ArangoSearch Functions

ArangoSearch AQL functions take either an expression or an attribute path expression as first argument.

ANALYZER(<expression>, …)
STARTS_WITH(doc.attribute, …)

If an expression is expected, it means that search conditions can expressed in AQL syntax. They are typically function calls to ArangoSearch search functions, possibly nested and/or using logical operators for multiple conditions.

STARTS_WITH(doc.text, "avoca") OR STARTS_WITH(doc.text, "arang")

The default Analyzer that will be used for searching is "identity". While many ArangoSearch functions accept an Analyzer argument, it is often easier and cleaner to wrap a search (sub-)expressions with an ANALYZER() call to set the Analyzer for these functions. Their Analyzer argument can then be left out.

// Analyzer specified in each function call
PHRASE(doc.text, "avocado dish", "text_en") AND PHRASE(doc.text, "lemon", "text_en")

// Analyzer specified using ANALYZER()
ANALYZER(PHRASE(doc.text, "avocado dish") AND PHRASE(doc.text, "lemon")

Certain expressions do not require any ArangoSearch functions, such as basic comparisons. However, the Analyzer used for searching will be "identity" unless ANALYZER() is used to set a different one.

// The "identity" Analyzer will be used by default
SEARCH doc.text == "avocado"

// Use the "text_en" Analyzer for searching instead
SEARCH ANALYZER(doc.text == "avocado", "text_en")

If an attribute path expressions is needed, then you have to reference a document object emitted by a View like FOR doc IN viewName and then specify which attribute you want to test for as an unquoted string literal. For example doc.attr or doc.deeply.nested.attr but not "doc.attr". You can also use the bracket notation doc["attr"].

FOR doc IN viewName
  SEARCH STARTS_WITH(doc.deeply.nested["attr"], "avoca")
  RETURN doc

Search Functions

Search functions can be used in a SEARCH operation to form an ArangoSearch expression to filter a View. The functions control the ArangoSearch functionality without having a returnable value in AQL.

ANALYZER()

ANALYZER(expr, analyzer)

Sets the Analyzer for the given search expression. The default Analyzer is identity for any ArangoSearch expression. This utility function can be used to wrap a complex expression to set a particular Analyzer. It also sets it for all the nested functions which require such an argument to avoid repeating the Analyzer parameter. If an Analyzer argument is passed to a nested function regardless, then it takes precedence over the Analyzer set via ANALYZER().

The TOKENS() function is an exception. It requires the Analyzer name to be passed in in all cases even if wrapped in an ANALYZER() call, because it is not an ArangoSearch function but a regular string function which can be used outside of SEARCH operations.

• expr (expression): any valid search expression
• analyzer (string): name of an Analyzer.
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

Assuming a View definition with an Analyzer whose name and type is delimiter:

{
  "links": {
    "coll": {
      "analyzers": [ "delimiter" ],
      "includeAllFields": true,
    }
  },
  ...
}

… with the Analyzer properties { "delimiter": "|" } and an example document { "text": "foo|bar|baz" } in the collection coll, the following query would return the document:

FOR doc IN viewName
  SEARCH ANALYZER(doc.text == "bar", "delimiter")
  RETURN doc

The expression doc.text == "bar" has to be wrapped by ANALYZER() in order to set the Analyzer to delimiter. Otherwise the expression would be evaluated with the default identity Analyzer. "foo|bar|baz" == "bar" would not match, but the View does not even process the indexed fields with the identity Analyzer. The following query would also return an empty result because of the Analyzer mismatch:

FOR doc IN viewName
  SEARCH doc.text == "foo|bar|baz"
  //SEARCH ANALYZER(doc.text == "foo|bar|baz", "identity")
  RETURN doc

In below query, the search expression is swapped by ANALYZER() to set the text_en Analyzer for both PHRASE() functions:

FOR doc IN viewName
  SEARCH ANALYZER(PHRASE(doc.text, "foo") OR PHRASE(doc.text, "bar"), "text_en")
  RETURN doc

Without the usage of ANALYZER():

FOR doc IN viewName
  SEARCH PHRASE(doc.text, "foo", "text_en") OR PHRASE(doc.text, "bar", "text_en")
  RETURN doc

