ck (seek) finds code by meaning, not just keywords. It's a drop-in replacement for grep that understands what you're looking for — search for "error handling" and find try/catch blocks, error returns, and exception handling code even when those exact words aren't present.
# Install from crates.io
cargo install ck-search
# Or build from source
git clone https://github.com/BeaconBay/ck
cd ck
cargo build --release# Index your project for semantic search
ck --index src/
# Search by meaning
ck --sem "error handling" src/
ck --sem "authentication logic" src/
ck --sem "database connection pooling" src/
# Traditional grep-compatible search still works
ck -n "TODO" *.rs
ck -R "TODO|FIXME" .
# Combine both: semantic relevance + keyword filtering
ck --hybrid "connection timeout" src/For Developers: Stop hunting through thousands of regex false positives. Find the code you actually need by describing what it does.
For AI Agents: Get structured, semantic search results in JSONL format. Stream-friendly, error-resilient output perfect for LLM workflows, code analysis, documentation generation, and automated refactoring.
Find code by concept, not keywords. Searches understand synonyms, related terms, and conceptual similarity.
# These find related code even without exact keywords:
ck --sem "retry logic" # finds backoff, circuit breakers
ck --sem "user authentication" # finds login, auth, credentials
ck --sem "data validation" # finds sanitization, type checking
# Get complete functions/classes containing matches (NEW!)
ck --sem --full-section "error handling" # returns entire functions
ck --full-section "async def" src/ # works with regex tooAll your muscle memory works. Same flags, same behavior, same output format.
ck -i "warning" *.log # Case-insensitive
ck -n -A 3 -B 1 "error" src/ # Line numbers + context
ck --no-filename "TODO" src/ # Suppress filenames (grep -h equivalent)
ck -l "error" src/ # List files with matches only (NEW!)
ck -L "TODO" src/ # List files without matches (NEW!)
ck -R --exclude "*.test.js" "bug" # Recursive with exclusions
ck "pattern" file1.txt file2.txt # Multiple filesCombine keyword precision with semantic understanding using Reciprocal Rank Fusion.
ck --hybrid "async timeout" src/ # Best of both worlds
ck --hybrid --scores "cache" src/ # Show relevance scores with color highlighting
ck --hybrid --threshold 0.02 query # Filter by minimum relevance
ck -l --hybrid "database" src/ # List files using hybrid searchPerfect structured output for LLMs, scripts, and automation. JSONL format provides superior parsing reliability for AI agents.
# JSONL format - one JSON object per line (recommended for agents)
ck --jsonl --sem "error handling" src/
ck --jsonl --no-snippet "function" . # Metadata only
ck --jsonl --topk 5 --threshold 0.7 "auth" # High-confidence results
# Traditional JSON (single array)
ck --json --sem "error handling" src/ | jq '.file'
ck --json --topk 5 "TODO" . | jq -r '.preview'
ck --json --full-section --sem "database" . | jq -r '.preview' # Complete functionsWhy JSONL for AI agents?
- ✅ Streaming friendly: Process results as they arrive, no waiting for complete response
- ✅ Memory efficient: Parse one result at a time, not entire array into memory
- ✅ Error resilient: One malformed line doesn't break entire response
- ✅ Composable: Works perfectly with Unix pipes and stream processing
- ✅ Standard format: Used by OpenAI API, Anthropic API, and modern ML pipelines
JSONL Output Format:
{"path":"./src/auth.rs","span":{"byte_start":1203,"byte_end":1456,"line_start":42,"line_end":58},"language":"rust","snippet":"fn authenticate(user: User) -> Result<Token> { ... }","score":0.89}
{"path":"./src/error.rs","span":{"byte_start":234,"byte_end":678,"line_start":15,"line_end":25},"language":"rust","snippet":"impl Error for AuthError { ... }","score":0.76}Agent Processing Example:
# Stream-process JSONL results (memory efficient)
import json, subprocess
proc = subprocess.Popen(['ck', '--jsonl', '--sem', 'error handling', 'src/'],
stdout=subprocess.PIPE, text=True)
for line in proc.stdout:
result = json.loads(line)
if result['score'] > 0.8: # High-confidence matches only
print(f"📍 {result['path']}:{result['span']['line_start']}")
print(f"🔍 {result['snippet'][:100]}...")Automatically excludes cache directories, build artifacts, and respects .gitignore files.
