> 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: ```json { "$search": { "index": "", // optional, defaults to "default" "vectorSearch": { "exact": true | false, "filter": {}, "limit": , "numCandidates": , "path": "", "queryVector": [], "score": {} } } } ``` ## Options `vectorSearch` uses the following fields to construct a query: | Field | Type | Necessity | Description | | --- | --- | --- | --- | | `exact` | Boolean | Optional | This is required if `numCandidates` is omitted. Flag that specifies whether to run ENN (Exact Nearest Neighbor) or ANN (Approximate Nearest Neighbor) search. Value can be one of the following: `false` - to run ANN (Approximate Nearest Neighbor) search; `true` - to run ENN (Exact Nearest Neighbor) search If omitted, defaults to `false`. MongoDB Vector Search supports ANN (Approximate Nearest Neighbor) search on Atlas clusters running MongoDB v6.0.11, v7.0.2, or later and ENN (Exact Nearest Neighbor) search on clusters running MongoDB v6.0.16, v7.0.10, v7.3.2, or later. You can also use MongoDB Vector Search with [self-managed](https://www.mongodb.com/docs/manual/administration/install-community.md#std-label-install-mdb-community-edition) or [local Atlas deployments](https://www.mongodb.com/docs/atlas/cli/current/atlas-cli-deploy-local/) that you create with the Atlas CLI. | | `filter` | Object | Optional | MongoDB Search [operator](https://www.mongodb.com/docs/search/query/operators-collectors/overview.md#std-label-operators-ref) to use to pre-filter documents based on metadata or specific search criteria. To learn more, see [Pre-Filter.](https://www.mongodb.com/docs/search/query/operators-collectors/vectorsearch.md#std-label-fts-vectorSearch-pre-filter) | | `limit` | Int | Required | Number (of type `int` only) of documents to return in the results. This value can't exceed the value of `numCandidates` if you specify `numCandidates`. On sharded clusters, you must use [`$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 [`$search`](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search) queries with the `vectorSearch` operator in the MongoDB Search Playground. ## Examples The following examples use the `sample_mflix.embedded_movies` collection from the sample data. If you [load the sample data](https://www.mongodb.com/docs/atlas/import/load-sample-data.md#std-label-sample-data) and create the [sample index](https://www.mongodb.com/docs/search/indexes/field-types/vector-type.md#std-label-fts-vector-field-type-example) on the collection, you can run the following ANN (Approximate Nearest Neighbor) and ENN (Exact Nearest Neighbor) queries against the collection after replacing the `` and `` placeholders in the queries. The following query uses `vectorSearch` operator in the [`$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": "", "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, 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-0.031498104333877563, 3.885456681018695e-05, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382], "limit": 10, "numCandidates": 1000 }, "concurrent": true } } ] ``` **Output:** ```javascript SCORE: 0.8064572811126709 _id: "573a139af29313caabcef3ea" plot: "A Harvard professor is lured back into the courtroom after 25 years to…" title: "Just Cause" SCORE: 0.7989137172698975 _id: "573a139df29313caabcfa561" plot: "An attorney defends an officer on trial for ordering his troops to fir…" title: "Rules of Engagement" SCORE: 0.7935066223144531 _id: "573a13d8f29313caabda84a9" plot: "A defense attorney begins to suspect that there might be more to her c…" title: "Wer" SCORE: 0.7793526649475098 _id: "573a13b6f29313caabd466b9" plot: "Jang is a homicide detective who likes to use violence with criminals,…" title: "Running Wild" SCORE: 0.7697937488555908 _id: "573a139af29313caabcf0e97" plot: "A lawyer becomes a target by a corrupt politician and his NSA goons wh…" title: "Enemy of the State" SCORE: 0.7680296897888184 _id: "573a13e4f29313caabdc160e" plot: "A French police magistrate spends years trying to take down one of the…" title: "The Connection" SCORE: 0.767859697341919 _id: "573a13cff29313caabd89a51" plot: "As a writer stymied by past success, writers block, substance abuse, r…" title: "The Adderall Diaries" SCORE: 0.7603211402893066 _id: "573a13b4f29313caabd3f550" plot: "A cop, a lawyer, and an assassin cross paths after the murder of a fed…" title: "Divergence" SCORE: 0.758400440216064 _id: "573a13cff29313caabd8aae0" plot: "In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the law…" title: "Two Rabbits" SCORE: 0.7571885585784912 _id: "573a13dbf29313caabdae597" plot: "A hilarious cat and mouse chase and mind game between two highly intel…" title: "Julayi" ``` 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 [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "compound": { "should": [{ "queryString": { "defaultPath": "fullplot", "query": "plot:courtroom OR lawyer" } }, { "range": { "path": "year", "gte": 2000, "lte": 2015 } }] } }, "queryVector": [-0.03177440166473389, 0.0047661601565778255, 0.007770913653075695, 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-0.031498104333877563, 3.885456681018695e-05, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382], "limit": 10, "numCandidates": 1000 }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ] ``` **Output:** ```javascript plot: "A Harvard professor is lured back into the courtroom after 25 years to…" title: "Just Cause" score: 0.8064572811126709 plot: "An attorney defends an officer on trial for ordering his troops to fir…" title: "Rules of Engagement" score: 0.7989137172698975 plot: "A defense attorney begins to suspect that there might be more to her c…" title: "Wer" score: 0.7935066223144531 plot: "Jang is a homicide detective who likes to use violence with criminals,…" title: "Running Wild" score: 0.7793526649475098 plot: "A lawyer becomes a target by a corrupt politician and his NSA goons wh…" title: "Enemy of the State" score: 0.7697937488555908 plot: "A French police magistrate spends years trying to take down one of the…" title: "The Connection" score: 0.7680296897888184 plot: "As a writer stymied by past success, writers block, substance abuse, r…" title: "The Adderall Diaries" score: 0.767859697341919 plot: "A cop, a lawyer, and an assassin cross paths after the murder of a fed…" title: "Divergence" score: 0.7603211402893066 plot: "In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the law…" title: "Two Rabbits" score: 0.7584004402160645 plot: "A hilarious cat and mouse chase and mind game between two highly intel…" title: "Julayi" score: 0.7571885585784912 ``` The following query uses `vectorSearch` operator in the [`$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. 