refactor(memory): consolidate memory config extraction and remove unused validator

- Add workspace default LLM fallback for emotion model in extraction orchestrator
- Consolidate memory config ID extraction logic into MemoryConfigService
- Remove duplicate extraction methods from AppService (_extract_memory_config_id_from_agent, _extract_memory_config_id_from_workflow)
- Remove unused validate_embedding_model function from validators
- Simplify AppService by delegating memory config extraction to MemoryConfigService
- Update validator exports to remove validate_embedding_model
- Improve code maintainability by centralizing memory configuration logic

api/app/core/validators/__init__.py

@@ -4,7 +4,6 @@ Validators package for various validation utilities.
 from app.core.validators.file_validator import FileValidator, ValidationResult
 from app.core.validators.memory_config_validators import (
     validate_and_resolve_model_id,
-    validate_embedding_model,
     validate_llm_model,
     validate_model_exists_and_active,
 )
@@ -16,6 +15,5 @@ __all__ = [
     # Memory config validators
     "validate_model_exists_and_active",
     "validate_and_resolve_model_id",
-    "validate_embedding_model",
     "validate_llm_model",
 ]

api/app/core/validators/memory_config_validators.py

@@ -6,7 +6,6 @@ This module provides validation functions for memory configuration models.
 Functions:
     validate_model_exists_and_active: Validate model exists and is active
     validate_and_resolve_model_id: Validate and resolve model ID with DB lookup
-    validate_embedding_model: Validate embedding model availability
     validate_llm_model: Validate LLM model availability
 """
 
@@ -203,58 +202,6 @@ def validate_and_resolve_model_id(
     return model_uuid, model_name
 
 
-def validate_embedding_model(
-    config_id: UUID,
-    embedding_id: Union[str, UUID, None],
-    db: Session,
-    tenant_id: Optional[UUID] = None,
-    workspace_id: Optional[UUID] = None
-) -> tuple[UUID, str]:
-    """Validate that embedding model is available and return its UUID and name.
-    
-    Returns:
-        Tuple of (embedding_uuid, embedding_name)
-    
-    Raises:
-        InvalidConfigError: If embedding_id is not provided or invalid
-        ModelNotFoundError: If embedding model does not exist
-        ModelInactiveError: If embedding model is inactive
-    """
-    if embedding_id is None or (isinstance(embedding_id, str) and not embedding_id.strip()):
-        raise InvalidConfigError(
-            f"Configuration {config_id} has no embedding model configured",
-            field_name="embedding_model_id",
-            invalid_value=embedding_id,
-            config_id=config_id,
-            workspace_id=workspace_id
-        )
-    
-    embedding_uuid, embedding_name = validate_and_resolve_model_id(
-        embedding_id, "embedding", db, tenant_id, required=True,
-        config_id=config_id, workspace_id=workspace_id
-    )
-    
-    logger.debug(
-        "Embedding model validated",
-        extra={
-            "embedding_uuid": str(embedding_uuid),
-            "embedding_name": embedding_name,
-            "config_id": config_id
-        }
-    )
-    
-    if embedding_uuid is None:
-        raise InvalidConfigError(
-            f"Configuration {config_id} has no embedding model configured",
-            field_name="embedding_model_id",
-            invalid_value=embedding_id,
-            config_id=config_id,
-            workspace_id=workspace_id
-        )
-    
-    return embedding_uuid, embedding_name
-
-
 def validate_llm_model(
     config_id: UUID,
     llm_id: Union[str, UUID, None],