embedding 模型独立为api

embedding/__init__.py

@@ -0,0 +1,17 @@
+"""
+Embedding Package
+提供文本编码和语义搜索功能
+"""
+
+from .manager import get_cache_manager, get_model_manager
+from .search_service import get_search_service
+from .embedding import embed_document, semantic_search, split_document_by_pages
+
+__all__ = [
+    'get_cache_manager',
+    'get_model_manager', 
+    'get_search_service',
+    'embed_document',
+    'semantic_search', 
+    'split_document_by_pages'
+]

embedding/embedding.py

@@ -1,42 +1,51 @@
 import pickle
 import re
 import numpy as np
-from sentence_transformers import SentenceTransformer, util
+import os
+from typing import Optional
+import requests
+import asyncio
 
-# 延迟加载模型
+def encode_texts_via_api(texts, batch_size=32):
-embedder = None
+    """通过 API 接口编码文本"""
-
+    if not texts:
-def get_model(model_name_or_path='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'):
+        return np.array([])
-    """获取模型实例（延迟加载）
     
-    Args:
+    try:
-        model_name_or_path (str): 模型名称或本地路径
+        # FastAPI 服务地址
-                                - 可以是 HuggingFace 模型名称
+        fastapi_url = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
-                                - 可以是本地模型路径
+        api_endpoint = f"{fastapi_url}/api/v1/embedding/encode"
-    """
-    global embedder
-    if embedder is None:
-        print("正在加载模型...")
-        print(f"模型路径: {model_name_or_path}")
         
-        # 检查是否是本地路径
+        # 调用编码接口
-        import os
+        request_data = {
-        if os.path.exists(model_name_or_path):
+            "texts": texts,
-            print("使用本地模型")
+            "batch_size": batch_size
+        }
+        
+        response = requests.post(
+            api_endpoint,
+            json=request_data,
+            timeout=60  # 增加超时时间
+        )
+        
+        if response.status_code == 200:
+            result_data = response.json()
+            
+            if result_data.get("success"):
+                embeddings_list = result_data.get("embeddings", [])
+                print(f"API编码成功，处理了 {len(texts)} 个文本，embedding维度: {len(embeddings_list[0]) if embeddings_list else 0}")
+                return np.array(embeddings_list)
+            else:
+                error_msg = result_data.get('error', '未知错误')
+                print(f"API编码失败: {error_msg}")
+                raise Exception(f"API编码失败: {error_msg}")
         else:
-            print("使用 HuggingFace 模型")
+            print(f"API请求失败: {response.status_code} - {response.text}")
-        
+            raise Exception(f"API请求失败: {response.status_code}")
-        # 从环境变量获取设备配置，默认为 CPU
+            
-        device = os.environ.get('SENTENCE_TRANSFORMER_DEVICE', 'cpu')
+    except Exception as e:
-        if device not in ['cpu', 'cuda', 'mps']:
+        print(f"API编码异常: {e}")
-            print(f"警告: 不支持的设备类型 '{device}'，使用默认 CPU")
+        raise
-            device = 'cpu'
-        
-        print(f"使用设备: {device}")
-        embedder = SentenceTransformer(model_name_or_path, device=device)
-        
-        print("模型加载完成")
-    return embedder
 
 def clean_text(text):
     """
@@ -192,23 +201,16 @@ def embed_document(input_file='document.txt', output_file='embedding.pkl',
         
         print(f"正在处理 {len(chunks)} 个内容块...")
         
-        # 在函数内部设置模型路径
+        # 使用API接口进行编码
-        local_model_path = "./models/paraphrase-multilingual-MiniLM-L12-v2"
+        print("使用API接口进行编码...")
-        import os
+        chunk_embeddings = encode_texts_via_api(chunks, batch_size=32)
-        if os.path.exists(local_model_path):
-            model_path = local_model_path
-        else:
-            model_path = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
-        
-        model = get_model(model_path)
-        chunk_embeddings = model.encode(chunks, convert_to_tensor=True)
         
         embedding_data = {
             'chunks': chunks,
             'embeddings': chunk_embeddings,
             'chunking_strategy': chunking_strategy,
             'chunking_params': chunking_params,
-            'model_path': model_path
+            'model_path': 'api_service'
         }
         
         with open(output_file, 'wb') as f:
@@ -254,18 +256,28 @@ def semantic_search(user_query, embeddings_file='embedding.pkl', top_k=20):
             chunking_strategy = 'line'
             content_type = "句子"
         
-        # 从embedding_data中获取模型路径（如果有的话）
+        # 使用API接口进行编码
-        model_path = embedding_data.get('model_path', 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+        print("使用API接口进行查询编码...")
-        model = get_model(model_path)
+        query_embeddings = encode_texts_via_api([user_query], batch_size=1)
-        query_embedding = model.encode(user_query, convert_to_tensor=True)
+        query_embedding = query_embeddings[0] if len(query_embeddings) > 0 else np.array([])
         
-        cos_scores = util.cos_sim(query_embedding, chunk_embeddings)[0]
+        # 计算相似度
-        
+        if len(chunk_embeddings.shape) > 1:
-        # 处理 GPU/CPU 环境下的 tensor 转换
+            cos_scores = np.dot(chunk_embeddings, query_embedding) / (
-        if cos_scores.is_cuda:
+                np.linalg.norm(chunk_embeddings, axis=1) * np.linalg.norm(query_embedding) + 1e-8
-            cos_scores_np = cos_scores.cpu().numpy()
+            )
         else:
-            cos_scores_np = cos_scores.numpy()
+            cos_scores = [0.0]  # 兼容性处理
+        
+        # 处理不同格式下的 cos_scores
+        if isinstance(cos_scores, np.ndarray):
+            cos_scores_np = cos_scores
+        else:
+            # PyTorch tensor
+            if hasattr(cos_scores, 'is_cuda') and cos_scores.is_cuda:
+                cos_scores_np = cos_scores.cpu().numpy()
+            else:
+                cos_scores_np = cos_scores.numpy()
         
         top_results = np.argsort(-cos_scores_np)[:top_k]
         
@@ -273,7 +285,7 @@ def semantic_search(user_query, embeddings_file='embedding.pkl', top_k=20):
         print(f"\n与查询最相关的 {top_k} 个{content_type} (分块策略: {chunking_strategy}):")
         for i, idx in enumerate(top_results):
             chunk = chunks[idx]
-            score = cos_scores[idx].item()
+            score = cos_scores_np[idx]
             results.append((chunk, score))
             # 显示内容预览（如果内容太长）
             preview = chunk[:100] + "..." if len(chunk) > 100 else chunk

