Nomic-embeddings-support (#280)

* Nomic-embeddings-support

* Jina models moved to pooled-normalized embeddings

* Canonical vector for nomic-ai/nomic-embed-text-v1.5-Q

* Moved all nomics to pooled_embeddings

---------

Co-authored-by: d.rudenko <dimitriyrudenk@gmail.com>

fastembed/text/mini_lm_embedding.py

@@ -1,58 +0,0 @@
-from typing import Any, Dict, Iterable, List, Type
-
-import numpy as np
-
-from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.utils import normalize
-from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
-from fastembed.text.onnx_text_model import TextEmbeddingWorker
-
-supported_mini_lm_models = [
-    {
-        "model": "sentence-transformers/all-MiniLM-L6-v2",
-        "dim": 384,
-        "description": "Sentence Transformer model, MiniLM-L6-v2",
-        "size_in_GB": 0.09,
-        "sources": {
-            "url": "https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
-            "hf": "qdrant/all-MiniLM-L6-v2-onnx",
-        },
-        "model_file": "model.onnx",
-    }
-]
-
-
-class MiniLMOnnxEmbedding(OnnxTextEmbedding):
-    @classmethod
-    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
-        return MiniLMEmbeddingWorker
-
-    @classmethod
-    def mean_pooling(cls, model_output: np.ndarray, attention_mask: np.ndarray) -> np.ndarray:
-        token_embeddings = model_output
-        input_mask_expanded = np.expand_dims(attention_mask, axis=-1)
-        input_mask_expanded = np.tile(input_mask_expanded, (1, 1, token_embeddings.shape[-1]))
-        input_mask_expanded = input_mask_expanded.astype(float)
-        sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
-        sum_mask = np.sum(input_mask_expanded, axis=1)
-        pooled_embeddings = sum_embeddings / np.maximum(sum_mask, 1e-9)
-        return pooled_embeddings
-
-    @classmethod
-    def list_supported_models(cls) -> List[Dict[str, Any]]:
-        """Lists the supported models.
-
-        Returns:
-            List[Dict[str, Any]]: A list of dictionaries containing the model information.
-        """
-        return supported_mini_lm_models
-
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
-        embeddings = output.model_output
-        attn_mask = output.attention_mask
-        return normalize(self.mean_pooling(embeddings, attn_mask)).astype(np.float32)
-
-
-class MiniLMEmbeddingWorker(OnnxTextEmbeddingWorker):
-    def init_embedding(self, model_name: str, cache_dir: str, **kwargs) -> OnnxTextEmbedding:
-        return MiniLMOnnxEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs)

fastembed/text/onnx_embedding.py

@@ -80,36 +80,6 @@ supported_onnx_models = [
         },
         "model_file": "model_optimized.onnx",
     },
-    {
-        "model": "nomic-ai/nomic-embed-text-v1",
-        "dim": 768,
-        "description": "8192 context length english model",
-        "size_in_GB": 0.52,
-        "sources": {
-            "hf": "nomic-ai/nomic-embed-text-v1",
-        },
-        "model_file": "onnx/model.onnx",
-    },
-    {
-        "model": "nomic-ai/nomic-embed-text-v1.5",
-        "dim": 768,
-        "description": "8192 context length english model",
-        "size_in_GB": 0.52,
-        "sources": {
-            "hf": "nomic-ai/nomic-embed-text-v1.5",
-        },
-        "model_file": "onnx/model.onnx",
-    },
-    {
-        "model": "nomic-ai/nomic-embed-text-v1.5-Q",
-        "dim": 768,
-        "description": "Quantized 8192 context length english model",
-        "size_in_GB": 0.13,
-        "sources": {
-            "hf": "nomic-ai/nomic-embed-text-v1.5",
-        },
-        "model_file": "onnx/model_quantized.onnx",
-    },
     {
         "model": "thenlper/gte-large",
         "dim": 1024,
@@ -274,7 +244,9 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[np.ndarray]):
         """
         return onnx_input
 
-    def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[np.ndarray]:
+    def _post_process_onnx_output(
+        self, output: OnnxOutputContext
+    ) -> Iterable[np.ndarray]:
         embeddings = output.model_output
         return normalize(embeddings[:, 0]).astype(np.float32)
 
