Slopo

A CLI tool for detecting non-exact code duplication using embedding models.

It focuses on the similar code that is hardest to detect and most harmful: snippets written similarly, sitting far apart in the codebase, often spread across different modules or separated within a large file. Exact copy-paste is easy to spot by other tools, and duplicates that are close together are easy to spot by humans or AI.

For high-level description of the problem and example LLM prompts, see slopo.dev.

For details, see Embedding models benchmark for code duplication detection. The author is also the developer of this tool, so both parts are compatible. The sample configuration in this documentation is the one that gives the best results based on this research.

Supported languages

Python, TypeScript, JavaScript, Java, Kotlin, C#, Go, Rust, PHP, Elixir

How it works

It takes a different approach than typical duplication detection. For every code unit, it calculates an embedding, then looks for pairs whose embeddings are close. Similar code is not necessarily a duplicate, so each pair is a potential duplicate to confirm.

The result is clusters of similar code units, ranked by similarity and by distance in the codebase. These clusters are meant as input for your AI coding agent, which can check whether a cluster is a real duplicate. Reviewed clusters can be marked as ignored or passed on for refactoring.

Example report generated from Slopo code (src directory, git tag v0.2.0).

Accessing embedding model

According to the benchmark, two providers are recommended:

1. Jina AI - code-focused models available via API and for local use
2. Voyage AI - general-purpose model only, not code-focused ones which are not suitable here

Jina AI offers API keys with free tokens for non-commercial use, without registration. Paid options are available for commercial use. Simply open the main page, and the token will be generated. You should see "You have 10,000,000 tokens left in the API key below." However, free tokens are not always granted - this is probably because of abuse prevention mechanisms based on IP address or other measures (it's a guess, not official information).

Local model

One tested solution is to use Ollama with jina-embeddings-v2-base-code. This is a small model that also runs on a CPU, but it may be (significantly) slower than the API, depending on the hardware you use. Pull the model from here.

Other models

Any model provider supported by LiteLLM can be configured. Additionally, any OpenAI-compatible server is supported.

Quick start

Once you figure out the model, the rest is quick.

Installation

uv tool install slopo

Update to the latest version (there are no automatic updates)

uv tool upgrade slopo

This command uses uv (installing uv), a Python package manager, to install Slopo from PyPI in an isolated virtual environment. No need to get Python separately.

Setup

Run slopo init to create a config file template containing further instructions. Only the directory with code for analysis and embedding model configuration is required.

Model configuration

Jina AI

embedding_model: jina_ai/jina-code-embeddings-0.5b
embedding_dimensions: 256
embedding_params:
  task: code2code.query

If you are embedding a large project with the free API key and hitting rate limits, add the embedding_request_delay: 6 option to slow down.

Voyage AI

embedding_model: voyage/voyage-4-large
embedding_dimensions: 256

Local model

embedding_model: ollama/unclemusclez/jina-embeddings-v2-base-code
embedding_dimensions: 768

Analysis

Run slopo show-config to validate your config and show all configurable parameters, most are optional with sensible defaults.

Now you are ready to index code, calculate embeddings and generate a report:

slopo index
slopo embed
slopo analyze

Real workflow

This section demonstrates how Slopo can be used in a real development workflow.

It utilizes incremental re-indexing (update index with changed files only) and slopo.ignore.txt to discard already reviewed clusters.

1. Create your first analysis and check results. You will notice index.md containing a list of all clusters and cluster details per file.
2. You may want to exclude some directories or file patterns, usually excluding tests is a good idea. You can also tune thresholds if the result is too big or too small.
3. Once satisfied with analysis results, ask your AI coding agent to filter out clusters that are not real duplicates. This is a common case because not every similar code is a duplication to act on. Ask the AI agent to add discarded cluster hashes to slopo.ignore.txt.
4. Re-run the analysis to generate a report without reviewed clusters. This is a basis for refactoring, which can be done by an AI agent.
5. ignore file can be committed to your Git repository and reused cross-team. New and modified clusters will reappear in the report. A configuration file without an API key can also be committed. Don't commit slopo.db, this is your local data.

Configuration

Run slopo --help and slopo show-config to explore it by yourself anytime.

Most configuration is done with a configuration file with two exceptions:

1. The location of the configuration file can be overridden with the --config option.
2. The API key can be set with the SLOPO_EMBEDDING_API_KEY environment variable, also picked up from a .env file in the current directory.

Be aware that some parameters can't be changed after first indexing. You need to remove slopo.db and index/embed from the beginning: source_dir, embedding_model, embedding_dimensions, body_node_count_threshold.

All configurable parameters

• source_dir: Source directory with code to index, absolute or relative path.
• source_dir_exclude: .gitignore-style patterns to exclude from indexing.
• db_file: SQLite database file with tool data.
• report_dir: Output directory for analysis report.
• ignore_file: Text file with ignored clusters.
• embedding_model: Embedding model name in LiteLLM format.
• embedding_dimensions: Embedding dimensions compatible with the used model. This value is also used to verify received embeddings dimensions.
• embedding_api_key: API key for embedding provider, alternatively configured with an environment variable. Optional, no need to set for local models.
• embedding_params: Additional properties passed to the LiteLLM and embedding API. Anything supported by the API provider is valid. Example:

embedding_params:
  api_base: http://example.com:123
  task: code2code.query

• embedding_batch_size and embedding_batch_chars: Requests to the embedding API are batched for performance. Defaults are fine for most cases.
• embedding_request_delay: Delay in seconds after every batched request, by default no delay. Increase if you reach rate limits.
• similarity_threshold: Controls minimal cosine similarity between embeddings.
• rerank_threshold: Controls minimal similarity after applying a boost reflecting distance in the codebase.
• body_node_count_threshold: Number of AST nodes inside the body (excluding signature and annotations). This value reflects the minimum code complexity of the included code unit, more precise than text length. Increase if you notice unwanted, too-small code units in the report.

Details

Ranking thresholds

Similar code units are filtered in two passes, each with its own configurable threshold. The pipeline is as follows:

1. similarity_threshold filters out code unit pairs whose embeddings are not similar enough. The calculated value is cosine similarity ranging from -1 to 1 where 1 means the same.
2. Similar pairs are grouped in clusters.
3. Units in clusters are reranked after applying a boost. Boost is calculated based on the number of directory hops required to reach the other file in the pair (max. 15%). If they are in the same file, the boost is calculated based on distance in number of lines (max. 10%). rerank_threshold filters out clusters whose highest-scoring pair is not high enough.

Exact-copy duplicates

The main goal of this tool is to detect non-exact code duplication, but exact copies (identical code at multiple paths) are reported too, just handled a little differently from merely similar code:

• The report shows the code once, listing every path where it appears, instead of repeating identical snippets.
• The analyze command reports the "similarity ratio" (the share of code units flagged as similar) in two variants: including and excluding exact copies.