feat: Add support for deepset-mxbai-embed-de-large-v1 German embedding model

fastembed/text/onnx_embedding.py

@@ -107,6 +107,28 @@
         size_in_GB=0.64,
         sources=ModelSource(hf="mixedbread-ai/mxbai-embed-large-v1"),
         model_file="onnx/model.onnx",
+        # Prefixes from https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1#usage
+        tasks={
+            "query_prefix": "Represent this sentence for searching relevant passages: ",
+            "passage_prefix": "",
+        },
+    ),
+    DenseModelDescription(
+        model="mixedbread-ai/deepset-mxbai-embed-de-large-v1",
+        dim=1024,
+        description=(
+            "Text embeddings, Unimodal (text), German/English, 512 input tokens truncation, "
+            "Prefixes for queries/documents: necessary, 2024 year."
+        ),
+        license="apache-2.0",
+        size_in_GB=1.94,
+        sources=ModelSource(hf="mixedbread-ai/deepset-mxbai-embed-de-large-v1"),
+        model_file="onnx/model.onnx",
+        # Prefixes from https://huggingface.co/mixedbread-ai/deepset-mxbai-embed-de-large-v1#usage
+        tasks={
+            "query_prefix": "query: ",
+            "passage_prefix": "passage: ",
+        },
     ),
     DenseModelDescription(
         model="snowflake/snowflake-arctic-embed-xs",
@@ -294,6 +316,50 @@ def embed(
             **kwargs,
         )
 
+    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+        """
+        Embeds queries with optional query prefix.
+
+        Args:
+            query (Union[str, Iterable[str]]): The query to embed, or an iterable e.g. list of queries.
+
+        Returns:
+            Iterable[NumpyArray]: The embeddings.
+        """
+        # Check if model has query prefix
+        query_prefix = self.model_description.tasks.get("query_prefix", "") if self.model_description.tasks else ""
+
+        # Apply prefix if specified
+        if query_prefix:
+            if isinstance(query, str):
+                query = [query_prefix + query]
+            else:
+                query = [query_prefix + q for q in query]
+        elif isinstance(query, str):
+            query = [query]
+
+        yield from self.embed(query, **kwargs)
+
+    def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
+        """
+        Embeds passages with optional passage prefix.
+
+        Args:
+            texts (Iterable[str]): The list of texts to embed.
+            **kwargs: Additional keyword arguments to pass to the embed method.
+
+        Yields:
+            Iterable[NumpyArray]: The embeddings.
+        """
+        # Check if model has passage prefix
+        passage_prefix = self.model_description.tasks.get("passage_prefix", "") if self.model_description.tasks else ""
+
+        # Apply prefix if specified
+        if passage_prefix:
+            texts = [passage_prefix + text for text in texts]
+
+        yield from self.embed(texts, **kwargs)
+
     @classmethod
     def _get_worker_class(cls) -> Type["TextEmbeddingWorker[NumpyArray]"]:
         return OnnxTextEmbeddingWorker

tests/test_text_onnx_embeddings.py

@@ -57,6 +57,9 @@
     "mixedbread-ai/mxbai-embed-large-v1": np.array(
         [0.02295546, 0.03196154, 0.016512, -0.04031524, -0.0219634]
     ),
+    "mixedbread-ai/deepset-mxbai-embed-de-large-v1": np.array(
+        [0.00574683, 0.00185086, 0.00910093, -0.03800965, 0.00805963]
+    ),
     "snowflake/snowflake-arctic-embed-xs": np.array([0.0092, 0.0619, 0.0196, 0.009, -0.0114]),
     "snowflake/snowflake-arctic-embed-s": np.array([-0.0416, -0.0867, 0.0209, 0.0554, -0.0272]),
     "snowflake/snowflake-arctic-embed-m": np.array([-0.0329, 0.0364, 0.0481, 0.0016, 0.0328]),