diff --git a/.gitignore b/.gitignore
index 232ea0c..02f7c61 100644
--- a/.gitignore
+++ b/.gitignore
@@ -162,5 +162,6 @@ cython_debug/
 
 # Project specific
 ai-service/uploads/
+ai-service/config/
 *.local
 
diff --git a/README.md b/README.md
index 96b84e5..8d1dcb0 100644
--- a/README.md
+++ b/README.md
@@ -105,7 +105,10 @@ docker exec -it ai-ollama ollama pull toshk0/nomic-embed-text-v2-moe:Q6_K
    - **Matryoshka 截断**：`true`
 3. 点击 **保存配置**
 
-> **注意**: 使用 Nomic Embed (优化版) provider 可启用完整的 RAG 优化功能：任务前缀、Matryoshka 多向量、两阶段检索。
+> **注意**: 
+> - 使用 Nomic Embed (优化版) provider 可启用完整的 RAG 优化功能：任务前缀、Matryoshka 多向量、两阶段检索。
+> - 嵌入模型配置会持久化保存到 `ai-service/config/embedding_config.json`，服务重启后自动加载。
+> - **重要**: 切换嵌入模型后，需要删除现有知识库并重新上传文档，因为不同模型生成的向量不兼容。
 
 #### 6. 验证服务
 
diff --git a/ai-service-admin/src/stores/embedding.ts b/ai-service-admin/src/stores/embedding.ts
index 2cfb0ea..3c2af55 100644
--- a/ai-service-admin/src/stores/embedding.ts
+++ b/ai-service-admin/src/stores/embedding.ts
@@ -74,7 +74,8 @@ export const useEmbeddingStore = defineStore('embedding', () => {
         provider: currentConfig.value.provider,
         config: currentConfig.value.config
       }
-      await saveConfig(updateData)
+      const response = await saveConfig(updateData)
+      return response
     } catch (error) {
       console.error('Failed to save config:', error)
       throw error
diff --git a/ai-service-admin/src/views/admin/embedding/index.vue b/ai-service-admin/src/views/admin/embedding/index.vue
index e38ad24..8ca96d9 100644
--- a/ai-service-admin/src/views/admin/embedding/index.vue
+++ b/ai-service-admin/src/views/admin/embedding/index.vue
@@ -169,8 +169,19 @@ const handleSave = async () => {
 
   saving.value = true
   try {
-    await embeddingStore.saveCurrentConfig()
+    const response: any = await embeddingStore.saveCurrentConfig()
     ElMessage.success('配置保存成功')
+    
+    if (response?.warning || response?.requires_reindex) {
+      ElMessageBox.alert(
+        response.warning || '嵌入模型已更改，请重新上传文档以确保检索效果正常。',
+        '重要提示',
+        {
+          confirmButtonText: '我知道了',
+          type: 'warning',
+        }
+      )
+    }
   } catch (error) {
     ElMessage.error('配置保存失败')
   } finally {
diff --git a/ai-service-admin/vite.config.ts b/ai-service-admin/vite.config.ts
index b25653b..6df2553 100644
--- a/ai-service-admin/vite.config.ts
+++ b/ai-service-admin/vite.config.ts
@@ -14,7 +14,7 @@ export default defineConfig({
     port: 3000,
     proxy: {
       '/api': {
-        target: 'http://localhost:8088',
+        target: 'http://localhost:8000',
         changeOrigin: true,
         rewrite: (path) => path.replace(/^\/api/, ''),
       },
diff --git a/ai-service/app/api/admin/embedding.py b/ai-service/app/api/admin/embedding.py
index 206b27d..5937296 100644
--- a/ai-service/app/api/admin/embedding.py
+++ b/ai-service/app/api/admin/embedding.py
@@ -78,12 +78,32 @@ async def update_embedding_config(
     
     manager = get_embedding_config_manager()
     
+    old_config = manager.get_full_config()
+    old_provider = old_config.get("provider")
+    old_model = old_config.get("config", {}).get("model", "")
+    
+    new_model = config.get("model", "")
+    
     try:
         await manager.update_config(provider, config)
-        return {
+        
+        response = {
             "success": True,
             "message": f"Configuration updated to use {provider}",
         }
+        
+        if old_provider != provider or old_model != new_model:
+            response["warning"] = (
+                "嵌入模型已更改。由于不同模型生成的向量不兼容，"
+                "请删除现有知识库并重新上传文档，以确保检索效果正常。"
+            )
+            response["requires_reindex"] = True
+            logger.warning(
+                f"[EMBEDDING] Model changed from {old_provider}/{old_model} to {provider}/{new_model}. "
+                f"Documents need to be re-uploaded."
+            )
+        
+        return response
     except EmbeddingException as e:
         raise InvalidRequestException(str(e))
 
diff --git a/ai-service/app/services/embedding/factory.py b/ai-service/app/services/embedding/factory.py
index d0e8ad6..90c7285 100644
--- a/ai-service/app/services/embedding/factory.py
+++ b/ai-service/app/services/embedding/factory.py
@@ -7,7 +7,9 @@ Design reference: progress.md Section 7.1 - Architecture
 - EmbeddingConfigManager: manages configuration with hot-reload support
 """
 
