fatcat-scholar: fulltext search over fatcat corpus of 25+ million open research papers
Translations
Update the .pot file and translation files:
pybabel extract -F extra/i18n/babel.cfg -o extra/i18n/web_interface.pot fatcat_scholar/
pybabel update -i extra/i18n/web_interface.pot -d fatcat_scholar/translations
Compile translated messages together:
pybabel compile -d fatcat_scholar/translations
Create initial .po file for a new language translation (then run the above update/compile after doing initial translations):
pybabel init -i extra/i18n/web_interface.pot -d fatcat_scholar/translations -l de
Production
Use gunicorn plus uvicorn, to get multiple worker processes, each running async:
gunicorn example:app -w 4 -k uvicorn.workers.UvicornWorker
Prototype Pipeline
Requires staff credentials in environment for internetarchive python library.
TODO: pass these credentials via ansible/dotenv
Generate complete SIM issue database:
ia search "collection:periodicals collection:sim_microfilm mediatype:collection" --itemlist | rg "^pub_" > data/sim_collections.tsv
ia search "collection:periodicals collection:sim_microfilm mediatype:texts" --itemlist | rg "^sim_" > data/sim_items.tsv
cat data/sim_collections.tsv | parallel -j4 ia metadata {} | jq . -c | pv -l > data/sim_collections.json
cat data/sim_items.tsv | parallel -j8 ia metadata {} | jq . -c | pv -l > data/sim_items.json
python -m fatcat_scholar.issue_db init_db
cat data/sim_collections.json | pv -l | python -m fatcat_scholar.issue_db load_pubs
cat data/sim_items.json | pv -l | python -m fatcat_scholar.issue_db load_issues
python -m fatcat_scholar.issue_db load_counts
Create QA elasticsearch index (localhost):
http put ":9200/qa_scholar_fulltext_v01?include_type_name=true" < schema/scholar_fulltext.v01.json
http put ":9200/qa_scholar_fulltext_v01/_alias/qa_scholar_fulltext"
Fetch "heavy" fulltext documents (JSON) for full SIM database:
python -m fatcat_scholar.sim_pipeline run_issue_db | pv -l | gzip > data/sim_intermediate.json.gz
Re-use existing COVID-19 database to index releases:
cat /srv/fatcat_covid19/metadata/2020-06-24/fatcat_hits.enrich.json \
| jq -c .fatcat_release \
| rg -v "^null" \
| parallel -j8 --linebuffer --round-robin --pipe python -m fatcat_scholar.work_pipeline run_releases \
| pv -l \
| gzip > data/work_intermediate.json.gz
=> 48.3k 0:17:58 [44.8 /s]
Transform and index both into local elasticsearch:
zcat data/work_intermediate.json.gz data/sim_intermediate.json.gz \
| parallel -j8 --linebuffer --round-robin --pipe python -m fatcat_scholar.transform run_transform \
| esbulk -verbose -size 100 -id key -w 4 -index qa_scholar_fulltext_v01 -type _doc
=> 132635 docs in 2m18.787824205s at 955.667 docs/s with 4 workers