persist dimensions

frontend/src/app/api/queries/useGetSearchQuery.ts

@@ -29,6 +29,8 @@ export interface ChunkResult {
   owner_email?: string;
   file_size?: number;
   connector_type?: string;
+  embedding_model?: string;
+  embedding_dimensions?: number;
   index?: number;
 }
 
@@ -43,6 +45,8 @@ export interface File {
   owner_email?: string;
   size: number;
   connector_type: string;
+  embedding_model?: string;
+  embedding_dimensions?: number;
   status?:
     | "processing"
     | "active"
@@ -134,6 +138,8 @@ export const useGetSearchQuery = (
           owner_email?: string;
           file_size?: number;
           connector_type?: string;
+          embedding_model?: string;
+          embedding_dimensions?: number;
         }
       >();
 
@@ -142,6 +148,15 @@ export const useGetSearchQuery = (
         if (existing) {
           existing.chunks.push(chunk);
           existing.totalScore += chunk.score;
+          if (!existing.embedding_model && chunk.embedding_model) {
+            existing.embedding_model = chunk.embedding_model;
+          }
+          if (
+            existing.embedding_dimensions == null &&
+            typeof chunk.embedding_dimensions === "number"
+          ) {
+            existing.embedding_dimensions = chunk.embedding_dimensions;
+          }
         } else {
           fileMap.set(chunk.filename, {
             filename: chunk.filename,
@@ -154,6 +169,8 @@ export const useGetSearchQuery = (
             owner_email: chunk.owner_email,
             file_size: chunk.file_size,
             connector_type: chunk.connector_type,
+            embedding_model: chunk.embedding_model,
+            embedding_dimensions: chunk.embedding_dimensions,
           });
         }
       });
@@ -169,6 +186,8 @@ export const useGetSearchQuery = (
         owner_email: file.owner_email || "",
         size: file.file_size || 0,
         connector_type: file.connector_type || "local",
+        embedding_model: file.embedding_model,
+        embedding_dimensions: file.embedding_dimensions,
         chunks: file.chunks,
       }));
 

frontend/src/app/api/queries/useGetTasksQuery.ts

@@ -19,6 +19,8 @@ export interface TaskFileEntry {
   updated_at?: string;
   duration_seconds?: number;
   filename?: string;
+  embedding_model?: string;
+  embedding_dimensions?: number;
   [key: string]: unknown;
 }
 

frontend/src/app/globals.css

@@ -168,7 +168,7 @@
   }
 
   .header-notifications {
-    @apply absolute right-[0px] top-[-4px] h-1 w-1 rounded-full bg-destructive;
+    @apply absolute right-1 top-1 h-2 w-2 rounded-full bg-destructive;
   }
 
   .header-menu-bar {

frontend/src/app/knowledge/page.tsx

@@ -86,6 +86,8 @@ function SearchPage() {
       connector_type: taskFile.connector_type,
       status: taskFile.status,
       error: taskFile.error,
+      embedding_model: taskFile.embedding_model,
+      embedding_dimensions: taskFile.embedding_dimensions,
     };
   });
 
@@ -124,7 +126,7 @@ function SearchPage() {
 
   const gridRef = useRef<AgGridReact>(null);
 
-  const columnDefs = [
+  const columnDefs: ColDef<File>[] = [
     {
       field: "filename",
       headerName: "Source",
@@ -200,6 +202,28 @@ function SearchPage() {
         );
       },
     },
+    {
+      field: "embedding_model",
+      headerName: "Embedding model",
+      minWidth: 200,
+      cellRenderer: ({ data }: CustomCellRendererProps<File>) => (
+        <span className="text-xs text-muted-foreground">
+          {data?.embedding_model || "—"}
+        </span>
+      ),
+    },
+    {
+      field: "embedding_dimensions",
+      headerName: "Dimensions",
+      width: 110,
+      cellRenderer: ({ data }: CustomCellRendererProps<File>) => (
+        <span className="text-xs text-muted-foreground">
+          {typeof data?.embedding_dimensions === "number"
+            ? data.embedding_dimensions.toString()
+            : "—"}
+        </span>
+      ),
+    },
     {
       field: "status",
       headerName: "Status",

