Day 5: Building a Multi-Source RAG AI Customer Support Agent with n8n + OpenAI 📚🤖

🚀 Day 5 of My AI Automation Engineering Journey

Today I upgraded my AI Customer Support Agent by implementing a Multi-Source Retrieval-Augmented Generation (RAG) architecture.

Instead of relying on a single knowledge source, the AI now retrieves information from multiple sources simultaneously, ranks the most relevant context, and generates more accurate and reliable responses.

🔹 Technologies

• n8n

• OpenAI

• RAG

• AI Agents

• Knowledge Base

• Google Sheets

• PDFs

• Website Documentation

• FAQs

✅ New Features

• Multi-Source Knowledge Retrieval

• Intent Detection

• Parallel Data Processing

• Context Ranking

• AI Reasoning

• Confidence Scoring

• Conversation Memory

• Structured JSON Responses

• Execution Logging

This enhancement brings the project closer to an enterprise-grade AI support platform capable of delivering more accurate and context-aware customer assistance.

Next milestone: Live Chat Widget, Human Handoff Dashboard, and Ticket Management.

Every project is an opportunity to build, learn, and improve.

Feedback is always welcome. 🚀

#n8n

#OpenAI

#RAG

#AIAgents

#ArtificialIntelligence

#WorkflowAutomation

#Automation

#CustomerSupport

#LLM