enh: RAG_EMBEDDING_CONCURRENT_REQUESTS

backend/open_webui/config.py

@@ -2903,6 +2903,12 @@ ENABLE_ASYNC_EMBEDDING = PersistentConfig(
     os.environ.get("ENABLE_ASYNC_EMBEDDING", "True").lower() == "true",
 )
 
+RAG_EMBEDDING_CONCURRENT_REQUESTS = PersistentConfig(
+    "RAG_EMBEDDING_CONCURRENT_REQUESTS",
+    "rag.embedding_concurrent_requests",
+    int(os.getenv("RAG_EMBEDDING_CONCURRENT_REQUESTS", "0")),
+)
+
 RAG_EMBEDDING_QUERY_PREFIX = os.environ.get("RAG_EMBEDDING_QUERY_PREFIX", None)
 
 RAG_EMBEDDING_CONTENT_PREFIX = os.environ.get("RAG_EMBEDDING_CONTENT_PREFIX", None)

backend/open_webui/main.py

@@ -240,6 +240,7 @@ from open_webui.config import (
     RAG_EMBEDDING_ENGINE,
     RAG_EMBEDDING_BATCH_SIZE,
     ENABLE_ASYNC_EMBEDDING,
+    RAG_EMBEDDING_CONCURRENT_REQUESTS,
     RAG_TOP_K,
     RAG_TOP_K_RERANKER,
     RAG_RELEVANCE_THRESHOLD,
@@ -980,6 +981,7 @@ app.state.config.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
 app.state.config.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
 app.state.config.RAG_EMBEDDING_BATCH_SIZE = RAG_EMBEDDING_BATCH_SIZE
 app.state.config.ENABLE_ASYNC_EMBEDDING = ENABLE_ASYNC_EMBEDDING
+app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS = RAG_EMBEDDING_CONCURRENT_REQUESTS
 
 app.state.config.RAG_RERANKING_ENGINE = RAG_RERANKING_ENGINE
 app.state.config.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
@@ -1133,6 +1135,7 @@ app.state.EMBEDDING_FUNCTION = get_embedding_function(
         else None
     ),
     enable_async=app.state.config.ENABLE_ASYNC_EMBEDDING,
+    concurrent_requests=app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
 )
 
 app.state.RERANKING_FUNCTION = get_reranking_function(

backend/open_webui/retrieval/utils.py

@@ -803,6 +803,7 @@ def get_embedding_function(
     embedding_batch_size,
     azure_api_version=None,
     enable_async=True,
+    concurrent_requests=0,
 ) -> Awaitable:
     if embedding_engine == "":
         # Sentence transformers: CPU-bound sync operation
@@ -844,10 +845,22 @@ def get_embedding_function(
                     log.debug(
                         f"generate_multiple_async: Processing {len(batches)} batches in parallel"
                     )
-                    # Execute all batches in parallel
+                    # Use semaphore to limit concurrent embedding API requests
-                    tasks = [
+                    # 0 = unlimited (no semaphore)
-                        embedding_function(batch, prefix=prefix, user=user)
+                    if concurrent_requests:
-                        for batch in batches
+                        semaphore = asyncio.Semaphore(concurrent_requests)
+
+                        async def generate_batch_with_semaphore(batch):
+                            async with semaphore:
+                                return await embedding_function(
+                                    batch, prefix=prefix, user=user
+                                )
+
+                        tasks = [generate_batch_with_semaphore(batch) for batch in batches]
+                    else:
+                        tasks = [
+                            embedding_function(batch, prefix=prefix, user=user)
+                            for batch in batches
                     ]
                     batch_results = await asyncio.gather(*tasks)
                 else:

backend/open_webui/routers/retrieval.py

@@ -270,6 +270,7 @@ async def get_status(request: Request):
         "RAG_RERANKING_MODEL": request.app.state.config.RAG_RERANKING_MODEL,
         "RAG_EMBEDDING_BATCH_SIZE": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
         "ENABLE_ASYNC_EMBEDDING": request.app.state.config.ENABLE_ASYNC_EMBEDDING,
+        "RAG_EMBEDDING_CONCURRENT_REQUESTS": request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
     }
 
 
@@ -281,6 +282,7 @@ async def get_embedding_config(request: Request, user=Depends(get_admin_user)):
         "RAG_EMBEDDING_MODEL": request.app.state.config.RAG_EMBEDDING_MODEL,
         "RAG_EMBEDDING_BATCH_SIZE": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
         "ENABLE_ASYNC_EMBEDDING": request.app.state.config.ENABLE_ASYNC_EMBEDDING,
+        "RAG_EMBEDDING_CONCURRENT_REQUESTS": request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
         "openai_config": {
             "url": request.app.state.config.RAG_OPENAI_API_BASE_URL,
             "key": request.app.state.config.RAG_OPENAI_API_KEY,
@@ -321,6 +323,7 @@ class EmbeddingModelUpdateForm(BaseModel):
     RAG_EMBEDDING_MODEL: str
     RAG_EMBEDDING_BATCH_SIZE: Optional[int] = 1
     ENABLE_ASYNC_EMBEDDING: Optional[bool] = True
+    RAG_EMBEDDING_CONCURRENT_REQUESTS: Optional[int] = 0
 
