You can measure the accuracy of your MongoDB Vector Search query by evaluating how closely the results for an ANN search match the results of an ENN search against quantized vectors by using the same query criteria. That is, you can compare the results of ANN search with the results of ENN search and measure how frequently the ANN search results include the nearest neighbors in the results from the ENN 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:
An Atlas cluster with, optionally, the sample dataset.
➤ Use the Select your language drop-down menu to select the interface you want to use to create your index.
Procedures
To evaluate the accuracy of your $vectorSearch query
results, you must do the following:
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
vectortype field.Construct and run the ENN query followed by the ANN query.
Compare the results of the ANN query with the results of the ENN 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 and ENN queries.
1
In Atlas, go to the Clusters page for your project.
WARNING: Navigation Improvements In Progress We're currently rolling out a new and improved navigation experience. If the following steps don't match your view in the Atlas UI, see the preview documentation.
If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it's not already displayed, select your desired project from the Projects menu in the navigation bar.
If it's not already displayed, click Clusters in the sidebar.
The Clusters page displays.
2
4
Start your index configuration.
Make the following selections on the page and then click Next.
Search Type | Select the Vector Search index type. |
Index Name and Data Source | Specify the following information:
|
Configuration Method | For a guided experience, select Visual Editor. To edit the raw index definition, select JSON Editor. |
IMPORTANT: .. include:: /includes/fts/facts/fact-default-index-name.rst
5
Specify the index definition.
Example
This index definition indexes the plot_embedding_voyage_3_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_3_large field contains embeddings created
using Voyage AI's voyage-3-large embedding model. The
index definition specifies 2048 vector dimensions and measures
distance using dotProduct similarity function.
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_3_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
genresfield to filter the data by.
Paste the following index definition in the JSON editor:
1 { 2 "fields": [ 3 { 4 "numDimensions": 2048, 5 "path": "plot_embedding_voyage_3_large", 6 "similarity": "dotProduct", 7 "type": "vector", 8 "quantization": "binary" 9 }, 10 { 11 "path": "genres", 12 "type": "filter" 13 } 14 ] 15 }
7
9
Check the status.
The newly created index displays on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.
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.
In your terminal, connect to your Atlas cloud-hosted
deployment or local deployment from mongosh. For detailed
instructions on how to connect, see
Connect to a Deployment.
2
3
Run the db.collection.createSearchIndex() method to create the index.
db.embedded_movies.createSearchIndex( "vector_index", "vectorSearch", { "fields": [ { "numDimensions": 2048, "path": "plot_embedding_voyage_3_large", "similarity": "dotProduct", "type": "vector", "quantization": "binary" }, { "path": "genres", "type": "filter" } ] } } )
vector_index
Run the Queries
You can't run MongoDB Vector Search queries in the Atlas UI
Search Tester. Use mongosh or a supported driver
to run the query.
This section demonstrates how to run the ENN and ANN queries against the indexed collection.
1
Prepare your query embeddings.
Save the following embeddings in a file named query-embeddings.js:
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2
Connect to your Atlas cluster using mongosh.
Open a terminal window and connect to your cluster by using
mongosh. To learn more, see Connect to a Cluster via mongosh.
3
4
Switch to your database.
Example
Use the sample_mflix database. To switch to the
sample_mflix database, run the following command at
mongosh prompt:
use sample_mflix
switched to db sample_mflix
5
Run your ENN query.
Example
Copy and paste the following sample query into your terminal and
then run it using mongosh. mongosh might lag slightly when
you paste in the query due to the number of characters in the
vector embedding.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "exact": true, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { 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: 0.760047972202301 }, { 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: 0.7576861381530762 }, { 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: 0.7576561570167542 }, { 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: 0.7509932518005371 }, { 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: 0.7502642869949341 }, { plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { 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: 0.7468316555023193 }, { 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: 0.7420939207077026 }, { 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: 0.7356286644935608 } ]
This query uses the following pipeline stages:
| |
|
6
Run your ANN query.
Example
Copy and paste the following sample query into your terminal and
then run it using mongosh. mongosh might lag slightly when
you paste in the query due to the number of characters in the
vector embedding.
1 db.embedded_movies.aggregate([ 2 { 3 "$vectorSearch": { 4 "index": "vector_index", 5 "path": "plot_embedding_voyage_3_large", 6 "filter": { 7 "$and": [ 8 { 9 "genres": { "$eq": "Action" } 10 }, 11 { 12 "genres": { "$ne": "Comedy" } 13 } 14 ] 15 }, 16 "queryVector": TIME_TRAVEL_EMBEDDING, 17 "numCandidates": 100, 18 "limit": 10 19 } 20 }, 21 { 22 "$project": { 23 "_id": 0, 24 "plot": 1, 25 "title": 1, 26 "genres": 1, 27 "score": { $meta: "vectorSearchScore" } 28 } 29 } 30 ])
[ { 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: 0.760047972202301 }, { 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: 0.7576861381530762 }, { 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: 0.7576561570167542 }, { 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: 0.7509932518005371 }, { 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: 0.7502642869949341 }, { plot: 'Hoping to alter the events of the past, a 19th century inventor instead travels 800,000 years into the future, where he finds humankind divided into two warring races.', genres: [ 'Sci-Fi', 'Adventure', 'Action' ], title: 'The Time Machine', score: 0.7502503395080566 }, { plot: 'A modern aircraft carrier is thrown back in time to 1941 near Hawaii, just hours before the Japanese attack on Pearl Harbor.', genres: [ 'Action', 'Sci-Fi' ], title: 'The Final Countdown', score: 0.7469133734703064 }, { 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: 0.7468316555023193 }, { 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: 0.7420939207077026 }, { 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: 0.7356286644935608 } ]
This query uses the following pipeline stages:
| |
|
Compare the Results
The top nine documents in the example ENN and ANN 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 and ANN query results is different, which reflects a slight variation in the exact and approximate nearest neighbor search.
ENN search examines all possible candidates and returns the
closest match to the query based on the similarity score. ANN 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 query, the results will be a closer match to the ENN
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 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 queries, including those performed against quantized vectors.
If you notice large discrepancies between your ENN and ANN 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 query or ENN ground truth values. You can use the ENN query results as the baseline judgement list and then evaluate ANN query results against this judgement list to measure recall, overlap, and performance. Judgment lists provide a way to evaluate whether ANN queries meet the desired accuracy or recall thresholds compared to the ENN baseline. Use LLMs to generate the example queries.