Scaling vector database imports beyond prototypes requires solving four core problems: embedding provider rate limits, silent per-object failures behind HTTP 200 responses, duplicate work on retries, and memory blowups with media. The guide covers Weaviate's server-side batching via `collection.batch.stream()` which lets the server control throughput automatically, a retry-with-checkpoint pattern using deterministic UUIDs (`generate_uuid5`) to make reruns idempotent, and the `blobHash` data type that vectorizes media at import time but stores only a SHA-256 hash instead of raw bytes — shrinking a 10 TB image corpus to gigabytes. PDF ingestion without an OCR pipeline is possible using multimodal models (Weaviate Embeddings, multi2vec-google, or ColPali for layout-heavy docs). A pre-import checklist covers schema decisions like tokenization, data types, and vectorizer configuration that are expensive to fix after the fact.
