ArangoSearch functions in AQL

You can form search expressions by composing ArangoSearch function calls, logical operators and comparison operators. This allows you to filter Views as well as to utilize inverted indexes to filter collections.

The AQL SEARCH operation accepts search expressions, such as PHRASE(doc.text, "foo bar", "text_en"), for querying Views. You can combine ArangoSearch filter and context functions as well as operators like AND and OR to form complex search conditions. Similarly, the FILTER operation accepts such search expressions when using inverted indexes.

Scoring functions allow you to rank matches and to sort results by relevance. They are limited to Views.

Search highlighting functions let you retrieve the string positions of matches. They are limited to Views.

You can use most functions also without an inverted index or a View and the SEARCH keyword, but then they are not accelerated by an index.

See Information Retrieval with ArangoSearch for an introduction.

Context Functions

ANALYZER()

ANALYZER(expr, analyzer) → retVal

Sets the Analyzer for the given search expression.

The default Analyzer is identity for any search expression that is used for filtering arangosearch Views. 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 retVal (any): the expression result that it wraps

Example: Using a custom Analyzer

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

Example: Setting the Analyzer context with and without ANALYZER()

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

Example: Analyzer precedence and specifics of the TOKENS() function

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 cannot 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. This is because the TOKENS() function is a regular string function that does not take the Analyzer context into account:

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

BOOST()

BOOST(expr, boost) → retVal

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 retVal (any): the expression result that it wraps

Example: Boosting a search sub-expression

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
  }
]

Filter Functions

EXISTS()

Testing for attribute presence

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 evaluates to a boolean, but this value cannot be returned. The function can only be called in a search expression. It throws an error if used outside of a SEARCH operation or a FILTER operation that uses an inverted index.

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

Testing for attribute type

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"
  • "type" (matches null, boolean, and numeric values)
  • "string"
  • "analyzer" (see below)
• returns nothing: the function evaluates to a boolean, but this value cannot be returned. The function can only be called in a search expression. It throws an error if used outside of a SEARCH operation or a FILTER operation that uses an inverted index.

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

Testing for Analyzer index status

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 evaluates to a boolean, but this value cannot be returned. The function can only be called in a search expression. It throws an error if used outside of a SEARCH operation or a FILTER operation that uses an inverted index.

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

Testing for nested fields

EXISTS(path, "nested")

Match documents where the attribute at path is present and is indexed as a nested field for nested search with Views or inverted indexes.

• path (attribute path expression): the attribute to test in the document
• type (string): string literal "nested"
• returns nothing: the function evaluates to a boolean, but this value cannot be returned. The function can only be called in a search expression. It throws an error if used outside of a SEARCH operation or a FILTER operation that uses an inverted index.

Examples

Only return documents from the View viewName whose text attribute is indexed as a nested field:

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

Only return documents whose attr attribute and its nested text attribute are indexed as nested fields:

FOR doc IN viewName
  SEARCH doc.attr[? FILTER EXISTS(CURRENT.text, "nested")]
  RETURN doc

Only return documents from the collection coll whose text attribute is indexed as a nested field by an inverted index:

FOR doc IN coll OPTIONS { indexHint: "inv-idx", forceIndexHint: true }
  FILTER EXISTS(doc.text, "nested")
  RETURN doc

Only return documents whose attr attribute and its nested text attribute are indexed as nested fields:

FOR doc IN coll OPTIONS { indexHint: "inv-idx", forceIndexHint: true }
  FILTER doc.attr[? FILTER EXISTS(CURRENT.text, "nested")]
  RETURN doc

IN_RANGE()

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

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

You can use IN_RANGE() for searching more efficiently compared to an equivalent expression that combines two comparisons with a logical conjunction:

• IN_RANGE(path, low, high, true, true) instead of low <= value AND value <= high
• IN_RANGE(path, low, high, true, false) instead of low <= value AND value < high
• IN_RANGE(path, low, high, false, true) instead of low < value AND value <= high
• IN_RANGE(path, low, high, false, false) instead of low < value AND value < 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.

There is a corresponding IN_RANGE() Miscellaneous Function that is used outside of SEARCH operations.

• 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 included (bool): whether value is in the range

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.

Example: Using numeric ranges

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.

Example: Using string ranges

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) → fulfilled

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

There is a corresponding MIN_MATCH() Miscellaneous function that is used outside of SEARCH operations.

• expr (expression, repeatable): any valid search expression
• minMatchCount (number): minimum number of search expressions that should be satisfied
• returns fulfilled (bool): whether at least minMatchCount of the specified expressions are true

Example: Matching a subset of search sub-expressions

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:

LET t = TOKENS("quick brown fox", "text_en")
FOR doc IN viewName
  SEARCH ANALYZER(MIN_MATCH(doc.text == t[0], doc.text == t[1], doc.text == t[2], 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.

