Merge branch 'main' into mean_pooling

fastembed/common/pooling.py

@@ -0,0 +1,38 @@
+import numpy as np
+from numpy import ufunc
+
+
+class LateInteractionPooler(object):
+    def __init__(self, agg_type="row", agg="mean"):
+        self.agg = agg
+        self.type = agg_type
+
+    def _pick_operation(self) -> ufunc:
+        if self.agg == "mean":
+            return np.mean
+        elif self.agg == "max":
+            return np.max
+        elif self.agg == "min":
+            return np.min
+        else:
+            raise NotImplementedError(
+                f"LateInteractionPooler only supports agg=mean,min,max, provided {self.agg}"
+            )
+
+    def pool(self, embeddings_batch) -> np.array:
+        if isinstance(embeddings_batch, np.ndarray) and len(embeddings_batch.shape) == 2:
+            embeddings_batch = [embeddings_batch]
+
+        if self.type == "row":
+            pooled_embedding = self.pool_row(embeddings_batch)
+        elif self.type == "col":
+            pooled_embedding = self.pool_col(embeddings_batch)
+        else:
+            raise ValueError("type must be 'row' or 'col'")
+        return pooled_embedding
+
+    def pool_row(self, embeddings_batch) -> np.array:
+        return self._pick_operation()(embeddings_batch, axis=-1)
+
+    def pool_col(self, embeddings_batch) -> np.array:
+        return self._pick_operation()(embeddings_batch, axis=-2)

tests/test_pooling.py

@@ -0,0 +1,34 @@
+import os
+
+import numpy as np
+
+from fastembed.late_interaction.late_interaction_text_embedding import (
+    LateInteractionTextEmbedding,
+)
+from fastembed.common.pooling import LateInteractionPooler
+
+from tests.utils import delete_model_cache
+
+CANONICAL_COLUMN_VALUES = {
+    "colbert-ir/colbertv2.0": np.array(
+        [4.0727495e-03, -2.4026826e-03, -6.8204990e-04, -7.1383954e-05, 4.4963313e-03]
+    ),
+}
+
+docs = ["Hello World"]
+
+
+def test_batch_embedding():
+    is_ci = os.getenv("CI")
+    docs_to_embed = docs * 10
+
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+        print("evaluating", model_name)
+        model = LateInteractionTextEmbedding(model_name=model_name)
+        pooler = LateInteractionPooler()
+        result = list(model.embed(docs_to_embed, batch_size=6))
+        pooled_result = pooler.pool(result)
+        assert np.allclose(pooled_result[0], expected_result, atol=2e-3)
+
+        if is_ci:
+            delete_model_cache(model.model._model_dir)