refactor: 将 n8n 工作流外部化并简化数据库 schema

- 移除 n8n-workflows 目录下的工作流配置文件
- 从 Project 模型移除 embedding 和 embeddingUpdatedAt 字段
- 添加 N8N_AI_SEARCH_WEBHOOK 环境变量配置

Co-Authored-By: Claude (glm-4.7) <noreply@anthropic.com>

.env.example

@@ -4,6 +4,9 @@ DATABASE_URL="postgresql://postgres:password@localhost:5432/agent_park"
 # Webhook API - Generate a secure key for production
 WEBHOOK_API_KEY="sk_live_your_secure_api_key_min_32_chars"
 
+# n8n AI Search Webhook
+N8N_AI_SEARCH_WEBHOOK="https://n8n.mzaxd.fun/webhook/ai-search"
+
 # Internationalization
 NEXT_INTL_DEFAULT_LOCALE="zh"
 NEXT_INTL_SUPPORTED_LOCALES="zh,en"

n8n-workflows/README.md

@@ -1,210 +0,0 @@
-# n8n 工作流导入指南
-
-本指南将帮助您导入和配置 AI 智能搜索系统的两个 n8n 工作流。
-
-## 前置条件
-
-确保您已经完成：
-- [ ] n8n 实例已运行
-- [ ] 已配置 `OpenAI Embeddings` 凭证
-- [ ] 已配置 `Neon Database` 凭证
-- [ ] Neon 数据库已应用迁移（添加 embedding 字段）
-
----
-
-## 工作流 1: Project Vectorization（项目向量化）
-
-### 功能说明
-每 5 分钟自动执行一次，查询未向量化的项目，生成 OpenAI embeddings 并存储到数据库。
-
-### 导入步骤
-
-1. **导入工作流**
-   - 打开 n8n 实例
-   - 点击右上角 **+** → **Import from File**
-   - 选择 `project-vectorization.json`
-   - 点击 **Import**
-
-2. **配置凭证**
-   - 点击 **查询未向量化项目** 节点
-   - 在 **Credentials** 下拉框中选择 `Neon Database`
-   - 点击 **Save**
-
-   - 点击 **OpenAI Embeddings** 节点
-   - 在 **Credentials** 下拉框中选择 `OpenAI Embeddings`
-   - 点击 **Save**
-
-   - 点击 **更新 Embedding** 节点
-   - 在 **Credentials** 下拉框中选择 `Neon Database`
-   - 点击 **Save**
-
-3. **测试工作流**
-   - 点击工作流右上角 **Test Workflow**
-   - 手动点击 **Cron** 节点的执行按钮
-   - 查看每个节点的输出：
-     - `查询未向量化项目` 应返回项目列表（或空数组）
-     - `构造文本内容` 应添加 `textContent` 字段
-     - `OpenAI Embeddings` 应返回向量数组
-     - `更新 Embedding` 应成功更新数据库
-
-4. **激活工作流**
-   - 点击左上角 **Inactive** 开关，变为 **Active**
-   - 工作流将每 5 分钟自动执行
-
-### 节点说明
-
-| 节点 | 功能 |
-|------|------|
-| Cron | 定时触发器（每 5 分钟） |
-| 查询未向量化项目 | 查询 embedding 为空的 ACTIVE 项目 |
-| 构造文本内容 | 合并项目字段生成用于向量化的文本 |
-| Split in Batches | 分批处理（每批 5 个，避免 API 限流） |
-| OpenAI Embeddings | 调用 OpenAI API 生成向量 |
-| 更新 Embedding | 将向量写入数据库 |
-
----
-
-## 工作流 2: AI Semantic Search（AI 语义搜索）
-
-### 功能说明
-接收 Webhook 请求，生成查询向量，执行向量相似度搜索，返回排序结果。
-
-### 导入步骤
-
-1. **导入工作流**
-   - 打开 n8n 实例
-   - 点击右上角 **+** → **Import from File**
-   - 选择 `ai-semantic-search.json`
-   - 点击 **Import**
-
-2. **配置凭证**
-   - 依次配置以下节点的凭证为 `OpenAI Embeddings`：
