NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
- Stars
- 30k
- Forks
- 3.7k
- Open issues
- 2
- Last push
- 1d ago
About RAG_Techniques
RAG_Techniques is a free, open-source RAG and AI search tool by NirDiamant, written mainly in Jupyter Notebook. This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial. It has 30k GitHub stars and 3.7k forks, and was last updated 1d ago. GitHub doesn't detect a standard license, so check the repository before using it commercially.
- License
- Not detected
- Language
- Jupyter Notebook
- Stars
- 29,962
- Last update
- 1d ago
- Runs on Mac
- Not stated
- GPU
- Not stated
- Paid API key
- Not stated
- Self-hostable
- Not stated
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RAG_Techniques FAQ
Is RAG_Techniques free?
Yes. RAG_Techniques is open source and free to use. GitHub doesn't detect a standard license, so check the repository before using it commercially.
Does RAG_Techniques run on a Mac?
Its README doesn't say. Check the installation guide on GitHub before you start.
Does RAG_Techniques need a GPU?
Its README doesn't state hardware needs. AI models generally run much faster with a GPU.
Is RAG_Techniques still maintained?
Yes. It was last updated 1d ago and has 2 open issues on GitHub.
What are alternatives to RAG_Techniques?
Similar free, open-source tools include ragflow, meilisearch, PageIndex, milvus.