TRUEREF-0007 — Embedding Generation & Vector Storage

Priority: P1 Status: Pending Depends On: TRUEREF-0001, TRUEREF-0005 Blocks: TRUEREF-0008

Overview

Implement a pluggable embedding generation system that produces vector representations of snippets and stores them in SQLite. Supports multiple embedding backends (OpenAI-compatible API, local ONNX models via transformers.js). When no embedding provider is configured, the system gracefully falls back to FTS5-only search.

Acceptance Criteria

Pluggable EmbeddingProvider interface with at least two implementations
OpenAIEmbeddingProvider — works with OpenAI, Azure OpenAI, Ollama, any OpenAI-compatible endpoint
LocalEmbeddingProvider — uses @xenova/transformers with a bundled ONNX model (optional dep)
NoopEmbeddingProvider — returns null, enables graceful FTS5-only mode
EmbeddingService that batches embedding requests and stores results
Embeddings stored as Float32Array blobs in snippet_embeddings table
Embedding provider configured via settings (stored in settings table)
GET /api/v1/settings/embedding — get current embedding config
PUT /api/v1/settings/embedding — set embedding provider configuration
Unit tests for provider abstraction and storage logic

Provider Interface

// src/lib/server/embeddings/provider.ts

export interface EmbeddingVector {
	values: Float32Array;
	dimensions: number;
	model: string;
}

export interface EmbeddingProvider {
	readonly name: string;
	readonly dimensions: number;
	readonly model: string;

	embed(texts: string[]): Promise<EmbeddingVector[]>;
	isAvailable(): Promise<boolean>;
}

OpenAI-Compatible Provider

export interface OpenAIProviderConfig {
	baseUrl: string; // e.g. "https://api.openai.com/v1" or "http://localhost:11434/v1"
	apiKey: string;
	model: string; // e.g. "text-embedding-3-small", "nomic-embed-text"
	dimensions?: number; // override for models that support it (e.g. text-embedding-3-small)
	maxBatchSize?: number; // default: 100
}

export class OpenAIEmbeddingProvider implements EmbeddingProvider {
	constructor(private config: OpenAIProviderConfig) {}

	async embed(texts: string[]): Promise<EmbeddingVector[]> {
		// Batch into groups of maxBatchSize
		const batches = chunk(texts, this.config.maxBatchSize ?? 100);
		const allEmbeddings: EmbeddingVector[] = [];

		for (const batch of batches) {
			const response = await fetch(`${this.config.baseUrl}/embeddings`, {
				method: 'POST',
				headers: {
					Authorization: `Bearer ${this.config.apiKey}`,
					'Content-Type': 'application/json'
				},
				body: JSON.stringify({
					model: this.config.model,
					input: batch,
					dimensions: this.config.dimensions
				})
			});

			if (!response.ok) {
				throw new EmbeddingError(`API error: ${response.status}`);
			}

			const data = await response.json();
			for (const item of data.data) {
				allEmbeddings.push({
					values: new Float32Array(item.embedding),
					dimensions: item.embedding.length,
					model: this.config.model
				});
			}
		}

		return allEmbeddings;
	}
}

Local Provider (Optional Dependency)

// Uses @xenova/transformers — only loaded if installed
export class LocalEmbeddingProvider implements EmbeddingProvider {
	private pipeline: unknown = null;

	readonly name = 'local';
	readonly model = 'Xenova/all-MiniLM-L6-v2'; // 384-dim, fast, small
	readonly dimensions = 384;

	async embed(texts: string[]): Promise<EmbeddingVector[]> {
		if (!this.pipeline) {
			const { pipeline } = await import('@xenova/transformers');
			this.pipeline = await pipeline('feature-extraction', this.model);
		}

		const results: EmbeddingVector[] = [];
		for (const text of texts) {
			const output = await (this.pipeline as Function)(text, {
				pooling: 'mean',
				normalize: true
			});
			results.push({
				values: new Float32Array(output.data),
				dimensions: this.dimensions,
				model: this.model
			});
		}
		return results;
	}

	async isAvailable(): Promise<boolean> {
		try {
			await import('@xenova/transformers');
			return true;
		} catch {
			return false;
		}
	}
}

Embedding Service

export class EmbeddingService {
	constructor(
		private db: BetterSQLite3.Database,
		private provider: EmbeddingProvider
	) {}

	async embedSnippets(
		snippetIds: string[],
		onProgress?: (done: number, total: number) => void
	): Promise<void> {
		const snippets = this.db
			.prepare(
				`SELECT id, content, type FROM snippets WHERE id IN (${snippetIds.map(() => '?').join(',')})`
			)
			.all(...snippetIds) as Snippet[];

		// Prepare text for embedding: combine title + content
		const texts = snippets.map((s) =>
			[s.title, s.breadcrumb, s.content].filter(Boolean).join('\n').slice(0, 2048)
		);

		const BATCH_SIZE = 50;
		const insert = this.db.prepare(`
      INSERT OR REPLACE INTO snippet_embeddings (snippet_id, model, dimensions, embedding, created_at)
      VALUES (?, ?, ?, ?, unixepoch())
    `);

		for (let i = 0; i < snippets.length; i += BATCH_SIZE) {
			const batch = snippets.slice(i, i + BATCH_SIZE);
			const batchTexts = texts.slice(i, i + BATCH_SIZE);

			const embeddings = await this.provider.embed(batchTexts);

			const insertMany = this.db.transaction(() => {
				for (let j = 0; j < batch.length; j++) {
					const snippet = batch[j];
					const embedding = embeddings[j];
					insert.run(
						snippet.id,
						embedding.model,
						embedding.dimensions,
						Buffer.from(embedding.values.buffer)
					);
				}
			});
			insertMany();

			onProgress?.(Math.min(i + BATCH_SIZE, snippets.length), snippets.length);
		}
	}
}

Provider Configuration

Stored in the settings table as JSON:

export interface EmbeddingConfig {
	provider: 'openai' | 'local' | 'none';
	openai?: {
		baseUrl: string;
		apiKey: string;
		model: string;
		dimensions?: number;
	};
}

// Settings key: 'embedding_config'

API Endpoints

GET /api/v1/settings/embedding

{
	"provider": "openai",
	"openai": {
		"baseUrl": "https://api.openai.com/v1",
		"model": "text-embedding-3-small",
		"dimensions": 1536
	}
}

PUT /api/v1/settings/embedding — same shape, validates provider connectivity before saving.

Blob Storage Format

Embeddings are stored as raw Float32Array binary blobs:

// Store
const buffer = Buffer.from(float32Array.buffer);

// Retrieve
const float32Array = new Float32Array(buffer.buffer, buffer.byteOffset, buffer.byteLength / 4);

Files to Create

src/lib/server/embeddings/provider.ts — interface + noop
src/lib/server/embeddings/openai.provider.ts
src/lib/server/embeddings/local.provider.ts
src/lib/server/embeddings/embedding.service.ts
src/lib/server/embeddings/factory.ts — create provider from config
src/routes/api/v1/settings/embedding/+server.ts
src/lib/server/embeddings/embedding.service.test.ts

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