { keyValue } from "./keys.ts";
|
||||
import { effectiveSettings } from "./stores.ts";
|
||||
|
||||
export const EMBEDDING_MODEL = "onnx-community/embeddinggemma-300m-ONNX";
|
||||
/** MRL truncation of the native 768 — negligible quality loss, half the
|
||||
* ledger size (2026-09-13 researcher sweep). */
|
||||
export const EMBEDDING_DIM = 512;
|
||||
/** Batch size bounds peak memory; propositions are tiny so 32 per call
|
||||
* keeps the pipeline warm without thrashing. */
|
||||
/** The wire-callable shape the adapters close over (tests inject a stub). */
|
||||
type FetchLike = (input: string, init: RequestInit) => Promise<Response>;
|
||||
|
||||
/** One provider embedding API call — kept small enough to never trip
|
||||
* per-request input limits (propositions are tiny; 32 was the local
|
||||
* embedder's batch and it stays). */
|
||||
const EMBED_BATCH = 32;
|
||||
/** Per-request timeout: a hung provider call must not hang an extraction
|
||||
* run (the wave-1 hung-job lesson). */
|
||||
const REQUEST_TIMEOUT_MS = 60_000;
|
||||
|
||||
// Model cache routing (Electron seam, 2026-09-17): the default cacheDir
|
||||
// lives inside node_modules — read-only inside a packaged .app bundle.
|
||||
// When LH_DATA_DIR is set the cache follows the data seam (userData);
|
||||
// in dev it stays at the package default (the model is already there).
|
||||
if (PATHS.modelCache) {
|
||||
fs.mkdirSync(PATHS.modelCache, { recursive: true });
|
||||
env.cacheDir = PATHS.modelCache;
|
||||
/** The providers this wave ships + their auth vars (the curated key
|
||||
* catalog, same ids the auth store and /api/keys use). */
|
||||
const PROVIDER_KEYS = {
|
||||
openrouter: "OPENROUTER_API_KEY",
|
||||
openai: "OPENAI_API_KEY",
|
||||
google: "GEMINI_API_KEY",
|
||||
} as const;
|
||||
export type EmbeddingProviderId = keyof typeof PROVIDER_KEYS;
|
||||
|
||||
/** "provider:id" → {provider, id}; null when malformed (no guessing). */
|
||||
export function parseEmbeddingModelSlug(slug: string): { provider: EmbeddingProviderId; id: string } | null {
|
||||
const colon = slug.indexOf(":");
|
||||
if (colon <= 0 || colon === slug.length - 1) return null;
|
||||
const provider = slug.slice(0, colon) as EmbeddingProviderId;
|
||||
if (!(provider in PROVIDER_KEYS)) return null;
|
||||
return { provider, id: slug.slice(colon + 1) };
|
||||
}
|
||||
|
||||
let pipelinePromise: Promise<FeatureExtractionPipeline> | null = null;
|
||||
// ─── Adapters (one per provider; pure injected-fetch factories) ──────────────
|
||||
|
||||
function getPipeline(): Promise<FeatureExtractionPipeline> {
|
||||
pipelinePromise ??= pipeline("feature-extraction", EMBEDDING_MODEL, { dtype: "q8" });
|
||||
return pipelinePromise;
|
||||
/** Shared: HTTP-error → clear Error (the search adapters' mapping). */
|
||||
async function demandOk(resp: Response, label: string): Promise<void> {
|
||||
if (resp.ok) return;
|
||||
const body = await resp.text();
|
||||
throw new Error(`${label} API ${resp.status}: ${body.slice(0, 200)}`);
|
||||
}
|
||||
|
||||
/** EmbeddingGemma's required document format (model card). */
|
||||
function embedInput(statement: string): string {
|
||||
return `title: none | text: ${statement}`;
|
||||
/** Shared: normalize a provider's {embedding, index} rows to input order. */
|
||||
function rowsInOrder(
|
||||
data: Array<{ embedding?: unknown; index?: unknown }>,
|
||||
count: number,
|
||||
label: string,
|
||||
): number[][] {
|
||||
const byIndex = new Map<number, number[]>();
|
||||
for (const row of data) {
|
||||
if (
|
||||
Array.isArray(row.embedding) &&
|
||||
row.embedding.every((x) => typeof x === "number") &&
|
||||
typeof row.index === "number"
|
||||
) {
|
||||
byIndex.set(row.index, row.embedding as number[]);
|
||||
}
|
||||
}
|
||||
const out: number[][] = [];
|
||||
for (let i = 0; i < count; i++) {
|
||||
const vec = byIndex.get(i);
|
||||
if (!vec) throw new Error(`${label} API returned ${byIndex.size}/${count} vectors`);
|
||||
out.push(vec);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
/** EmbeddingGemma's retrieval-QUERY format (model card) — the topic side of
|
||||
* graph context injection (vision-context-injection.md). Role-asymmetric
|
||||
* BY DESIGN: queries and documents use different trained templates; the
|
||||
* requirement is same model + same truncation (one vector space), each
|
||||
* side in the format the model was trained for. */
|
||||
function queryInput(text: string): string {
|
||||
return `task: search result | query: ${text}`;
|
||||
/** One API call's money-channel step: embeddings emit no output, cache
|
||||
* nothing, reason never — the fields exist (CallUsage requires them) as
|
||||
* absent-is-not-zero undefineds. */
|
||||
function embeddingStep(promptTokens: number): CallUsage {
|
||||
return {
|
||||
inputTokens: promptTokens,
|
||||
outputTokens: 0,
|
||||
cacheReadTokens: undefined,
|
||||
cacheWriteTokens: undefined,
|
||||
reasoningTokens: undefined,
|
||||
};
|
||||
}
|
||||
|
||||
/** Embed one retrieval query (the session's topic) — same warm singleton,
|
||||
* same 512-dim MRL truncation as the ledger. The one embedding call the
|
||||
* injection job makes; tens–~200ms warm. */
|
||||
export async function embedQuery(text: string): Promise<number[]> {
|
||||
const extractor = await getPipeline();
|
||||
const output = await extractor([queryInput(text)], { pooling: "mean", normalize: true });
|
||||
return truncateNormalize((output.tolist() as number[][])[0]!);
|
||||
/** OpenRouter: POST https://openrouter.ai/api/v1/embeddings — Bearer auth,
|
||||
* input_type hints the retrieval side (verified against the docs 2026-09-23). */
|
||||
export function openrouterEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
|
||||
const parsed = parseEmbeddingModelSlug(model);
|
||||
if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
|
||||
return {
|
||||
model,
|
||||
async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
|
||||
const resp = await fetchFn("https://openrouter.ai/api/v1/embeddings", {
|
||||
method: "POST",
|
||||
headers: { "content-type": "application/json", authorization: `Bearer ${apiKey}` },
|
||||
body: JSON.stringify({
|
||||
model: parsed.id,
|
||||
input: texts,
|
||||
input_type: task === "query" ? "search_query" : "search_document",
|
||||
}),
|
||||
signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
|
||||
});
|
||||
await demandOk(resp, "OpenRouter embeddings");
|
||||
const data = (await resp.json()) as {
|
||||
data?: Array<{ embedding?: unknown; index?: unknown }>;
|
||||
usage?: { prompt_tokens?: number };
|
||||
};
|
||||
return {
|
||||
vectors: rowsInOrder(data.data ?? [], texts.length, "OpenRouter embeddings"),
|
||||
model,
|
||||
dim: 0, // filled by the caller from the first vector (below)
|
||||
promptTokens: data.usage?.prompt_tokens,
|
||||
};
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/** Truncate an L2-normalized vector to EMBEDDING_DIM (MRL: leading dims
|
||||
* carry most of the signal) and re-normalize so cosine = dot product. */
|
||||
function truncateNormalize(vec: readonly number[]): number[] {
|
||||
const out = vec.slice(0, EMBEDDING_DIM);
|
||||
let mag = 0;
|
||||
for (const x of out) mag += x * x;
|
||||
mag = Math.sqrt(mag);
|
||||
return mag > 0 ? out.map((x) => x / mag) : out;
|
||||
/** OpenAI: POST https://api.openai.com/v1/embeddings — Bearer auth; the
|
||||
* response's usage carries prompt_tokens (verified 2026-09-23). */
|
||||
export function openaiEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
|
||||
const parsed = parseEmbeddingModelSlug(model);
|
||||
if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
|
||||
return {
|
||||
model,
|
||||
async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
|
||||
// OpenAI's embeddings API takes no task hint (one format per
|
||||
// model); the argument exists for seam parity and is unused here.
