LangGraph Series | Video 10 | Build a PDF RAG Agent with LangGraph and Gemini

Description

In this video, we take LangGraph even further by building a Retrieval-Augmented Generation (RAG) agent that can read and reason over PDF documents using Google Gemini and LangGraph. You’ll learn how to connect your AI to real-world data — enabling it to search, retrieve, and answer questions from a PDF file intelligently. Here’s what we’ll do step-by-step: 📄 Load a stock market performance report (PDF) ⚙️ Split it into chunks and create embeddings 🧠 Store the data in a Chroma vector database 🔍 Define a retriever tool to fetch relevant information dynamically 🕸️ Use LangGraph to decide when to call the retriever and when to respond directly By the end of this video, you’ll have a fully functional RAG pipeline powered by LangGraph and Gemini, capable of reading documents and answering context-aware questions. This marks a big step toward building intelligent, retrieval-based AI systems that understand and reason over your data — not just generate text.