Merge branch 'main' into fix/vertexai-py-batch-size

gemini-embedding-* accepts one input text per predict request, but
embed_batch() chunked at 250 for every model while defaulting to
gemini-embedding-001. Derive the chunk size from the model instead.

mem0/embeddings/vertexai.py

@@ -8,6 +8,14 @@ from mem0.embeddings.base import EmbeddingBase
 from mem0.utils.gcp_auth import GCPAuthenticator
 
 
+def _max_instances_per_request(model: str) -> int:
+    # Vertex AI's per-request input cap depends on the model family: gemini-embedding-*
+    # accepts exactly one input text per predict() call, while text-embedding-004/005 and
+    # text-multilingual-embedding-002 accept up to 250.
+    # https://docs.cloud.google.com/vertex-ai/docs/samples/aiplatform-sdk-embedding
+    return 1 if model.startswith("gemini-embedding") else 250
+
+
 class VertexAIEmbedding(EmbeddingBase):
     def __init__(self, config: Optional[BaseEmbedderConfig] = None):
         super().__init__(config)
@@ -72,8 +80,9 @@ class VertexAIEmbedding(EmbeddingBase):
                 raise ValueError(f"Invalid memory action: {memory_action}")
             embedding_type = self.embedding_types[memory_action]
         all_embeddings = []
-        for i in range(0, len(texts), 250):
+        chunk_size = _max_instances_per_request(self.config.model)
-            chunk = texts[i : i + 250]
+        for i in range(0, len(texts), chunk_size):
+            chunk = texts[i : i + chunk_size]
             inputs = [TextEmbeddingInput(text=t, task_type=embedding_type) for t in chunk]
             results = self.model.get_embeddings(texts=inputs, output_dimensionality=self.config.embedding_dims)
             all_embeddings.extend(r.values for r in results)

tests/embeddings/test_vertexai_embeddings.py

@@ -163,7 +163,9 @@ def test_invalid_memory_action(mock_text_embedding_model, mock_config):
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_single_call(mock_text_embedding_model, mock_os_environ, mock_config):
-    mock_config.return_value.model = "gemini-embedding-001"
+    # text-embedding-005 accepts up to 250 inputs per request, so 2 texts fit in one call.
+    # (gemini-embedding-* only accepts 1 input per request; see test_embed_batch_default_model_sends_one_text_per_call.)
+    mock_config.return_value.model = "text-embedding-005"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -198,7 +200,9 @@ def test_embed_batch_empty_list(mock_text_embedding_model, mock_os_environ, mock
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_count_mismatch_raises(mock_text_embedding_model, mock_os_environ, mock_config):
-    mock_config.return_value.model = "gemini-embedding-001"
+    # Use a 250-cap model so both texts land in a single chunk/call, keeping the mismatch
+    # (1 embedding returned for that one call) triggered by that single call.
+    mock_config.return_value.model = "text-embedding-005"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -261,8 +265,8 @@ def test_embed_batch_invalid_memory_action_raises(mock_text_embedding_model, moc
 
 @patch("mem0.embeddings.vertexai.TextEmbeddingModel")
 def test_embed_batch_chunking_triggers_two_api_calls(mock_text_embedding_model, mock_os_environ, mock_config):
-    """300 texts must produce exactly 2 get_embeddings calls (chunks of 250 and 50)."""
+    """text-embedding-005 accepts up to 250 inputs/request: 300 texts -> 2 calls (chunks of 250 and 50)."""
-    mock_config.return_value.model = "gemini-embedding-001"
+    mock_config.return_value.model = "text-embedding-005"
     mock_config.return_value.embedding_dims = 256
 
     config = mock_config()
@@ -278,3 +282,25 @@ def test_embed_batch_chunking_triggers_two_api_calls(mock_text_embedding_model,
 
     assert mock_text_embedding_model.from_pretrained.return_value.get_embeddings.call_count == 2
     assert len(result) == 300
+
+
+@patch("mem0.embeddings.vertexai.TextEmbeddingModel")
+def test_embed_batch_default_model_sends_one_text_per_call(mock_text_embedding_model, mock_os_environ, mock_config):
+    """gemini-embedding-001 (default) accepts 1 input/request: 300 texts -> 300 calls of 1 text each."""
+    mock_config.return_value.model = "gemini-embedding-001"
+    mock_config.return_value.embedding_dims = 256
+
+    config = mock_config()
+    embedder = VertexAIEmbedding(config)
+
+    def make_single_response(texts, output_dimensionality):
+        assert len(texts) == 1
+        return [Mock(values=[0.1, 0.2]) for _ in texts]
+
+    mock_text_embedding_model.from_pretrained.return_value.get_embeddings.side_effect = make_single_response
+
+    texts = [f"text {i}" for i in range(300)]
+    result = embedder.embed_batch(texts)
+
+    assert mock_text_embedding_model.from_pretrained.return_value.get_embeddings.call_count == 300
+    assert len(result) == 300