🦀 Qlue-ls 🦀

⚡Qlue-ls (pronounced "clueless") is a blazingly fast language server for SPARQL, written in Rust 🦀, build for the web.

💻 Demo
📚 Documentation
📝 Project Blog Post
🎓 Thesis

🚀 Capabilities

Qlue-ls offers a wide range of LSP features tailored to SPARQL.
For a complete overview, see the capabilities section. QLue-ls also provides custom LSP extentions for SPARQL specific features and server state management. See LSP Extensions for more.

✨ Completion

• Suggests valid continuations while typing SPARQL queries
• Backend-powered suggestions for subjects, predicates, and objects
• Note: Completion queries must be configured for each knowledge graph

📐 Formatting

• Auto-formats SPARQL queries for consistency and readability
• Fully customizable to match your preferred coding style
• On-type formatting: pressing Enter after ; in a triple pattern auto-indents to the predicate column

🛠️ Code Actions

• Provides smart quick-fixes for diagnostics
• Offers suggested improvements and automated edits

ℹ️ Hover

• View contextual information by hovering over tokens

🩺 Diagnostics

• Real-time feedback with severity levels: error, warning, and info
• Helps catch syntax issues and common mistakes

🕳 Jump

• Navigate quickly between key locations in a query

❓ operation identification

• Detects whether a SPARQL operation is a query or an update

⚙️ Configuration

Qlue-ls is configured via a qlue-ls.toml or qlue-ls.yml file.
Full configuration options are explained in the documentation.

Example Configuration

[format]
align_predicates = true
align_prefixes = true
separate_prologue = false
capitalize_keywords = true
insert_spaces = true
tab_size = 2
where_new_line = true
filter_same_line = true
line_length = 120
contract_triples = false
auto_line_break = false

[completion]
timeout_ms = 5000
result_size_limit = 100
subject_completion_trigger_length = 3
object_completion_suffix = true
variable_completion_limit = 10
same_subject_semicolon = true

[prefixes]
add_missing = true
remove_unused = false

🙏 Acknowledgements

• TJ DeVries - for the inspiration and fantastic tutorials
• Hannah Bast - for mentorship and guidance.

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