n8nauto
Get Started

RAG & GenAI App With WordPress Content

Build a powerful RAG (Retrieval Augmented Generation) and Generative AI application using your WordPress content with this n8n workflow. This comprehensive solution automatically retrieves, processes, and stores your WordPress posts and pages, making them available for AI-powered queries. It connects WordPress to a PostgreSQL database (often hosted on Supabase) for vector storage and chat memory, leveraging OpenAI for embeddings and chat model interactions. This workflow is ideal for businesses and content creators who want to transform their existing WordPress knowledge base into an interactive AI assistant, customer support chatbot, or internal knowledge retrieval system, allowing users to ask natural language questions and receive accurate, context-aware answers directly from their published content. By automating the content synchronization and AI integration, it significantly reduces the manual effort of maintaining an AI knowledge base, ensures your AI always has access to the latest information, and enhances user engagement by providing instant access to information.
53 nodesmanual trigger130 views0 copiesMarketing
WordPressPostgreSQLSupabaseOpenAI

Workflow JSON

{"id": "o8iTqIh2sVvnuWz5", "meta": {"instanceId": "b9faf72fe0d7c3be94b3ebff0778790b50b135c336412d28fd4fca2cbbf8d1f5"}, "name": "RAG & GenAI App With WordPress Content", "tags": [], "nodes": [{"id": "c3738490-ed39-4774-b337-bf5ee99d0c72", "name": "When clicking \u2018Test workflow\u2019", "type": "n8n-nodes-base.manualTrigger", "position": [500, 940], "parameters": {}, "typeVersion": 1}, {"id": "3ab719bd-3652-433f-a597-9cd28f8cfcea", "name": "Embeddings OpenAI", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [2580, 1320], "parameters": {"model": "text-embedding-3-small", "options": {}}, "typeVersion": 1}, {"id": "e8639569-2091-44de-a84d-c3fc3ce54de4", "name": "Default Data Loader", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [2800, 1260], "parameters": {"options": {"metadata": {"metadataValues": [{"name": "title", "value": "={{ $json.title }}"}, {"name": "url", "value": "={{ $json.url }}"}, {"name": "content_type", "value": "={{ $json.content_type }}"}, {"name": "publication_date", "value": "={{ $json.publication_date }}"}, {"name": "modification_date", "value": "={{ $json.modification_date }}"}, {"name": "id", "value": "={{ $json.id }}"}]}}, "jsonData": "={{ $json.data }}", "jsonMode": "expressionData"}, "typeVersion": 1}, {"id": "e7f858eb-4dca-40ea-9da9-af953687e63d", "name": "Token Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter", "position": [2900, 1480], "parameters": {"chunkSize": 300, "chunkOverlap": 30}, "typeVersion": 1}, {"id": "27585104-5315-4c11-b333-4b5d27d9bae4", "name": "Embeddings OpenAI1", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [1400, 2340], "parameters": {"model": "text-embedding-3-small", "options": {}}, "typeVersion": 1}, {"id": "35269a98-d905-4e4f-ae5b-dadad678f260", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "position": [2800, 2300], "parameters": {"model": "gpt-4o-mini", "options": {}}, "typeVersion": 1}, {"id": "cd26b6fa-a8bb-4139-9bec-8656d90d8203", "name": "Postgres Chat Memory", "type": "@n8n/n8n-nodes-langchain.memoryPostgresChat", "position": [2920, 2300], "parameters": {"tableName": "website_chat_histories"}, "typeVersion": 1.1}, {"id": "7c718e1b-1398-49f3-ba67-f970a82983e0", "name": "Respond to Webhook", "type": "n8n-nodes-base.respondToWebhook", "position": [3380, 2060], "parameters": {"options": {}}, "typeVersion": 1.1}, {"id": "f91f18e0-7a04-4218-8490-bff35dfbf7a8", "name": "Set fields", "type": "n8n-nodes-base.set", "position": [2360, 2060], "parameters": {"options": {}, "assignments": {"assignments": [{"id": "6888175b-853b-457a-96f7-33dfe952a05d", "name": "documents", "type": "string", "value": "={{ \n JSON.stringify(\n $json.documents.map(doc => ({\n metadata: \n 'URL: ' + doc.metadata.url.replaceAll('&rsquo;', \"'\").replaceAll(/[\"]/g, '') + '\\n' +\n 'Publication Date: ' + doc.metadata.publication_date.replaceAll(/[\"]/g, '') + '\\n' +\n 'Modification Date: ' + doc.metadata.modification_date.replaceAll(/[\"]/g, '') + '\\n' +\n 'Content Type: ' + doc.metadata.content_type.replaceAll(/[\"]/g, '') + '\\n' +\n 'Title: ' + doc.metadata.title.replaceAll('&rsquo;', \"'\").replaceAll(/[\"]/g, '') + '\\n',\n \n page_content: doc.pageContent\n }))\n ).replaceAll(/[\\[\\]{}]/g, '')\n}}"}, {"id": "ae310b77-4560-4f44-8c4e-8d13f680072e", "name": "sessionId", "type": "string", "value": "={{ $('When chat message received').item.json.sessionId }}"}, {"id": "8738f4de-b3c3-45ad-af4b-8311c8105c35", "name": "chatInput", "type": "string", "value": "={{ $('When chat message received').item.json.chatInput }}"}]}}, "typeVersion": 3.4}, {"id": "7f392a40-e353-4bb2-9ecf-3ee330110b95", "name": "Embeddings OpenAI2", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "position": [6400, 860], "parameters": {"model": "text-embedding-3-small", "options": {}}, "typeVersion": 1}, {"id": "9e045857-5fcd-4c4b-83ee-ceda28195b76", "name": "Default Data Loader1", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "position": [6500, 860], "parameters": {"options": {"metadata": {"metadataValues": [{"name": "title", "value": "={{ $json.title }}"}, {"name": "url", "value": "={{ $json.url }}"}, {"name": "content_type", "value": "={{ $json.content_type }}"}, {"name": "publication_date", "value": "={{ $json.publication_date }}"}, {"name": "modification_date", "value": "={{ $json.modification_date }}"}, {"name": "id", "value": "={{ $json.id }}"}]}}, "jsonData": "={{ $json.data }}", "jsonMode": "expressionData"}, "typeVersion": 1}, {"id": "d0c1144b-4542-470e-8cbe-f985e839d9d0", "name": "Token Splitter1", "type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter", "position": [6500, 980], "parameters": {"chunkSize": 300, "chunkOverlap": 30}, "typeVersion": 1}, {"id": "ec7cf1b2-f56f-45da-bb34-1dc8a66a7de6", "name": "Markdown1", "type": "n8n-nodes-base.markdown", "position": [6240, 900], "parameters": {"html": "={{ $json.content }}", "options": {}}, "typeVersion": 1}, {"id": "8399976b-340a-49ce-a5b6-f7339957aa9d", "name": "Postgres", "type": "n8n-nodes-base.postgres", "position": [4260, 900], "parameters": {"query": "select max(created_at) as last_workflow_execution from n8n_website_embedding_histories", "options": {}, "operation": "executeQuery"}, "typeVersion": 2.5}, {"id": "88e79403-06df-4f18-9e4c-a4c4e727aa17", "name": "Aggregate", "type": "n8n-nodes-base.aggregate", "position": [3300, 900], "parameters": {"options": {}, "aggregate": "aggregateAllItemData"}, "typeVersion": 1}, {"id": "db7241e8-1c3a-4f91-99b7-383000f41afe", "name": "Aggregate1", "type": "n8n-nodes-base.aggregate", "position": [6800, 680], "parameters": {"options": {}, "aggregate": "aggregateAllItemData"}, "typeVersion": 1}, {"id": "94bbba31-d83b-427f-a7dc-336725238294", "name": "Aggregate2", "type": "n8n-nodes-base.aggregate", "position": [7180, 1160], "parameters": {"options": {}, "fieldsToAggregate": {"fieldToAggregate": [{"fieldToAggregate": "metadata.id"}]}}, "typeVersion": 1}, {"id": "52a110fa-cdd6-4b1d-99fe-394b5dfa0a1f", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "position": [440, 600], "parameters": {"color": 5, "width": 3308.2687575224263, "height": 1015.3571428571431, "content": "# Workflow 1 : Initial Embedding \n## Use this workflow to create the initial embedding for your WordPress website content\n\n"}, "typeVersion": 1}, {"id": "4cbf8135-a52b-4a54-b7b0-15ea27ce7ae3", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "position": [3812, 605], "parameters": {"color": 5, "width": 3785.6673412474183, "height": 1020.4528919414245, "content": "# Workflow 2 : Upsert\n## Use this workflow to upsert embeddings for documents stored in the Supabase vector table\n"}, "typeVersion": 1}, {"id": "f6e954e0-a37a-45ac-9882-20f4f1944b70", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "position": [440, 1820], "parameters": {"color": 5, "width": 3235.199999999999, "height": 817.9199999999992, "content": "# Workflow 3 : Use this workflow to enable chat functionality with your website content. The chat can be embedded into your website to enhance user experience"}, "typeVersion": 1}, {"id": "acbdd54b-f02a-41aa-a0ce-8642db560151", "name": "Wordpress - Get all posts", "type": "n8n-nodes-base.wordpress", "position": [1260, 880], "parameters": {"options": {}, "operation": "getAll", "returnAll": true}, "typeVersion": 1}, {"id": "94fce59d-9336-4d49-a378-17335ec02e52", "name": "Wordpress - Get all pages", "type": "n8n-nodes-base.wordpress", "position": [1260, 1060], "parameters": {"options": {}, "resource": "page", "operation": "getAll", "returnAll": true}, "typeVersion": 1}, {"id": "b00c92e5-1765-4fd9-9981-e01053992a0a", "name": "Sticky Note3", "type": "n8n-nodes-base.stickyNote", "position": [1157, 727], "parameters": {"width": 1108.3519999999999, "height": 561.4080000000004, "content": "## Use filters to create embeddings only for content that you want to include in your GenAI application"}, "typeVersion": 