new: make model name mandatory in inference structures

qdrant_client/async_qdrant_fastembed.py

@@ -791,7 +791,7 @@ def _resolve_query(
             Optional[models.Query]: query as it was, models.Query(nearest=query) or None
 
         Raises:
-            ValueError: if query is not of supported type or query is models.Document without `model` field
+            ValueError: if query is not of supported type
         """
         if isinstance(query, get_args(types.Query)) or isinstance(query, grpc.Query):
             return query
@@ -807,9 +807,6 @@ def _resolve_query(
             )
             return models.NearestQuery(nearest=query)
         if isinstance(query, INFERENCE_OBJECT_TYPES):
-            model_name = query.model
-            if model_name is None:
-                raise ValueError(f"`model` field has to be set explicitly in the {type(query)}")
             return models.NearestQuery(nearest=query)
         if query is None:
             return None

qdrant_client/conversions/conversion.py

@@ -1036,23 +1036,23 @@ def convert_multi_dense_vector(cls, model: grpc.MultiDenseVector) -> List[List[f
     def convert_document(cls, model: grpc.Document) -> rest.Document:
         return rest.Document(
             text=model.text,
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 
     @classmethod
     def convert_image(cls, model: grpc.Image) -> rest.Image:
         return rest.Image(
             image=value_to_json(model.image),
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 
     @classmethod
     def convert_inference_object(cls, model: grpc.InferenceObject) -> rest.InferenceObject:
         return rest.InferenceObject(
             object=value_to_json(model.object),
-            model=model.model if model.HasField("model") else None,
+            model=model.model,
             options=grpc_to_payload(model.options),
         )
 

qdrant_client/http/models/models.py

@@ -607,9 +607,8 @@ class Document(BaseModel, extra="forbid"):
     """
 
     text: str = Field(..., description="Text of the document This field will be used as input for the embedding model")
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"
@@ -873,9 +872,8 @@ class Image(BaseModel, extra="forbid"):
     """
 
     image: Any = Field(..., description="Image data: base64 encoded image or an URL")
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"
@@ -921,9 +919,8 @@ class InferenceObject(BaseModel, extra="forbid"):
         ...,
         description="Arbitrary data, used as input for the embedding model Used if the model requires more than one input or a custom input",
     )
-    model: Optional[str] = Field(
-        default=None,
-        description="Name of the model used to generate the vector List of available models depends on a provider",
+    model: str = Field(
+        ..., description="Name of the model used to generate the vector List of available models depends on a provider"
     )
     options: Optional[Dict[str, Any]] = Field(
         default=None, description="Parameters for the model Values of the parameters are model-specific"

qdrant_client/proto/points.proto

@@ -48,19 +48,19 @@ message SparseIndices {
 
 message Document {
   string text = 1; // Text of the document
-  optional string model = 3; // Model name
+  string model = 3; // Model name
   map<string, Value> options = 4; // Model options
 }
 
 message Image {
   Value image = 1; // Image data, either base64 encoded or URL
-  optional string model = 2; // Model name
+  string model = 2; // Model name
   map<string, Value> options = 3; // Model options
 }
 
 message InferenceObject {
   Value object = 1; // Object to infer
-  optional string model = 2; // Model name
+  string model = 2; // Model name
   map<string, Value> options = 3; // Model options
 }
 

qdrant_client/qdrant_fastembed.py

@@ -872,7 +872,7 @@ def _resolve_query(
             Optional[models.Query]: query as it was, models.Query(nearest=query) or None
 
         Raises:
-            ValueError: if query is not of supported type or query is models.Document without `model` field
+            ValueError: if query is not of supported type
         """
         if isinstance(query, get_args(types.Query)) or isinstance(query, grpc.Query):
             return query
@@ -892,9 +892,6 @@ def _resolve_query(
             return models.NearestQuery(nearest=query)
 
         if isinstance(query, INFERENCE_OBJECT_TYPES):
-            model_name = query.model
-            if model_name is None:
-                raise ValueError(f"`model` field has to be set explicitly in the {type(query)}")
             return models.NearestQuery(nearest=query)
 
         if query is None:

tests/conversions/fixtures.py

@@ -312,21 +312,18 @@
     text="random text", model="bert", options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"})
 )
 document_without_options = grpc.Document(text="random text", model="bert")
-document_only_text = grpc.Document(text="random text")
 image_with_options = grpc.Image(
     image=json_to_value("path_to_image"),
     model="resnet",
     options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"}),
 )
 image_without_options = grpc.Image(image=json_to_value("path_to_image"), model="resnet")
-image_only_image = grpc.Image(image=json_to_value("path_to_image"))
 inference_object_with_options = grpc.InferenceObject(
     object=json_to_value("path_to_image"),
     model="bert",
     options=payload_to_grpc({"a": 2, "b": [1, 2], "c": "useful"}),
 )
 inference_object_without_options = grpc.InferenceObject(object=json_to_value("text"), model="bert")
-inference_object_only_object = grpc.InferenceObject(object=json_to_value("text"))
 order_value_int = grpc.OrderValue(int=42)
 order_value_float = grpc.OrderValue(float=42.0)
 single_vector_output = grpc.VectorsOutput(vector=grpc.VectorOutput(data=[1.0, 2.0, 3.0, 4.0]))
@@ -1254,13 +1251,10 @@
 vector_input_multi = grpc.VectorInput(multi_dense=multi_dense_vector)
 vector_input_doc_with_options = grpc.VectorInput(document=document_with_options)
 vector_input_doc_without_options = grpc.VectorInput(document=document_without_options)
-vector_input_doc_only_text = grpc.VectorInput(document=document_only_text)
 vector_input_image_with_options = grpc.VectorInput(image=image_with_options)
 vector_input_image_without_options = grpc.VectorInput(image=image_without_options)
-vector_input_image_only_image = grpc.VectorInput(image=image_only_image)
 vector_input_inference_with_options = grpc.VectorInput(object=inference_object_with_options)
 vector_input_inference_without_options = grpc.VectorInput(object=inference_object_without_options)
-vector_input_inference_only_object = grpc.VectorInput(object=inference_object_only_object)
 
 recommend_input = grpc.RecommendInput(
     positive=[
@@ -1272,15 +1266,12 @@
     positive=[
         vector_input_doc_with_options,
         vector_input_doc_without_options,
-        vector_input_doc_only_text,
     ],
     negative=[
         vector_input_image_with_options,
         vector_input_image_without_options,
-        vector_input_image_only_image,
         vector_input_inference_with_options,
         vector_input_inference_without_options,
-        vector_input_inference_only_object,
     ],
 )
 recommend_input_strategy = grpc.RecommendInput(