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Code-Specific Embeddings

Relace uses a specialized embeddings model designed specifically for code, not generic text. This model:
  • Understands programming language syntax and semantics
  • Recognizes relationships between functions, classes, and modules
  • Maps natural language queries to relevant code patterns
  • Works across all programming languages in your stack

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Automatic Indexing

When you upload a repository, Relace automatically:
  • Chunks your code at logical boundaries (functions, classes, modules)
  • Generates embeddings using our code-specific model
  • Keeps everything up-to-date with incremental indexing on each code change
  • Requires zero configuration - no chunking strategies or parameters to tune

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Two-Stage Retrieval

The Ask and Retrieve endpoints use a two-stage system:
  1. Semantic matching - Vector similarity search finds potentially relevant code
  2. Intelligent reranking - Our Code Reranker refines results for accuracy
This delivers sub-second response times while maintaining high relevance.

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Fast Git Performance

Relace maintains the git experience you’re used to:
  • Repository operations remain as fast as traditional git
  • Semantic indexing happens in the background without slowing you down
  • Multi-tenant architecture scales to thousands of repositories

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What This Means for You

You get semantic code understanding without the typical complexity:
  • No embedding model selection or vector database setup
  • No chunking configuration or index maintenance
  • No performance tuning or scaling concerns
  • Instant semantic search from upload to searchable in minutes
The infrastructure handles all the complexity so you can focus on building with semantic code understanding.