Note that you can also use the AT LEAST array comparison operator in the specific case of matching a subset of tokens against a single attribute:

FOR doc IN viewName
  SEARCH ANALYZER(TOKENS("quick brown fox", "text_en") AT LEAST (2) == doc.text, "text_en")
  RETURN doc.text

MINHASH_MATCH()

MINHASH_MATCH(path, target, threshold, analyzer) → fulfilled

Match documents with an approximate Jaccard similarity of at least the threshold, approximated with the specified minhash Analyzer.

To only compute the MinHash signatures, see the MINHASH() Miscellaneous function.

• path (attribute path expression|string): the path of the attribute in a document or a string
• target (string): the string to hash with the specified Analyzer and to compare against the stored attribute
• threshold (number, optional): a value between 0.0 and 1.0.
• analyzer (string): the name of a minhash Analyzer.
• returns fulfilled (bool): true if the approximate Jaccard similarity is greater than or equal to the specified threshold, false otherwise

Example: Find documents with a text similar to a target text

Assuming a View with a minhash Analyzer, you can use the stored MinHash signature to find candidates for the more expensive Jaccard similarity calculation:

LET target = "the quick brown fox jumps over the lazy dog"
LET targetSignature = TOKENS(target, "myMinHash")

FOR doc IN viewName
  SEARCH MINHASH_MATCH(doc.text, target, 0.5, "myMinHash") // approximation
  LET jaccard = JACCARD(targetSignature, TOKENS(doc.text, "myMinHash"))
  FILTER jaccard > 0.75
  SORT jaccard DESC
  RETURN doc.text

NGRAM_MATCH()

NGRAM_MATCH(path, target, threshold, analyzer) → fulfilled

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

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

The n-grams for both attribute and target are produced by the specified Analyzer. Increasing the n-gram length will increase accuracy, but reduce error tolerance. In most cases a size of 2 or 3 will be a good choice.

Also see the String Functions NGRAM_POSITIONAL_SIMILARITY() and NGRAM_SIMILARITY() for calculating n-gram similarity that cannot be accelerated by a View index.

• 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): a value between 0.0 and 1.0. Defaults to 0.7 if none is specified.
• analyzer (string): the name of an Analyzer.
• returns fulfilled (bool): true if the evaluated n-gram similarity value is greater than or equal to the specified threshold, false otherwise

Example: Using a custom bigram Analyzer

Given a View indexing an attribute text, a custom n-gram 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

Example: Using constant values

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")

PHRASE()

PHRASE(path, phrasePart, analyzer)

PHRASE(path, phrasePart1, skipTokens1, ... phrasePartN, skipTokensN, 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, or tokens interleaved with numbers of skipTokens. 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 evaluates to a boolean, but this value cannot be returned. The function can only be called in a search expression. It throws an error if used outside of a SEARCH operation or a FILTER operation that uses an inverted index.

Object tokens

• {IN_RANGE: [low, high, includeLow, includeHigh]}: see IN_RANGE(). low and high can only be strings.
• {LEVENSHTEIN_MATCH: [token, maxDistance, transpositions, maxTerms, prefix]}:
  • 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.
  • prefix (string, optional): if defined, then a search for the exact prefix is carried out, using the matches as candidates. The Levenshtein / Damerau-Levenshtein distance is then computed for each candidate using the remainders of the strings. This option can improve performance in cases where there is a known common prefix. The default value is an empty string (introduced in v3.7.13, v3.8.1).
• {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.

Also see Example: Using object tokens.

Example: Using a text Analyzer for a phrase search

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")

Example: Skip tokens for a proximity search

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")

Example: Using PHRASE() with an array of tokens

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

Example: Handling of arrays with no members

Empty arrays are skipped:

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

The query is equivalent to:

FOR doc IN myView SEARCH PHRASE(doc.title, "quick", 2 "jumps", "text_en") RETURN doc

Providing only empty arrays is valid, but will yield no results.

Example: Using object tokens

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) → startsWith

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.

There is a corresponding STARTS_WITH() String function that is used outside of SEARCH operations.

• 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 startsWith (bool): whether the specified attribute starts with the given prefix

STARTS_WITH(path, prefixes, minMatchCount) → startsWith

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 (see example). The default is 1
• returns startsWith (bool): whether the specified attribute starts with at least minMatchCount of the given prefixes

Example: Searching for an exact value prefix

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

Example: Searching for a prefix in text

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

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 built-in text_en Analyzer used for indexing has stemming enabled. So the indexed 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

Example: Searching for one or multiple prefixes

The STARTS_WITH() function accepts an array of prefix alternatives of which only one has to match:

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

It will match a document { "text": "lorem ipsum" } but also { "text": "that is something" }, as at least one of the words start with a given prefix.

