Kontextbasierte hybride RAG-KI-Texterstellung

• •Supabase URL und API Key
• •Google Drive API-Anmeldedaten
• •Möglicherweise sind Ziel-API-Anmeldedaten erforderlich
• •OpenAI API Key

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    "templateCredsSetupCompleted": true
  },
  "name": "Contextual Hybrid RAG AI copy",
  "tags": [
    {
      "id": "YINdrIOThMQjiVkB",
      "name": "RAG",
      "createdAt": "2025-06-18T21:07:48.174Z",
      "updatedAt": "2025-06-18T21:07:48.174Z"
    },
    {
      "id": "j0SNhalFSbPAhdWo",
      "name": "n8n creator",
      "createdAt": "2025-09-02T01:40:23.866Z",
      "updatedAt": "2025-09-02T01:40:23.866Z"
    }
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      "type": "@n8n/n8n-nodes-langchain.agent",
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      ],
      "parameters": {
        "options": {
          "systemMessage": "Only generate answers based on the results from the connected database. Do no under any circumstance, get answers from anywhere else.\nfor every query check the database to see if you can find an answer. If you can not or are not sure, then say 'Sorry, I don’t know'. Never come up with answers. Only get answers from the database"
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      "webhookId": "f4516070-1da1-41ad-8320-bd3a31cddf12",
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      ],
      "parameters": {
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      "typeVersion": 1.3
    },
    {
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    {
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      "name": "Google Drive",
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      "parameters": {
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        "operation": "download"
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        "googleDriveOAuth2Api": {
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    },
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    },
    {
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      "name": "Zeile in Datensatz-Manager erstellen",
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          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "9a68682b-7a2f-40ca-91a9-e0d935ec7ed3",
      "name": "Haftnotiz1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -4016,
        144
      ],
      "parameters": {
        "width": 160,
        "height": 304,
        "content": "### Watch GD folder\n"
      },
      "typeVersion": 1
    },
    {
      "id": "22acc35b-1370-450f-90f2-ff4a2c9f61e4",
      "name": "Haftnotiz6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -3136,
        144
      ],
      "parameters": {
        "width": 160,
        "height": 300,
        "content": "### Set Text"
      },
      "typeVersion": 1
    },
    {
      "id": "cb0d4e98-3716-4bba-8f33-a1c0ee5224a7",
      "name": "Haftnotiz7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2896,
        144
      ],
      "parameters": {
        "width": 172,
        "height": 308,
        "content": " ### Generate Hash based on the text. If text changes we have a different hash"
      },
      "typeVersion": 1
    },
    {
      "id": "6f0493ae-8973-48c8-9338-f62d1b826445",
      "name": "Haftnotiz8",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2656,
        144
      ],
      "parameters": {
        "width": 188,
        "height": 308,
        "content": "### Search the record manager to see if we have any files in the database, that have the same file id. If it does, it will return the hash"
      },
      "typeVersion": 1
    },
    {
      "id": "6336a34a-fade-435c-b0a7-5257b48c5579",
      "name": "Haftnotiz9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2432,
        112
      ],
      "parameters": {
        "width": 180,
        "height": 340,
        "content": "### Compare the Hash from generated hash and (if it exists) hash from record manager search to determine if file exists or not and if modified"
      },
      "typeVersion": 1
    },
    {
      "id": "be85f58e-3cff-42f7-b1ff-d1f22c781961",
      "name": "Haftnotiz10",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2080,
        256
      ],
      "parameters": {
        "width": 640,
        "height": 280,
        "content": "### If the doc is modified, we delete all the vectors related to that google id and update record manager id and hash"
      },
      "typeVersion": 1
    },
    {
      "id": "79ee3377-d001-4bd7-8d25-c48e5676aff7",
      "name": "Haftnotiz11",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1328,
        176
      ],
      "parameters": {
        "width": 380,
        "height": 220,
        "content": "### Create summary of document for metadata"
      },