|
||||
void task;
|
||||
const resp = await fetchFn("https://api.openai.com/v1/embeddings", {
|
||||
method: "POST",
|
||||
headers: { "content-type": "application/json", authorization: `Bearer ${apiKey}` },
|
||||
body: JSON.stringify({ model: parsed.id, input: texts }),
|
||||
signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
|
||||
});
|
||||
await demandOk(resp, "OpenAI embeddings");
|
||||
const data = (await resp.json()) as {
|
||||
data?: Array<{ embedding?: unknown; index?: unknown }>;
|
||||
usage?: { prompt_tokens?: number };
|
||||
};
|
||||
return {
|
||||
vectors: rowsInOrder(data.data ?? [], texts.length, "OpenAI embeddings"),
|
||||
model,
|
||||
dim: 0,
|
||||
promptTokens: data.usage?.prompt_tokens,
|
||||
};
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/** Google: POST …/models/{m}:batchEmbedContents — x-goog-api-key, one
|
||||
* request per text with taskType RETRIEVAL_QUERY|RETRIEVAL_DOCUMENT
|
||||
* (verified against the Gemini API docs 2026-09-23). */
|
||||
export function googleEmbeddingProvider(model: string, apiKey: string, fetchFn: FetchLike = fetch): EmbeddingPort {
|
||||
const parsed = parseEmbeddingModelSlug(model);
|
||||
if (!parsed) throw new Error(`invalid embedding model slug: ${model}`);
|
||||
return {
|
||||
model,
|
||||
async embed(texts: readonly string[], task: EmbeddingTask): Promise<EmbeddingResult> {
|
||||
const resp = await fetchFn(
|
||||
`https://generativelanguage.googleapis.com/v1beta/models/${encodeURIComponent(parsed.id)}:batchEmbedContents`,
|
||||
{
|
||||
method: "POST",
|
||||
headers: { "content-type": "application/json", "x-goog-api-key": apiKey },
|
||||
body: JSON.stringify({
|
||||
requests: texts.map((text) => ({
|
||||
model: `models/${parsed.id}`,
|
||||
content: { parts: [{ text }] },
|
||||
taskType: task === "query" ? "RETRIEVAL_QUERY" : "RETRIEVAL_DOCUMENT",
|
||||
})),
|
||||
}),
|
||||
signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS),
|
||||
},
|
||||
);
|
||||
await demandOk(resp, "Google embeddings");
|
||||
const data = (await resp.json()) as {
|
||||
embeddings?: Array<{ values?: unknown }>;
|
||||
/** embedContent responses carry usageMetadata; the batch
|
||||
* shape may too — parsed opportunistically, never guessed. */
|
||||
usageMetadata?: { promptTokenCount?: number };
|
||||
};
|
||||
const vectors = (data.embeddings ?? []).map((row, i) => {
|
||||
if (!Array.isArray(row.values) || !row.values.every((x) => typeof x === "number")) {
|
||||
throw new Error(`Google embeddings API returned a bad vector at index ${i}`);
|
||||
}
|
||||
return row.values as number[];
|
||||
});
|
||||
if (vectors.length !== texts.length) {
|
||||
throw new Error(`Google embeddings API returned ${vectors.length}/${texts.length} vectors`);
|
||||
}
|
||||
return { vectors, model, dim: 0, promptTokens: data.usageMetadata?.promptTokenCount };
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
// ─── Resolution (fresh per call — the search port's treatment) ───────────────
|
||||
|
||||
/** The resolved state: a live port, or the honest reason it isn't. */
|
||||
export type EmbeddingResolution =
|
||||
| { ok: true; port: EmbeddingPort }
|
||||
| { ok: false; reason: string };
|
||||
|
||||
/** Pure resolution from a setting slug + a key lookup (testable): null
|
||||
* slug = off; a set slug with no key = honest unavailability. */
|
||||
export function resolveEmbeddingPortFor(
|
||||
slug: string | null | undefined,
|
||||
keyOf: (envVar: string) => string | undefined,
|
||||
fetchFn?: FetchLike,
|
||||
): EmbeddingResolution {
|
||||
if (slug === null || slug === undefined || slug === "") {
|
||||
return { ok: false, reason: "no embedding model configured (graph.embeddingModel is null)" };
|
||||
}
|
||||
const parsed = parseEmbeddingModelSlug(slug);
|
||||
if (!parsed) {
|
||||
return { ok: false, reason: `invalid embedding model slug: ${slug}` };
|
||||
}
|
||||
const apiKey = keyOf(PROVIDER_KEYS[parsed.provider]);
|
||||
if (!apiKey) {
|
||||
return {
|
||||
ok: false,
|
||||
reason: `embedding model is set to "${slug}" but its ${PROVIDER_KEYS[parsed.provider]} key is missing`,
|
||||
};
|
||||
}
|
||||
const factory =
|
||||
parsed.provider === "openrouter"
|
||||
? openrouterEmbeddingProvider
|
||||
: parsed.provider === "openai"
|
||||
? openaiEmbeddingProvider
|
||||
: googleEmbeddingProvider;
|
||||
return { ok: true, port: factory(slug, apiKey, fetchFn) };
|
||||
}
|
||||
|
||||
/** The LIVE resolution — reads the settings record + auth store fresh on
|
||||
* every call, so a settings PUT or a key save lands on the next call. */
|
||||
export function embeddingPort(): EmbeddingResolution {
|
||||
return resolveEmbeddingPortFor(effectiveSettings().graph.embeddingModel, keyValue);
|
||||
}
|
||||
|
||||
// ─── The post-merge batch (the extraction pipeline's embedding tail) ────────
|
||||
|
||||
/** What the embedding tail reports back: counts for logs, the skip reason
|
||||
* when nothing ran (owner ruling: null model = info skip, not error), and
|
||||
* the money-channel stage when it billed (one CallUsage step per API call,
|
||||
* outputTokens: 0 — priced via EMBEDDING_PRICES). */
|
||||
export interface EmbedBatchResult {
|
||||
embedded: number;
|
||||
total: number;
|
||||
skipped?: string;
|
||||
usage?: ExtractionStageUsage;
|
||||
}
|
||||
|
||||
/**
|
||||
* Embed every node the ledger is missing — the post-merge hook (new
|
||||
* propositions only, typically a handful) AND the cold-start backfill
|
||||
* (empty ledger = every node) in one function. Model/dim mismatch voids
|
||||
* the ledger first (full re-embed). Also prunes vectors for nodes that
|
||||
* no longer exist. Writes the whole ledger and returns counts for logs.
|
||||
* Non-fatal by contract: the caller treats failure as "clustering runs
|
||||
* without the new nodes until the next merge retries".
|
||||
* (empty ledger = every node) in one function. A model change voids the
|
||||
* ledger first (full re-embed — vectors from different models live in
|
||||
* incompatible spaces). Also prunes vectors for nodes that no longer
|
||||
* exist, then re-clusters ONCE per batch (consumers read the persisted
|
||||
* partition). Non-fatal by contract: the caller treats failure as
|
||||
* "clustering runs without the new nodes until the next merge retries".
|
||||
*/
|
||||
export async function embedNewNodes(
|
||||
graphStore: {
|
||||
@@ -96,10 +295,21 @@ export async function embedNewNodes(
|
||||
readEmbeddings(): EmbeddingsLedger;
|
||||
writeEmbeddings(ledger: EmbeddingsLedger): void;
|
||||
},
|
||||
): Promise<{ embedded: number; total: number }> {
|
||||
/** Test seam: the live resolution by default; tests inject a scripted
|
||||
* port (they never read the machine's settings/auth files). */
|
||||
resolvePort: () => EmbeddingResolution = embeddingPort,
|
||||
): Promise<EmbedBatchResult> {
|
||||
const resolved = resolvePort();
|
||||
const ledger = graphStore.readEmbeddings();
|
||||
const stale = ledger.model !== EMBEDDING_MODEL || ledger.dim !== EMBEDDING_DIM;
|
||||
if (!resolved.ok) {
|
||||
console.info(`[embeddings] skipped — ${resolved.reason}`);
|
||||
return { embedded: 0, total: Object.keys(ledger.vectors).length, skipped: resolved.reason };
|
||||
}
|
||||
const port = resolved.port;
|
||||
const stale = ledger.model !== port.model;
|
||||
const vectors: Record<string, number[]> = stale ? {} : { ...ledger.vectors };
|
||||
const steps: NonNullable<EmbedBatchResult["usage"]>["steps"] = [];
|
||||
let dim = ledger.dim;
|
||||
|
||||
// Node LINES may repeat an id — dedupe, first defines (getGraph rule).
