n8n lokaler KI-Agent RAG-Vorlage

• •HTTP Webhook-Endpunkt (wird von n8n automatisch generiert)
• •PostgreSQL-Datenbankverbindungsdaten
• •OpenAI API Key

{
  "id": "dlA7uMt2f1hTW3xd",
  "meta": {
    "instanceId": "8cf060ebda3ec45b5ebb6a30779eaf0c03dfba83865feab3f32adb31b82caa08"
  },
  "name": "n8n Local AI Agentic RAG Template",
  "tags": [],
  "nodes": [
    {
      "id": "397d00eb-8034-49e5-a8f6-0a0fd9b97d5b",
      "name": "Standard-Datenlader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        3312,
        1280
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "=file_id",
                "value": "={{ $('Set File ID').first().json.file_id }}"
              },
              {
                "name": "file_title",
                "value": "={{ $('Set File ID').first().json.file_title }}"
              }
            ]
          }
        },
        "jsonData": "={{ $json.data || $json.text || $json.concatenated_data }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "e57065a2-9087-48e9-839e-d9c5c5fb477f",
      "name": "Haftnotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2304,
        144
      ],
      "parameters": {
        "color": 4,
        "width": 583.4552380860637,
        "height": 528.85546469693,
        "content": "## Agent Tools for RAG"
      },
      "typeVersion": 1
    },
    {
      "id": "f7efaf27-78fb-4429-beba-74ffcc700342",
      "name": "Haftnotiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        560,
        688
      ],
      "parameters": {
        "color": 5,
        "width": 3073,
        "height": 867,
        "content": "## Tool to Add a Google Drive File to Vector DB"
      },
      "typeVersion": 1
    },
    {
      "id": "a137d00b-fb01-408c-9963-645e2beb44d9",
      "name": "Dokumenttext extrahieren",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        2512,
        1280
      ],
      "parameters": {
        "options": {},
        "operation": "text"
      },
      "typeVersion": 1,
      "alwaysOutputData": true
    },
    {
      "id": "1aec304d-7264-4e65-8654-cb9294c96c82",
      "name": "Postgres-Chat-Speicher",
      "type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
      "position": [
        1712,
        512
      ],
      "parameters": {},
      "notesInFlow": false,
      "typeVersion": 1
    },
    {
      "id": "9c407f2b-4f6a-46d6-a607-225c1c628ae5",
      "name": "Datei-ID setzen",
      "type": "n8n-nodes-base.set",
      "position": [
        992,
        960
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "10646eae-ae46-4327-a4dc-9987c2d76173",
              "name": "file_id",
              "type": "string",
              "value": "={{ $json.path }}"
            },
            {
              "id": "f4536df5-d0b1-4392-bf17-b8137fb31a44",
              "name": "file_type",
              "type": "string",
              "value": "={{ $json.path.split(/[\\\\/]/).pop().split('.').pop(); }}"
            },
            {
              "id": "77d782de-169d-4a46-8a8e-a3831c04d90f",
              "name": "file_title",
              "type": "string",
              "value": "={{ $json.path.split(/[\\\\/]/).pop().split('.').slice(0, -1).join('.'); }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "bc93aa94-10ec-4670-99f4-3bcec36be1ce",
      "name": "Haftnotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1264,
        208
      ],
      "parameters": {
        "width": 1035.6381264595484,
        "height": 464.8027193303974,
        "content": "## RAG AI Agent with Chat Interface"
      },
      "typeVersion": 1
    },
    {
      "id": "8ccc451e-2fac-49b0-8700-085476add599",
      "name": "Auf Webhook antworten",
      "type": "n8n-nodes-base.respondToWebhook",