-     - **生成查询向量** 节点
-
-   - 依次配置以下节点的凭证为 `Neon Database`：
-     - **向量相似度搜索** 节点
-     - **查询标签** 节点
-
-3. **获取 Webhook URL**
-   - 点击 **Webhook** 节点
-   - 复制 **Production URL**（格式类似：`https://your-n8n.com/webhook/ai-search`）
-   - 将此 URL 更新到 `.env.local` 的 `N8N_AI_SEARCH_WEBHOOK`
-
-4. **测试工作流**
-   - 点击工作流右上角 **Test Workflow**
-   - 在 **Webhook** 节点中点击 **Listen for Test Event**
-   - 使用以下命令测试：
-
-```bash
-curl -X POST https://your-n8n.com/webhook/ai-search \
-  -H "Content-Type: application/json" \
-  -d '{"query":"视频生成工具","locale":"zh","limit":5}'
-```
-
-   - 预期响应：
-
-```json
-{
-  "results": [
-    {
-      "project": { /* 项目数据 */ },
-      "similarity": 0.89,
-      "matchReason": "相似度: 89%"
-    }
-  ],
-  "total": 5,
-  "searchTime": 1234
-}
-```
-
-5. **激活工作流**
-   - 点击左上角 **Inactive** 开关，变为 **Active**
-
-### 节点说明
-
-| 节点 | 功能 |
-|------|------|
-| Webhook | 接收搜索请求（POST /webhook/ai-search） |
-| 生成查询向量 | 将查询文本转换为向量 |
-| 向量相似度搜索 | 使用 pgvector 执行余弦相似度搜索 |
-| 准备标签查询 | 准备项目 ID 列表 |
-| 查询标签 | 查询每个项目的标签 |
-| 合并标签 | 将标签合并到搜索结果 |
-| 格式化响应 | 生成最终的 JSON 响应 |
-
----
-
-## 常见问题
-
-### Q1: 节点连接错误？
-导入后如果节点连接线丢失，手动按以下顺序连接：
-
-**工作流 1 连接顺序：**
-```
-Cron → 查询未向量化项目 → 构造文本内容 → Split in Batches → OpenAI Embeddings → 更新 Embedding → (循环回) Split in Batches
-```
-
-**工作流 2 连接顺序：**
-```
-Webhook → 生成查询向量 → 向量相似度搜索 → 准备标签查询 → 查询标签 → 合并标签 → 格式化响应
-```
-
-### Q2: 凭证选择框为空？
-- 确保已在 n8n 中创建了 `OpenAI Embeddings` 和 `Neon Database` 凭证
-- 如果凭证已创建但不可见，重新导入工作流
-
-### Q3: OpenAI API 错误？
-- 检查 API Key 是否有效
-- 确认 API Key 有足够的配额
-- 检查网络连接
-
-### Q4: 数据库连接错误？
-- 验证 Neon 数据库凭证配置正确
-- 检查数据库是否已应用迁移
-- 确认 pgvector 扩展已安装
-
----
-
-## 更新环境变量
-
-将获取的 Webhook URL 更新到项目的 `.env.local` 文件：
-
-```bash
-# n8n AI Search Webhook
-N8N_AI_SEARCH_WEBHOOK="https://your-n8n.com/webhook/ai-search"
-```
-
-然后重启开发服务器：
-
-```bash
-pnpm dev
-```
-
----
-
-## 验证完整流程
-
-1. **启动向量化**
-   - 确保 Project Vectorization 工作流已激活
-   - 等待 5 分钟或手动执行
-   - 在 Neon SQL Editor 中检查：
-
-```sql
-SELECT COUNT(*) FROM "projects" WHERE "embedding" IS NOT NULL;
-```
-
-2. **测试 AI 搜索**
-   - 访问 `http://localhost:3000/zh/projects`
-   - 点击 ✨ 按钮切换到 AI 模式
-   - 输入查询："帮我找能生成视频的 AI 工具"
-   - 验证返回相关结果
-
----
-
-## 完成后
-
-所有工作流配置完成后，您的 AI 智能搜索系统就已就绪！
-
-- 向量化工作流会在后台自动运行
-- AI 搜索 API 可供前端调用
-- 用户可以使用自然语言查询项目

