RAGLight is a lightweight, modular Python library designed to implement Retrieval Augmented Generation (RAG). Its main features include:
- Support for multiple data sources (local folders and GitHub repositories)
- Modular RAG pipeline supporting standard RAG, Agentic RAG and RAT modes
- Flexible integration of multilingual models (e.g., llama3) and embedded models (e.g., HuggingFace's all-MiniLM-L6-v2)
- Efficient vector storage with Chroma or HuggingFace
- Support for customized configurations such as embedding models, vector storage paths, and retrieval parameters (e.g., k-values)
- Automate document processing to simplify knowledge base building
These features make it particularly suitable for developers who need to build context-aware AI applications quickly.
This answer comes from the articleRAGLight: Lightweight Retrieval Augmentation Generation Python LibraryThe
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