Create a Knowledge-Powered Chatbot with Claude, Supabase & Postgres

Intelligent chatbot with custom knowledge base Who's it for Businesses, developers, and organizations who need a customizable AI chatbot for internal documentation access, customer support, e-commerce assistance, or any use case requiring intelligent conversation with access to specific knowledge bases. What it does This workflow creates a fully customizable AI chatbot that can be deployed on any platform supporting webhook triggers (websites, Slack, Teams, etc.). The chatbot accesses a personalized knowledge base stored in Supabase and can perform advanced actions like sending emails, scheduling appointments, or updating databases beyond simple conversation. How it works The workflow combines several powerful components:

Webhook Trigger: Accepts messages from any platform that supports webhooks AI Agent: Processes user queries with customizable personality and instructions Vector Database: Searches relevant information from your Supabase knowledge base Memory System: Maintains conversation history for context and traceability Action Tools: Performs additional tasks like email sending or calendar booking

Technical architecture

Chat trigger connects directly to AI Agent Language model, memory, and vector store all connect as tools/components to the AI Agent Embeddings connect specifically to the Supabase Vector Store for similarity search

Requirements

Supabase account and project AI model API key (any LLM provider of your choice) OpenAI API key (for embeddings - this is covered in Cole Medin's tutorial) n8n built-in PostgreSQL access (for conversation memory) Platform-specific webhook configuration (optional)

How to set up Step 1: Configure your trigger

The template uses n8n's default chat trigger For external platforms: Replace with webhook trigger and configure your platform's webhook URL Supported platforms: Any service with webhook capabilities (websites, Slack, Teams, Discord, etc.)

Step 2: Set up your knowledge base For creating and managing your vector database, follow this comprehensive guide:

Watch Cole Medin's tutorial on document vectorization This video shows how to build a complete knowledge base on Supabase The tutorial covers document processing, embedding creation, and database optimization Important: The video explains the OpenAI embeddings configuration required for vector search

Step 3: Configure the AI agent

Define your prompt: Customize the agent's personality and role

Example: "You are the virtual assistant for example.com. Help users by answering their questions about our products and services."

Select your language model: Choose any AI provider you prefer (OpenAI, Anthropic, Google, etc.) Set behavior parameters: Define response style, tone, and limitations

Step 4: Connect Supabase Vector Store

Add the "Supabase Vector Store" tool to your agent Configure your Supabase project credentials Mode: Set to "retrieve-as-tool" for automatic agent integration Tool Description: Customize description (default: "Database") to describe your knowledge base Table configuration:

Specify the table containing your knowledge base (example shows "growth_ai_documents") Ensure your table name matches your actual knowledge base structure Multiple tables: You can connect several tables for organized data structure

The agent will automatically decide when to search the knowledge base based on user queries

Step 5: Set up conversation memory (recommended)

Use "Postgres Chat Memory" with n8n's built-in PostgreSQL credentials Configure table name: Choose a name for your chat history table (will be auto-created) Context Window Length: Set to 20 messages by default (adjustable based on your needs) Benefits:

Conversation traceability and analytics Context retention across messages Unique conversation IDs for user sessions Stored in n8n's database, not Supabase

How to customize the workflow Basic conversation features

Response style: Modify prompts to change personality and tone Knowledge scope: Update Supabase tables to expand or focus the knowledge base Language support: Configure for multiple languages Response length: Set limits for concise or detailed answers Memory retention: Adjust context window length for longer or shorter conversation memory

Advanced action capabilities The chatbot can be extended with additional tools for:

Email automation: Send support emails when users request assistance Calendar integration: Book appointments directly in Google Calendar Database updates: Modify Airtable or other databases based on user interactions API integrations: Connect to external services and systems File handling: Process and analyze uploaded documents

Platform-specific deployments Website integration

Replace chat trigger with webhook trigger Configure your website's chat widget to send messages to the n8n webhook URL Handle response formatting for your specific chat interface

Slack/Teams deployment

Set up webhook trigger with Slack/Teams webhook URL Configure response formatting for platform-specific message structures Add platform-specific features (mentions, channels, etc.)

E-commerce integration

Connect to product databases Add order tracking capabilities Integrate with payment systems Configure support ticket creation

Results interpretation Conversation management

Chat history: All conversations stored in n8n's PostgreSQL database with unique IDs Context tracking: Agent maintains conversation flow and references previous messages Analytics potential: Historical data available for analysis and improvement

Knowledge retrieval

Semantic search: Vector database returns most relevant information based on meaning, not just keywords Automatic decision: Agent automatically determines when to search the knowledge base Source tracking: Ability to trace answers back to source documents Accuracy improvement: Continuously refine knowledge base based on user queries

Use cases Internal applications

Developer documentation: Quick access to technical guides and APIs HR support: Employee handbook and policy questions IT helpdesk: Troubleshooting guides and system information Training assistant: Learning materials and procedure guidance

External customer service

E-commerce support: Product information and order assistance Technical support: User manuals and troubleshooting Sales assistance: Product recommendations and pricing FAQ automation: Common questions and instant responses

Specialized implementations

Lead qualification: Gather customer information and schedule sales calls Appointment booking: Healthcare, consulting, or service appointments Order processing: Take orders and update inventory systems Multi-language support: Global customer service with language detection

Workflow limitations

Knowledge base dependency: Quality depends on source documentation and embedding setup Memory storage: Requires active n8n PostgreSQL connection for conversation history Platform restrictions: Some platforms may have webhook limitations Response time: Vector search may add slight delay to responses Token limits: Large context windows may increase API costs Embedding costs: OpenAI embeddings required for vector search functionality