In the following example ANALYZER() is used to set the Analyzer text_en, but in the second call to PHRASE() a different Analyzer is set (identity) which overrules ANALYZER(). Therefore, the text_en Analyzer is used to find the phrase foo and the identity Analyzer to find bar:

FOR doc IN viewName
  SEARCH ANALYZER(PHRASE(doc.text, "foo") OR PHRASE(doc.text, "bar", "identity"), "text_en")
  RETURN doc

Despite the wrapping ANALYZER() function, the Analyzer name can not be omitted in calls to the TOKENS() function. Both occurrences of text_en are required, to set the Analyzer for the expression doc.text IN ... and for the TOKENS() function itself:

FOR doc IN viewName
  SEARCH ANALYZER(doc.text IN TOKENS("foo", "text_en"), "text_en")
  RETURN doc

BOOST()

BOOST(expr, boost)

Override boost in the context of a search expression with a specified value, making it available for scorer functions. By default, the context has a boost value equal to 1.0.

• expr (expression): any valid search expression
• boost (number): numeric boost value
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

FOR doc IN viewName
  SEARCH ANALYZER(BOOST(doc.text == "foo", 2.5) OR doc.text == "bar", "text_en")
  LET score = BM25(doc)
  SORT score DESC
  RETURN { text: doc.text, score }

Assuming a View with the following documents indexed and processed by the text_en Analyzer:

{ "text": "foo bar" }
{ "text": "foo" }
{ "text": "bar" }
{ "text": "foo baz" }
{ "text": "baz" }

… the result of above query would be:

[
  {
    "text": "foo bar",
    "score": 2.787301540374756
  },
  {
    "text": "foo baz",
    "score": 1.6895781755447388
  },
  {
    "text": "foo",
    "score": 1.525835633277893
  },
  {
    "text": "bar",
    "score": 0.9913395643234253
  }
]

EXISTS()

EXISTS(path)

Match documents where the attribute at path is present.

• path (attribute path expression): the attribute to test in the document
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

FOR doc IN viewName
  SEARCH EXISTS(doc.text)
  RETURN doc

EXISTS(path, type)

Match documents where the attribute at path is present and is of the specified data type.

• path (attribute path expression): the attribute to test in the document
• type (string): data type to test for, can be one of:
  • "null"
  • "bool" / "boolean"
  • "numeric"
  • "string"
  • "analyzer" (see below)
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

FOR doc IN viewName
  SEARCH EXISTS(doc.text, "string")
  RETURN doc

EXISTS(path, "analyzer", analyzer)

Match documents where the attribute at path is present and was indexed by the specified analyzer.

• path (attribute path expression): the attribute to test in the document
• type (string): string literal "analyzer"
• analyzer (string, optional): name of an Analyzer. Uses the Analyzer of a wrapping ANALYZER() call if not specified or defaults to "identity"
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

FOR doc IN viewName
  SEARCH EXISTS(doc.text, "analyzer", "text_en")
  RETURN doc

IN_RANGE()

IN_RANGE(path, low, high, includeLow, includeHigh)

Match documents where the attribute at path is greater than (or equal to) low and less than (or equal to) high.

low and high can be numbers or strings (technically also null, true and false), but the data type must be the same for both.

• path (attribute path expression): the path of the attribute to test in the document
• low (number|string): minimum value of the desired range
• high (number|string): maximum value of the desired range
• includeLow (bool): whether the minimum value shall be included in the range (left-closed interval) or not (left-open interval)
• includeHigh (bool): whether the maximum value shall be included in the range (right-closed interval) or not (right-open interval)
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

If low and high are the same, but includeLow and/or includeHigh is set to false, then nothing will match. If low is greater than high nothing will match either.

To match documents with the attribute value >= 3 and value <= 5 using the default "identity" Analyzer you would write the following query:

FOR doc IN viewName
  SEARCH IN_RANGE(doc.value, 3, 5, true, true)
  RETURN doc.value

This will also match documents which have an array of numbers as value attribute where at least one of the numbers is in the specified boundaries.

Using string boundaries and a text Analyzer allows to match documents which have at least one token within the specified character range:

FOR doc IN valView
  SEARCH ANALYZER(IN_RANGE(doc.value, "a","f", true, false), "text_en")
  RETURN doc

This will match { "value": "bar" } and { "value": "foo bar" } because the b of bar is in the range ("a" <= "b" < "f"), but not { "value": "foo" } because the f of foo is excluded (high is “f” but includeHigh is false).

