Mark Shcherbakov This workflow corresponds to n8n.io template #5436 — we link there as the canonical source.
This workflow follows the Chat Trigger → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "3634e7f6-9c01-48f6-9ed0-b4c9514b134e",
"name": "Database retrieval1",
"type": "@n8n/n8n-nodes-langchain.toolCode",
"position": [
-100,
460
],
"parameters": {
"name": "knowledge_base",
"jsCode": "const axios = require('axios');\n\nconst OPENAI_API_KEY = '<openai_api_token>';\nconst SUPABASE_URL = 'https://<supabase_id>.supabase.co';\nconst SUPABASE_API_KEY = '<supabase_api_token>';\n\n\n// Get input from the previous node\nconst queryText = query.query;\nconst filters = $('Aggregate files').item.json || {}; // Should include { file_id: [\"uuid1\", \"uuid2\"] }\n\nasync function getEmbedding(text) {\n if (!text || typeof text !== 'string') {\n throw new Error(`Invalid input for embedding: \"${text}\"`);\n }\n\n try {\n console.log(\"\ud83d\udd39 Generating embedding for query:\", text);\n const response = await axios.post(\n 'https://api.openai.com/v1/embeddings',\n { input: text, model: 'text-embedding-3-small' },\n {\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${OPENAI_API_KEY}`\n }\n }\n );\n\n console.log(\"\u2705 OpenAI Embedding Generated Successfully!\");\n return response.data.data[0].embedding;\n } catch (error) {\n console.error(\"\u274c OpenAI Embedding Error:\", error.response?.data || error.message);\n throw new Error(`OpenAI Embedding Error: ${JSON.stringify(error.response?.data || error.message)}`);\n }\n}\n\nasync function fetchDocuments(queryEmbedding) {\n try {\n console.log(\"\ud83d\udd39 Sending request to Supabase...\");\n console.log(\"\ud83d\udd39 Query Embedding Length:\", queryEmbedding.length);\n console.log(\"\ud83d\udd39 Filters used:\", JSON.stringify(filters, null, 2));\n\n const response = await axios.post(\n `${SUPABASE_URL}/rest/v1/rpc/match_documents`,\n {\n query_embedding: queryEmbedding,\n match_count: 5,\n filter: filters\n },\n {\n headers: {\n 'Content-Type': 'application/json',\n 'apikey': SUPABASE_API_KEY,\n 'Authorization': `Bearer ${SUPABASE_API_KEY}`\n }\n }\n );\n\n console.log(\"\u2705 Supabase Response:\", response.data);\n return response.data;\n } catch (error) {\n console.error(\"\u274c Supabase Query Error:\", error.response?.data || error.message);\n throw new Error(`Supabase Query Error: ${error.message}`);\n }\n}\n\nasync function processQuery() {\n try {\n console.log(\"\ud83d\udd39 Processing Query:\", queryText);\n console.log(\"\ud83d\udd39 Filters:\", filters);\n console.log(\"\ud83d\udd0e queryText:\", JSON.stringify(queryText));\n\n const embedding = await getEmbedding(queryText);\n const results = await fetchDocuments(embedding);\n\n // Format results with file name and URL\n const formattedResults = results.map(item => ({\n id: item.id,\n content: item.content,\n similarity: item.similarity.toFixed(4),\n fileName: item.file_name,\n fileUrl: item.file_url\n }));\n\n // Combine content into a single string with file metadata\n const joinedContent = formattedResults.map(item => {\n return `\ud83d\udcc4 *${item.fileName || \"Unnamed file\"}* (${item.similarity} similarity)\\n\ud83d\udd17 ${item.fileUrl || \"No link\"}\\n\\n${item.content}`;\n }).join(\"\\n\\n---\\n\\n\");\n\n console.log(\"\u2705 Final Formatted Results:\", formattedResults);\n console.log(\"\u2705 Final Joined Content:\", joinedContent);\n\n return joinedContent;\n } catch (error) {\n console.error(\"\u274c Processing Error:\", error.message);\n throw new Error(`Processing Error: ${error.message}`);\n }\n}\n\nreturn processQuery();",
"jsonSchemaExample": "{\n\t\"query\": \"n8n\"\n}",
"specifyInputSchema": true
},
"typeVersion": 1.1
},
{
"id": "43eee475-c154-4baf-a3bd-f0c1ef0c3b5c",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
-100
],
"parameters": {
