Hi Allessandro, Thanks for your reply! Yes, the document are in the same result list and I'm not doing any indexing at the moment and executed a commit just to be sure. Still the same result. It is an environment with 4 shards. Perhaps that plays a factor?
Thanks, Sjoerd On Sun, Dec 5, 2021 at 11:02 AM Alessandro Benedetti <a.benede...@sease.io> wrote: > It's seems like the underline index changed. > Are those two documents in the same result set? > Is it just one query? > It's definitely curious, even if a commit happened search results are > consistent in one searcher. > > > On Sun, 5 Dec 2021, 16:28 Sjoerd Smeets, <ssme...@gmail.com> wrote: > >> Hi all, >> >> I'm debugging the relevancy scores of my query and I see the following for >> two documents hits. My question is, why is the idf score not the same for >> both documents? This is Solr 6.6. >> >> Any guidance would be much appreciated. >> >> Thanks! >> >> *Doc1* >> "71d72354eea23b9eae934ab616e8ce38de69d760": " >> 104.994415 = sum of: >> 104.994415 = sum of: >> 82.89969 = weight(stemmed_data.timenote.narratives:remedi in 22470) >> [SchemaSimilarity], result of: >> 82.89969 = score(freq=9.0), computed as boost * idf * tf from: >> 100.0 = boost >> 0.87546873 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >> from: >> *52 = n, number of documents containing term* >> *125 = N, total number of documents with field* >> 0.9469177 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >> avgdl)) from: >> 9.0 = freq, occurrences of term within document >> 1.2 = k1, term saturation parameter >> 0.75 = b, length normalization parameter >> 12312.0 = dl, length of field (approximate) >> 54179.03 = avgdl, average length of field >> 22.09473 = weight(stemmed_data.timenote.matters:remedi in 22470) >> [SchemaSimilarity], result of: >> 22.09473 = score(freq=4.0), computed as boost * idf * tf from: >> 10.0 = boost >> 2.4308395 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >> from: >> *9 = n, number of documents containing term* >> *107 = N, total number of documents with field* >> 0.9089341 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >> avgdl)) from: >> 4.0 = freq, occurrences of term within document >> 1.2 = k1, term saturation parameter >> 0.75 = b, length normalization parameter >> 5656.0 = dl, length of field (approximate) >> 50520.543 = avgdl, average length of field >> 0.0 = FunctionQuery(int(s_integer_search.previews)), product of: >> 0.0 = int(s_integer_search.previews)=0 >> 1.0 = boost >> 0.0 = FunctionQuery(int(s_integer_search.downloads)), product of: >> 0.0 = int(s_integer_search.downloads)=0 >> 1.0 = boost >> " >> >> *Doc2* >> "80302a1ecc44d1e556970ab96c25b1fd3328a854": " >> 84.61461 = sum of: >> 84.61461 = sum of: >> 64.68881 = weight(stemmed_data.timenote.narratives:remedi in 0) >> [SchemaSimilarity], result of: >> 64.68881 = score(freq=493.0), computed as boost * idf * tf from: >> 100.0 = boost >> 0.65094686 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >> from: >> *60 = n, number of documents containing term* >> *115 = N, total number of documents with field* >> 0.99376476 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >> avgdl)) from: >> 493.0 = freq, occurrences of term within document >> 1.2 = k1, term saturation parameter >> 0.75 = b, length normalization parameter >> 229400.0 = dl, length of field (approximate) >> 73913.91 = avgdl, average length of field >> 19.9258 = weight(stemmed_data.timenote.matters:remedi in 0) >> [SchemaSimilarity], result of: >> 19.9258 = score(freq=340.0), computed as boost * idf * tf from: >> 10.0 = boost >> 2.0024805 = idf, computed as log(1 + (N - n + 0.5) / (n + 0.5)) >> from: >> *13 = n, number of documents containing term* >> *99 = N, total number of documents with field* >> 0.99505585 = tf, computed as freq / (freq + k1 * (1 - b + b * dl / >> avgdl)) from: >> 340.0 = freq, occurrences of term within document >> 1.2 = k1, term saturation parameter >> 0.75 = b, length normalization parameter >> 147480.0 = dl, length of field (approximate) >> 95534.95 = avgdl, average length of field >> 0.0 = FunctionQuery(int(s_integer_search.previews)), product of: >> 0.0 = int(s_integer_search.previews)=0 >> 1.0 = boost >> 0.0 = FunctionQuery(int(s_integer_search.downloads)), product of: >> 0.0 = int(s_integer_search.downloads)=0 >> 1.0 = boost >> " >> >
