> For the complete MongoDB documentation index, see www.mongodb.com/docs/llms.txt # How to Measure the Accuracy of Your Query Results You can measure the accuracy of your MongoDB Vector Search query by evaluating how closely the results for an ANN (Approximate Nearest Neighbor) search match the results of an ENN (Exact Nearest Neighbor) search against quantized vectors by using the same query criteria. That is, you can compare the results of ANN (Approximate Nearest Neighbor) search with the results of ENN (Exact Nearest Neighbor) search and measure how frequently the ANN (Approximate Nearest Neighbor) search results include the nearest neighbors in the results from the ENN (Exact Nearest Neighbor) search. ## Use Cases You might want to measure the accuracy of the results if you have any of the following: - Quantized vectors - Large numbers of vectors - Low dimensional vectors ## Prerequisites To try the examples on this page, you need the following: - A cluster with, optionally, the sample dataset. - [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh) ## Procedures To evaluate the accuracy of your [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) query results, you must do the following: 1. Create a MongoDB Vector Search index on the vector field and on any other fields that you want to pre-filter the data by. We recommend using quantized vectors to improve the storage of your vectors and speed of your queries. If you don't have quantized vectors, you can enable automatic quantization when indexing your `vector` type field. 2. Construct and run the ENN (Exact Nearest Neighbor) query followed by the ANN (Approximate Nearest Neighbor) query. 3. Compare the results of the ANN (Approximate Nearest Neighbor) query with the results of the ENN (Exact Nearest Neighbor) query to evaluate the similarities and differences in the results. This section demonstrates how to perform the preceding 3 steps against data in the `sample_mflix.embedded_movies` collection. If you don't wish to use the sample dataset, you can perform the procedures against your own data. ### Create the MongoDB Vector Search Index This section demonstrates how to create a MongoDB Vector Search index for running MongoDB Vector Search ANN (Approximate Nearest Neighbor) and ENN (Exact Nearest Neighbor) queries. 1. In Atlas, go to the Search & Vector Search page for your cluster. You can go the MongoDB Search page from the Search & Vector Search option, or the Data Explorer. ###### Search & Vector Search If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar. If it's not already displayed, select your project from the Projects menu in the navigation bar. In the sidebar, click Search & Vector Search under the Database heading. - If you have no clusters, click Create cluster to create one. To learn more, see [Create an Atlas Infinite or Atlas Core Cluster.](https://www.mongodb.com/docs/atlas/tutorial/create-new-cluster.md#std-label-create-new-cluster) - If your project has multiple clusters, select the cluster you want to use from the Select cluster dropdown, then click Go to Search. The [Search & Vector Search](https://cloud.mongodb.com/go?l=https%3A%2F%2Fcloud.mongodb.com%2Fv2%2F%3Cproject%3E%23%2Fclusters%2FatlasSearch%2F%3Ccluster%3E) page displays. ###### Data Explorer If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar. If it's not already displayed, select your project from the Projects menu in the navigation bar. In the sidebar, click Data Explorer under the Database heading. Expand the database and select the collection. Click the Indexes tab for the collection. Click the Search and Vector Search link in the banner. The [Search & Vector Search](https://cloud.mongodb.com/go?l=https%3A%2F%2Fcloud.mongodb.com%2Fv2%2F%3Cproject%3E%23%2Fclusters%2FatlasSearch%2F%3Ccluster%3E%3Fdatabase%3Dsample_mflix%26collectionName%3Dusers) page displays. 2. Click Create Search Index. 