RAG_Techniques and raggenie
The first is an educational reference implementation of RAG techniques and patterns, while the second is a production-oriented low-code platform for deploying RAG applications—making them complementary resources where one teaches concepts and the other operationalizes them.
About RAG_Techniques
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Covers advanced RAG patterns including agentic retrieval loops, hybrid search strategies (dense-sparse retrieval fusion), query optimization techniques, and multi-document reasoning—beyond basic retrieval pipelines. Implementations target popular frameworks like LangChain and LlamaIndex with code-first Jupyter notebooks, focusing on practical enhancements for production-grade systems.
About raggenie
sirocco-ventures/raggenie
RAGGENIE: An open-source, low-code platform to build custom Retrieval-Augmented Generation (RAG) Copilets with your own data. Simplify AI development with ease!
Supports multiple structured (MySQL, PostgreSQL, BigQuery, Airtable) and unstructured data sources with automatic query generation, while integrating with major LLM providers (OpenAI, Together.ai, Ollama, AI71). Features a capabilities framework with intent extraction to bind chatbot actions to database operations, plus a JavaScript UI widget for seamless embedding. Built on Python with Zitadel identity management and Chroma vector database for semantic search.
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