ck

Local first semantic and hybrid BM25 grep / search tool for use by AI and humans!

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ck (seek) is a semantic grep tool that finds code by meaning, not just keywords. It replaces traditional grep by understanding the user's search intent. It allows users to search for code based on concepts like 'error handling' and retrieves relevant code even if the exact keywords are not present. ck offers semantic search, drop-in grep compatibility, hybrid search combining keyword precision with semantic understanding, agent-friendly output in JSONL format, smart file filtering, and various advanced features. It supports multiple search modes, relevance scoring, top-K results, and smart exclusions. Users can index projects for semantic search, choose embedding models, and search specific files or directories. The tool is designed to improve code search efficiency and accuracy for developers and AI agents.

README:

ck - Semantic Grep by Embedding

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.

Quick Start

# 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 (one-time setup)
ck --index src/

# Search by meaning - automatically updates index for changed files
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/

Why ck?

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.

Core Features

🔍 Semantic Search

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 too

⚡ 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 --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 files

🎯 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
ck -l --hybrid "database" src/      # List files using hybrid search

🤖 Agent-Friendly Output

Perfect 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 functions

Why 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]}...")

📁 Smart File Filtering

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

How It Works

1. Index Once, Search Many

# 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" .

2. Embedding Model Selection

Choose the right model for your needs when creating the index:

# 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 and classes
jina-code: 1024-token chunks with 8K model capacity, specialized for code understanding

New in v0.4.5: Token-aware chunking uses actual model tokenizers for precise sizing, with model-specific chunk configurations balancing precision vs context.

Note: Model choice is set during indexing. Existing indexes will automatically use their original model.

3. Three Search Modes

--regex (default): Classic grep behavior, no indexing required
--sem: Pure semantic search using embeddings (requires index)
--hybrid: Combines regex + semantic with intelligent ranking

4. Relevance Scoring

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...

Advanced Usage

Search Specific Files

# 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"

Threshold Filtering

# 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

Top-K Results

# 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

Directory Management

# Check index status
ck --status .

# Clean up and rebuild
ck --clean .
ck --index .

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

File Inspection (New in v0.4.5)

Analyze how files will be chunked for embedding with the enhanced --inspect command:

# Inspect file chunking and token usage
ck --inspect src/main.rs
# Output: File info, chunk count, token statistics, and chunk details

# Example output:
# File: src/main.rs (49.6 KB, 1378 lines, 12083 tokens)
# Language: rust
#
# Chunks: 17 (tokens: min=4, max=3942, avg=644)
#    1. mod: 4 tokens | L9-9 | mod progress;
#    2. func: 1185 tokens | L88-294 | struct Cli { ... }
#    3. func: 442 tokens | L296-341 | fn expand_glob_patterns(...

# Check different model configurations
ck --inspect --model bge-small src/main.rs      # 400-token chunking
ck --inspect --model nomic-v1.5 src/main.rs    # 1024-token chunking

File Support

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

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.

Installation

From Source

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

From crates.io

# Install latest release from crates.io
cargo install ck-search

Package Managers (Planned)

# Coming soon:
brew install ck-search
apt install ck-search

Architecture

ck uses a modular Rust workspace:

ck-cli - Command-line interface and argument parsing
ck-core - Shared types, configuration, and utilities
ck-search - Search engine implementations (regex, BM25, semantic)
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.

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/

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

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/
ck -l --hybrid --scores "security" src/  # Files with security-related code

Configuration

Default Exclusions

# 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

Custom Configuration (Planned)

# .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"

Performance

Indexing: ~1M LOC in under 2 minutes (with smart exclusions and token-aware chunking)
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
Token precision: HuggingFace tokenizers for exact model-specific token counting (v0.4.5+)

Testing

Run the comprehensive test suite:

# Full test suite (40+ tests)
./test_ck.sh

# Quick smoke test (14 core tests)
./test_ck_simple.sh

Tests cover grep compatibility, semantic search, index management, file filtering, and more.

Contributing

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

Development Setup

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/

Roadmap

Current (v0.4+)

✅ grep-compatible CLI with semantic search and file listing flags (-l, -L)
✅ 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 (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
✅ Token-aware chunking with HuggingFace tokenizers (v0.4.5)
✅ Model-specific chunk sizing and FastEmbed capacity utilization (v0.4.5)
✅ Enhanced --inspect command with token analysis (v0.4.5)
✅ Granular indexing progress with file-level and chunk-level progress bars (v0.4.5)

Next (v0.5+)

✅ Published to crates.io (cargo install ck-search)
🚧 Configuration file support
🚧 Package manager distributions (brew, apt)

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, 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.

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 build --release
./target/release/ck --index .
./target/release/ck --sem "the code you're looking for"

For Tasks:

Click tags to check more tools for each tasks

find authentication code identify refactoring candidates security audit code review prep documentation generation

For Jobs:

software developer ai engineer code reviewer automation engineer data scientist

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uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

github

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griptape

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.

github

: 2.2k