# Respects .gitignore by default (NEW!)
ck "pattern" . # Follows .gitignore rules
ck --no-ignore "pattern" . # Search all files including ignored ones
# These are also excluded by default:
# .git, node_modules, target/, __pycache__
# Override defaults:
ck --no-default-excludes "pattern" . # Search everything
ck --exclude "dist" --exclude "logs" . # Add custom exclusions
# Works with indexing too (NEW in v0.3.6!):
ck --index --exclude "node_modules" . # Exclude from index
ck --index --exclude "*.test.js" . # Support glob patterns# Create semantic index (one-time setup)
ck --index /path/to/project
# Now search instantly by meaning
ck --sem "database queries" .
ck --sem "error handling" .
ck --sem "authentication" .Choose the right model for your needs when creating the index:
# Default: BGE-Small (fast, 512 tokens, 384 dimensions)
ck --index .
# Enhanced: Nomic V1.5 (16x larger context, 2x dimensions)
ck --index --model nomic-v1.5 .
# Code-specialized: Jina Code (optimized for programming languages)
ck --index --model jina-code .Model Comparison:
bge-small(default): Fast, 512 token limit, good for most codenomic-v1.5: 8192 tokens (16x more context), 768 dimensions, better for large functionsjina-code: 8192 tokens, specialized for code understanding
Note: Model choice is set during indexing. Existing indexes will automatically use their original model.
--regex(default): Classic grep behavior, no indexing required--sem: Pure semantic search using embeddings (requires index)--hybrid: Combines regex + semantic with intelligent ranking
ck --sem --scores "machine learning" docs/
# [0.847] ./ai_guide.txt: Machine learning introduction...
# [0.732] ./statistics.txt: Statistical learning methods...
# [0.681] ./algorithms.txt: Classification algorithms...# Glob patterns work
ck --sem "authentication" *.py *.js *.rs
# Multiple files
ck --sem "error handling" src/auth.rs src/db.rs
# Quoted patterns prevent shell expansion
ck --sem "auth" "src/**/*.ts"# Only high-confidence semantic matches
ck --sem --threshold 0.7 "query"
# Low-confidence hybrid matches (good for exploration)
ck --hybrid --threshold 0.01 "concept"
# Get complete code sections instead of snippets (NEW!)
ck --sem --full-section "database queries"
ck --full-section "class.*Error" src/ # Complete classes# Limit results for focused analysis
ck --sem --topk 5 "authentication patterns"
# Great for AI agent consumption
ck --json --topk 10 "error handling" | process_results.py# Check index status
ck --status .
# Clean up and rebuild
ck --clean .
ck --index .
# Add single file to index
ck --add new_file.rs| Language | Indexing | Tree-sitter Parsing | Semantic Chunking |
|---|---|---|---|
| Python | ✅ | ✅ | ✅ Functions, classes |
| JavaScript | ✅ | ✅ | ✅ Functions, classes, methods |
| TypeScript | ✅ | ✅ | ✅ Functions, classes, methods |
| Haskell | ✅ | ✅ | ✅ Functions, types, instances |
Text Formats: Markdown, JSON, YAML, TOML, XML, HTML, CSS, shell scripts, SQL, log files, config files, and any other text format.
Smart Binary Detection: Uses ripgrep-style content analysis (NUL byte detection) instead of extension-based filtering, automatically indexing any text file regardless of extension while correctly excluding binary files.
Smart Exclusions: Automatically skips .git, node_modules, target/, build/, dist/, __pycache__/, .venv, venv, and other common build/cache/virtual environment directories.
git clone https://github.com/BeaconBay/ck
cd ck
cargo install --path ck-cli# Coming soon:
brew install ck-search
apt install ck-searchck uses a modular Rust workspace:
ck-cli- Command-line interface and argument parsingck-core- Shared types, configuration, and utilitiesck-search- Search engine implementations (regex, BM25, semantic)ck-index- File indexing, hashing, and sidecar managementck-embed- Text embedding providers (FastEmbed, API backends)ck-ann- Approximate nearest neighbor search indicesck-chunk- Text segmentation and language-aware parsingck-models- Model registry and configuration management
Indexes are stored in .ck/ directories alongside your code:
project/
├── src/
├── docs/
└── .ck/ # Semantic index (can be safely deleted)
├── embeddings.json
├── ann_index.bin
└── tantivy_index/
The .ck/ directory is a cache — safe to delete and rebuild anytime.
