feat(memory): implement step-based extraction pipeline architecture

Introduce ExtractionStep abstraction with modular pipeline stages:
- Add base ExtractionStep class with render/call/parse lifecycle
- Implement StatementExtractionStep, TripletExtractionStep,
  EmbeddingStep, EmotionStep, GraphBuildStep, and DedupStep
- Add SidecarStepFactory for hot-pluggable non-critical steps
- Define Pydantic I/O schemas for all pipeline stages
- Refactor WritePipeline to orchestrate new step-based flow
- Add NEW_PIPELINE_ENABLED env switch for old/new pipeline routing
- Add emotion_enabled config flag to MemoryConfig
- Fix workspace_id reference in get_end_user_connected_config

api/app/schemas/memory_config_schema.py

@@ -421,6 +421,9 @@ class MemoryConfig:
     pruning_scene: Optional[str] = "education"
     pruning_threshold: float = 0.5
     
+    # Pipeline config: Emotion extraction
+    emotion_enabled: bool = False
+    
     # Ontology scene association
     scene_id: Optional[UUID] = None
     ontology_class_infos: list[dict] = field(default_factory=list)