Jupyter Notebook
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
★ 30.2k
Stars
3.7k forks · 7 open issues
+24/day
Growth · measured
Stars counted between two of our own snapshots roughly a week apart. The repository page gives the exact span.
60.1
Momentum / 100
see the breakdown below
2y ago
Created
last push 6d ago
Star history
How this score was built
| Component | Weight | Earned | Why |
|---|---|---|---|
| Velocity | 55 | 26.7 | +24/day, log-scaled against a 750/day ceiling |
| Acceleration | 15 | 5.7 | last week ran 1.38× the monthly pace |
| Freshness | 15 | 14.7 | last push 6d ago; decays to zero at 180 days |
| Scale | 15 | 13.0 | 30.2k stars, log-scaled against 150k — stops a 3-star repo topping the board |
| Momentum | 100 | 60.1 | sum of the above |
Detail
| Topics | agentic-ragaiembeddingsgenerative-aigptlangchainllama-indexllmllmsmachine-learningnlpopenaipythonragretrieval-augmented-generationsemantic-searchtutorialsvector-database |
| Matched on | RAG (embeddings, rag, retrieval-augmented-generation, semantic-search, vector-database) LLM & Inference (llm) |
| Short window | +168 stars over 7 days = 24.0/day |
| Long window | +453 stars over 26 days = 17.4/day |
| Lifetime average | 35.79 stars/day over 845 days |
| First seen here | 2026-10-11 |
| Last read | 2d ago |