      "position": [
        2128,
        288
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "55abb8ac-7988-430a-ae41-5155471228a2",
      "name": "Felder bearbeiten",
      "type": "n8n-nodes-base.set",
      "position": [
        1568,
        288
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "9a9a245e-f1a1-4282-bb02-a81ffe629f0f",
              "name": "chatInput",
              "type": "string",
              "value": "={{ $json?.chatInput || $json.body.chatInput }}"
            },
            {
              "id": "b80831d8-c653-4203-8706-adedfdb98f77",
              "name": "sessionId",
              "type": "string",
              "value": "={{ $json?.sessionId || $json.body.sessionId}}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "78b3fd17-23e9-4693-b782-918a5a8e5aed",
      "name": "Bei Chat-Nachrichtenempfang",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        1312,
        288
      ],
      "webhookId": "e104e40e-6134-4825-a6f0-8a646d882662",
      "parameters": {
        "public": true,
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "06e362d1-d20c-407a-a75a-ed175c07439d",
      "name": "Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        1312,
        480
      ],
      "webhookId": "bf4dd093-bb02-472c-9454-7ab9af97bd1d",
      "parameters": {
        "path": "bf4dd093-bb02-472c-9454-7ab9af97bd1d",
        "options": {},
        "httpMethod": "POST",
        "responseMode": "responseNode"
      },
      "typeVersion": 2
    },
    {
      "id": "e8ba5c17-3426-4d76-b69b-ff91dff7958f",
      "name": "PDF-Text extrahieren",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        2512,
        720
      ],
      "parameters": {
        "options": {},
        "operation": "pdf"
      },
      "typeVersion": 1
    },
    {
      "id": "b40eb123-d7fc-4799-b248-4b9516aee49e",
      "name": "Aggregieren",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        2544,
        912
      ],
      "parameters": {
        "options": {},
        "aggregate": "aggregateAllItemData"
      },
      "typeVersion": 1
    },
    {
      "id": "0e3755e8-9532-447f-9137-f65d542c247e",
      "name": "Zusammenfassen",
      "type": "n8n-nodes-base.summarize",
      "position": [
        2752,
        992
      ],
      "parameters": {
        "options": {},
        "fieldsToSummarize": {
          "values": [
            {
              "field": "data",
              "aggregation": "concatenate"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "b185f2be-06bf-4a14-8d58-4b411a709f18",
      "name": "RAG-KI-Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1792,
        288
      ],
      "parameters": {
        "text": "={{ $json.chatInput }}",
        "options": {
          "systemMessage": "You are a personal assistant who helps answer questions from a corpus of documents. The documents are either text based (Txt, docs, extracted PDFs, etc.) or tabular data (CSVs or Excel documents).\n\nYou are given tools to perform RAG in the 'documents' table, look up the documents available in your knowledge base in the 'document_metadata' table, extract all the text from a given document, and query the tabular files with SQL in the 'document_rows' table.\n\nAlways start by performing RAG unless the users asks you to check a document or the question requires a SQL query for tabular data (fetching a sum, finding a max, something a RAG lookup would be unreliable for). If RAG doesn't help, then look at the documents that are available to you, find a few that you think would contain the answer, and then analyze those.\n\nAlways tell the user if you didn't find the answer. Don't make something up just to please them."