+import json
 import logging
+from pathlib import Path
 from typing import Any, Type
 
 from app.services.embedding.base import EmbeddingException, EmbeddingProvider
@@ -17,6 +19,8 @@ from app.services.embedding.nomic_provider import NomicEmbeddingProvider
 
 logger = logging.getLogger(__name__)
 
+EMBEDDING_CONFIG_FILE = Path("config/embedding_config.json")
+
 
 class EmbeddingProviderFactory:
     """
@@ -152,17 +156,46 @@ class EmbeddingProviderFactory:
 class EmbeddingConfigManager:
     """
     Manager for embedding configuration.
-    [AC-AISVC-31] Supports hot-reload of configuration.
+    [AC-AISVC-31] Supports hot-reload of configuration with persistence.
     """
 
     def __init__(self, default_provider: str = "ollama", default_config: dict[str, Any] | None = None):
-        self._provider_name = default_provider
-        self._config = default_config or {
+        self._default_provider = default_provider
+        self._default_config = default_config or {
             "base_url": "http://localhost:11434",
             "model": "nomic-embed-text",
             "dimension": 768,
         }
+        self._provider_name = default_provider
+        self._config = self._default_config.copy()
         self._provider: EmbeddingProvider | None = None
+        
+        self._load_from_file()
+
+    def _load_from_file(self) -> None:
+        """Load configuration from file if exists."""
+        try:
+            if EMBEDDING_CONFIG_FILE.exists():
+                with open(EMBEDDING_CONFIG_FILE, 'r', encoding='utf-8') as f:
+                    saved = json.load(f)
+                    self._provider_name = saved.get("provider", self._default_provider)
+                    self._config = saved.get("config", self._default_config.copy())
+                    logger.info(f"Loaded embedding config from file: provider={self._provider_name}")
+        except Exception as e:
+            logger.warning(f"Failed to load embedding config from file: {e}")
+
+    def _save_to_file(self) -> None:
+        """Save configuration to file."""
+        try:
+            EMBEDDING_CONFIG_FILE.parent.mkdir(parents=True, exist_ok=True)
+            with open(EMBEDDING_CONFIG_FILE, 'w', encoding='utf-8') as f:
+                json.dump({
+                    "provider": self._provider_name,
+                    "config": self._config,
+                }, f, indent=2, ensure_ascii=False)
+            logger.info(f"Saved embedding config to file: provider={self._provider_name}")
+        except Exception as e:
+            logger.error(f"Failed to save embedding config to file: {e}")
 
     def get_provider_name(self) -> str:
         """Get current provider name."""
@@ -201,7 +234,7 @@ class EmbeddingConfigManager:
     ) -> bool:
         """
         Update embedding configuration.
-        [AC-AISVC-31, AC-AISVC-40] Supports hot-reload.
+        [AC-AISVC-31, AC-AISVC-40] Supports hot-reload with persistence.
         
         Args:
             provider: New provider name
@@ -229,6 +262,8 @@ class EmbeddingConfigManager:
             self._config = config
             self._provider = new_provider_instance
             
+            self._save_to_file()
+            
             logger.info(f"Updated embedding config: provider={provider}")
             return True
             
diff --git a/ai-service/app/services/retrieval/optimized_retriever.py b/ai-service/app/services/retrieval/optimized_retriever.py
index 02cd28d..2150552 100644
--- a/ai-service/app/services/retrieval/optimized_retriever.py
+++ b/ai-service/app/services/retrieval/optimized_retriever.py
@@ -138,7 +138,6 @@ class OptimizedRetriever(BaseRetriever):
     def __init__(
         self,
         qdrant_client: QdrantClient | None = None,
-        embedding_provider: NomicEmbeddingProvider | None = None,
         top_k: int | None = None,
         score_threshold: float | None = None,
         min_hits: int | None = None,
@@ -148,7 +147,6 @@ class OptimizedRetriever(BaseRetriever):
         rrf_k: int | None = None,
     ):
         self._qdrant_client = qdrant_client
-        self._embedding_provider = embedding_provider
         self._top_k = top_k or settings.rag_top_k
         self._score_threshold = score_threshold or settings.rag_score_threshold
         self._min_hits = min_hits or settings.rag_min_hits
@@ -164,19 +162,17 @@ class OptimizedRetriever(BaseRetriever):
         return self._qdrant_client
     
     async def _get_embedding_provider(self) -> NomicEmbeddingProvider:
-        if self._embedding_provider is None:
-            from app.services.embedding.factory import get_embedding_config_manager
-            manager = get_embedding_config_manager()
-            provider = await manager.get_provider()
-            if isinstance(provider, NomicEmbeddingProvider):
-                self._embedding_provider = provider
-            else:
-                self._embedding_provider = NomicEmbeddingProvider(
-                    base_url=settings.ollama_base_url,
-                    model=settings.ollama_embedding_model,
-                    dimension=settings.qdrant_vector_size,
-                )
-        return self._embedding_provider
+        from app.services.embedding.factory import get_embedding_config_manager
+        manager = get_embedding_config_manager()
+        provider = await manager.get_provider()
+        if isinstance(provider, NomicEmbeddingProvider):
+            return provider
+        else:
+            return NomicEmbeddingProvider(
+                base_url=settings.ollama_base_url,
+                model=settings.ollama_embedding_model,
+                dimension=settings.qdrant_vector_size,
+            )
     
     async def retrieve(self, ctx: RetrievalContext) -> RetrievalResult:
         """