"content": "### Match_documents function\n\ncreate or replace function match_documents(\n query_embedding vector(1536),\n match_count int default null,\n filter jsonb default '{}'\n)\nreturns table (\n id bigint,\n content text,\n similarity float,\n file_name text,\n file_url text\n)\nlanguage plpgsql\nas $$\ndeclare\n filter_conditions text := '';\n file_id_list text[] := '{}';\nbegin\n -- Extract file_id values from filter\n if filter ? 'file_id' then\n select array_agg(value::text)\n into file_id_list\n from jsonb_array_elements_text(filter->'file_id');\n end if;\n\n -- Add file_id filter condition\n if array_length(file_id_list, 1) > 0 then\n filter_conditions := ' AND (d.metadata->>''file_id'') = ANY($3)';\n end if;\n\n -- Run the query with JOIN to files\n return query execute format(\n 'SELECT\n d.id,\n d.content,\n 1 - (d.embedding <=> $1) as similarity,\n f.name as file_name,\n f.google_drive_url as file_url\n FROM documents d\n JOIN files f ON (d.metadata->>''file_id'') = f.id::text\n WHERE true %s\n ORDER BY d.embedding <=> $1\n LIMIT $2',\n filter_conditions\n )\n using query_embedding, match_count, file_id_list;\nend;\n$$;"
},
"typeVersion": 1
},
{
"id": "ad163ed4-c900-4958-aaeb-b9d330e7c77a",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-840,
520
],
"parameters": {
"content": "### Match_files function\n\ncreate function match_files (\n query_embedding vector(1536),\n match_count int default null,\n filter jsonb default '{}'\n)\nreturns table (\n id uuid,\n description text,\n similarity float\n)\nlanguage plpgsql\nas $$\nbegin\n return query\n select\n f.id,\n f.description,\n 1 - (f.embedding <=> query_embedding) as similarity\n from public.files f\n where filter = '{}' or to_jsonb(f) @> filter\n order by f.embedding <=> query_embedding\n limit match_count;\nend;\n$$;"
},
"typeVersion": 1
},
{
"id": "723564a3-0417-49ac-b280-af3b3fee2d15",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-840,
720
],
"parameters": {
"height": 140,
"content": "### Files table\n\ncreate table public.files (\n id uuid not null default gen_random_uuid (),\n name text null,\n category text null,\n date_created timestamp with time zone null default (now() AT TIME ZONE 'utc'::text),\n google_drive_url text null,\n theme text null,\n description text null,\n embedding extensions.vector null,\n google_drive_id text null,\n status text null default 'ready'::text,\n type public.file_type null,\n video_transcription_url text null,\n constraint files_pkey primary key (id)\n) TABLESPACE pg_default;"
},
"typeVersion": 1
},
{
"id": "0250b36b-7a91-44b6-a084-f6536689c984",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-960,
280
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "343ba375-41ee-474e-968b-ca8e3da5f2f9",
"name": "OpenAI",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
-260,
280
],
"parameters": {
"modelId": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini",
"cachedResultName": "GPT-4.1-MINI"
},
"options": {},
"messages": {
"values": [
{
"content": "={{ $('When chat message received').item.json.chatInput }}"
},
{
"role": "system",
"content": "IMPORTANT Always use knowledge_base tool to retrieve data for answering.\nIMPORTANT Always return url of file as reference if exists."
}
]
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.8
},
{
"id": "fdd481bc-3b33-43ec-b33c-05b4adbd66c4",
"name": "GetFile",
"type": "n8n-nodes-base.supabase",
"position": [
-740,
1080
],
"parameters": {
"tableId": "files",
"matchType": "allFilters",
"operation": "getAll",
"returnAll": true
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"executeOnce": false,
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "f1f1c78e-5aaa-483e-b124-024a5b086561",
"name": "UpdateEmbedding",
"type": "n8n-nodes-base.supabase",
"position": [
-380,
1080
],
"parameters": {
"filters": {
"conditions": [
{
"keyName": "id",
"keyValue": "={{ $('GetFile').item.json.id }}",
"condition": "eq"
}
]
},
"tableId": "files",
"fieldsUi": {
"fieldValues": [
{
"fieldId": "embedding",
"fieldValue": "={{ $json.data[0].embedding }}"
}
]
},
"operation": "update"
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "451aceee-bcee-4247-9fb0-9c3048e302a1",
"name": "OpenAI CreateEmbeddings",
"type": "n8n-nodes-base.httpRequest",
"position": [
-560,
1080
],
"parameters": {
"url": "=https://api.openai.com/v1/embeddings",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"text-embedding-3-small\",\n \"input\": {{JSON.stringify( \"File Name:\" + $json.name + \"Description:\" + $json.description )}} \n }",
"sendBody": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "657334f2-80e0-402b-b150-52145fa81a69",
"name": "When clicking \u2018Execute workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-920,
1080
],
"parameters": {},
"typeVersion": 1
},
{
"id": "0420cc67-cd8c-492b-89ed-44912106d3e3",
"name": "Find Files",
"type": "n8n-nodes-base.code",
"position": [
-780,
280
