8
n8n 한국어amn8n.com

파일 검색을 위해 RAG을 생성하는 OpenAI 참조

고급

이것은AI분야의자동화 워크플로우로, 19개의 노드를 포함합니다.주로 Set, Code, Markdown, SplitOut, Aggregate 등의 노드를 사용하며인공지능 기술을 결합하여 스마트 자동화를 구현합니다. 파일 검색 RAG을 위해 OpenAI 참조 생성

사전 요구사항
  • 대상 API의 인증 정보가 필요할 수 있음
  • OpenAI API Key

카테고리

워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
  "id": "5NAbfX550LJsfz6f",
  "meta": {
    "instanceId": "00493e38fecfc163cb182114bc2fab90114038eb9aad665a7a752d076920d3d5",
    "templateCredsSetupCompleted": true
  },
  "name": "Make OpenAI Citation for File Retrieval RAG",
  "tags": [
    {
      "id": "urxRtGxxLObZWPvX",
      "name": "sample",
      "createdAt": "2024-09-13T02:43:13.014Z",
      "updatedAt": "2024-09-13T02:43:13.014Z"
    },
    {
      "id": "nMXS3c9l1WqDwWF5",
      "name": "assist",
      "createdAt": "2024-12-23T16:09:38.737Z",
      "updatedAt": "2024-12-23T16:09:38.737Z"
    }
  ],
  "nodes": [
    {
      "id": "b9033511-3421-467a-9bfa-73af01b99c4f",
      "name": "집계",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        740,
        120
      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "a61dd9d3-4faa-4878-a6f3-ba8277279002",
      "name": "윈도우 버퍼 메모리",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        980,
        -320
      ],
      "parameters": {},
      "typeVersion": 1.3
    },
    {
      "id": "2daabca5-37ec-4cad-9157-29926367e1a7",
      "name": "메모4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        220,
        320
      ],
      "parameters": {
        "color": 3,
        "width": 840,
        "height": 80,
        "content": "## Within N8N, there will be a chat button to test"
      },
      "typeVersion": 1
    },
    {
      "id": "bf4485b1-cd94-41c8-a183-bf1b785f2761",
      "name": "메모",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -440,
        -520
      ],
      "parameters": {
        "color": 5,
        "width": 500,
        "height": 720,
        "content": "## Make OpenAI Citation for File Retrieval RAG\n\n## Use case\n\nIn this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags.\n\nThis is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example.\n\n## What this workflow does\n\nIn this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval.\n\nThe assistant will perform the file search within the OpenAI infrastructure and will return the content with citations.\n\n- We will make an HTTP request to retrieve all the details we need to format the text output.\n\n## Setup\n\nInsert an OpenAI Key\n\n## How to adjust it to your needs\n\nAt the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.\n\n\nby Davi Saranszky Mesquita\nhttps://www.linkedin.com/in/mesquitadavi/"
      },
      "typeVersion": 1
    },
    {
      "id": "539a4e40-9745-4a26-aba8-2cc2b0dd6364",
      "name": "Create a simple Trigger to have the Chat button within N8N",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "notes": "https://www.npmjs.com/package/@n8n/chat",
      "position": [
        260,
        -520
      ],
      "webhookId": "8ccaa299-6f99-427b-9356-e783893a3d0c",
      "parameters": {
        "options": {}
      },
      "notesInFlow": true,
      "typeVersion": 1.1
    },
    {
      "id": "aa5b2951-df32-43ac-9939-83b02d818e73",
      "name": "OpenAI Assistant with Vector Store",
      "type": "@n8n/n8n-nodes-langchain.openAi",
      "position": [
        580,
        -520
      ],
      "parameters": {
        "options": {
          "preserveOriginalTools": false
        },
        "resource": "assistant",
        "assistantId": {
          "__rl": true,
          "mode": "list",
          "value": "asst_QAfdobVCVCMJz8LmaEC7nlId",
          "cachedResultName": "Teste"
        }
      },
      "credentials": {
        "openAiApi": {
          "id": "UfNrqPCRlD8FD9mk",
          "name": "OpenAi Lourival"
        }
      },
      "typeVersion": 1.7
    },
    {
      "id": "1817b673-6cb3-49aa-9f38-a5876eb0e6fa",
      "name": "메모1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        560,
        -680
      ],
      "parameters": {
        "width": 300,
        "content": "## Setup\n\n- Configure OpenAI Key\n\n### In this step, we will use an assistant created within the OpenAI platform that contains a vector store a.k.a file retrieval"
      },
      "typeVersion": 1
    },
    {
