Add Snowflake Arctic Embed L v2.0 model

fastembed/text/onnx_embedding.py

@@ -168,6 +168,26 @@
         sources=ModelSource(hf="snowflake/snowflake-arctic-embed-l"),
         model_file="onnx/model.onnx",
     ),
+    DenseModelDescription(
+        model="Snowflake/snowflake-arctic-embed-l-v2.0",
+        dim=1024,
+        tasks={
+            "embedding": {
+                "query_prefix": "query: ",
+                "passage_prefix": "",
+            }
+        },
+        description=(
+            "Text embeddings, Unimodal (text), Multilingual (74 languages), 8192 input tokens truncation, "
+            "Based on XLM-RoBERTa, supports Matryoshka learning for dimension truncation, "
+            "Prefixes for queries: recommended (query: ), 2024 year."
+        ),
+        license="apache-2.0",
+        size_in_GB=2.27,
+        sources=ModelSource(hf="Snowflake/snowflake-arctic-embed-l-v2.0"),
+        model_file="onnx/model.onnx",
+        additional_files=["onnx/model.onnx_data"],
+    ),
     DenseModelDescription(
         model="jinaai/jina-clip-v1",
         dim=768,
@@ -294,6 +314,60 @@ def embed(
             **kwargs,
         )
 
+    def query_embed(
+        self, query: Union[str, Iterable[str]], **kwargs: Any
+    ) -> Iterable[NumpyArray]:
+        """
+        Embeds queries using the model-specific query prefix if configured.
+
+        Args:
+            query: The query or queries to embed
+            **kwargs: Additional arguments to pass to embed
+
+        Yields:
+            Iterable[NumpyArray]: Query embeddings
+        """
+        # Check if model has task-specific prefixes configured
+        if hasattr(self.model_description, "tasks") and self.model_description.tasks:
+            embedding_task = self.model_description.tasks.get("embedding", {})
+            query_prefix = embedding_task.get("query_prefix", "")
+
+            if query_prefix:
+                # Add prefix to queries
+                if isinstance(query, str):
+                    query = f"{query_prefix}{query}"
+                else:
+                    query = [f"{query_prefix}{q}" for q in query]
+
+        # Use parent implementation
+        if isinstance(query, str):
+            yield from self.embed([query], **kwargs)
+        else:
+            yield from self.embed(query, **kwargs)
+
+    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
+        """
+        Embeds passages using the model-specific passage prefix if configured.
+
+        Args:
+            texts: The passages to embed
+            **kwargs: Additional arguments to pass to embed
+
+        Yields:
+            Iterable[NumpyArray]: Passage embeddings
+        """
+        # Check if model has task-specific prefixes configured
+        if hasattr(self.model_description, "tasks") and self.model_description.tasks:
+            embedding_task = self.model_description.tasks.get("embedding", {})
+            passage_prefix = embedding_task.get("passage_prefix", "")
+
+            if passage_prefix:
+                # Add prefix to passages
+                texts = [f"{passage_prefix}{t}" for t in texts]
+
+        # Use parent implementation
+        yield from self.embed(texts, **kwargs)
+
     @classmethod
     def _get_worker_class(cls) -> Type["TextEmbeddingWorker[NumpyArray]"]:
         return OnnxTextEmbeddingWorker

tests/test_text_onnx_embeddings.py

@@ -64,6 +64,9 @@
         [0.0080, -0.0266, -0.0335, 0.0282, 0.0143]
     ),
     "snowflake/snowflake-arctic-embed-l": np.array([0.0189, -0.0673, 0.0183, 0.0124, 0.0146]),
+    "Snowflake/snowflake-arctic-embed-l-v2.0": np.array(
+        [-0.0266, 0.0167, -0.0478, -0.0039, -0.0128]
+    ),
     "Qdrant/clip-ViT-B-32-text": np.array([0.0083, 0.0103, -0.0138, 0.0199, -0.0069]),
     "thenlper/gte-base": np.array([0.0038, 0.0355, 0.0181, 0.0092, 0.0654]),
     "jinaai/jina-clip-v1": np.array([-0.0862, -0.0101, -0.0056, 0.0375, -0.0472]),