1}, {"id": "f8a22739-898d-456b-93f8-79f74b60a00c", "name": "Set fields1", "type": "n8n-nodes-base.set", "position": [2320, 900], "parameters": {"options": {}, "assignments": {"assignments": [{"id": "de6711dc-d03c-488c-bef4-0a853e2d0a14", "name": "publication_date", "type": "string", "value": "={{ $json.date }}"}, {"id": "f8e35dcc-c96c-4554-b6bc-8e5d7eca90e3", "name": "modification_date", "type": "string", "value": "={{ $json.modified }}"}, {"id": "f6a6e3de-fe39-4cfc-ab07-c4ccfaef78f5", "name": "content_type", "type": "string", "value": "={{ $json.type }}"}, {"id": "b0428598-073f-4560-9a0c-01caf3708921", "name": "title", "type": "string", "value": "={{ $json.title.rendered }}"}, {"id": "534f51b4-b43a-40d3-8120-58df8043d909", "name": "url", "type": "string", "value": "={{ $json.link }}"}, {"id": "dbe0c559-90bd-49f8-960e-0d85d5ed4f5e", "name": "content", "type": "string", "value": "={{ $json.content.rendered }}"}, {"id": "892be7c6-b032-4129-b285-1986ed4ee046", "name": "protected", "type": "boolean", "value": "={{ $json.excerpt.protected }}"}, {"id": "06fac885-4431-41ff-a43b-6eb84ca57401", "name": "status", "type": "string", "value": "={{ $json.status }}"}, {"id": "43b1aea7-895e-41da-a0a6-2f1cec1f1b97", "name": "id", "type": "number", "value": "={{ $json.id }}"}]}}, "typeVersion": 3.4}, {"id": "404db031-f470-4e42-a3b3-66b849a86174", "name": "Filter - Only published & unprotected content", "type": "n8n-nodes-base.filter", "position": [2520, 900], "parameters": {"options": {}, "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"id": "1f708587-f3d3-487a-843a-b6a2bfad2ca9", "operator": {"type": "boolean", "operation": "false", "singleValue": true}, "leftValue": "={{ $json.protected }}", "rightValue": ""}, {"id": "04f47269-e112-44c3-9014-749898aca8bd", "operator": {"name": "filter.operator.equals", "type": "string", "operation": "equals"}, "leftValue": "={{ $json.status }}", "rightValue": "publish"}]}}, "typeVersion": 2.2}, {"id": "05bb6091-515e-4f22-a3fd-d25b2046a03d", "name": "HTML To Markdown", "type": "n8n-nodes-base.markdown", "position": [2740, 900], "parameters": {"html": "={{ $json.content}}", "options": {}}, "typeVersion": 1}, {"id": "391e9ea7-71dd-42ae-bee7-badcae32427c", "name": "Supabase - Store workflow execution", "type": "n8n-nodes-base.supabase", "position": [3520, 900], "parameters": {"tableId": "n8n_website_embedding_histories", "fieldsUi": {"fieldValues": [{"fieldId": "id", "fieldValue": "={{ $executionId }}"}]}}, "typeVersion": 1}, {"id": "47dad096-efc8-4bdd-9c22-49562325d8a0", "name": "Sticky Note4", "type": "n8n-nodes-base.stickyNote", "position": [460, 1320], "parameters": {"width": 851.1898437499999, "height": 275.2000000000001, "content": "## Run these two nodes if the \"documents\" table on Supabase and the \"n8n_website_embedding_histories\" table do not exist"}, "typeVersion": 1}, {"id": "d19f3a5f-fa42-46d0-a366-4c5a5d09f559", "name": "Every 30 seconds", "type": "n8n-nodes-base.scheduleTrigger", "position": [3940, 900], "parameters": {"rule": {"interval": [{"field": "seconds"}]}}, "typeVersion": 1.2}, {"id": "a22ab0dd-1da8-4fc2-8106-6130bf7938c8", "name": "Sticky Note5", "type": "n8n-nodes-base.stickyNote", "position": [3820, 740], "parameters": {"width": 336.25, "height": 292.5, "content": "## Set this node to match the frequency of publishing and updating on your website"}, "typeVersion": 1}, {"id": "ba25135b-6e6e-406b-b18a-f532a6e37276", "name": "Wordpress - Get posts modified after last workflow execution", "type": "n8n-nodes-base.httpRequest", "position": [4600, 840], "parameters": {"url": "https://mydomain.com/wp-json/wp/v2/posts", "options": {}, "sendQuery": true, "authentication": "predefinedCredentialType", "queryParameters": {"parameters": [{"name": "modified_after", "value": "={{ $json.last_workflow_execution }}"}]}, "nodeCredentialType": "wordpressApi"}, "typeVersion": 4.2}, {"id": "a1d8572e-2b0d-40a1-a898-bbd563a6b190", "name": "Wordpress - Get posts modified after last workflow execution1", "type": "n8n-nodes-base.httpRequest", "position": [4600, 1060], "parameters": {"url": "https://mydomain.com/wp-json/wp/v2/pages", "options": {}, "sendQuery": true, "authentication": "predefinedCredentialType", "queryParameters": {"parameters": [{"name": "modified_after", "value": "={{ $json.last_workflow_execution }}"}]}, "nodeCredentialType": "wordpressApi"}, "typeVersion": 4.2}, {"id": "c0839aaa-8ba7-47ff-8fa9-dc75e1c4da84", "name": "Set fields2", "type": "n8n-nodes-base.set", "position": [5420, 