I Built a RAG Chatbot That Answers Questions Directly From Your Documents (Telegram → Supabase)

Описание: Work with me: https://cal.com/mdamaanco/coffee-chat-30

In this video, I break down a RAG chatbot system that answers questions directly from uploaded documents using embeddings and vector search.

The workflow starts by uploading a PDF through Telegram, storing it in a Supabase vector database, and then using AI to scan the document and return exact, context-aware answers pulled from the source file. This is a system breakdown, not just a chatbot demo — showing how document ingestion, embeddings, vector storage, and retrieval actually work together.

If you’re a SaaS founder or agency owner looking to build internal knowledge bots, client-facing AI assistants, or document-based chat systems, this video shows how to design and deploy a real RAG pipeline, not just connect an API.

Tools used: Telegram, n8n, Supabase (vector store), OpenAI, embeddings, PDFs.

If you want help building or customizing a RAG chatbot or internal AI system for your SaaS or agency, you can book a quick strategy call above.

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