Update the result score result field with the result queue ranking value to reflect
the actual calculated/used score,
for rwi & solr stack results.
(calc. etc. is unchanged, it's just that result entry carries the latest val
as api retrieves the number from it)
Collection is not available in pure rwi entries (but in local solr metadata)
But if user wishes to filter by query constraint also rwi shall adhere to this
(even if only rwi entries with parsed or solr received metadata may fit)
1-char tokens and also more-than-1-char tokens, then remove the 1-char
tokens to prevent that we are to strict. This will make it possible to
be a bit more fuzzy in the search where it is appropriate.
(regardless if these fields part of update).
Switch partial update option off in postprocessing if schema contains *_dts (multivalued date field).
see http://mantis.tokeek.de/view.php?id=601
moved and was not cleared anymore. This results in an huge fieldcache.
(http://lucene.apache.org/#highlights-of-the-lucene-release-includehttps://issues.apache.org/jira/browse/LUCENE-5666)
Here I try to use DovValues where it is possible.
For this I used the Api-Scheme as new basis für the Solr-Schema.
This needs at least a complete optimization of the Solr-Index to get a
smaller FieldCache.
Everything that is indexed with these setting will not use the
Fieldcache at all.
bayesian filters. This can be used to classify documents during
indexing-time using a pre-definied bayesian filter.
New wordings:
- a context is a class where different categories are possible. The
context name is equal to a facet name.
- a category is a facet type within a facet navigation. Each context
must have several categories, at least one custom name (things you want
to discover) and one with the exact name "negative".
To use this, you must do:
- for each context, you must create a directory within
DATA/CLASSIFICATION with the name of the context (the facet name)
- within each context directory, you must create text files with one
document each per line for every categroy. One of these categories MUST
have the name 'negative.txt'.
Then, each new document is classified to match within one of the given
categories for each context.
- fixed superfluous space in query field list
- fixed filter query logic
- removed look-ahead query which caused that each new search page
submitted two solr queries
- fixed random solr result orders in case that the solr score was equal:
this was then re-ordered by YaCy using the document hash which came from
the solr object and that appeared to be random. Now the hash of the url
is used and the score is additionally modified by the url length to
prevent that this particular case appears at all.
during surrogate reading: those attributes from the dump are removed
during the import process and replaced by new detected attributes
according to the setting of the YaCy peer.
This may cause that all such attributes are removed if the importing
peer has no synonyms and/or no vocabularies defined.
reger