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Store Notion's Pages as Vector Documents into Supabase with OpenAI

Automatically transform your Notion pages into intelligent vector documents within Supabase, powered by OpenAI embeddings. This workflow triggers whenever a new page is added in Notion, retrieves its content, filters out non-textual elements, and then concatenates the remaining blocks into a coherent summary. This summarized content is then processed by OpenAI's Embeddings node to generate vector representations, which are subsequently stored in your Supabase Vector Store along with relevant metadata. This is ideal for teams and individuals who want to build AI-powered search, Q&A, or recommendation systems on their Notion knowledge base, enabling rapid retrieval of information and deeper insights without manual data migration. By automating the conversion and indexing of Notion content, this workflow significantly reduces the manual effort and development time required to integrate your knowledge base with AI applications, ensuring your information is always up-to-date and readily available for advanced analytical tasks.
9 nodesmanual trigger113 views0 copiesProductivity
SupabaseOpenAINotion

Workflow JSON

{"id": "DvP6IHWymTIVg8Up", "meta": {"instanceId": "b9faf72fe0d7c3be94b3ebff0778790b50b135c336412d28fd4fca2cbbf8d1f5", "templateCredsSetupCompleted": true}, "name": "Store Notion's Pages as Vector Documents into Supabase with OpenAI", "tags": [], "nodes": [{"id": "495609cd-4ca0-426d-8413-69e771398188", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [480, 400], "parameters": {"width": 637.1327972412109, "height": 1113.7434387207031, "content": "## Store Notion's Pages as Vector Documents into Supabase\n\n**This workflow assumes you have a Supabase project with a table that has a vector column. If you don't have it, follow the instructions here:** [Supabase Vector Columns Guide](https://supabase.com/docs/guides/ai/vector-columns)\n\n## Workflow Description\n\nThis workflow automates the process of storing Notion pages as vector documents in a Supabase database with a vector column. The steps are as follows:\n\n1. **Notion Page Added Trigger**:\n - Monitors a specified Notion database for newly added pages. You can create a specific Notion database where you copy the pages you want to store in Supabase.\n - Node: `Page Added in Notion Database`\n\n2. **Retrieve Page Content**:\n - Fetches all block content from the newly added Notion page.\n - Node: `Get Blocks Content`\n\n3. **Filter Non-Text Content**:\n - Excludes blocks of type \"image\" and \"video\" to focus on textual content.\n - Node: `Filter - Exclude Media Content`\n\n4. **Summarize Content**:\n - Concatenates the Notion blocks content to create a single text for embedding.\n - Node: `Summarize - Concatenate Notion's blocks content`\n\n5. **Store in Supabase**:\n - Stores the processed documents and their embeddings into a Supabase table with a vector column.\n - Node: `Store Documents in Supabase`\n\n6. **Generate Embeddings**:\n - Utilizes OpenAI's API to generate embeddings for the textual content.\n - Node: `Generate Text Embeddings`\n\n\n7. **Create Metadata and Load Content**:\n - Loads the block content and creates associated metadata, such as page ID and block ID.\n - Node: `Load Block Content & Create Metadata`\n\n8. **Split Content into Chunks**:\n - Divides the text into smaller chunks for easier processing and embedding generation.