luckyshot 0.1.0

A powerful CLI tool that enhances code understanding by using RAG (Retrieval Augmented Generation) to find and analyze relevant files in your codebase.

Features

• File scanning with customizable chunk sizes and overlap
• Semantic search using OpenAI embeddings
• Support for piped input and file suggestions
• Intelligent context expansion

Installation

cargo install luckyshot

Usage

Scanning Files

Generate embeddings for your codebase using the scan command:

# Basic scan of all Rust files
luckyshot scan "**/*.rs"

# Basic scan of all Rust and Markdown files
luckyshot scan "**/*{.rs,.md}"

# Scan with chunking enabled
luckyshot scan --chunk-size 1000 --overlap-size 100 "src/**/*.rs"

# Include file metadata in embeddings
luckyshot scan --embed-metadata "*.{rs,md}"

The scan command:

1. Finds files matching your pattern (respecting .gitignore)
2. Generates embeddings using OpenAI's API
3. Saves results to .luckyshot.file.vectors.v1

Finding Relevant Files

To find files related to a topic or question:

# Using command line arguments
luckyshot suggest-files "how does the scanning work?"

# Using piped input
echo "how does error handling work?" | luckyshot suggest-files

# Chain commands Unix-style
# This will:
# 1. Ask about OpenAI URLs
# 2. Expand it with Rust expertise context
# 3. Find relevant files
echo "what openai url am I using" | \
  luckyshot expand "you are a rust expert who describes their \
     question and the files you are looking for" | \
  luckyshot suggest-files

This will:

1. Convert your query into an embedding
2. Use BM25-style ranking to find similar files
3. Display relevant files with similarity scores

Expanding Context

To expand a query with additional context:

luckyshot expand "describe the implementation" --system-prompt "You are a helpful assistant"

Environment Setup

You'll need an OpenAI API key. Either:

export OPENAI_API_KEY="your-api-key"

Or create a .env file:

OPENAI_API_KEY=your-api-key

License

MIT