        },
        "promptType": "define"
      },
      "typeVersion": 1.6
    },
    {
      "id": "2ee45951-3553-49b7-9f79-3cef3d065e8a",
      "name": "Weiche",
      "type": "n8n-nodes-base.switch",
      "position": [
        1840,
        944
      ],
      "parameters": {
        "rules": {
          "values": [
            {
              "conditions": {
                "options": {
                  "version": 1,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "operator": {
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $('Set File ID').item.json.file_type }}",
                    "rightValue": "pdf"
                  }
                ]
              }
            },
            {
              "conditions": {
                "options": {
                  "version": 1,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "2ae7faa7-a936-4621-a680-60c512163034",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $('Set File ID').item.json.file_type }}",
                    "rightValue": "xlsx"
                  }
                ]
              }
            },
            {
              "conditions": {
                "options": {
                  "version": 1,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "fc193b06-363b-4699-a97d-e5a850138b0e",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $('Set File ID').item.json.file_type }}",
                    "rightValue": "=csv"
                  }
                ]
              }
            },
            {
              "conditions": {
                "options": {
                  "version": 1,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "b69f5605-0179-4b02-9a32-e34bb085f82d",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $('Set File ID').item.json.file_type }}",
                    "rightValue": "txt"
                  }
                ]
              }
            }
          ]
        },
        "options": {
          "fallbackOutput": 3
        }
      },
      "typeVersion": 3
    },
    {
      "id": "20bf7dde-e073-4288-a9d6-34df3973b5c3",
      "name": "Aus Excel extrahieren",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        2320,
        912
      ],
      "parameters": {
        "options": {},
        "operation": "xlsx"
      },
      "typeVersion": 1
    },
    {
      "id": "f1840995-3f1c-4f4e-9d78-bc9225ecbe2b",
      "name": "Schema setzen",
      "type": "n8n-nodes-base.set",
      "position": [
        3184,
        848
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "f422e2e0-381c-46ea-8f38-3f58c501d8b9",
              "name": "schema",
              "type": "string",
              "value": "={{ $('Extract from Excel').isExecuted ? $('Extract from Excel').first().json.keys().toJsonString() : $('Extract from CSV').first().json.keys().toJsonString() }}"
            },
            {
              "id": "bb07c71e-5b60-4795-864c-cc3845b6bc46",
              "name": "data",
              "type": "string",
              "value": "={{ $json.concatenated_data }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "b79ceb0b-f370-4ffb-9953-14b411acb5d9",
      "name": "Aus CSV extrahieren",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        2320,
        1088
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "7067874e-4123-4a6c-a94d-89e4d1878309",
      "name": "Haftnotiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        560,
        368
      ],
      "parameters": {
        "color": 3,
        "width": 680,
        "height": 300,
        "content": "## Run Each Node Once to Set Up Database Tables"
      },
      "typeVersion": 1
    },
    {
      "id": "130c53e8-d507-4b6f-b1cf-f79dbc571c46",
      "name": "Dokument-Metadaten-Tabelle erstellen",
      "type": "n8n-nodes-base.postgres",
      "position": [
        688,
        464
      ],
      "parameters": {
        "query": "CREATE TABLE document_metadata (\n    id TEXT PRIMARY KEY,\n    title TEXT,\n    created_at TIMESTAMP DEFAULT NOW(),\n    schema TEXT\n);",
        "options": {},
        "operation": "executeQuery"
      },
      "typeVersion": 2.5
    },
    {
      "id": "421d2123-b68a-4c51-a482-db5bdffd3f76",
      "name": "Dokument-Zeilen-Tabelle erstellen (für Tabellendaten)",
      "type": "n8n-nodes-base.postgres",
      "position": [
        992,
        464
      ],
      "parameters": {
        "query": "CREATE TABLE document_rows (\n    id SERIAL PRIMARY KEY,\n    dataset_id TEXT REFERENCES document_metadata(id),\n    row_data JSONB  -- Store the actual row data\n);",
        "options": {},
        "operation": "executeQuery"
      },
      "typeVersion": 2.5
    },
    {
      "id": "55ff6535-bedb-479f-b3da-eb45e1127e77",
      "name": "Dokumente auflisten",
      "type": "n8n-nodes-base.postgresTool",
      "position": [
        1840,
        512
      ],
      "parameters": {
        "table": {
          "__rl": true,
          "mode": "list",
          "value": "document_metadata",
          "cachedResultName": "document_metadata"
        },
        "schema": {
          "__rl": true,
          "mode": "list",
          "value": "public"
        },
        "options": {},
        "operation": "select",
        "returnAll": true,
        "descriptionType": "manual",
        "toolDescription": "Use this tool to fetch all available documents, including the table schema if the file is a CSV or Excel file."