\n - Node: `Token Splitter`\n\n\n\n"}, "typeVersion": 1}, {"id": "3f3e65dc-2b26-407c-87e5-52ba3b315fed", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [2200, 760], "parameters": {"options": {}}, "typeVersion": 1}, {"id": "6d2579b8-376f-44c3-82e8-9dc608efd98b", "name": "Token Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter", "position": [2340, 960], "parameters": {"chunkSize": 256, "chunkOverlap": 30}, "typeVersion": 1}, {"id": "79b3c147-08ca-4db4-9116-958a868cbfd9", "name": "Notion - Page Added Trigger", "type": "n8n-nodes-base.notionTrigger", "position": [1180, 520], "parameters": {"simple": false, "pollTimes": {"item": [{"mode": "everyMinute"}]}, "databaseId": {"__rl": true, "mode": "list", "value": "", "cachedResultUrl": "", "cachedResultName": ""}}, "typeVersion": 1}, {"id": "e4a6f524-e3f5-4d02-949a-8523f2d21965", "name": "Notion - Retrieve Page Content", "type": "n8n-nodes-base.notion", "position": [1400, 520], "parameters": {"blockId": {"__rl": true, "mode": "url", "value": "={{ $json.url }}"}, "resource": "block", "operation": "getAll", "returnAll": true}, "typeVersion": 2.2}, {"id": "bfebc173-8d4b-4f8f-a625-4622949dd545", "name": "Filter Non-Text Content", "type": "n8n-nodes-base.filter", "position": [1620, 520], "parameters": {"options": {}, "conditions": {"options": {"leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"id": "e5b605e5-6d05-4bca-8f19-a859e474620f", "operator": {"type": "string", "operation": "notEquals"}, "leftValue": "={{ $json.type }}", "rightValue": "image"}, {"id": "c7415859-5ffd-4c78-b497-91a3d6303b6f", "operator": {"type": "string", "operation": "notEquals"}, "leftValue": "={{ $json.type }}", "rightValue": "video"}]}}, "typeVersion": 2}, {"id": "b04939f9-355a-430b-a069-b11800066313", "name": "Summarize - Concatenate Notion's blocks content", "type": "n8n-nodes-base.summarize", "position": [1920, 520], "parameters": {"options": {"outputFormat": "separateItems"}, "fieldsToSummarize": {"values": [{"field": "content", "separateBy": "\n", "aggregation": "concatenate"}]}}, "typeVersion": 1}, {"id": "0e64dbb5-20c1-4b90-b818-a1726aaf5112", "name": "Create metadata and load content", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [2320, 760], "parameters": {"options": {"metadata": {"metadataValues": [{"name": "pageId", "value": "={{ $('Notion - Page Added Trigger').item.json.id }}"}, {"name": "createdTime", "value": "={{ $('Notion - Page Added Trigger').item.json.created_time }}"}, {"name": "pageTitle", "value": "={{ $('Notion - Page Added Trigger').item.json.properties.Page.title[0].text.content }}"}]}}, "jsonData": "={{ $('Summarize - Concatenate Notion's blocks content').item.json.concatenated_content }}", "jsonMode": "expressionData"}, "typeVersion": 1}, {"id": "187aba6f-eaed-4427-8d40-b9da025fb37d", "name": "Supabase Vector Store", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "position": [2200, 520], "parameters": {"mode": "insert", "options": {}, "tableName": {"__rl": true, "mode": "list", "value": "", "cachedResultName": ""}}, "typeVersion": 1}], "active": false, "pinData": {}, "settings": {"executionOrder": "v1"}, "versionId": "77f6b6f7-d699-4a7e-b3e7-fe8a60bde7ba", "connections": {"Token Splitter": {"ai_textSplitter": [[{"node": "Create metadata and load content", "type": "ai_textSplitter", "index": 0}]]}, "Embeddings OpenAI": {"ai_embedding": [[{"node": "Supabase Vector Store", "type": "ai_embedding", "index": 0}]]}, "Filter Non-Text Content": {"main": [[{"node": "Summarize - Concatenate Notion's blocks content", "type": "main", "index": 0}]]}, "Notion - Page Added Trigger": {"main": [[{"node": "Notion - Retrieve Page Content", "type": "main", "index": 0}]]}, "Notion - Retrieve Page Content": {"main": [[{"node": "Filter Non-Text Content", "type": "main", "index": 0}]]}, "Create metadata and load content": {"ai_document": [[{"node": "Supabase Vector Store", "type": "ai_document", "index": 0}]]}, "Summarize - Concatenate Notion's blocks content": {"main": [[{"node": "Supabase Vector Store", "type": "main", "index": 0}]]}}}

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  1. 1Copy the workflow JSON above using the Copy Workflow JSON button.
  2. 2Open your n8n instance and go to Workflows.
  3. 3Click Import from JSON and paste the copied workflow.

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