920], "parameters": {"options": {}, "assignments": {"assignments": [{"id": "de6711dc-d03c-488c-bef4-0a853e2d0a14", "name": "publication_date", "type": "string", "value": "={{ $json.date }}"}, {"id": "f8e35dcc-c96c-4554-b6bc-8e5d7eca90e3", "name": "modification_date", "type": "string", "value": "={{ $json.modified }}"}, {"id": "f6a6e3de-fe39-4cfc-ab07-c4ccfaef78f5", "name": "content_type", "type": "string", "value": "={{ $json.type }}"}, {"id": "b0428598-073f-4560-9a0c-01caf3708921", "name": "title", "type": "string", "value": "={{ $json.title.rendered }}"}, {"id": "534f51b4-b43a-40d3-8120-58df8043d909", "name": "url", "type": "string", "value": "={{ $json.link }}"}, {"id": "dbe0c559-90bd-49f8-960e-0d85d5ed4f5e", "name": "content", "type": "string", "value": "={{ $json.content.rendered }}"}, {"id": "892be7c6-b032-4129-b285-1986ed4ee046", "name": "protected", "type": "boolean", "value": "={{ $json.content.protected }}"}, {"id": "06fac885-4431-41ff-a43b-6eb84ca57401", "name": "status", "type": "string", "value": "={{ $json.status }}"}, {"id": "43b1aea7-895e-41da-a0a6-2f1cec1f1b97", "name": "id", "type": "number", "value": "={{ $json.id }}"}]}}, "typeVersion": 3.4}, {"id": "15b1d30a-5861-4380-89d5-0eef65240503", "name": "Filter - Only published and unprotected content", "type": "n8n-nodes-base.filter", "position": [5760, 920], "parameters": {"options": {}, "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"id": "c2b25d74-91d7-44ea-8598-422100947b07", "operator": {"type": "boolean", "operation": "false", "singleValue": true}, "leftValue": "={{ $json.protected }}", "rightValue": ""}, {"id": "3e63bf79-25ca-4ccf-aa86-ff5f90e1ece1", "operator": {"name": "filter.operator.equals", "type": "string", "operation": "equals"}, "leftValue": "={{ $json.status }}", "rightValue": "publish"}]}}, "typeVersion": 2.2}, {"id": "0990f503-8d6f-44f6-8d04-7e2f7d74301a", "name": "Loop Over Items", "type": "n8n-nodes-base.splitInBatches", "position": [6040, 920], "parameters": {"options": {}}, "typeVersion": 3}, {"id": "6cc4e46e-3884-4259-b7ed-51c5552cc3e0", "name": "Set fields3", "type": "n8n-nodes-base.set", "position": [7400, 1160], "parameters": {"options": {}, "assignments": {"assignments": [{"id": "de6711dc-d03c-488c-bef4-0a853e2d0a14", "name": "publication_date", "type": "string", "value": "={{ $('Loop Over Items').item.json.publication_date }}"}, {"id": "f8e35dcc-c96c-4554-b6bc-8e5d7eca90e3", "name": "modification_date", "type": "string", "value": "={{ $('Loop Over Items').item.json.modification_date }}"}, {"id": "f6a6e3de-fe39-4cfc-ab07-c4ccfaef78f5", "name": "content_type", "type": "string", "value": "={{ $('Loop Over Items').item.json.content_type }}"}, {"id": "b0428598-073f-4560-9a0c-01caf3708921", "name": "title", "type": "string", "value": "={{ $('Loop Over Items').item.json.title }}"}, {"id": "534f51b4-b43a-40d3-8120-58df8043d909", "name": "url", "type": "string", "value": "={{ $('Loop Over Items').item.json.url }}"}, {"id": "dbe0c559-90bd-49f8-960e-0d85d5ed4f5e", "name": "content", "type": "string", "value": "={{ $('Loop Over Items').item.json.content }}"}, {"id": "892be7c6-b032-4129-b285-1986ed4ee046", "name": "protected", "type": "boolean", "value": "={{ $('Loop Over Items').item.json.protected }}"}, {"id": "06fac885-4431-41ff-a43b-6eb84ca57401", "name": "status", "type": "string", "value": "={{ $('Loop Over Items').item.json.status }}"}, {"id": "43b1aea7-895e-41da-a0a6-2f1cec1f1b97", "name": "id", "type": "number", "value": "={{ $('Loop Over Items').item.json.id }}"}]}}, "typeVersion": 3.4}, {"id": "24f47982-a803-4848-8390-c400a8cebcee", "name": "Set fields4", "type": "n8n-nodes-base.set", "position": [6680, 1400], "parameters": {"options": {}, "assignments": {"assignments": [{"id": "de6711dc-d03c-488c-bef4-0a853e2d0a14", "name": "publication_date", "type": "string", "value": "={{ $('Loop Over Items').item.json.publication_date }}"}, {"id": "f8e35dcc-c96c-4554-b6bc-8e5d7eca90e3", "name": "modification_date", "type": "string", "value": "={{ $('Loop Over Items').item.json.modification_date }}"}, {"id": "f6a6e3de-fe39-4cfc-ab07-c4ccfaef78f5", "name": "content_type", "type": "string", "value": "={{ $('Loop Over Items').item.json.content_type }}"}, {"id": "b0428598-073f-4560-9a0c-01caf3708921", "name": "title", "type": "string", "value": "={{ $('Loop Over Items').item.json.title }}"}, {"id": "534f51b4-b43a-40d3-8120-58df8043d909", "name": "url", "type": "string", "value": "={{ $('Loop Over Items').item.json.url }}"}, {"id": "dbe0c559-90bd-49f8-960e-0d85d5ed4f5e", "name": "content", "type": "string", "value": "={{ $('Loop Over Items').item.json.content }}"}, {"id": "892be7c6-b032-4129-b285-1986ed4ee046", "name": "protected", "type": "boolean", "value": "={{ $('Loop Over Items').item.json.protected }}"}, {"id": "06fac885-4431-41ff-a43b-6eb84ca57401", "name": "status", "type": "string", "value": "={{ $('Loop Over Items').item.json.status }}"}, {"id": "43b1aea7-895e-41da-a0a6-2f1cec1f1b97", "name": "id", "type": "number", "value": "={{ $('Loop Over Items').item.json.id }}"}]}}, "typeVersion": 3.4}, {"id": "5f59ebbf-ca17-4311-809c-85b74ce624cc", "name": "Store documents on Supabase", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "position": [6380, 680], "parameters": {"mode": "insert", "options": {"queryName": "match_documents"}, "tableName": {"__rl": true, "mode": "list", "value": "documents", "cachedResultName": "documents"}}, "typeVersion": 1}, {"id": "2422562e-9c95-4d77-ae8c-485b06f9234e", "name": "Store workflow execution id and timestamptz", "type": "n8n-nodes-base.supabase", "position": [7060, 680], "parameters": {"tableId": "n8n_website_embedding_histories"}, "typeVersion": 1}, {"id": "5013f3a1-f7fb-4fa7-9ef2-3599f77f5fc8", "name": "Aggregate documents", "type": "n8n-nodes-base.aggregate", "position": [1960, 2060], "parameters": {"options": {}, "fieldsToAggregate": {"fieldToAggregate": [{"renameField": true, "outputFieldName": "documents", "fieldToAggregate": "document"}]}}, "typeVersion": 1}, {"id": "26532217-3206-4be3-b186-733bc364913b", "name": "Sticky Note6", "type": "n8n-nodes-base.stickyNote", "position": [1220, 1980], "parameters": {"width": 665.78125, "height": 507.65625, "content": "## Retrieve documents from Supabase immediately after chat input to send metadata to OpenAI"}, "typeVersion": 1}, {"id": "78d2806c-8d13-44b8-bd6d-866fa794edae", "name": "Sticky Note7", "type": "n8n-nodes-base.stickyNote", "position": [6375, 1090], "parameters": {"width": 1198.9843749999998, "height": 515.4687499999998, "content": "## Switch:\n- **If the document exists and has been updated:** delete rows and insert new embedding\n- **If it\u2019s a new document:** insert embedding"}, "typeVersion": 1}, {"id": "3b5ffada-ae2a-45a2-a76c-69732b05761c", "name": "Postgres - Create documents table", "type": "n8n-nodes-base.postgres", "position": [560, 1440], "parameters": {"query": "-- Enable the pgvector extension to work with embedding vectors\nCREATE EXTENSION vector;\n\n-- Create a table to store your documents with default RLS\nCREATE TABLE\n documents (\n id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\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 );\n\n-- Enable Row Level Security on the documents table\nALTER TABLE documents ENABLE ROW LEVEL SECURITY;\n\n-- Create a function to search for documents\nCREATE FUNCTION match_documents (\n query_embedding vector (1536),\n match_count INT DEFAULT NULL,\n FILTER jsonb DEFAULT '{}'\n) RETURNS TABLE (\n id BIGINT,\n CONTENT TEXT,\n metadata jsonb,\n similarity FLOAT\n) LANGUAGE plpgsql AS $$\n#variable_conflict use_column\nBEGIN\n RETURN QUERY\n SELECT\n id,\n content,\n metadata,\n 1 - (documents.embedding <=> query_embedding) AS similarity\n FROM documents\n WHERE metadata @> filter\n ORDER BY documents.embedding <=> query_embedding\n LIMIT match_count;\nEND;\n$$;", "options": {}, "operation": "executeQuery"}, "typeVersion": 2.5}, {"id": "632a7b44-a062-472e-a777-805ee74a4bd6", "name": "Postgres - Create workflow execution history table", "type": "n8n-nodes-base.postgres", "position": [920, 1440], "parameters": {"query": "CREATE TABLE\n n8n_website_embedding_histories (\n id BIGINT PRIMARY KEY GENERATED ALWAYS AS IDENTITY,\n created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()\n );", "options": {}, "operation": "executeQuery"}, "typeVersion": 2.5}, {"id": "7c55e08b-e116-4e22-bd1d-e4bec5107d89", "name": "Merge Wordpress Posts and Pages", "type": "n8n-nodes-base.merge", "position": [1660, 900], "parameters": {}, "typeVersion": 3}, {"id": "4520db6c-2e68-45ff-9439-6fd95f95dc85", "name": "Merge retrieved WordPress posts and pages", "type": "n8n-nodes-base.merge", "position": [5120, 920], "parameters": {}, "typeVersion": 3}, {"id": "d547a063-6b76-4bfd-ba0a-165181c4af19", "name": "Postgres - Filter on existing documents", "type": "n8n-nodes-base.postgres", "position": [6260, 1180], "parameters": {"query": "SELECT *\nFROM documents\nWHERE (metadata->>'id')::integer = {{ $json.id }};\n", "options": {}, "operation": "executeQuery"}, "typeVersion": 2.5, "alwaysOutputData": true}, {"id": "03456a81-d512-4fd8-842a-27b6d8b3f94e", "name": "Supabase - Delete row if documents exists", "type": "n8n-nodes-base.supabase", "position": [6900, 1160], "parameters": {"tableId": "documents", "operation": "delete", "filterType": "string", "filterString": "=metadata->>id=like.