Erstellung eines RAG-Vektordatenbanks aus Google Drive-Dokumenten mit Gemini und Supabase
Dies ist ein Document Extraction, AI RAG-Bereich Automatisierungsworkflow mit 16 Nodes. Hauptsächlich werden Code, Postgres, GoogleDrive, SplitInBatches, ExecuteWorkflowTrigger und andere Nodes verwendet. Erstellen Sie einen RAG-Vektordatenbank aus Google Drive-Dokumenten mit Gemini und Supabase
- •PostgreSQL-Datenbankverbindungsdaten
- •Google Drive API-Anmeldedaten
- •Supabase URL und API Key
Verwendete Nodes (16)
Kategorie
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"name": "Embeddings Google Gemini4",
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"name": "Eine SQL-Abfrage ausführen",
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"parameters": {
"query": "DROP TABLE IF EXISTS documents CASCADE;\n\nCREATE EXTENSION IF NOT EXISTS vector;\n\nCREATE TABLE IF NOT EXISTS documents (\n id bigserial PRIMARY KEY,\n content text,\n metadata jsonb,\n embedding vector(768)\n);\n\nCREATE OR REPLACE FUNCTION match_documents(\n query_embedding vector(768),\n match_count int DEFAULT NULL,\n filter jsonb DEFAULT '{}'::jsonb\n)\nRETURNS TABLE (\n id bigint,\n content text,\n metadata jsonb,\n similarity double precision\n)\nLANGUAGE sql\nAS $$\n SELECT\n d.id,\n d.content,\n d.metadata,\n 1 - (d.embedding <=> query_embedding) AS similarity\n FROM documents d\n WHERE (filter = '{}'::jsonb OR d.metadata @> filter)\n ORDER BY d.embedding <=> query_embedding\n LIMIT match_count;\n$$;\n",
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"jsCode": "// Get the Drive_Folder_link from the workflow input\nconst driveUrl = $input.first().json.Drive_Folder_link;\n\n// Extract Google Drive folder/file ID from URL\nfunction getDriveId(url) {\n const folderMatch = url.match(/\\/folders\\/([a-zA-Z0-9_-]+)/);\n const fileMatch = url.match(/\\/file\\/d\\/([a-zA-Z0-9_-]+)/);\n return folderMatch ? folderMatch[1] : (fileMatch ? fileMatch[1] : null);\n}\n\n// Process input items\nreturn items.map(item => {\n const chatInput = item.json.chatInput || driveUrl || '';\n const driveId = getDriveId(chatInput);\n\n return {\n json: {\n originalInput: chatInput,\n folderId: driveId,\n driveId: driveId\n }\n };\n});\n"
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"parameters": {
"inputSource": "jsonExample",
"jsonExample": "{\n \"Drive_Folder_link\": \"https://drive.google.com/drive/folders/example\"\n}"
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"typeVersion": 1.1
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"height": 512,
"content": "# 📁 Drive to Supabase Vector Store for Study RAG\n\nProcesses Google Drive folder files into Supabase vector embeddings for RAG applications.\n\n**Flow:** Drive URL → Parse ID → Init DB → Fetch Files → Loop → Download → Embed → Store\n\n**Requirements:**\n- Google Drive OAuth2\n- Supabase + Postgres credentials\n- Google Gemini API key\n\n**Input:** `{\"Drive_Folder_link\": \"your_drive_url\"}`\n**Output:** Vector embeddings in Supabase documents table\n"
},
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"type": "n8n-nodes-base.stickyNote",
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"parameters": {
"width": 176,
"height": 128,
"content": "**Trigger Node** - Starts workflow when called from another n8n workflow. Accepts Drive folder URL as input.\n"
},
"typeVersion": 1
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"name": "Notizzettel2",
"type": "n8n-nodes-base.stickyNote",
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"parameters": {
"width": 150,
"height": 128,
"content": "**Extract Folder ID** - Parses Google Drive URL using regex to extract folder/file ID for API calls.\n"
},
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{
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"name": "Notizzettel3",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"width": 176,
"content": "**Initialize Database** - Creates Supabase vector table with pgvector extension and match_documents search function. ⚠️ Drops existing table!\n"
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"id": "4ac27951-096f-44ba-8e66-be73b3c0d380",
"name": "Über Elemente iterieren",
"type": "n8n-nodes-base.splitInBatches",
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{
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"name": "Dateien und Ordner durchsuchen",
"type": "n8n-nodes-base.googleDrive",
"position": [
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"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Code in JavaScript').item.json.folderId }}"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "CVN95k3ctbjWs60e",
"name": "Google_Drive_gaming"
}
},
"typeVersion": 3
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{
"id": "db2f57b6-1aa9-4926-a211-7362d5d4533e",
"name": "In Supabase Vectorstore einfügen",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
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"parameters": {
"mode": "insert",
"options": {
"queryName": "match_documents"
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"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
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"name": "Datei herunterladen",
"type": "n8n-nodes-base.googleDrive",
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"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text/plain"
}
}
},
"operation": "download"
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"credentials": {
"googleDriveOAuth2Api": {
"id": "CVN95k3ctbjWs60e",
"name": "Google_Drive_gaming"
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"content": "**List Drive Files** - Retrieves all files from the specified Google Drive folder using extracted folder ID.\n"
},
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"parameters": {
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"content": "**List Drive Files** - Retrieves all files from the specified Google Drive folder using extracted folder ID.\n"
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"parameters": {
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"content": "**Store Embeddings** - Generates 768-dim vectors via Gemini and inserts documents into Supabase for semantic search.\n**AI Embeddings** - Converts text to 768-dimensional vectors using Google Gemini text-embedding-004 model.\n**Document Loader** - Extracts and formats text from binary files for the embedding generator.\n"
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}Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Experte - Dokumentenextraktion, KI RAG
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
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Mantaka Mahir
@mantakamahirAl Automation Expert || Al Agents || n8n || Python || LangChain || Helping businesses scale revenue and reduce costs with Al driven automation .
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