{
  "workflow": {
    "id": 5140,
    "name": "Build and update RAG system with Google Drive, Qdrant, and Gemini Chat",
    "views": 6935,
    "recentViews": 2,
    "totalViews": 6935,
    "createdAt": "2025-06-23T08:09:25.952Z",
    "description": "This workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It **enables full or incremental updates to documents** in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering.\n\nHere is a clear and professional description in English of the n8n workflow **“Create a RAG with Qdrant and update single files”**, including its benefits:\n\n\n---\n\n### **Benefits**\n\n* **Efficient RAG Setup**\n  Seamlessly integrates OpenAI, Qdrant, and Google Drive to create a scalable RAG pipeline.\n\n* **Single File Update**\n  You can replace the vector representation of a single file without reprocessing the entire collection—ideal for maintaining document freshness.\n\n* **Flexible File Source**\n  Works with Google Drive, allowing document management and updates from a familiar interface.\n---\n\n### **How It Works**  \nThis workflow is designed to create a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as a document source. It consists of four main phases:  \n\n- **Collection Setup**:  \n  - Creates or clears a Qdrant collection to store vectorized documents.  \n  - Configures the collection with cosine distance metrics and other parameters.  \n\n- **Document Processing**:  \n  - Retrieves files from a specified Google Drive folder.  \n  - Downloads and processes each file (text extraction, chunking, and embedding using OpenAI).  \n  - Stores the embeddings in Qdrant for vector search.  \n\n- **Single-File Update**:  \n  - Allows updating or deleting a specific file in the Qdrant collection by referencing its Google Drive ID.  \n  - Re-embeds the file and updates the vector store.  \n\n- **RAG Querying**:  \n  - Uses a chat trigger to receive user questions.  \n  - Retrieves relevant documents from Qdrant using vector similarity.  \n  - Generates answers using Google Gemini as the language model.  \n\n---\n\n### **Set Up Steps**  \n\n1. **Configure Qdrant**:  \n   - Replace `QDRANTURL` and `COLLECTION` in the \"Create collection\" and \"Clear collection\" HTTP nodes.  \n   - Ensure Qdrant API credentials are correctly set in the credentials section.  \n\n2. **Google Drive Integration**:  \n   - Specify the Google Drive folder ID in the \"Get files\" node.  \n   - Ensure Google Drive OAuth credentials are configured.  \n\n3. **OpenAI and Gemini Keys**:  \n   - Add OpenAI API credentials for embeddings (used in \"Embeddings OpenAI\" nodes).  \n   - Configure Google Gemini credentials for the chat model.  \n\n4. **Single-File Update**:  \n   - Set the `file_id` in the \"Edit Fields3\" node to target a specific Google Drive file for updates.  \n\n5. **Testing**:  \n   - Trigger the workflow manually to populate the Qdrant collection.  \n   - Use the chat interface to test RAG responses.  \n   \n---\n\n### **Need help customizing?**  \n[Contact me](mailto:info@n3w.it) for consulting and support or add me on [Linkedin](https://www.linkedin.com/in/davideboizza/). ",
    "workflow": {
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      "meta": {
        "instanceId": "a4bfc93e975ca233ac45ed7c9227d84cf5a2329310525917adaf3312e10d5462",
        "templateCredsSetupCompleted": true
      },
      "name": "Enables updates to documents in RAG",
      "tags": [
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          "id": "oFVn9SpkmyjWZwco",
          "name": "Qdrant",
          "createdAt": "2024-12-04T16:52:58.409Z",
          "updatedAt": "2024-12-04T16:52:58.409Z"
        }
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          "position": [
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          "parameters": {
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          },
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            "openAiApi": {
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                  {
                    "name": "file_id",
                    "value": "={{ $('Download files').item.json.id }}"
                  },
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            "options": {},
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              "value": "negozio-emporio-verde",
              "cachedResultName": "negozio-emporio-verde"
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          ],
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            "jsonBody": "{\n  \"vectors\": {\n    \"size\": 1536,\n    \"distance\": \"Cosine\"  \n  },\n  \"shard_number\": 1,  \n  \"replication_factor\": 1,  \n  \"write_consistency_factor\": 1 \n}",
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        },
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          "position": [
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          ],
          "parameters": {
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              "metadata": {
                "metadataValues": [
                  {
                    "name": "file_id",
                    "value": "={{ $('Download file').item.json.file_id }}"
                  },
                  {
                    "name": "file_name",
                    "value": "={{ $binary.data.fileName}}"
                  }
                ]
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            "binaryMode": "specificField"
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            "chunkOverlap": 50
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          "name": "Delete single file",
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          "position": [
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            720
          ],
          "parameters": {
            "url": "http:/YOUR_AWS_SECRET_KEY_HERE/delete",
            "method": "POST",
            "options": {},
            "jsonBody": "={\n  \"filter\": {\n    \"must\": [\n      {\n          \"key\": \"metadata.file_id\",\n          \"match\": { \"value\": \"{{$json.file_id}}\" }\n        }\n    ]\n  }\n}",
            "sendBody": true,
            "sendHeaders": true,
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                  "value": "application/json"
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              "id": "credential-id",
              "name": "httpHeaderAuth Credential"
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          "name": "Sticky Note3",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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            -280
          ],
          "parameters": {
