is there any way to figure out a maximum theoretical score for a non text
search query - something like:
"query": "location:(1)^3 accommodation_comfort:(137 OR
193)^2 accommodation_facilities:(459 OR 403 OR 319)",
At the moment I'm using Django Haystack to generate the query - so I could
build it differently if that helps.
Or is there a simple bit of maths I could do based on the number of
attributes and the boost scores I'm giving - (assuming that there's no
extra boosting going on at index time)?
Each document attribute I'm querying is just an array of integers.
The search is working fine - I'd just like to be able to know if I have a
100% match, or a 50% match and so on so I can decide if I want to use the
returned doc in the next step of the process.
if the max score is 2, I want to do something with the top 40 docs that are
over a score of 1.5 (if there's only 20 >1.5, then that's fine, just those
20, or if there's 60 >1.5 then only the top 40..)
and as the query can change, the max score changes, so "query":
"activity:(1) location:(1)^3" - is getting me a max score of 1.264911
because there's not much to search on - (I know because I can see the top
results do exactly match..)
but a more complex search like "query": "activity:(1) location:(2)^3
accommodation_facilities:(459 OR 347 OR 319)^1
accommodation_comfort:(193)^2", is scoring 2.1828206 as a 100% match
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