feat: 添加 DeepSeek API 支持（阿里云百炼平台）

- 新增 deepseek_service.py 调用阿里云百炼 DeepSeek API
- 配置默认使用 deepseek-v3.2-exp 模型
- NER 服务支持 deepseek 模式

python-services/ner-service/app/config.py

@@ -18,9 +18,9 @@ class Settings(BaseSettings):
     
     # NER 模型配置
     # rule: 基于规则的简单 NER（开发测试用）
-    # ollama: 使用本地 Ollama LLM（推荐生产环境）
-    # api: 使用远程 API（如百炼）
-    ner_model: str = "rule"
+    # ollama: 使用本地 Ollama LLM
+    # deepseek: 使用阿里云百炼 DeepSeek API（推荐）
+    ner_model: str = "deepseek"
     use_gpu: bool = False
     max_text_length: int = 50000
     
@@ -37,10 +37,14 @@ class Settings(BaseSettings):
     chunk_size: int = 2000  # 每个分块的最大字符数
     chunk_overlap: int = 200  # 分块重叠字符数
     
-    # API 配置（用于 api 模式）
-    api_base_url: Optional[str] = None
-    api_key: Optional[str] = None
-    api_model: str = "qwen-plus"
+    # DeepSeek API 配置（阿里云百炼平台）
+    deepseek_api_key: str = "sk-14a0d0b9eda54b3bb6f0e55a7b8df084"
+    deepseek_base_url: str = "https://dashscope.aliyuncs.com/compatible-mode"
+    deepseek_model: str = "deepseek-v3.2-exp"
+    deepseek_timeout: int = 600
+    deepseek_temperature: float = 0.1  # NER 任务使用低温度
+    deepseek_max_tokens: int = 4096
+    deepseek_max_retries: int = 3
     