embedding/manager.py

@@ -0,0 +1,333 @@
+#!/usr/bin/env python3
+"""
+模型池管理器和缓存系统
+支持高并发的 embedding 检索服务
+"""
+
+import os
+import asyncio
+import time
+import pickle
+import hashlib
+import logging
+from typing import Dict, List, Optional, Any, Tuple
+from dataclasses import dataclass
+from collections import OrderedDict
+import threading
+import psutil
+import numpy as np
+
+from sentence_transformers import SentenceTransformer
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CacheEntry:
+    """缓存条目"""
+    embeddings: np.ndarray
+    chunks: List[str]
+    chunking_strategy: str
+    chunking_params: Dict[str, Any]
+    model_path: str
+    file_path: str
+    file_mtime: float  # 文件修改时间
+    access_count: int  # 访问次数
+    last_access_time: float  # 最后访问时间
+    load_time: float  # 加载时间
+    memory_size: int  # 占用内存大小（字节）
+
+
+class EmbeddingCacheManager:
+    """Embedding 数据缓存管理器"""
+    
+    def __init__(self, max_cache_size: int = 5, max_memory_mb: int = 1024):
+        self.max_cache_size = max_cache_size  # 最大缓存条目数
+        self.max_memory_bytes = max_memory_mb * 1024 * 1024  # 最大内存使用量
+        self._cache: OrderedDict[str, CacheEntry] = OrderedDict()
+        self._lock = threading.RLock()
+        self._current_memory_usage = 0
+        
+        logger.info(f"EmbeddingCacheManager 初始化: max_size={max_cache_size}, max_memory={max_memory_mb}MB")
+    
+    def _get_file_key(self, file_path: str) -> str:
+        """生成文件缓存键"""
+        # 使用绝对路径和文件修改时间生成唯一键
+        try:
+            abs_path = os.path.abspath(file_path)
+            mtime = os.path.getmtime(abs_path)
+            key_data = f"{abs_path}:{mtime}"
+            return hashlib.md5(key_data.encode()).hexdigest()
+        except Exception as e:
+            logger.warning(f"生成文件键失败: {file_path}, {e}")
+            return hashlib.md5(file_path.encode()).hexdigest()
+    
+    def _estimate_memory_size(self, embeddings: np.ndarray, chunks: List[str]) -> int:
+        """估算数据内存占用"""
+        try:
+            embeddings_size = embeddings.nbytes
+            chunks_size = sum(len(chunk.encode('utf-8')) for chunk in chunks)
+            overhead = 1024 * 1024  # 1MB 开销
+            return embeddings_size + chunks_size + overhead
+        except Exception:
+            return 100 * 1024 * 1024  # 默认100MB
+    
+    def _cleanup_cache(self):
+        """清理缓存以释放内存"""
+        with self._lock:
+            # 按访问时间和次数排序，清理最少使用的条目
+            entries = list(self._cache.items())
+            
+            # 计算需要清理的条目
+            to_remove = []
+            current_size = len(self._cache)
+            
+            # 如果缓存条目超限，清理最老的条目
+            if current_size > self.max_cache_size:
+                to_remove.extend(entries[:current_size - self.max_cache_size])
+            
+            # 如果内存超限，按LRU策略清理
+            if self._current_memory_usage > self.max_memory_bytes:
+                # 按最后访问时间排序
+                entries.sort(key=lambda x: x[1].last_access_time)
+                
+                accumulated_size = 0
+                for key, entry in entries:
+                    if accumulated_size >= self._current_memory_usage - self.max_memory_bytes:
+                        break
+                    to_remove.append((key, entry))
+                    accumulated_size += entry.memory_size
+            
+            # 执行清理
+            for key, entry in to_remove:
+                if key in self._cache:
+                    del self._cache[key]
+                    self._current_memory_usage -= entry.memory_size
+                    logger.info(f"清理缓存条目: {key} ({entry.memory_size / 1024 / 1024:.1f}MB)")
+    
+    async def load_embedding_data(self, file_path: str) -> Optional[CacheEntry]:
+        """加载 embedding 数据"""
+        cache_key = self._get_file_key(file_path)
+        
+        # 检查缓存
+        with self._lock:
+            if cache_key in self._cache:
+                entry = self._cache[cache_key]
+                entry.access_count += 1
+                entry.last_access_time = time.time()
+                # 移动到末尾（最近使用）
+                self._cache.move_to_end(cache_key)
+                logger.info(f"缓存命中: {file_path}")
+                return entry
+        
+        # 缓存未命中，异步加载数据
+        try:
+            start_time = time.time()
+            
+            # 检查文件是否存在
+            if not os.path.exists(file_path):
+                logger.error(f"文件不存在: {file_path}")
+                return None
+            
+            # 加载 embedding 数据
+            with open(file_path, 'rb') as f:
+                embedding_data = pickle.load(f)
+            
+            # 兼容新旧数据结构
+            if 'chunks' in embedding_data:
+                chunks = embedding_data['chunks']
+                embeddings = embedding_data['embeddings']
+                chunking_strategy = embedding_data.get('chunking_strategy', 'unknown')
+                chunking_params = embedding_data.get('chunking_params', {})
+                model_path = embedding_data.get('model_path', 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
+            else:
+                chunks = embedding_data['sentences']
+                embeddings = embedding_data['embeddings']
+                chunking_strategy = 'line'
+                chunking_params = {}
+                model_path = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'
+            
+            # 确保 embeddings 是 numpy 数组
+            if hasattr(embeddings, 'cpu'):
+                embeddings = embeddings.cpu().numpy()
+            elif hasattr(embeddings, 'numpy'):
+                embeddings = embeddings.numpy()
+            elif not isinstance(embeddings, np.ndarray):
+                embeddings = np.array(embeddings)
+            
+            # 创建缓存条目
+            load_time = time.time() - start_time
+            file_mtime = os.path.getmtime(file_path)
+            memory_size = self._estimate_memory_size(embeddings, chunks)
+            
+            entry = CacheEntry(
+                embeddings=embeddings,
+                chunks=chunks,
+                chunking_strategy=chunking_strategy,
+                chunking_params=chunking_params,
+                model_path=model_path,
+                file_path=file_path,
+                file_mtime=file_mtime,
+                access_count=1,
+                last_access_time=time.time(),
+                load_time=load_time,
+                memory_size=memory_size
+            )
+            
+            # 添加到缓存
+            with self._lock:
+                self._cache[cache_key] = entry
+                self._current_memory_usage += memory_size
+                
+                # 清理缓存
+                self._cleanup_cache()
+            
+            logger.info(f"加载完成: {file_path} ({memory_size / 1024 / 1024:.1f}MB, {load_time:.2f}s)")
+            return entry
+            
+        except Exception as e:
+            logger.error(f"加载 embedding 数据失败: {file_path}, {e}")
+            return None
+    
+    def get_cache_stats(self) -> Dict[str, Any]:
+        """获取缓存统计信息"""
+        with self._lock:
+            return {
+                "cache_size": len(self._cache),
+                "max_cache_size": self.max_cache_size,
+                "memory_usage_mb": self._current_memory_usage / 1024 / 1024,
+                "max_memory_mb": self.max_memory_bytes / 1024 / 1024,
+                "memory_usage_percent": (self._current_memory_usage / self.max_memory_bytes) * 100,
+                "entries": [
+                    {
+                        "file_path": entry.file_path,
+                        "access_count": entry.access_count,
+                        "last_access_time": entry.last_access_time,
+                        "memory_size_mb": entry.memory_size / 1024 / 1024
+                    }
+                    for entry in self._cache.values()
+                ]
+            }
+    
+    def clear_cache(self):
+        """清空缓存"""
+        with self._lock:
+            cleared_count = len(self._cache)
+            cleared_memory = self._current_memory_usage
+            self._cache.clear()
+            self._current_memory_usage = 0
+            logger.info(f"清空缓存: {cleared_count} 个条目, {cleared_memory / 1024 / 1024:.1f}MB")
+
+
+class GlobalModelManager:
+    """全局模型管理器"""
+    
+    def __init__(self, model_name: str = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'):
+        self.model_name = model_name
+        self.local_model_path = "./models/paraphrase-multilingual-MiniLM-L12-v2"
+        self._model: Optional[SentenceTransformer] = None
+        self._lock = asyncio.Lock()
+        self._load_time = 0
+        self._device = 'cpu'
+        
+        logger.info(f"GlobalModelManager 初始化: {model_name}")
+    
+    async def get_model(self) -> SentenceTransformer:
+        """获取模型实例（延迟加载）"""
+        if self._model is not None:
+            return self._model
+        
+        async with self._lock:
+            # 双重检查
+            if self._model is not None:
+                return self._model
+            
+            try:
+                start_time = time.time()
+                
+                # 检查本地模型
+                model_path = self.local_model_path if os.path.exists(self.local_model_path) else self.model_name
+                
+                # 获取设备配置
+                self._device = os.environ.get('SENTENCE_TRANSFORMER_DEVICE', 'cpu')
+                if self._device not in ['cpu', 'cuda', 'mps']:
+                    self._device = 'cpu'
+                
+                logger.info(f"加载模型: {model_path} (device: {self._device})")
+                
+                # 在事件循环中运行阻塞操作
+                loop = asyncio.get_event_loop()
+                self._model = await loop.run_in_executor(
+                    None, 
+                    lambda: SentenceTransformer(model_path, device=self._device)
+                )
+                
+                self._load_time = time.time() - start_time
+                logger.info(f"模型加载完成: {self._load_time:.2f}s")
+                
+                return self._model
+                
+            except Exception as e:
+                logger.error(f"模型加载失败: {e}")
+                raise
+    
+    async def encode_texts(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
+        """编码文本为向量"""
+        if not texts:
+            return np.array([])
+        
+        model = await self.get_model()
+        
+        try:
+            # 在事件循环中运行阻塞操作
+            loop = asyncio.get_event_loop()
+            embeddings = await loop.run_in_executor(
+                None,
+                lambda: model.encode(texts, batch_size=batch_size, show_progress_bar=False)
+            )
+            
+            # 确保返回 numpy 数组
+            if hasattr(embeddings, 'cpu'):
+                embeddings = embeddings.cpu().numpy()
+            elif hasattr(embeddings, 'numpy'):
+                embeddings = embeddings.numpy()
+            elif not isinstance(embeddings, np.ndarray):
+                embeddings = np.array(embeddings)
+            
+            return embeddings
+            
+        except Exception as e:
+            logger.error(f"文本编码失败: {e}")
+            raise
+    
+    def get_model_info(self) -> Dict[str, Any]:
+        """获取模型信息"""
+        return {
+            "model_name": self.model_name,
+            "local_model_path": self.local_model_path,
+            "device": self._device,
+            "is_loaded": self._model is not None,
+            "load_time": self._load_time
+        }
+
+
+# 全局实例
+_cache_manager = None
+_model_manager = None
+
+def get_cache_manager() -> EmbeddingCacheManager:
+    """获取缓存管理器实例"""
+    global _cache_manager
+    if _cache_manager is None:
+        max_cache_size = int(os.getenv("EMBEDDING_MAX_CACHE_SIZE", "5"))
+        max_memory_mb = int(os.getenv("EMBEDDING_MAX_MEMORY_MB", "1024"))
+        _cache_manager = EmbeddingCacheManager(max_cache_size, max_memory_mb)
+    return _cache_manager
+
+def get_model_manager() -> GlobalModelManager:
+    """获取模型管理器实例"""
+    global _model_manager
+    if _model_manager is None:
+        model_name = os.getenv("SENTENCE_TRANSFORMER_MODEL", "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+        _model_manager = GlobalModelManager(model_name)
+    return _model_manager

