A deep dive into an agentic RAG system designed for finance and tax queries. The architecture uses LangChain and LangGraph for orchestration, Pinecone for vector storage, and a hybrid BM25+FAISS retrieval pipeline with Maximal Marginal Relevance (MMR) re-ranking. Key components include an intelligent query analyzer that
Table of contents
The Core Problem: Why Financial Queries Break Simple SystemsArchitecture Overview: A Symphony of Specialized ComponentsPillar 1: The Brain — Intelligent Query AnalysisPillar 2: The Librarian — High-Performance, Explainable RetrievalGet Yashgiri’s stories in your inboxPillar 3: The Conductor — Dynamic Orchestration with LangGraphPillar 4: The Engine Room — Performance & RobustnessConclusion: The Future of Financial AISort: