# Install qlever CLI (recommended: use pipx or uv)
pip install qlever
# or
pipx install qlever
# or
uv tool install qlever

# Get a preconfigured dataset
qlever setup-config olympics
qlever get-data
qlever index
qlever start

# Test with a query
qlever query "SELECT * WHERE { ?s ?p ?o } LIMIT 10"

# Launch the web UI
qlever ui

# List available preconfigured datasets
qlever setup-config --list

# Popular options:
qlever setup-config wikidata      # Complete Wikidata (18B+ triples)
qlever setup-config osm-planet    # OpenStreetMap (40B+ triples)
qlever setup-config olympics      # Small demo (2M triples)
qlever setup-config dblp          # Computer science bibliography
qlever setup-config uniprot       # Protein database

# Example Qleverfile for custom dataset
[data]
NAME = my-knowledge-graph
GET_DATA_CMD = curl -L -o data.ttl https://example.org/data.ttl
FORMAT = turtle

[index]
INPUT_FILES = data.ttl
SETTINGS_JSON = {"prefixes": {"": "http://example.org/"}}

[server]
PORT = 7001
MEMORY_FOR_QUERIES = 10G
CACHE_MAX_SIZE = 5G

[ui]
UI_PORT = 7000

# Find all types of entities
SELECT ?type (COUNT(?s) AS ?count)
WHERE { ?s a ?type }
GROUP BY ?type
ORDER BY DESC(?count)
LIMIT 20

# Get entity with all properties
SELECT ?predicate ?object
WHERE { <http://example.org/entity1> ?predicate ?object }

# Find entities by label
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?entity ?label
WHERE {
  ?entity rdfs:label ?label .
  FILTER (CONTAINS(LCASE(STR(?label)), "example"))
}
LIMIT 100

# Find entities mentioned with specific words
PREFIX ql: <http://qlever.cs.uni-freiburg.de/builtin/>

SELECT ?entity ?text (COUNT(?text) AS ?mentions)
WHERE {
  ?text ql:contains-entity ?entity .
  ?text ql:contains-word "artificial intelligence" .
}
GROUP BY ?entity ?text
ORDER BY DESC(?mentions)
LIMIT 20

# Wildcard text search
SELECT ?entity ?text
WHERE {
  ?text ql:contains-entity ?entity .
  ?text ql:contains-word "machine learn*" .  # Matches learning, learns, etc.
}

# Find all elements within a geographic region (e.g., France)
PREFIX ogc: <http://www.opengis.net/rdf#>
PREFIX osmrel: <https://www.openstreetmap.org/relation/>
PREFIX osmkey: <https://www.openstreetmap.org/wiki/Key:>

SELECT ?element ?name
WHERE {
  osmrel:2202162 ogc:sfContains ?element .  # France relation ID
  ?element osmkey:railway "station" ;
           osmkey:name ?name .
}
LIMIT 100

# Calculate distance between points
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>

SELECT ?place1 ?place2 ?distance
WHERE {
  ?place1 geo:hasGeometry/geo:asWKT ?geom1 .
  ?place2 geo:hasGeometry/geo:asWKT ?geom2 .
  BIND(geof:distance(?geom1, ?geom2, <http://www.opengis.net/def/uom/OGC/1.0/kilometre>) AS ?distance)
  FILTER(?distance < 10)
}

# Basic query (TSV output)
curl -s "https://qlever.dev/api/wikidata" \
  -H "Accept: text/tab-separated-values" \
  -H "Content-Type: application/sparql-query" \
  --data "SELECT * WHERE { ?s ?p ?o } LIMIT 10"

# JSON output (SPARQL Results JSON)
curl -s "https://qlever.dev/api/wikidata" \
  -H "Accept: application/sparql-results+json" \
  -H "Content-Type: application/sparql-query" \
  --data "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10"

# QLever custom JSON format
curl -s "https://qlever.dev/api/wikidata" \
  -H "Accept: application/qlever-results+json" \
  -H "Content-Type: application/sparql-query" \
  --data "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10"

PREFIX wd: <http://www.wikidata.org/entity/>
PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

# Find all universities with founding date
SELECT ?university ?name ?founded
WHERE {
  ?university wdt:P31 wd:Q3918 ;      # instance of university
              rdfs:label ?name ;
              wdt:P571 ?founded .      # inception date
  FILTER (LANG(?name) = "en")
}
ORDER BY ?founded
LIMIT 100

PREFIX osmkey: <https://www.openstreetmap.org/wiki/Key:>
PREFIX osm2rdf: <https://osm2rdf.cs.uni-freiburg.de/rdf#>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>

# Find all cafes with their locations
SELECT ?cafe ?name ?geometry
WHERE {
  ?cafe osmkey:amenity "cafe" ;
        osmkey:name ?name ;
        geo:hasGeometry/geo:asWKT ?geometry .
}
LIMIT 100

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>

SELECT ?item ?itemLabel ?osmElement
WHERE {
  # Local OSM data
  ?osmElement osmkey:wikidata ?wikidataId .

  # Federated query to Wikidata
  SERVICE <https://query.wikidata.org/sparql> {
    ?item wdt:P31 wd:Q515 ;       # instance of city
          rdfs:label ?itemLabel .
    FILTER (LANG(?itemLabel) = "en")
  }
  FILTER (STR(?item) = ?wikidataId)
}
LIMIT 50

# Show what command would execute
qlever index --show

# Check server status
qlever status

# View logs
qlever log

# Test with simple query
qlever query "SELECT (COUNT(*) AS ?count) WHERE { ?s ?p ?o }"