@@ -286,4 +258,6 @@ class OnnxTextEmbeddingWorker(TextEmbeddingWorker):
         cache_dir: str,
         **kwargs,
     ) -> OnnxTextEmbedding:
-        return OnnxTextEmbedding(model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs)
+        return OnnxTextEmbedding(
+            model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs
+        )

fastembed/text/pooled_embedding.py

@@ -0,0 +1,87 @@
+from typing import Any, Dict, Iterable, List, Type
+
+import numpy as np
+
+from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.utils import normalize
+from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
+from fastembed.text.onnx_text_model import TextEmbeddingWorker
+
+supported_pooled_models = [
+    {
+        "model": "nomic-ai/nomic-embed-text-v1.5",
+        "dim": 768,
+        "description": "8192 context length english model",
+        "size_in_GB": 0.52,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1.5-Q",
+        "dim": 768,
+        "description": "Quantized 8192 context length english model",
+        "size_in_GB": 0.13,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
+        },
+        "model_file": "onnx/model_quantized.onnx",
+    },
+    {
+        "model": "nomic-ai/nomic-embed-text-v1",
+        "dim": 768,
+        "description": "8192 context length english model",
+        "size_in_GB": 0.52,
+        "sources": {
+            "hf": "nomic-ai/nomic-embed-text-v1",
+        },
+        "model_file": "onnx/model.onnx",
+    },
+]
+
+
+class PooledEmbedding(OnnxTextEmbedding):
+    @classmethod
+    def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
+        return PooledEmbeddingWorker
+
+    @classmethod
+    def mean_pooling(
+        cls, model_output: np.ndarray, attention_mask: np.ndarray
+    ) -> np.ndarray:
+        token_embeddings = model_output
+        input_mask_expanded = np.expand_dims(attention_mask, axis=-1)
+        input_mask_expanded = np.tile(
+            input_mask_expanded, (1, 1, token_embeddings.shape[-1])
+        )
+        input_mask_expanded = input_mask_expanded.astype(float)
+        sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
+        sum_mask = np.sum(input_mask_expanded, axis=1)
+        pooled_embeddings = sum_embeddings / np.maximum(sum_mask, 1e-9)
+        return pooled_embeddings
+
+    @classmethod
+    def list_supported_models(cls) -> List[Dict[str, Any]]:
+        """Lists the supported models.
+
+        Returns:
+            List[Dict[str, Any]]: A list of dictionaries containing the model information.
+        """
+        return supported_pooled_models
+
+    def _post_process_onnx_output(
+        self, output: OnnxOutputContext
+    ) -> Iterable[np.ndarray]:
+        embeddings = output.model_output
+        attn_mask = output.attention_mask
+        return self.mean_pooling(embeddings, attn_mask).astype(np.float32)
+
+
+class PooledEmbeddingWorker(OnnxTextEmbeddingWorker):
+    def init_embedding(
+        self, model_name: str, cache_dir: str, **kwargs
+    ) -> OnnxTextEmbedding:
+        return PooledEmbedding(
+            model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs
+        )

fastembed/text/pooled_normalized_embedding.py

@@ -6,8 +6,20 @@ from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import normalize
 from fastembed.text.onnx_embedding import OnnxTextEmbedding, OnnxTextEmbeddingWorker
 from fastembed.text.onnx_text_model import TextEmbeddingWorker
+from fastembed.text.pooled_embedding import PooledEmbedding
 
-supported_jina_models = [
+supported_pooled_normalized_models = [
+    {
+        "model": "sentence-transformers/all-MiniLM-L6-v2",
+        "dim": 384,
+        "description": "Sentence Transformer model, MiniLM-L6-v2",
+        "size_in_GB": 0.09,
+        "sources": {
+            "url": "https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
+            "hf": "qdrant/all-MiniLM-L6-v2-onnx",
+        },
+        "model_file": "model.onnx",
+    },
     {
         "model": "jinaai/jina-embeddings-v2-base-en",
         "dim": 768,
@@ -35,20 +47,10 @@ supported_jina_models = [
 ]
 
