• how-to
    You can use the Full Text Search service (FTS) to create queryable full-text indexes in Couchbase Server.

    Full Text Search or FTS allows you to create, manage, and query full text indexes on JSON documents stored in Couchbase buckets. It uses natural language processing for querying documents, provides relevance scoring on the results of your queries, and has fast indexes for querying a wide range of possible text searches.

    Some of the supported query types include simple queries like Match and Term queries; range queries like Date Range and Numeric Range; and compound queries for conjunctions, disjunctions, and/or boolean queries.

    The PHP SDK exposes an API for performing FTS queries which abstracts some of the complexity of using the underlying REST API.

    There are two APIs for querying search: cluster.searchQuery(), and Both are also available at the Scope level.

    The former API supports FTS queries (SearchQuery), while the latter additionally supports the VectorSearch added in 7.6. Most of this documentation will focus on the former API, as the latter is in @Stability.Volatile status.

    We will perform an FTS query here - see the [vector search] section for examples of that.


    For the purposes of the below examples we will use the Travel Sample sample bucket with the below Full Text Search index (dynamic mapping for type hotel).

      "type": "fulltext-index",
      "name": "travel-sample-index",
      "uuid": "ea630dfe35e1f415",
      "sourceType": "couchbase",
      "sourceName": "travel-sample",
      "sourceUUID": "8ee9d874356f4c92a63a244f5e34210a",
      "planParams": {
        "maxPartitionsPerPIndex": 171,
        "indexPartitions": 6
      "params": {
        "doc_config": {
          "docid_prefix_delim": "",
          "docid_regexp": "",
          "mode": "type_field",
          "type_field": "type"
        "mapping": {
          "analysis": {},
          "default_analyzer": "standard",
          "default_datetime_parser": "dateTimeOptional",
          "default_field": "_all",
          "default_mapping": {
            "dynamic": true,
            "enabled": true
          "default_type": "_default",
          "docvalues_dynamic": true,
          "index_dynamic": true,
          "store_dynamic": false,
          "type_field": "_type"
        "store": {
          "indexType": "scorch"
      "sourceParams": {}

    Search queries are executed at Cluster level (not bucket or collection). As of Couchbase Server 6.5+ they do also not require a bucket to be opened first. In older versions of Couchbase Server, even though executed at Cluster level, a bucket must be opened before performing queries.

    Here is a simple MatchQuery that looks for the text “swanky” using a defined index:

    $matchQuery = new MatchSearchQuery("swanky");
    $opts = new SearchOptions();
    $res = $cluster->searchQuery("travel-sample-index", $matchQuery, $opts);
    printf("Match query: \"swanky\":\n");
    foreach ($res->rows() as $row) {
        printf("id: %s, score: %f\n", $row['id'], $row['score']);

    All simple query types are created in the same manner, although some have additional properties, which can be seen in common query type descriptions. Couchbase FTS’s range of query types enable powerful searching using multiple options, to ensure results are just within the range wanted. Here is a numeric range query that looks for hotels with "Cleanliness" ratings higher than 5:

    $numericRangeQuery = new NumericRangeSearchQuery();
    $opts = new SearchOptions();
    $res = $cluster->searchQuery("travel-sample-index", $numericRangeQuery, $opts);
    printf("Cleanliness 5+:\n");
    foreach ($res->rows() as $row) {
        printf("id: %s, score: %f\n", $row['id'], $row['score']);

    Queries can also be combined together. A conjunction query contains multiple child queries; its result documents must satisfy all of the child queries:

    $conjunction = new ConjunctionSearchQuery([$matchQuery, $numericRangeQuery]);
    $opts = new SearchOptions();
    $res = $cluster->searchQuery("travel-sample-index", $conjunction, $opts);
    printf("Swanky and with cleanliness 5+:\n");
    foreach ($res->rows() as $row) {
        printf("id: %s, score: %f\n", $row['id'], $row['score']);

    Working with Results

    The result of a search query has three components: rows, facets, and metdata. Rows are the documents that match the query. Facets allow the aggregation of information collected on a particular result set. Metdata holds additional information not directly related to your query, such as success total hits and how long the query took to execute in the cluster.

    Iterating Rows

    Here we are iterating over the rows that were returned in the results (for context, see the same in the two samples below this one):

    foreach ($res->rows() as $row) {
        printf("id: %s, score: %f\n", $row['id'], $row['score']);


    Facets can only be accessed once Close has been called on rows.

