Agentic RAG for Dummies

Skill by ara.so — AI Agent Skills collection.

This skill enables you to build modular Agentic RAG (Retrieval-Augmented Generation) systems using LangGraph. The framework provides hierarchical document indexing, conversation memory, query clarification with human-in-the-loop, multi-agent map-reduce for complex queries, self-correction, and context compression.

What This Project Does

Agentic RAG for Dummies is a production-ready framework for building intelligent document retrieval systems that go beyond basic RAG:

• Hierarchical Indexing: Search small child chunks for precision, retrieve large parent chunks for context
• Conversation Memory: Maintains dialogue context across multiple questions
• Query Clarification: Rewrites ambiguous queries or pauses for human clarification
• Multi-Agent Orchestration: Decomposes complex queries into parallel sub-agents using LangGraph
• Self-Correction: Automatically re-queries when initial results are insufficient
• Context Compression: Prevents redundant retrievals across long conversations
• Provider Agnostic: Works with Ollama, OpenAI, Anthropic, Google, or any LangChain-supported LLM

Installation