 
-class JinaOnnxEmbedding(OnnxTextEmbedding):
+class PooledNormalizedEmbedding(PooledEmbedding):
     @classmethod
     def _get_worker_class(cls) -> Type[TextEmbeddingWorker]:
-        return JinaEmbeddingWorker
-
-    @classmethod
-    def mean_pooling(cls, model_output, attention_mask) -> np.ndarray:
-        token_embeddings = model_output
-        input_mask_expanded = (np.expand_dims(attention_mask, axis=-1)).astype(float)
-
-        sum_embeddings = np.sum(token_embeddings * input_mask_expanded, axis=1)
-        mask_sum = np.clip(np.sum(input_mask_expanded, axis=1), a_min=1e-9, a_max=None)
-
-        return sum_embeddings / mask_sum
+        return PooledNormalizedEmbeddingWorker
 
     @classmethod
     def list_supported_models(cls) -> List[Dict[str, Any]]:
@@ -57,7 +59,7 @@ class JinaOnnxEmbedding(OnnxTextEmbedding):
         Returns:
             List[Dict[str, Any]]: A list of dictionaries containing the model information.
         """
-        return supported_jina_models
+        return supported_pooled_normalized_models
 
     def _post_process_onnx_output(
         self, output: OnnxOutputContext
@@ -67,10 +69,10 @@ class JinaOnnxEmbedding(OnnxTextEmbedding):
         return normalize(self.mean_pooling(embeddings, attn_mask)).astype(np.float32)
 
 
-class JinaEmbeddingWorker(OnnxTextEmbeddingWorker):
+class PooledNormalizedEmbeddingWorker(OnnxTextEmbeddingWorker):
     def init_embedding(
         self, model_name: str, cache_dir: str, **kwargs
     ) -> OnnxTextEmbedding:
-        return JinaOnnxEmbedding(
+        return PooledNormalizedEmbedding(
             model_name=model_name, cache_dir=cache_dir, threads=1, **kwargs
         )

fastembed/text/text_embedding.py

@@ -5,8 +5,8 @@ import numpy as np
 from fastembed.common import OnnxProvider
 from fastembed.text.clip_embedding import CLIPOnnxEmbedding
 from fastembed.text.e5_onnx_embedding import E5OnnxEmbedding
-from fastembed.text.jina_onnx_embedding import JinaOnnxEmbedding
-from fastembed.text.mini_lm_embedding import MiniLMOnnxEmbedding
+from fastembed.text.pooled_normalized_embedding import PooledNormalizedEmbedding
+from fastembed.text.pooled_embedding import PooledEmbedding
 from fastembed.text.onnx_embedding import OnnxTextEmbedding
 from fastembed.text.text_embedding_base import TextEmbeddingBase
 
@@ -15,9 +15,9 @@ class TextEmbedding(TextEmbeddingBase):
     EMBEDDINGS_REGISTRY: List[Type[TextEmbeddingBase]] = [
         OnnxTextEmbedding,
         E5OnnxEmbedding,
-        JinaOnnxEmbedding,
         CLIPOnnxEmbedding,
-        MiniLMOnnxEmbedding,
+        PooledNormalizedEmbedding,
+        PooledEmbedding,
     ]
 
     @classmethod

tests/test_text_onnx_embeddings.py

@@ -45,16 +45,16 @@ CANONICAL_VECTOR_VALUES = {
         [-0.0332, -0.0509, 0.0287, -0.0043, -0.0077]
     ),
     "jinaai/jina-embeddings-v2-base-de": np.array(
-      [-0.0085, 0.0417, 0.0342, 0.0309, -0.0149]
+        [-0.0085, 0.0417, 0.0342, 0.0309, -0.0149]
     ),
     "nomic-ai/nomic-embed-text-v1": np.array(
-        [0.0061, 0.0103, -0.0296, -0.0242, -0.0170]
+        [0.3708 ,  0.2031, -0.3406, -0.2114, -0.3230]
     ),
     "nomic-ai/nomic-embed-text-v1.5": np.array(
-        [-1.6531514e-02, 8.5380634e-05, -1.8171231e-01, -3.9333291e-03, 1.2763254e-02]
+        [-0.15407836, -0.03053198, -3.9138033, 0.1910364, 0.13224715]
     ),
     "nomic-ai/nomic-embed-text-v1.5-Q": np.array(
-        [-0.01554983, 0.0129992, -0.17909265, -0.01062993, 0.00512859]
+        [-0.12525563,  0.38030425, -3.961622 ,  0.04176439, -0.0758301]
     ),
     "thenlper/gte-large": np.array(
         [-0.01920587, 0.00113156, -0.00708992, -0.00632304, -0.04025577]