|
||||
const seen = new Set<string>();
|
||||
@@ -110,18 +320,20 @@ export async function embedNewNodes(
|
||||
if (!vectors[node.id]) todo.push(node);
|
||||
}
|
||||
|
||||
if (todo.length > 0) {
|
||||
const extractor = await getPipeline();
|
||||
for (let i = 0; i < todo.length; i += EMBED_BATCH) {
|
||||
const batch = todo.slice(i, i + EMBED_BATCH);
|
||||
const output = await extractor(batch.map((n) => embedInput(n.statement)), {
|
||||
pooling: "mean",
|
||||
normalize: true,
|
||||
});
|
||||
const rows = output.tolist() as number[][];
|
||||
for (let j = 0; j < batch.length; j++) {
|
||||
vectors[batch[j]!.id] = truncateNormalize(rows[j]!);
|
||||
}
|
||||
for (let i = 0; i < todo.length; i += EMBED_BATCH) {
|
||||
const batch = todo.slice(i, i + EMBED_BATCH);
|
||||
const result = await port.embed(
|
||||
batch.map((n) => n.statement),
|
||||
"document",
|
||||
);
|
||||
for (let j = 0; j < batch.length; j++) {
|
||||
vectors[batch[j]!.id] = result.vectors[j]!;
|
||||
}
|
||||
if (result.vectors[0]) dim = result.vectors[0].length;
|
||||
if (result.promptTokens !== undefined) {
|
||||
// outputTokens: 0 — embeddings emit no output; callCost needs it
|
||||
// defined to price the input at all.
|
||||
steps.push(embeddingStep(result.promptTokens));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -135,20 +347,112 @@ export async function embedNewNodes(
|
||||
}
|
||||
|
||||
// RECLUSTER AT THE END OF THE BATCH (user-decided 2026-09-13): the
|
||||
// agglomerative pass is ~1.5s at current scale, so it runs HERE —
|
||||
// once per embedding batch — and consumers (snapshot, settle, namer)
|
||||
// read the persisted partition instead of recomputing per read.
|
||||
// agglomerative pass runs HERE — once per embedding batch — and
|
||||
// consumers (snapshot, settle, namer) read the persisted partition.
|
||||
const clusters = semanticClusters(vectors, { similarityThreshold: SEMANTIC_SIM_THRESHOLD });
|
||||
|
||||
const wrote = todo.length > 0 || pruned > 0 || stale || ledger.clusters.length !== clusters.length;
|
||||
if (wrote) {
|
||||
graphStore.writeEmbeddings({
|
||||
model: EMBEDDING_MODEL,
|
||||
dim: EMBEDDING_DIM,
|
||||
model: port.model,
|
||||
dim,
|
||||
vectors,
|
||||
clusters,
|
||||
updatedAt: new Date().toISOString(),
|
||||
});
|
||||
}
|
||||
return { embedded: todo.length, total: Object.keys(vectors).length };
|
||||
return {
|
||||
embedded: todo.length,
|
||||
total: Object.keys(vectors).length,
|
||||
usage: steps.length > 0 ? { model: port.model, steps } : undefined,
|
||||
};
|
||||
}
|
||||
// ─── The federated embedding catalog (wave 6, owner ruling 2026-09-23) ──────
|
||||
|
||||
/** One catalog row: provider-qualified id (the setting's exact slug
|
||||
* format) + display name. */
|
||||
export interface EmbeddingCatalogModel {
|
||||
/** The full "provider:id" slug — what graph.embeddingModel stores. */
|
||||
id: string;
|
||||
/** Human label for the picker (the provider's name, when given). */
|
||||
name: string;
|
||||
}
|
||||
|
||||
/** The embedding models of every KEYED provider, fetched live (the
|
||||
* chat-models picker is a static pi-ai catalog, but pi-ai carries no
|
||||
* embedding models — so this endpoint federates the providers' own
|
||||
* lists). A provider without a key contributes nothing; a provider whose
|
||||
* catalog call fails is skipped with an error log (never poisons the
|
||||
* rest, never fails the endpoint). Empty array = nothing keyed. */
|
||||
export async function embeddingModelCatalog(
|
||||
fetchFn: FetchLike = fetch,
|
||||
/** Test seam: the live key lookup by default; tests inject scripted
|
||||
* keys (they never read the machine's auth file). */
|
||||
keyOf: (id: string) => string | undefined = keyValue,
|
||||
): Promise<EmbeddingCatalogModel[]> {
|
||||
const out: EmbeddingCatalogModel[] = [];
|
||||
const fetchJson = async (url: string, headers: Record<string, string>): Promise<unknown> => {
|
||||
const resp = await fetchFn(url, { headers, signal: AbortSignal.timeout(REQUEST_TIMEOUT_MS) });
|
||||
await demandOk(resp, "Embeddings catalog");
|
||||
return resp.json();
|
||||
};
|
||||
|
||||
// OpenRouter: GET /api/v1/embeddings/models — the dedicated embedding
|
||||
// list (VERIFIED live 2026-09-23: the inverse path /api/v1/models/
|
||||
// embeddings 404s; this one returns {data: [{id, name, …}]}). Paginated
|
||||
// — limit=500 covers the whole list at current size (33).
|
||||
if (keyOf(PROVIDER_KEYS.openrouter)) {
|
||||
try {
|
||||
const data = (await fetchJson("https://openrouter.ai/api/v1/embeddings/models?limit=500", {
|
||||
authorization: `Bearer ${keyOf(PROVIDER_KEYS.openrouter)}`,
|
||||
})) as { data?: Array<{ id?: unknown; name?: unknown }> };
|
||||
for (const m of data.data ?? []) {
|
||||
if (typeof m.id !== "string" || !m.id) continue;
|
||||
out.push({ id: `openrouter:${m.id}`, name: typeof m.name === "string" && m.name ? m.name : m.id });
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("[embeddings] OpenRouter catalog fetch failed:", err);
|
||||
}
|
||||
}
|
||||
|
||||
// OpenAI: GET /v1/models — no embedding-only list, so filter the
|
||||
// generic one on the embedding naming convention.
|
||||
if (keyOf(PROVIDER_KEYS.openai)) {
|
||||
try {
|
||||
const data = (await fetchJson("https://api.openai.com/v1/models", {
|
||||
authorization: `Bearer ${keyOf(PROVIDER_KEYS.openai)}`,
|
||||
})) as { data?: Array<{ id?: unknown }> };
|
||||
for (const m of data.data ?? []) {
|
||||
if (typeof m.id !== "string" || !/^text-embedding-/.test(m.id)) continue;
|
||||
out.push({ id: `openai:${m.id}`, name: m.id });
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("[embeddings] OpenAI catalog fetch failed:", err);
|
||||
}
|
||||
}
|
||||
|
||||
// Google: GET /v1beta/models — filter on the methods the model
|
||||
// actually serves (embedContent present).
|
||||
if (keyOf(PROVIDER_KEYS.google)) {
|
||||
try {
|
||||
const data = (await fetchJson("https://generativelanguage.googleapis.com/v1beta/models?pageSize=1000", {
|
||||
"x-goog-api-key": keyOf(PROVIDER_KEYS.google)!,
|
||||
})) as {
|
||||
models?: Array<{ name?: unknown; displayName?: unknown; supportedGenerationMethods?: unknown }>;
|
||||
};
|
||||
for (const m of data.models ?? []) {
|
||||
if (typeof m.name !== "string" || !m.name.startsWith("models/")) continue;
|
||||
if (!Array.isArray(m.supportedGenerationMethods) || !m.supportedGenerationMethods.includes("embedContent")) continue;
|
||||
const id = m.name.slice("models/".length);
|
||||
out.push({
|
||||
id: `google:${id}`,
|
||||
name: typeof m.displayName === "string" && m.displayName ? m.displayName : id,
|
||||
});
|
||||
}
|
||||
} catch (err) {
|
||||
console.error("[embeddings] Google catalog fetch failed:", err);
|
||||
}
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
@@ -6,6 +6,7 @@
|
||||
|
||||
export * from "./chat-handler.ts";
|
||||
export * from "./chats.ts";
|
||||
export * from "./embeddings.ts";
|
||||
export * from "./graph.ts";
|
||||
export * from "./commit-positions.ts";
|
||||
export * from "./jobs.ts";
|
||||
|
||||
@@ -277,7 +277,11 @@ export function parseAppearanceSettingsBody(body: unknown): SettingsBodyResult<A
|
||||
return { ok: true, settings };
|
||||
}
|
||||
|
||||
/** graph: the four-rung ladder { enabled?, embedding?, clustering?, labeling? }. */
|
||||
/** graph: the four-rung ladder { enabled?, embedding?, clustering?, labeling? }
|
||||
* + embeddingModel? — the BYOK provider embedding model (wave 6,
|
||||
* 2026-09-23): null or a "provider:id" slug of a shipped provider.
