Projects
LangGraph Orchestrated RAG Over Nike 10-K
This project demonstrates a LangGraph-orchestrated Retrieval-Augmented Generation (RAG) system for intelligent querying of Nike’s 2023 10-K filing. It extends prior modules by introducing a graph-based control flow, allowing the app to analyze questions, retrieve relevant vector data using metadata filters, and generate structured responses, all as composable LangGraph nodes.
This approach reflects a production-ready design for enterprise AI, combining semantic search, structured reasoning, and orchestration to deliver scalable, transparent question-answering pipelines.
⸻
Features
- LangGraph Orchestration: Constructs a structured execution flow using LangGraph, where each step (query analysis retrieval generation) is defined as a node.
- Mermaid Diagram Compatibility: Outputs the graph as a Mermaid diagram for inspection and transparency.
- Checkpointed Memory: Supports state management between nodes with LangGraph’s
MemorySaverfor persistence and reproducibility. - Metadata-Filtered Search: Performs section-specific retrievals (e.g., only from “middle” of the document).
- Modular Design: Each RAG component, from question parsing to chunk filtering to response generation, is encapsulated as a separate function node.
- Prompt Customization: Uses minimal, safety-conscious RAG prompts for controlled output.
⸻
Key Insight
Orchestration is the secret to reliable AI systems. LangGraph makes it possible to coordinate retrieval, reasoning, and memory across steps, turning one-off queries into robust workflows and enabling LLMs to act as structured agents in dynamic environments.