],
"parameters": {
"jsCode": "const axios = require('axios');\n\nconst OPENAI_API_KEY = '<openai_api_token>';\nconst SUPABASE_URL = 'https://<supabase_id>.supabase.co';\nconst SUPABASE_API_KEY = '<supabase_api_token>';\n\n\n// Get input from the previous node\nconst queryText = $('When chat message received').item.json.chatInput ;\nconst filters = {}; // Dynamic filter object\n\nasync function getEmbedding(text) {\n try {\n console.log(\"\ud83d\udd39 Generating embedding for query:\", text);\n const response = await axios.post(\n 'https://api.openai.com/v1/embeddings',\n { input: text, model: 'text-embedding-3-small' },\n {\n headers: {\n 'Content-Type': 'application/json',\n 'Authorization': `Bearer ${OPENAI_API_KEY}`\n }\n }\n );\n\n console.log(\"\u2705 OpenAI Embedding Generated Successfully!\");\n return response.data.data[0].embedding;\n } catch (error) {\n console.error(\"\u274c OpenAI Embedding Error:\", error.response?.data || error.message);\n throw new Error(`OpenAI Embedding Error: ${error.message}`);\n }\n}\n\nasync function fetchFiles(queryEmbedding) {\n try {\n console.log(\"\ud83d\udd39 Sending request to Supabase (match_files)...\");\n console.log(\"\ud83d\udd39 Query Embedding Length:\", queryEmbedding.length);\n console.log(\"\ud83d\udd39 Filters used:\", JSON.stringify(filters, null, 2));\n\n const response = await axios.post(\n `${SUPABASE_URL}/rest/v1/rpc/match_files`,\n {\n query_embedding: queryEmbedding,\n match_count: 5,\n filter: filters\n },\n {\n headers: {\n 'Content-Type': 'application/json',\n 'apikey': SUPABASE_API_KEY,\n 'Authorization': `Bearer ${SUPABASE_API_KEY}`\n }\n }\n );\n\n console.log(\"\u2705 Supabase Response:\", response.data);\n return response.data;\n } catch (error) {\n console.error(\"\u274c Supabase Query Error:\", error.response?.data || error.message);\n throw new Error(`Supabase Query Error: ${error.message}`);\n }\n}\n\nasync function processQuery() {\n try {\n console.log(\"\ud83d\udd39 Processing Query:\", queryText);\n console.log(\"\ud83d\udd39 Filters:\", filters);\n\n const embedding = await getEmbedding(queryText);\n const results = await fetchFiles(embedding);\n\n const formattedResults = results.map(item => ({\n id: item.id,\n description: item.description,\n similarity: item.similarity\n }));\n return formattedResults;\n } catch (error) {\n console.error(\"\u274c Processing Error:\", error.message);\n throw new Error(`Processing Error: ${error.message}`);\n }\n}\n\n// Execute and return results\nreturn processQuery();"
},
"typeVersion": 2
},
{
"id": "b29ed780-bf05-4069-b5e5-257472a4cd8a",
"name": "Filter similarity",
"type": "n8n-nodes-base.filter",
"position": [
-620,
280
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "a9268663-5051-4cb3-a51c-c77fd81d5135",
"operator": {
"type": "number",
"operation": "gt"
},
"leftValue": "={{ $(\"Find Files\").item.json[\"similarity\"] }}",
"rightValue": 0.2
}
]
}
},
"typeVersion": 2.2
},
{
"id": "81875856-45a0-48ee-95a6-10cc640eea9f",
"name": "Aggregate files",
"type": "n8n-nodes-base.aggregate",
"position": [
-440,
280
],
"parameters": {
"options": {},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"renameField": true,
"outputFieldName": "file_id",
"fieldToAggregate": "id"
}
]
}
},
"typeVersion": 1
},
{
"id": "17f29fea-6b0d-477c-9e9c-53ed5e896c72",
"name": "Set Output",
"type": "n8n-nodes-base.set",
"position": [
100,
280
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "db940cb7-dc10-4b53-a310-53d15e9f2feb",
"name": "output",
"type": "string",
"value": "={{ $json.message.content }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "ea9cfacd-4e01-4809-9da0-afd8c58c3689",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-240,
80
],
"parameters": {
"height": 140,
"content": "### Documents table\n\n-- Enable the pgvector extension to work with embedding vectors\ncreate extension vector;\n\n-- Create a table to store your documents\ncreate table documents (\n id bigserial primary key,\n content text, -- corresponds to Document.pageContent\n metadata jsonb, -- corresponds to Document.metadata\n embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n);"
},
"typeVersion": 1
},
{
"id": "018eb8c5-7504-4e99-92a9-2cd0ef0ab9de",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-660,
420
],
"parameters": {
"width": 180,
"height": 80,
"content": "Change similarity to get less/more results"
},
