Scoring In RediSearch¶
RediSearch comes with a few very basic scoring functions to evaluate document relevance. They are all based on document scores and term frequency. This is regardless of the ability to use sortable fields. Scoring functions are specified by adding the SCORER {scorer_name}
argument to a search query.
If you prefer a custom scoring function, it is possible to add more functions using the Extension API.
These are the prebunldled scoring functions availabe in RediSearch and how they work. Each function is mentioned by registered name, that can be passed as a SCORER argument in FT.SEARCH.
TFIDF (Default)¶
Basic TFIDF scoring with a few extra features thrown inside:

For each term in each result we calculate the TFIDF score of that term to that document. Frequencies are weighted based on field weights that are predetermined, and each term's frequency is normalized by the highest term frequency in each document.

We multiply the total TFIDF for the query term by the a priory document score given on
FT.ADD
. 
We give a penalty to each result based on "slop" or cumulative distance between the search terms: exact matches will get no penlty, but matches where the search terms are distant see their score reduced significantly. For each 2gram of consecutive terms, we find the minimal distance between them. The penalty is the square root of the sum of the distances, squared 
1/sqrt(d(t2t1)^2 + d(t3t2)^2 + ...)
.
So for N terms in a document D, T1...Tn
, the resulting score could be described with this python function:
def get_score(terms, doc): # the sum of tfidf score = 0 # the distance penalty for all terms dist_penalty = 0 for i, term in enumerate(terms): # tf normalized by maximum frequency tf = doc.freq(term) / doc.max_freq # idf is global for the index, and not calculated each time in real life idf = log2(1 + total_docs / docs_with_term(term)) score += tf*idf # sum up the distance penalty if i > 0: dist_penalty += min_distance(term, terms[i1])**2 # multiply the score by the document score score *= doc.score # divide the score by the root of the cumulative distance if len(terms) > 1: score /= sqrt(dist_penalty) return score
TFIDF.DOCNORM¶
Identical to the default TFIDF scorer, with one important distinction:
Term frequencies are normalized by the length of the document (in number of terms). The length is weighted, so that if a document contains two terms, one in a feild that has a weight 1 and one in a field with a weight of 5, the total frequency is 6, not 2.
FT.SEARCH myIndex "foo" SCORER TFIDF.DOCNORM
BM25¶
A vraiation on the basic TFIDF scorer, see this Wikipedia article for more info.
We also multiply the relevance score for each document by the a priory docment score, and apply a penalty based on slop as in TFIDF.
FT.SEARCH myIndex "foo" SCORER BM25
DISMAX¶
A simple scorer that sums up the frequencies of the matched terms; in the case of union clauses, it will give the maximum value of those matches. No other penalties or factors are applied.
It is not a 1 to 1 implementation of Solr's DISMAX algorithm, but follows it in broad terms.
FT.SEARCH myIndex "foo" SCORER DISMAX
DOCSCORE¶
A scoring function that just returns the a priory score of the document without applying any calculations to it. Since document scores can be updates, this can be useful if you'd like to use an external score and nothing further.
FT.SEARCH myIndex "foo" SCORER DOCSCORE