      "typeVersion": 1
    },
    {
      "id": "2943dad5-ae6b-4fde-a293-eb8e319bc41b",
      "name": "Haftnotiz12",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -896,
        176
      ],
      "parameters": {
        "width": 160,
        "height": 224,
        "content": "### Set text to send to sub-workflow"
      },
      "typeVersion": 1
    },
    {
      "id": "6109b822-9860-4a85-860b-15dd4494bac7",
      "name": "Haftnotiz14",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2464,
        864
      ],
      "parameters": {
        "width": 156,
        "height": 280,
        "content": "### Watch GD Trash folder"
      },
      "typeVersion": 1
    },
    {
      "id": "4522d72b-9a92-4614-a2c8-f6a739d2169d",
      "name": "Haftnotiz15",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1984,
        864
      ],
      "parameters": {
        "width": 192,
        "height": 264,
        "content": "### Search record manager on corresponding GD file id"
      },
      "typeVersion": 1
    },
    {
      "id": "c57592c9-e02e-4861-a1e3-189f3b24f7cf",
      "name": "Haftnotiz16",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1760,
        864
      ],
      "parameters": {
        "width": 160,
        "height": 264,
        "content": "### if records exist for this id or not\n"
      },
      "typeVersion": 1
    },
    {
      "id": "9af264c1-de52-4fd7-9366-7d573499b02a",
      "name": "Haftnotiz17",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1520,
        864
      ],
      "parameters": {
        "width": 580,
        "height": 264,
        "content": "### Delete records from supabase"
      },
      "typeVersion": 1
    },
    {
      "id": "0403746d-e354-4d88-93d4-a05f9faa4aea",
      "name": "Haftnotiz18",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -880,
        864
      ],
      "parameters": {
        "width": 150,
        "height": 264,
        "content": "### Delete file from GD"
      },
      "typeVersion": 1
    },
    {
      "id": "d48e00db-570d-41b5-a5db-4435059fe3ec",
      "name": "Haftnotiz19",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2304,
        -624
      ],
      "parameters": {
        "width": 1020,
        "height": 240,
        "content": "## Query Seach Supabase Database Tool with workflow"
      },
      "typeVersion": 1
    },
    {
      "id": "1af9acb3-e6a8-435d-9d91-cb84c5fd608b",
      "name": "Haftnotiz20",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -688,
        192
      ],
      "parameters": {
        "width": 150,
        "height": 248,
        "content": "### Set up data\n"
      },
      "typeVersion": 1
    },
    {
      "id": "936e7ead-1a8c-4337-b784-5732ef2784ac",
      "name": "Haftnotiz",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -6000,
        -16
      ],
      "parameters": {
        "width": 752,
        "height": 800,
        "content": "## Setting up Supabase\n\n\n### Create a project, and fill in the details as guided by Supabase.  On the left hand side, you will find the Project Overview section. Scroll down to connecting your project section and you will find the url and api key, which you can place in the credentials section or in the create new credentials in a supabase node.\n\n\n\n\n\n## Setting up the Hybrid Search in Supabase\n### You can learn more about hybrid search here: https://supabase.com/docs/guides/ai/hybrid-search\n\n### Go to the SQL Editor tab, and delete the existing code. First we will create the documents for the Hybrid Search called documentsHS. Find the code in a note in this workflow to the left of this sticky note. Copy & paste the code in the SQL Editor and click run. You will get a message saying pg_notify. The documentsHS is complete. Now delete the code and copy paste the code for the record manager called record_managerHS in the sticky note on the left. You will get a message saying Success. No Rows Returned. You have now created the tables for the hybrid search. \n\n## To call the hybrid search, we will use the edge function.\n### To create the edge function, go to the edge function (left hand side bar), click deploy a new function, and select Via AI Assistant.  Paste \"Create a new edge function that calls on the match_documentshs_hybrid so it can be called through API\". This will create your edge function and you can click on deploy to deploy it. In the details section, you will find the endpoint URL that you can paste into the URL in the HTTP Request (called Edge Function). Make sure to copy the Bearer YOUR_API_KEY as well and create a Header Auth to store it. You will find that in the Generic Auth Type under Header Auth. That will go in the Value section and the word 'Authorization' in the Name. You have now successfully completed the setup for the hybrid search."
