chore: format

backend/open_webui/routers/audio.py

@@ -1025,7 +1025,9 @@ def transcription_handler(request, file_path, metadata, user=None):
             )
 
 
-def transcribe(request: Request, file_path: str, metadata: Optional[dict] = None, user=None):
+def transcribe(
+    request: Request, file_path: str, metadata: Optional[dict] = None, user=None
+):
     log.info(f"transcribe: {file_path} {metadata}")
 
     if is_audio_conversion_required(file_path):
@@ -1052,7 +1054,9 @@ def transcribe(request: Request, file_path: str, metadata: Optional[dict] = None
         with ThreadPoolExecutor() as executor:
             # Submit tasks for each chunk_path
             futures = [
-                executor.submit(transcription_handler, request, chunk_path, metadata, user)
+                executor.submit(
+                    transcription_handler, request, chunk_path, metadata, user
+                )
                 for chunk_path in chunk_paths
             ]
             # Gather results as they complete

backend/open_webui/routers/memories.py

@@ -52,9 +52,7 @@ async def add_memory(
 ):
     memory = Memories.insert_new_memory(user.id, form_data.content)
 
-    vector = await request.app.state.EMBEDDING_FUNCTION(
-        memory.content, user=user
-    )
+    vector = await request.app.state.EMBEDDING_FUNCTION(memory.content, user=user)
 
     VECTOR_DB_CLIENT.upsert(
         collection_name=f"user-memory-{user.id}",
@@ -112,10 +110,12 @@ async def reset_memory_from_vector_db(
     memories = Memories.get_memories_by_user_id(user.id)
 
     # Generate vectors in parallel
-    vectors = await asyncio.gather(*[
-        request.app.state.EMBEDDING_FUNCTION(memory.content, user=user)
-        for memory in memories
-    ])
+    vectors = await asyncio.gather(
+        *[
+            request.app.state.EMBEDDING_FUNCTION(memory.content, user=user)
+            for memory in memories
+        ]
+    )
 
     VECTOR_DB_CLIENT.upsert(
         collection_name=f"user-memory-{user.id}",
@@ -174,9 +174,7 @@ async def update_memory_by_id(
         raise HTTPException(status_code=404, detail="Memory not found")
 
     if form_data.content is not None:
-        vector = await request.app.state.EMBEDDING_FUNCTION(
-            memory.content, user=user
-        )
+        vector = await request.app.state.EMBEDDING_FUNCTION(memory.content, user=user)
 
         VECTOR_DB_CLIENT.upsert(
             collection_name=f"user-memory-{user.id}",

backend/open_webui/routers/ollama.py

@@ -53,7 +53,6 @@ from open_webui.utils.auth import get_admin_user, get_verified_user
 from open_webui.utils.access_control import has_access
 
 
-
 from open_webui.config import (
     UPLOAD_DIR,
 )

backend/open_webui/routers/retrieval.py

@@ -1468,11 +1468,13 @@ def save_docs_to_vector_db(
         )
 
         # Run async embedding in sync context
-        embeddings = asyncio.run(embedding_function(
-            list(map(lambda x: x.replace("\n", " "), texts)),
-            prefix=RAG_EMBEDDING_CONTENT_PREFIX,
-            user=user,
-        ))
+        embeddings = asyncio.run(
+            embedding_function(
+                list(map(lambda x: x.replace("\n", " "), texts)),
+                prefix=RAG_EMBEDDING_CONTENT_PREFIX,
+                user=user,
+            )
+        )
         log.info(f"embeddings generated {len(embeddings)} for {len(texts)} items")
 
         items = [