refac

backend/open_webui/utils/embeddings.py

@@ -10,12 +10,11 @@ from open_webui.env import GLOBAL_LOG_LEVEL, BYPASS_MODEL_ACCESS_CONTROL
 
 from open_webui.routers.openai import embeddings as openai_embeddings
 from open_webui.routers.ollama import (
-    embeddings as ollama_embeddings,
+    embed as ollama_embed,
-    GenerateEmbeddingsForm,
+    GenerateEmbedForm,
 )
 
-
+from open_webui.utils.payload import convert_embed_payload_openai_to_ollama
-from open_webui.utils.payload import convert_embedding_payload_openai_to_ollama
 from open_webui.utils.response import convert_embedding_response_ollama_to_openai
 
 logging.basicConfig(stream=sys.stdout, level=GLOBAL_LOG_LEVEL)
@@ -71,12 +70,12 @@ async def generate_embeddings(
         if not bypass_filter and user.role == "user":
             check_model_access(user, model)
 
-    # Ollama backend
+    # Ollama backend — use /api/embed which supports batch input natively
     if model.get("owned_by") == "ollama":
-        ollama_payload = convert_embedding_payload_openai_to_ollama(form_data)
+        ollama_payload = convert_embed_payload_openai_to_ollama(form_data)
-        response = await ollama_embeddings(
+        response = await ollama_embed(
             request=request,
-            form_data=GenerateEmbeddingsForm(**ollama_payload),
+            form_data=GenerateEmbedForm(**ollama_payload),
             user=user,
         )
         return convert_embedding_response_ollama_to_openai(response)
@@ -87,3 +86,4 @@ async def generate_embeddings(
         form_data=form_data,
         user=user,
     )
+

backend/open_webui/utils/payload.py

@@ -395,3 +395,30 @@ def convert_embedding_payload_openai_to_ollama(openai_payload: dict) -> dict:
             ollama_payload[optional_key] = openai_payload[optional_key]
 
     return ollama_payload
+
+
+def convert_embed_payload_openai_to_ollama(openai_payload: dict) -> dict:
+    """
+    Convert an embeddings request payload from OpenAI format to Ollama's
+    /api/embed format, which supports batch input natively.
+
+    Args:
+        openai_payload (dict): The original payload designed for OpenAI API usage.
+            Expected keys: "model", "input" (str or list[str]).
+
+    Returns:
+        dict: A payload compatible with the Ollama /api/embed endpoint.
+    """
+    ollama_payload = {"model": openai_payload.get("model")}
+    input_value = openai_payload.get("input")
+
+    # /api/embed accepts 'input' as a string or list of strings directly
+    ollama_payload["input"] = input_value
+
+    # Optionally forward other fields if present
+    for optional_key in ("truncate", "options", "keep_alive"):
+        if optional_key in openai_payload:
+            ollama_payload[optional_key] = openai_payload[optional_key]
+
+    return ollama_payload
+

backend/open_webui/utils/response.py

@@ -192,17 +192,29 @@ def convert_embedding_response_ollama_to_openai(response) -> dict:
                 "model": "...",
             }
     """
-    # Ollama batch-style output
+    # Ollama batch-style output from /api/embed
+    # Response format: {"embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]], "model": "..."}
     if isinstance(response, dict) and "embeddings" in response:
         openai_data = []
         for i, emb in enumerate(response["embeddings"]):
-            openai_data.append(
+            # /api/embed returns embeddings as plain float lists
-                {
+            if isinstance(emb, list):
-                    "object": "embedding",
+                openai_data.append(
-                    "embedding": emb.get("embedding"),
+                    {
-                    "index": emb.get("index", i),
+                        "object": "embedding",
-                }
+                        "embedding": emb,
-            )
+                        "index": i,
+                    }
+                )
+            # Also handle dict format for robustness
+            elif isinstance(emb, dict):
+                openai_data.append(
+                    {
+                        "object": "embedding",
+                        "embedding": emb.get("embedding"),
+                        "index": emb.get("index", i),
+                    }
+                )
         return {
             "object": "list",
             "data": openai_data,