# Find authentication/authorization code
ck --sem "user permissions" src/
ck --sem "access control" src/
ck --sem "login validation" src/
# Find error handling strategies
ck --sem "exception handling" src/
ck --sem "error recovery" src/
ck --sem "fallback mechanisms" src/
# Find performance-related code
ck --sem "caching strategies" src/
ck --sem "database optimization" src/
ck --sem "memory management" src/# Git hooks
git diff --name-only | xargs ck --sem "TODO"
# CI/CD pipeline
ck --json --sem "security vulnerability" . | security_scanner.py
# Code review prep
ck --hybrid --scores "performance" src/ > review_notes.txt
# Documentation generation
ck --json --sem "public API" src/ | generate_docs.py# Find related test files
ck --sem "unit tests for authentication" tests/
ck -l --sem "test" tests/ # List test files by semantic content
# Identify refactoring candidates
ck --sem "duplicate logic" src/
ck --sem "code complexity" src/
ck -L "test" src/ # Find source files without tests
# Security audit
ck --hybrid "password|credential|secret" src/
ck --sem "input validation" src/
ck -l --hybrid --scores "security" src/ # Files with security-related code# View current exclusion patterns
ck --help | grep -A 20 exclude
# These directories are excluded by default:
# .git, .svn, .hg # Version control
# node_modules, target, build # Build artifacts
# .cache, __pycache__ # Caches
# .vscode, .idea # IDE files# .ck/config.toml
[search]
default_mode = "hybrid"
default_threshold = 0.05
[indexing]
exclude_patterns = ["*.log", "temp/"]
chunk_size = 512
overlap = 64
[models]
embedding_model = "BAAI/bge-small-en-v1.5"- Indexing: ~1M LOC in under 2 minutes (with smart exclusions and optimized embedding computation)
- Search: Sub-500ms queries on typical codebases
- Index size: ~2x source code size with compression
- Memory: Efficient streaming for large repositories with span-based content extraction
- File filtering: Automatic exclusion of virtual environments and build artifacts
- Output: Clean stdout/stderr separation for reliable piping and scripting
Run the comprehensive test suite:
# Full test suite (40+ tests)
./test_ck.sh
# Quick smoke test (14 core tests)
./test_ck_simple.shTests cover grep compatibility, semantic search, index management, file filtering, and more.
ck is actively developed and welcomes contributions:
- Issues: Report bugs, request features
- Code: Submit PRs for bug fixes, new features
- Documentation: Improve examples, guides, tutorials
- Testing: Help test on different codebases and languages
git clone https://github.com/your-org/ck
cd ck
cargo build
cargo test
./target/debug/ck --index test_files/
./target/debug/ck --sem "test query" test_files/- ✅ grep-compatible CLI with semantic search and file listing flags (
-l,-L) - ✅ FastEmbed integration with BGE models
- ✅ File exclusion patterns and glob support
- ✅ Threshold filtering and relevance scoring with visual highlighting
- ✅ Tree-sitter parsing and intelligent chunking (Python, TypeScript, JavaScript, Go, Haskell, Rust, Ruby)
- ✅ Complete code section extraction (
--full-section) - ✅ Enhanced indexing strategy with v3 semantic search optimization
- ✅ Clean stdout/stderr separation for reliable scripting
- ✅ Incremental index updates with hash-based change detection
- 🚧 Configuration file support
- 🚧 Package manager distributions
Q: How is this different from grep/ripgrep/silver-searcher?
A: ck includes all the features of traditional search tools, but adds semantic understanding. Search for "error handling" and find relevant code even when those exact words aren't used.
Q: Does it work offline?
A: Yes, completely offline. The embedding model runs locally with no network calls.
Q: How big are the indexes?
A: Typically 1-3x the size of your source code, depending on content. The .ck/ directory can be safely deleted to reclaim space.
Q: Is it fast enough for large codebases?
A: Yes. Indexing is a one-time cost, and searches are sub-second even on large projects. Regex searches require no indexing and are as fast as grep.
Q: Can I use it in scripts/automation?
A: Absolutely. The --json flag provides structured output perfect for automated processing. Use --full-section to get complete functions for AI analysis.
Q: What about privacy/security?
A: Everything runs locally. No code or queries are sent to external services. The embedding model is downloaded once and cached locally.
Q: Where are the embedding models cached?
A: The embedding models (ONNX format) are downloaded and cached in platform-specific directories:
- Linux/macOS:
~/.cache/ck/models/(or$XDG_CACHE_HOME/ck/models/if set) - Windows:
%LOCALAPPDATA%\ck\cache\models\ - Fallback:
.ck_models/models/in the current directory (only if no home directory is found)
The models are downloaded automatically on first use and reused for subsequent runs.
Licensed under either of:
- Apache License, Version 2.0 (LICENSE-APACHE)
- MIT License (LICENSE-MIT)
at your option.
Built with:
- Rust - Systems programming language
- FastEmbed - Fast text embeddings
- Tantivy - Full-text search engine
- clap - Command line argument parsing
Inspired by the need for better code search tools in the age of AI-assisted development.
Start finding code by what it does, not what it says.
cargo build --release
./target/release/ck --index .
./target/release/ck --sem "the code you're looking for"