feat(embeddings): add native embed_batch to 5 embedders (LMStudio, Together, HuggingFace, VertexAI, GoogleGenAI) (#5609)

tests/embeddings/test_lm_studio_embeddings.py

@@ -6,6 +6,7 @@ from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.lmstudio import LMStudioEmbedding
 
 
+
 @pytest.fixture
 def mock_lm_studio_client():
     with patch("mem0.embeddings.lmstudio.OpenAI") as mock_openai:
@@ -27,3 +28,55 @@ def test_embed_text(mock_lm_studio_client):
     )
 
     assert embedding == [0.1, 0.2, 0.3, 0.4, 0.5]
+
+
+def test_embed_batch_single_call(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
+    mock_item1 = Mock(index=1, embedding=[0.4, 0.5, 0.6])
+    mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0, mock_item1])
+
+    texts = ["First text.", "Second text."]
+    embeddings = embedder.embed_batch(texts)
+
+    mock_lm_studio_client.embeddings.create.assert_called_once_with(
+        input=texts, model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+    )
+    assert embeddings == [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]
+
+
+def test_embed_batch_empty_list(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    result = embedder.embed_batch([])
+
+    assert result == []
+    mock_lm_studio_client.embeddings.create.assert_not_called()
+
+
+def test_embed_batch_strips_newlines(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
+    mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0])
+
+    embedder.embed_batch(["line one\nline two"])
+
+    mock_lm_studio_client.embeddings.create.assert_called_once_with(
+        input=["line one line two"], model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf"
+    )
+
+
+def test_embed_batch_count_mismatch_raises(mock_lm_studio_client):
+    config = BaseEmbedderConfig(model="nomic-embed-text-v1.5-GGUF/nomic-embed-text-v1.5.f16.gguf", embedding_dims=512)
+    embedder = LMStudioEmbedding(config)
+
+    mock_item0 = Mock(index=0, embedding=[0.1, 0.2, 0.3])
+    mock_lm_studio_client.embeddings.create.return_value = Mock(data=[mock_item0])
+
+    with pytest.raises(ValueError, match="returned 1 embeddings for 2 texts"):
+        embedder.embed_batch(["first text", "second text"])