agentic-rag-for-dummies and Controllable-RAG-Agent
These are complementary educational resources that address different aspects of agentic RAG development—the first provides a beginner-friendly modular introduction using LangGraph, while the second offers an advanced implementation with graph-based algorithms for handling complex question-answering scenarios.
About agentic-rag-for-dummies
GiovanniPasq/agentic-rag-for-dummies
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
Built on LangGraph's agentic framework, this system implements hierarchical parent-child chunk indexing for precision search paired with context-rich retrieval, conversation memory across turns, and human-in-the-loop query clarification. Multi-agent map-reduce parallelizes sub-query resolution with self-correction and context compression, while supporting pluggable LLM providers (Ollama, OpenAI, Anthropic, Google) and Qdrant vector storage—all orchestrated through observable graph execution with Langfuse integration.
About Controllable-RAG-Agent
NirDiamant/Controllable-RAG-Agent
This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.
Implements a deterministic graph-based agent that breaks down complex questions through multi-step reasoning—anonymizing queries to avoid pre-trained knowledge bias, decomposing tasks into retrieval or answer generation steps, and verifying outputs against source documents. Built on LangChain and FAISS with Streamlit visualization, it processes PDFs into chunked content, LLM-generated summaries, and quote databases to enable grounded, hallucination-resistant responses.
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