embedding/model_client.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+"""
+模型服务客户端
+提供与 model_server.py 交互的客户端接口
+"""
+
+import os
+import asyncio
+import logging
+from typing import List, Union, Dict, Any
+import numpy as np
+import aiohttp
+import json
+from sentence_transformers import util
+
+logger = logging.getLogger(__name__)
+
+class ModelClient:
+    """模型服务客户端"""
+    
+    def __init__(self, base_url: str = "http://127.0.0.1:8000"):
+        self.base_url = base_url.rstrip('/')
+        self.session = None
+        
+    async def __aenter__(self):
+        self.session = aiohttp.ClientSession(
+            timeout=aiohttp.ClientTimeout(total=300)  # 5分钟超时
+        )
+        return self
+    
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        if self.session:
+            await self.session.close()
+    
+    async def health_check(self) -> Dict[str, Any]:
+        """检查服务健康状态"""
+        try:
+            async with self.session.get(f"{self.base_url}/health") as response:
+                return await response.json()
+        except Exception as e:
+            logger.error(f"健康检查失败: {e}")
+            return {"status": "unavailable", "error": str(e)}
+    
+    async def load_model(self) -> bool:
+        """预加载模型"""
+        try:
+            async with self.session.post(f"{self.base_url}/load_model") as response:
+                result = await response.json()
+                return response.status == 200
+        except Exception as e:
+            logger.error(f"模型加载失败: {e}")
+            return False
+    
+    async def encode_texts(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
+        """编码文本为向量"""
+        if not texts:
+            return np.array([])
+        
+        try:
+            data = {
+                "texts": texts,
+                "batch_size": batch_size
+            }
+            
+            async with self.session.post(
+                f"{self.base_url}/encode", 
+                json=data
+            ) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    raise Exception(f"编码失败: {error_text}")
+                
+                result = await response.json()
+                return np.array(result["embeddings"])
+                
+        except Exception as e:
+            logger.error(f"文本编码失败: {e}")
+            raise
+    
+    async def compute_similarity(self, text1: str, text2: str) -> float:
+        """计算两个文本的相似度"""
+        try:
+            data = {
+                "text1": text1,
+                "text2": text2
+            }
+            
+            async with self.session.post(
+                f"{self.base_url}/similarity", 
+                json=data
+            ) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    raise Exception(f"相似度计算失败: {error_text}")
+                
+                result = await response.json()
+                return result["similarity_score"]
+                
+        except Exception as e:
+            logger.error(f"相似度计算失败: {e}")
+            raise
+
+# 同步版本的客户端包装器
+class SyncModelClient:
+    """同步模型客户端"""
+    
+    def __init__(self, base_url: str = "http://127.0.0.1:8000"):
+        self.base_url = base_url
+        self._async_client = ModelClient(base_url)
+    
+    def _run_async(self, coro):
+        """运行异步协程"""
+        try:
+            loop = asyncio.get_event_loop()
+        except RuntimeError:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+        
+        return loop.run_until_complete(coro)
+    
+    def encode_texts(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
+        """同步版本的文本编码"""
+        async def _encode():
+            async with self._async_client as client:
+                return await client.encode_texts(texts, batch_size)
+        
+        return self._run_async(_encode())
+    
+    def compute_similarity(self, text1: str, text2: str) -> float:
+        """同步版本的相似度计算"""
+        async def _similarity():
+            async with self._async_client as client:
+                return await client.compute_similarity(text1, text2)
+        
+        return self._run_async(_similarity())
+    
+    def health_check(self) -> Dict[str, Any]:
+        """同步版本的健康检查"""
+        async def _health():
+            async with self._async_client as client:
+                return await client.health_check()
+        
+        return self._run_async(_health())
+    
+    def load_model(self) -> bool:
+        """同步版本的模型加载"""
+        async def _load():
+            async with self._async_client as client:
+                return await client.load_model()
+        
+        return self._run_async(_load())
+
+# 全局客户端实例（单例模式）
+_client_instance = None
+
+def get_model_client(base_url: str = None) -> SyncModelClient:
+    """获取模型客户端实例（单例）"""
+    global _client_instance
+    
+    if _client_instance is None:
+        url = base_url or os.environ.get('MODEL_SERVER_URL', 'http://127.0.0.1:8000')
+        _client_instance = SyncModelClient(url)
+    
+    return _client_instance
+
+# 便捷函数
+def encode_texts(texts: List[str], batch_size: int = 32) -> np.ndarray:
+    """便捷的文本编码函数"""
+    client = get_model_client()
+    return client.encode_texts(texts, batch_size)
+
+def compute_similarity(text1: str, text2: str) -> float:
+    """便捷的相似度计算函数"""
+    client = get_model_client()
+    return client.compute_similarity(text1, text2)
+
+def is_service_available() -> bool:
+    """检查模型服务是否可用"""
+    client = get_model_client()
+    health = client.health_check()
+    return health.get("status") == "running" or health.get("status") == "ready"