MIN_MATCH()

MIN_MATCH(expr1, ... exprN, minMatchCount)

Match documents where at least minMatchCount of the specified search expressions are satisfied.

• expr (expression, repeatable): any valid search expression
• minMatchCount (number): minimum number of search expressions that should be satisfied
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

Assuming a View with a text Analyzer, you may use it to match documents where the attribute contains at least two out of three tokens:

FOR doc IN viewName
  SEARCH ANALYZER(MIN_MATCH(doc.text == 'quick', doc.text == 'brown', doc.text == 'fox', 2), "text_en")
  RETURN doc.text

This will match { "text": "the quick brown fox" } and { "text": "some brown fox" }, but not { "text": "snow fox" } which only fulfills one of the conditions.

NGRAM_MATCH()

Introduced in: v3.7.0

NGRAM_MATCH(path, target, threshold, analyzer)

Match documents whose attribute value has an ngram similarity higher than the specified threshold compared to the target value.

The similarity is calculated by counting how long the longest sequence of matching ngrams is, divided by the target’s total ngram count. Only fully matching ngrams are counted.

The ngrams for both attribute and target are produced by the specified Analyzer. It is recommended to use an Analyzer of type ngram with preserveOriginal: false and min equal to max. Increasing the ngram length will increase accuracy, but reduce error tolerance. In most cases a size of 2 or 3 will be a good choice.

• path (attribute path expression|string): the path of the attribute in a document or a string
• target (string): the string to compare against the stored attribute
• threshold (number, optional): value between 0.0 and 1.0. Defaults to 0.7 if none is specified.
• analyzer (string): name of an Analyzer.
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

Given a View indexing an attribute text, a custom ngram Analyzer "bigram" (min: 2, max: 2, preserveOriginal: false, streamType: "utf8") and a document { "text": "quick red fox" }, the following query would match it (with a threshold of 1.0):

FOR doc IN viewName
  SEARCH NGRAM_MATCH(doc.text, "quick fox", "bigram")
  RETURN doc.text

The following will also match (note the low threshold value):

FOR doc IN viewName
  SEARCH NGRAM_MATCH(doc.text, "quick blue fox", 0.4, "bigram")
  RETURN doc.text

The following will not match (note the high threshold value):

FOR doc IN viewName
  SEARCH NGRAM_MATCH(doc.text, "quick blue fox", 0.9, "bigram")
  RETURN doc.text

NGRAM_MATCH() can be called with constant arguments, but for such calls the analyzer argument is mandatory (even for calls inside of a SEARCH clause):

FOR doc IN viewName
  SEARCH NGRAM_MATCH("quick fox", "quick blue fox", 0.9, "bigram")
  RETURN doc.text

RETURN NGRAM_MATCH("quick fox", "quick blue fox", "bigram")

NGRAM_POSITIONAL_SIMILARITY()

See String Functions.

NGRAM_SIMILARITY()

See String Functions.

PHRASE()

PHRASE(path, phrasePart, analyzer)

PHRASE(path, phrasePart1, skipTokens1, ... phrasePartN, skipTokensN, analyzer)

Search for a phrase in the referenced attribute. It only matches documents in which the tokens appear in the specified order. To search for tokens in any order use TOKENS() instead.

The phrase can be expressed as an arbitrary number of phraseParts separated by skipTokens number of tokens (wildcards), either as separate arguments or as array as second argument.

• path (attribute path expression): the attribute to test in the document
• phrasePart (string|array|object): text to search for in the tokens. Can also be an array comprised of string, array and object tokens (object tokens introduced in v3.7.0, see below) or tokens interleaved with numbers of skipTokens (introduced in v3.6.0). The specified analyzer is applied to string and array tokens, but not for object tokens.
• skipTokens (number, optional): amount of tokens to treat as wildcards
• analyzer (string, optional): name of an Analyzer. Uses the Analyzer of a wrapping ANALYZER() call if not specified or defaults to "identity"
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

Object tokens:

