Upsert huge documents in a vector store with Supabase and Notion.json

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"content": "## Switch trigger (optional)\nIf you are on the cloud plan, consider switching to the Notion Trigger Node instead, to save on executions."
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"parameters": {
"tableId": "documents",
"operation": "delete",
"filterType": "string",
"filterString": "=metadata->>id=eq.{{ $('Input Reference').item.json.id }}"
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"credentials": {
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"id": "DjIb4HMTYXhTU8Uc",
"name": "Supabase (VectorStore)"
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"alwaysOutputData": true
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"blockId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Input Reference').item.json.id }}"
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"resource": "block",
"operation": "getAll",
"returnAll": true,
"fetchNestedBlocks": true
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"credentials": {
"notionApi": {
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"name": "Notion (octionicsolutions / Test)"
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"metadata": {
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{
"name": "id",
"value": "={{ $('Input Reference').item.json.id }}"
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{
"name": "name",
"value": "={{ $('Input Reference').item.json.name }}"
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"height": 264.37628764336097,
"content": "## Adjust chunk size and overlap\nFor more accurate search results, increase the overlap. For the *text-embedding-ada-002* model the chunk size plus overlap must not exceed 8191"
},
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"event": "pagedUpdatedInDatabase",
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
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},
"databaseId": {
"__rl": true,
"mode": "list",
"value": "ec6dc7b4-9ce0-47f7-8025-ef09295999fd",
"cachedResultUrl": "https://www.notion.so/ec6dc7b49ce047f78025ef09295999fd",
"cachedResultName": "Knowledge Base"
}
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"resource": "databasePage",
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"content": "This placeholder serves as a reference point so it is easier to swap the data source with a different service"
},
"typeVersion": 1
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"content": "All chunks of a previous version of the document are being deleted by filtering the meta data by the given ID"
},
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"content": "Retrieve all page contents/blocks"
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"content": "Embed item and store in Vector Store. Depending on the length the content is being split up into multiple chunks/embeds"
},
"typeVersion": 1
},
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"parameters": {
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
}
},
"credentials": {
"supabaseApi": {
"id": "DjIb4HMTYXhTU8Uc",
"name": "Supabase (VectorStore)"
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"parameters": {
"color": 7,
"width": 730.7522093855692,
"height": 668.724737081502,
"content": "Simple chat bot to ask specific questions while having access to the context of the Notion Knowledge Base which was stored in the Vector Store"
},
"typeVersion": 1
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"parameters": {
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"width": 219.31927574471658,
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"content": "Model used for both creating and reading embeddings"
},
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{
"id": "2f59cba1-4318-47e7-bf0b-b908d4186b86",
"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
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"parameters": {
"mode": "insert",
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
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},
"credentials": {
"supabaseApi": {
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"name": "Supabase (VectorStore)"
}
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"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Process each page/document separately."
},
"typeVersion": 1
},
{
"id": "3f632a24-ca0a-45c4-801d-041aa3f887a7",
"name": "Sticky Note29",
"type": "n8n-nodes-base.stickyNote",
"position": [
2220,
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"parameters": {
"color": 7,
"width": 376.0759088111347,
"height": 275.841854198618,
"content": "Store additional meta data with each embed, especially the Notion ID, which can be later used to find all belonging entries of one page, even if they got split into multiple embeds."
},
"typeVersion": 1
},
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"position": [
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"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Using a manual approach for polling data from Notion for more accuracy."
},
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"name": "Sticky Note34",
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"parameters": {
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "## Select Database\nChoose the database which represents your Knowledge Base"
},
"typeVersion": 1
},
{
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"name": "Sticky Note35",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 7,
"width": 216.47293010628914,
"height": 275.841854198618,
"content": "Combine all contents to a single text formatted into one line which can be easily stored as an embed"
},
"typeVersion": 1
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{
"id": "cdff1756-77d7-421e-8672-25c9862840b0",
"name": "Concatenate to single string",
"type": "n8n-nodes-base.summarize",
"position": [
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"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "content",
"separateBy": "\n",
"aggregation": "concatenate"
}
]
}
},
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"active": false,
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"Limit": {
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"Limit1": {
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"Notion Trigger": {
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"Token Splitter": {
"ai_textSplitter": [
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"node": "Default Data Loader",
"type": "ai_textSplitter",
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]
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"Get page blocks": {
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"Loop Over Items": {
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[
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},
"Embeddings OpenAI": {
"ai_embedding": [
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{
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"Get updated pages": {
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"Default Data Loader": {
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"type": "ai_document",
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}
]
]
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"Supabase Vector Store": {
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},
"Supabase Vector Store1": {
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"type": "ai_vectorStore",
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]
]
},
"Vector Store Retriever": {
"ai_retriever": [
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{
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}
]
]
},
"When chat message received": {
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}
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},
"Concatenate to single string": {
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[
{
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}
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},
"Delete old embeddings if exist": {
"main": [
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}
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]
}
}
}