RAGパイプライン

中級

これはEngineering, AI分野の自動化ワークフローで、13個のノードを含みます。主にFormTrigger, Agent, ChatTrigger, LmChatOllama, EmbeddingsOllamaなどのノードを使用、AI技術を活用したスマート自動化を実現。 検索拡張生成(RAG)ベースのローカルチャットボット

前提条件
  • Qdrantサーバー接続情報
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
  "id": "L9nteAq0NLYqIGxH",
  "meta": {
    "instanceId": "558d88703fb65b2d0e44613bc35916258b0f0bf983c5d4730c00c424b77ca36a",
    "templateCredsSetupCompleted": true
  },
  "name": "RAG Pipeline",
  "tags": [],
  "nodes": [
    {
      "id": "a00e5b5b-1cc1-4272-9790-8ffde3c92efb",
      "name": "フォーム送信時",
      "type": "n8n-nodes-base.formTrigger",
      "position": [
        0,
        0
      ],
      "webhookId": "4e1e20d4-f759-42c8-8439-87b93f43aa7c",
      "parameters": {
        "options": {},
        "formTitle": "Add your file here",
        "formFields": {
          "values": [
            {
              "fieldType": "file",
              "fieldLabel": "File",
              "requiredField": true,
              "acceptFileTypes": ".pdf"
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "1218186e-a93e-4e05-b47e-a395f28cf5f9",
      "name": "Qdrant Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        220,
        0
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "qdrantCollection": {
          "__rl": true,
          "mode": "id",
          "value": "rag_collection"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "sFfERYppMeBnFNeA",
          "name": "Local QdrantApi database"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "9c7fb858-b571-4626-b976-d3e1995c464b",
      "name": "Embeddings Ollama",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
      "position": [
        60,
        220
      ],
      "parameters": {
        "model": "mxbai-embed-large:latest"
      },
      "credentials": {
        "ollamaApi": {
          "id": "xHuYe0MDGOs9IpBW",
          "name": "Local Ollama service"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "af14443b-ae01-48dc-8552-5ded7a27fce2",
      "name": "デフォルトデータローダー",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        360,
        220
      ],
      "parameters": {
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "660380c5-63da-4404-98e6-f9c0ee9aaa90",
      "name": "再帰的文字テキスト分割器",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        460,
        440
      ],
      "parameters": {
        "options": {},
        "chunkSize": 200,
        "chunkOverlap": 50
      },
      "typeVersion": 1
    },
    {
      "id": "49dbe387-751f-4a2e-8803-290bc2c06ec5",
      "name": "付箋ノート",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -140,
        -100
      ],
      "parameters": {
        "color": 3,
        "width": 840,
        "height": 700,
        "content": "## Data Ingestion\n**Add data to the semantic database"
      },
      "typeVersion": 1
    },
    {
      "id": "45683271-af59-41d0-9e69-af721d566661",
      "name": "チャットメッセージ受信時",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        940,
        -20
      ],
      "webhookId": "5e56a263-3a40-44bd-bc9d-1cfb3bc2a87d",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "af562588-2e8c-4c0b-b041-d6fc8c0affd0",
      "name": "AIエージェント",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1220,
        -20
      ],
      "parameters": {
        "options": {
          "systemMessage": "You are a helpful assistant. You have access to a tool to retrieve data from a semantic database to answer questions. Always provide arguments when you execute the tool"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "4d924b4a-fe07-4606-8385-613d6ea14991",
      "name": "Ollama チャットモデル",
      "type": "@n8n/n8n-nodes-langchain.lmChatOllama",
      "position": [
        1060,
        220
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "ollamaApi": {
          "id": "xHuYe0MDGOs9IpBW",
          "name": "Local Ollama service"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "de87b7bb-6fec-4d8f-a77a-25bc3a30a038",
      "name": "シンプルメモリ",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        1260,
        220
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "16261539-5218-4df1-8b14-915dd3377167",
      "name": "Qdrant Vector Store1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
      "position": [
        1540,
        240
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "options": {},
        "toolName": "retriever",
        "toolDescription": "Retrieve data from a semantic database to answer questions",
        "qdrantCollection": {
          "__rl": true,
          "mode": "id",
          "value": "rag_collection"
        }
      },
      "credentials": {
        "qdrantApi": {
          "id": "sFfERYppMeBnFNeA",
          "name": "Local QdrantApi database"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "57d3be1d-73cd-4464-a3f3-7dd4a3157cdf",
      "name": "Embeddings Ollama1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
      "position": [
        1460,
        440
      ],
      "parameters": {
        "model": "mxbai-embed-large:latest"
      },
      "credentials": {
        "ollamaApi": {
          "id": "xHuYe0MDGOs9IpBW",
          "name": "Local Ollama service"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "5919cc58-05f4-42c8-aada-3782a16574d9",
      "name": "付箋ノート1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        740,
        -100
      ],
      "parameters": {
        "color": 4,
        "width": 1200,
        "height": 700,
        "content": "## RAG Chatbot\n**Chat with your data"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "895c0261-fbf5-4bb6-9581-4cea3c4d20bd",
  "connections": {
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    "9c7fb858-b571-4626-b976-d3e1995c464b": {
      "ai_embedding": [
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            "index": 0
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        ]
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    },
    "4d924b4a-fe07-4606-8385-613d6ea14991": {
      "ai_languageModel": [
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    "57d3be1d-73cd-4464-a3f3-7dd4a3157cdf": {
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            "type": "ai_embedding",
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    "a00e5b5b-1cc1-4272-9790-8ffde3c92efb": {
      "main": [
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    "af14443b-ae01-48dc-8552-5ded7a27fce2": {
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    "16261539-5218-4df1-8b14-915dd3377167": {
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    "45683271-af59-41d0-9e69-af721d566661": {
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    "660380c5-63da-4404-98e6-f9c0ee9aaa90": {
      "ai_textSplitter": [
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            "type": "ai_textSplitter",
            "index": 0
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}
よくある質問

このワークフローの使い方は?

上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。

このワークフローはどんな場面に適していますか?

中級 - エンジニアリング, 人工知能

有料ですか?

このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス(OpenAI APIなど)は別途料金が発生する場合があります。

ワークフロー情報
難易度
中級
ノード数13
カテゴリー2
ノードタイプ10
難易度説明

経験者向け、6-15ノードの中程度の複雑さのワークフロー

外部リンク
n8n.ioで表示

このワークフローを共有

カテゴリー

カテゴリー: 34