• {IN_RANGE: [low, high, includeLow, includeHigh]}: see IN_RANGE(). low and high can only be strings.
• {LEVENSHTEIN_MATCH: [token, maxDistance, transpositions, maxTerms]}:
  • token (string): a string to search
  • maxDistance (number): maximum Levenshtein / Damerau-Levenshtein distance
  • transpositions (bool, optional): if set to false, a Levenshtein distance is computed, otherwise a Damerau-Levenshtein distance (default)
  • maxTerms (number, optional): consider only a specified number of the most relevant terms. One can pass 0 to consider all matched terms, but it may impact performance negatively. The default value is 64.
• {STARTS_WITH: [prefix]}: see STARTS_WITH(). Array brackets are optional
• {TERM: [token]}: equal to token but without Analyzer tokenization. Array brackets are optional
• {TERMS: [token1, ..., tokenN]}: one of token1, ..., tokenN can be found in specified position. Inside an array the object syntax can be replaced with the object field value, e.g., [..., [token1, ..., tokenN], ...].
• {WILDCARD: [token]}: see LIKE(). Array brackets are optional

An array token inside an array can be used in the TERMS case only.

Given a View indexing an attribute text with the "text_en" Analyzer and a document { "text": "Lorem ipsum dolor sit amet, consectetur adipiscing elit" }, the following query would match it:

FOR doc IN viewName
  SEARCH PHRASE(doc.text, "lorem ipsum", "text_en")
  RETURN doc.text

However, this search expression does not because the tokens "ipsum" and "lorem" do not appear in this order:

PHRASE(doc.text, "ipsum lorem", "text_en")

To match "ipsum" and "amet" with any two tokens in between, you can use the following search expression:

PHRASE(doc.text, "ipsum", 2, "amet", "text_en")

The skipTokens value of 2 defines how many wildcard tokens have to appear between ipsum and amet. A skipTokens value of 0 means that the tokens must be adjacent. Negative values are allowed, but not very useful. These three search expressions are equivalent:

PHRASE(doc.text, "lorem ipsum", "text_en")
PHRASE(doc.text, "lorem", 0, "ipsum", "text_en")
PHRASE(doc.text, "ipsum", -1, "lorem", "text_en")

PHRASE(path, [ phrasePart1, skipTokens1, ... phrasePartN, skipTokensN ], analyzer)

The PHRASE() function also accepts an array as second argument with phrasePart and skipTokens parameters as elements.

FOR doc IN myView SEARCH PHRASE(doc.title, ["quick brown fox"], "text_en") RETURN doc
FOR doc IN myView SEARCH PHRASE(doc.title, ["quick", "brown", "fox"], "text_en") RETURN doc

This syntax variation enables the usage of computed expressions:

LET proximityCondition = [ "foo", ROUND(RAND()*10), "bar" ]
FOR doc IN viewName
  SEARCH PHRASE(doc.text, proximityCondition, "text_en")
  RETURN doc

LET tokens = TOKENS("quick brown fox", "text_en") // ["quick", "brown", "fox"]
FOR doc IN myView SEARCH PHRASE(doc.title, tokens, "text_en") RETURN doc

Above example is equivalent to the more cumbersome and static form:

FOR doc IN myView SEARCH PHRASE(doc.title, "quick", 0, "brown", 0, "fox", "text_en") RETURN doc

You can optionally specify the number of skipTokens in the array form before every string element:

FOR doc IN myView SEARCH PHRASE(doc.title, ["quick", 1, "fox", "jumps"], "text_en") RETURN doc

It is the same as the following:

FOR doc IN myView SEARCH PHRASE(doc.title, "quick", 1, "fox", 0, "jumps", "text_en") RETURN doc

Using object tokens STARTS_WITH, WILDCARD, LEVENSHTEIN_MATCH, TERMS and IN_RANGE:

FOR doc IN myView SEARCH PHRASE(doc.title,
  {STARTS_WITH: ["qui"]}, 0,
  {WILDCARD: ["b%o_n"]}, 0,
  {LEVENSHTEIN_MATCH: ["foks", 2]}, 0,
  {TERMS: ["jump", "run"]}, 0, // Analyzer not applied!
  {IN_RANGE: ["over", "through", true, false]},
  "text_en") RETURN doc

Note that the text_en Analyzer has stemming enabled, but for object tokens the Analyzer isn’t applied. {TERMS: ["jumps", "runs"]} would not match the indexed (and stemmed!) attribute value. Therefore, the trailing s which would be stemmed away is removed from both words manually in the example.

