Improve development of retrieval augmented generation (RAG) applications at the BR AI + Automation Lab.
Project description
rag-tools-library
Library to support common tasks in retrieval augmented generation (RAG).
This library is in a very early stage and all the documentation is AI generated.
Tutorial and Documentation
You find a brief tutorial and the documentation under br-data.github.io/rag-tools-library.
Roadmap
- Add Google Bison to available LLMs
- Add an offline database alternative
- FAISS and SQLite
- Allow users to register their own LLMs
- Allow users to register their own Embedding models
- Support Semantic Scholar endpoint to generate embeddings for scientific papers.
- Support chat functionality; e.g. let the user give feedback on the result to the LLM.
Deployment
Run the build_and_deploy.sh
script in the root folder. Once prompted for the username, pass __token__
and the pypi API
token you've received. If you don't have an API token and feel like you should, feel free to contact the maintainers.
Contact
Marco Lehner
Project details
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