Build a RAG Based PDF Chatbot with LangChain, ChromaDB & Gemini API | Fundamentals of RAG

Learn how to build a powerful Retrieval-Augmented Generation (RAG) chatbot using LangChain, ChromaDB, and Google's Gemini API! In this step-by-step tutorial, we’ll walk through integrating a custom knowledge base with an LLM for real-time, context-aware conversations. 🔍 What You’ll Learn: How RAG works in modern AI applications Setting up LangChain for chaining prompts and logic Using ChromaDB for efficient vector-based document retrieval Integrating Gemini API for intelligent language understanding Deploying a smart chatbot that can answer from custom data Perfect for developers, AI enthusiasts, and anyone building next-gen chatbots with RAG and LLMs! 📌 Tools Used: - LangChain - ChromaDB (Vector DB) - Gemini API (by Google) - Python Resources & Codes : https://github.com/snsupratim/rag_pdf_chatbot/blob/main/langchain_rag_chatbot.ipynb 👉 Don't forget to like, subscribe, and hit the 🔔 bell icon for more AI & Dev tutorials! #LangChain #GeminiAPI #ChromaDB #RAGChatbot #AIchatbot #LangChainTutorial #GeminiLLM #PythonAI