n8n-workflows/ai-semantic-search.json

@@ -1,180 +0,0 @@
-{
-  "name": "AI Semantic Search",
-  "nodes": [
-    {
-      "parameters": {
-        "path": "ai-search",
-        "responseMode": "whenLastNodeFinishes",
-        "options": {}
-      },
-      "id": "webhook-node",
-      "name": "Webhook",
-      "type": "n8n-nodes-base.webhook",
-      "typeVersion": 2,
-      "position": [250, 300],
-      "webhookId": "ai-search-webhook"
-    },
-    {
-      "parameters": {
-        "resource": "embedding",
-        "model": "text-embedding-3-small",
-        "input": "={{ $json.query }}",
-        "options": {}
-      },
-      "id": "openai-embeddings",
-      "name": "生成查询向量",
-      "type": "@n8n/n8n-nodes-langchain.openai",
-      "typeVersion": 1.4,
-      "position": [470, 300],
-      "credentials": {
-        "openaiApi": {
-          "id": "OPENAI_CREDENTIAL_ID",
-          "name": "OpenAI Embeddings"
-        }
-      }
-    },
-    {
-      "parameters": {
-        "operation": "executeQuery",
-        "query": "=SELECT\n  p.id,\n  p.name,\n  p.\"nameEn\",\n  p.slug,\n  p.description,\n  p.\"descriptionEn\",\n  p.status,\n  p.\"createdAt\",\n  1 - (p.\"embedding\" <=> '{{ $json.data[0].embedding }}'::vector) as similarity\nFROM \"projects\" p\nWHERE p.\"embedding\" IS NOT NULL\n  AND p.status = 'ACTIVE'\nORDER BY p.\"embedding\" <=> '{{ $json.data[0].embedding }}'::vector\nLIMIT {{ $json.limit || 20 }}",
-        "options": {}
-      },
-      "id": "postgres-vector-search",
-      "name": "向量相似度搜索",
-      "type": "n8n-nodes-base.postgres",
-      "typeVersion": 2.5,
-      "position": [690, 300],
-      "credentials": {
-        "postgres": {
-          "id": "NEON_DATABASE_CREDENTIAL_ID",
-          "name": "Neon Database"
-        }
-      }
-    },
-    {
-      "parameters": {
-        "jsCode": "// 为每个项目查询标签\nconst results = $input.all();\n\nreturn results.map(item => {\n  return {\n    json: {\n      ...item.json,\n      projectId: item.json.id\n    }\n  };\n});"
-      },
-      "id": "prepare-tag-query",
-      "name": "准备标签查询",
-      "type": "n8n-nodes-base.code",
-      "typeVersion": 2,
-      "position": [910, 300]
-    },
-    {
-      "parameters": {
-        "operation": "executeQuery",
-        "query": "=SELECT\n  t.id,\n  t.name,\n  t.\"nameEn\",\n  t.slug,\n  pt.\"projectId\"\nFROM \"tags\" t\nINNER JOIN \"project_tags\" pt ON t.id = pt.\"tagId\"\nWHERE pt.\"projectId\" IN (SELECT UNNEST(STRING_TO_ARRAY('{{ $json.projectIds }}', ','))::INTEGER)",
-        "options": {}
-      },
-      "id": "query-tags",
-      "name": "查询标签",
-      "type": "n8n-nodes-base.postgres",
-      "typeVersion": 2.5,
-      "position": [1130, 300],
-      "credentials": {
-        "postgres": {
-          "id": "NEON_DATABASE_CREDENTIAL_ID",
-          "name": "Neon Database"
-        }
-      }
-    },
-    {
-      "parameters": {
-        "jsCode": "// 合并标签到项目\nconst projects = $('向量相似度搜索').all();\nconst tags = $('查询标签').all();\n\n// 合并标签到项目\nconst results = projects.map(project => {\n  const projectTags = tags\n    .filter(t => t.json.projectId === project.json.id)\n    .map(t => ({\n      id: t.json.id,\n      name: t.json.name,\n      nameEn: t.json.nameEn,\n      slug: t.json.slug\n    }));\n\n  return {\n    json: {\n      project: {\n        ...project.json,\n        tags: projectTags\n      },\n      similarity: project.json.similarity,\n      matchReason: `相似度: ${(project.json.similarity * 100).toFixed(0)}%`\n    }\n  };\n});\n\nreturn results;"
-      },
-      "id": "merge-tags",
-      "name": "合并标签",
-      "type": "n8n-nodes-base.code",
-      "typeVersion": 2,
-      "position": [1350, 300]
-    },
-    {
-      "parameters": {
-        "jsCode": "// 格式化最终响应\nconst results = $input.all();\n\nreturn {\n  json: {\n    results: results.map(r => r.json),\n    total: results.length,\n    searchTime: Date.now() - $('Webhook').item.json.startTime\n  }\n};"
-      },
-      "id": "format-response",
-      "name": "格式化响应",
-      "type": "n8n-nodes-base.code",
-      "typeVersion": 2,
-      "position": [1570, 300]
-    }
-  ],
-  "connections": {
-    "Webhook": {
-      "main": [
-        [
-          {
-            "node": "生成查询向量",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "生成查询向量": {
-      "main": [
-        [
-          {
-            "node": "向量相似度搜索",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "向量相似度搜索": {
-      "main": [
-        [
-          {
-            "node": "准备标签查询",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "准备标签查询": {
-      "main": [
-        [
-          {
-            "node": "查询标签",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "查询标签": {
-      "main": [
-        [
-          {
-            "node": "合并标签",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "合并标签": {
-      "main": [
-        [
-          {
-            "node": "格式化响应",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    }
-  },
-  "pinData": {},
-  "settings": {
-    "executionOrder": "v1"
-  },
-  "staticData": null,
-  "tags": [],
-  "triggerCount": 0,
-  "updatedAt": "2026-01-26T00:00:00.000Z",
-  "versionId": "1"
-}