      "id": "16429226-e850-4698-b419-fd9805a03fb7",
      "name": "Get ALL Thread Content",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        1260,
        -520
      ],
      "parameters": {
        "url": "=https://api.openai.com/v1/threads/{{ $json.threadId }}/messages",
        "options": {},
        "sendHeaders": true,
        "authentication": "predefinedCredentialType",
        "headerParameters": {
          "parameters": [
            {
              "name": "OpenAI-Beta",
              "value": "assistants=v2"
            }
          ]
        },
        "nodeCredentialType": "openAiApi"
      },
      "credentials": {
        "openAiApi": {
          "id": "UfNrqPCRlD8FD9mk",
          "name": "OpenAi Lourival"
        }
      },
      "typeVersion": 4.2,
      "alwaysOutputData": true
    },
    {
      "id": "e8c88b08-5be2-4f7e-8b17-8cf804b3fe9f",
      "name": "메모2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1160,
        -620
      ],
      "parameters": {
        "content": "### Retrieving all thread content is necessary because the OpenAI tool does not retrieve all citations upon request."
      },
      "typeVersion": 1
    },
    {
      "id": "0f51e09f-2782-4e2d-b797-d4d58fcabdaf",
      "name": "Split all message iterations from a thread",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        220,
        -300
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "data"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "4d569993-1ce3-4b32-beaf-382feac25da9",
      "name": "Split all content from a single message",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        460,
        -300
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "content"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "999e1c2b-1927-4483-aac1-6e8903f7ed25",
      "name": "Split all citations from a single message",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        700,
        -300
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "text.annotations"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "98af62f5-adb0-4e07-a146-fc2f13b851ce",
      "name": "Retrieve file name from a file ID",
      "type": "n8n-nodes-base.httpRequest",
      "onError": "continueRegularOutput",
      "position": [
        220,
        120
      ],
      "parameters": {
        "url": "=https://api.openai.com/v1/files/{{ $json.file_citation.file_id }}",
        "options": {},
        "sendQuery": true,
        "authentication": "predefinedCredentialType",
        "queryParameters": {
          "parameters": [
            {
              "name": "limit",
              "value": "1"
            }
          ]
        },
        "nodeCredentialType": "openAiApi"
      },
      "credentials": {
        "openAiApi": {
          "id": "UfNrqPCRlD8FD9mk",
          "name": "OpenAi Lourival"
        }
      },
      "typeVersion": 4.2,
      "alwaysOutputData": true
    },
    {
      "id": "b11f0d3d-bdc4-4845-b14b-d0b0de214f01",
      "name": "Regularize output",
      "type": "n8n-nodes-base.set",
      "position": [
        480,
        120
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "2dcaafee-5037-4a97-942a-bcdd02bc2ad9",
              "name": "id",
              "type": "string",
              "value": "={{ $json.id }}"
            },
            {
              "id": "b63f967d-ceea-4aa8-98b9-91f5ab21bfe8",
              "name": "filename",
              "type": "string",
              "value": "={{ $json.filename }}"
            },
            {
              "id": "f611e749-054a-441d-8610-df8ba42de2e1",
              "name": "text",
              "type": "string",
              "value": "={{ $('Split all citations from a single message').item.json.text }}"
            }
          ]
        }
      },
      "typeVersion": 3.4,
      "alwaysOutputData": true
    },
    {
      "id": "0e999a0e-76ed-4897-989b-228f075e9bfb",
      "name": "메모3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        440,
        -60
      ],
      "parameters": {
        "width": 200,
        "height": 220,
        "content": "### A file retrieval request contains a lot of information, and we want only the text that will be substituted and the file name.\n\n- id\n- filename\n- text\n"
      },
      "typeVersion": 1
    },
    {