|
||||
* Catalog validity is network-dependent — the parser checks SHAPE only
|
||||
* (the federated /api/models/embeddings gives the picker real options). */
|
||||
export function parseGraphSettingsBody(body: unknown): SettingsBodyResult<GraphSettings> {
|
||||
const raw = asRecord(body);
|
||||
if (!raw) return { ok: false, error: "Invalid JSON body" };
|
||||
@@ -290,6 +294,25 @@ export function parseGraphSettingsBody(body: unknown): SettingsBodyResult<GraphS
|
||||
settings[key] = raw[key];
|
||||
}
|
||||
}
|
||||
if (raw.embeddingModel !== undefined) {
|
||||
if (raw.embeddingModel !== null) {
|
||||
// NOTE: the id may itself contain colons (OpenRouter suffixes like
|
||||
// "liquid/lfm-2.5-embedding-350m:free") — the provider is the part
|
||||
// before the FIRST colon, so the id half just forbids whitespace.
|
||||
if (
|
||||
typeof raw.embeddingModel !== "string" ||
|
||||
!/^(openrouter|openai|google):\S+$/.test(raw.embeddingModel)
|
||||
) {
|
||||
return {
|
||||
ok: false,
|
||||
error: 'embeddingModel must be null or a "openrouter|openai|google:model-id" slug',
|
||||
};
|
||||
}
|
||||
settings.embeddingModel = raw.embeddingModel;
|
||||
} else {
|
||||
settings.embeddingModel = null;
|
||||
}
|
||||
}
|
||||
return { ok: true, settings };
|
||||
}
|
||||
|
||||
|
||||
@@ -96,6 +96,39 @@ describe("graphCostSummary (the cost pill's fold)", () => {
|
||||
assert.equal(s.unpricedRuns, 1); // run-level: only the both-stages run
|
||||
});
|
||||
|
||||
test("the embedding stage (wave 6): folds when present, absent when the run embedded nothing", () => {
|
||||
const EMBED_MODEL = "openai:text-embedding-3-small";
|
||||
const embStep = callCost(step(5000, 0), EMBED_MODEL)!;
|
||||
assert.ok(embStep > 0, "the overlay prices the known embedding models");
|
||||
const withEmbed = extractionRun(100, PRICED, PRICED);
|
||||
withEmbed.stages.embedding = { model: EMBED_MODEL, steps: [step(5000, 0)] };
|
||||
const s = graphCostSummary({
|
||||
chats: [{ title: "a", extractions: [withEmbed] }, { title: "b", extractions: [extractionRun(200, PRICED, PRICED)] }],
|
||||
sources: [],
|
||||
naming: [],
|
||||
});
|
||||
assert.equal(s.stages.embedding.runs, 1, "only the run that embedded");
|
||||
assert.equal(s.stages.embedding.dollars, embStep);
|
||||
assert.equal(s.stages.embedding.tokens.inputTokens, 5000);
|
||||
assert.equal(s.stages.extraction.runs, 2, "the two-stage era still folds");
|
||||
assert.equal(s.stages.extraction.runs, 2);
|
||||
// Run-level: the embedding dollars ride the run's total.
|
||||
const extStep = callCost(step(1000, 200), PRICED)!;
|
||||
const audStep = callCost(step(2000, 100), PRICED)!;
|
||||
assert.equal(s.runs.find((r) => r.startedAt === 100)!.dollars, extStep + audStep + embStep);
|
||||
assert.equal(s.runs.find((r) => r.startedAt === 200)!.dollars, extStep + audStep);
|
||||
assert.equal(s.totalDollars, (extStep + audStep) * 2 + embStep);
|
||||
});
|
||||
|
||||
test("an UNPRICED embedding model stays honest: counted, never a silent zero; a priced sibling saves the run total", () => {
|
||||
const unpricedEmbed = extractionRun(100, PRICED, "unknown-model");
|
||||
unpricedEmbed.stages.embedding = { model: "some/new-embed", steps: [step(900, 0)] };
|
||||
const s = graphCostSummary({ chats: [{ title: "x", extractions: [unpricedEmbed] }], sources: [], naming: [] });
|
||||
assert.equal(s.stages.embedding.unpricedRuns, 1);
|
||||
assert.equal(s.stages.embedding.dollars, 0);
|
||||
assert.notEqual(s.runs[0]!.dollars, null, "the priced extractor saves the run total");
|
||||
});
|
||||
|
||||
test("recent runs cap: newest survive", () => {
|
||||
const extractions = [1, 2, 3, 4].map((n) => extractionRun(n * 100, PRICED, PRICED));
|
||||
const s = graphCostSummary({
|
||||
|
||||
@@ -19,7 +19,7 @@ const REPO_ROOT = path.resolve(import.meta.dirname, "..");
|
||||
const TSX_LOADER = path.join(REPO_ROOT, "node_modules", "tsx", "dist", "loader.mjs");
|
||||
|
||||
describe("LH_DATA_DIR seam", () => {
|
||||
it("relocates data/chats/graph + modelCache under LH_DATA_DIR, keeps prompts ROOT-anchored", async () => {
|
||||
it("relocates data/chats/graph under LH_DATA_DIR, keeps prompts ROOT-anchored", async () => {
|
||||
const dir = fs.mkdtempSync(path.join(os.tmpdir(), "lh-seam-"));
|
||||
process.env.LH_DATA_DIR = dir;
|
||||
try {
|
||||
@@ -27,7 +27,6 @@ describe("LH_DATA_DIR seam", () => {
|
||||
assert.equal(PATHS.data, dir, "PATHS.data resolves the env value (resolved absolute)");
|
||||
assert.equal(PATHS.chats, path.join(dir, "chats"));
|
||||
assert.equal(PATHS.graph, path.join(dir, "graph"));
|
||||
assert.equal(PATHS.modelCache, path.join(dir, "models"), "the HF model cache follows the seam");
|
||||
assert.equal(
|
||||
PATHS.teacherPrompt,
|
||||
path.join(ROOT, "src", "agent", "system-prompt.md"),
|
||||
@@ -41,12 +40,11 @@ describe("LH_DATA_DIR seam", () => {
|
||||
it("unset = the repo data/ exactly as before (fresh child process, no env)", () => {
|
||||
const out = execFileSync(
|
||||
process.execPath,
|
||||
["--import", TSX_LOADER, "--eval", `const m = await import(${JSON.stringify(path.join(REPO_ROOT, "config.ts"))}); console.log(JSON.stringify({ data: m.PATHS.data, chats: m.PATHS.chats, modelCache: m.PATHS.modelCache }));`],
|
||||
["--import", TSX_LOADER, "--eval", `const m = await import(${JSON.stringify(path.join(REPO_ROOT, "config.ts"))}); console.log(JSON.stringify({ data: m.PATHS.data, chats: m.PATHS.chats }));`],
|
||||
{ cwd: REPO_ROOT, encoding: "utf-8" },
|
||||
);
|
||||
const parsed = JSON.parse(out) as { data: string; chats: string | null; modelCache: string | null };
|
||||
const parsed = JSON.parse(out) as { data: string; chats: string | null };
|
||||
assert.equal(parsed.data, path.join(REPO_ROOT, "data"));
|
||||
assert.equal(parsed.chats, path.join(REPO_ROOT, "data", "chats"));
|
||||
assert.equal(parsed.modelCache, null, "dev keeps the transformers package default cache");
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,401 @@
|
||||
/**
|
||||
* BYOK provider embeddings (wave 6, 2026-09-23): the adapters' wire
|
||||
* protocols (injected fetch — never the network), the slug/keys
|
||||
* resolution rules (null = honestly off; a set model with a missing key
|
||||
* is an honest error), the post-merge batch's staleness/re-embed/skip
|
||||
* semantics (scripted port — never the machine's settings/auth files),
|
||||
* and the federated catalog's federation rules.