1. Create a file named `query-embeddings.js`. ```javascript touch query-embeddings.js ``` 2. Copy and paste the following embeddings in to the `query-embeddings` file. ```javascript INTENSE_COURTROOM_DRAMA = [-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, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 0.0020031689200550318, 0.004317174199968576, -0.004904309753328562, -0.001459204824641347, -0.015127378515899181, -0.0027802600525319576, 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Connect to your cluster and switch to the database that contains the collection. ```javascript use sample_mflix ``` 4. Load the embeddings. ```javascript load('/query-embeddings.js'); ``` 5. Run the following query in your terminal. ```javascript db.embedded_movies.aggregate([ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "compound": { "should": [{ "queryString": { "defaultPath": "fullplot", "query": "plot:courtroom OR lawyer" } }, { "range": { "path": "year", "gte": 2000, "lte": 2015 } }] } }, "queryVector": INTENSE_COURTROOM_DRAMA, "limit": 10, "numCandidates": 1000 }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ]) ``` **Output:** ```javascript [ { plot: 'A Harvard professor is lured back into the courtroom after 25 years to take the case of a young black man condemned to death for the horrific murder of a child.', title: 'Just Cause', score: 0.8064572811126709 }, { plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', title: 'Rules of Engagement', score: 0.7989137172698975 }, { plot: 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', title: 'Wer', score: 0.7935066223144531 }, { plot: 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', title: 'Running Wild', score: 0.7793526649475098 }, { plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', title: 'Enemy of the State', score: 0.7697937488555908 }, { plot: "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", title: 'The Connection', score: 0.7680296897888184 }, { plot: "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", title: 'The Adderall Diaries', score: 0.767859697341919 }, { plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', title: 'Divergence', score: 0.7603211402893066 }, { plot: 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', title: 'Two Rabbits', score: 0.7584004402160645 }, { plot: 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', title: 'Julayi', score: 0.7571885585784912 } ] ``` 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 import pymongo client = pymongo.MongoClient("") database = client["sample_mflix"] collection = database["embedded_movies"] # define query query = [ { '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', 'filter': { 'compound': { 'should': [ { 'queryString': { 'defaultPath': 'fullplot', 'query': 'plot:courtroom OR lawyer' } }, { 'range': { 'path': 'year', 'gte': 2000, 'lte': 2015 } } ] } }, 'queryVector': [ -0.03177440166473389, 0.0047661601565778255, 0.007770913653075695, -0.027906212955713272, 0.0038336506113409996, -0.01650887355208397, 0.02196578122675419, -0.011397339403629303, -0.006458492483943701, -0.015956275165081024, -0.002452154876664281, 0.0014160331338644028, 0.032603297382593155, 0.04089227318763733, -0.03177440166473389, -0.03329404816031456, -0.004282636567950249, 0.007321927230805159, -0.03868187963962555, 0.02099873498082161, 0.014989228919148445, -0.010499367490410805, 0.024866923689842224, 0.03329404816031456, 0.031221803277730942, -0.02680101804435253, -0.007460076827555895, 0.056917622685432434, 0.03218885138630867, -0.0011008793953806162, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 0.0020031689200550318, 0.004317174199968576, -0.004904309753328562, -0.001459204824641347, -0.015127378515899181, -0.0027802600525319576, -0.016854247078299522, 0.008427123539149761, 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-0.005802282132208347, -0.02113688550889492, -0.020169837400317192, 0.029287708923220634, -0.005146071780472994, -0.014643854461610317, -0.0017786757089197636, 0.03785298392176628, -0.031498104333877563, 3.885456681018695e-5, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382 ], 'limit': 10, 'numCandidates': 1000 }, "concurrent": True } }, { '$project': { '_id': 0, 'title': 1, 'plot': 1, 'score': { '$meta': 'searchScore' } } } ] # run query and print results results = collection.aggregate(query) for document in results: print(document) ``` **Output:** ```javascript {'plot': 'A Harvard professor is lured back into the courtroom after 25 years to take the case of a young black man condemned to death for the horrific murder of a child.', 'title': 'Just Cause', 'score': 0.8064572811126709} {'plot': 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', 'title': 'Rules of Engagement', 'score': 0.7989137172698975} {'plot': 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', 'title': 'Wer', 'score': 0.7935066223144531} {'plot': 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', 'title': 'Running Wild', 'score': 0.7793526649475098} {'plot': 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', 'title': 'Enemy of the State', 'score': 0.7697937488555908} {'plot': "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", 'title': 'The Connection', 'score': 0.7680296897888184} {'plot': "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", 'title': 'The Adderall Diaries', 'score': 0.767859697341919} {'plot': 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', 'title': 'Divergence', 'score': 0.7603211402893066} {'plot': 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', 'title': 'Two Rabbits', 'score': 0.7584004402160645} {'plot': 