{
  "name": "RAG AI Agent with Milvus and Cohere",
  "nodes": [
    {
      "id": "361065cc-edbf-47da-8da7-c59b564db6f3",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        0,
        320
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "a01b9512-ced1-4e28-a2aa-88077ab79d9a",
      "name": "Embeddings Cohere",
      "type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
      "position": [
        -140,
        320
      ],
      "parameters": {
        "modelName": "embed-multilingual-v3.0"
      },
      "typeVersion": 1
    },
    {
      "id": "1da6ea4b-de88-44d3-a215-78c55b5592a2",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -800,
        520
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "23004477-3f6d-4909-a626-0eba0557a5bd",
      "name": "Watch New Files",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
        -800,
        100
      ],
      "parameters": {
        "event": "fileCreated",
        "options": {},
        "pollTimes": {
          "item": [
            {
              "mode": "everyMinute"
            }
          ]
        },
        "triggerOn": "specificFolder",
        "folderToWatch": {
          "__rl": true,
          "mode": "list",
          "value": "REPLACE_WITH_RESOURCE"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "001fbdbe-dfcb-4552-bf09-de416b253389",
      "name": "Download New",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        -580,
        100
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {},
        "operation": "download"
      },
      "typeVersion": 3
    },
    {
      "id": "c1116cba-beb9-4d28-843d-c5c21c0643de",
      "name": "Insert into Milvus",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
      "position": [
        -124,
        100
      ],
      "parameters": {
        "mode": "insert",
        "options": {
          "clearCollection": false
        },
        "milvusCollection": {
          "__rl": true,
          "mode": "list",
          "value": "REPLACE_WITH_RESOURCE"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "2dbc7139-46f6-41d8-8c13-9fafad5aec55",
      "name": "RAG Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -540,
        520
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.8
    },
    {
      "id": "a103506e-9019-41f2-9b0d-9b831434c9e9",
      "name": "Retrieve from Milvus",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
      "position": [
        -340,
        740
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "topK": 10,
        "toolName": "vector_store",
        "toolDescription": "You are an AI agent that responds based on information received from a vector database.",
        "milvusCollection": {
          "__rl": true,
          "mode": "list",
          "value": "REPLACE_WITH_RESOURCE"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "74ccdff1-b976-4e1c-a2c4-237ffff19e34",
      "name": "OpenAI 4o",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        -580,
        740
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "REPLACE_WITH_RESOURCE"
        },
        "options": {}
      },
      "typeVersion": 1.2
    },
    {
      "id": "36e35eaf-f723-4eeb-9658-143d5bc390a0",
      "name": "Memory",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        -460,
        740
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "ec7b6b92-065c-455c-a3f0-17586d9e48d7",
      "name": "Cohere embeddings",
      "type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
      "position": [
        -220,
        900
      ],
      "parameters": {
        "modelName": "embed-multilingual-v3.0"
      },
      "typeVersion": 1
    },
    {
      "id": "3c3a8900-0b98-4479-8602-16b21e011ba1",
      "name": "Set Chunks",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        80,
        480
      ],
      "parameters": {
        "options": {},
        "chunkSize": 700,
        "chunkOverlap": 60
      },
      "typeVersion": 1
    },
    {
      "id": "3a43bf1a-7e22-4b5e-bbb1-6bb2c1798c07",
      "name": "Extract from File",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        -360,
        100
      ],
      "parameters": {
        "options": {},
        "operation": "pdf"
      },
      "typeVersion": 1
    },
    {
      "id": "e0c9d4d7-5e3e-4e47-bb1f-dbdca360b20a",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1440,
        120
      ],
      "parameters": {
        "color": 2,
        "width": 540,
        "height": 600,
        "content": "## Why Milvus\nBased on comparisons and user feedback, **Milvus is often considered a more performant and scalable vector database solution compared to Supabase**, particularly for demanding use cases involving large datasets, high-volume vector search operations, and multilingual support.\n\n\n### Requirements\n- Create an account on [Zilliz](https://zilliz.com/) to generate the Milvus cluster. \n- There is no need to create docker containers or your own instance, Zilliz provides the cloud infraestructure to build it easily\n- Get your credentials ready from Drive, Milvus (Zilliz), and [Cohere](https://cohere.com)\n\n### Usage\nEvery time a new pdf is added into the Drive folder, it will be inserted into the Milvus Vector Store, allowing for the interaction with the RAG agent in seconds.\n\n## Calculate your company's RAG costs\n\nWant to run Milvus on your own server on n8n? Zilliz provides a great [cost calculator](https://zilliz.com/rag-cost-calculator/)\n\n### Get in touch with us\nWant to implement a RAG AI agent for your company? [Shoot us a message](https://1node.ai)\n"
      },
      "typeVersion": 1
    },
    {
      "id": "fd0a7633-23a5-5b9e-aa5a-506281a5acfd",
      "name": "Publication · Overview and setup",
      "type": "n8n-nodes-base.stickyNote",
      "typeVersion": 1,
      "position": [
        -2240,
        0
      ],
      "parameters": {
        "content": "# RAG AI Agent with Milvus and Cohere\n\n## Purpose and structure\nThis folder contains the **RAG AI Agent with Milvus and Cohere** workflow. Its executable nodes use `agent`, `chatTrigger`, `documentDefaultDataLoader`, `embeddingsCohere`, `extractFromFile`, `googleDrive`, `googleDriveTrigger`, `lmChatOpenAi`, `memoryBufferWindow`, `textSplitterRecursiveCharacterTextSplitter`, `vectorStoreMilvus`. The table and connection map below are derived from the JSON, not an inferred guarantee of behavior.\n\n## Setup\nImport inactive. Configure your own credentials and replace REPLACE_WITH_* values. Review external destinations and Code nodes before running.\n\n## Entry points\nWhen chat message received, Watch New Files\n\n## Verification\nSanitized source candidate; not runtime-tested. This overview is generated from node metadata. Read the folder README for the connection map, credential types, and license.\n\n## Attribution\nhttps://github.com/Zie619/n8n-workflows/blob/94007c1445d9258a7da116646b79473e7c7c3282/workflows/Extractfromfile/0741_Extractfromfile_Stickynote_Automation_Triggered.json\nMIT; preserve source notices.",
        "width": 700,
        "height": 800,
        "color": 1
      }
    }
  ],
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "connections": {
    "Memory": {
      "ai_memory": [
        [
          {
            "node": "RAG Agent",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "OpenAI 4o": {
      "ai_languageModel": [
        [
          {
            "node": "RAG Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Set Chunks": {
      "ai_textSplitter": [
        [
          {
            "node": "Default Data Loader",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    },
    "Download New": {
      "main": [
        [
          {
            "node": "Extract from File",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Watch New Files": {
      "main": [
        [
          {
            "node": "Download New",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Cohere embeddings": {
      "ai_embedding": [
        [
          {
            "node": "Retrieve from Milvus",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Cohere": {
      "ai_embedding": [
        [
          {
            "node": "Insert into Milvus",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Extract from File": {
      "main": [
        [
          {
            "node": "Insert into Milvus",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "Insert into Milvus",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "Retrieve from Milvus": {
      "ai_tool": [
        [
          {
            "node": "RAG Agent",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "RAG Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}