agentic-rag-for-dummies and oreilly-retrieval-augmented-gen-ai
One project provides a modular LangGraph-based Agentic RAG implementation for learning, while the other offers a broader educational resource demonstrating RAG with agents and GraphRAG, making them complementary for understanding and implementing Agentic RAG systems.
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 oreilly-retrieval-augmented-gen-ai
sinanuozdemir/oreilly-retrieval-augmented-gen-ai
See how to augment LLMs with real-time data for dynamic, context-aware apps - Rag + Agents + GraphRAG.
Implements end-to-end RAG workflows using vector databases (Pinecone), multiple LLM providers (OpenAI, Anthropic, Gemini, Cohere), and LangGraph for orchestration with built-in evaluation components. Covers advanced patterns including knowledge graph-based retrieval (GraphRAG with Neo4j), embedding fine-tuning with synthetic data, multimodal search, and agentic workflows with semantic re-ranking.
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