Projects
Conversational RAG with LangGraph Agents
This project extends a Retrieval-Augmented Generation (RAG) pipeline into a stateful, conversational assistant using LangChain, LangGraph, and OpenAI’s GPT-4o-mini. Designed for the MSDS 442 course at Northwestern University, it builds on previous modules by introducing agent reasoning, memory persistence, and multi-step retrieval workflows.
This reinforces the principle that AI systems are most powerful when embedded into structured, state-aware workflows, not standalone black boxes. LangGraph offers a clean architecture to reason over conversational history, perform multi-step tool calls, and allow LLMs to act as autonomous decision-makers.
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Features
- Memory Persistence: Tracks multi-turn conversations using LangGraph’s
MemorySavercheckpoints. - Chat State as Messages: Uses LangChain’s message schema (
user,AI,tool) for clean session tracking. - Agent Reasoning (ReAct): Implements LangGraph’s ReAct-style executor to dynamically decide when and how to retrieve and respond.
- Multi-Step Retrieval: Empowers the agent to issue follow-up queries without requiring further user input.
- Visualization: Supports generation of Mermaid-compatible control flow graphs to inspect reasoning structure.
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Key Insight
Agents are ideal for dynamic and ambiguous queries. This project illustrates how LLMs can be embedded in intelligent systems that retrieve, reason, and respond across evolving conversations, making them more like collaborative assistants than static tools.