feat: integrate spring ai vectorstore fallback

src/main/resources/application.yml

@@ -93,6 +93,30 @@ spring:
           min-idle: 0
 
   ai:
+    vectorstore:
+      type: milvus
+      milvus:
+        initialize-schema: false
+        database-name: ${milvus.database}
+        collection-name: business_knowledge
+        embedding-dimension: ${milvus.vector-dim}
+        index-type: IVF_FLAT
+        metric-type: L2
+        index-parameters: '{"nlist":128}'
+        id-field-name: id
+        auto-id: false
+        content-field-name: content
+        metadata-field-name: metadata
+        embedding-field-name: vector
+        client:
+          host: ${milvus.host}
+          port: ${milvus.port}
+          token: ${milvus.token}
+          username: ${milvus.username}
+          password: ${milvus.password}
+          secure: ${milvus.secure}
+          connect-timeout-ms: ${milvus.timeout}
+
     # --- Chat: DeepSeek (原生) ---
     deepseek:
       api-key: sk-1f44696abe644bd684f09cc43f12c557
@@ -133,6 +157,8 @@ rag:
 
 # 检索归一化配置
 retrieval:
+  vector-store:
+    mode: auto # auto | spring-ai | sdk
   normalization:
     max-l2-distance: 2.0              # L2 距离上界（BGE-M3 单位向量 = 2.0）
     highly-relevant-threshold: 0.75   # similarity >= 0.75 → HIGHLY_RELEVANT