Modular Agentic RAG System with LangGraph

Описание и применение

Модульная система RAG на LangGraph с иерархическим индексированием, памятью диалога и самокоррекцией повышает релевантность ответов и поддерживает контекст.

Задача

Need a flexible, self‑correcting Retrieval‑Augmented Generation (RAG) system that can handle hierarchical data, maintain conversation context, and adapt its responses.

Результат

The modular LangGraph RAG system improves answer relevance, maintains context across conversations, and automatically corrects low‑confidence responses, leading to higher user satisfaction.

Рабочий процесс

• 1. Define hierarchical data sources and index them with LangGraph’s indexing nodes.
• 2. Configure a conversation memory node to store dialogue context across turns.
• 3. Implement a self‑correction node that re‑queries or re‑generates answers when confidence is low.
• 4. Connect nodes in a LangGraph workflow to form an agentic RAG pipeline.
• 5. Deploy the workflow and monitor performance, iterating on indexing or memory settings as needed.

Доказательства

Builds a modular agentic RAG system using LangGraph with hierarchical indexing, conversation memory, and self-correction.

Источник