      },
      "typeVersion": 2.5
    },
    {
      "id": "ffcb630b-5119-4ff6-b85a-d77eeb8d5713",
      "name": "Dateiinhalte abrufen",
      "type": "n8n-nodes-base.postgresTool",
      "position": [
        1984,
        512
      ],
      "parameters": {
        "query": "SELECT \n    string_agg(text, ' ') as document_text\nFROM documents_pg\n  WHERE metadata->>'file_id' = $1\nGROUP BY metadata->>'file_id';",
        "options": {
          "queryReplacement": "={{ $fromAI('file_id') }}"
        },
        "operation": "executeQuery",
        "descriptionType": "manual",
        "toolDescription": "Given a file ID, fetches the text from the document."
      },
      "typeVersion": 2.5
    },
    {
      "id": "f504b2f4-ffb5-4ef7-ba93-753151b77d9e",
      "name": "Dokument-Zeilen abfragen",
      "type": "n8n-nodes-base.postgresTool",
      "position": [
        2144,
        512
      ],
      "parameters": {
        "query": "{{ $fromAI('sql_query') }}",
        "options": {},
        "operation": "executeQuery",
        "descriptionType": "manual",
        "toolDescription": "Run a SQL query - use this to query from the document_rows table once you know the file ID (which is the file path) you are querying. dataset_id is the file_id (file path) and you are always using the row_data for filtering, which is a jsonb field that has all the keys from the file schema given in the document_metadata table.\n\nExample query:\n\nSELECT AVG((row_data->>'revenue')::numeric)\nFROM document_rows\nWHERE dataset_id = '/data/shared/document.csv';\n\nExample query 2:\n\nSELECT \n    row_data->>'category' as category,\n    SUM((row_data->>'sales')::numeric) as total_sales\nFROM dataset_rows\nWHERE dataset_id = '/data/shared/document2.csv'\nGROUP BY row_data->>'category';"
      },
      "typeVersion": 2.5
    },
    {
      "id": "4abe03ca-297c-4509-b0db-7bed4338a158",
      "name": "Über Elemente iterieren",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        800,
        800
      ],
      "parameters": {
        "options": {
          "reset": false
        }
      },
      "typeVersion": 3
    },
    {
      "id": "e382d750-85ba-492d-9d3e-eb839af0bfc1",
      "name": "Dokument-Metadaten einfügen",
      "type": "n8n-nodes-base.postgres",
      "position": [
        1488,
        832
      ],
      "parameters": {
        "table": {
          "__rl": true,
          "mode": "list",
          "value": "document_metadata",
          "cachedResultName": "document_metadata"
        },
        "schema": {
          "__rl": true,
          "mode": "list",
          "value": "public"
        },
        "columns": {
          "value": {
            "id": "={{ $('Set File ID').item.json.file_id }}",
            "title": "={{ $('Set File ID').item.json.file_title }}"
          },
          "schema": [
            {
              "id": "id",
              "type": "string",
              "display": true,
              "removed": false,
              "required": true,
              "displayName": "id",
              "defaultMatch": true,
              "canBeUsedToMatch": true
            },
            {
              "id": "title",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "title",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "url",
              "type": "string",
              "display": true,
              "removed": true,
              "required": false,
              "displayName": "url",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "created_at",
              "type": "dateTime",
              "display": true,
              "required": false,
              "displayName": "created_at",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "schema",
              "type": "string",
              "display": true,
              "removed": true,
              "required": false,
              "displayName": "schema",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "id"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "upsert"
      },
      "executeOnce": true,
      "typeVersion": 2.5
    },
    {
      "id": "bbf6f704-b4a2-4ff2-ac09-27626526b35f",
      "name": "Tabellenzeilen einfügen",
      "type": "n8n-nodes-base.postgres",
      "position": [
        2544,
        1088
      ],
      "parameters": {
        "table": {
          "__rl": true,
          "mode": "list",
          "value": "document_rows",
          "cachedResultName": "document_rows"
        },
        "schema": {
          "__rl": true,
          "mode": "list",
          "value": "public"
        },
        "columns": {
          "value": {
            "row_data": "={{ $json.toJsonString().replaceAll(/'/g, \"''\") }}",
            "dataset_id": "={{ $('Set File ID').item.json.file_id }}"
          },
          "schema": [
            {
              "id": "id",
              "type": "number",
              "display": true,
              "removed": true,
              "required": false,
              "displayName": "id",
              "defaultMatch": true,