frontend/src/components/layout-wrapper.tsx

@@ -129,7 +129,7 @@ export function LayoutWrapper({ children }: { children: React.ReactNode }) {
             {/* Task Notification Bell */}
             <button
               onClick={toggleMenu}
-              className="h-8 w-8 hover:bg-muted rounded-lg flex items-center justify-center"
+              className="relative h-8 w-8 hover:bg-muted rounded-lg flex items-center justify-center"
             >
               <Bell size={16} className="text-muted-foreground" />
               {activeTasks.length > 0 && (

frontend/src/contexts/task-context.tsx

@@ -33,6 +33,8 @@ export interface TaskFile {
   created_at: string;
   updated_at: string;
   error?: string;
+  embedding_model?: string;
+  embedding_dimensions?: number;
 }
 interface TaskContextType {
   tasks: Task[];
@@ -108,6 +110,8 @@ export function TaskProvider({ children }: { children: React.ReactNode }) {
         created_at: now,
         updated_at: now,
         error: file.error,
+        embedding_model: file.embedding_model,
+        embedding_dimensions: file.embedding_dimensions,
       }));
 
       setFiles((prevFiles) => [...prevFiles, ...filesToAdd]);
@@ -214,6 +218,14 @@ export function TaskProvider({ children }: { children: React.ReactNode }) {
                       ? fileInfoEntry.updated_at
                       : now,
                   error: fileError,
+                  embedding_model:
+                    typeof fileInfoEntry.embedding_model === "string"
+                      ? fileInfoEntry.embedding_model
+                      : undefined,
+                  embedding_dimensions:
+                    typeof fileInfoEntry.embedding_dimensions === "number"
+                      ? fileInfoEntry.embedding_dimensions
+                      : undefined,
                 };
 
                 if (existingFileIndex >= 0) {

src/models/processors.py

@@ -226,6 +226,7 @@ class TaskProcessor:
                 embedding_field_name: vect,
                 # Track which model was used
                 "embedding_model": embedding_model,
+                "embedding_dimensions": len(vect),
                 "file_size": file_size,
                 "connector_type": connector_type,
                 "indexed_time": datetime.datetime.now().isoformat(),
@@ -763,4 +764,4 @@ class LangflowFileProcessor(TaskProcessor):
             file_task.error_message = str(e)
             file_task.updated_at = time.time()
             upload_task.failed_files += 1
-            raise
+            raise

src/services/search_service.py

@@ -282,6 +282,7 @@ class SearchService:
                 "file_size",
                 "connector_type",
                 "embedding_model",  # Include embedding model in results
+                "embedding_dimensions",
                 "allowed_users",
                 "allowed_groups",
             ],
@@ -333,6 +334,7 @@ class SearchService:
                     "file_size": hit["_source"].get("file_size"),
                     "connector_type": hit["_source"].get("connector_type"),
                     "embedding_model": hit["_source"].get("embedding_model"),  # Include in results
+                    "embedding_dimensions": hit["_source"].get("embedding_dimensions"),
                 }
             )
 

src/utils/embedding_fields.py

@@ -116,7 +116,10 @@ async def ensure_embedding_field_exists(
             # Also ensure the embedding_model tracking field exists as keyword
             "embedding_model": {
                 "type": "keyword"
-            }
+            },
+            "embedding_dimensions": {
+                "type": "integer"
+            },
         }
     }
 

src/utils/embeddings.py

@@ -54,6 +54,7 @@ def create_dynamic_index_body(embedding_model: str) -> dict:
                 },
                 # Track which embedding model was used for this chunk
                 "embedding_model": {"type": "keyword"},
+                "embedding_dimensions": {"type": "integer"},
                 "source_url": {"type": "keyword"},
                 "connector_type": {"type": "keyword"},
                 "owner": {"type": "keyword"},
@@ -67,4 +68,4 @@ def create_dynamic_index_body(embedding_model: str) -> dict:
                 "metadata": {"type": "object"},
             }
         },
-    }
+    }