Building a RAG Pipeline for 10M+ Documents With Near-Zero Hallucination
A step-by-step, notebook-based tutorial that builds a retrieval-augmented-generation pipeline meant to stay accurate at a scale of ten million or more documents, ending in a self-correcting agent that cites and verifies its own claims.
The tutorial builds each pipeline component in turn: downloading and inspecting a large document corpus, cleaning and chunking text with near-duplicate removal, building a hybrid dense-and-sparse index in LanceDB with an on-disk BM25 index for scale, and retrieving and reranking a fused set of candidates with reciprocal rank fusion.
On the generation side, each question is classified and multi-hop questions are decomposed before retrieval; answers are generated strictly from retrieved context with a citation on every sentence, or an abstain token when support is thin. A separate faithfulness judge splits an answer into atomic claims and checks each against its cited text, and the signals are folded into one calibrated decision to answer or abstain, wired into a self-correcting loop that re-retrieves on weak evidence.
The pipeline is evaluated on a 200-question golden set and benchmarked up to a real ten-million-vector index. It is distributed as a Jupyter Notebook and is MIT licensed.