The same query again, but with an explicit minMatchCount:

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

The number can be increased to require that at least this many prefixes must be present:

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

This will still match { "text": "lorem ipsum" } because at least two prefixes (lo and ips) are found, but not { "text": "that is something" } which only contains one of the prefixes (something).

LEVENSHTEIN_MATCH()

LEVENSHTEIN_MATCH(path, target, distance, transpositions, maxTerms, prefix) → fulfilled

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|string): the path of the attribute to compare against in the document or a string
• 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.
• returns fulfilled (bool): true if the calculated distance is less than or equal to distance, false otherwise
• prefix (string, optional): if defined, then a search for the exact prefix is carried out, using the matches as candidates. The Levenshtein / Damerau-Levenshtein distance is then computed for each candidate using the target value and the remainders of the strings, which means that the prefix needs to be removed from target (see example). This option can improve performance in cases where there is a known common prefix. The default value is an empty string (introduced in v3.7.13, v3.8.1).

Example: Matching with and without transpositions

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

Example: Matching with prefix search

Match documents with a Levenshtein distance of 1 with the prefix qui. The edit distance is calculated using the search term kc (quikc with the prefix qui removed) and the stored value without the prefix (e.g. ck). The prefix qui is constant.

FOR doc IN viewName
  SEARCH LEVENSHTEIN_MATCH(doc.text, "kc", 1, false, 64, "qui") // matches "quick"
  RETURN doc.text

You may compute the prefix and suffix from the input string as follows:

LET input = "quikc"
LET prefixSize = 3
LET prefix = LEFT(input, prefixSize)
LET suffix = SUBSTRING(input, prefixSize)
FOR doc IN viewName
  SEARCH LEVENSHTEIN_MATCH(doc.text, suffix, 1, false, 64, prefix) // matches "quick"
  RETURN doc.text

Example: Basing the edit distance on string length

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, maxDistance, true)
  RETURN doc.text

LIKE()

LIKE(path, search) → bool

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

• _: A single arbitrary character
• %: Zero, one or many arbitrary characters
• \\_: A literal underscore
• \\%: A literal percent sign

Searching with the LIKE() function in the context of a SEARCH operation is backed by View indexes. The String LIKE() function is used in other contexts such as in FILTER operations and cannot be accelerated by any sort of index on the other hand. Another difference is that the ArangoSearch variant does not accept a third argument to enable case-insensitive matching. This can be controlled with Analyzers instead.

• 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 backslashes.
• returns bool (bool): true if the pattern is contained in text, and false otherwise

Example: Searching with wildcards

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

Geo functions

The following functions can be accelerated by View indexes. There are corresponding Geo Functions for the regular geo index type, but also general purpose functions such as GeoJSON constructors that can be used in conjunction with ArangoSearch.

GEO_CONTAINS()

Introduced in: v3.8.0

GEO_CONTAINS(geoJsonA, geoJsonB) → bool

Checks whether the GeoJSON object geoJsonA fully contains geoJsonB (every point in B is also in A).

• geoJsonA (object|array): first GeoJSON object or coordinate array (in longitude, latitude order)
• geoJsonB (object|array): second GeoJSON object or coordinate array (in longitude, latitude order)
• returns bool (bool): true when every point in B is also contained in A, false otherwise

GEO_DISTANCE()

Introduced in: v3.8.0

GEO_DISTANCE(geoJsonA, geoJsonB) → distance

Return the distance between two GeoJSON objects, measured from the centroid of each shape.

• geoJsonA (object|array): first GeoJSON object or coordinate array (in longitude, latitude order)
• geoJsonB (object|array): second GeoJSON object or coordinate array (in longitude, latitude order)
• returns distance (number): the distance between the centroid points of the two objects on the reference ellipsoid

GEO_IN_RANGE()

Introduced in: v3.8.0

GEO_IN_RANGE(geoJsonA, geoJsonB, low, high, includeLow, includeHigh) → bool

Checks whether the distance between two GeoJSON objects lies within a given interval. The distance is measured from the centroid of each shape.

