ivygrep 1.3.0

What's new in 1.3.0

Upgrade notes

• The default neural model is now potion-code-16m-v2. Indexes keep working without a rebuild and the index format is unchanged：search ignores neural vectors built by static-retrieval-v1 and answers from lexical, literal, symbol, and hash evidence until the next neural enhancement re-embeds the index, which the daemon schedules after the first search (done within 8 seconds for 11,214 chunks in a local run; a direct rebuild embeds 25,460 chunks in 6 seconds). Until then --force-neural reports an incompatible neural model, and ig --status and ig --doctor report a healthy index that still lists static-retrieval-v1. The first neural use downloads 33.5 MB instead of 125.7 MB. Hosts without network access need the new model in the Hugging Face cache (uv run scripts/cache_neural_model.py --profile potion-code-v2 --cache "$HF_HOME"); without it neural enhancement fails and search stays on lexical and hash results. Set IVYGREP_MODEL_PROFILE=static-retrieval-v1 (or static) to keep the previous model, its cached download, and existing vectors.

Changed

• Neural search results now depend on how good the embedding model is, and the default neural profile is potion-code-16m-v2 instead of static-retrieval-v1; an unset or unknown IVYGREP_MODEL_PROFILE selects it (see the upgrade notes). Until now the neural tier was one reciprocal-rank vote of weight 1.0 among lexical, literal, path, and symbol votes worth several times more, and only files among the 50 nearest chunks got it, so a better model changed almost nothing：on the public benchmarks potion-code-v2 beat static-retrieval-v1 by .003 nDCG@10 in default mode although its own ranking is far better. A late dense-fusion stage now runs after the ranking heuristics and before result filtering and the learned reranker. Search scores every candidate that lexical, literal, path, or symbol search found against the neural query vector, then reorders files by two signals. A file whose best cosine beats the runner-up file by a ratio of 1.05 moves to the first position. Profiles with a measured calibration (DenseFusionCalibration in src/embedding.rs; potion-code-16m-v2 uses 0.25 for pasted source and for multi-line or long prose) also add a standing reciprocal-rank vote, and search consults them for every query whose route allows neural retrieval instead of only when lexical confidence is low. …

Full release notes »