# Qleverfile for Olympics, use with https://github.com/ad-freiburg/qlever-control
#
# qlever get-data  # downloads .zip file of size 13 MB, uncompressed to 323 MB
# qlever index     # takes ~10 seconds and ~1 GB RAM (on an AMD Ryzen 9 5900X)
# qlever start     # starts the server (instant)
[data]
NAME              = huggingface
FILE_URL          = https://huggingface.co/datasets/david4096/huggingface-ttl/resolve/main/huggingface240k.ttl
GET_DATA_CMD      = curl -sLo huggingface240.ttl -C - ${FILE_URL}
DESCRIPTION       = Metadata of all huggingface datasets, converted to RDF using croissant-rdf
TEXT_DESCRIPTION  = All literals, search with FILTER CONTAINS(?var, "...")
[index]
INPUT_FILES       = huggingface240.ttl
CAT_INPUT_FILES   = cat ${INPUT_FILES}
#MULTI_INPUT_JSON = [{"cmd": "cat huggingface240.ttl", "format": "ttl", "parallel":"false"}]
PARALLEL_PARSING  = false
SETTINGS_JSON   = { "ascii-prefixes-only": false, "num-triples-per-batch": 100000 }
[server]
PORT               = 7088
ACCESS_TOKEN       = ${data:NAME}_7643543846_jxJWYyAKQiP8
MEMORY_FOR_QUERIES = 5G
CACHE_MAX_SIZE     = 2G
TIMEOUT            = 30s
[runtime]
SYSTEM = docker
IMAGE  = docker.io/adfreiburg/qlever:latest
[ui]
UI_CONFIG = olympics
UI_PORT = 9085