• geoJsonA (object|array): first GeoJSON object or coordinate array (in longitude, latitude order)
• geoJsonB (object|array): second GeoJSON object or coordinate array (in longitude, latitude order)
• low (number): minimum value of the desired range
• high (number): maximum value of the desired range
• includeLow (bool, optional): whether the minimum value shall be included in the range (left-closed interval) or not (left-open interval). The default value is true
• includeHigh (bool): whether the maximum value shall be included in the range (right-closed interval) or not (right-open interval). The default value is true
• returns bool (bool): whether the evaluated distance lies within the range

GEO_INTERSECTS()

Introduced in: v3.8.0

GEO_INTERSECTS(geoJsonA, geoJsonB) → bool

Checks whether the GeoJSON object geoJsonA intersects with geoJsonB (i.e. at least one point of B is in A or vice versa).

• geoJsonA (object|array): first GeoJSON object or coordinate array (in longitude, latitude order)
• geoJsonB (object|array): second GeoJSON object or coordinate array (in longitude, latitude order)
• returns bool (bool): true if A and B intersect, false otherwise

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  (Okapi BM25).

• doc (document): must be emitted by FOR ... IN viewName
• k (number, optional): calibrates the text term frequency scaling. The value needs to be non-negative (0.0 or higher), or the returned score is an undefined value that may cause unpredictable results. 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 value needs to be between 0.0 and 1.0 (inclusive), or the returned score is an undefined value that may cause unpredictable results. 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

Example: Sorting by default BM25() score

Sorting by relevance with BM25 at default settings:

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

Example: Sorting with tuned BM25() ranking

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

Example: Sorting by default TFIDF() score

Sort by relevance using the TF-IDF score:

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

Example: Sorting by TFIDF() score with normalization

Sort by relevance using a normalized TF-IDF score:

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

Example: Sort by value and TFIDF()

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

Search Highlighting Functions

ArangoDB Enterprise Edition ArangoGraph

OFFSET_INFO()

OFFSET_INFO(doc, paths) → offsetInfo

Returns the attribute paths and substring offsets of matched terms, phrases, or n-grams for search highlighting purposes.

• doc (document): must be emitted by FOR ... IN viewName
• paths (string|array): a string or an array of strings, each describing an attribute and array element path you want to get the offsets for. Use . to access nested objects, and [n] with n being an array index to specify array elements. The attributes need to be indexed by Analyzers with the offset feature enabled.
• returns offsetInfo (array): an array of objects, limited to a default of 10 offsets per path. Each object has the following attributes:

  • name (array): the attribute and array element path as an array of strings and numbers. You can pass this name to the VALUE() function to dynamically look up the value.

  • offsets (array): an array of arrays with the matched positions. Each inner array has two elements with the start offset and the length of a match.

    The offsets describe the positions in bytes, not characters. You may need to account for characters encoded using multiple bytes.

OFFSET_INFO(doc, rules) → offsetInfo

• doc (document): must be emitted by FOR ... IN viewName
• rules (array): an array of objects with the following attributes:
  • name (string): an attribute and array element path you want to get the offsets for. Use . to access nested objects, and [n] with n being an array index to specify array elements. The attributes need to be indexed by Analyzers with the offset feature enabled.
  • options (object): an object with the following attributes:
    • maxOffsets (number, optional): the total number of offsets to collect per path. Default: 10.
    • limits (object, optional): an object with the following attributes:
      • term (number, optional): the total number of term offsets to collect per path. Default: 2³².
      • phrase (number, optional): the total number of phrase offsets to collect per path. Default: 2³².
      • ngram (number, optional): the total number of n-gram offsets to collect per path. Default: 2³².
• returns offsetInfo (array): an array of objects, each with the following attributes:

  • name (array): the attribute and array element path as an array of strings and numbers. You can pass this name to the VALUE() to dynamically look up the value.

  • offsets (array): an array of arrays with the matched positions, capped to the specified limits. Each inner array has two elements with the start offset and the length of a match.

    The start offsets and lengths describe the positions in bytes, not characters. You may need to account for characters encoded using multiple bytes.

Examples

Search a View and get the offset information for the matches:

db._query(`
  FOR doc IN food_view
    SEARCH ANALYZER(TOKENS("avocado tomato", "text_en_offset") ANY == doc.description.en, "text_en_offset")
    RETURN OFFSET_INFO(doc, ["description.en"])`);

[object ArangoQueryCursor, count: 2, cached: false, hasMore: false]

[ 
  [ 
    { 
      "name" : [ 
        "description", 
        "en" 
      ], 
      "offsets" : [ 
        [ 
          4, 
          7 
        ] 
      ] 
    } 
  ], 
  [ 
    { 
      "name" : [ 
        "description", 
        "en" 
      ], 
      "offsets" : [ 
        [ 
          4, 
          6 
        ], 
        [ 
          38, 
          6 
        ] 
      ] 
    } 
  ] 
]

For full examples, see Search Highlighting.