improve-accuracy

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

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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.

  1. Construct and run the ENN (Exact Nearest Neighbor) query followed by the ANN (Approximate Nearest Neighbor) query.

  2. 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 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 page displays.

  1. Click Create Search Index.

  2. 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.
  1. 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.

  1. Click Next to review the index.

  2. Click Create Vector Search Index.

Atlas displays a modal window to let you know your index is building.

  1. Close the You're All Set! Modal Window by clicking the Close button.

  2. 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.

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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  1. 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

  1. 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('/<path-to-file>/query-embeddings.js');

  1. 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

  1. 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.
  1. 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 pur

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