Smart Email Assistant: Automate Customer Support with AI & Supabase

Description

Intelligent Email Support System with Vector Database

Overview

This n8n workflow automates email support using AI and vector database technology to provide smart, context-aware responses. It seamlessly integrates email automation and document management, ensuring efficient customer support.

📌 System Components

✉️ Email Support System

Email Monitoring & Classification

Gmail trigger node monitoring inbox
AI-powered email classification
Intelligent routing (support vs non-support inquiries)

AI Response Generation

LangChain agent for response automation
OpenAI integration for NLP-driven replies
Vector-based knowledge retrieval
Automated draft creation in Gmail

Vector Database System

Supabase vector store for document management
OpenAI embeddings for vector conversion
Fast and efficient similarity search

📂 Document Management System

Google Drive Integration

Monitors specific folders for new/updated files
Automatic document processing
Supports various file formats

Document Processing Pipeline

Auto file download & text extraction
Smart text chunking for better indexing
Embedding generation via OpenAI
Storage in Supabase vector database

🔄 Workflow Processes

📧 Email Support Flow

Monitor Gmail inbox for new emails
AI classification of incoming messages
Route support emails to AI response generator
Perform vector similarity search for knowledge retrieval
Generate personalized AI-driven response
Create email drafts in Gmail

📁 Document Management Flow

Monitor Google Drive for new/updated files
Auto-download and process documents
Clean up outdated vector entries for updated files
Extract and split document text efficiently
Generate OpenAI embeddings
Store processed data in Supabase vector DB

⚙️ Setup Instructions

1️⃣ Prerequisites

Supabase** account & project
OpenAI API key**
Gmail account** with OAuth2 setup
Google Drive API** access
n8n installation**

2️⃣ Supabase Database Setup

-- Create the vector extension
create extension if not exists vector;

-- Create the documents table
create table documents (
id bigserial primary key,
content text,
metadata jsonb,
embedding vector(1536)
);

-- Create an index for similarity search
create index on documents using ivfflat (embedding vector_cosine_ops)
with (lists = 100);

3️⃣ Google Drive Setup

Create & configure two monitored folders:
RAG folder for new documents
documents
Assign correct folder permissions
Add folder IDs to the workflow

4️⃣ Document Processing Configuration

Set up triggers for file creation and file updates
Configure text extraction:
Define chunk size & overlap settings
Set document metadata processing

🔍 Maintenance & Optimization

📌 Regular Tasks

Monitor system performance
Update the knowledge base regularly
Review AI response quality
Optimize vector search parameters
Clean up outdated document embeddings

✅ Best Practices

Document Organization

Maintain structured folders & naming conventions
Keep knowledge base content updated

System Optimization

Track AI classification accuracy
Tune response times & chunk sizes
Perform regular database maintenance

🛠️ Troubleshooting

Email Issues

Verify Gmail API credentials
Check AI service uptime
Monitor classification performance

Document Processing Issues

Ensure correct file permissions
Validate extraction & embedding processes
Debug vector database insertions