ck - Semantic Code Search

ck (seek) finds code by meaning, not just keywords. It's 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.

🚀 Quick Start

# Install from crates.io
cargo install ck-search

# Just search — ck builds and updates indexes automatically
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/

✨ Headline Features

🤖 AI Agent Integration (MCP Server)

Connect ck directly to Claude Desktop, Cursor, or any MCP-compatible AI client for seamless code search integration:

# Start MCP server for AI agent integration
ck --serve

Claude Desktop Setup:

# Install via Claude Code CLI (recommended)
claude mcp add ck-search -s user -- ck --serve

# Note: You may need to restart Claude Code after installation
# Verify installation with:
claude mcp list  # or use /mcp in Claude Code

Manual Configuration (alternative):

{
  "mcpServers": {
    "ck": {
      "command": "ck",
      "args": ["--serve"],
      "cwd": "/path/to/your/codebase"
    }
  }
}

Tool Permissions: When prompted by Claude Code, approve permissions for ck-search tools (semantic_search, regex_search, hybrid_search, etc.)

Available MCP Tools:

• semantic_search - Find code by meaning using embeddings
• regex_search - Traditional grep-style pattern matching
• hybrid_search - Combined semantic and keyword search
• index_status - Check indexing status and metadata
• reindex - Force rebuild of search index
• health_check - Server status and diagnostics

Built-in Pagination: Handles large result sets gracefully with page_size controls, cursors, and snippet length management.

🔍 Semantic Search

Find code by concept, not keywords. Understands 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
ck --sem --full-section "error handling"  # returns entire functions

⚡ Drop-in grep Compatibility

All 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 -l "error" src/                  # List files with matches only
ck -L "TODO" src/                   # List files without matches
ck -R --exclude "*.test.js" "bug"  # Recursive with exclusions

🎯 Hybrid Search

Combine 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

⚙️ Automatic Delta Indexing

Semantic and hybrid searches transparently create and refresh their indexes before running. The first search builds what it needs; subsequent searches only touch files that changed.

📁 Smart File Filtering

Automatically excludes cache directories, build artifacts, and respects .gitignore and .ckignore files:

# ck respects multiple exclusion layers (all are additive):
ck "pattern" .                           # Uses .gitignore + .ckignore + defaults
ck --no-ignore "pattern" .               # Skip .gitignore (still uses .ckignore)
ck --no-ckignore "pattern" .             # Skip .ckignore (still uses .gitignore)
ck --exclude "dist" --exclude "logs" .   # Add custom exclusions

# .ckignore file (created automatically on first index):
# - Excludes images, videos, audio, binaries, archives by default
# - Excludes JSON/YAML config files (issue #27)
# - Uses same syntax as .gitignore (glob patterns, ! for negation)
# - Persists across searches (issue #67)
# - Located at repository root, editable for custom patterns

# Exclusion patterns use .gitignore syntax:
ck --exclude "node_modules" .            # Exclude directory and all contents
ck --exclude "*.test.js" .                # Exclude files matching pattern
ck --exclude "build/" --exclude "*.log" . # Multiple exclusions
# Note: Patterns are relative to the search root

Why .ckignore? While .gitignore handles version control exclusions, many files that should be in your repo aren't ideal for semantic search. Config files (package.json, tsconfig.json), images, videos, and data files add noise to search results and slow down indexing. .ckignore lets you focus semantic search on actual code while keeping everything else in git. Think of it as "what should I search" vs "what should I commit".

🛠 Advanced Usage

AI Agent Integration

MCP Server (Recommended)

# Example usage in AI agents
response = await client.call_tool("semantic_search", {
    "query": "authentication logic",
    "path": "/path/to/code",
    "page_size": 25,
    "top_k": 50,           # Limit total results (default: 100 for MCP)
    "snippet_length": 200
})

# Handle pagination
if response["pagination"]["next_cursor"]:
    next_response = await client.call_tool("semantic_search", {
        "query": "authentication logic",
        "path": "/path/to/code",
        "cursor": response["pagination"]["next_cursor"]
    })

JSONL Output (Custom Workflows)

Perfect structured output for LLMs, scripts, and automation:

# 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'

Why JSONL for AI agents?

• ✅ Streaming friendly: Process results as they arrive
• ✅ Memory efficient: Parse one result at a time
• ✅ Error resilient: One malformed line doesn't break entire response
• ✅ Standard format: Used by OpenAI API, Anthropic API, and modern ML pipelines

Search & Filter Options

# Threshold filtering
ck --sem --threshold 0.7 "query"           # Only high-confidence matches
ck --hybrid --threshold 0.01 "concept"     # Low-confidence (exploration)

# Limit results
ck --sem --topk 5 "authentication patterns"

# Complete code sections
ck --sem --full-section "database queries"  # Complete functions
ck --full-section "class.*Error" src/       # Complete classes (works with regex too)

# Relevance scoring
ck --sem --scores "machine learning" docs/
# [0.847] ./ai_guide.txt: Machine learning introduction...
# [0.732] ./statistics.txt: Statistical learning methods...

