fixed: EMBEDDING_PROVIDER=mock

examples/agent_memory.py

@@ -30,7 +30,7 @@ def load_oceanbase_config():
 
     Uses the auto_config() utility function to automatically load from .env.
     """
-    oceanbase_env_path = os.path.join(os.path.dirname(__file__), '..', 'configs', '.env')
+    oceanbase_env_path = os.path.join(os.path.dirname(__file__), '..', '.env')
 
     if os.path.exists(oceanbase_env_path):
         load_dotenv(oceanbase_env_path, override=True)

examples/basic_usage.py

@@ -23,8 +23,8 @@ def main():
     print("=" * 60)
 
     # Check if .env exists and load it
-    env_path = os.path.join(os.path.dirname(__file__), "..", "configs", ".env")
-    env_example_path = os.path.join(os.path.dirname(__file__), "..", "configs", "env.example")
+    env_path = os.path.join(os.path.dirname(__file__), "..", ".env")
+    env_example_path = os.path.join(os.path.dirname(__file__), "..", "env.example")
 
     if not os.path.exists(env_path):
         print(f"\n No .env file found at: {env_path}")

examples/intelligent_memory_demo.py

@@ -38,7 +38,7 @@ def load_config():
 
     Uses the auto_config() utility function to automatically load from .env.
     """
-    oceanbase_env_path = os.path.join(os.path.dirname(__file__), '..', 'configs', '.env')
+    oceanbase_env_path = os.path.join(os.path.dirname(__file__), '..', '.env')
 
     if os.path.exists(oceanbase_env_path):
         load_dotenv(oceanbase_env_path, override=True)

src/powermem/core/async_memory.py

@@ -159,7 +159,8 @@ def __init__(
 
         # Extract embedder config
         embedder_config = self._get_component_config('embedder')
-        self.embedding = EmbedderFactory.create(self.embedding_provider, embedder_config, None)
+        # Pass vector_store_config so factory can extract embedding_model_dims for mock embeddings
+        self.embedding = EmbedderFactory.create(self.embedding_provider, embedder_config, vector_store_config)
 
         # Initialize storage adapter with embedding service
         # Automatically select adapter based on sub_stores configuration
@@ -1339,10 +1340,12 @@ def _init_single_sub_store(
                 if key not in sub_embedding_params and key in main_embedding_config:
                     sub_embedding_params[key] = main_embedding_config[key]
 
+            # Create a config dict with embedding_model_dims for mock embeddings
+            sub_vector_config = {'embedding_model_dims': embedding_model_dims}
             sub_embedding = EmbedderFactory.create(
                 sub_embedding_provider,
                 sub_embedding_params,
-                None
+                sub_vector_config
             )
             logger.info(f"Created sub embedding service for store {index}: {sub_embedding_provider}")
         else:

src/powermem/core/memory.py

@@ -228,7 +228,8 @@ def __init__(
 
         # Extract embedder config
         embedder_config = self._get_component_config('embedder')
-        self.embedding = EmbedderFactory.create(self.embedding_provider, embedder_config, None)
+        # Pass vector_store_config so factory can extract embedding_model_dims for mock embeddings
+        self.embedding = EmbedderFactory.create(self.embedding_provider, embedder_config, vector_store_config)
 
         # Initialize storage adapter with embedding service
         # Automatically select adapter based on sub_stores configuration
@@ -1404,10 +1405,12 @@ def _init_single_sub_store(
                 if key not in sub_embedding_params and key in main_embedding_config:
                     sub_embedding_params[key] = main_embedding_config[key]
 
+            # Create a config dict with embedding_model_dims for mock embeddings
+            sub_vector_config = {'embedding_model_dims': embedding_model_dims}
             sub_embedding = EmbedderFactory.create(
                 sub_embedding_provider,
                 sub_embedding_params,
-                None
+                sub_vector_config
             )
             logger.info(f"Created sub embedding service for store {index}: {sub_embedding_provider}")
         else:

src/powermem/integrations/embeddings/factory.py

@@ -34,11 +34,38 @@ class EmbedderFactory:
 
     @classmethod
     def create(cls, provider_name, config, vector_config: Optional[dict]):
+        # Helper function to extract dimension from vector_config (handles both dict and object)
+        def get_dimension_from_vector_config(vector_config, default=1536):
+            if not vector_config:
+                return default
+            if isinstance(vector_config, dict):
+                return vector_config.get('embedding_model_dims', default)
+            else:
+                return getattr(vector_config, 'embedding_model_dims', default)
+
         # Handle mock provider directly
         if provider_name == "mock":
-            return MockEmbeddings()
-        if provider_name == "upstash_vector" and vector_config and vector_config.enable_embeddings:
-            return MockEmbeddings()
+            # Extract dimension from vector_config or embedder config, default to 1536
+            dimension = 1536  # Default dimension
+            dimension = get_dimension_from_vector_config(vector_config, dimension)
+            if config:
+                dimension = config.get('embedding_dims', dimension)
+            return MockEmbeddings(dimension=dimension)
+        if provider_name == "upstash_vector" and vector_config:
+            # Check enable_embeddings (handles both dict and object)
+            enable_embeddings = False
+            if isinstance(vector_config, dict):
+                enable_embeddings = vector_config.get('enable_embeddings', False)
+            else:
+                enable_embeddings = getattr(vector_config, 'enable_embeddings', False)
+
+            if enable_embeddings:
+                # Extract dimension from vector_config or embedder config, default to 1536
+                dimension = 1536  # Default dimension
+                dimension = get_dimension_from_vector_config(vector_config, dimension)
+                if config:
+                    dimension = config.get('embedding_dims', dimension)
+                return MockEmbeddings(dimension=dimension)
         class_type = cls.provider_to_class.get(provider_name)
         if class_type:
             embedder_instance = load_class(class_type)

src/powermem/integrations/embeddings/mock.py

@@ -4,8 +4,26 @@
 
 
 class MockEmbeddings(EmbeddingBase):
+    def __init__(self, dimension: int = 1536):
+        """
+        Initialize MockEmbeddings with specified dimension.
+
+        Args:
+            dimension: Dimension of the mock embedding vector. Defaults to 1536 to match
+                      common embedding models and OceanBase default.
+        """
+        self.dimension = dimension
+
     def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None):
         """
-        Generate a mock embedding with dimension of 10.
+        Generate a mock embedding with the configured dimension.
+
+        Returns a vector with values [0.1, 0.2, 0.3, ...] repeated to fill the dimension.
         """
-        return [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+        # Generate a simple pattern that repeats to fill the dimension
+        base_values = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
+        # Repeat the pattern to fill the required dimension
+        result = []
+        for i in range(self.dimension):
+            result.append(base_values[i % len(base_values)])
+        return result