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/llm_named_entity_recognition/__init__.py

@@ -0,0 +1,10 @@
+# -*- coding: utf-8 -*-
+from datamate.core.base_op import OPERATORS
+from .process import LLMNamedEntityRecognition
+
+OPERATORS.register_module(
+    module_name="LLMNamedEntityRecognition",
+    module_path="ops.annotation.llm_named_entity_recognition.process",
+)
+
+__all__ = ["LLMNamedEntityRecognition"]

runtime/ops/annotation/llm_named_entity_recognition/metadata.yml

@@ -0,0 +1,29 @@
+name: 'LLM命名实体识别'
+name_en: 'LLM Named Entity Recognition'
+description: '基于大语言模型的命名实体识别算子，支持自定义实体类型。'
+description_en: 'LLM-based NER operator with custom entity types.'
+language: 'python'
+vendor: 'datamate'
+raw_id: 'LLMNamedEntityRecognition'
+version: '1.0.0'
+types:
+  - 'annotation'
+modal: 'text'
+inputs: 'text'
+outputs: 'text'
+settings:
+  modelId:
+    name: '模型ID'
+    description: '已配置的 LLM 模型 ID（留空使用系统默认模型）。'
+    type: 'input'
+    defaultVal: ''
+  entityTypes:
+    name: '实体类型'
+    description: '逗号分隔的实体类型，如：PER,ORG,LOC,DATE'
+    type: 'input'
+    defaultVal: 'PER,ORG,LOC,DATE'
+  outputDir:
+    name: '输出目录'
+    description: '算子输出目录（由运行时自动注入）。'
+    type: 'input'
+    defaultVal: ''

