自中国去年4月实施相关出口限制以来，尽管北京已允许大量稀土出口恢复，但海关数据显示，这类材料运往美国的货流仍十分稀少，未因去年10月中美关系缓和而明显回升。
Index size is bounded by your infrastructure. The LMDB-backed index performs best when the working set fits in RAM. For very large datasets — tens of millions of documents with many text-heavy fields — Meilisearch becomes expensive to run because you need enough RAM to hold the hot index pages. The engine can handle datasets larger than RAM via memory-mapped I/O and OS page cache management, but query latency will degrade if the index doesn't fit. Elasticsearch's disk-based indexes handle this more gracefully at large scale.
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This is probably due to the way larger numbers are tokenised, as big numbers can be split up into arbitrary forms. Take the integer 123456789. A BPE tokenizer (e.g., GPT-style) might split it like: ‘123’ ‘456’ ‘789’ or: ‘12’ ‘345’ ‘67’ ‘89’