      },
      "typeVersion": 1
    },
    {
      "id": "76cf1951-f712-4d05-ab00-c5201a4bdf15",
      "name": "Haftnotiz2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -7456,
        -784
      ],
      "parameters": {
        "width": 656,
        "height": 2416,
        "content": "# documentHS\n\n\n-- Extensions\ncreate extension if not exists vector;\ncreate extension if not exists pg_trgm;\n\n-- Table\ncreate table if not exists documentsHS (\n  id bigserial primary key,\n  content text,\n  metadata jsonb,\n  embedding vector(1536),\n  tokens tsvector generated always as (\n    to_tsvector('english', coalesce(content, ''))\n  ) stored\n);\n\n-- Indexes\ncreate index if not exists documentsHS_tokens_gin\n  on documentsHS using gin (tokens);\n\ncreate index if not exists documentsHS_embedding_ivfflat\n  on documentsHS using ivfflat (embedding vector_cosine_ops) with (lists = 100);\n\ncreate index if not exists documentsHS_metadata_gin\n  on documentsHS using gin (metadata);\n\n-- ==========================================\n-- Single HYBRID function exposed via RPC\n-- ==========================================\ncreate or replace function public.match_documentshs_hybrid(\n  query_embedding float4[],           -- required; send as JSON array\n  query_text text,                    -- required\n  match_count int default 10,\n  semantic_weight float default 0.5,  -- 0..1 (higher = more semantic)\n  filter jsonb default '{}'           -- optional structured filter\n)\nreturns table (\n  id bigint,\n  content text,\n  metadata jsonb,\n  similarity double precision,\n  keyword_rank double precision,\n  hybrid_score double precision\n)\nlanguage sql\nstable\nas $$\n  with base as (\n    select\n      d.id,\n      d.content,\n      d.metadata,\n      (1 - (d.embedding <=> (query_embedding::vector(1536)))) as sim,\n      ts_rank(d.tokens, plainto_tsquery('english', query_text)) as kw_rank\n    from documentsHS d\n    where d.metadata @> filter\n  ),\n  stats as (\n    select max(sim) as max_sim, max(kw_rank) as max_kw from base\n  )\n  select\n    b.id,\n    b.content,\n    b.metadata,\n    b.sim as similarity,\n    b.kw_rank as keyword_rank,\n    (\n      coalesce(case when s.max_sim > 0 then b.sim / s.max_sim else 0 end, 0) * semantic_weight\n      +\n      coalesce(case when s.max_kw  > 0 then b.kw_rank / s.max_kw else 0 end, 0) * (1 - semantic_weight)\n    ) as hybrid_score\n  from base b cross join stats s\n  where (b.sim > 0) or (b.kw_rank > 0)\n  order by hybrid_score desc\n  limit match_count;\n$$;\n\n-- Optional helpers\ncreate or replace function public.match_documentshs_vector_only(\n  query_embedding float4[],\n  match_count int default 10,\n  filter jsonb default '{}'\n)\nreturns table (\n  id bigint,\n  content text,\n  metadata jsonb,\n  similarity double precision\n)\nlanguage sql\nstable\nas $$\n  select\n    d.id, d.content, d.metadata,\n    (1 - (d.embedding <=> (query_embedding::vector(1536)))) as similarity\n  from documentsHS d\n  where d.metadata @> filter\n  order by d.embedding <=> (query_embedding::vector(1536))\n  limit match_count;\n$$;\n\ncreate or replace function public.match_documentshs_keyword_only(\n  query_text text,\n  match_count int default 10,\n  filter jsonb default '{}'\n)\nreturns table (\n  id bigint,\n  content text,\n  metadata jsonb,\n  keyword_rank double precision\n)\nlanguage sql\nstable\nas $$\n  select\n    d.id, d.content, d.metadata,\n    ts_rank(d.tokens, plainto_tsquery('english', query_text)) as keyword_rank\n  from documentsHS d\n  where d.metadata @> filter\n    and plainto_tsquery('english', query_text) @@ d.tokens\n  order by keyword_rank desc\n  limit match_count;\n$$;\n\n-- Reload API schema\nselect pg_notify('pgrst', 'reload schema');"
      },
      "typeVersion": 1
    },
    {
      "id": "ae127c8a-15e8-485a-913b-932e7a3f0f15",
      "name": "Haftnotiz3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -6784,
        -784
      ],
      "parameters": {
        "width": 592,
        "height": 224,