|
||||
*/
|
||||
|
||||
import assert from "node:assert/strict";
|
||||
import { describe, it } from "node:test";
|
||||
import { callCost, EMBEDDING_PRICES } from "../src/core/prices.ts";
|
||||
import {
|
||||
embedNewNodes,
|
||||
embeddingModelCatalog,
|
||||
googleEmbeddingProvider,
|
||||
openaiEmbeddingProvider,
|
||||
openrouterEmbeddingProvider,
|
||||
parseEmbeddingModelSlug,
|
||||
resolveEmbeddingPortFor,
|
||||
type EmbeddingResolution,
|
||||
} from "../src/server/embeddings.ts";
|
||||
import type { EmbeddingPort, EmbeddingTask } from "../src/core/ports.ts";
|
||||
import type { EmbeddingsLedger, GraphNode } from "../src/core/contracts/graph.ts";
|
||||
|
||||
// ─── scripted fetch + in-memory graph (no fs, no network) ────────────────────
|
||||
|
||||
type RecordedCall = { url: string; init: RequestInit };
|
||||
|
||||
function jsonResponse(body: unknown, status = 200): Response {
|
||||
return new Response(JSON.stringify(body), {
|
||||
status,
|
||||
headers: { "content-type": "application/json" },
|
||||
});
|
||||
}
|
||||
|
||||
function recordingFetch(
|
||||
route: (url: string, body: unknown) => Response | Promise<Response>,
|
||||
): { calls: RecordedCall[]; fetch: (input: string, init: RequestInit) => Promise<Response> } {
|
||||
const calls: RecordedCall[] = [];
|
||||
const fetchFn = async (input: string, init: RequestInit): Promise<Response> => {
|
||||
calls.push({ url: input, init });
|
||||
const body = init.body ? (JSON.parse(String(init.body)) as unknown) : null;
|
||||
return route(input, body);
|
||||
};
|
||||
return { calls, fetch: fetchFn };
|
||||
}
|
||||
|
||||
function node(id: string, statement = `statement of ${id}`): GraphNode {
|
||||
return { id, name: id, statement, aliases: [], provenance: [], createdAt: "2026-09-23T00:00:00.000Z" };
|
||||
}
|
||||
|
||||
function fakeGraphStore(start?: Partial<EmbeddingsLedger>): {
|
||||
ledger: EmbeddingsLedger;
|
||||
writes: EmbeddingsLedger[];
|
||||
store: {
|
||||
readNodes(): GraphNode[];
|
||||
readEmbeddings(): EmbeddingsLedger;
|
||||
writeEmbeddings(ledger: EmbeddingsLedger): void;
|
||||
};
|
||||
nodes: GraphNode[];
|
||||
} {
|
||||
const nodes = [node("n1", "alpha fact"), node("n2", "beta fact")];
|
||||
const ledger: EmbeddingsLedger = {
|
||||
model: start?.model ?? "test-embed",
|
||||
dim: start?.dim ?? 2,
|
||||
vectors: { ...start?.vectors },
|
||||
clusters: [...start?.clusters ?? []],
|
||||
updatedAt: start?.updatedAt ?? "2026-09-23T00:00:00.000Z",
|
||||
};
|
||||
const writes: EmbeddingsLedger[] = [];
|
||||
return {
|
||||
ledger,
|
||||
writes,
|
||||
nodes,
|
||||
store: {
|
||||
readNodes: () => nodes,
|
||||
readEmbeddings: () => ledger,
|
||||
writeEmbeddings: (next) => {
|
||||
writes.push(next);
|
||||
Object.assign(ledger, next);
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
/** A scripted port: unit vectors by text hash, records (texts, task) calls. */
|
||||
function scriptedPort(model = "openai:test-embed", dim = 2, tokensPerCall = 120): {
|
||||
calls: Array<{ texts: string[]; task: EmbeddingTask }>;
|
||||
port: EmbeddingPort;
|
||||
} {
|
||||
const calls: Array<{ texts: string[]; task: EmbeddingTask }> = [];
|
||||
return {
|
||||
calls,
|
||||
port: {
|
||||
model,
|
||||
async embed(texts, task) {
|
||||
calls.push({ texts: [...texts], task });
|
||||
return {
|
||||
vectors: texts.map((_, i) => Array.from({ length: dim }, (_, j) => (j === i ? 1 : 0))),
|
||||
model,
|
||||
dim,
|
||||
promptTokens: tokensPerCall,
|
||||
};
|
||||
},
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
const okPort = (port: EmbeddingPort): EmbeddingResolution => ({ ok: true, port });
|
||||
|
||||
// ─── the money channel's embedding overlay ─────────────────────────────────
|
||||
|
||||
describe("the embedding price overlay (hand-maintained, outside the sync block)", () => {
|
||||
it("prices the known embedding models at their verified input rates", () => {
|
||||
// USD per 1M input tokens, verified against provider pages 2026-09-23.
|
||||
assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openai:text-embedding-3-small"), 0.02);
|
||||
assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openai:text-embedding-3-large"), 0.13);
|
||||
assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "google:gemini-embedding-001"), 0.15);
|
||||
assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "google:gemini-embedding-2"), 0.2);
|
||||
assert.equal(callCost({ inputTokens: 1_000_000, outputTokens: 0 }, "openrouter:openai/text-embedding-3-small"), 0.02);
|
||||
});
|
||||
|
||||
it("unknown embedding models stay unpriced (—, never zero), tokens still captured", () => {
|
||||
assert.equal(callCost({ inputTokens: 100, outputTokens: 0 }, "openrouter:mystery/embed"), null);
|
||||
assert.equal(!("openrouter:mystery/embed" in EMBEDDING_PRICES), true);
|
||||
});
|
||||
});
|
||||
|
||||
// ─── the slug grammar ─────────────────────────────────────────────────────────
|
||||
|
||||
describe("parseEmbeddingModelSlug", () => {
|
||||
it("parses the three shipped providers", () => {
|
||||
assert.deepEqual(parseEmbeddingModelSlug("openai:text-embedding-3-small"), { provider: "openai", id: "text-embedding-3-small" });
|
||||
assert.deepEqual(parseEmbeddingModelSlug("openrouter:openai/text-embedding-3-large"), { provider: "openrouter", id: "openai/text-embedding-3-large" });
|
||||
assert.deepEqual(parseEmbeddingModelSlug("google:gemini-embedding-001"), { provider: "google", id: "gemini-embedding-001" });
|
||||
// OpenRouter ids may carry a :free suffix (live catalog fact) — the
|
||||
// provider is the part before the FIRST colon.
