使用Google Gemini和PostgreSQL构建GLPI知识库RAG管道

Name: 使用Google Gemini和PostgreSQL构建GLPI知识库RAG管道
Rating: 4.5 (10 reviews)
Author: Thiago Vazzoler Loureiro

中级

这是一个Internal Wiki, Multimodal AI领域的自动化工作流，包含 9 个节点。主要使用 Agent, ChatTrigger, LmChatGoogleGemini, MemoryBufferWindow, VectorStorePGVector 等节点。使用Google Gemini和PostgreSQL构建GLPI知识库RAG管道

前置要求

•Google Gemini API Key

使用的节点 (9)

EmbeddingsGoogleGemini

分类

内部知识库

多模态 AI

工作流预览

可视化展示节点连接关系，支持缩放和平移

AI Agent

Google Gemini聊天模型

当收到聊天消息时

嵌入 Google Gemini1

嵌入 Google Gemini3

IT知识库_GLPI

知识库_TI_CONFLUENCE_SGU_GPL

简单记忆

React Flow

导出工作流

复制以下 JSON 配置到 n8n 导入，即可使用此工作流

{
  "meta": {
    "instanceId": "98986c4dc0b4b2af9fd0666d254f8b80e6e6b2044e2f3f3eedc76242999f4b5e",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "faa18c92-30d0-481f-b073-0b5efa68fbdf",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        3824,
        992
      ],
      "parameters": {},
      "typeVersion": 1.8
    },
    {
      "id": "dd67e0b2-9986-4506-813e-d9d79b0b9f7b",
      "name": "Google Gemini聊天模型",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        3392,
        1232
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "0667b08a-42f9-4c74-9560-75f196147468",
      "name": "当收到聊天消息时",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        3488,
        992
      ],
      "webhookId": "8850cfe1-5a35-4bc9-9c02-91ccf4b82c58",
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "e828b8eb-b88f-425b-aaca-7081a0145c08",
      "name": "嵌入 Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        4192,
        1440
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "6f220ede-5bbf-48b7-a5b1-48052fb1dd87",
      "name": "便签",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        3488,
        800
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "761568e7-36aa-4dd2-8dc1-a037b0c27218",
      "name": "嵌入 Google Gemini3",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        3904,
        1424
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "e25c8e1e-a871-491e-ad86-fd9575700f96",
      "name": "IT知识库_GLPI",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
      "position": [
        4208,
        1280
      ],
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "19487e55-30d4-4570-a9e2-6ff9ce92c615",
      "name": "知识库_TI_CONFLUENCE_SGU_GPL",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
      "position": [
        3824,
        1264
      ],
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "2fe1ca70-36ee-4593-bf7d-33bc1d4ea9f9",
      "name": "简单记忆",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        3648,
        1248
      ],
      "parameters": {},
      "typeVersion": 1.3
    }
  ],
  "pinData": {},
  "connections": {
    "AI Agent": {
      "main": [
        []
      ]
    },
    "Simple Memory": {
      "ai_memory": [
        [
          {
            "node": "AI Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "CONHECIMENTO_TI_GLPI": {
      "ai_tool": [
        [
          {
            "node": "AI Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Google Gemini1": {
      "ai_embedding": [
        [
          {
            "node": "CONHECIMENTO_TI_GLPI",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Google Gemini3": {
      "ai_embedding": [
        [
          {
            "node": "CONFLUENCE_TI_CONFLUENCE_SGU_GPL",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "CONFLUENCE_TI_CONFLUENCE_SGU_GPL": {
      "ai_tool": [
        []
      ]
    }
  }
}

常见问题

如何使用这个工作流？

复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。

这个工作流适合什么场景？

中级 - 内部知识库, 多模态 AI

需要付费吗？

本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。

使用Google Gemini和PostgreSQL构建GLPI知识库RAG管道

使用的节点 (9)

分类

如何使用这个工作流？

这个工作流适合什么场景？

需要付费吗？

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