            "color": 6,
            "width": 880,
            "height": 220,
            "content": "# STEP 1\n\n## Create Qdrant Collection\nChange:\n- QDRANTURL\n- COLLECTION"
          },
          "typeVersion": 1
        },
        {
          "id": "56e6534e-e191-4756-8bcf-d9ff8fc88b5f",
          "name": "Sticky Note",
          "type": "n8n-nodes-base.stickyNote",
          "position": [
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            700
          ],
          "parameters": {
            "height": 360,
            "content": "# STEP 3\n\n## FILE ID\nSet Google Drive File ID to update\n"
          },
          "typeVersion": 1
        },
        {
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          "name": "Clear collection",
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            "url": "http:/YOUR_AWS_SECRET_KEY_HERE/delete",
            "method": "POST",
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            "jsonBody": "{\n  \"filter\": {}\n}",
            "sendBody": true,
            "sendHeaders": true,
            "specifyBody": "json",
            "authentication": "genericCredentialType",
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              "id": "credential-id",
              "name": "httpHeaderAuth Credential"
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          "name": "Get files",
          "type": "n8n-nodes-base.googleDrive",
          "position": [
            120,
            220
          ],
          "parameters": {
            "filter": {
              "driveId": {
                "__rl": true,
                "mode": "list",
                "value": "My Drive",
                "cachedResultUrl": "https://drive.google.com/drive/my-drive",
                "cachedResultName": "My Drive"
              },
              "folderId": {
                "__rl": true,
                "mode": "list",
                "value": "1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
                "cachedResultUrl": "https://drive.google.com/drive/folders/1RO5ByPhq2yvYLmbapTNC_kKdU5lZd4W5",
                "cachedResultName": "Test Negozio"
              }
            },
            "options": {},
            "resource": "fileFolder"
          },
          "credentials": {
            "googleDriveOAuth2Api": {
              "id": "credential-id",
              "name": "googleDriveOAuth2Api Credential"
            }
          },
          "typeVersion": 3
        },
        {
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          ],
          "parameters": {
            "fileId": {
              "__rl": true,
              "mode": "id",
              "value": "={{ $json.id }}"
            },
            "options": {
              "googleFileConversion": {
                "conversion": {
                  "docsToFormat": "text/plain"
                }
              }
            },
            "operation": "download"
          },
          "credentials": {
            "googleDriveOAuth2Api": {
              "id": "credential-id",
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            "modelName": "models/gemini-1.5-flash"
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          "credentials": {
            "googlePalmApi": {
              "id": "credential-id",
              "name": "googlePalmApi Credential"
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          "name": "Vector Store Retriever",
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            1820
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          "typeVersion": 1
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          "name": "Qdrant Vector Store1",
          "type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
          "position": [
            -60,
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          ],
          "parameters": {
            "options": {},
            "qdrantCollection": {
              "__rl": true,
              "mode": "list",
              "value": "ocr_mistral_test",
              "cachedResultName": "ocr_mistral_test"
            }
          },
          "credentials": {
            "qdrantApi": {
              "id": "credential-id",
              "name": "qdrantApi Credential"
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          "position": [
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          ],
          "parameters": {
            "width": 500,
            "height": 120,
            "content": "## STEP 4\nTest the RAG"
          },
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          "name": "Embeddings OpenAI",
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          ],
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              "id": "credential-id",
              "name": "openAiApi Credential"
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          "parameters": {
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            "content": "# STEP 2\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n## Documents vectorization with Qdrant and Google Drive\nChange:\n- QDRANTURL\n- COLLECTION"
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                  "value": "DRIVEFILE_ID"
                }
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          "name": "Update single file",
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            "mode": "insert",
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            "qdrantCollection": {
              "__rl": true,
              "mode": "list",
              "value": "negozio-emporio-verde",
              "cachedResultName": "negozio-emporio-verde"
            }
          },
          "credentials": {
            "qdrantApi": {
              "id": "credential-id",
              "name": "qdrantApi Credential"
            }
          },
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        },
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          "name": "Sticky Note2",
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          "position": [
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          ],
          "parameters": {
            "color": 3,
            "width": 840,
            "height": 220,
            "content": "# Enables full or incremental updates to documents in RAG system using Qdrant \nThis workflow automates the creation and management of a Retrieval-Augmented Generation (RAG) system using Qdrant as a vector store and Google Drive as the document source. It **enables full or incremental updates to documents** in the Qdrant vector database and integrates with a chatbot using Google Gemini for question answering."