embedding/model_server.py

@@ -0,0 +1,240 @@
+#!/usr/bin/env python3
+"""
+独立的模型服务器
+提供统一的 embedding 服务，减少内存占用
+"""
+
+import os
+import asyncio
+import logging
+from typing import List, Dict, Any
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from pydantic import BaseModel
+
+# 配置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# 全局模型实例
+model = None
+model_config = {
+    "model_name": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+    "local_model_path": "./models/paraphrase-multilingual-MiniLM-L12-v2",
+    "device": "cpu"
+}
+
+# Pydantic 模型
+class TextRequest(BaseModel):
+    texts: List[str]
+    batch_size: int = 32
+
+class SimilarityRequest(BaseModel):
+    text1: str
+    text2: str
+
+class HealthResponse(BaseModel):
+    status: str
+    model_loaded: bool
+    model_path: str
+    device: str
+
+class EmbeddingResponse(BaseModel):
+    embeddings: List[List[float]]
+    shape: List[int]
+
+class SimilarityResponse(BaseModel):
+    similarity_score: float
+
+class ModelServer:
+    def __init__(self):
+        self.model = None
+        self.model_loaded = False
+        
+    async def load_model(self):
+        """延迟加载模型"""
+        if self.model_loaded:
+            return
+            
+        logger.info("正在加载模型...")
+        
+        # 检查本地模型是否存在
+        if os.path.exists(model_config["local_model_path"]):
+            model_path = model_config["local_model_path"]
+            logger.info(f"使用本地模型: {model_path}")
+        else:
+            model_path = model_config["model_name"]
+            logger.info(f"使用 HuggingFace 模型: {model_path}")
+        
+        # 从环境变量获取设备配置
+        device = os.environ.get('SENTENCE_TRANSFORMER_DEVICE', model_config["device"])
+        if device not in ['cpu', 'cuda', 'mps']:
+            logger.warning(f"不支持的设备类型 '{device}'，使用默认 CPU")
+            device = 'cpu'
+        
+        logger.info(f"使用设备: {device}")
+        
+        try:
+            self.model = SentenceTransformer(model_path, device=device)
+            self.model_loaded = True
+            model_config["device"] = device
+            model_config["current_model_path"] = model_path
+            logger.info("模型加载完成")
+        except Exception as e:
+            logger.error(f"模型加载失败: {e}")
+            raise HTTPException(status_code=500, detail=f"模型加载失败: {str(e)}")
+    
+    async def encode_texts(self, texts: List[str], batch_size: int = 32) -> np.ndarray:
+        """编码文本为向量"""
+        if not texts:
+            return np.array([])
+        
+        await self.load_model()
+        
+        try:
+            embeddings = self.model.encode(
+                texts, 
+                batch_size=batch_size,
+                convert_to_tensor=True,
+                show_progress_bar=False
+            )
+            
+            # 转换为 CPU numpy 数组
+            if embeddings.is_cuda:
+                embeddings = embeddings.cpu().numpy()
+            else:
+                embeddings = embeddings.numpy()
+                
+            return embeddings
+        except Exception as e:
+            logger.error(f"编码失败: {e}")
+            raise HTTPException(status_code=500, detail=f"编码失败: {str(e)}")
+    
+    async def compute_similarity(self, text1: str, text2: str) -> float:
+        """计算两个文本的相似度"""
+        if not text1 or not text2:
+            raise HTTPException(status_code=400, detail="文本不能为空")
+        
+        await self.load_model()
+        
+        try:
+            embeddings = self.model.encode([text1, text2], convert_to_tensor=True)
+            from sentence_transformers import util
+            similarity = util.cos_sim(embeddings[0:1], embeddings[1:2])[0][0]
+            return similarity.item()
+        except Exception as e:
+            logger.error(f"相似度计算失败: {e}")
+            raise HTTPException(status_code=500, detail=f"相似度计算失败: {str(e)}")
+
+# 创建服务器实例
+server = ModelServer()
+
+# 创建 FastAPI 应用
+app = FastAPI(
+    title="Embedding Model Server",
+    description="统一的句子嵌入和相似度计算服务",
+    version="1.0.0"
+)
+
+# 添加 CORS 中间件
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 健康检查
+@app.get("/health", response_model=HealthResponse)
+async def health_check():
+    """健康检查"""
+    try:
+        if not server.model_loaded:
+            return HealthResponse(
+                status="ready",
+                model_loaded=False,
+                model_path="",
+                device=model_config["device"]
+            )
+        
+        return HealthResponse(
+            status="running",
+            model_loaded=True,
+            model_path=model_config.get("current_model_path", ""),
+            device=model_config["device"]
+        )
+    except Exception as e:
+        logger.error(f"健康检查失败: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 预加载模型
+@app.post("/load_model")
+async def preload_model():
+    """预加载模型"""
+    await server.load_model()
+    return {"message": "模型加载完成"}
+
+# 文本嵌入
+@app.post("/encode", response_model=EmbeddingResponse)
+async def encode_texts(request: TextRequest):
+    """编码文本为向量"""
+    try:
+        embeddings = await server.encode_texts(request.texts, request.batch_size)
+        return EmbeddingResponse(
+            embeddings=embeddings.tolist(),
+            shape=list(embeddings.shape)
+        )
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 相似度计算
+@app.post("/similarity", response_model=SimilarityResponse)
+async def compute_similarity(request: SimilarityRequest):
+    """计算两个文本的相似度"""
+    try:
+        similarity = await server.compute_similarity(request.text1, request.text2)
+        return SimilarityResponse(similarity_score=similarity)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 批量编码（用于文档处理）
+@app.post("/batch_encode")
+async def batch_encode(texts: List[str], batch_size: int = 64):
+    """批量编码大量文本"""
+    if not texts:
+        return {"embeddings": [], "shape": [0, 0]}
+    
+    try:
+        embeddings = await server.encode_texts(texts, batch_size)
+        return {
+            "embeddings": embeddings.tolist(),
+            "shape": list(embeddings.shape)
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="启动嵌入模型服务器")
+    parser.add_argument("--host", default="127.0.0.1", help="服务器地址")
+    parser.add_argument("--port", type=int, default=8000, help="服务器端口")
+    parser.add_argument("--workers", type=int, default=1, help="工作进程数")
+    parser.add_argument("--reload", action="store_true", help="开发模式自动重载")
+    
+    args = parser.parse_args()
+    
+    logger.info(f"启动模型服务器: http://{args.host}:{args.port}")
+    
+    uvicorn.run(
+        "model_server:app",
+        host=args.host,
+        port=args.port,
+        workers=args.workers,
+        reload=args.reload,
+        log_level="info"
+    )

embedding/search_service.py

@@ -0,0 +1,259 @@
+#!/usr/bin/env python3
+"""
+语义检索服务
+支持高并发的语义搜索功能
+"""
+
+import asyncio
+import time
+import logging
+from typing import List, Dict, Any, Optional, Tuple
+import numpy as np
+
+from .manager import get_cache_manager, get_model_manager, CacheEntry
+
+logger = logging.getLogger(__name__)
+
+
+class SemanticSearchService:
+    """语义检索服务"""
+    
+    def __init__(self):
+        self.cache_manager = get_cache_manager()
+        self.model_manager = get_model_manager()
+        
+    async def semantic_search(
+        self, 
+        embedding_file: str, 
+        query: str, 
+        top_k: int = 20,
+        min_score: float = 0.0
+    ) -> Dict[str, Any]:
+        """
+        执行语义搜索
+        
+        Args:
+            embedding_file: embedding.pkl 文件路径
+            query: 查询关键词
+            top_k: 返回结果数量
+            min_score: 最小相似度阈值
+            
+        Returns:
+            搜索结果
+        """
+        start_time = time.time()
+        
+        try:
+            # 验证输入参数
+            if not embedding_file:
+                return {
+                    "success": False,
+                    "error": "embedding_file 参数不能为空"
+                }
+            
+            if not query or not query.strip():
+                return {
+                    "success": False,
+                    "error": "query 参数不能为空"
+                }
+            
+            query = query.strip()
+            
+            # 加载 embedding 数据
+            cache_entry = await self.cache_manager.load_embedding_data(embedding_file)
+            if cache_entry is None:
+                return {
+                    "success": False,
+                    "error": f"无法加载 embedding 文件: {embedding_file}"
+                }
+            
+            # 编码查询
+            query_embeddings = await self.model_manager.encode_texts([query], batch_size=1)
+            if len(query_embeddings) == 0:
+                return {
+                    "success": False,
+                    "error": "查询编码失败"
+                }
+            
+            query_embedding = query_embeddings[0]
+            
+            # 计算相似度
+            similarities = self._compute_similarities(query_embedding, cache_entry.embeddings)
+            
+            # 获取 top_k 结果
+            top_results = self._get_top_results(
+                similarities, 
+                cache_entry.chunks, 
+                top_k, 
+                min_score
+            )
+            
+            processing_time = time.time() - start_time
+            
+            return {
+                "success": True,
+                "query": query,
+                "embedding_file": embedding_file,
+                "top_k": top_k,
+                "processing_time": processing_time,
+                "chunking_strategy": cache_entry.chunking_strategy,
+                "total_chunks": len(cache_entry.chunks),
+                "results": top_results,
+                "cache_stats": {
+                    "access_count": cache_entry.access_count,
+                    "load_time": cache_entry.load_time
+                }
+            }
+            
+        except Exception as e:
+            logger.error(f"语义搜索失败: {embedding_file}, {query}, {e}")
+            return {
+                "success": False,
+                "error": f"搜索失败: {str(e)}"
+            }
+    
+    def _compute_similarities(self, query_embedding: np.ndarray, embeddings: np.ndarray) -> np.ndarray:
+        """计算相似度分数"""
+        try:
+            # 确保数据类型和形状正确
+            if embeddings.ndim == 1:
+                embeddings = embeddings.reshape(1, -1)
+            
+            # 使用余弦相似度
+            query_norm = np.linalg.norm(query_embedding)
+            embeddings_norm = np.linalg.norm(embeddings, axis=1)
+            
+            # 避免除零错误
+            if query_norm == 0:
+                query_norm = 1e-8
+            embeddings_norm = np.where(embeddings_norm == 0, 1e-8, embeddings_norm)
+            
+            # 计算余弦相似度
+            similarities = np.dot(embeddings, query_embedding) / (embeddings_norm * query_norm)
+            
+            # 确保结果在合理范围内
+            similarities = np.clip(similarities, -1.0, 1.0)
+            
+            return similarities
+            
+        except Exception as e:
+            logger.error(f"相似度计算失败: {e}")
+            return np.array([])
+    
+    def _get_top_results(
+        self, 
+        similarities: np.ndarray, 
+        chunks: List[str], 
+        top_k: int, 
+        min_score: float
+    ) -> List[Dict[str, Any]]:
+        """获取 top_k 结果"""
+        try:
+            if len(similarities) == 0:
+                return []
+            
+            # 获取排序后的索引
+            top_indices = np.argsort(-similarities)[:top_k]
+            
+            results = []
+            for rank, idx in enumerate(top_indices):
+                score = similarities[idx]
+                
+                # 过滤低分结果
+                if score < min_score:
+                    continue
+                
+                chunk = chunks[idx]
+                
+                # 创建结果条目
+                result = {
+                    "rank": rank + 1,
+                    "score": float(score),
+                    "content": chunk,
+                    "content_preview": self._get_preview(chunk)
+                }
+                
+                results.append(result)
+            
+            return results
+            
+        except Exception as e:
+            logger.error(f"获取 top_k 结果失败: {e}")
+            return []
+    
+    def _get_preview(self, content: str, max_length: int = 200) -> str:
+        """获取内容预览"""
+        if not content:
+            return ""
+        
+        # 清理内容
+        preview = content.replace('\n', ' ').strip()
+        
+        # 截断
+        if len(preview) > max_length:
+            preview = preview[:max_length] + "..."
+        
+        return preview
+    
+    async def batch_search(
+        self, 
+        requests: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
+        """
+        批量搜索
+        
+        Args:
+            requests: 搜索请求列表，每个请求包含 embedding_file, query, top_k
+            
+        Returns:
+            搜索结果列表
+        """
+        if not requests:
+            return []
+        
+        # 并发执行搜索
+        tasks = []
+        for req in requests:
+            embedding_file = req.get("embedding_file", "")
+            query = req.get("query", "")
+            top_k = req.get("top_k", 20)
+            min_score = req.get("min_score", 0.0)
+            
+            task = self.semantic_search(embedding_file, query, top_k, min_score)
+            tasks.append(task)
+        
+        # 等待所有任务完成
+        results = await asyncio.gather(*tasks, return_exceptions=True)
+        
+        # 处理异常
+        processed_results = []
+        for i, result in enumerate(results):
+            if isinstance(result, Exception):
+                processed_results.append({
+                    "success": False,
+                    "error": f"搜索异常: {str(result)}",
+                    "request_index": i
+                })
+            else:
+                result["request_index"] = i
+                processed_results.append(result)
+        
+        return processed_results
+    
+    def get_service_stats(self) -> Dict[str, Any]:
+        """获取服务统计信息"""
+        return {
+            "cache_manager_stats": self.cache_manager.get_cache_stats(),
+            "model_manager_info": self.model_manager.get_model_info()
+        }
+
+
+# 全局实例
+_search_service = None
+
+def get_search_service() -> SemanticSearchService:
+    """获取搜索服务实例"""
+    global _search_service
+    if _search_service is None:
+        _search_service = SemanticSearchService()
+    return _search_service

