feat: Introduce AI Brand Monitoring System V5 technical design document

- Added comprehensive technical design document for AI Brand Monitoring System V5, outlining system architecture, data models, sampling strategies, and monitoring protocols.
- Key changes include a shift to a sampling-based trend monitoring approach, updated data collection and storage strategies, and new metrics for performance evaluation.
- Implemented migration scripts to support the flattening of brand questions and versioning of question texts, ensuring historical data integrity and version control.

server/internal/tenant/repository/generated/workspace.sql.go

@@ -98,7 +98,7 @@ SELECT a.id, a.generate_status, a.publish_status, a.source_type, a.created_at,
        t.template_name,
        COALESCE(
          NULLIF(gt.input_params_json ->> 'generation_mode', ''),
-         CASE WHEN a.source_type = 'custom_generation' THEN 'instant' ELSE NULL END
+         CASE WHEN a.source_type = 'custom_generation' THEN 'instant'::TEXT ELSE NULL::TEXT END
        ) AS generation_mode
 FROM articles a
 LEFT JOIN article_versions av ON av.id = a.current_version_id
@@ -126,7 +126,7 @@ type GetRecentArticlesRow struct {
 	WordCount       pgtype.Int4        `json:"word_count"`
 	SourceLabel     pgtype.Text        `json:"source_label"`
 	TemplateName    pgtype.Text        `json:"template_name"`
-	GenerationMode  pgtype.Text        `json:"generation_mode"`
+	GenerationMode  interface{}        `json:"generation_mode"`
 }
 
 func (q *Queries) GetRecentArticles(ctx context.Context, tenantID int64) ([]GetRecentArticlesRow, error) {