n8n-workflows/project-vectorization.json

@@ -1,175 +0,0 @@
-{
-  "name": "Project Vectorization",
-  "nodes": [
-    {
-      "parameters": {
-        "rule": {
-          "interval": [
-            {
-              "field": "minutes",
-              "minutesInterval": 5
-            }
-          ]
-        }
-      },
-      "id": "cron-node",
-      "name": "Cron",
-      "type": "n8n-nodes-base.cron",
-      "typeVersion": 1.2,
-      "position": [250, 300]
-    },
-    {
-      "parameters": {
-        "operation": "executeQuery",
-        "query": "SELECT id, name, \"nameEn\", description, \"descriptionEn\", content, \"contentEn\"\nFROM \"projects\"\nWHERE \"embedding\" IS NULL\n  AND \"status\" = 'ACTIVE'\nLIMIT 20",
-        "options": {}
-      },
-      "id": "postgres-query",
-      "name": "查询未向量化项目",
-      "type": "n8n-nodes-base.postgres",
-      "typeVersion": 2.5,
-      "position": [470, 300],
-      "credentials": {
-        "postgres": {
-          "id": "NEON_DATABASE_CREDENTIAL_ID",
-          "name": "Neon Database"
-        }
-      }
-    },
-    {
-      "parameters": {
-        "jsCode": "// 为每个项目构造用于向量化的文本内容\nconst projects = $input.all();\n\nreturn projects.map(item => {\n  const project = item.json;\n\n  // 合并字段，按权重构造\n  const parts = [\n    project.name || '',\n    project.nameEn || '',\n    project.description || '',\n    project.descriptionEn || '',\n    (project.content || '').substring(0, 500),\n    (project.contentEn || '').substring(0, 500)\n  ].filter(Boolean);\n\n  const textContent = parts.join('\\n\\n');\n\n  return {\n    json: {\n      ...project,\n      textContent: textContent\n    }\n  };\n});"
-      },
-      "id": "construct-content",
-      "name": "构造文本内容",
-      "type": "n8n-nodes-base.code",
-      "typeVersion": 2,
-      "position": [690, 300]
-    },
-    {
-      "parameters": {
-        "batchSize": 5,
-        "options": {}
-      },
-      "id": "split-batches",
-      "name": "Split in Batches",
-      "type": "n8n-nodes-base.splitInBatches",
-      "typeVersion": 3,
-      "position": [910, 300]
-    },
-    {
-      "parameters": {
-        "resource": "embedding",
-        "model": "text-embedding-3-small",
-        "input": "={{ $json.textContent }}",
-        "options": {}
-      },
-      "id": "openai-embeddings",
-      "name": "OpenAI Embeddings",
-      "type": "@n8n/n8n-nodes-langchain.openai",
-      "typeVersion": 1.4,
-      "position": [1130, 300],
-      "credentials": {
-        "openaiApi": {
-          "id": "OPENAI_CREDENTIAL_ID",
-          "name": "OpenAI Embeddings"
-        }
-      }
-    },
-    {
-      "parameters": {
-        "operation": "executeQuery",
-        "query": "=UPDATE \"projects\"\nSET\n  \"embedding\" = '{{ $json.data[0].embedding }}'::vector,\n  \"embeddingUpdatedAt\" = NOW()\nWHERE \"id\" = {{ $json.id }}",
-        "options": {}
-      },
-      "id": "postgres-update",