fastapi_app.py

@@ -23,6 +23,8 @@ from file_manager_api import router as file_manager_router
 from qwen_agent.llm.schema import ASSISTANT, FUNCTION
 from pydantic import BaseModel, Field
 
+# 导入语义检索服务
+from embedding import get_search_service
 
 # Import utility modules
 from utils import (
@@ -126,6 +128,56 @@ def get_versioned_filename(upload_dir: str, name_without_ext: str, file_extensio
     return versioned_filename, next_version
 
 
+# Models are now imported from utils module
+
+
+# 语义检索请求模型
+class SemanticSearchRequest(BaseModel):
+    embedding_file: str = Field(..., description="embedding.pkl 文件路径")
+    query: str = Field(..., description="搜索关键词")
+    top_k: int = Field(default=20, description="返回结果数量", ge=1, le=100)
+    min_score: float = Field(default=0.0, description="最小相似度阈值", ge=0.0, le=1.0)
+
+
+class BatchSearchRequest(BaseModel):
+    requests: List[SemanticSearchRequest] = Field(..., description="搜索请求列表")
+
+
+# 语义检索响应模型
+class SearchResult(BaseModel):
+    rank: int = Field(..., description="排名")
+    score: float = Field(..., description="相似度分数")
+    content: str = Field(..., description="匹配的内容")
+    content_preview: str = Field(..., description="内容预览")
+
+
+class SemanticSearchResponse(BaseModel):
+    success: bool = Field(..., description="是否成功")
+    query: str = Field(..., description="查询关键词")
+    embedding_file: str = Field(..., description="embedding 文件路径")
+    processing_time: float = Field(..., description="处理时间（秒）")
+    total_chunks: int = Field(..., description="总文档块数")
+    chunking_strategy: str = Field(..., description="分块策略")
+    results: List[SearchResult] = Field(..., description="搜索结果")
+    cache_stats: Optional[Dict[str, Any]] = Field(None, description="缓存统计")
+    error: Optional[str] = Field(None, description="错误信息")
+
+
+# 编码请求和响应模型
+class EncodeRequest(BaseModel):
+    texts: List[str] = Field(..., description="要编码的文本列表")
+    batch_size: int = Field(default=32, description="批次大小", ge=1, le=128)
+
+
+class EncodeResponse(BaseModel):
+    success: bool = Field(..., description="是否成功")
+    embeddings: List[List[float]] = Field(..., description="编码结果")
+    shape: List[int] = Field(..., description="embeddings 形状")
+    processing_time: float = Field(..., description="处理时间（秒）")
+    total_texts: int = Field(..., description="总文本数量")
+    error: Optional[str] = Field(None, description="错误信息")
+
+
 # Custom version for qwen-agent messages - keep this function as it's specific to this app
 def get_content_from_messages(messages: List[dict], tool_response: bool = True) -> str:
     """Extract content from qwen-agent messages with special formatting"""
@@ -1536,6 +1588,205 @@ async def reset_files_processing(dataset_id: str):
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"重置文件处理状态失败: {str(e)}")
 
+
+# ============ 语义检索 API 端点 ============
+
+@app.post("/api/v1/semantic-search", response_model=SemanticSearchResponse)
+async def semantic_search(request: SemanticSearchRequest):
+    """
+    语义搜索 API
+
+    Args:
+        request: 包含 embedding_file 和 query 的搜索请求
+
+    Returns:
+        语义搜索结果
+    """
+    try:
+        search_service = get_search_service()
+        result = await search_service.semantic_search(
+            embedding_file=request.embedding_file,
+            query=request.query,
+            top_k=request.top_k,
+            min_score=request.min_score
+        )
+
+        if result["success"]:
+            return SemanticSearchResponse(
+                success=True,
+                query=result["query"],
+                embedding_file=result["embedding_file"],
+                processing_time=result["processing_time"],
+                total_chunks=result["total_chunks"],
+                chunking_strategy=result["chunking_strategy"],
+                results=[
+                    SearchResult(
+                        rank=r["rank"],
+                        score=r["score"],
+                        content=r["content"],
+                        content_preview=r["content_preview"]
+                    )
+                    for r in result["results"]
+                ],
+                cache_stats=result.get("cache_stats")
+            )
+        else:
+            return SemanticSearchResponse(
+                success=False,
+                query=request.query,
+                embedding_file=request.embedding_file,
+                processing_time=0.0,
+                total_chunks=0,
+                chunking_strategy="",
+                results=[],
+                error=result.get("error", "未知错误")
+            )
+
+    except Exception as e:
+        logger.error(f"语义搜索 API 错误: {e}")
+        raise HTTPException(status_code=500, detail=f"语义搜索失败: {str(e)}")
+
+
+@app.post("/api/v1/semantic-search/batch")
+async def batch_semantic_search(request: BatchSearchRequest):
+    """
+    批量语义搜索 API
+
+    Args:
+        request: 包含多个搜索请求的批量请求
+
+    Returns:
+        批量搜索结果
+    """
+    try:
+        search_service = get_search_service()
+
+        # 转换请求格式
+        search_requests = [
+            {
+                "embedding_file": req.embedding_file,
+                "query": req.query,
+                "top_k": req.top_k,
+                "min_score": req.min_score
+            }
+            for req in request.requests
+        ]
+
+        results = await search_service.batch_search(search_requests)
+
+        return {
+            "success": True,
+            "total_requests": len(request.requests),
+            "results": results
+        }
+
+    except Exception as e:
+        logger.error(f"批量语义搜索 API 错误: {e}")
+        raise HTTPException(status_code=500, detail=f"批量语义搜索失败: {str(e)}")
+
+
+@app.get("/api/v1/semantic-search/stats")
+async def get_semantic_search_stats():
+    """
+    获取语义搜索服务统计信息
+
+    Returns:
+        服务统计信息
+    """
+    try:
+        search_service = get_search_service()
+        stats = search_service.get_service_stats()
+
+        return {
+            "success": True,
+            "timestamp": int(time.time()),
+            "stats": stats
+        }
+
+    except Exception as e:
+        logger.error(f"获取语义搜索统计信息失败: {e}")
+        raise HTTPException(status_code=500, detail=f"获取统计信息失败: {str(e)}")
+
+
+@app.post("/api/v1/semantic-search/clear-cache")
+async def clear_semantic_search_cache():
+    """
+    清空语义搜索缓存
+
+    Returns:
+        清理结果
+    """
+    try:
+        from manager import get_cache_manager
+        cache_manager = get_cache_manager()
+        cache_manager.clear_cache()
+
+        return {
+            "success": True,
+            "message": "缓存已清空"
+        }
+
+    except Exception as e:
+        logger.error(f"清空语义搜索缓存失败: {e}")
+        raise HTTPException(status_code=500, detail=f"清空缓存失败: {str(e)}")
+
+
+@app.post("/api/v1/embedding/encode", response_model=EncodeResponse)
+async def encode_texts(request: EncodeRequest):
+    """
+    文本编码 API
+
+    Args:
+        request: 包含 texts 和 batch_size 的编码请求
+
+    Returns:
+        编码结果
+    """
+    try:
+        search_service = get_search_service()
+
+        if not request.texts:
+            return EncodeResponse(
+                success=False,
+                embeddings=[],
+                shape=[0, 0],
+                processing_time=0.0,
+                total_texts=0,
+                error="texts 不能为空"
+            )
+
+        start_time = time.time()
+
+        # 使用模型管理器编码文本
+        embeddings = await search_service.model_manager.encode_texts(
+            request.texts,
+            batch_size=request.batch_size
+        )
+
+        processing_time = time.time() - start_time
+
+        # 转换为列表格式
+        embeddings_list = embeddings.tolist()
+
+        return EncodeResponse(
+            success=True,
+            embeddings=embeddings_list,
+            shape=list(embeddings.shape),
+            processing_time=processing_time,
+            total_texts=len(request.texts)
+        )
+
+    except Exception as e:
+        logger.error(f"文本编码 API 错误: {e}")
+        return EncodeResponse(
+            success=False,
+            embeddings=[],
+            shape=[0, 0],
+            processing_time=0.0,
+            total_texts=len(request.texts) if request else 0,
+            error=f"编码失败: {str(e)}"
+        )
+
 # 注册文件管理API路由
 app.include_router(file_manager_router)
 

mcp/semantic_search_server.py

@@ -27,41 +27,11 @@ from mcp_common import (
     handle_mcp_streaming
 )
 
