Text Summarization Using Agentic RAG

A text summarisation system that uses agentic RAG.

A text summarisation system that uses agentic 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, cooperating agents with defined roles and tools, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses LangChain, FAISS, LLM APIs, Python, and Hugging Face Transformers, with a clear project structure, setup instructions and sample data so that it can be run and extended.