tests: Updated tests

tests/test_image_onnx_embeddings.py

@@ -8,7 +8,7 @@
 
 from fastembed import ImageEmbedding
 from tests.config import TEST_MISC_DIR
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_VECTOR_VALUES = {
     "Qdrant/clip-ViT-B-32-vision": np.array([-0.0098, 0.0128, -0.0274, 0.002, -0.0059]),
@@ -33,10 +33,7 @@ def test_embedding(model_name: str) -> None:
     is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
     for model_desc in ImageEmbedding._list_supported_models():
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         dim = model_desc.dim

tests/test_late_interaction_embeddings.py

@@ -6,7 +6,7 @@
 from fastembed.late_interaction.late_interaction_text_embedding import (
     LateInteractionTextEmbedding,
 )
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 # vectors are abridged and rounded for brevity
 CANONICAL_COLUMN_VALUES = {
@@ -177,10 +177,7 @@ def test_single_embedding(model_name: str):
     docs_to_embed = docs
 
     for model_desc in LateInteractionTextEmbedding._list_supported_models():
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         print("evaluating", model_name)
@@ -201,10 +198,7 @@ def test_single_embedding_query(model_name: str):
     queries_to_embed = docs
 
     for model_desc in LateInteractionTextEmbedding._list_supported_models():
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         print("evaluating", model_name)

tests/test_sparse_embeddings.py

@@ -5,7 +5,7 @@
 
 from fastembed.sparse.bm25 import Bm25
 from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_COLUMN_VALUES = {
     "prithivida/Splade_PP_en_v1": {
@@ -73,10 +73,7 @@ def test_single_embedding(model_name: str) -> None:
     for model_desc in SparseTextEmbedding._list_supported_models():
         if model_desc.model not in CANONICAL_COLUMN_VALUES:
             continue
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         model = SparseTextEmbedding(model_name=model_name)

tests/test_text_cross_encoder.py

@@ -4,7 +4,7 @@
 import pytest
 
 from fastembed.rerank.cross_encoder import TextCrossEncoder
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_SCORE_VALUES = {
     "Xenova/ms-marco-MiniLM-L-6-v2": np.array([8.500708, -2.541011]),
@@ -22,10 +22,7 @@ def test_rerank(model_name: str) -> None:
     is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
     for model_desc in TextCrossEncoder._list_supported_models():
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         model = TextCrossEncoder(model_name=model_name)

tests/test_text_multitask_embeddings.py

@@ -229,8 +229,6 @@ def test_task_assignment():
             continue
 
         model_name = model_desc.model
-        if model_name not in CANONICAL_VECTOR_VALUES:
-            continue
 
         model = TextEmbedding(model_name=model_name)
 

tests/test_text_onnx_embeddings.py

@@ -5,7 +5,7 @@
 import pytest
 
 from fastembed.text.text_embedding import TextEmbedding
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_VECTOR_VALUES = {
     "BAAI/bge-small-en": np.array([-0.0232, -0.0255, 0.0174, -0.0639, -0.0006]),
@@ -83,10 +83,7 @@ def test_embedding(model_name: str) -> None:
             is_mac and model_desc.model == "nomic-ai/nomic-embed-text-v1.5-Q"
         ):
             continue
-        if not is_ci:
-            if model_desc.size_in_GB > 1:
-                continue
-        elif not is_manual and model_desc.model != model_name:
+        if not should_test_model(model_name, model_desc, is_ci, is_manual):
             continue
 
         dim = model_desc.dim

tests/utils.py

@@ -3,7 +3,9 @@
 
 from pathlib import Path
 from types import TracebackType
-from typing import Union, Callable, Any, Type
+from typing import Union, Callable, Any, Type, Optional
+
+from fastembed.common.model_description import BaseModelDescription
 
 
 def delete_model_cache(model_dir: Union[str, Path]) -> None:
@@ -35,3 +37,15 @@ def on_error(
     if model_dir.exists():
         # todo: PermissionDenied is raised on blobs removal in Windows, with blobs > 2GB
         shutil.rmtree(model_dir, onerror=on_error)
+
+
+def should_test_model(
+    model_name: str, model_desc: BaseModelDescription, is_ci: Optional[str], is_manual: bool
+):
+    """Determine if a model should be tested based on environment"""
+    if not is_ci:
+        if model_desc.size_in_GB > 1:
+            return False
+    elif not is_manual and model_desc.model != model_name:
+        return False
+    return True