|
||||
assert.deepEqual(parseEmbeddingModelSlug("openrouter:liquid/lfm-2.5-embedding-350m:free"), { provider: "openrouter", id: "liquid/lfm-2.5-embedding-350m:free" });
|
||||
});
|
||||
|
||||
it("rejects malformed slugs (no guessing)", () => {
|
||||
assert.equal(parseEmbeddingModelSlug("text-embedding-3-small"), null, "no provider prefix");
|
||||
assert.equal(parseEmbeddingModelSlug("anthropic:embed"), null, "unshipped provider");
|
||||
assert.equal(parseEmbeddingModelSlug("openai:"), null, "empty id");
|
||||
assert.equal(parseEmbeddingModelSlug(":id"), null, "empty provider");
|
||||
});
|
||||
});
|
||||
|
||||
// ─── resolution: BYOK honesty rules ───────────────────────────────────────────
|
||||
|
||||
describe("resolveEmbeddingPortFor", () => {
|
||||
it("null/absent setting = honestly off", () => {
|
||||
for (const slug of [null, undefined, ""]) {
|
||||
const res = resolveEmbeddingPortFor(slug, () => "key");
|
||||
assert.equal(res.ok, false);
|
||||
assert.match((res as { reason: string }).reason, /no embedding model configured/);
|
||||
}
|
||||
});
|
||||
|
||||
it("a set model with no key is an honest error naming the key", () => {
|
||||
const res = resolveEmbeddingPortFor("openai:text-embedding-3-small", () => undefined);
|
||||
assert.equal(res.ok, false);
|
||||
assert.match((res as { reason: string }).reason, /OPENAI_API_KEY/);
|
||||
});
|
||||
|
||||
it("an invalid slug never resolves", () => {
|
||||
const res = resolveEmbeddingPortFor("bedrock:whatever", () => "key");
|
||||
assert.equal(res.ok, false);
|
||||
assert.match((res as { reason: string }).reason, /invalid embedding model slug/);
|
||||
});
|
||||
|
||||
it("a keyed model resolves to its provider adapter", () => {
|
||||
const res = resolveEmbeddingPortFor("openai:text-embedding-3-small", () => "sk-test");
|
||||
assert.equal(res.ok, true);
|
||||
assert.equal((res as { port: EmbeddingPort }).port.model, "openai:text-embedding-3-small");
|
||||
});
|
||||
});
|
||||
|
||||
// ─── the adapters' wire protocols (injected fetch) ────────────────────────────
|
||||
|
||||
describe("openrouterEmbeddingProvider", () => {
|
||||
it("POSTs /api/v1/embeddings with Bearer auth and the task input_type", async () => {
|
||||
const { calls, fetch } = recordingFetch(() =>
|
||||
jsonResponse({ data: [{ embedding: [1, 0], index: 0 }], usage: { prompt_tokens: 42 } }),
|
||||
);
|
||||
const port = openrouterEmbeddingProvider("openrouter:openai/text-embedding-3-small", "k", fetch);
|
||||
const result = await port.embed(["hello"], "document");
|
||||
assert.equal(calls.length, 1);
|
||||
assert.equal(calls[0]!.url, "https://openrouter.ai/api/v1/embeddings");
|
||||
assert.equal((calls[0]!.init.headers as Record<string, string>).authorization, "Bearer k");
|
||||
assert.deepEqual(JSON.parse(String(calls[0]!.init.body)), {
|
||||
model: "openai/text-embedding-3-small",
|
||||
input: ["hello"],
|
||||
input_type: "search_document",
|
||||
});
|
||||
assert.deepEqual(result.vectors, [[1, 0]]);
|
||||
assert.equal(result.promptTokens, 42);
|
||||
|
||||
await port.embed(["q"], "query");
|
||||
assert.equal((JSON.parse(String(calls[1]!.init.body)) as { input_type: string }).input_type, "search_query");
|
||||
});
|
||||
});
|
||||
|
||||
describe("openaiEmbeddingProvider", () => {
|
||||
it("POSTs /v1/embeddings, Bearer auth, no task hint; index ordering honored", async () => {
|
||||
const { calls, fetch } = recordingFetch(() =>
|
||||
jsonResponse({
|
||||
data: [{ embedding: [0, 1], index: 1 }, { embedding: [1, 0], index: 0 }],
|
||||
usage: { prompt_tokens: 7, total_tokens: 7 },
|
||||
}),
|
||||
);
|
||||
const port = openaiEmbeddingProvider("openai:text-embedding-3-small", "sk", fetch);
|
||||
const result = await port.embed(["a", "b"], "document");
|
||||
assert.equal(calls[0]!.url, "https://api.openai.com/v1/embeddings");
|
||||
assert.equal((calls[0]!.init.headers as Record<string, string>).authorization, "Bearer sk");
|
||||
const body = JSON.parse(String(calls[0]!.init.body)) as Record<string, unknown>;
|
||||
assert.deepEqual(body.input, ["a", "b"]);
|
||||
assert.equal("input_type" in body, false, "OpenAI takes no task hint");
|
||||
// Rows arrive out of order — the adapter must restore input order.
|
||||
assert.deepEqual(result.vectors, [[1, 0], [0, 1]]);
|
||||
assert.equal(result.promptTokens, 7);
|
||||
});
|
||||
});
|
||||
|
||||
describe("googleEmbeddingProvider", () => {
|
||||
it("POSTs :batchEmbedContents with x-goog-api-key and per-text taskType", async () => {
|
||||
const { calls, fetch } = recordingFetch((_url, body) => {
|
||||
const n = ((body as { requests?: unknown[] } | null)?.requests ?? []).length;
|
||||
return jsonResponse({ embeddings: Array.from({ length: n }, (_, i) => ({ values: i === 0 ? [1, 0] : [0, 1] })) });
|
||||
});
|
||||
const port = googleEmbeddingProvider("google:gemini-embedding-001", "gk", fetch);
|
||||
const result = await port.embed(["a", "b"], "document");
|
||||
assert.equal(calls[0]!.url, "https://generativelanguage.googleapis.com/v1beta/models/gemini-embedding-001:batchEmbedContents");
|
||||
assert.equal((calls[0]!.init.headers as Record<string, string>)["x-goog-api-key"], "gk");
|
||||
assert.deepEqual(JSON.parse(String(calls[0]!.init.body)), {
|
||||
requests: [
|
||||
{ model: "models/gemini-embedding-001", content: { parts: [{ text: "a" }] }, taskType: "RETRIEVAL_DOCUMENT" },
|
||||
{ model: "models/gemini-embedding-001", content: { parts: [{ text: "b" }] }, taskType: "RETRIEVAL_DOCUMENT" },
|
||||
],
|
||||
});
|
||||
assert.deepEqual(result.vectors, [[1, 0], [0, 1]]);
|
||||
assert.equal(result.promptTokens, undefined, "batch responses carry no token count we can trust");
|
||||
|
||||
await port.embed(["q"], "query");
|
||||
const second = JSON.parse(String(calls[1]!.init.body)) as { requests: Array<{ taskType: string }> };
|
||||
assert.equal(second.requests[0]!.taskType, "RETRIEVAL_QUERY");
|
||||
});
|
||||
|
||||
it("a short/missing vector is an honest error, never a silent misalignment", async () => {
|
||||
const { fetch } = recordingFetch(() => jsonResponse({ embeddings: [{ values: [1, 0] }] }));
|
||||
const port = googleEmbeddingProvider("google:gemini-embedding-001", "gk", fetch);
|
||||
await assert.rejects(port.embed(["a", "b"], "document"), /1\/2 vectors/);
|
||||
});
|
||||
});
|
||||
|
||||
describe("adapter error mapping (all providers)", () => {
|
||||
it("HTTP failures become clear Errors carrying the status", async () => {
|
||||
for (const factory of [openrouterEmbeddingProvider, openaiEmbeddingProvider, googleEmbeddingProvider]) {
|
||||
const { fetch } = recordingFetch(() => jsonResponse({ error: { message: "bad key" } }, 401));
|
||||
const port = factory("openai:text-embedding-3-small", "k", fetch);
|
||||
await assert.rejects(port.embed(["x"], "document"), /API 401/);
|
||||
}
|
||||
});
|
||||
});
|
||||
|
||||
// ─── the post-merge batch (scripted port — the machine's files stay unread) ───
|
||||
|
||||
describe("embedNewNodes (the extraction tail)", () => {
|
||||
it("embeds missing nodes, stamps model/dim, records one usage step per API call", async () => {
|
||||
const g = fakeGraphStore();
|
||||
const scripted = scriptedPort("openai:text-embedding-3-small", 2, 120);
|
||||
const result = await embedNewNodes(g.store, () => okPort(scripted.port));
|
||||
assert.deepEqual(result, {
|
||||
embedded: 2,
|
||||
total: 2,
|
||||
usage: {
|
||||
model: "openai:text-embedding-3-small",
|
||||
steps: [
|
||||
{ inputTokens: 120, outputTokens: 0, cacheReadTokens: undefined, cacheWriteTokens: undefined, reasoningTokens: undefined },
|
||||
],
|
||||
},
|
||||
});
|
||||
assert.equal(g.writes.length, 1, "the ledger is written once per batch");
|
||||
assert.equal(g.ledger.model, "openai:text-embedding-3-small");
|
||||
assert.equal(g.ledger.dim, 2);
|
||||
assert.ok(g.ledger.vectors.n1 && g.ledger.vectors.n2);
|
||||
assert.deepEqual(scripted.calls[0]!.texts, ["alpha fact", "beta fact"]);
|
||||
assert.equal(scripted.calls[0]!.task, "document");
|
||||
assert.equal(result.usage?.steps.length, 1);
|
||||
});
|
||||
|
||||
it("a MODEL CHANGE voids the ledger and re-embeds everything", async () => {
|
||||
// The ledger holds a stale-model vector for a node that still exists.