      "id": "53c79a6c-7543-435f-b40e-966dff0904d4",
      "name": "메모5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        700,
        -60
      ],
      "parameters": {
        "width": 200,
        "height": 220,
        "content": "### With the last three splits, we may have many citations and texts to substitute. By doing an aggregation, it will be possible to handle everything as a single request."
      },
      "typeVersion": 1
    },
    {
      "id": "381fb6d6-64fc-4668-9d3c-98aaa43a45ca",
      "name": "메모6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        960,
        -60
      ],
      "parameters": {
        "height": 220,
        "content": "### This simple code will take all the previous files and citations and alter the original text, formatting the output. In this way, we can use Markdown tags to create links, or if you prefer, we can add an HTML transformation node."
      },
      "typeVersion": 1
    },
    {
      "id": "d0cbb943-57ab-4850-8370-1625610a852a",
      "name": "Optional Markdown to HTML",
      "type": "n8n-nodes-base.markdown",
      "disabled": true,
      "position": [
        1220,
        120
      ],
      "parameters": {
        "html": "={{ $json.output }}",
        "options": {},
        "destinationKey": "output"
      },
      "typeVersion": 1
    },
    {
      "id": "589e2418-5dec-47d0-ba08-420d84f09da7",
      "name": "Finnaly format the output",
      "type": "n8n-nodes-base.code",
      "position": [
        980,
        120
      ],
      "parameters": {
        "mode": "runOnceForEachItem",
        "jsCode": "let saida = $('OpenAI Assistant with Vector Store').item.json.output;\n\nfor (let i of $input.item.json.data) {\n  saida = saida.replaceAll(i.text, \"  _(\"+ i.filename+\")_  \");\n}\n\n$input.item.json.output = saida;\nreturn $input.item;"
      },
      "typeVersion": 2
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "0e621a5a-d99d-4db3-9ae4-ea98c31467e9",
  "connections": {
    "Aggregate": {
      "main": [
        [
          {
            "node": "589e2418-5dec-47d0-ba08-420d84f09da7",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "b11f0d3d-bdc4-4845-b14b-d0b0de214f01": {
      "main": [
        [
          {
            "node": "Aggregate",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Window Buffer Memory": {
      "ai_memory": [
        [
          {
            "node": "aa5b2951-df32-43ac-9939-83b02d818e73",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "16429226-e850-4698-b419-fd9805a03fb7": {
      "main": [
        [
          {
            "node": "0f51e09f-2782-4e2d-b797-d4d58fcabdaf",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "589e2418-5dec-47d0-ba08-420d84f09da7": {
      "main": [
        [
          {
            "node": "d0cbb943-57ab-4850-8370-1625610a852a",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "98af62f5-adb0-4e07-a146-fc2f13b851ce": {
      "main": [
        [
          {
            "node": "b11f0d3d-bdc4-4845-b14b-d0b0de214f01",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "aa5b2951-df32-43ac-9939-83b02d818e73": {
      "main": [
        [
          {
            "node": "16429226-e850-4698-b419-fd9805a03fb7",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "4d569993-1ce3-4b32-beaf-382feac25da9": {
      "main": [
        [
          {
            "node": "999e1c2b-1927-4483-aac1-6e8903f7ed25",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "999e1c2b-1927-4483-aac1-6e8903f7ed25": {
      "main": [
        [
          {
            "node": "98af62f5-adb0-4e07-a146-fc2f13b851ce",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "0f51e09f-2782-4e2d-b797-d4d58fcabdaf": {
      "main": [
        [
          {
            "node": "4d569993-1ce3-4b32-beaf-382feac25da9",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "539a4e40-9745-4a26-aba8-2cc2b0dd6364": {
      "main": [
        [
          {
            "node": "aa5b2951-df32-43ac-9939-83b02d818e73",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
자주 묻는 질문

이 워크플로우를 어떻게 사용하나요?

위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.

이 워크플로우는 어떤 시나리오에 적합한가요?

고급 - 인공지능

유료인가요?

이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.

워크플로우 정보
난이도
고급
노드 수19
카테고리1
노드 유형10
난이도 설명

고급 사용자를 위한 16+개 노드의 복잡한 워크플로우

외부 링크
n8n.io에서 보기

이 워크플로우 공유

카테고리

카테고리: 34