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Harrison BoothandGitHub
9690140bf3
The ollama recipe declared a single `default_dims: 768` (nomic-embed-text's width) while serving models spanning 384..4096. Every non-nomic model resolved to 768, so `gbrain init --embedding-model ollama:bge-m3` built a 768-wide `content_chunks.embedding` column for a model that emits 1024. The schema looked fine and only failed at first insert with `expected 768 dimensions, not 1024`. Adds an optional `model_dims` map to `EmbeddingTouchpoint` and an `embeddingDimsForModel()` resolver that prefers the per-model entry and falls back to `default_dims`. The ollama recipe declares real widths for the models it lists; bge-m3 is added to that list. The three `init` call sites that read `default_dims` now resolve per model. Partial by design: unlisted models still fall back to `default_dims`, and `trust_custom_dims` keeps an explicit `--embedding-dimensions` override working. `user_provided_models` recipes (litellm, llama-server) still resolve to 0, so they continue to require explicit dimensions. Verified end to end against an OpenAI-compatible stub standing in for Ollama, using an isolated GBRAIN_HOME: before: config 768, content_chunks.embedding vector(768), insert fails after: config 1024, content_chunks.embedding vector(1024), insert succeeds
69 lines
2.6 KiB
TypeScript
69 lines
2.6 KiB
TypeScript
/**
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* #2051 — per-model embedding dimensions for local recipes.
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*
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* Ollama serves models spanning 384..4096 dims, but the recipe declared a
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* single `default_dims: 768` (nomic-embed-text's width). Every other model
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* resolved to 768, so `gbrain init --embedding-model ollama:bge-m3` created a
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* 768-wide column for a model emitting 1024 and the mismatch only surfaced at
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* first insert.
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*
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* `embeddingDimsForModel()` consults the recipe's `model_dims` map first and
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* falls back to `default_dims`, so:
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* 1. Known models resolve to their true native width.
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* 2. Unlisted models still fall back (no regression for arbitrary pulls).
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* 3. `user_provided_models` recipes keep returning 0, which is what forces
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* an explicit `--embedding-dimensions`.
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*/
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import { test, expect, describe } from 'bun:test';
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import { getRecipe } from '../src/core/ai/recipes/index.ts';
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import { embeddingDimsForModel } from '../src/core/ai/model-resolver.ts';
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describe('embeddingDimsForModel — per-model dims (#2051)', () => {
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const ollama = getRecipe('ollama')!;
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test('bge-m3 resolves to its native 1024, not the recipe default 768', () => {
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expect(embeddingDimsForModel(ollama, 'bge-m3')).toBe(1024);
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});
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test('accepts a provider-qualified id', () => {
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expect(embeddingDimsForModel(ollama, 'ollama:bge-m3')).toBe(1024);
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});
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test.each([
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['nomic-embed-text', 768],
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['mxbai-embed-large', 1024],
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['all-minilm', 384],
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['qwen3-embed-8b', 4096],
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['snowflake-arctic-embed-l-v2', 1024],
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])('%s resolves to %i', (model, dims) => {
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expect(embeddingDimsForModel(ollama, model as string)).toBe(dims as number);
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});
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test('every declared model_dims entry is also a listed model', () => {
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const listed = new Set(ollama.touchpoints.embedding!.models);
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for (const model of Object.keys(ollama.touchpoints.embedding!.model_dims ?? {})) {
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expect(listed.has(model)).toBe(true);
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}
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});
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test('an unlisted model falls back to the recipe default', () => {
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expect(embeddingDimsForModel(ollama, 'some-model-pulled-locally')).toBe(768);
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});
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test('a missing model id falls back to the recipe default', () => {
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expect(embeddingDimsForModel(ollama, undefined)).toBe(768);
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});
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test('llama-server still returns 0 so explicit dimensions stay required', () => {
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const llamaServer = getRecipe('llama-server')!;
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expect(embeddingDimsForModel(llamaServer, 'anything')).toBe(0);
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});
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test('fixed-dim hosted recipes are unaffected', () => {
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const openai = getRecipe('openai')!;
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const tp = openai.touchpoints.embedding!;
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expect(embeddingDimsForModel(openai, tp.models[0])).toBe(tp.default_dims);
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});
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});
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