          },
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        }
      ],
      "active": false,
      "pinData": {},
      "settings": {
        "executionOrder": "v1"
      },
      "versionId": "630e34d7-6558-49c3-b861-520b33fd0f91",
      "connections": {
        "Wait": {
          "main": [
            [
              {
                "node": "Loop Over Items",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Get files": {
          "main": [
            [
              {
                "node": "Loop Over Items",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Edit Fields3": {
          "main": [
            [
              {
                "node": "Download file",
                "type": "main",
                "index": 0
              },
              {
                "node": "Delete single file",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Download file": {
          "main": [
            [
              {
                "node": "Update single file",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Download files": {
          "main": [
            [
              {
                "node": "Qdrant Vector Store",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Loop Over Items": {
          "main": [
            [],
            [
              {
                "node": "Download files",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Clear collection": {
          "main": [
            [
              {
                "node": "Get files",
                "type": "main",
                "index": 0
              }
            ]
          ]
        },
        "Embeddings OpenAI": {
          "ai_embedding": [
            [
              {
                "node": "Qdrant Vector Store1",
                "type": "ai_embedding",
                "index": 0
              }
            ]
          ]
        },
        "Embeddings OpenAI1": {
          "ai_embedding": [
            [
              {
                "node": "Qdrant Vector Store",
                "type": "ai_embedding",
                "index": 0
              }
            ]
          ]
        },
        "Embeddings OpenAI2": {
          "ai_embedding": [
            [
              {
                "node": "Update single file",
                "type": "ai_embedding",
                "index": 0
              }
            ]
          ]
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            "name": "Core Nodes"
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        "icon": "file:googleDrive.svg",
        "name": "n8n-nodes-base.googleDrive",
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                {
                  "url": "https://n8n.io/blog/your-business-doesnt-need-you-to-operate/",
                  "icon": " 🖥️",
                  "label": "Hey founders! Your business doesn't need you to operate"
                },
                {
                  "url": "https://n8n.io/blog/why-this-product-manager-loves-workflow-automation-with-n8n/",
                  "icon": "🧠",
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            "nodeVersion": "1.0",
            "codexVersion": "1.0"
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        "name": "n8n-nodes-base.wait",
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              "sleep",
              "delay",
              "timeout"
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                  "icon": "👥",
                  "label": "How to get started with CRM automation (with 3 no-code workflow ideas"
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            "subcategories": {
              "Core Nodes": [
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                "Flow"
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          }
        },
        "group": "[\"organization\"]",
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          "color": "#804050"
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          "type": "icon"
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        "displayName": "Wait",
        "typeVersion": 1,
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        "icon": "fa:sticky-note",
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              "Notes",
              "Sticky"
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            "subcategories": {
              "Core Nodes": [
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        "group": "[\"input\"]",
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            "name": "Core Nodes"
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          "color": "#909298"
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        "icon": "fa:link",
        "name": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
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              "Langchain"
