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

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.

rag chatbot, rag chatbot from documents, document question answering ai, ai chatbot from pdf, supabase vector database, rag system architecture, ai document search, embeddings and vector search, n8n ai automation, ai knowledge base chatbot, saas ai chatbot, internal ai systems, telegram ai bot, ai document ingestion, retrieval augmented generation