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', 'title': 'Julayi', 'score': 0.7571885585784912} ``` 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 const { MongoClient } = require("mongodb"); async function main() { // Replace the placeholder with your connection string const uri = ""; const client = new MongoClient(uri); try { await client.connect(); const database = client.db("sample_mflix"); const movies = database.collection("embedded_movies"); const query = [{ '$search': { 'index': '', '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, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 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child.', title: 'Just Cause', score: 0.8064572811126709 } { plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', title: 'Rules of Engagement', score: 0.7989137172698975 } { plot: 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', title: 'Wer', score: 0.7935066223144531 } { plot: 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', title: 'Running Wild', score: 0.7793526649475098 } { plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', title: 'Enemy of the State', score: 0.7697937488555908 } { plot: "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", title: 'The Connection', score: 0.7680296897888184 } { plot: "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", title: 'The Adderall Diaries', score: 0.767859697341919 } { plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', title: 'Divergence', score: 0.7603211402893066 } { plot: 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', title: 'Two Rabbits', score: 0.7584004402160645 } { plot: 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', title: 'Julayi', score: 0.7571885585784912 } ``` 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 ENN (Exact 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*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "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, 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3.885456681018695e-05, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382], "limit": 10, "exact": true }, "concurrent": true } } ] ``` **Output:** ```javascript SCORE: 0.806577742099762 _id: "573a139af29313caabcef3ea" plot: "A Harvard professor is lured back into the courtroom after 25 years to…" title: "Just Cause" SCORE: 0.7987366914749146 _id: "573a139df29313caabcfa561" plot: "An attorney defends an officer on trial for ordering his troops to fir…" title: "Rules of Engagement" SCORE: 0.7937260866165161 _id: "573a13d8f29313caabda84a9" plot: "A defense attorney begins to suspect that there might be more to her c…" title: "Wer" SCORE: 0.7793828845024109 _id: "573a13b6f29313caabd466b9" plot: "Jang is a homicide detective who likes to use violence with criminals,…" title: "Running Wild" SCORE: 0.769807755947113 _id: "573a139af29313caabcf0e97" plot: "A lawyer becomes a target by a corrupt politician and his NSA goons wh…" title: "Enemy of the State" SCORE: 0.7680113315582275 _id: "573a13e4f29313caabdc160e" plot: "A French police magistrate spends years trying to take down one of the…" title: "The Connection" SCORE: 0.7679274082183838 _id: "573a13cff29313caabd89a51" plot: "As a writer stymied by past success, writers block, substance abuse, r…" title: "The Adderall Diaries" SCORE: 0.7603133916854858 _id: "573a13b4f29313caabd3f550" plot: "A cop, a lawyer, and an assassin cross paths after the murder of a fed…" title: "Divergence" SCORE: 0.7586807608604431 _id: "573a13cff29313caabd8aae0" plot: "In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the law…" title: "Two Rabbits" SCORE: 0.7570918798446655 _id: "573a13dbf29313caabdae597" plot: "A hilarious cat and mouse chase and mind game between two highly intel…" title: "Julayi" ``` 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 ENN (Exact 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*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "compound": { "should": [{ "queryString": { "defaultPath": "fullplot", "query": "plot:courtroom OR lawyer" } }, { "range": { "path": "year", "gte": 2000, "lte": 2015 } }] } }, "queryVector": [-0.03177440166473389, 0.0047661601565778255, 0.007770913653075695, -0.027906212955713272, 0.0038336506113409996, 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3.885456681018695e-05, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382], "limit": 10, "exact": true }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ] ``` **Output:** ```javascript plot: "A Harvard professor is lured back into the courtroom after 25 years to…" title: "Just Cause" score: 0.806577742099762 plot: "An attorney defends an officer on trial for ordering his troops to fir…" title: "Rules of Engagement" score: 0.7987366914749146 plot: "A defense attorney begins to suspect that there might be more to her c…" title: "Wer" score: 0.7937260866165161 plot: "Jang is a homicide detective who likes to use violence with criminals,…" title: "Running Wild" score: 0.7793828845024109 plot: "A lawyer becomes a target by a corrupt politician and his NSA goons wh…" title: "Enemy of the State" score: 0.769807755947113 plot: "A French police magistrate spends years trying to take down one of the…" title: "The Connection" score: 0.7680113315582275 plot: "As a writer stymied by past success, writers block, substance abuse, r…" title: "The Adderall Diaries" score: 0.7679274082183838 plot: "A cop, a lawyer, and an assassin cross paths after the murder of a fed…" title: "Divergence" score: 0.7603133916854858 plot: "In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the law…" title: "Two Rabbits" score: 0.7586807608604431 plot: "A hilarious cat and mouse chase and mind game between two highly intel…" title: "Julayi" score: 0.7570918798446655 ``` The following query uses `vectorSearch` operator in the [`$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 ENN (Exact 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*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. 