        "content": "# record_managerHS code\n\ncreate table public.record_managerHS (\n  id bigint generated by default as identity not null,\n  created_at timestamp with time zone not null default now(),\n  gd_file_id text not null,\n  hash text not null,\n  constraint record_managerHS_pkey primary key (id)\n) TABLESPACE pg_default;"
      },
      "typeVersion": 1
    },
    {
      "id": "4e8a1b14-2fd5-4dfc-89f4-73898134d384",
      "name": "Edge Function",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -1552,
        -544
      ],
      "parameters": {
        "url": "Your Edge Function URL Here",
        "method": "POST",
        "options": {
          "redirect": {
            "redirect": {}
          }
        },
        "jsonBody": "={\n    \"query_text\":       \"{{ $('When Executed by Another Workflow').item.json.query }}\",\n    \"query_embedding\":  [{{ $json.data[0].embedding }}],\n    \"match_count\":      5, \n    \"full_text_weight\": 1.0,\n    \"semantic_weight\": 1.0,\n    \"rrf_k\":            50\n  } ",
        "sendBody": true,
        "specifyBody": "json",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth"
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "T59KJDlDMiYYary2",
          "name": "Supabase CHRAG"
        }
      },
      "typeVersion": 4.2,
      "alwaysOutputData": true
    },
    {
      "id": "cf18ef2b-6044-4526-9f0f-b1ec4d7a547d",
      "name": "Meinen Sub-Workflow aufrufen",
      "type": "n8n-nodes-base.executeWorkflow",
      "position": [
        -656,
        336
      ],
      "parameters": {
        "options": {},
        "workflowId": {
          "__rl": true,
          "mode": "list",
          "value": "OB2T8YYdXtHcujuT",
          "cachedResultName": "My Sub-workflow"
        },
        "workflowInputs": {
          "value": {
            "content": "={{ $json.content }}",
            "file_id": "={{ $json.file_id }}",
            "fileName": "={{ $json.fileName }}",
            "documentSummary": "={{ $json.documentSummary }}"
          },
          "schema": [
            {
              "id": "content",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "content",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "documentSummary",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "documentSummary",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "file_id",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "file_id",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "fileName",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "fileName",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "content",
            "documentSummary",
            "file_id",
            "fileName"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": true
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "61e693a0-bebc-4f32-8531-67110fc08e38",
      "name": "Supabase Vektorspeicher1",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
        1440,
        336
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "documentshs",
          "cachedResultName": "documentshs"
        }
      },
      "credentials": {
        "supabaseApi": {
          "id": "GZCrvWKfz1aQNVve",
          "name": "Supabase - RAG"
        }
      },
      "typeVersion": 1.3
    },
    {
      "id": "7588feea-e4d2-4a67-91a8-2a35d4439293",
      "name": "Embeddings OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        1440,
        512
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "QsPyEUltWeliSiFb",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "db23a77d-2805-421d-a3c0-43de5a78e0a5",
      "name": "Standard-Datenlader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        1568,
        528
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "file_id",
                "value": "={{ $('Metadata').item.json.file_id }}"
              },
              {
                "name": "motorsport_category",
                "value": "={{ $('Metadata').item.json.motorsportCategory }}"
              },
              {
                "name": "file_name",
                "value": "={{ $('Metadata').item.json.fileName }}"
              },
              {
                "name": "file_summary",
