agentic-rag-for-dummies and Building-Natural-Language-and-LLM-Pipelines
These are complementary educational resources that teach overlapping agentic RAG concepts using the same core frameworks (LangGraph, Haystack), so a learner might use the simpler introductory project (A) first, then progress to the more comprehensive Packt book (B) for production-grade implementation patterns.
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 Building-Natural-Language-and-LLM-Pipelines
PacktPublishing/Building-Natural-Language-and-LLM-Pipelines
Building RAG and Agentic Applications with Haystack 2.0, RAGAS and LangGraph 1.0 published by Packt
Covers deterministic pipeline design with strict tool contracts, context engineering for agent reliability, and production deployment patterns including microservices via FastAPI/Hayhooks and multi-agent orchestration with LangGraph's supervisor-worker patterns. Integrates evaluation frameworks (RAGAS, Weights & Biases) for cost and quality tracking, plus practical NLP tasks like NER and sentiment analysis as agentic tools within observable, fault-tolerant workflows.
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