AI Engineering/Phase 19/Lesson 42

Large Corpus Downloader

Training a language model begins long before the first forward pass. The corpus has to land on disk, decompressed, deduplicated, and addressable, with the resume story already worked out before the network drops at 4 percent. This lesson builds a streaming downloader that pulls compressed shards, decompresses on the fly with Zstandard, fingerprints near-duplicates via MinHash plus locality-sensitive hashing, and writes a shard manifest the rest of the pipeline can trust.

Build 1.5 hr Python

Prerequisites: Phase 19 lessons 30-37

#Learning Objectives

Stream remote shards with urllib and decompress with zstandard without buffering the whole file in memory.
Resume partial downloads by issuing HTTP Range requests against a verified byte offset.
Build a MinHash signature per document and bucket it with LSH so near-duplicates collide.
Emit a shard manifest with content hash, byte size, document count, and dedup verdict.

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.