search-bikeshed 0.1.0

Index and search Bikeshed (.bs) source documents from URLs with efficient change detection

search-bs

Index and search Bikeshed (.bs) source documents from URLs with efficient change detection.

Features

• Efficient change detection: Only re-indexes when content actually changes
  • Uses HTTP conditional requests (ETag, Last-Modified)
  • Falls back to SHA256 content hashing
• Full-text search: Powered by SQLite FTS5 with BM25 ranking
• Context-aware search: Show N lines around each match with --around flag
• Exact line retrieval: Pull specific line ranges for agent consumption
• Batch indexing: Index multiple documents from a config file with --all
• Markdown context: Tracks current heading for each search result
• GitHub integration: Automatically converts GitHub blob URLs to raw URLs
• JSON output: Machine-readable output for all commands

Installation

pip install search-bikeshed

Or install from source:

git clone https://github.com/tarekziade/search-bikeshed
cd search-bs
pip install -e .

Usage

Index a document

# Index from a URL
search-bs index https://github.com/webmachinelearning/webnn/blob/main/index.bs --name webnn

# GitHub blob URLs are automatically converted to raw URLs
search-bs index https://raw.githubusercontent.com/w3c/webrtc-pc/main/webrtc.bs --name webrtc

# Index all documents from config file
search-bs index --all

Batch indexing with config file

Create a config file at ~/.config/search-bs/sources.json:

[
  {
    "name": "webnn",
    "url": "https://github.com/webmachinelearning/webnn/blob/main/index.bs"
  },
  {
    "name": "webrtc",
    "url": "https://raw.githubusercontent.com/w3c/webrtc-pc/main/webrtc.bs"
  }
]

Then index all at once:

search-bs index --all

Search indexed documents

# Basic search
search-bs search --name webnn "MLTensor"

# Phrase search
search-bs search --name webnn "graph builder"

# Search with context lines (show 3 lines around each match)
search-bs search --name webnn "MLContext" --around 3

# With JSON output
search-bs search --name webnn "MLContext" --json

# Show URLs in results
search-bs search --name webnn "operator" --show-url --max-results 10

Get exact line ranges

Retrieve specific line ranges from indexed documents (useful for agents):

# Get 40 lines starting from line 1234
search-bs get --name webnn --line 1234 --count 40

# JSON output
search-bs get --name webnn --line 1234 --count 40 --json

List indexed documents

# Human-readable list
search-bs docs

# JSON output
search-bs docs --json

How it works

1. Indexing: Fetches .bs files from URLs and indexes them line-by-line with heading context
2. Change detection: Uses HTTP conditional requests and content hashing to skip unchanged documents
3. Search: Uses SQLite FTS5 full-text search with BM25 ranking for relevance

Data storage

By default, the index is stored in ~/.cache/search-bs/search-bs.sqlite3

You can override this location with the BIKESEARCH_HOME environment variable:

export BIKESEARCH_HOME=/custom/path
search-bs index ...

Requirements

• Python 3.8 or later
• SQLite 3 with FTS5 support (included in Python 3.6+)
• No external dependencies (stdlib only)

License

MIT