                "value": "={{ $('Metadata').item.json.documentSummary }}"
              }
            ]
          }
        },
        "textSplittingMode": "custom"
      },
      "typeVersion": 1.1
    },
    {
      "id": "fa1ce78e-ef3c-45e9-9b89-b6afa3e3a119",
      "name": "Rekursiver Zeichentext-Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        1536,
        656
      ],
      "parameters": {
        "options": {},
        "chunkSize": 1400
      },
      "typeVersion": 1
    },
    {
      "id": "c4771f8f-dabe-4815-8fe2-d862234cbec7",
      "name": "Rekursiver Splitter2",
      "type": "n8n-nodes-base.code",
      "position": [
        -80,
        336
      ],
      "parameters": {
        "mode": "runOnceForEachItem",
        "jsCode": "const chunkSize = 1000;\nconst chunkOverlap = 200;\nconst text = $input.item.json.content.replace(/\\n/, '');\n\nconst chunks = [];\nlet remainingText = text;\n\nwhile (remainingText.length > 0) {\n    let splitPoint;\n\n    // Try splitting at paragraph level first\n    splitPoint = remainingText.lastIndexOf(\"\\n\\n\", chunkSize);\n    \n    // If no paragraph split, try splitting at sentence level\n    if (splitPoint === -1) {\n        splitPoint = remainingText.lastIndexOf(\". \", chunkSize);\n    }\n\n    // If no sentence split, try splitting at word level\n    if (splitPoint === -1) {\n        splitPoint = remainingText.lastIndexOf(\" \", chunkSize);\n    }\n\n    // If still no split point, force cut at chunkSize\n    if (splitPoint === -1 || splitPoint < chunkSize * 0.5) {  \n        splitPoint = chunkSize; // Hard split if no good split point\n    }\n\n    // Extract chunk and adjust remaining text with overlap\n    let chunk = remainingText.substring(0, splitPoint).trim();\n    chunks.push(chunk);\n\n    // Move the pointer forward while keeping the overlap\n    remainingText = remainingText.substring(Math.max(0, splitPoint - chunkOverlap)).trim();\n\n    // Break if remaining text is too small to form another chunk\n    if (remainingText.length < chunkSize * 0.2) {\n        chunks.push(remainingText);\n        break;\n    }\n}\n\nreturn { chunks };"
      },
      "typeVersion": 2
    },
    {
      "id": "0969dc28-0e90-47ad-9dde-b4425978a6eb",
      "name": "Split Out",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        128,
        336
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "chunks"
      },
      "typeVersion": 1
    },
    {
      "id": "b279aabb-d27f-44d4-9290-e0bf9abca567",
      "name": "Chunks für Embedding vorbereiten",
      "type": "n8n-nodes-base.set",
      "position": [
        912,
        336
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "caed5bb3-bd1e-475f-94ad-db0b73cdedf0",
              "name": "text",
              "type": "string",
              "value": "={{ $json.text }} \n"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "2f58e5f1-afe1-4e34-8cf1-e02f932a2e99",
      "name": "Metadaten",
      "type": "n8n-nodes-base.set",
      "position": [
        -288,
        336
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "93527bdc-6f91-4942-b5da-d7d5f1a67f9d",
              "name": "content",
              "type": "string",
              "value": "={{ $json.content }}"
            },
            {
              "id": "15ac4402-e7a1-40fc-a45d-bafd8302358a",
              "name": "documentSummary",
              "type": "string",
              "value": "={{ $json.documentSummary }}"
            },
            {
              "id": "79781e9e-8d64-4ebb-b96a-5d9954e62939",
              "name": "motorsportCategory",
              "type": "string",
              "value": "={{ $json.motorsportCategory }}"
            },
            {
              "id": "ca42a5a1-d11b-4a1e-a9f1-b696d31b4251",
              "name": "file_id",
              "type": "string",
              "value": "={{ $json.file_id }}"
            },
            {
              "id": "97242570-a638-454d-8d5e-62a1eb6c8376",
              "name": "fileName",
              "type": "string",
              "value": "={{ $json.fileName }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "02d36503-b3ad-451b-8f90-9ee50d9e3c53",