Above example is equivalent to:

FOR doc IN myView SEARCH PHRASE(doc.title,
[
  {STARTS_WITH: "qui"}, 0,
  {WILDCARD: "b%o_n"}, 0,
  {LEVENSHTEIN_MATCH: ["foks", 2]}, 0,
  ["jumps", "runs"], 0, // Analyzer is applied using this syntax
  {IN_RANGE: ["over", "through", true, false]}
], "text_en") RETURN doc

STARTS_WITH()

STARTS_WITH(path, prefix)

Match the value of the attribute that starts with prefix. If the attribute is processed by a tokenizing Analyzer (type "text" or "delimiter") or if it is an array, then a single token/element starting with the prefix is sufficient to match the document.

• path (attribute path expression): the path of the attribute to compare against in the document
• prefix (string): a string to search at the start of the text
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

STARTS_WITH(path, prefixes, minMatchCount)

Introduced in: v3.7.1

Match the value of the attribute that starts with one of the prefixes, or optionally with at least minMatchCount of the prefixes.

• path (attribute path expression): the path of the attribute to compare against in the document
• prefixes (array): an array of strings to search at the start of the text
• minMatchCount (number, optional): minimum number of search prefixes that should be satisfied. The default is 1
• returns nothing: the function can only be called in a SEARCH operation and throws an error otherwise

To match a document { "text": "lorem ipsum..." } using a prefix and the "identity" Analyzer you can use it like this:

FOR doc IN viewName
  SEARCH STARTS_WITH(doc.text, "lorem ip")
  RETURN doc

This query will match { "text": "lorem ipsum" } as well as { "text": [ "lorem", "ipsum" ] } given a View which indexes the text attribute and processes it with the "text_en" Analyzer:

FOR doc IN viewName
  SEARCH ANALYZER(STARTS_WITH(doc.text, "ips"), "text_en")
  RETURN doc.text

For { "text": "lorem ipsum" } it is the same as the following:

FOR doc IN viewName
  SEARCH ANALYZER(STARTS_WITH(doc.text, ["wrong", "ips"], 1), "text_en")
  RETURN doc.text

Or the following:

FOR doc IN viewName
  SEARCH ANALYZER(STARTS_WITH(doc.text, ["lo", "ips", "other"], 2), "text_en")
  RETURN doc.text

Note that it will not match { "text": "IPS (in-plane switching)" } without modification to the query. The prefixes were passed to STARTS_WITH() as-is, but the Analyzer used for indexing has stemming enabled. So the indexes values are the following:

RETURN TOKENS("IPS (in-plane switching)", "text_en")

[
  [
    "ip",
    "in",
    "plane",
    "switch"
  ]
]

The s is removed from ips, which leads to the prefix ips not matching the indexed token ip. You may either create a custom text Analyzer with stemming disabled to avoid this issue, or apply stemming to the prefixes:

FOR doc IN viewName
  SEARCH ANALYZER(STARTS_WITH(doc.text, TOKENS("ips", "text_en")), "text_en")
  RETURN doc.text

LEVENSHTEIN_MATCH()

Introduced in: v3.7.0

LEVENSHTEIN_MATCH(path, target, distance, transpositions, maxTerms)

Match documents with a Damerau-Levenshtein distance lower than or equal to distance between the stored attribute value and target. It can optionally match documents using a pure Levenshtein distance.

See LEVENSHTEIN_DISTANCE() if you want to calculate the edit distance of two strings.

• path (attribute path expression): the path of the attribute to compare against in the document
• target (string): the string to compare against the stored attribute
• distance (number): the maximum edit distance, which can be between 0 and 4 if transpositions is false, and between 0 and 3 if it is true
• transpositions (bool, optional): if set to false, a Levenshtein distance is computed, otherwise a Damerau-Levenshtein distance (default)
• maxTerms (number, optional): consider only a specified number of the most relevant terms. One can pass 0 to consider all matched terms, but it may impact performance negatively. The default value is 64.

The Levenshtein distance between quick and quikc is 2 because it requires two operations to go from one to the other (remove k, insert k at a different position).

FOR doc IN viewName
  SEARCH LEVENSHTEIN_MATCH(doc.text, "quikc", 2, false) // matches "quick"
  RETURN doc.text

The Damerau-Levenshtein distance is 1 (move k to the end).