     # 实体类型配置
     entity_types: List[str] = [

python-services/ner-service/app/services/deepseek_service.py

@@ -0,0 +1,263 @@
+"""
+DeepSeek API 服务（阿里云百炼平台）
+用于调用 DeepSeek 模型进行 NER 提取
+"""
+import json
+import re
+import uuid
+import httpx
+from typing import List, Optional, Dict, Any
+from loguru import logger
+
+from ..config import settings
+from ..models import EntityInfo, PositionInfo
+
+
+class DeepSeekService:
+    """DeepSeek API 服务"""
+    
+    def __init__(self):
+        self.api_key = settings.deepseek_api_key
+        self.base_url = settings.deepseek_base_url
+        self.model = settings.deepseek_model
+        self.timeout = settings.deepseek_timeout
+        self.temperature = settings.deepseek_temperature
+        self.max_tokens = settings.deepseek_max_tokens
+        self.max_retries = settings.deepseek_max_retries
+        self.chunk_size = settings.chunk_size
+        self.chunk_overlap = settings.chunk_overlap
+        
+        logger.info(f"初始化 DeepSeek 服务: model={self.model}, base_url={self.base_url}")
+    
+    def _split_text(self, text: str) -> List[Dict[str, Any]]:
+        """
+        将长文本分割成多个块
+        """
+        if len(text) <= self.chunk_size:
+            return [{"text": text, "start_pos": 0, "end_pos": len(text)}]
+        
+        chunks = []
+        start = 0
+        
+        while start < len(text):
+            end = min(start + self.chunk_size, len(text))
+            
+            # 尝试在句号、换行处分割
+            if end < len(text):
+                for sep in ['\n\n', '\n', '。', '；', '！', '？', '.']:
+                    sep_pos = text.rfind(sep, start + self.chunk_size // 2, end)
+                    if sep_pos > start:
+                        end = sep_pos + len(sep)
+                        break
+            
+            chunk_text = text[start:end]
+            chunks.append({
+                "text": chunk_text,
+                "start_pos": start,
+                "end_pos": end
+            })
+            
+            start = end - self.chunk_overlap if end < len(text) else end
+        
+        logger.info(f"文本分割完成: 总长度={len(text)}, 分块数={len(chunks)}")
+        return chunks
+    
+    def _build_ner_prompt(self, text: str, entity_types: Optional[List[str]] = None) -> str:
+        """
+        构建 NER 提取的 Prompt
+        """
+        types = entity_types or settings.entity_types
+        types_desc = ", ".join(types)
+        
+        example = '{"entities": [{"name": "成都市", "type": "LOC", "charStart": 10, "charEnd": 13}, {"name": "2024年5月", "type": "DATE", "charStart": 0, "charEnd": 7}]}'
+        
+        prompt = f"""请从以下文本中提取命名实体，直接输出JSON，不要解释。
+
+实体类型: {types_desc}
+
+输出格式示例:
+{example}
+
+文本:
+{text}
+
+请直接输出JSON:"""
+        return prompt
+    
+    async def _call_api(self, prompt: str) -> Optional[str]:
+        """
+        调用 DeepSeek API（OpenAI 兼容格式）
+        """
+        url = f"{self.base_url}/v1/chat/completions"
+        headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": self.model,
+            "messages": [
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            "temperature": self.temperature,
+            "max_tokens": self.max_tokens,
+        }
+        
+        for attempt in range(self.max_retries):
+            try:
+                async with httpx.AsyncClient(timeout=self.timeout) as client:
+                    response = await client.post(url, headers=headers, json=payload)
+                    response.raise_for_status()
+                    result = response.json()
+                    
+                    # OpenAI 格式响应
+                    choices = result.get("choices", [])
+                    if choices:
+                        message = choices[0].get("message", {})
+                        return message.get("content", "")
+                    return None
+                    
+            except httpx.TimeoutException:
+                logger.warning(f"DeepSeek API 请求超时 (尝试 {attempt + 1}/{self.max_retries})")
+                if attempt == self.max_retries - 1:
+                    logger.error(f"DeepSeek API 请求超时: timeout={self.timeout}s")
+                    return None
+            except httpx.HTTPStatusError as e:
+                logger.error(f"DeepSeek API HTTP 错误: {e.response.status_code} - {e.response.text}")
+                return None
+            except Exception as e:
+                logger.error(f"DeepSeek API 请求失败: {e}")
+                if attempt == self.max_retries - 1:
+                    return None
+        
+        return None
+    
+    def _parse_response(self, response: str, chunk_start_pos: int = 0) -> List[EntityInfo]:
+        """
+        解析 API 返回的 JSON 结果
+        """
+        entities = []
+        
+        try:
+            # 移除 markdown code block 标记
+            response = re.sub(r'```json\s*', '', response)
+            response = re.sub(r'```\s*', '', response)
+            response = response.strip()
+            
+            # 方法1：直接解析
+            data = None
+            try:
+                data = json.loads(response)
+            except json.JSONDecodeError:
+                pass
+            
+            # 方法2：查找 JSON 对象
+            if not data or "entities" not in data:
+                json_match = re.search(r'\{\s*"entities"\s*:\s*\[[\s\S]*\]\s*\}', response)
+                if json_match:
+                    try:
+                        data = json.loads(json_match.group())
+                    except json.JSONDecodeError:
+                        pass
+            
+            if not data or "entities" not in data:
+                logger.warning(f"未找到有效的 entities JSON, response={response[:300]}...")
+                return entities
+            
+            entity_list = data.get("entities", [])
+            
+            for item in entity_list:
+                name = item.get("name", "").strip()
+                entity_type = item.get("type", "").upper()
+                char_start = item.get("charStart", 0)
+                char_end = item.get("charEnd", 0)
+                
+                if not name or len(name) < 2:
+                    continue
+                
+                # 校正位置
+                adjusted_start = char_start + chunk_start_pos
+                adjusted_end = char_end + chunk_start_pos
+                
+                entity = EntityInfo(
+                    name=name,
+                    type=entity_type,
+                    value=name,
+                    position=PositionInfo(
+                        char_start=adjusted_start,
+                        char_end=adjusted_end,
+                        line=1
+                    ),
+                    confidence=0.95,  # DeepSeek 置信度较高
+                    temp_id=str(uuid.uuid4())[:8]
+                )
+                entities.append(entity)
+                
+        except Exception as e:
+            logger.error(f"解析响应失败: {e}")
+        
+        return entities
+    
+    async def extract_entities(
+        self, 
+        text: str, 
+        entity_types: Optional[List[str]] = None
+    ) -> List[EntityInfo]:
+        """
+        使用 DeepSeek API 提取实体
+        """
+        if not text or not text.strip():
+            return []
+        
+        # 分割长文本
+        chunks = self._split_text(text)
+        
+        all_entities = []
+        seen_entities = set()
+        
+        for i, chunk in enumerate(chunks):
+            logger.info(f"处理分块 {i+1}/{len(chunks)}: 长度={len(chunk['text'])}")
+            
+            prompt = self._build_ner_prompt(chunk["text"], entity_types)
+            response = await self._call_api(prompt)
+            
+            if not response:
+                logger.warning(f"分块 {i+1} API 返回为空")
+                continue
+            
+            logger.debug(f"分块 {i+1} API 响应: {response[:500]}...")
+            
+            entities = self._parse_response(response, chunk["start_pos"])
+            
+            # 去重
+            for entity in entities:
+                entity_key = f"{entity.type}:{entity.name}"
+                if entity_key not in seen_entities:
+                    seen_entities.add(entity_key)
+                    all_entities.append(entity)
+            
+            logger.info(f"分块 {i+1} 提取实体: {len(entities)} 个")
+        
+        logger.info(f"DeepSeek NER 提取完成: 总实体数={len(all_entities)}")
+        return all_entities
+    
+    async def check_health(self) -> bool:
+        """
+        检查 DeepSeek API 是否可用
+        """
+        try:
+            url = f"{self.base_url}/v1/models"
+            headers = {
+                "Authorization": f"Bearer {self.api_key}"
+            }
+            async with httpx.AsyncClient(timeout=10) as client:
+                response = await client.get(url, headers=headers)
+                return response.status_code == 200
+        except Exception:
+            return False
+
+
+# 创建单例
+deepseek_service = DeepSeekService()

python-services/ner-service/app/services/ner_service.py

@@ -45,6 +45,8 @@ class NerService:
             return await self._extract_by_rules(text, entity_types)
         elif self.model_type == "ollama":
             return await self._extract_by_ollama(text, entity_types)
+        elif self.model_type == "deepseek":
+            return await self._extract_by_deepseek(text, entity_types)
         elif self.model_type == "spacy":
             return await self._extract_by_spacy(text, entity_types)
         elif self.model_type == "transformers":
@@ -204,6 +206,21 @@ class NerService:
             logger.error(f"Ollama NER 失败: {e}，回退到规则模式")
             return await self._extract_by_rules(text, entity_types)
     
+    async def _extract_by_deepseek(
+        self, 
+        text: str, 
+        entity_types: Optional[List[str]] = None
+    ) -> List[EntityInfo]:
+        """
+        使用阿里云百炼 DeepSeek API 进行 NER 提取
+        """
+        try:
+            from .deepseek_service import deepseek_service
+            return await deepseek_service.extract_entities(text, entity_types)
+        except Exception as e:
+            logger.error(f"DeepSeek NER 失败: {e}，回退到规则模式")
+            return await self._extract_by_rules(text, entity_types)
+    
     async def _extract_by_spacy(
         self, 
         text: str,