              "canBeUsedToMatch": true
            },
            {
              "id": "dataset_id",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "dataset_id",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "row_data",
              "type": "object",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "row_data",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "id"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {}
      },
      "typeVersion": 2.5
    },
    {
      "id": "3265a7df-dd40-421e-b1fb-53293a7460f8",
      "name": "Schema für Dokument-Metadaten aktualisieren",
      "type": "n8n-nodes-base.postgres",
      "position": [
        3408,
        848
      ],
      "parameters": {
        "table": {
          "__rl": true,
          "mode": "list",
          "value": "document_metadata",
          "cachedResultName": "document_metadata"
        },
        "schema": {
          "__rl": true,
          "mode": "list",
          "value": "public"
        },
        "columns": {
          "value": {
            "id": "={{ $('Set File ID').item.json.file_id }}",
            "schema": "={{ $json.schema }}"
          },
          "schema": [
            {
              "id": "id",
              "type": "string",
              "display": true,
              "removed": false,
              "required": true,
              "displayName": "id",
              "defaultMatch": true,
              "canBeUsedToMatch": true
            },
            {
              "id": "title",
              "type": "string",
              "display": true,
              "removed": true,
              "required": false,
              "displayName": "title",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "url",
              "type": "string",
              "display": true,
              "removed": true,
              "required": false,
              "displayName": "url",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "created_at",
              "type": "dateTime",
              "display": true,
              "required": false,
              "displayName": "created_at",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "schema",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "schema",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "id"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "upsert"
      },
      "typeVersion": 2.5
    },
    {
      "id": "53f9f045-bb08-4b22-a11e-dfd2c964b687",
      "name": "Haftnotiz9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        0,
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      ],
      "parameters": {
        "color": 6,
        "width": 540,
        "height": 1320,
        "content": "## 🚀 n8n Local AI Agentic RAG Template\n\n**Author:** [Jadai kongolo](https://my.jadaikongolo.tech)\n\n## What is this?\nThis template provides an entirely local implementation of an **Agentic RAG (Retrieval Augmented Generation)** system in n8n that can be extended easily for your specific use case and knowledge base. Unlike standard RAG which only performs simple lookups, this agent can reason about your knowledge base, self-improve retrieval, and dynamically switch between different tools based on the specific question. \n\n## Why Agentic RAG?\nStandard RAG has significant limitations:\n- Poor analysis of numerical/tabular data\n- Missing context due to document chunking\n- Inability to connect information across documents\n- No dynamic tool selection based on question type\n\n## What makes this template powerful:\n- **Intelligent tool selection**: Switches between RAG lookups, SQL queries, or full document retrieval based on the question\n- **Complete document context**: Accesses entire documents when needed instead of just chunks\n- **Accurate numerical analysis**: Uses SQL for precise calculations on spreadsheet/tabular data\n- **Cross-document insights**: Connects information across your entire knowledge base\n- **Multi-file processing**: Handles multiple documents in a single workflow loop\n- **Efficient storage**: Uses JSONB in Supabase to store tabular data without creating new tables for each CSV\n\n## Getting Started\n1. Run the table creation nodes first to set up your database tables in Supabase\n2. Upload your documents to the folder on your computer that is mounted to /data/shared in the n8n container. This folder by default is the \"shared\" folder in the local AI package.\n3. The agent will process them automatically (chunking text, storing tabular data in Supabase)\n4. Start asking questions that leverage the agent's multiple reasoning approaches\n\n## Customization\nThis template provides a solid foundation that you can extend by:\n- Tuning the system prompt for your specific use case\n- Adding document metadata like summaries\n- Implementing more advanced RAG techniques\n- Optimizing for larger knowledge bases\n\n---\n\nThe non-local (\"cloud\") version of this Agentic RAG agent can be [found here](https://kongolo.gumroad.com/l/anxwv)."
