feat(TRUEREF-0020): add embedding profiles, default local embeddings, and version-scoped semantic retrieval

- Add embedding_profiles table with provider registry pattern
- Install @xenova/transformers as runtime dependency
- Update snippet_embeddings with composite PK (snippet_id, profile_id)
- Seed default local profile using Xenova/all-MiniLM-L6-v2
- Add provider registry (local-transformers, openai-compatible)
- Update EmbeddingService to persist and retrieve by profileId
- Add version-scoped VectorSearch with optional versionId filtering
- Add searchMode (auto|keyword|semantic|hybrid) to HybridSearchService
- Update API /context route to load active profile, support searchMode/alpha params
- Extend MCP query-docs tool with searchMode and alpha parameters
- Update settings API to work with embedding_profiles table
- Add comprehensive test coverage for profiles, registry, version scoping

Status: 445/451 tests passing, core feature complete

src/lib/server/search/vector.search.ts

@@ -21,6 +21,13 @@ export interface VectorSearchResult {
 	score: number;
 }
 
+export interface VectorSearchOptions {
+	repositoryId: string;
+	versionId?: string;
+	profileId?: string;
+	limit?: number;
+}
+
 /** Raw DB row from snippet_embeddings joined with snippets. */
 interface RawEmbeddingRow {
 	snippet_id: string;
@@ -64,32 +71,33 @@ export function cosineSimilarity(a: Float32Array, b: Float32Array): number {
 // ---------------------------------------------------------------------------
 
 export class VectorSearch {
-	private readonly stmt: Database.Statement<[string], RawEmbeddingRow>;
-
-	constructor(private readonly db: Database.Database) {
-		// Prepare once — reused for every call.
-		this.stmt = this.db.prepare<[string], RawEmbeddingRow>(`
-			SELECT se.snippet_id, se.embedding
-			FROM snippet_embeddings se
-			JOIN snippets s ON s.id = se.snippet_id
-			WHERE s.repository_id = ?
-		`);
-	}
+	constructor(private readonly db: Database.Database) {}
 
 	/**
 	 * Search stored embeddings by cosine similarity to the query embedding.
 	 *
 	 * @param queryEmbedding - The embedded representation of the search query.
-	 * @param repositoryId   - Scope the search to a single repository.
-	 * @param limit          - Maximum number of results to return. Default: 50.
+	 * @param options - Search options including repositoryId, optional versionId, profileId, and limit.
 	 * @returns Results sorted by descending cosine similarity score.
 	 */
-	vectorSearch(
-		queryEmbedding: Float32Array,
-		repositoryId: string,
-		limit = 50
-	): VectorSearchResult[] {
-		const rows = this.stmt.all(repositoryId);
+	vectorSearch(queryEmbedding: Float32Array, options: VectorSearchOptions): VectorSearchResult[] {
+		const { repositoryId, versionId, profileId = 'local-default', limit = 50 } = options;
+
+		let sql = `
+			SELECT se.snippet_id, se.embedding
+			FROM snippet_embeddings se
+			JOIN snippets s ON s.id = se.snippet_id
+			WHERE s.repository_id = ?
+			  AND se.profile_id = ?
+		`;
+		const params: unknown[] = [repositoryId, profileId];
+
+		if (versionId) {
+			sql += ' AND s.version_id = ?';
+			params.push(versionId);
+		}
+
+		const rows = this.db.prepare<unknown[], RawEmbeddingRow>(sql).all(...params);
 
 		const scored: VectorSearchResult[] = rows.map((row) => {
 			const embedding = new Float32Array(