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ivygrep 1.3.0

Local code search and relationship-aware context packs for coding agents

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About ivygrep

Search source by intent or exact identifiers and build bounded task packs from Git changes, definitions, callers, dependents, tests, configuration, and documentation. Source, queries, embeddings, and indexes stay local.

License: MIT
Release info New Release on Sep 26, 2026
Release notes ### Upgrade notes

o 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

o 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...
Related tags cli code-search coding-agents context developer-tools
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