|
||||
const g = fakeGraphStore({ model: "openai:old-embed", vectors: { n1: [9, 9] } });
|
||||
const scripted = scriptedPort("openai:new-embed", 2);
|
||||
const result = await embedNewNodes(g.store, () => okPort(scripted.port));
|
||||
assert.equal(result.embedded, 2, "the stale vector dies — BOTH nodes re-embed");
|
||||
assert.deepEqual(scripted.calls[0]!.texts, ["alpha fact", "beta fact"]);
|
||||
assert.equal(g.ledger.model, "openai:new-embed");
|
||||
assert.ok(!g.ledger.vectors.n1!.every((x, i) => x === [9, 9][i]), "the old vector was replaced");
|
||||
});
|
||||
|
||||
it("null model = info skip: nothing written, nothing embedded, the reason rides the result", async () => {
|
||||
const g = fakeGraphStore({ vectors: { n1: [1, 0] } });
|
||||
const result = await embedNewNodes(g.store, () => ({
|
||||
ok: false,
|
||||
reason: "no embedding model configured (graph.embeddingModel is null)",
|
||||
}));
|
||||
assert.deepEqual(result, { embedded: 0, total: 1, skipped: "no embedding model configured (graph.embeddingModel is null)" });
|
||||
assert.equal(g.writes.length, 0, "a skipped batch never touches the ledger");
|
||||
});
|
||||
|
||||
it("a set model with a missing key is the same skip path (its reason says so)", async () => {
|
||||
const g = fakeGraphStore();
|
||||
const result = await embedNewNodes(g.store, () => ({
|
||||
ok: false,
|
||||
reason: 'embedding model is set to "openai:x" but its OPENAI_API_KEY key is missing',
|
||||
}));
|
||||
assert.match(result.skipped ?? "", /OPENAI_API_KEY/);
|
||||
});
|
||||
|
||||
it("prunes ghost vectors for nodes that no longer exist", async () => {
|
||||
const g = fakeGraphStore({ vectors: { n1: [1, 0], ghost: [0, 1] } });
|
||||
const scripted = scriptedPort(g.ledger.model, 2);
|
||||
const result = await embedNewNodes(g.store, () => okPort(scripted.port));
|
||||
assert.equal(result.embedded, 1, "n1 already has a vector; only n2 embeds");
|
||||
assert.ok(!("ghost" in g.ledger.vectors), "the ghost vector is pruned");
|
||||
});
|
||||
|
||||
it("batches at 32 (a 40-node backfill = two API calls)", async () => {
|
||||
const many: GraphNode[] = Array.from({ length: 40 }, (_, i) => node(`n${i}`, `statement ${i}`));
|
||||
const ledger: EmbeddingsLedger = { model: "m", dim: 2, vectors: {}, clusters: [], updatedAt: "" };
|
||||
const writes: EmbeddingsLedger[] = [];
|
||||
const scripted = scriptedPort("m", 2);
|
||||
const result = await embedNewNodes(
|
||||
{
|
||||
readNodes: () => many,
|
||||
readEmbeddings: () => ledger,
|
||||
writeEmbeddings: (next) => writes.push(next),
|
||||
},
|
||||
() => okPort(scripted.port),
|
||||
);
|
||||
assert.equal(result.embedded, 40);
|
||||
assert.equal(scripted.calls.length, 2);
|
||||
assert.equal(scripted.calls[0]!.texts.length, 32);
|
||||
assert.equal(scripted.calls[1]!.texts.length, 8);
|
||||
assert.equal(writes.length, 1);
|
||||
});
|
||||
});
|
||||
|
||||
// ─── the federated catalog (scripted keys + fetch) ────────────────────────────
|
||||
|
||||
describe("embeddingModelCatalog", () => {
|
||||
it("no keys = empty catalog", async () => {
|
||||
const models = await embeddingModelCatalog(
|
||||
recordingFetch(() => jsonResponse({})).fetch,
|
||||
() => undefined,
|
||||
);
|
||||
assert.deepEqual(models, []);
|
||||
});
|
||||
|
||||
it("federates every keyed provider with its filter rules", async () => {
|
||||
const { fetch, calls } = recordingFetch((url) => {
|
||||
if (url.includes("openrouter.ai/api/v1/embeddings/models")) {
|
||||
return jsonResponse({ data: [{ id: "openai/text-embedding-3-small", name: "Text Embedding 3 Small" }, { id: "other/embed" }] });
|
||||
}
|
||||
if (url.includes("api.openai.com")) {
|
||||
return jsonResponse({ data: [{ id: "text-embedding-3-large" }, { id: "text-embedding-3-small" }, { id: "gpt-6-sol" }] });
|
||||
}
|
||||
if (url.includes("generativelanguage")) {
|
||||
return jsonResponse({
|
||||
models: [
|
||||
{ name: "models/gemini-embedding-001", displayName: "Gemini Embedding", supportedGenerationMethods: ["embedContent", "generateContent"] },
|
||||
{ name: "models/gemini-6-pro", supportedGenerationMethods: ["generateContent"] },
|
||||
],
|
||||
});
|
||||
}
|
||||
return jsonResponse({});
|
||||
});
|
||||
const models = await embeddingModelCatalog(fetch, (id) => (id === "OPENROUTER_API_KEY" || id === "OPENAI_API_KEY" || id === "GEMINI_API_KEY" ? "k" : undefined));
|
||||
assert.equal(calls[0]!.url, "https://openrouter.ai/api/v1/embeddings/models?limit=500", "the real catalog path (the inverse 404s — verified live)");
|
||||
assert.deepEqual(models, [
|
||||
{ id: "openrouter:openai/text-embedding-3-small", name: "Text Embedding 3 Small" },
|
||||
{ id: "openrouter:other/embed", name: "other/embed" },
|
||||
{ id: "openai:text-embedding-3-large", name: "text-embedding-3-large" },
|
||||
{ id: "openai:text-embedding-3-small", name: "text-embedding-3-small" },
|
||||
{ id: "google:gemini-embedding-001", name: "Gemini Embedding" },
|
||||
]);
|
||||
});
|
||||
|
||||
it("a failing provider never poisons the rest", async () => {
|
||||
const { fetch } = recordingFetch((url) => {
|
||||
if (url.includes("openrouter.ai")) return jsonResponse({}, 500); // their side broke
|
||||
if (url.includes("api.openai.com")) return jsonResponse({ data: [{ id: "text-embedding-3-small" }] });
|
||||
return jsonResponse({});
|
||||
});
|
||||
const models = await embeddingModelCatalog(fetch, (id) => (id === "OPENROUTER_API_KEY" || id === "OPENAI_API_KEY" ? "k" : undefined));
|
||||
assert.deepEqual(models, [{ id: "openai:text-embedding-3-small", name: "text-embedding-3-small" }]);
|
||||
});
|
||||
});
|
||||
@@ -15,7 +15,7 @@ import { after, describe, it } from "node:test";
|
||||
import { deliverJobResult, setTurnRunner } from "../src/server/jobs.ts";
|
||||
import { abortJob } from "../src/server/jobs.ts";
|
||||
import { persistTurn } from "../src/server/turn-service.ts";
|
||||
import { startGraphContextInjection, runGraphContextJob, type GraphContextDeps } from "../src/agent/subagents/graph-context.ts";
|
||||
import { startGraphContextInjection, runGraphContextJob, type GraphContextDeps, type GraphContextEmbedder } from "../src/agent/subagents/graph-context.ts";
|
||||
import { chatStore } from "../src/store/chats.ts";
|
||||
import { jobStore } from "../src/store/jobs.ts";
|
||||
import { PATHS } from "../config.ts";
|
||||
@@ -104,17 +104,18 @@ function newTopicChat(label: string, settings?: Record<string, unknown>): { id:
|
||||
return { id: chat.id };
|
||||
}
|
||||
|
||||
/** The scripted embedder: deterministic query vectors, recorded topics. */
|
||||
/** The scripted embedder: deterministic query vectors, recorded topics.
|
||||
* Wave 6 shape: the query carries its (absent) prompt-token count; the
|
||||
* resolver seam wraps the static fake (fresh resolution per run). */
|
||||
function fakeEmbedder(vec: number[] = unit(0)) {
|
||||
const topics: string[] = [];
|
||||
return {
|
||||
topics,
|
||||
embedder: {
|
||||
model: "test-embed",
|
||||
dim: 8,
|
||||
embedQuery: async (text: string) => {
|
||||
topics.push(text);
|
||||
return vec;
|
||||
return { vector: vec, promptTokens: undefined };
|
||||
},
|
||||
},
|
||||
};
|
||||
@@ -122,10 +123,10 @@ function fakeEmbedder(vec: number[] = unit(0)) {
|
||||
|
||||
function deps(
|
||||
graph: Pick<GraphContextDeps["graphStore"], "readNodes" | "readEvents" | "readEmbeddings">,
|
||||
embedder: GraphContextDeps["embedder"],
|
||||
embedder: GraphContextEmbedder,
|
||||
deliver: (steer: SteerMessage) => Promise<void>,
|
||||
): GraphContextDeps {
|
||||
return { chatStore, jobStore, graphStore: graph, embedder, deliver };
|
||||
return { chatStore, jobStore, graphStore: graph, embedder: () => embedder, deliver };
|
||||
}
|
||||
|
||||
describe("startGraphContextInjection — the one-shot gates", () => {
|
||||
@@ -205,7 +206,9 @@ describe("runGraphContextJob — failure modes are honest errors, never phantom
|
||||
trackChat(chat.id);
|
||||
const job = jobStore.createJob({ kind: "graph-context", chatId, toolCallId: `ctx_${chatId}` });
|
||||
trackJob(job.id);
|
||||
// Ledger stamped with a DIFFERENT model than the embedder.