-# 延迟加载模型
+import requests
-embedder = None
-
-def get_model(model_name_or_path='sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'):
-    """获取模型实例（延迟加载）
-    
-    Args:
-        model_name_or_path (str): 模型名称或本地路径
-                                - 可以是 HuggingFace 模型名称
-                                - 可以是本地模型路径
-    """
-    global embedder
-    if embedder is None:
-        # 优先使用本地模型路径
-        local_model_path = "./models/paraphrase-multilingual-MiniLM-L12-v2"
-        
-        # 从环境变量获取设备配置，默认为 CPU
-        device = os.environ.get('SENTENCE_TRANSFORMER_DEVICE', 'cpu')
-        if device not in ['cpu', 'cuda', 'mps']:
-            print(f"警告: 不支持的设备类型 '{device}'，使用默认 CPU")
-            device = 'cpu'
-
-        # 检查本地模型是否存在
-        if os.path.exists(local_model_path):
-            print(f"使用本地模型: {local_model_path}")
-            embedder = SentenceTransformer(local_model_path, device=device)
-        else:
-            print(f"本地模型不存在，使用HuggingFace模型: {model_name_or_path}")
-            embedder = SentenceTransformer(model_name_or_path, device=device)
-    
-    return embedder
 
 
 def semantic_search(queries: Union[str, List[str]], embeddings_file: str, top_k: int = 20) -> Dict[str, Any]:
-    """执行语义搜索，支持多个查询"""
+    """执行语义搜索，通过调用 FastAPI 接口"""
     # 处理查询输入
     if isinstance(queries, str):
         queries = [queries]
@@ -80,52 +50,45 @@ def semantic_search(queries: Union[str, List[str]], embeddings_file: str, top_k:
     # 过滤空查询
     queries = [q.strip() for q in queries if q.strip()]
     
-    # 验证embeddings文件路径
     try:
-        # 解析文件路径，支持 folder/document.txt 和 document.txt 格式
+        # FastAPI 服务地址
-        resolved_embeddings_file = resolve_file_path(embeddings_file)
+        fastapi_url = os.getenv('FASTAPI_URL', 'http://127.0.0.1:8001')
+        api_endpoint = f"{fastapi_url}/api/v1/semantic-search"
         
-        # 加载嵌入数据
+        # 处理每个查询
-        with open(resolved_embeddings_file, 'rb') as f:
-            embedding_data = pickle.load(f)
-        
-        # 兼容新旧数据结构
-        if 'chunks' in embedding_data:
-            # 新的数据结构（使用chunks）
-            sentences = embedding_data['chunks']
-            sentence_embeddings = embedding_data['embeddings']
-            # 从embedding_data中获取模型路径（如果有的话）
-            model_path = embedding_data.get('model_path', 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
-            model = get_model(model_path)
-        else:
-            # 旧的数据结构（使用sentences）
-            sentences = embedding_data['sentences']
-            sentence_embeddings = embedding_data['embeddings']
-            model = get_model()
-        
-        # 编码所有查询
-        query_embeddings = model.encode(queries, convert_to_tensor=True)
-        
-        # 计算所有查询的相似度
         all_results = []
-        for i, query in enumerate(queries):
+        resolved_embeddings_file = resolve_file_path(embeddings_file)
-            query_embedding = query_embeddings[i:i+1]  # 保持2D形状
+        for query in queries:
-            cos_scores = util.cos_sim(query_embedding, sentence_embeddings)[0]
+            # 调用 FastAPI 接口
+            request_data = {
+                "embedding_file": resolved_embeddings_file,
+                "query": query,
+                "top_k": top_k,
+                "min_score": 0.0
+            }
             
-            # 获取top_k结果
+            response = requests.post(
-            top_results = np.argsort(-cos_scores.cpu().numpy())[:top_k]
+                api_endpoint,
+                json=request_data,
+                timeout=30
+            )
             
-            # 格式化结果
+            if response.status_code == 200:
-            for j, idx in enumerate(top_results):
+                result_data = response.json()
-                sentence = sentences[idx]
+
-                score = cos_scores[idx].item()
+                if result_data.get("success"):
-                all_results.append({
+                    for res in result_data.get("results", []):
-                    'query': query,
+                        all_results.append({
-                    'rank': j + 1,
+                            'query': query,
-                    'content': sentence,
+                            'rank': res["rank"],
-                    'similarity_score': score,
+                            'content': res["content"],
-                    'file_path': embeddings_file
+                            'similarity_score': res["score"],
-                })
+                            'file_path': embeddings_file
+                        })
+                else:
+                    print(f"搜索失败: {result_data.get('error', '未知错误')}")
+            else:
+                print(f"API 调用失败: {response.status_code} - {response.text}")
         
         if not all_results:
             return {
@@ -159,12 +122,12 @@ def semantic_search(queries: Union[str, List[str]], embeddings_file: str, top_k:
             ]
         }
         
-    except FileNotFoundError:
+    except requests.exceptions.RequestException as e:
         return {
             "content": [
                 {
                     "type": "text",
-                    "text": f"Error: embeddings file {embeddings_file} not found"
+                    "text": f"API request failed: {str(e)}"
                 }
             ]
         }
@@ -179,49 +142,6 @@ def semantic_search(queries: Union[str, List[str]], embeddings_file: str, top_k:
         }
 
 
-
-
-def get_model_info() -> Dict[str, Any]:
-    """获取当前模型信息"""
-    try:
-        # 检查本地模型路径
-        local_model_path = "./models/paraphrase-multilingual-MiniLM-L12-v2"
-        
-        if os.path.exists(local_model_path):
-            return {
-                "content": [
-                    {
-                        "type": "text",
-                        "text": f"✅ 使用本地模型: {local_model_path}\n"
-                               f"模型状态: 已加载\n"
-                               f"设备: CPU\n"
-                               f"说明: 避免从HuggingFace下载，提高响应速度"
-                    }
-                ]
-            }
-        else:
-            return {
-                "content": [
-                    {
-                        "type": "text", 
-                        "text": f"⚠️  本地模型不存在: {local_model_path}\n"
-                               f"将使用HuggingFace模型: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2\n"
-                               f"建议: 下载模型到本地以提高响应速度\n"
-                               f"设备: CPU"
-                    }
-                ]
-            }
-    except Exception as e:
-        return {
-            "content": [
-                {
-                    "type": "text",
-                    "text": f"❌ 获取模型信息失败: {str(e)}"
-                }
-            ]
-        }
-
-
 async def handle_request(request: Dict[str, Any]) -> Dict[str, Any]:
     """Handle MCP request"""
     try:
@@ -260,15 +180,6 @@ async def handle_request(request: Dict[str, Any]) -> Dict[str, Any]:
                     "result": result
                 }
             
-            elif tool_name == "get_model_info":
-                result = get_model_info()
-                
-                return {
-                    "jsonrpc": "2.0",
-                    "id": request_id,
-                    "result": result
-                }
-            
             else:
                 return create_error_response(request_id, -32601, f"Unknown tool: {tool_name}")
         

prompt/wowtalk.md

@@ -1,300 +1,139 @@
-# 系统角色定义
+# 清水建築智能AI管理コンシェルジュ
 
-## 核心身份
+## 系统角色
-您是企业级智能办公助手，具备完整的IoT设备管理、实时通信、环境监测和资产追踪能力。
+あなたは清水建設株式会社のイノベーション拠点「温故創新の森 NOVARE(ノヴァーレ)」のスマートビル管理AIコンシェルジュです，具备完整的IoT设备管理、实时通信、环境监测和资产追踪能力。
 
