Smaller Embeddings for Code Search: How to Test the Tradeoff

Smaller code embeddings can reduce vector storage and distance-computation work, but the useful question is whether they still retrieve the right implementation. Evaluate shortened embeddings against actual repository questions before trading away dimensions. A storage reduction is easy to calculate; preserved answer quality has to be measured.

Check whether the model supports shortening

Matryoshka-trained embedding models are designed to make selected prefixes useful at smaller dimensions. This does not mean an arbitrary embedding can be truncated safely. Follow the model's supported dimensions and normalization instructions, and use compatible settings for documents and queries. The Sentence Transformers Matryoshka guide describes the training approach.

For an illustrative collection of one million vectors stored as float32, 1,024 dimensions require 4.096 GB of raw vector values. At 256 dimensions, that becomes 1.024 GB: a 75% reduction in that component. Metadata, indexes, replicas, source code, and service overhead remain, so the total bill will not necessarily fall by 75%.

Build code-specific retrieval cases

Include questions that require distinguishing nearly identical functions: the public wrapper versus the underlying implementation, an obsolete API versus its replacement, or two classes with the same name in different packages. Add exact-symbol queries, natural-language behavior questions, and questions whose answer spans a caller and a callee.

Label the files or ranges needed to answer each question. Keep a separate set of recently changed files so an old but semantically similar implementation does not count as a success. An embedding can retrieve a plausible function while still sending a coding agent to the wrong revision.

Hold the rest of the pipeline constant

Compare supported dimensions with the same repository snapshot, chunk boundaries, filters, candidate count, and reranking policy. Record retrieval quality by query type, search latency, index size, and downstream answer correctness. Then test the complete system with its normal caching and concurrency.

If shorter vectors lose exact identifiers but retain conceptual matches, try explicit symbol retrieval alongside semantic search before deciding the model is unusable. If the missing evidence spans files, the problem may be chunking or expansion rather than dimensionality.

Roll out behind a reversible index choice

Keep the full-size index available during the pilot. Route a sampled workload to both versions and inspect disagreements. Rebuild or re-embed only after recording the model, dimension, normalization, and repository revision in the index configuration.

Use the embedding compatibility guide to check the migration boundary. If your goal is persistent coding context beyond repository search, try Supermemory with a recurring task and evaluate the complete memory workflow separately from this vector-size experiment.