1. Create a file named `query-embeddings`. ```javascript touch query-embeddings ``` 2. Copy and paste the following embeddings in to the `query-embeddings` file. ```javascript INTENSE_COURTROOM_DRAMA = [-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, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 0.0020031689200550318, 0.004317174199968576, -0.004904309753328562, -0.001459204824641347, -0.015127378515899181, -0.0027802600525319576, 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Connect to your cluster and switch to the database that contains the collection. ```javascript use sample_mflix ``` 4. Load the embeddings. ```javascript load('/query-embeddings.js'); ``` 5. Run the following query in your terminal. ```javascript db.embedded_movies.aggregate([ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "compound": { "should": [{ "queryString": { "defaultPath": "fullplot", "query": "plot:courtroom OR lawyer" } }, { "range": { "path": "year", "gte": 2000, "lte": 2015 } }] } }, "queryVector": INTENSE_COURTROOM_DRAMA, "limit": 10, "exact": true }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ]) ``` **Output:** ```javascript [ { plot: 'A Harvard professor is lured back into the courtroom after 25 years to take the case of a young black man condemned to death for the horrific murder of a child.', title: 'Just Cause', score: 0.806577742099762 }, { plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', title: 'Rules of Engagement', score: 0.7987366914749146 }, { plot: 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', title: 'Wer', score: 0.7937260866165161 }, { plot: 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', title: 'Running Wild', score: 0.7793828845024109 }, { plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', title: 'Enemy of the State', score: 0.769807755947113 }, { plot: "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", title: 'The Connection', score: 0.7680113315582275 }, { plot: "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", title: 'The Adderall Diaries', score: 0.7679274082183838 }, { plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', title: 'Divergence', score: 0.7603133916854858 }, { plot: 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', title: 'Two Rabbits', score: 0.7586807608604431 }, { plot: 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', title: 'Julayi', score: 0.7570918798446655 } ] ``` 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 ENN (Exact 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*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript import pymongo client = pymongo.MongoClient("") database = client["sample_mflix"] collection = database["embedded_movies"] # define query query = [ { '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', 'filter': { 'compound': { 'should': [ { 'queryString': { 'defaultPath': 'fullplot', 'query': 'plot:courtroom OR lawyer' } }, { 'range': { 'path': 'year', 'gte': 2000, 'lte': 2015 } } ] } }, 'queryVector': [ -0.03177440166473389, 0.0047661601565778255, 0.007770913653075695, -0.027906212955713272, 0.0038336506113409996, -0.01650887355208397, 0.02196578122675419, -0.011397339403629303, -0.006458492483943701, -0.015956275165081024, -0.002452154876664281, 0.0014160331338644028, 0.032603297382593155, 0.04089227318763733, -0.03177440166473389, -0.03329404816031456, -0.004282636567950249, 0.007321927230805159, -0.03868187963962555, 0.02099873498082161, 0.014989228919148445, -0.010499367490410805, 0.024866923689842224, 0.03329404816031456, 0.031221803277730942, -0.02680101804435253, -0.007460076827555895, 0.056917622685432434, 0.03218885138630867, -0.0011008793953806162, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 0.0020031689200550318, 0.004317174199968576, -0.004904309753328562, -0.001459204824641347, -0.015127378515899181, -0.0027802600525319576, -0.016854247078299522, 0.008427123539149761, -0.027906212955713272, 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-0.02113688550889492, -0.020169837400317192, 0.029287708923220634, -0.005146071780472994, -0.014643854461610317, -0.0017786757089197636, 0.03785298392176628, -0.031498104333877563, 3.885456681018695e-5, -0.006942016072571278, 0.007356464862823486, -0.014298480935394764, 0.040615975856781006, -0.014229405671358109, 0.007149240467697382 ], 'limit': 10, 'exact': True }, 'concurrent': True } }, { '$project': { '_id': 0, 'title': 1, 'plot': 1, 'score': { '$meta': 'searchScore' } } } ] # run query and print results results = collection.aggregate(query) for document in results: print(document) ``` **Output:** ```javascript {'plot': 'A Harvard professor is lured back into the courtroom after 25 years to take the case of a young black man condemned to death for the horrific murder of a child.', 'title': 'Just Cause', 'score': 0.806577742099762} {'plot': 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', 'title': 'Rules of Engagement', 'score': 0.7987366914749146} {'plot': 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', 'title': 'Wer', 'score': 0.7937260866165161} {'plot': 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', 'title': 'Running Wild', 'score': 0.7793828845024109} {'plot': 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', 'title': 'Enemy of the State', 'score': 0.769807755947113} {'plot': "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", 'title': 'The Connection', 'score': 0.7680113315582275} {'plot': "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", 'title': 'The Adderall Diaries', 'score': 0.7679274082183838} {'plot': 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', 'title': 'Divergence', 'score': 0.7603133916854858} {'plot': 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', 'title': 'Two Rabbits', 'score': 0.7586807608604431} {'plot': 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', 'title': 'Julayi', 'score': 0.7570918798446655} ``` 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 ENN (Exact 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*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript const { MongoClient } = require("mongodb"); async function main() { // Replace the placeholder with your connection string const uri = ""; const client = new MongoClient(uri); try { await client.connect(); const database = client.db("sample_mflix"); const movies = database.collection("embedded_movies"); const