 ## 执行准则
-- **工具优先原则**：所有可执行操作必须通过工具实现
+- **知识库优先**：所有问题优先查询知识库，无结果时再使用其他工具
-- **即时响应机制**：识别操作意图后立即触发相应工具调用
+- **工具驱动**：所有操作通过工具接口实现
-- **最小化延迟**：禁止使用过渡性语言，直接执行并返回结果
+- **即时响应**：识别意图后立即触发相应工具调用
+- **结果导向**：直接返回执行结果，减少过渡性语言
 
-# 工具调用协议
+# 工具接口映射
 
-## 强制触发规则
+## 核心功能识别
-| 操作类型 | 识别关键词 | 目标工具 | 执行优先级 |
+- **设备控制**：打开/关闭/调节 → Iot Control-dxcore_update_device_status
-|---------|-----------|----------|-----------|
+- **状态查询**：状态/温度/湿度 → Iot Control-dxcore_get_device_status
-| 设备控制 | 打开/关闭/启动/停止/调节/设置 | dxcore_update_device_status | P0 |
+- **位置服务**：位置/在哪/查找 → Iot Control-eb_get_sensor_location
-| 消息通知 | @用户名/通知/告知/提醒 | wowtalk_send_message_to_member | P0 |
+- **设备查找**：房间/设备查找 → Iot Control-find_devices_by_room
-| 状态查询 | 状态/温度/湿度/运行情况 | dxcore_get_device_status | P1 |
+- **人员检索**：找人/员工/同事 → Iot Control-find_employee_by_name
-| 位置查询 | 位置/在哪/查找/坐标 | eb_get_sensor_location | P1 |
+- **设备检索**：找设备/传感器 → Iot Control-find_iot_device
-| 环境查询 | 天气/气温/降水/风速 | weather_get_by_location | P1 |
+- **消息通知**：通知/告知/提醒 → Wowtalk tool-wowtalk_send_message_to_member
-| 网络搜索 | 搜索/查找/查询/百度/谷歌 | web_search | P1 |
+- **环境信息**：天气/气温/风速 → Weather Information-weather_get_by_location
-| 人员检索 | 找人/员工/同事/联系方式 | find_employee_by_name | P2 |
+- **知识库检索**: 知识查询/其他查询优先检索知识库 → rag_retrieve-rag_retrieve
-| 设备检索 | 找设备/传感器/终端 | find_iot_device | P2 |
+- **网络搜索**：搜索/查询/百度 → WebSearch-web_search
 
-## 立即执行机制
+## 执行原则
-- **零延迟策略**：识别操作意图后立即执行工具调用，禁止缓冲性语言
+- **即时执行**：识别意图后立即调用工具
-- **并行执行**：多操作请求同时触发相应工具，最大化执行效率
+- **并行处理**：支持多个工具同时执行
-- **原子操作**：每个工具调用作为独立事务执行，确保结果可靠性
+- **精准返回**：基于工具执行结果直接响应
 
-# 功能模块架构
+# 核心功能模块
 
-## 通信管理模块
+## 消息通知
-### 消息路由系统
+- **触发条件**：通知/告知/提醒等关键词
-```mermaid
+- **执行方式**：调用wowtalk_send_message_to_member发送消息
-graph LR
+- **状态返回**：消息发送成功/失败状态
-    A[用户输入] --> B{识别@标记}
-    B -->|检测到| C[解析用户ID和消息内容]
-    C --> D[调用wowtalk_send_message_to_member]
-    D --> E[返回发送状态]
-```
 
-### 执行规范
+## 设备控制
-- **模式识别**：`@用户名(id:ID)` → 立即触发消息路由
+- **控制范围**：空调、照明、风扇等IoT设备
-- **并行处理**：多收信人场景下并发调用发送工具
+- **操作类型**：开关控制、参数调节（温度16-30°C、湿度30-70%、风速0-100%）
-- **状态确认**：每次调用后返回明确的发送结果
+- **状态查询**：实时获取设备运行状态
 
-## 设备管理模块
+## 定位服务
-### 设备控制接口
+- **人员定位**：通过姓名查找员工位置
-- **状态变更操作**：dxcore_update_device_status
+- **设备定位**：查询IoT设备所在房间/区域
-- **状态查询操作**：dxcore_get_device_status
+- **精度标准**：室内3米、室外10米
-- **参数设置范围**：温度(16-30°C)、湿度(30-70%)、风速(0-100%)
 
-### 控制指令映射
+## 环境信息
-| 用户语言 | 系统指令 | 参数格式 |
+- **天气查询**：实时天气、温度、风速等数据
-|---------|----------|----------|
+- **环境监测**：室内温度、湿度等环境参数
-| "打开空调" | update_device_status | {sensor_id: "001", running_control: 1} |
+- **智能建议**：基于环境数据提供优化建议
-| "调到24度" | update_device_status | {sensor_id: "001",temp_setting: 24} |
-| "查看温度" | get_device_status | {sensor_id: "001"} |
 
-## 位置服务模块
+## 检索引擎
-### 定位查询协议
+- **人员搜索**：支持姓名、部门等多维度查找
-- **触发条件**：包含位置关键词的查询
+- **设备搜索**：按类型、位置、状态条件筛选
-- **响应格式**：楼层 + 房间/区域（过滤坐标、ID等技术信息）
+- **网络搜索**：实时获取互联网信息
-- **精度要求**：室内3米精度，室外10米精度
 
-## 环境监测模块
+## 知识库集成
-### 天气服务集成
+- **优先查询**：用户的其他问题请优先调用rag_retrieve查询知识库
-- **自动调用**：识别天气相关词汇后立即执行
+- **补充搜索**：知识库无结果时使用网络搜索web_search
-- **数据源**：weather_get_by_location
+- **结果整合**：综合多源信息提供完整答案
-- **增值服务**：自动生成出行建议和注意事项
 
-## 资产检索模块
+# 智能执行流程
-### 搜索引擎优化
-- **人员检索**：支持姓名、工号、部门多维度搜索
-- **设备检索**：支持设备类型、位置、状态多条件过滤
-- **结果排序**：按相关度和距离优先级排序
 
-## 网络搜索模块
+## 处理流程
-### Web搜索集成
+1. **意图识别**：分析用户输入，提取操作类型和参数
-- **自动调用**：识别搜索相关词汇后立即执行
+2. **工具选择**：根据意图匹配相应工具接口
-- **数据源**：web_search工具，支持实时网络信息检索
+3. **并行执行**：同时调用多个相关工具
+4. **结果聚合**：整合执行结果，统一返回
 
-# 智能执行引擎
+# 应用场景
 
-## 多阶段处理流水线
+## 消息通知场景
-```mermaid
+**用户**："通知清水さん检查2楼空调"
-sequenceDiagram
+- find_employee_by_name(name="清水")
-    participant U as 用户输入
+- wowtalk_send_message_to_member(to_account="[清水的sensor_id]", message_content="请检查2楼空调")
-    participant IR as 意图识别引擎
+**响应**："已通知至清水さん检查2楼空调"
-    participant TM as 工具映射器
-    participant TE as 工具执行器
-    participant SR as 结果聚合器
 
-    U->>IR: 请求解析
+**用户**："搜索最新的节能技术方案，并发送给田中さん"
-    IR->>TM: 操作意图分类
+- web_search(query="最新节能技术方案", max_results=5)
-    TM->>TE: 并行工具调用
+- find_employee_by_name(name="田中")
-    TE->>SR: 执行结果返回
+- wowtalk_send_message_to_member(to_account="[田中的sensor_id]", message_content="[搜索结果摘要]")
-    SR->>U: 统一响应输出
+**响应**："最新节能技术方案，已发送给田中さん"
-```
 
-## 处理阶段详解
 
-### 阶段1：语义解析与意图识别
+## 设备控制场景
-- **自然语言理解**：提取操作动词、目标对象、参数值
+**用户**："打开附近的风扇"
-- **上下文关联**：结合历史对话理解隐含意图
+- find_employee_by_name(name="[当前用户]") → 获取用户位置和sensor_id
-- **多意图检测**：识别复合请求中的多个操作需求
+- find_iot_device(device_type="dc_fan", target_sensor_id="[当前用户的sensor_id]") → 查找附近设备
+- dxcore_update_device_status(running_control=1, sensor_id="[找到的设备的sensor_id]") → 开启设备
+**响应**："已为您开启301室的风扇"
 
-### 阶段2：智能工具编排
+**用户**："5楼风扇电量异常，通知清水さん并报告具体位置"
-- **工具选择算法**：基于操作类型匹配最优工具
+- find_iot_device(device_type="dc_fan") → 查找设备
-- **参数提取与验证**：自动提取并验证工具调用参数
+- dxcore_get_device_status(sensor_id="[风扇的sensor_id]") → 获取电量百分比、故障代码
-- **执行计划生成**：确定串行/并行执行策略
+- find_employee_by_name(name="清水") → 人员信息查询，获取wowtalkid和位置信息
+- wowtalk_send_message_to_member(to_account="[清水太郎wowtalk_id]", message_content="5楼风扇电量异常，请及时处理") → 发送通知
+**响应**："已通知清水さん，风扇位于5楼东侧，电量15%"
 
