Add gcp auth for better supporton service account

mem0/embeddings/vertexai.py

@@ -5,6 +5,7 @@ from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
 
 from mem0.configs.embeddings.base import BaseEmbedderConfig
 from mem0.embeddings.base import EmbeddingBase
+from mem0.utils.gcp_auth import GCPAuthenticator
 
 
 class VertexAIEmbedding(EmbeddingBase):
@@ -20,14 +21,23 @@ class VertexAIEmbedding(EmbeddingBase):
             "search": self.config.memory_search_embedding_type or "RETRIEVAL_QUERY",
         }
 
-        credentials_path = self.config.vertex_credentials_json
-
-        if credentials_path:
-            os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
-        elif not os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
-            raise ValueError(
-                "Google application credentials JSON is not provided. Please provide a valid JSON path or set the 'GOOGLE_APPLICATION_CREDENTIALS' environment variable."
+        # Set up authentication using centralized GCP authenticator
+        # This supports multiple authentication methods while preserving environment variable support
+        try:
+            GCPAuthenticator.setup_vertex_ai(
+                service_account_json=getattr(self.config, 'google_service_account_json', None),
+                credentials_path=self.config.vertex_credentials_json,
+                project_id=getattr(self.config, 'google_project_id', None)
             )
+        except Exception as e:
+            # Fall back to original behavior for backward compatibility
+            credentials_path = self.config.vertex_credentials_json
+            if credentials_path:
+                os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
+            elif not os.getenv("GOOGLE_APPLICATION_CREDENTIALS"):
+                raise ValueError(
+                    "Google application credentials JSON is not provided. Please provide a valid JSON path or set the 'GOOGLE_APPLICATION_CREDENTIALS' environment variable."
+                )
 
         self.model = TextEmbeddingModel.from_pretrained(self.config.model)