{{ $json.metadata.id }}"}, "executeOnce": false, "typeVersion": 1, "alwaysOutputData": false}, {"id": "72e5bf4b-c413-4fb7-acb8-59e7abee60f7", "name": "Switch", "type": "n8n-nodes-base.switch", "position": [6580, 1180], "parameters": {"rules": {"values": [{"outputKey": "existing_documents", "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"operator": {"type": "number", "operation": "exists", "singleValue": true}, "leftValue": "={{ $json.metadata.id }}", "rightValue": ""}]}, "renameOutput": true}, {"outputKey": "new_documents", "conditions": {"options": {"version": 2, "leftValue": "", "caseSensitive": true, "typeValidation": "strict"}, "combinator": "and", "conditions": [{"id": "696d1c1b-8674-4549-880e-e0d0ff681905", "operator": {"type": "number", "operation": "notExists", "singleValue": true}, "leftValue": "={{ $json.metadata.id }}", "rightValue": ""}]}, "renameOutput": true}]}, "options": {}}, "typeVersion": 3.2}, {"id": "6c5d8f6a-569e-4f1e-99a6-07ec492575ff", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "position": [660, 2060], "webhookId": "4e762668-c19f-40ec-83bf-302bb9fc6527", "parameters": {"mode": "webhook", "public": true, "options": {}}, "typeVersion": 1.1}, {"id": "9a2f17ba-902f-4528-9eef-f8c0e4ddf516", "name": "Supabase - Retrieve documents from chatinput", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "position": [1380, 2060], "parameters": {"mode": "load", "prompt": "={{ $json.chatInput }}", "options": {}, "tableName": {"__rl": true, "mode": "list", "value": "documents", "cachedResultName": "documents"}}, "typeVersion": 1}, {"id": "43607f23-d33f-4aca-b478-f20ba8c218cf", "name": "AI Agent", "type": "@n8n/n8n-nodes-langchain.agent", "position": [2780, 2060], "parameters": {"text": "=Visitor's question : {{ $json.chatInput }}\nDocuments found: {{ $json.documents }}", "agent": "conversationalAgent", "options": {"systemMessage": "You are an assistant tasked with answering questions from visitors to the website {{your_website_url}}.\n\nInput:\nVisitor's question: The question posed by the visitor.\nDocuments found: A selection of documents from the vector database that match the visitor's question. These documents are accompanied by the following metadata:\nurl: The URL of the page or blog post found.\ncontent_type: The type of content (e.g., page or blog article).\npublication_date: The publication date of the document.\nmodification_date: The last modification date of the document.\nObjective:\nProvide a helpful answer using the relevant information from the documents found.\nIMPORTANT : You must always include all metadata (url, content_type, publication_date, and modification_date) directly in the main answer to the visitor to indicate the source of the information. These should not be separated from the main answer, and must be naturally integrated into the response.\nIf multiple documents are used in your response, mention each one with its respective metadata.\nIf no relevant documents are found, or if the documents are insufficient, clearly indicate this in your response.\nImportant: Respond in the language used by the visitor who asked the question.\nExample of forced metadata integration:\n\"The cost of a home charging station for an electric vehicle varies depending on several factors. According to [title of the page](https://example.com/charging-point-price), published on April 8, 2021, and updated on July 24, 2022, the price for a 7kW station is \u20ac777.57 including VAT. This page provides further details about the price range and installation considerations.\""}, "promptType": "define"}, "typeVersion": 1.6}, {"id": "cd4107cb-e521-4c1e-88e2-3417a12fd585", "name": "Supabase Vector Store", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "position": [2940, 900], "parameters": {"mode": "insert", "options": {"queryName": "match_documents"}, "tableName": {"__rl": true, "mode": "list", "value": "documents", "cachedResultName": "documents"}}, "typeVersion": 1}], "active": false, "pinData": {}, "settings": {"executionOrder": "v1"}, "versionId": "fe2a25f4-04b3-462c-97cd-a173b4a0631b", "connections": {"Switch": {"main": [[{"node": "Supabase - Delete row if documents exists", "type": "main", "index": 0}], [{"node": "Set fields4", "type": "main", "index": 0}]]}, "AI Agent": {"main": [[{"node": "Respond to Webhook", "type": "main", "index": 0}]]}, "Postgres": {"main": [[{"node": "Wordpress - Get posts modified after last workflow execution", "type": "main", "index": 0}, {"node": "Wordpress - Get posts modified after last workflow execution1", "type": "main", "index": 0}]]}, "Aggregate": {"main": [[{"node": "Supabase - Store workflow execution", "type": "main", "index": 0}]]}, "Markdown1": {"main": [[{"node": "Store documents on Supabase", "type": "main", "index": 0}]]}, "Aggregate1": {"main": [[{"node": "Store workflow execution id and timestamptz", "type": "main", "index": 0}]]}, "Aggregate2": {"main": [[{"node": "Set fields3", "type": "main", "index": 0}]]}, "Set fields": {"main": [[{"node": "AI Agent", "type": "main", "index": 0}]]}, "Set fields1": {"main": [[{"node": "Filter - Only published & unprotected content", "type": "main", "index": 0}]]}, "Set fields2": {"main": [[{"node": "Filter - Only published and unprotected content", "type": "main", "index": 0}]]}, "Set fields3": {"main": [[{"node": "Loop Over Items", "type": "main", "index": 0}]]}, "Set fields4": {"main": [[{"node": "Loop Over Items", "type": "main", "index": 0}]]}, "Token Splitter": {"ai_textSplitter": [[{"node": "Default Data Loader", "type": "ai_textSplitter", "index": 0}]]}, "Loop Over Items": {"main": [[{"node": "Markdown1", "type": "main", "index": 0}], [{"node": "Postgres - Filter on existing documents", "type": "main", "index": 0}]]}, "Token Splitter1": {"ai_textSplitter": [[{"node": "Default Data Loader1", "type": "ai_textSplitter", "index": 0}]]}, "Every 30 seconds": {"main": [[{"node": "Postgres", "type": "main", "index": 0}]]}, "HTML To Markdown": {"main": [[{"node": "Supabase Vector Store", "type": "main", "index": 0}]]}, "Embeddings OpenAI": {"ai_embedding": [[{"node": "Supabase Vector Store", "type": "ai_embedding", "index": 0}]]}, "OpenAI Chat Model": {"ai_languageModel": [[{"node": "AI Agent", "type": "ai_languageModel", "index": 0}]]}, "Embeddings OpenAI1": {"ai_embedding": [[{"node": "Supabase - Retrieve documents from chatinput", "type": "ai_embedding", "index": 0}]]}, "Embeddings OpenAI2": {"ai_embedding": [[{"node": "Store documents on Supabase", "type": "ai_embedding", "index": 0}]]}, "Aggregate documents": {"main": [[{"node": "Set fields", "type": "main", "index": 0}]]}, "Default Data Loader": {"ai_document": [[{"node": "Supabase Vector Store", "type": "ai_document", "index": 0}]]}, "Default Data Loader1": {"ai_document": [[{"node": "Store documents on Supabase", "type": "ai_document", "index": 0}]]}, "Postgres Chat Memory": {"ai_memory": [[{"node": "AI Agent", "type": "ai_memory", "index": 0}]]}, "Supabase Vector Store": {"main": [[{"node": "Aggregate", "type": "main", "index": 0}]]}, "Wordpress - Get all pages": {"main": [[{"node": "Merge Wordpress Posts and Pages", "type": "main", "index": 1}]]}, "Wordpress - Get all posts": {"main": [[{"node": "Merge Wordpress Posts and Pages", "type": "main", "index": 0}]]}, "When chat message received": {"main": [[{"node": "Supabase - Retrieve documents from chatinput", "type": "main", "index": 0}]]}, "Store documents on Supabase": {"main": [[{"node": "Aggregate1", "type": "main", "index": 0}]]}, "Merge Wordpress Posts and Pages": {"main": [[{"node": "Set fields1", "type": "main", "index": 0}]]}, "Postgres - Create documents table": {"main": [[{"node": "Postgres - Create workflow execution history table", "type": "main", "index": 0}]]}, "When clicking \u2018Test workflow\u2019": {"main": [[{"node": "Wordpress - Get all posts", "type": "main", "index": 0}, {"node": "Wordpress - Get all pages", "type": "main", "index": 0}]]}, "Postgres - Filter on existing documents": {"main": [[{"node": "Switch", "type": "main", "index": 0}]]}, "Merge retrieved WordPress posts and pages": {"main": [[{"node": "Set fields2", "type": "main", "index": 0}]]}, "Supabase - Delete row if documents exists": {"main": [[{"node": "Aggregate2", "type": "main", "index": 0}]]}, "Supabase - Retrieve documents from chatinput": {"main": [[{"node": "Aggregate documents", "type": "main", "index": 0}]]}, "Filter - Only published & unprotected content": {"main": [[{"node": "HTML To Markdown", "type": "main", "index": 0}]]}, "Filter - Only published and unprotected content": {"main": [[{"node": "Loop Over Items", "type": "main", "index": 0}]]}, "Wordpress - Get posts modified after last workflow execution": {"main": [[{"node": "Merge retrieved WordPress posts and pages", "type": "main", "index": 0}]]}, "Wordpress - Get posts modified after last workflow execution1": {"main": [[{"node": "Merge retrieved WordPress posts and pages", "type": "main", "index": 1}]]}}}

