Introduction

semanticastindexer (SAI) is a Rust CLI and MCP server for semantic code search and near-duplicate function detection over any codebase. You index your source once, then you can do two things with it:

• Search in plain English — ask “where do we open the DuckDB connection?” and get the matching code, ranked by meaning rather than keywords.
• Surface near-duplicate functions — find the functions across the repo that are near-copies of each other, so you can de-duplicate or refactor.

SAI works the same whether you drive it yourself from the terminal or wire it into an AI coding agent as an MCP server.

Highlights

• Local-first and offline. Embeddings run on-device through ONNX Runtime (via ort) — no API keys, nothing leaves your machine. A compact code model is pulled once from Hugging Face (jina-embeddings-v2-base-code, or e5-small). If you prefer, you can instead point at an Ollama server over HTTP.
• Pluggable backends. Store vectors in a local DuckDB file (VSS/HNSW, fully offline) or in Qdrant Cloud with server-side inference.
• Symbol-aware AST chunking. Code is split per symbol using tree-sitter for TypeScript/TSX, Rust, Go, and Python; every other language falls back to a line-based chunker.
• You control what leaves the repo. A YAML config filters out tests, generated files, comments, and more, and sai-noindexing / sai-noduplicate opt-out markers let you exclude code inline.
• Read-only by default. The MCP server only searches; the write tool (sai_refresh) requires --allow-write.

Who it is for

• Developers navigating a large or unfamiliar codebase who want to find code by intent instead of grepping for exact names.
• Teams fighting duplication who want to detect copy-pasted or near-identical functions across the whole repo.
• AI coding agents (Claude Code, Cursor, Windsurf, Codex, and others) that need a fast, local, semantic view of the code they are editing.

The mental model

Think of SAI as a search index for meaning, built once and refreshed as code changes:

1. Index. SAI walks your source, filters it, splits it into chunks (per symbol for AST languages, by lines otherwise), embeds each chunk into a vector, and upserts it into your chosen backend. Point IDs are a deterministic hash of path + start_line, so re-running updates points in place instead of duplicating them.
2. Query. Your natural-language question is embedded the same way, and the backend returns the nearest chunks.
3. Duplicates. SAI compares chunk vectors across the codebase and clusters the near-neighbours into near-duplicate groups.

You can expose the same index through the CLI or the MCP server: the shipped tools are sai_search_code, sai_find_similar, sai_find_duplicates, sai_index_status, and sai_refresh.

Next steps

• Getting Started — install SAI, index a project, and run your first search and duplicate scan.
• How It Works — the indexing pipeline, point IDs, and payload shape in detail.
• Glossary — the key terms (chunk, embedder, backend, symbol, point) used throughout the book.