|
||||
// Ledger stamped with a DIFFERENT model than the embedder (wave 6:
|
||||
// staleness is the model identity — dim is checked against the
|
||||
// served vector below).
|
||||
const d = deps(fakeGraphStore({ vectors: { n1: unit(0) }, model: "other-model" }), fakeEmbedder().embedder, async () => {});
|
||||
await runGraphContextJob(d, job.id);
|
||||
const got = jobStore.getJob(job.id)!;
|
||||
@@ -280,8 +283,7 @@ describe("the injection + the wake pipeline (end to end, fake turn runner)", ()
|
||||
});
|
||||
const embedder = {
|
||||
model: "test-embed",
|
||||
dim: 8,
|
||||
embedQuery: () => hanging.then(() => unit(0)),
|
||||
embedQuery: () => hanging.then(() => ({ vector: unit(0), promptTokens: undefined })),
|
||||
};
|
||||
const fired: number[] = [];
|
||||
setTurnRunner(async () => {
|
||||
|
||||
@@ -11,9 +11,12 @@ import { providerSafePng } from "../src/agent/subagents/headless.ts";
|
||||
* passed. providerSafePng merges the split back into the canonical ONE-IDAT
|
||||
* form (PNG spec: the IDAT chunks are an arbitrary split of ONE zlib stream —
|
||||
* the merged form carries the byte-identical stream). Pure Node on purpose:
|
||||
* the process already loads transformers' nested sharp/libvips; a second copy
|
||||
* (root sharp) triggered macOS's duplicate-libvips ObjC warning. These tests
|
||||
* pin the property with no native deps at all.
|
||||
* historically the process already carried a native image lib via
|
||||
* @huggingface/transformers' nested sharp/libvips, and a second copy (root
|
||||
* sharp) triggered macOS's duplicate-libvips ObjC warning — the transformers
|
||||
* embedder died with wave 6 (2026-09-23) but the no-native-deps rule lives
|
||||
* on: app code carries no image library. These tests pin the property with
|
||||
* no native deps at all.
|
||||
*/
|
||||
|
||||
// ── a tiny PNG toolchain (spec-level, no image library) ─────────────────────
|
||||
|
||||
@@ -156,6 +156,30 @@ describe("parseGraphSettingsBody", () => {
|
||||
const r = parseGraphSettingsBody({ enabled: false, clustering: true, labeling: true });
|
||||
assert.ok(r.ok);
|
||||
});
|
||||
|
||||
it("embeddingModel (wave 6): null or a shipped-provider slug; anything else is a 400", () => {
|
||||
const nulled = parseGraphSettingsBody({ embeddingModel: null });
|
||||
assert.ok(nulled.ok);
|
||||
assert.deepEqual(nulled.settings, { embeddingModel: null });
|
||||
for (const slug of [
|
||||
"openai:text-embedding-3-small",
|
||||
"openrouter:openai/text-embedding-3-large",
|
||||
"google:gemini-embedding-001",
|
||||
// OpenRouter ids may carry a :free-style suffix — the provider is
|
||||
// the part before the FIRST colon (live catalog fact, 2026-09-23).
|
||||
"openrouter:liquid/lfm-2.5-embedding-350m:free",
|
||||
]) {
|
||||
const r = parseGraphSettingsBody({ embeddingModel: slug });
|
||||
assert.ok(r.ok, slug);
|
||||
assert.deepEqual(r.settings, { embeddingModel: slug });
|
||||
}
|
||||
// Bad shapes never become a live model.
|
||||
assert.equal(parseGraphSettingsBody({ embeddingModel: "text-embedding-3-small" }).ok, false, "no provider prefix");
|
||||
assert.equal(parseGraphSettingsBody({ embeddingModel: "bedrock:whatever" }).ok, false, "unshipped provider");
|
||||
assert.equal(parseGraphSettingsBody({ embeddingModel: "openai:" }).ok, false, "empty id");
|
||||
assert.equal(parseGraphSettingsBody({ embeddingModel: 42 }).ok, false, "non-string");
|
||||
assert.equal(parseGraphSettingsBody({ embeddingModel: "openai:has spaces" }).ok, false, "whitespace id");
|
||||
});
|
||||
});
|
||||
|
||||
describe("SETTINGS_SECTIONS (the route's section map)", () => {
|
||||
|
||||
@@ -42,7 +42,7 @@ describe("resolveSettings", () => {
|
||||
assert.equal(eff.appearance.proseFontSize, 16);
|
||||
assert.equal(eff.appearance.sessionNameSerif, true);
|
||||
assert.equal(eff.appearance.mdRendering.headingWeight, 900);
|
||||
assert.deepEqual(eff.graph, { enabled: true, embedding: true, clustering: true, labeling: true });
|
||||
assert.deepEqual(eff.graph, { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // BYOK wave 6: no factory embedding model
|
||||
});
|
||||
|
||||
it("explicit record values win over every factory default", () => {
|
||||
@@ -77,7 +77,7 @@ describe("resolveSettings", () => {
|
||||
assert.equal(eff.appearance.mdRendering.headingWeight, 600);
|
||||
// graph: enabled=false explicit; absent rungs stay factory-on (stored
|
||||
// but inert — the ladder grays them out in the UI).
|
||||
assert.deepEqual(eff.graph, { enabled: false, embedding: true, clustering: true, labeling: true });
|
||||
assert.deepEqual(eff.graph, { enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // wave 6: unset embeddingModel resolves to the null floor
|
||||
});
|
||||
|
||||
it("partial role override: thinkingLevel alone borrows the SLOT's model (or nothing, without one)", () => {
|
||||
@@ -224,15 +224,15 @@ describe("resolveConversationSpec (the turn gate's chain)", () => {
|
||||
|
||||
describe("the graph channel gates (the wave's seam honoring)", () => {
|
||||
it("WRITE channel (extraction): dies with the master switch only", () => {
|
||||
assert.equal(extractionEnabled({ enabled: true, embedding: true, clustering: true, labeling: true }), true);
|
||||
assert.equal(extractionEnabled({ enabled: true, embedding: false, clustering: false, labeling: false }), true);
|
||||
assert.equal(extractionEnabled({ enabled: false, embedding: true, clustering: true, labeling: true }), false);
|
||||
assert.equal(extractionEnabled({ enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }), true);
|
||||
assert.equal(extractionEnabled({ enabled: true, embedding: false, clustering: false, labeling: false, embeddingModel: null }), true);
|
||||
assert.equal(extractionEnabled({ enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }), false);
|
||||
});
|
||||
|
||||
it("READ channel (graph-context): needs master AND the embedding rung", () => {
|
||||
assert.equal(graphContextEnabled({ enabled: true, embedding: true, clustering: false, labeling: false }), true);
|
||||
assert.equal(graphContextEnabled({ enabled: true, embedding: false, clustering: true, labeling: true }), false);
|
||||
assert.equal(graphContextEnabled({ enabled: false, embedding: true, clustering: true, labeling: true }), false);
|
||||
assert.equal(graphContextEnabled({ enabled: true, embedding: true, clustering: false, labeling: false, embeddingModel: null }), true);
|
||||
assert.equal(graphContextEnabled({ enabled: true, embedding: false, clustering: true, labeling: true, embeddingModel: null }), false);
|
||||
assert.equal(graphContextEnabled({ enabled: false, embedding: true, clustering: true, labeling: true, embeddingModel: null }), false);
|
||||
});
|
||||
|
||||
it("both gates ride resolveSettings' factory defaults (all on → both open)", () => {
|
||||
|
||||
@@ -21,7 +21,7 @@ describe("FACTORY_SETTINGS (the compiled factory layer)", () => {
|
||||
it("pins the owner-decided defaults", () => {
|
||||
assert.equal(CURRENT_SETTINGS_VERSION, 1);
|
||||
assert.deepEqual(FACTORY_SETTINGS.general, { autoUpdate: true });
|
||||
assert.deepEqual(FACTORY_SETTINGS.graph, { enabled: true, embedding: true, clustering: true, labeling: true });
|
||||
assert.deepEqual(FACTORY_SETTINGS.graph, { enabled: true, embedding: true, clustering: true, labeling: true, embeddingModel: null }); // BYOK wave 6: no factory embedding model
|
||||
const a = FACTORY_SETTINGS.appearance;
|
||||
assert.equal(a.mode, "dark");
|
||||
assert.equal(a.background, null); // the mode's own default, not a factory hex
|
||||
|
||||
Reference in New Issue
Block a user