refac

backend/open_webui/retrieval/utils.py

@@ -721,7 +721,7 @@ def generate_ollama_batch_embeddings(
 ) -> Optional[list[list[float]]]:
     try:
         log.debug(f'generate_ollama_batch_embeddings:model {model} batch size: {len(texts)}')
-        json_data = {'input': texts, 'model': model}
+        json_data = {'input': texts, 'model': model, 'truncate': True}
         if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
             json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
 
@@ -737,7 +737,9 @@ def generate_ollama_batch_embeddings(
             headers=headers,
             json=json_data,
         )
-        r.raise_for_status()
+        if r.status_code != 200:
+            error_detail = r.json().get('error', r.text)
+            raise Exception(f'Ollama embed error ({r.status_code}): {error_detail}')
         data = r.json()
 
         if 'embeddings' in data:
@@ -759,7 +761,7 @@ async def agenerate_ollama_batch_embeddings(
 ) -> Optional[list[list[float]]]:
     try:
         log.debug(f'agenerate_ollama_batch_embeddings:model {model} batch size: {len(texts)}')
-        form_data = {'input': texts, 'model': model}
+        form_data = {'input': texts, 'model': model, 'truncate': True}
         if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
             form_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
 
@@ -779,7 +781,10 @@ async def agenerate_ollama_batch_embeddings(
                 json=form_data,
                 ssl=AIOHTTP_CLIENT_SESSION_SSL,
             ) as r:
-                r.raise_for_status()
+                if r.status != 200:
+                    error_data = await r.json()
+                    error_detail = error_data.get('error', str(error_data))
+                    raise Exception(f'Ollama embed error ({r.status}): {error_detail}')
                 data = await r.json()
                 if 'embeddings' in data:
                     return data['embeddings']
@@ -901,11 +906,15 @@ async def generate_embeddings(
                 'user': user,
             }
         )
+        if embeddings is None:
+            return None
         return embeddings[0] if isinstance(text, str) else embeddings
     elif engine == 'openai':
         embeddings = await agenerate_openai_batch_embeddings(
             model, text if isinstance(text, list) else [text], url, key, prefix, user
         )
+        if embeddings is None:
+            return None
         return embeddings[0] if isinstance(text, str) else embeddings
     elif engine == 'azure_openai':
         azure_api_version = kwargs.get('azure_api_version', '')
@@ -918,6 +927,8 @@ async def generate_embeddings(
             prefix,
             user,
         )
+        if embeddings is None:
+            return None
         return embeddings[0] if isinstance(text, str) else embeddings