feat(backfill): implement backfill tool for generating missing embeddings

internal/tools/retrieval.go

@@ -0,0 +1,41 @@
+package tools
+
+import (
+	"context"
+
+	"github.com/google/uuid"
+
+	"git.warky.dev/wdevs/amcs/internal/ai"
+	"git.warky.dev/wdevs/amcs/internal/config"
+	"git.warky.dev/wdevs/amcs/internal/store"
+	thoughttypes "git.warky.dev/wdevs/amcs/internal/types"
+)
+
+// semanticSearch runs vector similarity search if embeddings exist for the active model
+// in the given scope, otherwise falls back to Postgres full-text search.
+func semanticSearch(
+	ctx context.Context,
+	db *store.DB,
+	provider ai.Provider,
+	search config.SearchConfig,
+	query string,
+	limit int,
+	threshold float64,
+	projectID *uuid.UUID,
+	excludeID *uuid.UUID,
+) ([]thoughttypes.SearchResult, error) {
+	hasEmbeddings, err := db.HasEmbeddingsForModel(ctx, provider.EmbeddingModel(), projectID)
+	if err != nil {
+		return nil, err
+	}
+
+	if hasEmbeddings {
+		embedding, err := provider.Embed(ctx, query)
+		if err != nil {
+			return nil, err
+		}
+		return db.SearchSimilarThoughts(ctx, embedding, provider.EmbeddingModel(), threshold, limit, projectID, excludeID)
+	}
+
+	return db.SearchThoughtsText(ctx, query, limit, projectID, excludeID)
+}