feat(auto-annotation): add LLM-based annotation operators

Add three new LLM-powered auto-annotation operators:
- LLMTextClassification: Text classification using LLM
- LLMNamedEntityRecognition: Named entity recognition with type validation
- LLMRelationExtraction: Relation extraction with entity and relation type validation

Key features:
- Load LLM config from t_model_config table via modelId parameter
- Lazy loading of LLM configuration on first execute()
- Result validation with whitelist checking for entity/relation types
- Fault-tolerant: returns empty results on LLM failure instead of throwing
- Fully compatible with existing Worker pipeline

Files added:
- runtime/ops/annotation/_llm_utils.py: Shared LLM utilities
- runtime/ops/annotation/llm_text_classification/: Text classification operator
- runtime/ops/annotation/llm_named_entity_recognition/: NER operator
- runtime/ops/annotation/llm_relation_extraction/: Relation extraction operator

Files modified:
- runtime/ops/annotation/__init__.py: Register 3 new operators
- runtime/python-executor/datamate/auto_annotation_worker.py: Add to Worker whitelist
- frontend/src/pages/DataAnnotation/OperatorCreate/hooks/useOperatorOperations.ts: Add to frontend whitelist

runtime/ops/annotation/__init__.py

@@ -1,10 +1,16 @@
 # -*- coding: utf-8 -*-
-"""Annotation-related operators (e.g. YOLO detection)."""
+"""Annotation-related operators (e.g. YOLO detection, LLM-based NLP annotation)."""
 
 from . import image_object_detection_bounding_box
 from . import test_annotation_marker
+from . import llm_text_classification
+from . import llm_named_entity_recognition
+from . import llm_relation_extraction
 
 __all__ = [
     "image_object_detection_bounding_box",
     "test_annotation_marker",
+    "llm_text_classification",
+    "llm_named_entity_recognition",
+    "llm_relation_extraction",
 ]