The IR community has always aimed to improve the realism of retrieval experiments by increasing the size of the document collections. As collection sizes grow from megabytes to giga-, tera-, and maybe soon petabytes, IR labs are challenged to keep pace. Herein, we describe our work on integrating ChatNoir with ir_datasets and PyTerrier to create chatnoir-pyterrier, a Python package for using ChatNoir in multi-stage pipelines. ChatNoir provides BM25-based first-stage retrieval on all ClueWeb crawls and all MS MARCO variants with a collective index size of about 20TB. This improves inclusivity by lowering the barrier to entry for web-scale IR, and reduces redundant first-stage indexing overhead across IR labs. We show how chatnoir-pyterrier simplifies a wide range of re-ranking approaches and facilitates retrieval-augmented generation setups against large corpora.