3. Start your index configuration. Make the following selections on the page and then click Next. | Search Type | Select the Vector Search index type. | | --- | --- | | How do you want to set up your vector data? | Select one of the following: Automated Embedding if you want MongoDB to generate and manage vector embeddings for your text fields.; Bring your own embeddings if you have already generated vector embeddings for your data. Choose Bring your own embeddings. | | Index Name and Data Source | Specify the following information: Index Name: `vector_index`; Database and Collection:`sample_mflix`; `movies` | | Configuration Method | For a guided experience, select Visual Editor.To edit the raw index definition, select JSON Editor. | 4. Specify the index definition. **Example:** This index definition indexes the `plot_embedding_voyage_4_large` field as the `vector` type with automatic binary `quantization` enabled and the `genres` field as the `filter` type in a MongoDB Vector Search index. The `plot_embedding_voyage_4_large` field contains embeddings created using Voyage AI's `voyage-4-large` embedding model. The index definition specifies `2048` vector dimensions and measures distance using `dotProduct` similarity function. ### Visual Editor Atlas automatically detects fields that contain vector embeddings, as well as their corresponding dimensions. For the `sample_mflix.embedded_movies` collection, select the `plot_embedding_voyage_4_large` field. To configure the index, do the following: Select Dot Product from the Similarity Method dropdown. Click Advanced, then select Binary quantization from the dropdown menu. In the Filter Field section, specify the `genres` field to filter the data by. 5. Click Next to review the index. 6. Click Create Vector Search Index. Atlas displays a modal window to let you know your index is building. 7. Close the You're All Set! Modal Window by clicking the Close button. 8. Check the status. The newly created index displays on the Search & Vector Search page. While the index is building, the Status field reads Pending. When the index is finished building, the Status field reads Ready. **Note:** Larger collections take longer to index. You will receive an email notification when your index is finished building. 1) Connect to the deployment using [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh). In your terminal, connect to your Atlas cloud-hosted deployment or local deployment from [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh). For detailed instructions on how to connect, see [Connect to a Deployment.](https://www.mongodb.com/docs/mongodb-shell/connect/) 2) Switch to the database that contains the collection for which you want to create the index. ```shell use sample_mflix ``` **Output:** ```shell switched to db sample_mflix ``` 3) Run the `db.collection.createSearchIndex()` method to create the index. **Example:** This index definition indexes the `plot_embedding_voyage_4_large` field as the `vector` type with automatic binary `quantization` enabled and the `genres` field as the `filter` type in a MongoDB Vector Search index. The `plot_embedding_voyage_4_large` field contains embeddings created using Voyage AI's `voyage-4-large` embedding model. The index definition specifies `2048` vector dimensions and measures distance using `dotProduct` similarity function. ```shell db.embedded_movies.createSearchIndex( "vector_index", "vectorSearch", { "fields": [ { "numDimensions": 2048, "path": "plot_embedding_voyage_4_large", "similarity": "dotProduct", "type": "vector", "quantization": "binary" }, { "path": "genres", "type": "filter" } ] } ) ``` **Output:** ```text vector_index ``` ### Run the Queries You can't run MongoDB Vector Search queries in the Atlas UI Search Tester. Use [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh) or a supported driver to run the query. This section demonstrates how to run the ENN (Exact Nearest Neighbor) and ANN (Approximate Nearest Neighbor) queries against the indexed collection. 1. Prepare your query embeddings. Save the following embeddings in a file named `query-embeddings.js`: ```javascript 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``` 2. Connect to your cluster using [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh). Open a terminal window and connect to your cluster by using [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh). To learn more, see [Connect to a Cluster via mongosh.](https://www.mongodb.com/docs/atlas/mongo-shell-connection.md#std-label-connect-mongo-shell) 3. Load the query embeddings into [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh). Load the file into [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh) to use the embeddings in your query: ```javascript load('//query-embeddings.js'); ``` 4. Switch to your database. **Example:** Use the `sample_mflix` database. To switch to the `sample_mflix` database, run the following command at [`mongosh`](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh) prompt: ```sh use sample_mflix ``` **Output:** ```sh switched to db sample_mflix ``` 5. Run your ENN (Exact Nearest Neighbor) query. The following query uses the [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) stage to search the `plot_embedding_voyage_4_large` field for movies. The query uses `exact` to perform an ENN (Exact Nearest Neighbor) search and limits the results to `10` documents only. The query also specifies a [`$project`](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project) stage to exclude all fields except `plot`, `title`, and `genres` from the documents in the results. It also adds a field named `score` that shows the vector search score of the documents in the results. ```shell db.embedded_movies.aggregate([ { "$vectorSearch": { "index": "vector_index", "path": "plot_embedding_voyage_4_large", "filter": { "$and": [ { "genres": { "$eq": "Action" } }, { "genres": { "$ne": "Comedy" } } ] }, "queryVector": TIME_TRAVEL_EMBEDDING, "exact": true, "limit": 10 } }, { "$project": { "_id": 0, "plot": 1, "title": 1, "genres": 1, "score": { $meta: "vectorSearchScore" } } } ]) ``` **Output:** ```shell [ { plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7673629522323608 }, { plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7462635636329651 }, { plot: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...", genres: [ 'Action', 'Drama', 'Fantasy' ], title: 'My iz budushchego', score: 0.7433278560638428 }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.7427071332931519 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7418472170829773 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7339942455291748 }, { plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7168680429458618 }, { plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', genres: [ 'Action', 'Adventure', 'Family' ], title: 'Tomorrowland', score: 0.7159395217895508 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7109469175338745 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7034971714019775 } ] ``` This query uses the following pipeline stages: | [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) | Prefilters the documents to search for movies in the `Action` genre, and not in the `Comedy` genre.; Searches the `plot_embedding_voyage_3_large` field for exact nearest neighbors by using vector embeddings for the string `time travel`.; Limits the output to only 10 results. | | --- | --- | | [`$project`](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project) | Excludes all fields except `plot`, `title`, and `genres` from the documents in the results.; Adds a field named `score` that shows the score of the documents the results. | 6. Run your ANN (Approximate Nearest Neighbor) query. The following query uses the [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) stage to search the `plot_embedding_voyage_4_large` field for movies. The query uses `numCandidates` to perform an ANN (Approximate Nearest Neighbor) search and limits the results to `10` documents only. The query also specifies a [`$project`](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project) stage to exclude all fields except `plot`, `title`, and `genres` from the documents in the results. It also adds a field named `score` that shows the vector search score of the documents in the results. ```shell db.embedded_movies.aggregate([ { "$vectorSearch": { "index": "vector_index", "path": "plot_embedding_voyage_4_large", "filter": { "$and": [ { "genres": { "$eq": "Action" } }, { "genres": { "$ne": "Comedy" } } ] }, "queryVector": TIME_TRAVEL_EMBEDDING, "numCandidates": 100, "limit": 10 } }, { "$project": { "_id": 0, "plot": 1, "title": 1, "genres": 1, "score": { $meta: "vectorSearchScore" } } } ]) ``` **Output:** ```shell [ { plot: 'An officer for a security agency that regulates time travel, must fend for his life against a shady politician who has a tie to his past.', genres: [ 'Action', 'Crime', 'Sci-Fi' ], title: 'Timecop', score: 0.7673629522323608 }, { plot: 'A time-travel experiment in which a robot probe is sent from the year 2073 to the year 1973 goes terribly wrong thrusting one of the project scientists, a man named Nicholas Sinclair into a...', genres: [ 'Action', 'Sci-Fi' ], title: 'A.P.E.X.', score: 0.7462635636329651 }, { plot: "My iz budushchego, or We Are from the Future, is a movie about time travel. Four 21st century treasure seekers are transported back into the middle of a WWII battle in Russia. The movie's ...", genres: [ 'Action', 'Drama', 'Fantasy' ], title: 'My iz budushchego', score: 0.7433278560638428 }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.7427071332931519 }, { plot: 'A reporter, learning of time travelers visiting 20th century disasters, tries to change the history they know by averting upcoming disasters.', genres: [ 'Action', 