runtime/ops/annotation/llm_named_entity_recognition/process.py

@@ -0,0 +1,174 @@
+# -*- coding: utf-8 -*-
+"""LLM 命名实体识别 (NER) 算子。
+
+基于大语言模型从文本中识别命名实体（人名、地名、机构名等），
+输出实体列表（含文本片段、实体类型、在原文中的起止位置）。
+"""
+
+import json
+import os
+import shutil
+import time
+from typing import Any, Dict, List
+
+from loguru import logger
+
+from datamate.core.base_op import Mapper
+
+
+SYSTEM_PROMPT = (
+    "你是一个专业的命名实体识别（NER）专家。根据给定的实体类型列表，"
+    "从输入文本中识别所有命名实体。\n"
+    "你必须严格输出 JSON 格式，不要输出任何其他内容。"
+)
+
+USER_PROMPT_TEMPLATE = """请从以下文本中识别所有命名实体。
+
+实体类型列表：{entity_types}
+
+实体类型说明：
+- PER：人名
+- ORG：组织/机构名
+- LOC：地点/地名
+- DATE：日期/时间
+- EVENT：事件
+- PRODUCT：产品名
+- MONEY：金额
+- PERCENT：百分比
+
+文本内容：
+{text}
+
+请以如下 JSON 格式输出（entities 为实体数组，每个实体包含 text、type、start、end 四个字段）：
+{{"entities": [{{"text": "实体文本", "type": "PER", "start": 0, "end": 3}}]}}
+
+注意：
+- type 必须是实体类型列表中的值之一
+- start 和 end 是实体在原文中的字符偏移位置（从 0 开始，左闭右开）
+- 如果没有找到任何实体，返回 {{"entities": []}}"""
+
+
+class LLMNamedEntityRecognition(Mapper):
+    """基于 LLM 的命名实体识别算子。"""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._model_id: str = kwargs.get("modelId", "")
+        self._entity_types: str = kwargs.get("entityTypes", "PER,ORG,LOC,DATE")
+        self._output_dir: str = kwargs.get("outputDir", "") or ""
+        self._llm_config = None
+
+    def _get_llm_config(self) -> Dict[str, Any]:
+        if self._llm_config is None:
+            from ops.annotation._llm_utils import get_llm_config
+
+            self._llm_config = get_llm_config(self._model_id)
+        return self._llm_config
+
+    @staticmethod
+    def _validate_entities(
+        entities_raw: Any, allowed_types: List[str]
+    ) -> List[Dict[str, Any]]:
+        """校验并过滤实体列表，确保类型在允许范围内。"""
+
+        if not isinstance(entities_raw, list):
+            return []
+
+        validated: List[Dict[str, Any]] = []
+        allowed_set = {t.strip().upper() for t in allowed_types}
+
+        for ent in entities_raw:
+            if not isinstance(ent, dict):
+                continue
+            ent_type = str(ent.get("type", "")).strip().upper()
+            ent_text = str(ent.get("text", "")).strip()
+            if not ent_text:
+                continue
+            # 保留匹配的类型，或在类型列表为空时全部保留
+            if allowed_set and ent_type not in allowed_set:
+                continue
+            validated.append(
+                {
+                    "text": ent_text,
+                    "type": ent_type,
+                    "start": ent.get("start"),
+                    "end": ent.get("end"),
+                }
+            )
+        return validated
+
+    def execute(self, sample: Dict[str, Any]) -> Dict[str, Any]:
+        start = time.time()
+
+        text_path = sample.get(self.text_key)
+        if not text_path or not os.path.exists(str(text_path)):
+            logger.warning("Text file not found: {}", text_path)
+            return sample
+
+        text_path = str(text_path)
+        with open(text_path, "r", encoding="utf-8") as f:
+            text_content = f.read()
+
+        if not text_content.strip():
+            logger.warning("Empty text file: {}", text_path)
+            return sample
+
+        max_chars = 8000
+        truncated = text_content[:max_chars]
+
+        from ops.annotation._llm_utils import call_llm, extract_json
+
+        config = self._get_llm_config()
+        prompt = USER_PROMPT_TEMPLATE.format(
+            entity_types=self._entity_types,
+            text=truncated,
+        )
+
+        allowed_types = [t.strip() for t in self._entity_types.split(",") if t.strip()]
+
+        try:
+            raw_response = call_llm(config, prompt, system_prompt=SYSTEM_PROMPT)
+            parsed = extract_json(raw_response)
+            entities_raw = parsed.get("entities", []) if isinstance(parsed, dict) else parsed
+            entities = self._validate_entities(entities_raw, allowed_types)
+        except Exception as e:
+            logger.error("LLM NER failed for {}: {}", text_path, e)
+            entities = []
+
+        annotation = {
+            "file": os.path.basename(text_path),
+            "task_type": "ner",
+            "entity_types": self._entity_types,
+            "model": config.get("model_name", ""),
+            "entities": entities,
+        }
+
+        # 写入输出
+        output_dir = self._output_dir or os.path.dirname(text_path)
+        annotations_dir = os.path.join(output_dir, "annotations")
+        data_dir = os.path.join(output_dir, "data")
+        os.makedirs(annotations_dir, exist_ok=True)
+        os.makedirs(data_dir, exist_ok=True)
+
+        base_name = os.path.splitext(os.path.basename(text_path))[0]
+
+        dst_data = os.path.join(data_dir, os.path.basename(text_path))
+        if not os.path.exists(dst_data):
+            shutil.copy2(text_path, dst_data)
+
+        json_path = os.path.join(annotations_dir, f"{base_name}.json")
+        with open(json_path, "w", encoding="utf-8") as f:
+            json.dump(annotation, f, indent=2, ensure_ascii=False)
+
+        sample["detection_count"] = len(entities)
+        sample["annotations_file"] = json_path
+        sample["annotations"] = annotation
+
+        elapsed = time.time() - start
+        logger.info(
+            "NER: {} -> {} entities, Time: {:.2f}s",
+            os.path.basename(text_path),
+            len(entities),
+            elapsed,
+        )
+        return sample