Similarity Search
Similarity Search is an automated part of the code review process that finds the patterns, implementations and definations to verify the code implemented is correct
Similarity Search is only available on PRO plan.
Overview
Similarity Search is an automatic review step that compares every new or modified function call in your pull request with existing definitions across the repository.
It then checks whether the way the engineer is using that function matches the original implementation’s contract (required parameters, return value, side-effects, etc.).
When a mismatch is detected, the reviewer leaves an inline suggestion that pin-points:
- The issue – what is being mis-used and why.
- The fix – the minimal code change required to align the call-site with the definition.
- The potential impact if the mismatch is shipped.
Example The screenshot below shows
isValidUrlbeing invoked with two parameters even though its definition only accepts one. Similarity Search detected the mismatch and suggested the one-line fix.
How it works
We use ast-grep for syntax-aware matching between new or modified call-sites and existing symbol definitions across the repository.
Why you’ll love it
- Catch bugs early – Prevent invalid API usage before it reaches CI.
- Shorter reviews – Focus human reviewers on architectural decisions, not boilerplate mistakes.
- Faster onboarding – New engineers quickly learn correct library usage by example.
Enable / disable
Similarity Search is on by default for all repositories. You can use the setting Enable Similarity Search option in the Matter AI configurations to enable or disable it.
Supported languages
- JavaScript / TypeScript
- Python
- Go
- Java
- Kotlin
- Python
- C# / C++
- Rust
Limitations & future work
- Only analyses intra-repository symbols today, cross-repo support is on the roadmap using Semantic Search.