How to Import This Workflow

  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.

Don't have an n8n instance? Start your free trial at n8nautomation.cloud

Related Templates

Automate Blog Creation in Brand Voice with AI

Generate blog posts in your brand's unique voice and style directly within WordPress using this powerful AI-driven workflow. It begins by fetching existing articles from your blog via an HTTP request, then intelligently extracts their content and structure using an HTML node and an AI chain LLM to understand your established writing patterns. Concurrently, an AI information extractor analyzes these articles to pinpoint and define your brand's distinct voice characteristics. This extracted style and voice, along with the structural insights, are then fed into an AI content generation agent which crafts new blog post drafts. Finally, these AI-generated articles are automatically saved as drafts in your WordPress instance, ready for review and publication. This workflow is ideal for marketing teams, content creators, and agencies looking to scale their content production while maintaining consistent brand messaging and reducing the manual effort involved in drafting new posts, ultimately saving significant time and resources.

27 nodes

Auto-Tag Blog Posts in WordPress with AI

Automatically tag your WordPress blog posts with relevant keywords using the power of AI. This n8n workflow connects your WordPress site with OpenAI's advanced language models to intelligently analyze new or existing articles and assign appropriate tags, streamlining your content management process. It starts by either triggering manually or by an RSS Feed Trigger monitoring your blog for new content, then sends the article text to an OpenAI Chat Model for tag generation. The workflow then checks your existing WordPress tags via an HTTP Request to avoid duplicates, creates any new tags needed with another HTTP Request, and finally updates the WordPress post with the newly generated and existing tags. This automation is perfect for content managers, marketing teams, and bloggers who want to improve SEO, enhance content discoverability, and reduce the manual effort of categorizing a large volume of articles, ultimately saving significant time and ensuring consistent tagging across your entire blog.

32 nodes

Extract spend details (template)

Automate the extraction and tracking of spend details directly from your email inbox into Google Sheets. This workflow connects Gmail to automatically retrieve invoice and payment emails, then uses AI models like Google Gemini Chat Model and Groq Chat Model to intelligently parse and extract key financial data. The extracted information, including vendor, amount, date, and itemized details, is then seamlessly organized and sent to Google Sheets for centralized record-keeping. This powerful automation is ideal for marketing teams, small businesses, and freelancers who need to efficiently monitor expenses, reconcile accounts, and gain better financial visibility without manual data entry, saving significant time and reducing errors in financial tracking.

24 nodes

Ready to automate with n8n?

Get affordable managed n8n hosting with 24/7 support.