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- Clean up outdated vector entries for updated files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