Scoring documents

Full-text scoring functions

When searching, documents are scored based on their relevance to the query. The score is a floating point number between 0.0 and 1.0, where 1.0 is the highest score. The score is returned as part of the search results and can be used to sort the results.

Redis Stack 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.

The following is a list of the pre-bundled scoring functions available in Redis Stack and a short explanation about how they work. Each function is mentioned by registered name, which can be passed as a SCORER argument in FT.SEARCH.

TFIDF (default)

Basic TF-IDF scoring with a few extra features:

  1. For each term in each result, the TF-IDF score of that term is calculated to that document. Frequencies are weighted based on field weights that are pre-determined, and each term's frequency is normalized by the highest term frequency in each document.

  2. The total TF-IDF for the query term is multiplied by the presumptive document score given on FT.CREATE via SCORE_FIELD.

  3. A penalty is assigned to each result based on "slop" or cumulative distance between the search terms. Exact matches will get no penalty, but matches where the search terms are distant will have their score reduced significantly. For each bigram of consecutive terms, the minimal distance between them is determined. The penalty is the square root of the sum of the distances squared; e.g., 1/sqrt(d(t2-t1)^2 + d(t3-t2)^2 + ...).

Given N terms in document D, T1...Tn, the resulting score could be described with this Python function:

def get_score(terms, doc):
    # the sum of tf-idf
    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[i-1])**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, expressed as the total number of terms. The length is weighted, so that if a document contains two terms, one in a field 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 variation on the basic TFIDF scorer, see this Wikipedia article for more info.

The relevance score for each document is multiplied by the presumptive document score and a penalty is applied based on slop as in TFIDF.

FT.SEARCH myIndex "foo" SCORER BM25

DISMAX

A simple scorer that sums up the frequencies of 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 one-to-one implementation of Solr's DISMAX algorithm, but it follows it in broad terms.

FT.SEARCH myIndex "foo" SCORER DISMAX

DOCSCORE

A scoring function that just returns the presumptive score of the document without applying any calculations to it. Since document scores can be updated, this can be useful if you'd like to use an external score and nothing further.

FT.SEARCH myIndex "foo" SCORER DOCSCORE

HAMMING

Scoring by the inverse Hamming distance between the document's payload and the query payload is performed. Since the nearest neighbors are of interest, the inverse Hamming distance (1/(1+d)) is used so that a distance of 0 gives a perfect score of 1 and is the highest rank.

This only works if:

  1. The document has a payload.
  2. The query has a payload.
  3. Both are exactly the same length.

Payloads are binary-safe, and having payloads with a length that is a multiple of 64 bits yields slightly faster results.

Example:

> HSET key:1 foo hello payload aaaabbbb
(integer) 2

> HSET key:2 foo bar payload aaaacccc 
(integer) 2

> FT.CREATE idx ON HASH PREFIX 1 key: PAYLOAD_FIELD payload SCHEMA foo TEXT
"OK"

> FT.SEARCH idx "*" PAYLOAD "aaaabbbc" SCORER HAMMING WITHSCORES
1) "2"
2) "key:1"
3) "0.5"
4) 1) "foo"
   2) "hello"
5) "key:2"
6) "0.25"
7) 1) "foo"
   2) "bar"
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