    $query = (new TermSearchQuery("beer"))->field("type");
    $options = new SearchOptions();
        "foo" => new TermSearchFacet("name", 3),
        "bar" => (new DateRangeSearchFacet("updated", 1))
                    ->addRange("old", NULL,  mktime(0, 0, 0, 1, 1, 2014)), // "2014-01-01T00:00:00" also acceptable
        "baz" => (new NumericRangeSearchFacet("abv", 2))
                    ->addRange("strong", 4.9, NULL)
                    ->addRange("light", NULL, 4.89)
    $res = $cluster->searchQuery("beer-search", $query, $options);
    $facet = $res->facets()["foo"];
    printf("Term facet \"foo\" on field \"%s\". Total: %d, missing: %d: other: %d\n",
        $facet["field"], $facet["total"], $facet["missing"], $facet["other"]);
    foreach ($facet["terms"] as $term) {
        printf(" * %-5s ... %d\n", $term["term"], $term["count"]);
    $facet = $res->facets()["bar"];
    printf("Date range facet \"bar\" on field \"%s\". Total: %d, missing: %d: other: %d\n",
        $facet["field"], $facet["total"], $facet["missing"], $facet["other"]);
    foreach ($facet["date_ranges"] as $range) {
        printf(" * %-20s ... %d\n", $range["end"], $range["count"]);
    $facet = $res->facets()["baz"];
    printf("Numeric range facet \"baz\" on field \"%s\". Total: %d, missing: %d: other: %d\n",
        $facet["field"], $facet["total"], $facet["missing"], $facet["other"]);
    foreach ($facet["numeric_ranges"] as $range) {
        if (isset($range["max"])) {
            printf(" * max %-4s ... %d\n", $range["max"], $range["count"]);
        } else {
            printf(" * min %-4s ... %d\n", $range["min"], $range["count"]);

    Scoped vs Global Indexes

    The FTS APIs exist at both the Cluster and Scope levels.

    This is because FTS supports, as of Couchbase Server 7.6, a new form of "scoped index" in addition to the traditional "global index".

    It’s important to use the Cluster.searchQuery() / for global indexes, and for scoped indexes.

    As of Couchbase Server 7.6, the FTS service supports vector search in additional to traditional full text search queries.


    Single vector query

    In this first example we are performing a single vector query:

    $request = SearchRequest::build(VectorSearch::build([
        VectorQuery::build("vector_field", $vectorQuery)
    $result = $scope->search("vector-index", $request);

    Let’s break this down. We create a SearchRequest, which can contain a traditional FTS query SearchQuery and/or the new VectorSearch. Here we are just using the latter.

    The VectorSearch allows us to perform one or more VectorQuery s.

    The VectorQuery itself takes the name of the document field that contains embedded vectors ("vector_field" here), plus actual vector query in the form of a float[].

    (Note that Couchbase itself is not involved in generating the vectors, and these will come from an external source such as an embeddings API.)

    Finally we execute the SearchRequest against the FTS index "vector-index", which has previously been setup to vector index the "vector_field" field.

    This happens to be a scoped index so we are using If it was a global index we would use instead - see Scoped vs Global Indexes.

    It returns the same SearchResult detailed earlier.

    Multiple vector queries

    You can run multiple vector queries together:

    $request = SearchRequest::build(VectorSearch::build([
        VectorQuery::build("vector_field", $vectorQuery)->numCandidates(2)->boost(0.3),
        VectorQuery::build("vector_field", $anotherVectorQuery)->numCandidates(5)->boost(0.7)
    $result = $scope->search("vector-index", $request);

    How the results are combined (ANDed or ORed) can be controlled with VectorSearchOptions→vectorQueryCombination().

    Combining FTS and vector queries

    You can combine a traditional FTS query with vector queries:

    $request = SearchRequest::build(MatchAllSearchQuery::build());
        VectorQuery::build("vector_field", $vectorQuery)
    $result = $scope->search("vector-and-fts-index", $request);

    FTS queries

    And note that traditional FTS queries, without vector search, are also supported with the new / APIs:

    $request = SearchRequest::build(MatchAllSearchQuery::build());
    $result = $scope->search("travel-sample-index", $request);

    The SearchQuery is created in the same way as detailed earlier.


    Like the Couchbase Query Service, FTS allows RequestPlus queries — Read-Your-Own_Writes (RYOW) consistency, ensuring results contain information from updated indexes:

    // Create new hotel document and demonstrate query with consistency requirement
    $scope = $cluster->bucket('travel-sample')->scope('inventory');
    $collection = $scope->collection('hotel');
    $hotel = [
        "name" => "super hotel",
        "reviews" => [
                "content" => "Super swanky hotel!",
                "ratings" => [
                    "Cleanliness" => 6
    $res = $collection->upsert("a-new-hotel", $hotel);
    $mutationState = new MutationState();
    $opts = new SearchOptions();
    $opts->consistentWith("travel-sample-index", $mutationState);
    $res = $cluster->searchQuery("travel-sample-index", $matchQuery, $opts);
    printf("Match query: \"swanky\":\n");
    foreach ($res->rows() as $row) {
        printf("id: %s, score: %f\n", $row['id'], $row['score']);

    Further Reading