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            "subcategories": {
              "AI": [
                "Chains",
                "Root Nodes"
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        "group": "[\"transform\"]",
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          "type": "icon"
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            "name": "AI"
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            "id": 26,
            "name": "Langchain"
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        "icon": "file:openAiLight.svg",
        "name": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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              "Langchain"
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            "subcategories": {
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            "name": "AI"
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          {
            "id": 26,
            "name": "Langchain"
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        "id": 1183,
        "icon": "fa:box-open",
        "name": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
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                }
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              "Langchain"
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            "subcategories": {
              "AI": [
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              ]
            }
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        },
        "group": "[\"transform\"]",
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          "name": "Vector Store Retriever"
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          "icon": "box-open",
          "type": "icon"
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        "displayName": "Vector Store Retriever",
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            "name": "AI"
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            "name": "Langchain"
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                }
              ]
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
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              ]
            }
          }
        },
        "group": "[\"transform\"]",
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          "name": "Recursive Character Text Splitter"
        },
        "iconData": {
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          "type": "icon"
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        "displayName": "Recursive Character Text Splitter",
        "typeVersion": 1,
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            "name": "AI"
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            "id": 26,
            "name": "Langchain"
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        "icon": "file:binary.svg",
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                }
              ]
            },
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              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Document Loaders"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Default Data Loader"
        },
        "iconData": {
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          "fileBuffer": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI3NjgiIGhlaWdodD0iMTAyNCI+PHBhdGggZmlsbD0iIzdEN0Q4NyIgZD0iTTAgOTYwVjY0aDU3NmwxOTIgMTkydjcwNHptNzA0LTY0MEw1MTIgMTI4SDY0djc2OGg2NDB6TTMyMCA1MTJIMTI4VjI1NmgxOTJ6bS02NC0xOTJoLTY0djEyOGg2NHptMCA0NDhoNjR2NjRIMTI4di02NGg2NFY2NDBoLTY0di02NGgxMjh6bTI1Ni0zMjBoNjR2NjRIMzg0di02NGg2NFYzMjBoLTY0di02NGgxMjh6bTY0IDM4NEgzODRWNTc2aDE5MnptLTY0LTE5MmgtNjR2MTI4aDY0eiIvPjwvc3ZnPg=="
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            "id": 25,
            "name": "AI"
          },
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            "id": 26,
            "name": "Langchain"
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        ]
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        "id": 1247,
        "icon": "fa:comments",
        "name": "@n8n/n8n-nodes-langchain.chatTrigger",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.chattrigger/"
                }
              ]
            },
            "categories": [
              "Core Nodes",
              "Langchain"
            ]
          }
        },
        "group": "[\"trigger\"]",
        "defaults": {
          "name": "When chat message received"
        },
        "iconData": {
          "icon": "comments",
          "type": "icon"
        },
        "displayName": "Chat Trigger",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 9,
            "name": "Core Nodes"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
