Agentic RAG - Building an AI Financial Analyst Team
RAG pipelines work great, but they follow a fixed script - retrieve, augment, generate. What happens when a user asks a complex, multi-faceted question like "Analyze Tesla's recent performance and risks"? A simple retriever can't create a plan, it just fetches whatever seems closest. The result is often a shallow answer that misses the big picture.
This is where an Agentic RAG shines. Instead of a fixed process, we'll build a team of AI agents that can reason, create a multi-step research plan, use different tools to gather information, and synthesize a comprehensive analysis.
In this tutorial, you will build FinVault, an agentic RAG system powered by LangGraph. You will learn to construct a "supervisor" agent that delegates tasks to specialized "worker" agents - one for stock prices, another for SEC filings, and a third "synthesizer" to craft a final, data-driven report.
Tutorial Goals
- Understand the difference between simple RAG and agentic RAG
- Implement a multi-agent Supervisor pattern
- Build specialized retrieval tools for financial data (stock prices and SEC filings)
- Orchestrate agent collaboration using LangGraph
- Develop a Streamlit UI application to talk with the agents
