Smaller Embeddings for Code Search: How to Test the Tradeoff

Reducing code-embedding dimensions can lower vector storage and distance-computation costs, but the tradeoff should be evaluated through repository-specific retrieval quality rather than savings alone. Matryoshka-trained models may support useful shortened embeddings at approved dimensions, whereas arbitrary truncation can harm results, so model guidance for dimensions, normalization, and query-document settings should be followed. For example, reducing one million float32 vectors from 1,024 to 256 dimensions cuts raw vector storage from 4.096 GB to 1.024 GB, although metadata, indexes, replicas, and service overhead limit total savings. Evaluation should use realistic code-search cases involving similar functions, API replacements, duplicate class names, exact symbols, behavioral questions, multi-file evidence, and recent changes, while holding the repository snapshot, chunking, filters, candidate counts, and reranking constant. Teams should measure retrieval accuracy, latency, index size, and downstream answer correctness, investigate whether failures stem from missing symbol search or chunking rather than dimensionality, and deploy shortened indexes reversibly alongside full-size versions before committing to a migration.

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