Build a customer support RAG agent with GPT-5, Telegram & Pinecone

🧠 RAG-Based Customer Support Agent (GPT-5 + Telegram) Description:

This workflow builds a powerful Retrieval-Augmented Generation (RAG) Customer Support Agent that interacts with users directly through Telegram using the GPT-5 model. It combines real-time conversational capabilities with context-aware responses by leveraging vector search via Pinecone, making it ideal for automated, intelligent support systems.

Watch Video Tutorial Build on Workflows Like These: https://www.youtube.com/@Automatewithmarc

💬 Key Features:

Telegram Integration: Listens to customer queries via the Telegram Trigger node and sends back intelligent responses in the same chat. GPT-5 Agent (LangChain): A powerful AI agent node orchestrates the conversation using OpenAI's GPT-5 model. Contextual Memory: A Memory Buffer stores the last 15 interactions per user to provide more personalized and coherent multi-turn conversations.

RAG with Pinecone: Integrates with Pinecone to fetch relevant answers from your “Customer FAQ” vector namespace, enabling grounded and accurate responses. Embeddings Generation: Uses OpenAI’s Embeddings node to process and vectorize documents for retrieval. End-to-End AI Pipeline: Connects all components from input to output, providing seamless and intelligent customer support.

🔧 Tech Stack:

GPT-5 via OpenAI API Pinecone vector store (namespace: Customer FAQ) Telegram Bot API LangChain agent, memory, and embedding tools n8n self-hosted or cloud instance