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Postgres Chat Memory and Supabase integration

Save yourself the work of writing custom integrations for Postgres Chat Memory and Supabase and use n8n instead. Build adaptable and scalable AI, Langchain, and Data & Storage workflows that work with your technology stack. All within a building experience you will love.

How to connect Postgres Chat Memory and Supabase

  • Step 1: Create a new workflow
  • Step 2: Add and configure nodes
  • Step 3: Connect
  • Step 4: Customize and extend your integration
  • Step 5: Test and activate your workflow

Step 1: Create a new workflow and add the first step

In n8n, click the "Add workflow" button in the Workflows tab to create a new workflow. Add the starting point – a trigger on when your workflow should run: an app event, a schedule, a webhook call, another workflow, an AI chat, or a manual trigger. Sometimes, the HTTP Request node might already serve as your starting point.

Postgres Chat Memory and Supabase integration: Create a new workflow and add the first step

Step 2: Add and configure Postgres Chat Memory and Supabase nodes

You can find Postgres Chat Memory and Supabase in the nodes panel. Drag them onto your workflow canvas, selecting their actions. Click each node, choose a credential, and authenticate to grant n8n access. Configure Postgres Chat Memory and Supabase nodes one by one: input data on the left, parameters in the middle, and output data on the right.

Postgres Chat Memory and Supabase integration: Add and configure Postgres Chat Memory and Supabase nodes

Step 3: Connect Postgres Chat Memory and Supabase

A connection establishes a link between Postgres Chat Memory and Supabase (or vice versa) to route data through the workflow. Data flows from the output of one node to the input of another. You can have single or multiple connections for each node.

Postgres Chat Memory and Supabase integration: Connect Postgres Chat Memory and Supabase

Step 4: Customize and extend your Postgres Chat Memory and Supabase integration

Use n8n's core nodes such as If, Split Out, Merge, and others to transform and manipulate data. Write custom JavaScript or Python in the Code node and run it as a step in your workflow. Connect Postgres Chat Memory and Supabase with any of n8n’s 1000+ integrations, and incorporate advanced AI logic into your workflows.

Postgres Chat Memory and Supabase integration: Customize and extend your Postgres Chat Memory and Supabase integration

Step 5: Test and activate your Postgres Chat Memory and Supabase workflow

Save and run the workflow to see if everything works as expected. Based on your configuration, data should flow from Postgres Chat Memory to Supabase or vice versa. Easily debug your workflow: you can check past executions to isolate and fix the mistake. Once you've tested everything, make sure to save your workflow and activate it.

Postgres Chat Memory and Supabase integration: Test and activate your Postgres Chat Memory and Supabase workflow

WordPress - AI Chatbot to enhance user experience - with Supabase and OpenAI

This is the first version of a template for a RAG/GenAI App using WordPress content.

As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template!

How It Works

This template includes three workflows:

Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store.
Workflow 2**: Handle upserts for WordPress content when edits are made.
Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents.

Why use this template?

This template can be applied to various use cases:

Build a GenAI application that requires embedded documents from your website's content.
Embed or create a chatbot page on your website to enhance user experience as visitors search for information.
Gain insights into the types of questions visitors are asking on your website.
Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking.

Prerequisites
Access to Supabase for storing embeddings.
Basic knowledge of Postgres and pgvector.
A WordPress website with content to be embedded.
An OpenAI API key
Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency.

Workflow 1 : Initial Embedding

This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector.

Step 0 : Create Supabase tables

Nodes :
Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions
Postgres - Create Workflow Execution History Table

These two nodes create tables in Supabase:

The documents table, which stores embeddings of your website content.
The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp.

Step 1 : Retrieve and Merge WordPress Pages and Posts

Nodes :
WordPress - Get All Posts
WordPress - Get All Pages
Merge WordPress Posts and Pages

These three nodes retrieve all content and metadata from your posts and pages and merge them.
Important: ** **Apply filters to avoid generating embeddings for all site content.

Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown

Nodes :
Set Fields
Filter - Only Published & Unprotected Content
HTML to Markdown

These three nodes prepare the content for embedding by:

Setting up the necessary fields for content embeddings and document metadata.
Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application.
Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings.

Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution

Nodes:
Supabase Vector Store
Sub-nodes:
Embeddings OpenAI
Default Data Loader
Token Splitter
Aggregate
Supabase - Store Workflow Execution

This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details.

Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content.
Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts.

⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers.

Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits.
Aggregate: Ensure the last node is run only for 1 item.
Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed.

This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking.

This workflow should be executed only once for the initial embedding.
Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed.

Workflow 2: Handle document upserts

Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted.

In this first version of the template, the upsert workflow manages:
Newly added content**
Updated content**

Step 1: Retrieve WordPress Content with Regular CRON

Nodes:
CRON - Every 30 Seconds
Postgres - Get Last Workflow Execution
WordPress - Get Posts Modified After Last Workflow Execution
WordPress - Get Pages Modified After Last Workflow Execution
Merge Retrieved WordPress Posts and Pages

A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution.

Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing.

Step 2 : Set fields, use filter

Nodes :
Set fields2
Filter - Only published and unprotected content

The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step.

Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content

Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :)

Nodes:
Loop Over Items
Postgres - Filter on Existing Documents
Switch

Route existing_documents (if documents with matching IDs are found in metadata):
Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update.
Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution.
Set Fields3: Sets fields required for embedding updates.

Route new_documents (if no matching documents are found with IDs in metadata):
Set Fields4: Configures fields for embedding newly added content.

In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process.

Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table

The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance.

Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes.

Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website

Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input

Nodes:
When Chat Message Received
Supabase - Retrieve Documents from Chat Input
Embeddings OpenAI1
Aggregate Documents
Set Fields

When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response.

In this step:
The Supabase vector store retriever fetches documents that match the user’s question, including metadata.
The Aggregate Documents node consolidates the retrieved data.
Finally, Set Fields organizes the data to create a more readable input for the AI agent.

Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative.

Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History

Nodes:
AI Agent**
Sub-nodes:
OpenAI Chat Model
Postgres Chat Memories
Respond to Webhook**

This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

Nodes used in this workflow

Popular Postgres Chat Memory and Supabase workflows

+6

WordPress - AI Chatbot to enhance user experience - with Supabase and OpenAI

This is the first version of a template for a RAG/GenAI App using WordPress content. As creating, sharing, and improving templates brings me joy 😄, feel free to reach out on LinkedIn if you have any ideas to enhance this template! How It Works This template includes three workflows: Workflow 1**: Generate embeddings for your WordPress posts and pages, then store them in the Supabase vector store. Workflow 2**: Handle upserts for WordPress content when edits are made. Workflow 3**: Enable chat functionality by performing Retrieval-Augmented Generation (RAG) on the embedded documents. Why use this template? This template can be applied to various use cases: Build a GenAI application that requires embedded documents from your website's content. Embed or create a chatbot page on your website to enhance user experience as visitors search for information. Gain insights into the types of questions visitors are asking on your website. Simplify content management by asking the AI for related content ideas or checking if similar content already exists. Useful for internal linking. Prerequisites Access to Supabase for storing embeddings. Basic knowledge of Postgres and pgvector. A WordPress website with content to be embedded. An OpenAI API key Ensure that your n8n workflow, Supabase instance, and WordPress website are set to the same timezone (or use GMT) for consistency. Workflow 1 : Initial Embedding This workflow retrieves your WordPress pages and posts, generates embeddings from the content, and stores them in Supabase using pgvector. Step 0 : Create Supabase tables Nodes : Postgres - Create Documents Table: This table is structured to support OpenAI embedding models with 1536 dimensions Postgres - Create Workflow Execution History Table These two nodes create tables in Supabase: The documents table, which stores embeddings of your website content. The n8n_website_embedding_histories table, which logs workflow executions for efficient management of upserts. This table tracks the workflow execution ID and execution timestamp. Step 1 : Retrieve and Merge WordPress Pages and Posts Nodes : WordPress - Get All Posts WordPress - Get All Pages Merge WordPress Posts and Pages These three nodes retrieve all content and metadata from your posts and pages and merge them. Important: * *Apply filters to avoid generating embeddings for all site content. Step 2 : Set Fields, Apply Filter, and Transform HTML to Markdown Nodes : Set Fields Filter - Only Published & Unprotected Content HTML to Markdown These three nodes prepare the content for embedding by: Setting up the necessary fields for content embeddings and document metadata. Filtering to include only published and unprotected content (protected=false), ensuring private or unpublished content is excluded from your GenAI application. Converting HTML to Markdown, which enhances performance and relevance in Retrieval-Augmented Generation (RAG) by optimizing document embeddings. Step 3: Generate Embeddings, Store Documents in Supabase, and Log Workflow Execution Nodes: Supabase Vector Store Sub-nodes: Embeddings OpenAI Default Data Loader Token Splitter Aggregate Supabase - Store Workflow Execution This step involves generating embeddings for the content and storing it in Supabase, followed by logging the workflow execution details. Generate Embeddings: The Embeddings OpenAI node generates vector embeddings for the content. Load Data: The Default Data Loader prepares the content for embedding storage. The metadata stored includes the content title, publication date, modification date, URL, and ID, which is essential for managing upserts. ⚠️ Important Note : Be cautious not to store any sensitive information in metadata fields, as this information will be accessible to the AI and may appear in user-facing answers. Token Management: The Token Splitter ensures that content is segmented into manageable sizes to comply with token limits. Aggregate: Ensure the last node is run only for 1 item. Store Execution Details: The Supabase - Store Workflow Execution node saves the workflow execution ID and timestamp, enabling tracking of when each content update was processed. This setup ensures that content embeddings are stored in Supabase for use in downstream applications, while workflow execution details are logged for consistency and version tracking. This workflow should be executed only once for the initial embedding. Workflow 2, described below, will handle all future upserts, ensuring that new or updated content is embedded as needed. Workflow 2: Handle document upserts Content on a website follows a lifecycle—it may be updated, new content might be added, or, at times, content may be deleted. In this first version of the template, the upsert workflow manages: Newly added content** Updated content** Step 1: Retrieve WordPress Content with Regular CRON Nodes: CRON - Every 30 Seconds Postgres - Get Last Workflow Execution WordPress - Get Posts Modified After Last Workflow Execution WordPress - Get Pages Modified After Last Workflow Execution Merge Retrieved WordPress Posts and Pages A CRON job (set to run every 30 seconds in this template, but you can adjust it as needed) initiates the workflow. A Postgres SQL query on the n8n_website_embedding_histories table retrieves the timestamp of the latest workflow execution. Next, the HTTP nodes use the WordPress API (update the example URL in the template with your own website’s URL and add your WordPress credentials) to request all posts and pages modified after the last workflow execution date. This process captures both newly added and recently updated content. The retrieved content is then merged for further processing. Step 2 : Set fields, use filter Nodes : Set fields2 Filter - Only published and unprotected content The same that Step 2 in Workflow 1, except that HTML To Makrdown is used in further Step. Step 3: Loop Over Items to Identify and Route Updated vs. Newly Added Content Here, I initially aimed to use 'update documents' instead of the delete + insert approach, but encountered challenges, especially with updating both content and metadata columns together. Any help or suggestions are welcome! :) Nodes: Loop Over Items Postgres - Filter on Existing Documents Switch Route existing_documents (if documents with matching IDs are found in metadata): Supabase - Delete Row if Document Exists: Removes any existing entry for the document, preparing for an update. Aggregate2: Used to aggregate documents on Supabase with ID to ensure that Set Fields3 is executed only once for each WordPress content to avoid duplicate execution. Set Fields3: Sets fields required for embedding updates. Route new_documents (if no matching documents are found with IDs in metadata): Set Fields4: Configures fields for embedding newly added content. In this step, a loop processes each item, directing it based on whether the document already exists. The Aggregate2 node acts as a control to ensure Set Fields3 runs only once per WordPress content, effectively avoiding duplicate execution and optimizing the update process. Step 4 : HTML to Markdown, Supabase Vector Store, Update Workflow Execution Table The HTML to Markdown node mirrors Workflow 1 - Step 2. Refer to that section for a detailed explanation on how HTML content is converted to Markdown for improved embedding performance and relevance. Following this, the content is stored in the Supabase vector store to manage embeddings efficiently. Lastly, the workflow execution table is updated. These nodes mirros the **Workflow 1 - Step 3 nodes. Workflow 3 : An example of GenAI App with Wordpress Content : Chatbot to be embed on your website Step 1: Retrieve Supabase Documents, Aggregate, and Set Fields After a Chat Input Nodes: When Chat Message Received Supabase - Retrieve Documents from Chat Input Embeddings OpenAI1 Aggregate Documents Set Fields When a user sends a message to the chat, the prompt (user question) is sent to the Supabase vector store retriever. The RPC function match_documents (created in Workflow 1 - Step 0) retrieves documents relevant to the user’s question, enabling a more accurate and relevant response. In this step: The Supabase vector store retriever fetches documents that match the user’s question, including metadata. The Aggregate Documents node consolidates the retrieved data. Finally, Set Fields organizes the data to create a more readable input for the AI agent. Directly using the AI agent without these nodes would prevent metadata from being sent to the language model (LLM), but metadata is essential for enhancing the context and accuracy of the AI’s response. By including metadata, the AI’s answers can reference relevant document details, making the interaction more informative. Step 2: Call AI Agent, Respond to User, and Store Chat Conversation History Nodes: AI Agent** Sub-nodes: OpenAI Chat Model Postgres Chat Memories Respond to Webhook** This step involves calling the AI agent to generate an answer, responding to the user, and storing the conversation history. The model used is gpt4-o-mini, chosen for its cost-efficiency.

Build your own Postgres Chat Memory and Supabase integration

Create custom Postgres Chat Memory and Supabase workflows by choosing triggers and actions. Nodes come with global operations and settings, as well as app-specific parameters that can be configured. You can also use the HTTP Request node to query data from any app or service with a REST API.

Supabase supported actions

Create
Create a new row
Delete
Delete a row
Get
Get a row
Get Many
Get many rows
Update
Update a row

FAQs

  • Can Postgres Chat Memory connect with Supabase?

  • Can I use Postgres Chat Memory’s API with n8n?

  • Can I use Supabase’s API with n8n?

  • Is n8n secure for integrating Postgres Chat Memory and Supabase?

  • How to get started with Postgres Chat Memory and Supabase integration in n8n.io?

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