      },
      "typeVersion": 1
    },
    {
      "id": "cdee87fe-e154-47ab-9330-32dee5c213d3",
      "name": "Lokaler Datei-Trigger",
      "type": "n8n-nodes-base.localFileTrigger",
      "position": [
        608,
        800
      ],
      "parameters": {
        "path": "/data/shared",
        "events": [
          "add",
          "change"
        ],
        "options": {
          "usePolling": true,
          "followSymlinks": true
        },
        "triggerOn": "folder"
      },
      "typeVersion": 1
    },
    {
      "id": "67311475-7928-4ddc-957a-79817c98d26d",
      "name": "Dateien von Festplatte lesen/schreiben",
      "type": "n8n-nodes-base.readWriteFile",
      "position": [
        1648,
        960
      ],
      "parameters": {
        "options": {
          "dataPropertyName": "=data"
        },
        "fileSelector": "={{ $('Set File ID').item.json.file_id }}"
      },
      "typeVersion": 1
    },
    {
      "id": "366e800a-9bd7-4822-a11c-f555800bbba6",
      "name": "Embeddings Ollama",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
      "position": [
        3072,
        1280
      ],
      "parameters": {
        "model": "nomic-embed-text:latest"
      },
      "typeVersion": 1
    },
    {
      "id": "be37cfb9-ea40-4244-87d7-b562be315573",
      "name": "Embeddings Ollama1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
      "position": [
        2560,
        480
      ],
      "parameters": {
        "model": "nomic-embed-text:latest"
      },
      "typeVersion": 1
    },
    {
      "id": "1306b972-2b24-4c62-846e-f1c5b3d0482c",
      "name": "Rekursiver Zeichentext-Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        3200,
        1408
      ],
      "parameters": {
        "options": {},
        "chunkSize": 400
      },
      "typeVersion": 1
    },
    {
      "id": "677ad468-8118-4f8f-9a47-f5429cdc7582",
      "name": "Ollama (Basis-URL ändern)",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1568,
        512
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "qwen2.5:14b-8k",
          "cachedResultName": "qwen2.5:14b-8k"
        },
        "options": {}
      },
      "typeVersion": 1.2
    },
    {
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      "name": "Haftnotiz4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
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        "width": 540,
        "height": 200,
        "content": "## NOTE\n\nThe Ollama chat model node doesn't work with the RAG nodes - known issue with n8n.\n\nSo for now, we are using the OpenAI chat model but changing the base URL to Ollama when creating the credentials (i.e. http://ollama:11434/v1). The API key can be set to whatever, it isn't used for local LLMs."
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      "type": "n8n-nodes-base.postgres",
      "position": [
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        832
      ],
      "parameters": {
        "query": "DO $$\nBEGIN\n    IF EXISTS (SELECT 1 FROM information_schema.tables WHERE table_name = 'documents_pg') THEN\n        EXECUTE 'DELETE FROM documents_pg WHERE metadata->>''file_id'' LIKE ''%' || $1 || '%''';\n    END IF;\nEND\n$$;",
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          "queryReplacement": "={{ $json.file_id }}"
        },
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      "name": "Alte Dateneinträge löschen",
      "type": "n8n-nodes-base.postgres",
      "position": [
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      ],
      "parameters": {
        "query": "DELETE FROM document_rows\nWHERE dataset_id LIKE '%' || $1 || '%';",
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        "operation": "executeQuery"
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    {
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      "name": "Postgres PGVector Store",
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      "position": [
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      ],
      "parameters": {
        "mode": "insert",
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        "tableName": "documents_pg"
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      "typeVersion": 1
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    {
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        "mode": "retrieve-as-tool",
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        "toolName": "documents",
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