 
 def unload_embedding_model(request: Request):
@@ -355,6 +358,9 @@ async def update_embedding_config(
         request.app.state.config.ENABLE_ASYNC_EMBEDDING = (
             form_data.ENABLE_ASYNC_EMBEDDING
         )
+        request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS = (
+            form_data.RAG_EMBEDDING_CONCURRENT_REQUESTS
+        )
 
         if request.app.state.config.RAG_EMBEDDING_ENGINE in [
             "ollama",
@@ -422,6 +428,7 @@ async def update_embedding_config(
                 else None
             ),
             enable_async=request.app.state.config.ENABLE_ASYNC_EMBEDDING,
+            concurrent_requests=request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
         )
 
         return {
@@ -430,6 +437,7 @@ async def update_embedding_config(
             "RAG_EMBEDDING_MODEL": request.app.state.config.RAG_EMBEDDING_MODEL,
             "RAG_EMBEDDING_BATCH_SIZE": request.app.state.config.RAG_EMBEDDING_BATCH_SIZE,
             "ENABLE_ASYNC_EMBEDDING": request.app.state.config.ENABLE_ASYNC_EMBEDDING,
+            "RAG_EMBEDDING_CONCURRENT_REQUESTS": request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
             "openai_config": {
                 "url": request.app.state.config.RAG_OPENAI_API_BASE_URL,
                 "key": request.app.state.config.RAG_OPENAI_API_KEY,
@@ -1589,6 +1597,7 @@ def save_docs_to_vector_db(
                 else None
             ),
             enable_async=request.app.state.config.ENABLE_ASYNC_EMBEDDING,
+            concurrent_requests=request.app.state.config.RAG_EMBEDDING_CONCURRENT_REQUESTS,
         )
 
         # Run async embedding in sync context using the main event loop
@@ -2329,7 +2338,7 @@ async def process_web_search(
             # Limited concurrency with semaphore
             semaphore = asyncio.Semaphore(concurrent_limit)
 
-            async def search_with_limit(query):
+            async def search_query_with_semaphore(query):
                 async with semaphore:
                     return await run_in_threadpool(
                         search_web,
@@ -2339,7 +2348,7 @@ async def process_web_search(
                         user,
                     )
 
-            search_tasks = [search_with_limit(query) for query in form_data.queries]
+            search_tasks = [search_query_with_semaphore(query) for query in form_data.queries]
         else:
             # Unlimited parallel execution (previous behavior)
             search_tasks = [

src/lib/components/admin/Settings/Documents.svelte

@@ -42,6 +42,7 @@
 	let RAG_EMBEDDING_MODEL = '';
 	let RAG_EMBEDDING_BATCH_SIZE = 1;
 	let ENABLE_ASYNC_EMBEDDING = true;
+	let RAG_EMBEDDING_CONCURRENT_REQUESTS = 0;
 
 	let rerankingModel = '';
 
@@ -104,7 +105,8 @@
 			RAG_EMBEDDING_ENGINE,
 			RAG_EMBEDDING_MODEL,
 			RAG_EMBEDDING_BATCH_SIZE,
-			ENABLE_ASYNC_EMBEDDING
+			ENABLE_ASYNC_EMBEDDING,
+			RAG_EMBEDDING_CONCURRENT_REQUESTS
 		});
 
 		updateEmbeddingModelLoading = true;
@@ -113,6 +115,7 @@
 			RAG_EMBEDDING_MODEL: RAG_EMBEDDING_MODEL,
 			RAG_EMBEDDING_BATCH_SIZE: RAG_EMBEDDING_BATCH_SIZE,
 			ENABLE_ASYNC_EMBEDDING: ENABLE_ASYNC_EMBEDDING,
+			RAG_EMBEDDING_CONCURRENT_REQUESTS: RAG_EMBEDDING_CONCURRENT_REQUESTS,
 			ollama_config: {
 				key: OllamaKey,
 				url: OllamaUrl
@@ -241,6 +244,7 @@
 			RAG_EMBEDDING_MODEL = embeddingConfig.RAG_EMBEDDING_MODEL;
 			RAG_EMBEDDING_BATCH_SIZE = embeddingConfig.RAG_EMBEDDING_BATCH_SIZE ?? 1;
 			ENABLE_ASYNC_EMBEDDING = embeddingConfig.ENABLE_ASYNC_EMBEDDING ?? true;
+			RAG_EMBEDDING_CONCURRENT_REQUESTS = embeddingConfig.RAG_EMBEDDING_CONCURRENT_REQUESTS ?? 0;
 
 			OpenAIKey = embeddingConfig.openai_config.key;
 			OpenAIUrl = embeddingConfig.openai_config.url;
@@ -1043,6 +1047,28 @@
 									<Switch bind:state={ENABLE_ASYNC_EMBEDDING} />
 								</div>
 							</div>
+
+							<div class="  mb-2.5 flex w-full justify-between">
+								<div class="self-center text-xs font-medium">
+									<Tooltip
+										content={$i18n.t(
+											'Limits the number of concurrent embedding requests. Set to 0 for unlimited.'
+										)}
+										placement="top-start"
+									>
+										{$i18n.t('Embedding Concurrent Requests')}
+									</Tooltip>
+								</div>
+								<div class="">
+									<input
+										bind:value={RAG_EMBEDDING_CONCURRENT_REQUESTS}
+										type="number"
+										class=" bg-transparent text-center w-14 outline-none"
+										min="0"
+										step="1"
+									/>
+								</div>
+							</div>
 						{/if}
 					</div>