'Sci-Fi', 'Thriller' ], title: 'Thrill Seekers', score: 0.7418472170829773 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7339942455291748 }, { plot: 'Lyle, a motorcycle champion is traveling the Mexican desert, when he find himself in the action radius of a time machine. So he find himself one century back in the past between rapists, ...', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Timerider: The Adventure of Lyle Swann', score: 0.7168680429458618 }, { plot: 'Bound by a shared destiny, a teen bursting with scientific curiosity and a former boy-genius inventor embark on a mission to unearth the secrets of a place somewhere in time and space that exists in their collective memory.', genres: [ 'Action', 'Adventure', 'Family' ], title: 'Tomorrowland', score: 0.7159395217895508 }, { plot: "Captain Picard and his crew pursue the Borg back in time to stop them from preventing Earth's first contact with an alien species. They also make sure that Zefram Cochrane makes his famous maiden flight at warp speed.", genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Star Trek: First Contact', score: 0.7109469175338745 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7034971714019775 } ] ``` This query uses the following pipeline stages: | [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) | Prefilters the documents to search by movies in the `Action` genre, and not in the `Comedy` genre.; Searches the `plot_embedding_voyage_3_large` field for approximate nearest neighbors by using vector embeddings for the string `time travel`.; Considers up to `100` nearest neighbors, but limits the output to only 10 results. | | --- | --- | | [`$project`](https://www.mongodb.com/docs/manual/reference/operator/aggregation/project.md#mongodb-pipeline-pipe.-project) | Excludes all fields except `plot`, `title`, and `genres` from the documents in the results.; Adds a field named `score` that shows the score of the documents the results. | 7. Rerank your ANN (Approximate Nearest Neighbor) results. The following query uses the [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) stage to search the `plot_embedding_voyage_4_large` field for movies. The query passes up to `50` documents from the ANN (Approximate Nearest Neighbor) search to the [`$rerank`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/rerank.md#mongodb-pipeline-pipe.-rerank) stage to rerank the results by semantic relevance to the query. The query uses the `rerank-2.5` model to rerank the results. The query also adds a field named `rerankScore` that shows the rerank score of the documents in the results. ```shell db.embedded_movies.aggregate([ { "$vectorSearch": { "index": "vector_index", "path": "plot_embedding_voyage_4_large", "filter": { "$and": [ { "genres": { "$eq": "Action" } }, { "genres": { "$ne": "Comedy" } } ] }, "queryVector": TIME_TRAVEL_EMBEDDING, "numCandidates": 100, "limit": 50 } }, { "$project": { "_id": 0, "plot": 1, "title": 1, "genres": 1, "score": { $meta: "vectorSearchScore" } } }, { "$rerank": { "query": { "text": "time travel" }, "path": "plot", "numDocsToRerank": 50, "model": "rerank-2.5" } }, { "$addFields": { "rerankScore": { "$meta": "score" } } }, { "$project": { "_id": 0, "plot": 1, "title": 1, "genres": 1, "score": { $meta: "vectorSearchScore" }, "rerankScore": { $meta: "score" } } }, { "$limit": 10 } ]) ``` **Output:** ```shell [ { plot: 'A detective investigates a mysterious motel room, which acts as a portal to an alternate universe.', genres: [ 'Action', 'Fantasy', 'Mystery' ], title: 'The Lost Room', score: 0.6741988658905029, rerankScore: 0.5986876487731934 }, { plot: 'A psychiatrist makes multiple trips through time to save a woman that was murdered by her brutal husband.', genres: [ 'Action', 'Crime', 'Drama' ], title: 'Retroactive', score: 0.7427071332931519, rerankScore: 0.5986876487731934 }, { plot: 'An executed samurai takes an existential journey throughout time, space and eternity in search of bloody vengeance.', genres: [ 'Action', 'Drama', 'Fantasy' ], title: 'Izo', score: 0.6576001644134521, rerankScore: 0.5986876487731934 }, { plot: 'An executed samurai takes an existential journey throughout time, space and eternity in search of bloody vengeance.', genres: [ 'Action', 'Drama', 'Fantasy' ], title: 'Izo', score: 0.6576001644134521, rerankScore: 0.5986876487731934 }, { plot: 'With the help of his uncle, a man travels to the future to try and bring his girlfriend back to life.', genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Love Story 2050', score: 0.7339942455291748, rerankScore: 0.5986876487731934 }, { plot: 'In an alternate Victorian Age world, a group of famous contemporary fantasy, SF and adventure characters team up on a secret mission.', genres: [ 'Action', 'Adventure', 'Fantasy' ], title: 'The League of Extraordinary Gentlemen', score: 0.6491703987121582, rerankScore: 0.5986876487731934 }, { plot: "Ba'al travels back in time and prevents the Stargate program from being started. SG-1 must somehow restore history.", genres: [ 'Action', 'Adventure', 'Drama' ], title: 'Stargate: Continuum', score: 0.7034971714019775, rerankScore: 0.5986876487731934 }, { plot: 'A young woman from the future forces a local gunman to help her stop an impending alien invasion which will wipe out the human race.', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Returner', score: 0.6581127643585205, rerankScore: 0.5986876487731934 }, { plot: 'An interstellar teleportation device, found in Egypt, leads to a planet with humans resembling ancient Egyptians who worship the god Ra.', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Stargate', score: 0.6534044146537781, rerankScore: 0.5986876487731934 }, { plot: 'A teenager with teleportation abilities suddenly finds himself in the middle of an ancient war between those like him and their sworn annihilators.', genres: [ 'Action', 'Adventure', 'Sci-Fi' ], title: 'Jumper', score: 0.6926636695861816, rerankScore: 0.5986876487731934 } ] ``` ### Compare the Results The top nine documents in the example ENN (Exact Nearest Neighbor) and ANN (Approximate Nearest Neighbor) query results are the same and have the same score. This shows a high-level of similarity in the top results for the query. However, the tenth document in the ENN (Exact Nearest Neighbor) and ANN (Approximate Nearest Neighbor) query results is different, which reflects a slight variation in the exact and approximate nearest neighbor search. ENN (Exact Nearest Neighbor) search examines all possible candidates and returns the closest match to the query based on the similarity score. ANN (Approximate Nearest Neighbor) search uses approximations to speed up the search, which might alter the score of the documents. If you increase the `numCandidates` value in the ANN (Approximate Nearest Neighbor) query, the results will be a closer match to the ENN (Exact Nearest Neighbor) query results. However, this would consume additional computational resources and might reduce query speed. The tenth document in the results reflects the trade-off between accuracy and speed. After quantitatively evaluating results against the ENN (Exact Nearest Neighbor) ground truth, we recommend testing a set of 100 queries in the same manner and computing the "jaccard similarity" between the result sets. Jaccard similarity can be computed by dividing the intersection between two sets, that is, the overlapping items, by the total set size. This gives a sense for recall performance of ANN (Approximate Nearest Neighbor) queries, including those performed against quantized vectors. If you notice large discrepancies between your ENN (Exact Nearest Neighbor) and ANN (Approximate Nearest Neighbor) query results, we recommend tuning the `numCandidates` value to strike an ideal balance between accuracy and speed for your application. We recommend that you use judgement lists for a structured list of queries with their ideal results for the ANN (Approximate Nearest Neighbor) query or ENN (Exact Nearest Neighbor) ground truth values. You can use the ENN (Exact Nearest Neighbor) query results as the baseline judgement list and then evaluate ANN (Approximate Nearest Neighbor) query results against this judgement list to measure recall, overlap, and performance. Judgment lists provide a way to evaluate whether ANN (Approximate Nearest Neighbor) queries meet the desired accuracy or recall thresholds compared to the ENN (Exact Nearest Neighbor) baseline. Use LLMs to generate the example queries. ## Improve Accuracy with Reranking To improve the accuracy of your [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) results, you can rerank them by semantic relevance to the query. Reranking reorders your results to prioritize the most pertinent documents. You can apply the [`$rerank`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/rerank.md#mongodb-pipeline-pipe.-rerank) stage after your [`$vectorSearch`](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch) stage to reorder the results using a reranker model. To learn more, see [`$rerank` Aggregation Pipeline Stage.](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/rerank.md#std-label-rerank-agg-pipeline)