• Being a lightweight option of RAG, with just the necessary dependencies.
• Focused on RAG with local and open source models, not focused on API calls.
• Try out different strategies and data-structures that can be used to have better results. (Such as dataframes, can try out with polars as its really performant)
• Multimodal support, combine image, text and audio to get the best results
• Solve some of the storage challenges RAG faces, and provide good solutions for updating documents and embeddings as well as loading them.
• Use it as a educational library to demostrate on some of the main concepts llama-index or other RAG framworks use.
• The base for the implementation of some agentic strategies I will try out on other library.

pip install nanorag

You can take a look at the notebooks to understand how it works.