-      "name": "更新 Embedding",
-      "type": "n8n-nodes-base.postgres",
-      "typeVersion": 2.5,
-      "position": [1350, 300],
-      "credentials": {
-        "postgres": {
-          "id": "NEON_DATABASE_CREDENTIAL_ID",
-          "name": "Neon Database"
-        }
-      }
-    }
-  ],
-  "connections": {
-    "Cron": {
-      "main": [
-        [
-          {
-            "node": "查询未向量化项目",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "查询未向量化项目": {
-      "main": [
-        [
-          {
-            "node": "构造文本内容",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "构造文本内容": {
-      "main": [
-        [
-          {
-            "node": "Split in Batches",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "Split in Batches": {
-      "main": [
-        [
-          {
-            "node": "OpenAI Embeddings",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "OpenAI Embeddings": {
-      "main": [
-        [
-          {
-            "node": "更新 Embedding",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    },
-    "更新 Embedding": {
-      "main": [
-        [
-          {
-            "node": "Split in Batches",
-            "type": "main",
-            "index": 0
-          }
-        ]
-      ]
-    }
-  },
-  "pinData": {},
-  "settings": {
-    "executionOrder": "v1"
-  },
-  "staticData": null,
-  "tags": [],
-  "triggerCount": 0,
-  "updatedAt": "2026-01-26T00:00:00.000Z",
-  "versionId": "1"
-}

prisma/schema.prisma

@@ -39,25 +39,23 @@ enum TaskStatus {
 // ================================
 
 model Project {
-  id                String        @id @default(cuid())
-  name              String
-  nameEn            String?
-  slug              String        @unique
-  description       String
-  descriptionEn     String?
-  content           String?       @db.Text
-  contentEn         String?       @db.Text
-  status            ProjectStatus @default(ACTIVE)
-  source            String?
-  embedding         Unsupported("vector(1536)")?
-  embeddingUpdatedAt DateTime?
-  createdAt         DateTime      @default(now())
-  updatedAt         DateTime      @updatedAt
+  id             String        @id @default(cuid())
+  name           String
+  nameEn         String?
+  slug           String        @unique
+  description    String
+  descriptionEn  String?
+  content        String?       @db.Text
+  contentEn      String?       @db.Text
+  status         ProjectStatus @default(ACTIVE)
+  source         String?
+  createdAt      DateTime      @default(now())
+  updatedAt      DateTime      @updatedAt
 
   // Relations
-  tags              ProjectTag[]
-  links             ExternalLink[]
-  discoveryTasks    ProjectDiscoveryTask[]
+  tags           ProjectTag[]
+  links          ExternalLink[]
+  discoveryTasks ProjectDiscoveryTask[]
 
   // Indexes
   @@index([status, createdAt], map: "idx_project_status_createdAt")