Supabase와 GPT-5를 기반으로 한 고급 다중 쿼리 RAG 시스템 구축

• •OpenAI API Key
• •Supabase URL과 API Key

{
  "nodes": [
    {
      "id": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
      "name": "AI 에이전트",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        208,
        -704
      ],
      "parameters": {
        "options": {
          "systemMessage": "=You are a helpful assistant that answers based on a biology course.\n\nFor that, you always start by calling the tool \"Query knowledge base\" to send an array of 1 to 5 questions that are relevant to ask to the RAG knowledge base that contains all the content of the course and get as an output all chunks that seem to help to craft the final answer. The more the user query is complex, the more you will break it down into sub-queries (up to 5).\n\nFrom there, use the Think tool to critically analyse the initial user query and the content you've retrieved from the knowledge retrieval tool and reason to prepare the best answer possible, challenge the content to be sure that you actually have the right information to be able to respond.\n\nOnly answer based on the course content that you get from using the tool, if you receive any question outside that scope, redirect the conversation, if you don't have the right information to answer, be transparent and say so - don't try to reply anyway with general knowledge.",
          "enableStreaming": false
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "4df46be3-c8b7-4f88-9af2-a644ca1bab2d",
      "name": "채팅 메시지 수신 시",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -256,
        -704
      ],
      "webhookId": "19fb162f-87ff-454f-96b2-cce0aaa6e22b",
      "parameters": {
        "public": true,
        "options": {
          "responseMode": "lastNode"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "5f07d924-7727-478a-abf6-eaf11543e19b",
      "name": "OpenAI 채팅 모델",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        48,
        -480
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-5-mini",
          "cachedResultName": "gpt-5-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "dMiSy27YCK6c6rra",
          "name": "Duv's OpenAI"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "dfc7c805-79cc-4326-8edb-f53a88af285d",
      "name": "심플 메모리",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        240,
        -480
      ],
      "parameters": {
        "contextWindowLength": 8
      },
      "typeVersion": 1.3
    },
    {
      "id": "7ade6fc1-84cc-48b2-bb20-672f0c5b4c27",
      "name": "분할",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        -160,
        96
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "queries"
      },
      "typeVersion": 1
    },
    {
      "id": "f4c92e45-e037-4477-ac50-1d6096fd902e",
      "name": "청크 집계",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        1312,
        0
      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData",
        "destinationFieldName": "All chunks for this question"
      },
      "typeVersion": 1
    },
    {
      "id": "cb5d42fe-9e27-4117-8a1c-9a78da8e770f",
      "name": "항목 집계",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        352,
        -208
      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData",
        "destinationFieldName": "Knowledge base retrieval"
      },
      "typeVersion": 1
    },
    {
      "id": "4e7f3e28-c316-4e21-b505-a211c1b23841",
      "name": "청크 존재 여부",
      "type": "n8n-nodes-base.if",
      "position": [
        1088,
        96
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "66402fe0-918e-4268-8928-f4e83cbb3c4f",
              "operator": {
                "type": "string",
                "operation": "exists",
                "singleValue": true
              },
              "leftValue": "={{ $json['Chunk content'] }}",
              "rightValue": ""
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "26d04029-da7f-4292-802a-4c233caef219",
      "name": "RAG 출력 정리",
      "type": "n8n-nodes-base.set",
      "position": [
        640,
        96
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "1eddb72f-9c99-465b-8f94-0ff0f686b542",
              "name": "Chunk content",
              "type": "string",
              "value": "={{ $json.document.pageContent }}"
            },
            {
              "id": "09fe6c91-2cce-40ff-9f8c-86a6857f0772",
              "name": "=Chunk metadata",
              "type": "object",
              "value": "={\n  \"Resource chapter name\": \"{{ $json.document.metadata['Chapter name'] }}\",\n  \"Retrieval relevance score\": {{ $json.score.round(2) }}\n}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "545514d9-107e-4af9-b407-7cdfc3770e3f",
      "name": "항목 루프 처리1",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        64,
        96
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "ebdbaea5-405f-4a58-b0b4-198154344329",
      "name": "RAG 서브 워크플로우",
      "type": "n8n-nodes-base.executeWorkflowTrigger",
      "position": [
        -384,
        96
      ],
      "parameters": {
        "workflowInputs": {
          "values": [
            {
              "name": "queries",
              "type": "array"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "d2362d6f-a6a0-4651-9f2b-827b8f7eb1c1",
      "name": "지식 베이스 쿼리",
      "type": "@n8n/n8n-nodes-langchain.toolWorkflow",
      "position": [
        432,
        -480
      ],
      "parameters": {