      "name": "Warten",
      "type": "n8n-nodes-base.wait",
      "position": [
        1104,
        336
      ],
      "webhookId": "9ddc5ae1-c2d9-4031-aa38-dafc7cdda091",
      "parameters": {},
      "typeVersion": 1.1
    },
    {
      "id": "f0e1c571-d615-4942-a9cc-2529aa401cef",
      "name": "Kontext hinzufügen",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        576,
        336
      ],
      "parameters": {
        "text": "=<document> \n{{ $('Metadata').item.json.content}}\n</document> \n\nHere is the chunk we want to situate within the overall document:\n\n<chunk> \n{{ $('Loop Over Items2').item.json.chunks }}\n</chunk> \n\nPlease:\n- Provide a short and succinct **context** to situate this chunk within the document for improved search retrieval.\n- Return the **original chunk** exactly as provided unless a correction is necessary.\n- If the chunk contains an **incomplete number, percentage, or entity**, correct it using the full document.\n- If part of a **sentence is cut off**, reconstruct the missing words only if necessary for clarity.\n- If the chunk is part of a table, include the complete table entry to maintain data integrity\n- Do not add any additional explanations or formatting beyond the required output.\n\nFill in the following format:\n[succinct context] : [original chunk or corrected version if necessary]\n\nYour response should contain only the text that replaces these placeholders, without including the placeholder labels themselves.",
        "promptType": "define"
      },
      "typeVersion": 1.5
    },
    {
      "id": "9153a959-d4a9-4392-a072-8db62ecd4008",
      "name": "OpenAI Chat-Modell2",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        576,
        544
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4.1-nano",
          "cachedResultName": "gpt-4.1-nano"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "QsPyEUltWeliSiFb",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "25cb00db-559f-4eb0-baf6-3118fc6c6bf0",
      "name": "Über Elemente iterieren2",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        352,
        336
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "756c06ae-1b66-44a0-b4a6-076856cf01b5",
      "name": "Aggregieren2",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        544,
        80
      ],
      "parameters": {
        "options": {},
        "fieldsToAggregate": {
          "fieldToAggregate": [
            {}
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "1b2f5f20-f2f3-49c4-900a-d63d297b3fc2",
      "name": "Haftnotiz5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -320,
        224
      ],
      "parameters": {
        "width": 176,
        "height": 280,
        "content": "### Set up data\n"
      },
      "typeVersion": 1
    },
    {
      "id": "a321d862-fcb5-4895-bb44-2f1d038c4614",
      "name": "Haftnotiz13",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -112,
        224
      ],
      "parameters": {
        "width": 356,
        "height": 280,
        "content": "### Chuck document into multiple chunks based chunk size"
      },
      "typeVersion": 1
    },
    {
      "id": "dfb50197-9be2-4189-ab3c-0f11de6b62c1",
      "name": "Haftnotiz21",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        544,
        224
      ],
      "parameters": {
        "width": 264,
        "height": 280,
        "content": "### Add context to each chunk\n"
      },
      "typeVersion": 1
    },
    {
      "id": "48bbb8a8-56de-4e68-acf2-beb25a583145",
      "name": "Haftnotiz22",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        832,
        224
      ],
      "parameters": {
        "width": 420,
        "height": 284,
        "content": "### Set up text chunks and add timer so create limits are not reached"
      },
      "typeVersion": 1
    },
    {
      "id": "044811b8-f03e-4954-a6b0-1522030aa109",
      "name": "Haftnotiz23",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1344,
        224
      ],
      "parameters": {
        "width": 472,
        "height": 576,
        "content": "### Upsert into vector database."
