Permission-Aware Hybrid Retrieval for Enterprise RAG: A Benchmark and Selectivity-Adaptive Method

A benchmark and selectivity-adaptive method for permission-aware hybrid retrieval in enterprise RAG.

A benchmark and selectivity-adaptive method for permission-aware hybrid retrieval in enterprise RAG. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are retrieval over an indexed document collection that grounds the language model's answers, text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses LangChain, FAISS, Python, Hugging Face Transformers, and spaCy, with a clear project structure, setup instructions and sample data so that it can be run and extended.