This workflow corresponds to n8n.io template #7171 — we link there as the canonical source.

This workflow follows the Agent → Chat Trigger recipe pattern — see all workflows that pair these two integrations.

The workflow JSON

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{
  "meta": {
    "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 Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        3392,
        1232
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "0667b08a-42f9-4c74-9560-75f196147468",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        3488,
        992
      ],
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "e828b8eb-b88f-425b-aaca-7081a0145c08",
      "name": "Embeddings Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        4192,
        1440
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "6f220ede-5bbf-48b7-a5b1-48052fb1dd87",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        3488,
        800
      ],
      "parameters": {
        "content": ""
      },
      "typeVersion": 1
    },
    {
      "id": "761568e7-36aa-4dd2-8dc1-a037b0c27218",
      "name": "Embeddings Google Gemini3",
      "type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
      "position": [
        3904,
        1424
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "e25c8e1e-a871-491e-ad86-fd9575700f96",
      "name": "CONHECIMENTO_TI_GLPI",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
      "position": [
        4208,
        1280
      ],
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "19487e55-30d4-4570-a9e2-6ff9ce92c615",
      "name": "CONFLUENCE_TI_CONFLUENCE_SGU_GPL",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
      "position": [
        3824,
        1264
      ],
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "2fe1ca70-36ee-4593-bf7d-33bc1d4ea9f9",
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        3648,
        1248
      ],
      "parameters": {},
      "typeVersion": 1.3
    }
  ],
  "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": [
        []
      ]
    }
  }
}

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About this workflow

This workflow automates the creation of a Retrieval-Augmented Generation (RAG) pipeline using content from the GLPI Knowledge Base. It retrieves and processes FAQ articles directly via the GLPI API, cleans and vectorizes the content using pgvector in PostgreSQL, and prepares the…

Source: https://n8n.io/workflows/7171/ — original creator credit. Request a take-down →

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The workflow JSON

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