An Alfred workflow for searching Rust crate documentations on docs.rs. It uses
the search index file built during cargo doc to generate typeahead list in
Alfred, and it opens docs.rs for you.
It supports the same query syntax in cargo-doc. For example, you can use fn:
to priortize functions in the search result, or use vec -> usize to search all
the functions that accepts a Vec as parameter and returns an usize. For
details, please read the "search tricks" section from cargo doc (press "h" in
any documentation generated by cargo doc):
Prefix searches with a type followed by a colon (e.g., fn:) to restrict the search to a given type.
Accepted types are: fn, mod, struct, enum, trait, type, macro, and const.
Search functions by type signature (e.g., vec -> usize or * -> vec)
Search multiple things at once by splitting your query with comma (e.g., str,u8 or String,struct:Vec,test)
You can look for items with an exact name by putting double quotes around your request: "string"
Look for items inside another one by searching for a path: vec::Vec
This workflow requires you have NodeJS installed in your system. To install NodeJS, run (w/ Homebrew):
brew install node
Then download the .alfredworkflow file from the Release page.
If you want the workflow to use a copy of NodeJS that is not located at
/usr/local/bin/node, please configure the environment variable
ALFRED_NODE_PATH in Aflred to your copy of NodeJS.
This project directly uses the initSearch function in
rust-lang/rust (hence written in NodeJS) with some stubs to make it work
outside the browser. Then it fetches the search-index.js file generated during
the documentation building process (cargo doc) from docs.rs to serve the
search result in Alfred. The search-index.js is evaluated inside a sandbox
provided by vm2. This means this workflow will be running code downloaded
from the Internet inside a sandbox. This is really unfortunate since some big
crates rely on some JavaScript features to do compression (for example, this
search index file). Please let me know if there is an
alternative way to acquire the search index without actually executing any
remote code.
MIT