feat: avoid validating return_multivector

modeling_jina_embeddings_v4.py

@@ -357,7 +357,6 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
 
     def _validate_encoding_params(
         self,
-        return_multivector: Optional[bool] = None,
         truncate_dim: Optional[int] = None,
         prompt_name: Optional[str] = None,
     ) -> Dict[str, Any]:
@@ -374,9 +373,6 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
                     else PREFIX_DICT["query"]
                 )
 
-        return_multivector = return_multivector or False
-        encode_kwargs["return_multivector"] = return_multivector
-
         truncate_dim = truncate_dim or self.config.truncate_dim
         if truncate_dim is not None and truncate_dim not in self.config.matryoshka_dims:
             raise ValueError(
@@ -429,9 +425,7 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
             List of text embeddings as tensors or numpy arrays when encoding multiple texts, or single text embedding as tensor when encoding a single text
         """
         prompt_name = prompt_name or "query"
-        encode_kwargs = self._validate_encoding_params(
-            return_multivector=return_multivector, truncate_dim=truncate_dim, prompt_name=prompt_name
-        )
+        encode_kwargs = self._validate_encoding_params(truncate_dim=truncate_dim, prompt_name=prompt_name)
 
         task = self._validate_task(task)
 
@@ -449,6 +443,7 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
             processor_fn=processor_fn,
             desc="Encoding texts...",
             task_label=task,
+            return_multivector=return_multivector,
             return_numpy=return_numpy,
             batch_size=batch_size,
             **encode_kwargs,
@@ -499,7 +494,7 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
             self.processor.image_processor.max_pixels = (
                 max_pixels  # change during encoding
             )
-        encode_kwargs = self._validate_encoding_params(return_multivector=return_multivector, truncate_dim=truncate_dim)
+        encode_kwargs = self._validate_encoding_params(truncate_dim=truncate_dim)
         task = self._validate_task(task)
         
         # Convert single image to list
@@ -513,6 +508,7 @@ class JinaEmbeddingsV4Model(Qwen2_5_VLForConditionalGeneration):
             desc="Encoding images...",
             task_label=task,
             batch_size=batch_size,
+            return_multivector=return_multivector,
             return_numpy=return_numpy,
             **encode_kwargs,
         )