agentic-rag-for-dummies and deep-thinking-rag
These are ecosystem siblings: one provides a foundational, modular agentic RAG framework optimized for learning and implementation, while the other extends that paradigm with advanced reasoning capabilities (deep thinking) for handling more complex query resolution.
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 deep-thinking-rag
FareedKhan-dev/deep-thinking-rag
A Deep Thinking RAG Pipeline to Solve Complex Queries
Implements a multi-stage agentic RAG system that decomposes complex queries into structured research plans, then iteratively retrieves, reranks, and synthesizes evidence using supervisor agents, cross-encoders, and hybrid search strategies (vector/keyword/semantic). Built on LangChain with configurable LLM providers, it includes self-critique and policy-based control flow to decide when to refine the plan, continue research, or synthesize final answers—enabling multi-hop reasoning across both internal documents and web sources.
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