FOR doc IN viewName
  SEARCH LEVENSHTEIN_MATCH(doc.text, "quikc", 1) // matches "quick"
  RETURN doc.text

You may want to pick the maximum edit distance based on string length. If the stored attribute is the string quick and the target string is quicksands, then the Levenshtein distance is 5, with 50% of the characters mismatching. If the inputs are q and qu, then the distance is only 1, although it is also a 50% mismatch.

LET target = "input"
LET targetLength = LENGTH(target)
LET maxDistance = (targetLength > 5 ? 2 : (targetLength >= 3 ? 1 : 0))
FOR doc IN viewName
  SEARCH LEVENSHTEIN_MATCH(doc.text, target, true, maxDistance)
  RETURN doc.text

LIKE()

Introduced in: v3.7.2

LIKE(path, search)

Check whether the pattern search is contained in the attribute denoted by path, using wildcard matching.

• path (attribute path expression): the path of the attribute to compare against in the document
• search (string): a search pattern that can contain the wildcard characters % (meaning any sequence of characters, including none) and _ (any single character). Literal % and _ must be escaped with two backslashes (four in arangosh).

FOR doc IN viewName
  SEARCH ANALYZER(LIKE(doc.text, "foo%b_r"), "text_en")
  RETURN doc.text

LIKE can also be used in operator form:

FOR doc IN viewName
  SEARCH ANALYZER(doc.text LIKE "foo%b_r", "text_en")
  RETURN doc.text

TOKENS()

See String Functions.

Scoring Functions

Scoring functions return a ranking value for the documents found by a SEARCH operation. The better the documents match the search expression the higher the returned number.

The first argument to any scoring function is always the document emitted by a FOR operation over an ArangoSearch View.

To sort the result set by relevance, with the more relevant documents coming first, sort in descending order by the score (e.g. SORT BM25(...) DESC).

You may calculate custom scores based on a scoring function using document attributes and numeric functions (e.g. TFIDF(doc) * LOG(doc.value)):

FOR movie IN imdbView
  SEARCH PHRASE(movie.title, "Star Wars", "text_en")
  SORT BM25(movie) * LOG(movie.runtime + 1) DESC
  RETURN movie

Sorting by more than one score is allowed. You may also sort by a mix of scores and attributes from multiple Views as well as collections:

FOR a IN viewA
  FOR c IN coll
    FOR b IN viewB
      SORT TFIDF(b), c.name, BM25(a)
      ...

BM25()

BM25(doc, k, b) → score

Sorts documents using the Best Matching 25 algorithm (BM25).

• doc (document): must be emitted by FOR ... IN viewName
• k (number, optional): calibrates the text term frequency scaling. The default is 1.2. A k value of 0 corresponds to a binary model (no term frequency), and a large value corresponds to using raw term frequency
• b (number, optional): determines the scaling by the total text length. The default is 0.75. At the extreme values of the coefficient b, BM25 turns into the ranking functions known as:
  • BM11 for b = 1 (corresponds to fully scaling the term weight by the total text length)
  • BM15 for b = 0 (corresponds to no length normalization)
• returns score (number): computed ranking value

Sorting by relevance with BM25 at default settings:

FOR doc IN viewName
  SEARCH ...
  SORT BM25(doc) DESC
  RETURN doc

Sorting by relevance, with double-weighted term frequency and with full text length normalization:

FOR doc IN viewName
  SEARCH ...
  SORT BM25(doc, 2.4, 1) DESC
  RETURN doc

TFIDF()

TFIDF(doc, normalize) → score

Sorts documents using the term frequency–inverse document frequency algorithm (TF-IDF).

• doc (document): must be emitted by FOR ... IN viewName
• normalize (bool, optional): specifies whether scores should be normalized. The default is false.
• returns score (number): computed ranking value

Sort by relevance using the TF-IDF score:

FOR doc IN viewName
  SEARCH ...
  SORT TFIDF(doc) DESC
  RETURN doc

Sort by relevance using a normalized TF-IDF score:

FOR doc IN viewName
  SEARCH ...
  SORT TFIDF(doc, true) DESC
  RETURN doc

Sort by the value of the text attribute in ascending order, then by the TFIDF score in descending order where the attribute values are equivalent:

FOR doc IN viewName
  SEARCH ...
  SORT doc.text, TFIDF(doc) DESC
  RETURN doc