query = [{ '$search': { 'index': '', '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, -0.03205070272088051, -0.017337771132588387, -0.04779975116252899, 0.0020031689200550318, 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title: 'Just Cause', score: 0.806577742099762 } { plot: 'An attorney defends an officer on trial for ordering his troops to fire on civilians after they stormed a U.S. embassy in a third world country.', title: 'Rules of Engagement', score: 0.7987366914749146 } { plot: 'A defense attorney begins to suspect that there might be more to her client, who is charged with the murders of a vacationing family, than meets the eye.', title: 'Wer', score: 0.7937260866165161 } { plot: 'Jang is a homicide detective who likes to use violence with criminals, while Oh is a prosecutor who believes in the importance of evidence. After the murder of his younger half-brother ...', title: 'Running Wild', score: 0.7793828845024109 } { plot: 'A lawyer becomes a target by a corrupt politician and his NSA goons when he accidentally receives key evidence to a serious politically motivated crime.', title: 'Enemy of the State', score: 0.769807755947113 } { plot: "A French police magistrate spends years trying to take down one of the country's most powerful drug rings.", title: 'The Connection', score: 0.7680113315582275 } { plot: "As a writer stymied by past success, writers block, substance abuse, relationship problems and a serious set of father issues, Elliott's cracked-out chronicle of a bizarre murder trial ...", title: 'The Adderall Diaries', score: 0.7679274082183838 } { plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.', title: 'Divergence', score: 0.7603133916854858 } { plot: 'In Sèo Paulo, the scheme of the corrupt DA Jèlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the Internal Affairs. Maicom is responsible for ...', title: 'Two Rabbits', score: 0.7586807608604431 } { plot: 'A hilarious cat and mouse chase and mind game between two highly intelligent young men, who stands on the two sides of law and order.', title: 'Julayi', score: 0.7570918798446655 } ``` 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 [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 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"numCandidates": 1000 }, "concurrent": true } } ] ``` **Output:** ```javascript SCORE: 0.8071866035461426 _id: "573a1397f29313caabce80f6" plot: "A cellist in a small orchestra receives help from animals to help him …" title: "Gauche the Cellist" SCORE: 0.7872991561889648 _id: "573a139af29313caabcf089f" plot: "A Doctor finds out that he can understand what animals are saying. And…" title: "Doctor Dolittle" SCORE: 0.779412031173706 _id: "573a13b4f29313caabd4070d" plot: "This is a wonderful movie about a group of men frontier the Nashville …" title: "Paragraph 78" SCORE: 0.777996301651001 _id: "573a13ccf29313caabd821f7" plot: "Young Ondra has asthma and so his mom throws away his favorite toy: a …" title: "Kooky" SCORE: 0.7747676372528076 _id: "573a13b1f29313caabd35987" plot: "Wilbur the pig is scared of the end of the season, because he knows th…" title: "Charlotte's Web" SCORE: 0.7687718868255615 _id: "573a13a5f29313caabd16308" plot: "A cat who turns into a young woman helps a journalist protect their to…" title: "Miss Minoes" SCORE: 0.7673406600952148 _id: "573a1394f29313caabce074d" plot: "This True Life Fantasy follows and shows how the life of a female squi…" title: "Perri" SCORE: 0.764977216720581 _id: "573a13bcf29313caabd55f51" plot: "A group of animals look for a way off of an eccentric billionaire's ow…" title: "The Missing Lynx" SCORE: 0.7573268413543701 _id: "573a1395f29313caabce1b5f" plot: "A group of men trap wild animals in Africa and sell them to zoos. Will…" title: "Hatari!" SCORE: 0.7563502788543701 _id: "573a1393f29313caabcde3af" plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives …" title: "Mighty Joe Young" ``` 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 [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 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"numCandidates": 1000 }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ] ``` **Output:** ```javascript plot: "A cellist in a small orchestra receives help from animals to help him …" title: "Gauche the Cellist" score: 0.8071866035461426 plot: "A Doctor finds out that he can understand what animals are saying. And…" title: "Doctor Dolittle" score: 0.7872991561889648 plot: "This is a wonderful movie about a group of men frontier the Nashville …" title: "Paragraph 78" score: 0.779412031173706 plot: "Young Ondra has asthma and so his mom throws away his favorite toy: a …" title: "Kooky" score: 0.777996301651001 plot: "Wilbur the pig is scared of the end of the season, because he knows th…" title: "Charlotte's Web" score: 0.7747676372528076 plot: "A cat who turns into a young woman helps a journalist protect their to…" title: "Miss Minoes" score: 0.7687718868255615 plot: "This True Life Fantasy follows and shows how the life of a female squi…" title: "Perri" score: 0.7673406600952148 plot: "A group of animals look for a way off of an eccentric billionaire's ow…" title: "The Missing Lynx" score: 0.764977216720581 plot: "A group of men trap wild animals in Africa and sell them to zoos. Will…" title: "Hatari!" score: 0.7573268413543701 plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives …" title: "Mighty Joe Young" score: 0.7563502788543701 ``` The following query uses `vectorSearch` operator in the [`$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 [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. 