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Voyage's tokenizer is 3-4x denser than OpenAI tiktoken, causing batches of 50+ texts to exceed the 120K token-per-batch limit even when DB token counts (from tiktoken) suggest they'd fit. Changes: - Add max_batch_tokens to EmbeddingTouchpoint type (provider-declared limit) - Set Voyage recipe to 120K token limit - Gateway embed() now auto-splits batches using conservative char-to-token estimate (1:1 ratio, 80% budget utilization) - On token-limit errors, embedSubBatch recursively halves and retries (down to single-text batches before giving up) - Reduce embedding.ts BATCH_SIZE from 100 to 50 as a secondary guard - Add tests for batch splitting logic and error pattern matching Fixes infinite retry loops where the same oversized batch would fail repeatedly because WHERE embedding IS NULL re-fetches identical rows.