RAG Chatbot System Vector Database + Embedding

Description

*Welcome to Subhan Kaladi’s Official YouTube Channel!* *Integrated RAG Chatbot + Vector Database Embedding – Full Implementation Guide!* In this video, you’ll learn how to build a fully integrated RAG (Retrieval-Augmented Generation) Chatbot connected with a Vector Database using modern AI tools. This tutorial covers everything from generating embeddings, storing them in a vector database (Qdrant / Pinecone), and retrieving relevant context to power your chatbot’s responses. *Resource:* - Code URL: https://github.com/subhankaladi/RAG-DOCS - Join Whatsapp Channel: https://whatsapp.com/channel/0029Vavb6Jy6buMQjQ0l8A45 *What You Will Learn* ✔ What is RAG and why it matters ✔ How vector embeddings work ✔ How to connect a vector database (Qdrant / Pinecone) ✔ How to store documents and generate embeddings ✔ How retrieval improves chatbot accuracy ✔ How to integrate embeddings with your chatbot agent ✔ How to perform similarity search ✔ Live implementation step by step *Social Media Links:* - WhatsApp Channel: https://whatsapp.com/channel/0029Vavb6Jy6buMQjQ0l8A45 - Tech YouTube Channel: https://www.youtube.com/@subhankaladi - Instagram: https://www.instagram.com/subhan_kaladi/ - TikTok: https://www.tiktok.com/@subhan_kaladi - X (Twitter): https://x.com/subhankaladi15 - LinkedIn: https://www.linkedin.com/in/subhankaladi - Facebook: https://www.facebook.com/subhanallahkaladi *Don’t forget to Like, Share, and Subscribe. Let’s build the future with AI together!* #SubhanKaladi #RAGChatbot #VectorDatabase #Embeddings #AIChatbot #Qdrant #Pinecone #RetrievalAugmentedGeneration #AIEngineering #OpenAI #ClaudeCode