1. Create a file named `query-embeddings`. ```javascript touch query-embeddings ``` 2. Copy and paste the following embeddings in to the `query-embeddings` file. ```javascript CHARMING_ANIMAL_STORY = [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 0.02030903473496437, -0.036051537841558456, 0.008652369491755962, -0.006939921528100967, -0.0379742868244648, -0.0010515032336115837, 0.03172535449266434, -0.02836054377257824, -0.00841202586889267, 0.01297855470329523, -0.042300473898649216, 0.005648074671626091, -0.0009763958514668047, -0.006879835389554501, -0.03845497593283653, -0.00871245563030243, 0.012077265419065952, -0.006399148143827915, -0.007150222081691027, -0.004596571438014507, -0.017545083537697792, -0.05239490419626236, 0.020068690180778503, -0.01514164637774229, -0.031244667246937752, -0.0036351969465613365, -0.012257523834705353, -0.02595710940659046, 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Connect to your cluster and switch to the database that contains the collection. ```javascript use sample_mflix ``` 4. Load the embeddings. ```javascript load('/query-embeddings.js'); ``` 5. Run the following query in your terminal. ```javascript db.embedded_movies.aggregate([ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": CHARMING_ANIMAL_STORY, "limit": 10, "numCandidates": 1000 }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ]) ``` **Output:** ```javascript [ { plot: 'A cellist in a small orchestra receives help from animals to help him practice his music.', title: 'Gauche the Cellist', score: 0.8071866035461426 }, { plot: 'A Doctor finds out that he can understand what animals are saying. And the animals find out that he understands.', title: 'Doctor Dolittle', score: 0.7872991561889648 }, { plot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', title: 'Paragraph 78', score: 0.779412031173706 }, { plot: 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', title: 'Kooky', score: 0.777996301651001 }, { plot: 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', title: "Charlotte's Web", score: 0.7747676372528076 }, { plot: 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', title: 'Miss Minoes', score: 0.7687718868255615 }, { plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', title: 'Perri', score: 0.7673406600952148 }, { plot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", title: 'The Missing Lynx', score: 0.764977216720581 }, { plot: 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', title: 'Hatari!', score: 0.7573268413543701 }, { plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", title: 'Mighty Joe Young', score: 0.7563502788543701 } ] ``` 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 [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript import pymongo client = pymongo.MongoClient("") database = client["sample_mflix"] collection = database["embedded_movies"] # define query query = [ { '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, 'queryVector': [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 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0.022351954132318497, 0.00600859010592103], 'limit': 10, 'numCandidates': 1000 }, "concurrent": True } }, { '$project': { '_id': 0, 'title': 1, 'plot': 1, 'score': { '$meta': 'searchScore' } } } ] # run query and print results results = collection.aggregate(query) for document in results: print(document) ``` **Output:** ```javascript {'plot': 'A cellist in a small orchestra receives help from animals to help him practice his music.', 'title': 'Gauche the Cellist', 'score': 0.8071866035461426} {'plot': 'A Doctor finds out that he can understand what animals are saying. And the animals find out that he understands.', 'title': 'Doctor Dolittle', 'score': 0.7872991561889648} {'plot': 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', 'title': 'Paragraph 78', 'score': 0.779412031173706} {'plot': 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', 'title': 'Kooky', 'score': 0.777996301651001} {'plot': 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', 'title': "Charlotte's Web", 'score': 0.7747676372528076} {'plot': 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', 'title': 'Miss Minoes', 'score': 0.7687718868255615} {'plot': 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', 'title': 'Perri', 'score': 0.7673406600952148} {'plot': "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", 'title': 'The Missing Lynx', 'score': 0.764977216720581} {'plot': 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', 'title': 'Hatari!', 'score': 0.7573268413543701} {'plot': "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", 'title': 'Mighty Joe Young', 'score': 0.7563502788543701} ``` 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 [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript import { MongoClient } from "mongodb"; async function main() { // Replace the placeholder with your connection string const uri = ""; const client = new MongoClient(uri); try { await client.connect(); const database = client.db("sample_mflix"); const movies = database.collection("embedded_movies"); const query = [{ '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', "filter": { "text": { "path": "fullplot", "query": 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-7.557679782621562e-05, 6.008589843986556e-05, -0.010815462097525597, 0.007420608773827553, 0.0019527917029336095, 5.633052933262661e-05, 0.029081575572490692, 0.022351954132318497, 0.00600859010592103], 'limit': 10, 'numCandidates': 1000 }, 'concurrent': true } }, { '$project': { '_id': 0, 'title': 1, 'plot': 1, 'score': { '$meta': 'searchScore' } } }]; const cursor = movies.aggregate(query); await cursor.forEach(doc => console.log(doc)); } finally { await client.close(); } } main().catch(console.error); ``` **Output:** ```javascript { plot: 'A cellist in a small orchestra receives help from animals to help him practice his music.', title: 'Gauche the Cellist', score: 0.8071866035461426 } { plot: 'A Doctor finds out that he can understand what animals are saying. And the animals find out that he understands.', title: 'Doctor Dolittle', score: 0.7872991561889648 } { plot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', title: 'Paragraph 78', score: 0.779412031173706 } { plot: 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', title: 'Kooky', score: 0.777996301651001 } { plot: 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', title: "Charlotte's Web", score: 0.7747676372528076 } { plot: 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', title: 'Miss Minoes', score: 0.7687718868255615 } { plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', title: 'Perri', score: 0.7673406600952148 } { plot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", title: 'The Missing Lynx', score: 0.764977216720581 } { plot: 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', title: 'Hatari!', score: 0.7573268413543701 } { plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", title: 'Mighty Joe Young', score: 0.7563502788543701 } ``` 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 ENN (Exact Nearest Neighbor) search for movies. Specifically, the query: - Uses the [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 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"exact": true }, "concurrent": true } } ] ``` **Output:** ```javascript SCORE: 0.807614803314209 _id: "573a1397f29313caabce80f6" plot: "A cellist in a small orchestra receives help from animals to help him …" title: "Gauche the Cellist" SCORE: 0.7874959111213684 _id: "573a139af29313caabcf089f" plot: "A Doctor finds out that he can understand what animals are saying. And…" title: "Doctor Dolittle" SCORE: 0.7798824310302734 _id: "573a13b4f29313caabd4070d" plot: "This is a wonderful movie about a group of men frontier the Nashville …" title: "Paragraph 78" SCORE: 0.7778682708740234 _id: "573a13ccf29313caabd821f7" plot: "Young Ondra has asthma and so his mom throws away his favorite toy: a …" title: "Kooky" SCORE: 0.7750082015991211 _id: "573a13b1f29313caabd35987" plot: "Wilbur the pig is scared of the end of the season, because he knows th…" title: "Charlotte's Web" SCORE: 0.769116222858429 _id: "573a13a5f29313caabd16308" plot: "A cat who turns into a young woman helps a journalist protect their to…" title: "Miss Minoes" SCORE: 0.7673614025115967 _id: "573a1394f29313caabce074d" plot: "This True Life Fantasy follows and shows how the life of a female squi…" title: "Perri" SCORE: 0.7652119398117065 _id: "573a13bcf29313caabd55f51" plot: "A group of animals look for a way off of an eccentric billionaire's ow…" title: "The Missing Lynx" SCORE: 0.7575701475143433 _id: "573a1395f29313caabce1b5f" plot: "A group of men trap wild animals in Africa and sell them to zoos. Will…" title: "Hatari!" SCORE: 0.7564237117767334 _id: "573a1393f29313caabcde3af" plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives …" title: "Mighty Joe Young" ``` 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 ENN (Exact Nearest Neighbor) search for movies. Specifically, the query: - Uses the [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript [ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 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"exact": true }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ] ``` **Output:** ```javascript plot: "A cellist in a small orchestra receives help from animals to help him …" title: "Gauche the Cellist" score: 0.807614803314209 plot: "A Doctor finds out that he can understand what animals are saying. And…" title: "Doctor Dolittle" score: 0.7874959111213684 plot: "This is a wonderful movie about a group of men frontier the Nashville …" title: "Paragraph 78" score: 0.7798824310302734 plot: "Young Ondra has asthma and so his mom throws away his favorite toy: a …" title: "Kooky" score: 0.7778682708740234 plot: "Wilbur the pig is scared of the end of the season, because he knows th…" title: "Charlotte's Web" score: 0.7750082015991211 plot: "A cat who turns into a young woman helps a journalist protect their to…" title: "Miss Minoes" score: 0.769116222858429 plot: "This True Life Fantasy follows and shows how the life of a female squi…" title: "Perri" score: 0.7673614025115967 plot: "A group of animals look for a way off of an eccentric billionaire's ow…" title: "The Missing Lynx" score: 0.7652119398117065 plot: "A group of men trap wild animals in Africa and sell them to zoos. Will…" title: "Hatari!" score: 0.7575701475143433 plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives …" title: "Mighty Joe Young" score: 0.7564237117767334 ``` The following query uses `vectorSearch` operator in the [`$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 ENN (Exact Nearest Neighbor) search for movies. Specifically, the query: - Uses the [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. 1. Create a file named `query-embeddings`. ```javascript touch query-embeddings ``` 2. Copy and paste the following embeddings in to the `query-embeddings` file. ```javascript CHARMING_ANIMAL_STORY = [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 0.009072970598936081, -0.007390565704554319, 0.01406010054051876, -0.0030794022604823112, -0.006729620508849621, 0.02127040922641754, 0.02030903473496437, -0.036051537841558456, 0.008652369491755962, -0.006939921528100967, -0.0379742868244648, -0.0010515032336115837, 0.03172535449266434, -0.02836054377257824, -0.00841202586889267, 0.01297855470329523, -0.042300473898649216, 0.005648074671626091, -0.0009763958514668047, -0.006879835389554501, -0.03845497593283653, -0.00871245563030243, 0.012077265419065952, -0.006399148143827915, -0.007150222081691027, -0.004596571438014507, -0.017545083537697792, -0.05239490419626236, 0.020068690180778503, -0.01514164637774229, -0.031244667246937752, -0.0036351969465613365, -0.012257523834705353, -0.02595710940659046, 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Connect to your cluster and switch to the database that contains the collection. ```javascript use sample_mflix ``` 4. Load the embeddings. ```javascript load('/query-embeddings.js'); ``` 5. Run the following query in your terminal. ```javascript db.embedded_movies.aggregate([ { "$search": { "index": "", "vectorSearch": { "path": "plot_embedding_voyage_3_large", "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, "queryVector": CHARMING_ANIMAL_STORY, "limit": 10, "exact": true }, "concurrent": true } }, { "$project": { "_id": 0, "title": 1, "plot": 1, "score": { "$meta": "searchScore" } } } ]) ``` **Output:** ```javascript [ { plot: 'A cellist in a small orchestra receives help from animals to help him practice his music.', title: 'Gauche the Cellist', score: 0.807614803314209 }, { plot: 'A Doctor finds out that he can understand what animals are saying. And the animals find out that he understands.', title: 'Doctor Dolittle', score: 0.7874959111213684 }, { plot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', title: 'Paragraph 78', score: 0.7798824310302734 }, { plot: 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', title: 'Kooky', score: 0.7778682708740234 }, { plot: 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', title: "Charlotte's Web", score: 0.7750082015991211 }, { plot: 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', title: 'Miss Minoes', score: 0.769116222858429 }, { plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', title: 'Perri', score: 0.7673614025115967 }, { plot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", title: 'The Missing Lynx', score: 0.7652119398117065 }, { plot: 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', title: 'Hatari!', score: 0.7575701475143433 }, { plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", title: 'Mighty Joe Young', score: 0.7564237117767334 } ] ``` 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 ENN (Exact Nearest Neighbor) search for movies. Specifically, the query: - Uses the [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript import pymongo client = pymongo.MongoClient("") database = client["sample_mflix"] collection = database["embedded_movies"] # define query query = [ { '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', "filter": { "text": { "path": "fullplot", "query": "charming animal", "fuzzy": {} } }, 'queryVector': [-0.020669549703598022, 0.02307298593223095, -0.018145941197872162, -0.015622333623468876, -0.007420608773827553, 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0.022351954132318497, 0.00600859010592103], 'limit': 10, 'exact': True }, "concurrent": True } }, { '$project': { '_id': 0, 'title': 1, 'plot': 1, 'score': { '$meta': 'searchScore' } } } ] # run query and print results results = collection.aggregate(query) for document in results: print(document) ``` **Output:** ```javascript {'plot': 'A cellist in a small orchestra receives help from animals to help him practice his music.', 'title': 'Gauche the Cellist', 'score': 0.807614803314209} {'plot': 'A Doctor finds out that he can understand what animals are saying. And the animals find out that he understands.', 'title': 'Doctor Dolittle', 'score': 0.7874959111213684} {'plot': 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', 'title': 'Paragraph 78', 'score': 0.7798824310302734} {'plot': 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', 'title': 'Kooky', 'score': 0.7778682708740234} {'plot': 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', 'title': "Charlotte's Web", 'score': 0.7750082015991211} {'plot': 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', 'title': 'Miss Minoes', 'score': 0.769116222858429} {'plot': 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', 'title': 'Perri', 'score': 0.7673614025115967} {'plot': "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", 'title': 'The Missing Lynx', 'score': 0.7652119398117065} {'plot': 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', 'title': 'Hatari!', 'score': 0.7575701475143433} {'plot': "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", 'title': 'Mighty Joe Young', 'score': 0.7564237117767334} ``` 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 ENN (Exact Nearest Neighbor) search for movies. Specifically, the query: - Uses the [`text` (MongoDB Search Operator)](https://www.mongodb.com/docs/search/query/operators-collectors/text.md#std-label-text-ref) to pre-filter the documents by a fuzzy search for movies that contain phrase similar to `charming animal` in the `fullplot` field. - Searches using the 2048 dimension vector embeddings generated from the `voyage-3-large` model for the string *charming animal story*. - Limits the output to `10` documents only. - Sets `concurrent` to `true` to improve query latency. ```javascript import { MongoClient } from "mongodb"; async function main() { // Replace the placeholder with your connection string const uri = ""; const client = new MongoClient(uri); try { await client.connect(); const database = client.db("sample_mflix"); const movies = database.collection("embedded_movies"); const query = [{ '$search': { 'index': '', 'vectorSearch': { 'path': 'plot_embedding_voyage_3_large', "filter": { "text": { "path": "fullplot", "query": 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And the animals find out that he understands.', title: 'Doctor Dolittle', score: 0.7874959111213684 } { plot: 'This is a wonderful movie about a group of men frontier the Nashville area who like to make puppets out of real animals', title: 'Paragraph 78', score: 0.7798824310302734 } { plot: 'Young Ondra has asthma and so his mom throws away his favorite toy: a musty old stuffed bear named Kooky. That night Ondra dreams that Kooky is determined to find his way back home from the...', title: 'Kooky', score: 0.7778682708740234 } { plot: 'Wilbur the pig is scared of the end of the season, because he knows that come that time, he will end up on the dinner table. He hatches a plan with Charlotte, a spider that lives in his pen, to ensure that this will never happen.', title: "Charlotte's Web", score: 0.7750082015991211 } { plot: 'A cat who turns into a young woman helps a journalist protect their town from a factory boss with an evil plan.', title: 'Miss Minoes', score: 0.769116222858429 } { plot: 'This True Life Fantasy follows and shows how the life of a female squirrel, Perri, in the forest is filled with danger and fraught with peril. When not fleeing her natural enemy, the Marten...', title: 'Perri', score: 0.7673614025115967 } { plot: "A group of animals look for a way off of an eccentric billionaire's own personal Noah's Ark.", title: 'The Missing Lynx', score: 0.7652119398117065 } { plot: 'A group of men trap wild animals in Africa and sell them to zoos. Will the arrival of a female wildlife photographer change their ways ?', title: 'Hatari!', score: 0.7575701475143433 } { plot: "In Africa, the girl Jill Young trades a baby gorilla with two natives and raises the animal. Twelve years later, the talkative and persuasive promoter Max O'Hara organizes a safari to ...", title: 'Mighty Joe Young', score: 0.7564237117767334 } ```