`vectorSearch` (MongoDB Search Operator)

vectorSearch (MongoDB Search Operator)

For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt

`vectorSearch` (MongoDB Search Operator)

Definition

The vectorSearch operator performs an ANN (Approximate Nearest Neighbor) or ENN (Exact Nearest Neighbor) search on vector embeddings in the specified field. Use this operator to add analyzed text capabilities such as fuzzy search, phrase matching, location filtering, wildcard pattern matching, and more alongside semantic search.

Syntax

vectorSearch has the following syntax:

{
  "$search": {
    "index": "<index name>", // optional, defaults to "default"
    "vectorSearch": {
      "exact": true | false,
      "filter": {<operator-specification>},
      "limit": <number-of-results>,
      "numCandidates": <number-of-candidates>,
      "path": "<field-to-search>",
      "queryVector": [<array-of-numbers>],
      "score": {<options>}
    }
  }
}

Options

vectorSearch uses the following fields to construct a query:

Field	Type	Necessity	Description
`exact`	Boolean	Optional	This is required if `numCandidates` is omitted. Flag that specifies whether to run ENN (Exact Nearest Neighbor) or ANN (Approximate Nearest Neighbor) search. Value can be one of the following: `false` - to run ANN (Approximate Nearest Neighbor) search; `true` - to run ENN (Exact Nearest Neighbor) search If omitted, defaults to `false`. MongoDB Vector Search supports ANN (Approximate Nearest Neighbor) search on Atlas clusters running MongoDB v6.0.11, v7.0.2, or later and ENN (Exact Nearest Neighbor) search on clusters running MongoDB v6.0.16, v7.0.10, v7.3.2, or later. You can also use MongoDB Vector Search with [self-managed](https://www.mongodb.com/docs/manual/administration/install-community.md#std-label-install-mdb-community-edition or local Atlas deployments that you create with the Atlas CLI.
`filter`	Object	Optional	MongoDB Search [operator](https://www.mongodb.com/docs/search/query/operators-collectors/overview.md#std-label-operators-ref to use to pre-filter documents based on metadata or specific search criteria. To learn more, see [Pre-Filter.](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-pre-filter
`limit`	Int	Required	Number (of type `int` only) of documents to return in the results. This value can't exceed the value of `numCandidates` if you specify `numCandidates`. On sharded clusters, you must use [`$limit`](https://www.mongodb.com/docs/manual/reference/operator/aggregation/limit.md#mongodb-pipeline-pipe.-limit after the [`$search`](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search stage to limit the number of documents in the results.
`numCandidates`	Int	Optional	This field is required if `exact` is `false` or omitted. Number of nearest neighbors to use during the search. Value must be less than or equal to (`<=`) `10000`. You can't specify a number less than the number of documents to return (`limit`). We recommend that you specify a number at least 20 times higher than the number of documents to return (`limit`) to increase accuracy. This overrequest pattern is the recommended way to trade off latency and :recall in your ANN (Approximate Nearest Neighbor) searches, and we recommend tuning this parameter based on your specific dataset size and query requirements.
`path`	String	Required	Indexed [vector](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-bson-data-types-vector type field to search.
`queryVector`	Array of Integers or Floats	Required	Array of numbers of `float32`, BSON (Binary Javascript Object Notation) [BinData](https://www.mongodb.com/docs/manual/reference/method/BinData.md#std-label-server-binData-method vectors with subtype `float32`, or BSON (Binary Javascript Object Notation) [BinData](https://www.mongodb.com/docs/manual/reference/method/BinData.md#std-label-server-binData-method vectors with subtype `int1` or `int8` type that represent the query vector. To learn more about generating BSON (Binary Javascript Object Notation) `binData` vectors with subtype `float32`, `int8`, or `int1`, see [How to Ingest Pre-Quantized Vectors.](https://www.mongodb.com/docs/vector-search/about/vector-quantization.md#std-label-avs-bindata-vector-subtype The array size must match the number of vector dimensions (`numDimensions`) specified in the [index definition](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-bson-data-types-vector for the field. You must embed your query with the same model that you used to embed the data. You can query your embeddings with full-fidelity vectors, as long as the vector subtype is the same. This is only possible with `binData` vectors with subtype `float32`. If you use any other subtype (`int8` or `int1`), MongoDB Search doesn't return any results or errors.
`score`	Object	Optional	Score assigned to matching search term results. Use one of the following options to modify the score: `boost`: multiply the result score by the given number.; `constant`: replace the result score with the given number.; `function`: replace the result score using the given expression. For information on using `score` in your query, see [Score the Documents in the Results.](https://www.mongodb.com/docs/search/query/score/overview.md#std-label-scoring-ref

Behavior

MongoDB Search Index

You must index the fields to search using the vectorSearch operator. You can index the following types of fields in the MongoDB Search index definition:

Field that contains vector embeddings. This is the field you specify in the query path option.
Fields to pre-filter the documents. These are the fields you specify in the query filter option.

