Article illustration 1

For decades, developers have relied on grep and its modern successors like ripgrep to search codebases. But as systems grow more complex, traditional pattern-matching shows its limits. How do you find "error handling" patterns when they manifest as try/catch, Result types, or except blocks across different languages? Enter ck ("seek")—a Rust-powered tool that understands code semantics, not just syntax.

Beyond Keywords: The Semantic Search Revolution

ck uses text embedding models (like BAAI/bge-small-en-v1.5) to convert code into mathematical representations of meaning. This lets you search conceptually:

ck --sem "error handling" src/  # Finds try/catch, Results, exceptions
ck --sem "retry logic"          # Discovers backoff/circuit breakers
ck --sem "data validation"      # Surfaces sanitization checks

Unlike regex-based tools, ck understands synonyms and related concepts. Searching for "authentication" returns login flows, JWT checks, and OAuth handlers—even if those exact terms are absent.

Practical Magic for Developers

Hybrid Workflows

ck retains full grep compatibility while adding semantic intelligence:

# Traditional regex
ck -n "TODO" *.rs

# Semantic + keyword fusion
ck --hybrid "connection timeout" src/

# Full-function extraction
ck --full-section "database queries" # Returns entire methods/classes

AI Agent Ready

Structured JSON output enables automation:

ck --json --sem "error handling" | jq '.file'

This allows LLMs to analyze code, generate documentation, or suggest refactors using semantic context.

Enterprise-Grade Smarts

  • Auto-excludes node_modules, .git, and build artifacts
  • Tree-sitter parsing for precise code chunking
  • Incremental indexing (1M LOC in ~2 minutes)
  • Sub-500ms query responses

Under the Hood

ck's Rust architecture is modular:
- ck-embed: Computes text embeddings
- ck-ann: Approximate nearest-neighbor search
- ck-chunk: Language-aware code segmentation
- ck-index: Manages project-specific .ck/ cache directories

Indexes use efficient compression (~2x source size) and support 10+ languages including Python, TypeScript, and Haskell.

Why Teams Are Adopting ck

  1. Refactoring: Find "duplicate logic" across microservices
  2. Security Audits: Hybrid search for "password|credential|secret"
  3. Documentation: Extract "public API" functions via --full-section
  4. Onboarding: Locate "authentication patterns" in unfamiliar repos

"We're bridging the gap between what code says and what it does," the project maintainers note. With a roadmap including IDE integrations and distributed indexing, ck signals a fundamental shift in how we navigate code.

Try it: cargo install ck-search \
Repo: github.com/BeaconBay/ck