Initial commit: AuraK人才测评系统基础框架

## 已实现功能
- 题库管理后端API完整实现
- 模板管理页面(Settings-测评模板)
- 评估统计页面
- 人才测评页面(AssessmentView)
- QuestionBank前端服务层

## 技术栈
- 后端: Node.js + NestJS + TypeORM
- 前端: React + TypeScript
- 容器化: Docker Compose

## 已知待完善
- 题库列表页缺少删除按钮
- 题库详情页未实现(题目管理/AI生成/审核)

server/src/knowledge-base/embedding.service.ts

@@ -128,14 +128,23 @@ export class EmbeddingService {
     }
   }
 
-  /**
-   * Process single batch embedding
-   */
+/**
+    * Process single batch embedding
+    */
   private async getEmbeddingsForBatch(
     texts: string[],
     modelConfig: any,
     maxBatchSize: number,
   ): Promise<number[][]> {
+    // Detect Ollama by port 11434 or /api/embeddings path
+    const isOllama =
+      modelConfig.baseUrl.includes(':11434') ||
+      modelConfig.baseUrl.includes('/api/embeddings');
+
+    if (isOllama) {
+      return await this.getOllamaEmbeddings(texts, modelConfig);
+    }
+
     const apiUrl = modelConfig.baseUrl.endsWith('/embeddings')
       ? modelConfig.baseUrl
       : `${modelConfig.baseUrl}/embeddings`;
@@ -283,4 +292,59 @@ export class EmbeddingService {
     // Use default dimensions from environment variable
     return this.defaultDimensions;
   }
+
+  /**
+   * Get embeddings from local Ollama
+   */
+  private async getOllamaEmbeddings(
+    texts: string[],
+    modelConfig: any,
+  ): Promise<number[][]> {
+    const baseUrl = modelConfig.baseUrl || 'http://localhost:11434';
+    const modelName = modelConfig.modelId || 'nomic-embed-text';
+
+    this.logger.log(
+      `[Ollama] Generating embeddings for ${texts.length} texts using ${modelName}`,
+    );
+
+    const embeddings: number[][] = [];
+
+    for (let i = 0; i < texts.length; i++) {
+      try {
+        const url = baseUrl.endsWith('/api/embeddings')
+          ? baseUrl
+          : `${baseUrl}/api/embeddings`;
+
+        const response = await fetch(url, {
+          method: 'POST',
+          headers: {
+            'Content-Type': 'application/json',
+          },
+          body: JSON.stringify({
+            model: modelName,
+            prompt: texts[i],
+          }),
+        });
+
+        if (!response.ok) {
+          const errorText = await response.text();
+          throw new Error(`Ollama API error: ${response.status} - ${errorText}`);
+        }
+
+        const data = await response.json();
+        embeddings.push(data.embedding);
+      } catch (error) {
+        this.logger.error(
+          `Ollama embedding error for text ${i}: ${error.message}`,
+        );
+        throw error;
+      }
+    }
+
+    this.logger.log(
+      `[Ollama] Got ${embeddings.length} embeddings, dimensions: ${embeddings[0]?.length || 0}`,
+    );
+
+    return embeddings;
+  }
 }