        "workflowId": {
          "__rl": true,
          "mode": "list",
          "value": "c9FlK6mLuWAwqLsP",
          "cachedResultName": "TEMPLATE RAG with Supabase and GPT5"
        },
        "description": "Call this tool to get content about the biology course before crafting your final user answer. Send an array of queries to the knowledge base.",
        "workflowInputs": {
          "value": {
            "queries": "={{ $fromAI('queries', `The array of queries (between 1 and 5) that you've planned to ask to the RAG knowledge base of the course. \nUse an Array format even if there's only one question - this is necessary to not break the workflow format!\n\nExample array output: \n\n[\n  {\n    \"query\": \"What is Lorem ipsum sir amet?\"\n  },\n  {\n    \"query\": \"How to lorem ipsum dolor sir lorem when lorem ipsum?'?\"\n  },\n  {\n    \"query\": \"Lorem ipsum lorem ipsum dolor sir lorem when lorem ipsum??\"\n  }\n]\n`, 'json') }}"
          },
          "schema": [
            {
              "id": "queries",
              "type": "array",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "queries",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "queries"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "db958756-f1a2-4162-afcf-2b6a0f936200",
      "name": "Supabase 벡터 스토어1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
        288,
        96
      ],
      "parameters": {
        "mode": "load",
        "prompt": "={{ $json.query }}",
        "options": {
          "queryName": "match_documents"
        },
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "documents",
          "cachedResultName": "documents"
        }
      },
      "credentials": {
        "supabaseApi": {
          "id": "WuxmgfzPKmocqt0M",
          "name": "Supabase account 2"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "478c2c07-ec28-427e-b33a-85a0f72c576f",
      "name": "임베딩 OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        368,
        320
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "G6pwE0s12sGlHRe3",
          "name": "1 - Plan A's OpenAI"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "da138097-8c28-4662-b916-8de388894330",
      "name": "스티키 노트1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -480,
        -832
      ],
      "parameters": {
        "color": 5,
        "width": 1472,
        "height": 528,
        "content": "# AI agent"
      },
      "typeVersion": 1
    },
    {
      "id": "93a8e212-2a8f-4e9f-8956-b1cca02da212",
      "name": "스티키 노트2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -480,
        -272
      ],
      "parameters": {
        "color": 4,
        "width": 2320,
        "height": 768,
        "content": "# Sub-workflow, tool for agent\n"
      },
      "typeVersion": 1
    },
    {
      "id": "21ade708-3f0e-4419-9edb-bc57fb543963",
      "name": "스티키 노트3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        816,
        -80
      ],
      "parameters": {
        "color": 7,
        "width": 688,
        "height": 432,
        "content": "## Filtering system\nOnly keeping chunks that have a score >0.4"
      },
      "typeVersion": 1
    },
    {
      "id": "ce4ce8ce-0f12-4dc6-ab24-585a81d71ca5",
      "name": "사고",
      "type": "@n8n/n8n-nodes-langchain.toolThink",
      "position": [
        608,
        -480
      ],
      "parameters": {
        "description": "Use this tool after you got the output of the knowledge retrieval tool to critically analyse the initial user query and the content you've retrieved from the knowledge retrieval tool and reason to prepare the best answer possible, challenge the content to be sure that you actually have the right information to be able to respond.\n\nBe very token efficient when using this tool, write 50 words max which is enough to reason."
      },
      "typeVersion": 1.1
    },
    {
      "id": "f1d619f3-42fb-4f48-83b3-3c0d1c43d574",
      "name": "스티키 노트",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1024,
        -832
      ],
      "parameters": {
        "width": 512,
        "height": 784,
        "content": "# Advanced Multi-Query RAG Agent\n\nThis template demonstrates a sophisticated RAG (Retrieval-Augmented Generation) pattern for building high-quality AI agents. It's designed to overcome the limitations of a basic RAG setup.\n\n## How it works\n\nInstead of a simple query, this agent uses a more intelligent, four-step process:\n1.  **Decompose:** It breaks complex questions into multiple, simpler sub-queries.\n2.  **Retrieve:** It sends these queries to a smart sub-workflow that fetches data from your vector store.\n3.  **Filter:** The sub-workflow filters out any retrieved information that doesn't meet a minimum relevance score, ensuring high-quality context.\n4.  **Synthesize:** The agent uses a \"Think\" tool to reason over the filtered information before crafting a final, comprehensive answer.\n\n## How to use\n\n1.  **Connect your accounts:** You need to connect **Supabase** and **OpenAI** in both this main workflow and in the \"RAG sub-workflow\".\n2.  **Customize the agent:** Edit the **AI Agent's system prompt** to match your specific knowledge base (e.g., \"You are a helpful assistant that answers based on our company's internal documents.\").\n3.  **Adjust the relevance filter:** In the sub-workflow, you can change the similarity score in the **Filter** node (default is >0.4) to control the quality of the retrieved information."
