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Built to explore and learn Retrieval‑Augmented Generation (RAG) techniques through practical implementations. Developed a collection of RAG workflows—from simple baselines to advanced methods like self‑refinement, reranking, and agentic RAG—across linked repositories.

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RAG Implementations

This repository serves as a central hub for all my Retrieval-Augmented Generation (RAG) experiments and implementations.

Each project explores a different technique, enhancement, or research direction within the RAG ecosystem — ranging from simple baselines to advanced agentic and self-refining pipelines.


📚 Project Index

# Name Description Link
1 Agentic_RAG Agent-powered RAG system integrating planning, tool use, and dynamic multi-step reasoning. https://github.com/supraja777/Agentic_RAG
2 Self-RAG Implements self-reflective refinement where the model evaluates and improves its own retrieved context and responses. https://github.com/supraja777/Self-RAG
3 CRAG Corrective RAG: applies post-retrieval validation and filtering to improve factual grounding and reduce noisy context. https://github.com/supraja777/CRAG
4 RAG-Reranking Enhances retrieval via cross-encoder–based re-ranking to deliver higher-quality context before generation. https://github.com/supraja777/RAG-Reranking
5 Reliable-RAG Focuses on stability and consistency — introducing checks to reduce hallucination and improve response robustness. https://github.com/supraja777/Reliable-RAG
6 Hypothetical-Document-Embedding Implements HDE-based retrieval using synthetic text embeddings to improve semantic similarity search. https://github.com/supraja777/Hypothetical-Document-Embedding
7 Simple-RAG A clean and minimal baseline RAG pipeline implementing standard retrieval + generation. https://github.com/supraja777/Simple-RAG
8 Query-Transformations-RAG Demonstrates query rewriting, step-back prompting, and sub-query decomposition to enhance retrieval in RAG pipelines. https://github.com/supraja777/Query-Transformations-RAG

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Built to explore and learn Retrieval‑Augmented Generation (RAG) techniques through practical implementations. Developed a collection of RAG workflows—from simple baselines to advanced methods like self‑refinement, reranking, and agentic RAG—across linked repositories.

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