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使用OpenAI、RAG和MongoDB向量嵌入构建知识库聊天机器人

中级

这是一个Support, AI领域的自动化工作流,包含 15 个节点。主要使用 GoogleDocs, ManualTrigger, Agent, ChatTrigger, LmChatOpenAi 等节点,结合人工智能技术实现智能自动化。 使用OpenAI、RAG和MongoDB向量嵌入构建知识库聊天机器人

前置要求
  • OpenAI API Key
  • MongoDB 连接字符串
工作流预览
可视化展示节点连接关系,支持缩放和平移
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": "074f90e2bb5206c5f405a8aac6551497c72005283a5405fb08207b1b3a78c2b8",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "5cb0a836-f9a1-4f92-9326-cd82a392d0da",
      "name": "Knowledge Base Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        220,
        0
      ],
      "parameters": {
        "text": "={{ $json.chatInput }}",
        "options": {
          "systemMessage": "You are the AI assistant for an internal support team at a technology company specializing in advanced software solutions. Your task is to assist internal users by consulting the official product documentation stored in the company’s knowledge base.\n\nAvailable references:\n\nproductDocs: Step-by-step guides, technical configurations, and official manuals extracted from the product’s documentation.\n\nBehavior rules for answering questions:\nAlways consult the official product documentation first using the productDocs tool.\n\nRespond clearly and directly, explaining how to do what is requested.\n\nDo not filter by category unless explicitly asked by the user.\n\nDetect the language of each incoming message individually and respond in that language. Do not use prior conversation language or history to decide the response language.\n\nNever provide links, even if requested. If a user asks for a link, reply:\n“I cannot provide links. If you need specific information, please let me know and I will help with the details.”\n\nUse a professional, direct, and human tone.\n\nKeep answers between 2 and 4 lines unless the user requests more detail.\n\nDo not invent information that is not in the knowledge base.\n\nIf you give numbered steps or lists, number them sequentially (1, 2, 3...) without skipping or repeating numbers, even if the source content uses different numbering."
        },
        "promptType": "define"
      },
      "typeVersion": 1.9
    },
    {
      "id": "56e6fb75-6a97-4466-9e7f-70710c2740d7",
      "name": "OpenAI Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        60,
        240
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "e352c32e-7108-4a0d-b081-b2532d96d092",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        680,
        380
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "74bbfb00-1a00-4131-a291-bce5b79628b4",
      "name": "When clicking \"Execute Workflow\"",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        -60,
        -420
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "f720a4b0-6239-4a0b-bb61-1e43f78f8e40",
      "name": "Simple Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        320,
        220
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "94561d61-4a01-48b6-b114-dc4d47546ff3",
      "name": "MongoDB Vector Search",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        560,
        220
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "options": {},
        "toolName": "productDocs",
        "mongoCollection": {
          "__rl": true,
          "mode": "list",
          "value": "n8n-template",
          "cachedResultName": "n8n-template"
        },
        "toolDescription": "retreive documentation",
        "vectorIndexName": "data_index"
      },
      "credentials": {
        "mongoDb": {
          "id": "7riubYENUDZsmjyK",
          "name": "MongoDB account 2"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "c473c33d-5681-4f3a-ac36-0d3012e7251f",
      "name": "Document Section Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        740,
        -260
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "doc_id",
                "value": "={{ $json.documentId }}"
              }
            ]
          }
        },
        "jsonData": "={{ $json.content }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "321222cb-1daf-4be2-a6ca-1a03d24f670f",
      "name": "Document Chunker",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        860,
        -100
      ],
      "parameters": {
        "options": {
          "splitCode": "markdown"
        },
        "chunkSize": 3000,
        "chunkOverlap": 200
      },
      "typeVersion": 1
    },
    {
      "id": "716519f5-cec1-4bfe-afbe-614fc23e74b5",
      "name": "MongoDB Vector Store Inserter",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas",
      "position": [
        540,
        -420
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "mongoCollection": {
          "__rl": true,
          "mode": "list",
          "value": "n8n-template",
          "cachedResultName": "n8n-template"
        },
        "vectorIndexName": "data_index"
      },
      "credentials": {
        "mongoDb": {
          "id": "7riubYENUDZsmjyK",
          "name": "MongoDB account 2"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "a49c19fc-f5f5-4381-b6ba-1bfc12b96135",
      "name": "OpenAI Embeddings Generator",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        480,
        -180
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "cJRah9hGPQ7eX4jd",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "6de724d5-2941-4e72-af8b-302ca2cf2ca0",
      "name": "Google Docs Importer",
      "type": "n8n-nodes-base.googleDocs",
      "position": [
        200,
        -420
      ],
      "parameters": {
        "operation": "get",
        "documentURL": "https://docs.google.com/document/d/1gvgp71e9edob8WLqFIYCdzC7kUq3pLO37VKb-a-vVW4/edit?tab=t.0"
      },
      "credentials": {
        "googleDocsOAuth2Api": {
          "id": "FNXMwqMf7vl1WUFj",
          "name": "Google Docs account"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "4f30bb21-72f0-4d13-b610-2ec218ad31b1",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -420,
        -440
      ],
      "parameters": {
        "color": 5,
        "content": "Run this workflow manually to import and index Google Docs product documentation into MongoDB with vector embeddings for fast search."
      },
      "typeVersion": 1
    },
    {
      "id": "25fd33d5-041b-4f01-a46b-1bacabd88376",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        40,
        0
      ],
      "webhookId": "427ead97-647d-49c7-82d7-e76b40664fd1",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "f1f3fadd-d5e6-45df-b810-1616531dffcb",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -420,
        40
      ],
      "parameters": {
        "color": 4,
        "content": "This workflow uses retrieval-augmented generation (RAG) to answer user questions by searching the MongoDB vector store and generating AI responses with context."
      },
      "typeVersion": 1
    },
    {
      "id": "39eee95c-b332-4ae4-bde9-aaf0fe5e0546",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1060,
        -380
      ],
      "parameters": {
        "height": 520,
        "content": "Search Index Example \n\n{\n  \"mappings\": {\n    \"dynamic\": false,\n    \"fields\": {\n      \"_id\": {\n        \"type\": \"string\"\n      },\n      \"text\": {\n        \"type\": \"string\"\n      },\n      \"embedding\": {\n        \"type\": \"knnVector\",\n        \"dimensions\": 1536,\n        \"similarity\": \"cosine\"\n      },\n      \"source\": {\n        \"type\": \"string\"\n      },\n      \"doc_id\": {\n        \"type\": \"string\"\n      }\n    }\n  }\n}\n"
      },
      "typeVersion": 1
    }
  ],
  "pinData": {},
  "connections": {
    "Simple Memory": {
      "ai_memory": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "Document Chunker": {
      "ai_textSplitter": [
        [
          {
            "node": "Document Section Loader",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings OpenAI": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Vector Search",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Google Docs Importer": {
      "main": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Knowledge Base Agent": {
      "main": [
        []
      ]
    },
    "MongoDB Vector Search": {
      "ai_tool": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "Document Section Loader": {
      "ai_document": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Knowledge Base Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI Embeddings Generator": {
      "ai_embedding": [
        [
          {
            "node": "MongoDB Vector Store Inserter",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "When clicking \"Execute Workflow\"": {
      "main": [
        [
          {
            "node": "Google Docs Importer",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
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 - 客户支持, 人工智能

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。

工作流信息
难度等级
中级
节点数量15
分类2
节点类型11
难度说明

适合有一定经验的用户,包含 6-15 个节点的中等复杂度工作流

外部链接
在 n8n.io 查看

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