Model Selection

Choose the right embedding model for your needs:

# Default: BGE-Small (fast, precise chunking)
ck --index .

# Enhanced: Nomic V1.5 (8K context, optimal for large functions)
ck --index --model nomic-v1.5 .

# Code-specialized: Jina Code (optimized for programming languages)
ck --index --model jina-code .

Model Comparison:

• bge-small (default): 400-token chunks, fast indexing, good for most code
• nomic-v1.5: 1024-token chunks with 8K model capacity, better for large functions
• jina-code: 1024-token chunks with 8K model capacity, specialized for code understanding

Index Management

# Check index status
ck --status .

# Clean up and rebuild / switch models
ck --clean .
ck --switch-model nomic-v1.5 .
ck --switch-model nomic-v1.5 --force .     # Force rebuild

# Add single file to index
ck --add new_file.rs

# File inspection (analyze chunking and token usage)
ck --inspect src/main.rs
ck --inspect --model bge-small src/main.rs  # Test different models

Interrupting Operations: Indexing can be safely interrupted with Ctrl+C. The partial index is saved, and the next operation will resume from where it stopped, only processing new or changed files.

📚 Language Support

Language	Indexing	Tree-sitter Parsing	Semantic Chunking
Python	✅	✅	✅ Functions, classes
JavaScript/TypeScript	✅	✅	✅ Functions, classes, methods
Rust	✅	✅	✅ Functions, structs, traits
Go	✅	✅	✅ Functions, types, methods
Ruby	✅	✅	✅ Classes, methods, modules
Haskell	✅	✅	✅ Functions, types, instances
C#	✅	✅	✅ Classes, interfaces, methods

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, automatically indexing any text file while correctly excluding binary files.

Unsupported File Types: Text files with unrecognized extensions (like .org, .adoc, etc.) are automatically indexed as plain text. ck detects text vs binary based on file contents, not extensions.

🏗 Installation

From crates.io

cargo install ck-search

From Source

git clone https://github.com/BeaconBay/ck
cd ck
cargo install --path ck-cli

Package Managers

# Currently available:
cargo install ck-search    # ✅ Available now via crates.io

# Coming soon:
brew install ck-search     # 🚧 In development (use cargo for now)
apt install ck-search      # 🚧 In development

💡 Examples

Finding Code Patterns

# 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/

Team Workflows

# 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/

Integration Examples

# 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

⚡ Performance

Field-tested on real codebases:

• Indexing: ~1M LOC in under 2 minutes
• Search: Sub-500ms queries on typical codebases
• Index size: ~2x source code size with compression
• Memory: Efficient streaming for large repositories
• Token precision: HuggingFace tokenizers for exact model-specific token counting

🔧 Architecture

ck uses a modular Rust workspace:

• ck-cli - Command-line interface and MCP server
• ck-core - Shared types, configuration, and utilities
• ck-engine - Search engine implementations (regex, semantic, hybrid)
• ck-index - File indexing, hashing, and sidecar management
• ck-embed - Text embedding providers (FastEmbed, API backends)
• ck-ann - Approximate nearest neighbor search indices
• ck-chunk - Text segmentation and language-aware parsing
• ck-models - Model registry and configuration management

Index Storage

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.

🧪 Testing

# Run the full test suite
cargo test --workspace

# Test with each feature combination
cargo hack test --each-feature --workspace

🤝 Contributing

ck is actively developed and welcomes contributions:

1. Issues: Report bugs, request features
2. Code: Submit PRs for bug fixes, new features
3. Documentation: Improve examples, guides, tutorials
4. Testing: Help test on different codebases and languages

Development Setup

git clone https://github.com/BeaconBay/ck
cd ck
cargo build --workspace
cargo test --workspace
./target/debug/ck --index test_files/
./target/debug/ck --sem "test query" test_files/

CI Requirements

Before submitting a PR, ensure your code passes all CI checks:

# Format code (required)
cargo fmt --all

# Run clippy linter (required - must have no warnings)
cargo clippy --workspace --all-features --all-targets -- -D warnings

# Run tests (required)
cargo test --workspace

# Check minimum supported Rust version (MSRV)
cargo hack check --each-feature --locked --rust-version --workspace

The CI pipeline runs on Ubuntu, Windows, and macOS to ensure cross-platform compatibility.

🗺 Roadmap

Current (v0.5+)

• ✅ MCP (Model Context Protocol) server for AI agent integration
• ✅ grep-compatible CLI with semantic search and file listing flags
• ✅ FastEmbed integration with BGE models and enhanced model selection
• ✅ File exclusion patterns and glob support
• ✅ Threshold filtering and relevance scoring with visual highlighting
• ✅ Tree-sitter parsing and intelligent chunking for 7+ languages
• ✅ Complete code section extraction (--full-section)
• ✅ Clean stdout/stderr separation for reliable scripting
• ✅ Incremental index updates with hash-based change detection
• ✅ Token-aware chunking with HuggingFace tokenizers
• ✅ Published to crates.io (cargo install ck-search)

Next (v0.6+)

• 🚧 Configuration file support
• 🚧 Package manager distributions (brew, apt)
• 🚧 Enhanced MCP tools (file writing, refactoring assistance)
• 🚧 VS Code extension
• 🚧 JetBrains plugin
• 🚧 Additional language chunkers (Java, PHP, Swift)

❓ FAQ

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. The .ck/ directory can be safely deleted to reclaim space.

Q: Is it fast enough for large codebases? A: Yes. The first semantic search builds the index automatically; after that only changed files are reprocessed, keeping searches sub-second even on large projects.

Q: Can I use it in scripts/automation? A: Absolutely. The --json and --jsonl flags provide structured output perfect for automated processing and AI agent integration.

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: Models are cached in platform-specific directories:

• Linux/macOS: ~/.cache/ck/models/
• Windows: %LOCALAPPDATA%\ck\cache\models\
• Fallback: .ck_models/models/ in current directory

📄 License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT License (LICENSE-MIT)

at your option.

🙏 Credits

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 install ck-search
ck --sem "the code you're looking for"