Telegram AI chatbot with document-based answers using OpenAI and PGVector RAG

🤖 AI Q&A Chatbot Workflow – Build Your Own AI Agent Trained on Private Documents

This powerful AI automation add-on upgrades your [Telegram Bot Starter Template](https://n8n.io/workflows/2402-telegram-bot-starter-template-setup-and-ai-chatbot/) by integrating a fully functional AI chatbot and a context-aware AI agent that answers user questions using your internal documents.

Unlike generic bots, this chatbot uses your own data to respond with deeply personalized, context-relevant information — perfect for support, onboarding, internal knowledge access, and client-facing interactions.

It connects to any PostgreSQL database — including [Neon.tech](https://neon.tech), Supabase, or a self-hosted Postgres setup — allowing you to build custom AI-powered FAQ assistants, internal support bots, or knowledge-based customer service tools.

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🧠 Why It Works: Contextual Retrieval

The secret is Contextual Retrieval — a powerful technique where your documents are stored in a way that preserves meaning and context. This allows the AI to fetch highly relevant, source-backed responses, eliminating hallucinations and guesswork.

> Data is embedded, chunked, and saved in a vector database (Postgres + PGVector), enabling smart semantic search tailored to your needs.