      },
      "typeVersion": 1
    },
    {
      "id": "b26b291d-9f95-4012-b830-cd07a9b8015f",
      "name": "0.4 초과 점수 유지",
      "type": "n8n-nodes-base.filter",
      "position": [
        864,
        96
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "9a3f844e-7d19-4631-9876-140118e61b6b",
              "operator": {
                "type": "number",
                "operation": "gt"
              },
              "leftValue": "={{ $json['Chunk metadata']['Retrieval relevance score'] }}",
              "rightValue": 0.4
            }
          ]
        }
      },
      "typeVersion": 2.2,
      "alwaysOutputData": true
    },
    {
      "id": "14d3efaf-dc35-491f-91df-f085829812ee",
      "name": "일치 청크 없음 알림",
      "type": "n8n-nodes-base.set",
      "position": [
        1312,
        192
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "245fe8f8-b217-4626-bc4d-84f53e47fbbf",
              "name": "Retrieval output",
              "type": "string",
              "value": "=No chunks reached the relevance threshold, the knowledge base was unable to provide information that would be helpful to answer this question."
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "e9eb2328-e9e2-4138-9d9e-468359a5e49d",
      "name": "루프 출력 준비",
      "type": "n8n-nodes-base.set",
      "position": [
        1568,
        192
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "838f21a4-f7bc-414e-83da-99fbaca4fcca",
              "name": "Query to the knowledge base",
              "type": "string",
              "value": "={{ $('Loop Over Items1').first().json.query }}"
            },
            {
              "id": "10a89085-1937-459f-9721-8715cd51ad39",
              "name": "Chunks returned",
              "type": "string",
              "value": "={{ JSON.stringify($json, null, 2) }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    }
  ],
  "connections": {
    "ce4ce8ce-0f12-4dc6-ab24-585a81d71ca5": {
      "ai_tool": [
        [
          {
            "node": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "14e54443-1722-476a-9f7a-44be7bd2b2bf": {
      "main": [
        []
      ]
    },
    "7ade6fc1-84cc-48b2-bb20-672f0c5b4c27": {
      "main": [
        [
          {
            "node": "545514d9-107e-4af9-b407-7cdfc3770e3f",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "4e7f3e28-c316-4e21-b505-a211c1b23841": {
      "main": [
        [
          {
            "node": "f4c92e45-e037-4477-ac50-1d6096fd902e",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "14d3efaf-dc35-491f-91df-f085829812ee",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "dfc7c805-79cc-4326-8edb-f53a88af285d": {
      "ai_memory": [
        [
          {
            "node": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "f4c92e45-e037-4477-ac50-1d6096fd902e": {
      "main": [
        [
          {
            "node": "e9eb2328-e9e2-4138-9d9e-468359a5e49d",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "26d04029-da7f-4292-802a-4c233caef219": {
      "main": [
        [
          {
            "node": "b26b291d-9f95-4012-b830-cd07a9b8015f",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "545514d9-107e-4af9-b407-7cdfc3770e3f": {
      "main": [
        [
          {
            "node": "cb5d42fe-9e27-4117-8a1c-9a78da8e770f",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "db958756-f1a2-4162-afcf-2b6a0f936200",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "ebdbaea5-405f-4a58-b0b4-198154344329": {
      "main": [
        [
          {
            "node": "7ade6fc1-84cc-48b2-bb20-672f0c5b4c27",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "5f07d924-7727-478a-abf6-eaf11543e19b": {
      "ai_languageModel": [
        [
          {
            "node": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "478c2c07-ec28-427e-b33a-85a0f72c576f": {
      "ai_embedding": [
        [
          {
            "node": "db958756-f1a2-4162-afcf-2b6a0f936200",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "14d3efaf-dc35-491f-91df-f085829812ee": {
      "main": [
        [
          {
            "node": "e9eb2328-e9e2-4138-9d9e-468359a5e49d",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "b26b291d-9f95-4012-b830-cd07a9b8015f": {
      "main": [
        [
          {
            "node": "4e7f3e28-c316-4e21-b505-a211c1b23841",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "e9eb2328-e9e2-4138-9d9e-468359a5e49d": {
      "main": [
        [
          {
            "node": "545514d9-107e-4af9-b407-7cdfc3770e3f",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "d2362d6f-a6a0-4651-9f2b-827b8f7eb1c1": {
      "ai_tool": [
        [
          {
            "node": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "db958756-f1a2-4162-afcf-2b6a0f936200": {
      "main": [
        [
          {
            "node": "26d04029-da7f-4292-802a-4c233caef219",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "4df46be3-c8b7-4f88-9af2-a644ca1bab2d": {
      "main": [
        [
          {
            "node": "14e54443-1722-476a-9f7a-44be7bd2b2bf",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}