feat: v1.6.0 search quality improvements #26
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…nker - Reciprocal Rank Fusion (k=60, rank-based scoring) - Intent-aware query classification (5 types) - Hard test-file filtering (0% spec contamination) - Import-graph proximity reranking - File-level deduplication - Chunk optimization (100→50 lines, 0 overlap) - Embedding upgrade (@huggingface/transformers v3) BREAKING CHANGE: Re-indexing required due to model and chunking changes
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* feat: evaluation framework and frozen fixture set * feat: v1.6.0 search quality - RRF fusion, intent classification, reranker - Reciprocal Rank Fusion (k=60, rank-based scoring) - Intent-aware query classification (5 types) - Hard test-file filtering (0% spec contamination) - Import-graph proximity reranking - File-level deduplication - Chunk optimization (100→50 lines, 0 overlap) - Embedding upgrade (@huggingface/transformers v3) Note: Re-indexing recommended for best results due to chunking changes. Existing indices remain readable — search still works without re-indexing.
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Summary
@xenova/transformersv2 →@huggingface/transformersv3 (granite model, 8192 context)BREAKING CHANGE
Re-indexing required after upgrade. Existing
.codebase-context/indices from v1.5.x are incompatible.Test plan
pnpm buildsucceedspnpm test— 135/135 passing