      },
      "typeVersion": 1
    },
    {
      "id": "57986be6-d5b8-47d8-8e14-f9be28282e59",
      "name": "Haftnotiz24",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -352,
        64
      ],
      "parameters": {
        "color": 3,
        "width": 2192,
        "height": 752,
        "content": "# This is the Call My Sub-Workflow "
      },
      "typeVersion": 1
    },
    {
      "id": "f28c07f0-e1d7-482f-bacc-bd2bc3ba011b",
      "name": "Haftnotiz4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -6000,
        -784
      ],
      "parameters": {
        "width": 752,
        "height": 752,
        "content": "# Workflow Summary\n\n### This automation keeps your Supabase vector database synchronized with documents stored in Google Drive, while also making the data contextual and vector based for better retrieval.\n\n### When a file is added or modified, the workflow extracts its text, splits it into smaller chunks, and enriches each chunk with contextual metadata (such as summaries and document details). It then generates embeddings using OpenAI and stores both the vector data and metadata in Supabase. If a file changes, the old records are replaced with updated, contextualized content.\n\n### The result is a continuously updated and context-aware vector database, enabling highly accurate hybrid search and retrieval. \n\n\n# To setup\n\n## 1. Connect Google Drive\n•\tCreate a Google Drive folder to watch.\n•\tConnect your Google Drive account in n8n and authorize access.\n•\tPoint the Google Drive Trigger node to this folder (new/modified files trigger the flow).\n\n## 2. Configure Supabase\n•\tPlease refer to the Setting Up Supabase Sticky Note. \n\n## 3. Connect OpenAI (or your embedding model)\n•\tAdd your OpenAI API key in n8n credentials.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "7c719413-7945-4e8e-9db2-02c6f3ee49c2",
      "name": "Haftnotiz26",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -3808,
        144
      ],
      "parameters": {
        "width": 150,
        "height": 304,
        "content": "### Loop over each item, as more than 1 file can be placed in the Google Drive"
      },
      "typeVersion": 1
    },
    {
      "id": "f5d30b0f-d7cc-4574-9939-9a53e7b02d12",
      "name": "Haftnotiz28",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -3568,
        144
      ],
      "parameters": {
        "width": 160,
        "height": 304,
        "content": "### Download file"
      },
      "typeVersion": 1
    },
    {
      "id": "f9c3a219-dbf0-470a-a54a-2c5775da45c4",
      "name": "Haftnotiz29",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -3376,
        144
      ],
      "parameters": {
        "width": 160,
        "height": 304,
        "content": "### Extract text from pdf"
      },
      "typeVersion": 1
    },
    {
      "id": "aced0ee1-a1d5-40fd-b866-c2640136802e",
      "name": "Haftnotiz30",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1840,
        -96
      ],
      "parameters": {
        "width": 176,
        "height": 304,
        "content": "### Create new record in record manager since file is new and doesn't exist in database"
      },
      "typeVersion": 1
    },
    {
      "id": "ae132431-fb89-45b5-9849-96812a46dcfd",
      "name": "Haftnotiz25",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -2592,
        -832
      ],
      "parameters": {
        "width": 336,
        "height": 208,
        "content": "## AI Agent to communicate with the database"
      },
      "typeVersion": 1
    },
    {
      "id": "c8d17104-e110-453d-97ca-e8e79bf589c1",
      "name": "Embedding",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        -1792,
        -544
      ],
      "parameters": {
        "url": "https://api.openai.com/v1/embeddings",
        "method": "POST",
        "options": {},
        "sendBody": true,
        "authentication": "genericCredentialType",
        "bodyParameters": {
          "parameters": [
            {
              "name": "input",
              "value": "={{ $json.query }}"
            },
            {
              "name": "model",
              "value": "text-embedding-3-small"
            }
          ]
        },
        "genericAuthType": "httpHeaderAuth"
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "IZ2NWEGrJPwf73xf",
          "name": "Openai"
        }
      },
      "typeVersion": 4.2
    }
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    "executionOrder": "v1"
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