Pre-Filter

You can pre-filter the documents to narrow the scope of your semantic search. Use any supported MongoDB Search operator in the filter field to query and filter the documents.

Important:

Filtered queries are typically slower than an otherwise equivalent unfiltered query.

You must index the fields that you want to filter your data in the index definition for the vector type.

Score

You can include the score of each document in your search results. Specify the [$meta](https://www.mongodb.com/docs/manual/reference/operator/aggregation/meta.md#mongodb-expression-exp.-meta expression with the searchScore value in the [$project](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project stage. You can also specify the searchScoreDetails value for the scoreDetails field [$meta](https://www.mongodb.com/docs/manual/reference/operator/aggregation/meta.md#mongodb-expression-exp.-meta expression for a detailed breakdown of the score.

To learn more, see [Score the Documents in the Results](https://www.mongodb.com/docs/search/query/score/overview.md#std-label-scoring-ref and [Return the Score Details.](https://www.mongodb.com/docs/search/query/score/get-details.md#std-label-fts-score-details

`vectorSearch` Operator Limitations

The vectorSearch operator must be the top-level operator in your queries. Therefore, you can't use the vectorSearch operator inside the following MongoDB Search [operators:](https://www.mongodb.com/docs/search/query/operators-collectors/overview.md#std-label-operators-ref

[embeddedDocument](https://www.mongodb.com/docs/search/query/operators-collectors/embedded-document.md#std-label-embedded-document-ref
[compound](https://www.mongodb.com/docs/search/query/operators-collectors/compound.md#std-label-compound-ref
[facet](https://www.mongodb.com/docs/search/query/operators-collectors/facet.md#std-label-fts-facet-ref

You can't use the vectorSearch operator to query fields indexed using a [vectorSearch](https://www.mongodb.com/docs/vector-search/indexes/vector-search-type.md#std-label-avs-types-vector-search type index.

You can't use the following [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search options with the vectorSearch operator:

[highlight](https://www.mongodb.com/docs/search/query/highlighting.md#std-label-highlight-ref
[sort](https://www.mongodb.com/docs/search/query/sort.md#std-label-sort-ref
[searchSequenceToken](https://www.mongodb.com/docs/search/query/paginate-results.md#std-label-fts-paginate-results

Instead, use [$skip](https://www.mongodb.com/docs/manual/reference/operator/aggregation/skip.md#mongodb-pipeline-pipe.-skip and [$limit](https://www.mongodb.com/docs/manual/reference/operator/aggregation/limit.md#mongodb-pipeline-pipe.-limit after the [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search stage.

You can't run [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search queries with the vectorSearch operator in the MongoDB Search Playground.

Examples

The following examples use the sample_mflix.embedded_movies collection from the sample data. If you [load the sample data](https://www.mongodb.com/docs/atlas/import/load-sample-data.md#std-label-sample-data and create the [sample index](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-fts-vector-field-type-example on the collection, you can run the following ANN (Approximate Nearest Neighbor) and ENN (Exact Nearest Neighbor) queries against the collection after replacing the <connection-string> and <index-name> placeholders in the queries.

The following query uses vectorSearch operator in the [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search stage to pre-filter the movie documents and then perform an ANN (Approximate Nearest Neighbor) search for movies. Specifically, the query:

Uses the [compound Operator](https://www.mongodb.com/docs/search/query/operators-collectors/compound.md#std-label-compound-ref to pre-filter the documents by:
Movies that contain the terms courtroom or lawyer in the fullplot field
Movies that were released between 2000 and 2015 in the year field
Searches using the 2048 dimension vector embeddings generated from the voyage-3-large model for the string intense courtroom drama.
Considers up to 1000 nearest neighbors.
Limits the output to 10 documents only.
Sets concurrent to true to improve query latency.

```javascript [ { "$search": { "index": "<index-name>", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "compound": { "should": [{ "queryString": { "defaultPath": "fullplot", "query": "plot:courtroom OR lawyer" } }, { "range": { "path": "year", "gte": 2000, "lte": 2015 } }] } }, "queryVector": [-0.03177440166473389, 0.0047661601565778255, 0.007770913653075695, -0.027906212955713272, 0.0038336506113409996, -0.01650887355208397, 0.02196578122675419, -0.011397339403629303, -0.006458492483943701, -0.015956275165081024, -0.002452154876664281, 0.0014160331338644028, 0.032603297382593155, 0.04089227318763733, -0.03177440166473389, -0.03329404816031456, -0.004282636567950249, 0.007321927230805159, -0.03868187963962555, 0.02099873498082161, 0.014989228919148445, -0.010499367490410805, 0.024866923689842224, 0.03329404816031456, 0.031221803277730942, -0.02680101804435253, -0.007460076827555895, 0.056917622685432434, 0.03218885138630867, -0.0011008793953806162, 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