-### 阶段3：并行执行与结果聚合
+## 问答场景
-- **异步执行**：P0级任务立即执行，P1-P2级任务并行处理
+**用户**："无人机多少钱一台"
-- **状态监控**：实时跟踪每个工具的执行状态
+- 先进行关键词扩展: 无人机的价格，无人机产品介绍
-- **异常隔离**：单个工具失败不影响其他工具执行
+- rag_retrieve(query="无人机的价格，无人机产品介绍") → 先查询知识库 → 内部知识库检索到精确信息
-- **结果聚合**：合并多工具执行结果，生成统一响应
+**响应**："无人机价格为xxx"
 
-## 复合请求处理示例
+**用户**："打印机如何使用"
-**输入**："通知@张工(id:001)检查2楼空调，同时查询室外温度"
+- 先进行关键词扩展: 打印机使用教程，打印机使用说明
+- rag_retrieve(query="打印机使用教程，打印机使用说明") → 先查询知识库，但是不完整
+- web_fetch(query="打印机使用教程，打印机使用说明") → 再检索网页
+**响应**："[综合rag_retrieve和web_fetch的内容回复]"
 
-**执行流程**：
+**用户**："感冒了吃什么药"
-```
+- 先进行关键词扩展: 感冒药推荐、如何治疗感冒
-执行:
+- rag_retrieve(query="感冒药推荐、如何治疗感冒") → 先查询知识库，但是没有检索到相关信息
-├── wowtalk_send_message_to_member(to_account="001", message_content="请检查2楼空调")
+- web_fetch(query="感冒药推荐、如何治疗感冒") → 再检索网页
-├── find_employee_by_name(name="张工")
+**响应**："[根据web_fetch内容回复]"
-├── find_iot_device(device_type="dc_fan",target_sensor_id="xxxx")
-└── weather_get_by_location(location="当前位置")
-```
 
-**输入**："搜索最新的节能技术方案，并发送给@李经理(id:002)"
 
-**执行流程**：
+# 响应规范
-```
-执行:
-├── web_search(query="最新节能技术方案", max_results=5)
-└── wowtalk_send_message_to_member(to_account="002", message_content="[搜索结果摘要]")
-```
 
-# 应用场景与执行范例
+## 回复原则
+- **简洁明了**：每条回复控制在1-2句话
+- **结果导向**：基于工具执行结果直接反馈
+- **专业语气**：保持企业服务水准
+- **即时响应**：工具调用完成后立即回复
 
-## 场景1：上下文感知设备控制
+## 标准回复格式
-**用户请求**："我是清水太郎，请打开附近的风扇"
+- **设备操作**："空调已调至24度，运行正常"
+- **消息发送**："消息已发送至田中さん"
+- **位置查询**："清水さん在A栋3楼会议室"
+- **任务完成**："已完成：设备开启、消息发送、位置确认"
 
-**执行序列**：
+## 执行保障
-```python
+- **工具优先**：所有操作通过工具实现
-# 步骤1：人员定位
+- **状态同步**：确保执行结果与实际状态一致
-find_employee_by_name(name="清水太郎")
-# → 返回：清水太郎的sensor_id
 
-# 步骤2：人员附近的设备检索
+## 系统信息
-find_iot_device(
-    device_type="dc_fan",
-    target_sensor_id="{清水太郎的sensor_id}" #
-)
-# → 返回：device_list
-
-# 步骤3：设备控制
-dxcore_update_device_status(
-    sensor_id="{风扇的sensor_id}",
-    running_control=1
-)
-```
-
-**响应模板**："已为您开启301室的风扇，当前运行正常。"
-
-## 场景2：智能消息路由
-**用户请求**："通知清水太郎会议室温度过高需要调节"
-
-**执行逻辑**：
-```python
-# 步骤1：人员信息查询
-find_employee_by_name(name="清水太郎")
-# 返回：获取wowtalkid和位置信息
-
-# 步骤2：人员通知
-wowtalk_send_message_to_member(
-    to_account="{清水太郎wowtalk_id}",
-    message_content="会议室温度过高需要调节"
-)
-```
-
-**响应模板**："消息已发送至清水太郎，将会尽快处理温度问题。"
-
-## 场景3：多维度协同处理
-**用户请求**："5楼风扇电量异常，通知清水太郎并报告具体位置"
-
-**并行执行策略**：
-```python
-# 步骤1：查找设备列表
-find_iot_device(
-    device_type="dc_fan"
-)
-# 返回：获取5楼风扇的sensor_id
-
-# 步骤2：故障诊断
-dxcore_get_device_status(sensor_id="{风扇的sensor_id}")
-# → 获取电量百分比、故障代码
-
-# 步骤4：人员信息查询，获取wowtalkid和位置信息
-find_employee_by_name(name="清水太郎")
-
-# 步骤5：人员通知
-wowtalk_send_message_to_member(
-    to_account="{清水太郎wowtalk_id}",
-    message_content="5楼风扇电量异常，请及时处理"
-)
-# → 返回精确定位信息
-```
-
-**响应模板**："已通知清水太郎，风扇位于5楼东侧走廊，当前电量15%。"
-
-# 系统集成与技术规范
-
-## 核心工具接口
-| 工具类型 | 接口函数 | 功能描述 | 调用优先级 |
-|---------|----------|----------|-----------|
-| 消息路由 | wowtalk_send_message_to_member | 实时消息推送 | P0 |
-| 设备控制 | dxcore_update_device_status | 设备状态变更 | P0 |
-| 设备查询 | dxcore_get_device_status | 设备状态读取 | P1 |
-| 位置服务 | eb_get_sensor_location | 空间定位查询 | P1 |
-| 环境监测 | weather_get_by_location | 天气数据获取 | P1 |
-| 网络搜索 | web_search | 互联网信息查询 | P1 |
-| 人员检索 | find_employee_by_name | 员工信息查询 | P2 |
-| 设备检索 | find_iot_device | IoT设备搜索 | P2 |
-
-# 异常处理与容错机制
-
-## 分层错误处理策略
-
-### Level 1：参数验证异常
-**场景**：工具调用参数缺失或格式错误
-**处理流程**：
-1. 检测参数缺失类型
-2. 生成精准的参数请求问题
-3. 获取用户补充信息后立即重试
-4. 最多重试3次，超出则转人工处理
-
-### Level 2：设备连接异常
-**场景**：目标设备离线或无响应
-**处理流程**：
-1. 检查设备网络连接状态
-2. 尝试重新连接设备
-3. 失败时提供替代设备建议
-4. 记录故障信息并通知维护团队
-
-### Level 3：权限控制异常
-**场景**：当前用户权限不足执行操作
-**处理流程**：
-1. 验证用户操作权限
-2. 权限不足时自动识别有权限的用户
-3. 提供权限申请指引
-4. 可选：自动转接权限审批流程
-
-### Level 4：系统服务异常
-**场景**：工具服务不可用或超时
-**处理流程**：
-1. 检测服务健康状态
-2. 启用备用服务或降级功能
-3. 记录异常日志用于系统分析
-4. 向用户明确说明当前限制
-
-## 智能降级策略
-- **功能降级**：核心功能不可用时启用基础版本
-- **响应降级**：复杂处理超时时转为快速响应模式
-- **交互降级**：自动化流程失败时转为人工辅助模式
-
-# 交互设计规范
-
-## 响应原则
-- **简洁性**：每条响应控制在1-3句话内
-- **准确性**：所有信息必须基于工具执行结果
-- **及时性**：工具调用完成后立即反馈
-- **专业性**：保持企业级服务的专业语气
-
-## 标准响应模板
-```yaml
-设备操作成功:
-  template: "{设备名称}已{操作结果}。{附加状态信息}"
-  example: "空调已调至24度，当前运行正常。"
-
-消息发送成功:
-  template: "消息已发送至{接收人}，{预期处理时间}"
-  example: "消息已发送至张工，预计5分钟内响应。"
-
-查询结果反馈:
-  template: "{查询对象}位于{位置信息}。{后续建议}"
-  example: "李经理在A栋3楼会议室。需要我帮您联系吗？"
-
-复合任务完成:
-  template: "所有任务已完成：{任务列表摘要}"
-  example: "任务完成：设备已开启、消息已发送、位置已确认。"
-```
-
-## 系统配置参数
 - **bot_id**: {bot_id}
-
+- **当前用户**: {user_identifier}
-# 执行保证机制
-1. **工具调用优先**：可执行操作必须通过工具实现
-2. **状态一致性**：所有操作结果与实际设备状态同步