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        "id": 1248,
        "icon": "file:qdrant.svg",
        "name": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
        "codex": {
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            "resources": {
              "primaryDocumentation": [
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                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Vector Stores",
                "Tools",
                "Root Nodes"
              ],
              "Tools": [
                "Other Tools"
              ],
              "Vector Stores": [
                "Other Vector Stores"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Qdrant Vector Store"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,PD94bWwgdmVyc2lvbj0iMS4wIiBlbmNvZGluZz0iVVRGLTgiPz4KPHN2ZyBkYXRhLW5hbWU9IkNhcGEgMiIgdmlld0JveD0iMCAwIDM0Ni40MiA0MDAiIHhtbG5zPSJodHRwOi8vd3d3LnczLm9yZy8yMDAwL3N2ZyI+CjxkZWZzPgo8c3R5bGU+LmNscy0xIHsKICAgICAgICBmaWxsOiAjOWUwZDM4OwogICAgICB9CgogICAgICAuY2xzLTIgewogICAgICAgIGZpbGw6ICNkYzI0NGM7CiAgICAgIH0KCiAgICAgIC5jbHMtMyB7CiAgICAgICAgZmlsbDogI2ZmNTE2YjsKICAgICAgfTwvc3R5bGU+CjwvZGVmcz4KPHBvbHlnb24gY2xhc3M9ImNscy0yIiBwb2ludHM9IjE3My4yMSAwIDAgMTAwIDAgMzAwIDE3My4yMSA0MDAgMjM4LjE2IDM2Mi41IDIzOC4xNiAyODcuNSAxNzMuMjEgMzI1IDY0Ljk2IDI2Mi41IDY0Ljk2IDEzNy41IDE3My4yMSA3NSAyODEuNDYgMTM3LjUgMjgxLjQ2IDM4Ny41IDM0Ni40MiAzNTAgMzQ2LjQyIDEwMCIvPgo8cG9seWdvbiBjbGFzcz0iY2xzLTIiIHBvaW50cz0iMTA4LjI2IDE2Mi41IDEwOC4yNiAyMzcuNSAxNzMuMjEgMjc1IDIzOC4xNiAyMzcuNSAyMzguMTYgMTYyLjUgMTczLjIxIDEyNSIvPgo8cG9seWdvbiBjbGFzcz0iY2xzLTEiIHBvaW50cz0iMjM4LjE2IDI4Ny41IDIzOC4xNiAzNjIuNSAxNzMuMjEgNDAwIDE3My4yMSAzMjUiLz4KPHBvbHlnb24gY2xhc3M9ImNscy0xIiBwb2ludHM9IjM0Ni40MiAxMDAgMzQ2LjQyIDM1MCAyODEuNDYgMzg3LjUgMjgxLjQ2IDEzNy41Ii8+Cjxwb2x5Z29uIGNsYXNzPSJjbHMtMyIgcG9pbnRzPSIzNDYuNDIgMTAwIDI4MS40NiAxMzcuNSAxNzMuMjEgNzUgNjQuOTYgMTM3LjUgMCAxMDAgMTczLjIxIDAiLz4KPHBvbHlnb24gY2xhc3M9ImNscy0yIiBwb2ludHM9IjE3My4yMSAzMjUgMTczLjIxIDQwMCAwIDMwMCAwIDEwMCA2NC45NiAxMzcuNSA2NC45NiAyNjIuNSIvPgo8cG9seWdvbiBjbGFzcz0iY2xzLTMiIHBvaW50cz0iMjM4LjE2IDE2Mi41IDE3My4yMSAyMDAgMTA4LjI2IDE2Mi41IDE3My4yMSAxMjUiLz4KPHBvbHlnb24gY2xhc3M9ImNscy0yIiBwb2ludHM9IjE3My4yMSAyMDAgMTczLjIxIDI3NSAxMDguMjYgMjM3LjUgMTA4LjI2IDE2Mi41Ii8+Cjxwb2x5Z29uIGNsYXNzPSJjbHMtMSIgcG9pbnRzPSIyMzguMTYgMTYyLjUgMjM4LjE2IDIzNy41IDE3My4yMSAyNzUgMTczLjIxIDIwMCIvPgo8L3N2Zz4K"
        },
        "displayName": "Qdrant Vector Store",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      },
      {
        "id": 1262,
        "icon": "file:google.svg",
        "name": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
        "codex": {
          "data": {
            "resources": {
              "primaryDocumentation": [
                {
                  "url": "https://docs.n8n.io/integrations/builtin/cluster-nodes/sub-nodes/n8n-nodes-langchain.lmchatgooglegemini/"
                }
              ]
            },
            "categories": [
              "AI",
              "Langchain"
            ],
            "subcategories": {
              "AI": [
                "Language Models",
                "Root Nodes"
              ],
              "Language Models": [
                "Chat Models (Recommended)"
              ]
            }
          }
        },
        "group": "[\"transform\"]",
        "defaults": {
          "name": "Google Gemini Chat Model"
        },
        "iconData": {
          "type": "file",
          "fileBuffer": "data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHhtbG5zOnhsaW5rPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5L3hsaW5rIiB2aWV3Qm94PSIwIDAgNDggNDgiPjxkZWZzPjxwYXRoIGlkPSJhIiBkPSJNNDQuNSAyMEgyNHY4LjVoMTEuOEMzNC43IDMzLjkgMzAuMSAzNyAyNCAzN2MtNy4yIDAtMTMtNS44LTEzLTEzczUuOC0xMyAxMy0xM2MzLjEgMCA1LjkgMS4xIDguMSAyLjlsNi40LTYuNEMzNC42IDQuMSAyOS42IDIgMjQgMiAxMS44IDIgMiAxMS44IDIgMjRzOS44IDIyIDIyIDIyYzExIDAgMjEtOCAyMS0yMiAwLTEuMy0uMi0yLjctLjUtNCIvPjwvZGVmcz48Y2xpcFBhdGggaWQ9ImIiPjx1c2UgeGxpbms6aHJlZj0iI2EiIG92ZXJmbG93PSJ2aXNpYmxlIi8+PC9jbGlwUGF0aD48cGF0aCBmaWxsPSIjRkJCQzA1IiBkPSJNMCAzN1YxMWwxNyAxM3oiIGNsaXAtcGF0aD0idXJsKCNiKSIvPjxwYXRoIGZpbGw9IiNFQTQzMzUiIGQ9Im0wIDExIDE3IDEzIDctNi4xTDQ4IDE0VjBIMHoiIGNsaXAtcGF0aD0idXJsKCNiKSIvPjxwYXRoIGZpbGw9IiMzNEE4NTMiIGQ9Im0wIDM3IDMwLTIzIDcuOSAxTDQ4IDB2NDhIMHoiIGNsaXAtcGF0aD0idXJsKCNiKSIvPjxwYXRoIGZpbGw9IiM0Mjg1RjQiIGQ9Ik00OCA0OCAxNyAyNGwtNC0zIDM1LTEweiIgY2xpcC1wYXRoPSJ1cmwoI2IpIi8+PC9zdmc+"
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        "displayName": "Google Gemini Chat Model",
        "typeVersion": 1,
        "nodeCategories": [
          {
            "id": 25,
            "name": "AI"
          },
          {
            "id": 26,
            "name": "Langchain"
          }
        ]
      }
    ],
    "categories": [
      {
        "id": 42,
        "name": "Internal Wiki"
      },
      {
        "id": 48,
        "name": "AI RAG"
      }
    ],
    "image": []
  }
}