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View Code? Open in Web Editor NEWSearch COVID-19 Open Research Dataset (CORD-19) using Vespa - the open source big data serving engine.
License: Apache License 2.0
Search COVID-19 Open Research Dataset (CORD-19) using Vespa - the open source big data serving engine.
License: Apache License 2.0
Some best practises would help if one wants different authentication than what Vespa cloud offers (mTLS).
Action: Select a Source, Journal, Published, etc from the site.
What Happens: All other options go away except for the selected facet in its group and the other facets recalculate their sizes and values.
What's Expected: All facets recalculate their sizes and values. The selected facet's group's values may or may not change, but the select value does not change. The selected facet's groups alternative values are not zero - they are the count as if no selection were made in that group only.
Example where the behavior is as expected on a different site on what is probably a lucene-based stack - this may be hard-coded.
LinkedIn's search has a better example where there is actually value discovery (current/past companies)
For example, if the corpus is:
Doc Source Journal Publish
1 WHO BMJ 2020
2 WHO BMJ 2019
3 WHO Lancet 2020
4 Medline Lancet 2020
With no selections:
Total: 4 - Docs 1,2,3,4
Source: WHO(3) Medline(1)
Journal: BMJ(2) Lancet(2)
Publish: 2020(3) 2019(1)
With Source WHO selection, expected:
Total: 3 - Docs 1,2,3
Source: WHO(3) Medline(1) <- notice Medline is 1 even though WHO is selected. This is the only filter WHO is not applied on.
Journal: BMJ(2) Lancet(1) <- Lancet is 1 because the WHO filter is applied here
Publish: 2020(2) 2019(1) <- 2020 goes to 2 because the WHO filter is applied here
With Source WHO selection and Lancet selection:
Total: 1 - Doc 3
Source: WHO(1, selected) Medline(1, unselected) <- Lancet filter applied, but not WHO at this group
Journal: Lancet(1, selected) BMJ(2, unselected) <- WHO filter applied, but not Lancet filter at this group
Publish: 2020(1, selected) <- WHO and Lancet filter applied at this group - there are no other publish years
I think this would be a better experience overall - removing all the unselected options doesn't make much sense for the user and the UI should encourage further discovery of available query expansions. I'm mostly looking to find out if Vespa offers this type of user experience / facet discovery without a separate query for each facet group - and if so - how?
In the readme file of the repository, there are link to backend code of the page.
https://github.com/vespa-engine/sample-apps/tree/master/vespa-cloud/cord-19-search
But, link is giving 404 page not found error. Kindly update and provide link to backed code of CORD-19.
Hi,
My company have a Vespa.ai plugin to do "Search maps".
https://github.com/Escavador/vespa-searcher-match-map
You can see it working, here:
https://www.escavador.com/jurisprudencia/busca?q=cloroquina
It shows a nice map of where the term was found in the text. For scientific texts that have a structured order, I think this would be a great thing.
I would like to filter results by journal but the API does not seem to be respecting the filter and returns random journals while the FE search works as expected:
api: https://api.cord19.vespa.ai/search/?query=covid-19&summary=short&ranking=freshness&year=2020&fieldset=all&journal=The+Lancet
FE: https://cord19.vespa.ai/search?query=covid-19&summary=short&ranking=freshness&year=2020&fieldset=all&journal=The+Lancet
Am I doing something wrong?
https://cord19.vespa.ai/search?query=sddocname%3Adoc&ranking=freshness
The freshness ranking profile uses the freshness(timestamp) ranking feature, instead of timestamp directly. The dataset has for some odd reason dates which are in the future so when sorting by decreasing timestamp to order by date we get ordering which is hard to explain.
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