Do you mean to verify if similar code units produce the same result?
The goal of the tool is to also detect code that is similar and behaves differently. There are not ideal duplicates, but still a code that can be refactored, abstracted, or fixed (because the variance may be the result of bug).
I've used jscpd, it does "non-exact code duplication" (regardless of what its readme says) which is why I asked how Slopo compares. I'm surprised you've not tried the competition!
* cluster-01.md has the highest similarity, and jscpd probably will detect this too.
* cluster-10.md has the lowest similarity still above the threshold, and I don't think jscpd will detect this as clones. Because they are not clones, this is a false positive that needs to be discarded. But in other cases this kind of similar code may be worth acting on.
I didn't compare with jscpd because I don't consider it a competition. Embedding-based duplication detection works differently, gives different results and has its own trade-offs.
Finding similar code is something different than deduplication, even when the final goal looks similar.
Deduplication backend is the easiest part of the tool and it doesn't need any additional libraries. Just calculate embeddings and find close pairs. The complexity is everything around.
Using local models is worth considering and the tool already uses the LiteLLM wrapper, allowing it to configure different models, including local. I left this part for the user.
I don't understand what you mean with your first sentence. Both SemHash and Slopo are deduplication libraries right? Regardless, finding similar code is the core functionality that enables (semantic) deduplication.
I also don't think the backend is the "easiest part" here, a lot of the scalability lives there, which is important for monorepos, or cases where you want to deduplicate across projects. For example, from looking at the implementation, you use exact brute-force similarity search (comparing every item to every other item) which is an O(n^2) operation. It also allocates large dense similarity blocks in memory, so memory use won’t scale well either.
Slopo doesn't deduplicate. It only reports similar code units, which in most cases are not duplicates. This needs to be cleaned up by the user, which is fast and easy with coding agents.
Embedding calculation is also outsourced externally: it's only a simple API call with a LiteLLM wrapper.
"brute-force similarity search" and O(n^2) may sound scary, but it works fine and this is not a bottleneck. For large projects, other parts are much slower, which has room for improvement. [1] is an implementation you probably saw. It uses NumPy, spreads work across all CPU cores and there is also a split into blocks (block_size = 1000). In larger sets, all vectors are not loaded into memory at once. Where I need to be honest, I didn't measure actual memory usage. I just tested this on large repos, so I'm aware of bottlenecks.
From my perspective, this is the easiest part. Code extraction, chunking, applying boost, clustering, generating report and designing everything as a single user-friendly tool is a real challenge. Architectural and product decisions are more difficult than implementation and solving performance issues.
"it works fine and this is not a bottleneck. For large projects, other parts are much slower": I don't think this is true, especially for large projects. I just ran your tool on the Kubernetes repo with 1536-dim embeddings. The isolated similar-pair search took ~130s and peaked at ~4.8 GB RSS, and the total runtime was ~250s with the same memory peak.
"In larger sets, all vectors are not loaded into memory at once": this is also not correct, at least in the implementation you shared. The similarity matrix is processed in blocks, but the embeddings themselves are loaded all at once and stacked into one NumPy matrix, hence the memory peak.
So in larger projects, more than half of the time is spent on the similarity search step, and almost all the memory is spent there as well.
To give more context, the current version is already an optimized one I considered good enough and didn't spend more time on it. In the first attempt, I used a vector database with indexes, trying to query for similar vectors. This was uselessly slow even in medium-sized repos. The brute-force NumPy solution is a significant improvement, making it faster than other calculations like clustering.
"vectors are not loaded into memory at once" is not true, I had in mind splitting computation into blocks.
One possible simple optimization is to use 16-bit floats in vector instead of 32-bit. I used this in a different project (halfvec in pgvector) without affecting results.
1536-dim embeddings from your case also can be reduced. This large vector usually doesn't give much benefit compared to smaller ones. And this is something I will compare in my own tests.
I just focused on embeddings without comparing them to deterministic solutions.
But I plan to do my own analysis of different embedding models in the context of code similarity detection. Including BM25 in the comparison is a very good idea.
Based on your example there is only a single function a() which is embedded. The rest is just a code and dependencies are not resolved. Did you think about adding this feature in your tool?
Generally, I chunk by function/method (not by whole class), but different languages have specific concepts and features. Nested code units, anonymous functions, lambdas, closures are extracted as separate chunks.
The chunk size has allowed range and those outside are simply ignored.
- Upper limit is hardcoded with a body size of 10k chars
- Lower limit is configurable with a default of 10 AST nodes inside the body
The chunking strategy is something that can be improved in future versions.
There are good mature tools for deterministic duplication detection and I intentionally focused on embedding-based to fill this gap (I didn't find other tools using this approach).
If by "more efficient" you mean to avoid embedding of the same code multiple times, this optimization is already implemented internally.
The goal of the tool is to also detect code that is similar and behaves differently. There are not ideal duplicates, but still a code that can be refactored, abstracted, or fixed (because the variance may be the result of bug).