RAGLight uses Chroma as its default vector storage solution, and also supports multiple HuggingFace embedding models (e.g. all-MiniLM-L6-v2) for document vectorization. This combination ensures efficient similarity search performance, while users can also customize vector storage paths and collection names via VectorStoreConfig. The choice of a high-quality embedding model has a direct impact on retrieval accuracy, and the design provides users with the flexibility to balance the space between efficiency and accuracy.
This answer comes from the articleRAGLight: Lightweight Retrieval Augmentation Generation Python LibraryThe
May not be reproduced without permission:AI productivity tools " Chroma and HuggingFace embedding models are the default vector storage and embedding choices for RAGLight
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