Build a Local Agentic RAG System to Analyze Financial Data and Stocks with LangGraph and Ollama

Learn to build "FinVault," a complete agentic RAG (Retrieval-Augmented Generation) application that analyzes financial documents and stock data entirely locally using Ollama with Qwen 3 or Gemini 2.5 and LangGraph. Discover the differences between simple and agentic RAG systems, then dive into constructing an agentic workflow architecture that can process SEC filings and historical stock price data. Explore the project structure and configuration setup, implement tools for retrieving historical price data and SEC filings, and build the core agentic workflow using LangGraph. Create a user-friendly Streamlit interface for the application and see the complete FinVault system in action through a comprehensive demonstration that showcases its ability to understand and analyze complex financial documents without requiring manual analysis.