Chat with local LLMs using n8n and Ollama

工作流概述

这是一个包含5个节点的中等工作流,主要用于自动化处理各种任务。

工作流源代码

下载
{
  "id": "af8RV5b2TWB2LclA",
  "meta": {
    "instanceId": "95f2ab28b3dabb8da5d47aa5145b95fe3845f47b20d6343dd5256b6a28ba8fab",
    "templateCredsSetupCompleted": true
  },
  "name": "Chat with local LLMs using n8n and Ollama",
  "tags": [],
  "nodes": [
    {
      "id": "475385fa-28f3-45c4-bd1a-10dde79f74f2",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        700,
        460
      ],
      "webhookId": "ebdeba3f-6b4f-49f3-ba0a-8253dd226161",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "61133dc6-dcd9-44ff-85f2-5d8cc2ce813e",
      "name": "Ollama Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "position": [
        900,
        680
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "ollamaApi": {
          "id": "MyYvr1tcNQ4e7M6l",
          "name": "Local Ollama"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "3e89571f-7c87-44c6-8cfd-4903d5e1cdc5",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        160,
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      ],
      "parameters": {
        "width": 485,
        "height": 473,
        "content": "## Chat with local LLMs using n8n and Ollama
This n8n workflow allows you to seamlessly interact with your self-hosted Large Language Models (LLMs) through a user-friendly chat interface. By connecting to Ollama, a powerful tool for managing local LLMs, you can send prompts and receive AI-generated responses directly within n8n.

### How it works
1. When chat message received: Captures the user's input from the chat interface.
2. Chat LLM Chain: Sends the input to the Ollama server and receives the AI-generated response.
3. Delivers the LLM's response back to the chat interface.

### Set up steps
* Make sure Ollama is installed and running on your machine before executing this workflow.
* Edit the Ollama address if different from the default.
"
      },
      "typeVersion": 1
    },
    {
      "id": "9345cadf-a72e-4d3d-b9f0-d670744065fe",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1040,
        660
      ],
      "parameters": {
        "color": 6,
        "width": 368,
        "height": 258,
        "content": "## Ollama setup
* Connect to your local Ollama, usually on http://localhost:11434
* If running in Docker, make sure that the n8n container has access to the host's network in order to connect to Ollama. You can do this by passing `--net=host` option when starting the n8n Docker container"
      },
      "typeVersion": 1
    },
    {
      "id": "eeffdd4e-6795-4ebc-84f7-87b5ac4167d9",
      "name": "Chat LLM Chain",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        920,
        460
      ],
      "parameters": {},
      "typeVersion": 1.4
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "3af03daa-e085-4774-8676-41578a4cba2d",
  "connections": {
    "Ollama Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Chat LLM Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Chat LLM Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}

功能特点

  • 自动检测新邮件
  • AI智能内容分析
  • 自定义分类规则
  • 批量处理能力
  • 详细的处理日志

技术分析

节点类型及作用

  • @N8N/N8N Nodes Langchain.Chattrigger
  • @N8N/N8N Nodes Langchain.Lmchatollama
  • Stickynote
  • @N8N/N8N Nodes Langchain.Chainllm

复杂度评估

配置难度:
★★★☆☆
维护难度:
★★☆☆☆
扩展性:
★★★★☆

实施指南

前置条件

  • 有效的Gmail账户
  • n8n平台访问权限
  • Google API凭证
  • AI分类服务订阅

配置步骤

  1. 在n8n中导入工作流JSON文件
  2. 配置Gmail节点的认证信息
  3. 设置AI分类器的API密钥
  4. 自定义分类规则和标签映射
  5. 测试工作流执行
  6. 配置定时触发器(可选)

关键参数

参数名称 默认值 说明
maxEmails 50 单次处理的最大邮件数量
confidenceThreshold 0.8 分类置信度阈值
autoLabel true 是否自动添加标签

最佳实践

优化建议

  • 定期更新AI分类模型以提高准确性
  • 根据邮件量调整处理批次大小
  • 设置合理的分类置信度阈值
  • 定期清理过期的分类规则

安全注意事项

  • 妥善保管API密钥和认证信息
  • 限制工作流的访问权限
  • 定期审查处理日志
  • 启用双因素认证保护Gmail账户

性能优化

  • 使用增量处理减少重复工作
  • 缓存频繁访问的数据
  • 并行处理多个邮件分类任务
  • 监控系统资源使用情况

故障排除

常见问题

邮件未被正确分类

检查AI分类器的置信度阈值设置,适当降低阈值或更新训练数据。

Gmail认证失败

确认Google API凭证有效且具有正确的权限范围,重新进行OAuth授权。

调试技巧

  • 启用详细日志记录查看每个步骤的执行情况
  • 使用测试邮件验证分类逻辑
  • 检查网络连接和API服务状态
  • 逐步执行工作流定位问题节点

错误处理

工作流包含以下错误处理机制:

  • 网络超时自动重试(最多3次)
  • API错误记录和告警
  • 处理失败邮件的隔离机制
  • 异常情况下的回滚操作