"typeVersion": 1
},
{
"id": "1c56abbb-fe0a-478c-9bbf-d77484d885be",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-840,
140
],
"parameters": {
"width": 200,
"height": 100,
"content": "### Replace Supabase and OpenAI credentials"
},
"typeVersion": 1
},
{
"id": "6076f007-7ec7-400c-849c-1adee9e11a5d",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
-160,
720
],
"parameters": {
"width": 220,
"height": 120,
"content": "Change match_count variable (5 default) to increase/decrease number of returned vectors"
},
"typeVersion": 1
},
{
"id": "81689d9a-8702-467f-bab0-9295385fcd1c",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-840,
20
],
"parameters": {
"width": 200,
"height": 100,
"content": "Change match_count variable (5 default) to increase/decrease number of returned files"
},
"typeVersion": 1
},
{
"id": "8cf014f1-8796-46dc-8077-8cf442bebcf8",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-160,
600
],
"parameters": {
"width": 200,
"height": 100,
"content": "### Replace Supabase and OpenAI credentials"
},
"typeVersion": 1
},
{
"id": "3acda68c-af77-41ad-aaae-6c08f4220aaf",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1040,
-180
],
"parameters": {
"color": 5,
"width": 1360,
"height": 1160,
"content": "## Main workflow"
},
"typeVersion": 1
},
{
"id": "2453f44d-f01f-45bc-8273-eaa0cd5da858",
"name": "Sticky Note10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1040,
1000
],
"parameters": {
"color": 5,
"width": 880,
"height": 300,
"content": "## Files description embedding workflow"
},
"typeVersion": 1
},
{
"id": "f9bea260-c665-41ab-aec7-99f094f3e0f7",
"name": "Sticky Note11",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1720,
-180
],
"parameters": {
"color": 7,
"width": 636.2128494576581,
"height": 497.1532689930921,
"content": "\n## AI Agent To Chat With Files In Supabase Storage\n**Made by [Mark Shcherbakov](https://www.linkedin.com/in/marklowcoding/) from community [5minAI](https://www.skool.com/5minai-pro)**\n\nManually searching and reviewing info across many documents is inefficient. This workflow automates the process: first finding the right files using metadata, then running a filtered vector search, so your queries always return the best-matching results from your uploads.\n\nThe workflow integrates Supabase and an AI chatbot to process, filter, and query text or PDF files. Steps include:\n\u2022 Searching file descriptions for initial filtering\n\u2022 Filtering file results by similarity threshold\n\u2022 Aggregating relevant file_ids for next-stage search\n\u2022 Running semantic (vector) queries only on those filtered files\n\u2022 Returning matched content with file names and URLs\n"
},
"typeVersion": 1
},
{
"id": "f49e8db9-244f-49e4-887a-2a5754f7c22c",
"name": "Sticky Note12",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1420,
340
],
"parameters": {
"color": 7,
"width": 330.5152611046425,
"height": 240.6839895136402,
"content": "### ... or watch set up video [15 min]\n[](https://www.youtube.com/watch?v=asXVOHg89hs)\n"
},
"typeVersion": 1
}
],
"connections": {
"OpenAI": {
"main": [
[
{
"node": "Set Output",
"type": "main",
"index": 0
}
]
]
},
"GetFile": {
"main": [
[
{
"node": "OpenAI CreateEmbeddings",
"type": "main",
"index": 0
}
]
]
},
"Find Files": {
"main": [
[
{
"node": "Filter similarity",
"type": "main",
"index": 0
}
]
]
},
"Aggregate files": {
"main": [
[
{
"node": "OpenAI",
"type": "main",
"index": 0
}
]
]
},
"Filter similarity": {
"main": [
[
{
"node": "Aggregate files",
"type": "main",
"index": 0
}
]
]
},
"Database retrieval1": {
"ai_tool": [
[
{
"node": "OpenAI",
"type": "ai_tool",
"index": 0
}
]
]
},
"OpenAI CreateEmbeddings": {
"main": [
[
{
"node": "UpdateEmbedding",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Find Files",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Execute workflow\u2019": {
"main": [
[
{
"node": "GetFile",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
openAiApisupabaseApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
I prepared a comprehensive guide demonstrating how to build a multi-level retrieval AI agent in n8n that smartly narrows down search results first by file descriptions, then retrieves detailed vector data for improved relevance and answer quality.
Source: https://n8n.io/workflows/5436/ — original creator credit. Request a take-down →
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