How to Perform Semantic Search Against Data in Your Atlas Cluster

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Prerequisites

Create the Atlas Vector Search Index
Required Access
Procedure
Run Queries Using the $vectorSearch Aggregation Pipeline Stage
Overview
Procedure

This tutorial describes how to perform an ANN search on a vector in the plot_embeddings field in the sample_mflix.embedded_movies collection on your Atlas cluster. To demonstrate this, it takes you through the following steps:

Create an Atlas Vector Search index on the numeric field named plot_embeddings in the sample_mflix.embedded_movies collection.
Run Atlas Vector Search queries against the plot_embeddings field in the sample_mflix.embedded_movies collection.

Prerequisites

To complete this tutorial, you must have the following:

An Atlas cluster with MongoDB version 6.0.11, or v7.0.2 or later (including RCs).
The sample data loaded into your Atlas cluster.
Note
Convert the embeddings in the sample data to BSON vectors for efficient storage and ingestion of vectors in Atlas. To learn more, see how to convert native embeddings to BSON vectors.
One of the following applications to run queries on your Atlas cluster:
You can also use Atlas Vector Search with local Atlas deployments that you create with the Atlas CLI. To learn more, see Create a Local Atlas Deployment.

Create the Atlas Vector Search Index

This section demonstrates how to create an Atlas Vector Search index on the plot_embeddings field in the sample_mflix.embedded_movies collection for running vector queries against the field.

Required Access

To create an Atlas Vector Search index, you must have Project Data Access Admin or higher access to the project.

Procedure

In Atlas, go to the Clusters page for your project.

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 the Clusters page is not already displayed, click Database in the sidebar.
The Clusters page displays.

Go to the Atlas Search page for your cluster.

You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.

In the sidebar, click Atlas Search under the Services heading.
From the Select data source dropdown, select your cluster and click Go to Atlas Search.
The Atlas Search page displays.

Click the Browse Collections button for your cluster.
Expand the database and select the collection.
Click the Search Indexes tab for the collection.
The Atlas Search page displays.

Click the cluster's name.
Click the Atlas Search tab.
The Atlas Search page displays.

Define the Atlas Vector Search index.

Click Create Search Index.
Under Atlas Vector Search, select JSON Editor and then click Next.
In the Database and Collection section, find the sample_mflix database, and select the embedded_movies collection.
In the Index Name field, enter vector-search-tutorial.
Replace the default definition with the following index definition and then click Next.

Define the Atlas Vector Search index.

Replace the default definition with the following index definition.

This index definition specifies indexing the following fields in an index of the vectorSearch type:

plot_embedding field as the vector type. The plot_embedding field contains embeddings created using OpenAI's text-embedding-ada-002 embedding model. The index definition specifies 1536 vector dimensions and measures similarity using euclidean.
genres field as the filter type for pre-filtering data by string values in the field.
year field as the filter type for pre-filtering data by numeric values in the field.

1 {
2   "fields": [
3     {
4       "type": "vector",
5       "path": "plot_embedding",
6       "numDimensions": 1536,
7       "similarity": "euclidean"
8     },
9     {
10       "type": "filter",
11       "path": "genres"
12     },
13     {
14       "type": "filter",
15       "path": "year"
16     }
17   ]
18 }

Review the index definition and then click Create Search Index.

A modal window displays to let you know that your index is building.

Click Close to close the You're All Set! modal window and wait for the index to finish building.

The index should take about one minute to build. While it builds, the Status column reads Initial Sync. When it finishes building, the Status column reads Active.

Run Queries Using the `$vectorSearch` Aggregation Pipeline Stage

➤ Use the Select your language drop-down menu to select the client to use to run the example queries in this section.

Overview

This section demonstrates how to query the indexed vector data in the sample_mflix.embedded_movies collection using the the $vectorSearch stage. These sample queries also demonstrate the various query and aggregation pipeline operators that we can use in the query to pre-filter the data that we perform the semantic search on.

Procedure

In Atlas, go to the Clusters page for your project.

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 the Clusters page is not already displayed, click Database in the sidebar.
The Clusters page displays.

Go to the Collections page.

Click the Browse Collections button for your cluster.

The Data Explorer displays.

Go to the Aggregation tab for the collection.

Expand sample_mflix under the list of databases and select embedded_movies from the list of collections in that database.
Click the Aggregation tab for the sample_mflix.embedded_movies collection.

Run the Atlas Vector Search queries against the indexed field.

The following queries use the $vectorSearch pipeline stage to search for movies that match the specified vector embeddings. Note that the queries use ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. The queries specify a search for up to 100 nearest neighbors and limit the results to 10 documents only. The queries also specify a $project stage to perform the following actions:

Exclude the _id field and include only the title, genres, plot, and year fields in the results.
Add a field named score that shows the vector search score for each document in the results.

Click </> Text.

Replace [ ] in the left pane with the query for the pre-filter operator that you want to try.

The following query searches the plot_embedding field using the vector embeddings for the string historical heist. The query uses ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. It specifies multiple comparison query operators per field to pre-filter the documents against which to perform the semantic search.

The filter uses the $and aggregation pipeline operator to find movie documents that match both the following criteria:

Filter by the genres field to find movies that aren't in the drama, western, or crime genre, but in the action, adventure, or family genre.
Filter by the year field to find movies that were released between the years 1960 and 2000, both inclusive.

1 [
2   {
3     "$vectorSearch": {
4       "index": "vector-search-tutorial",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [{
8           "genres": { 
9             "$nin": ["Drama", "Western", "Crime"] ,
10             "$in": ["Action", "Adventure", "Family"]
11           }, 
12         }, {
13           "year": { 
14             "$gte": 1960,
15             "$lte": 2000 
16           }
17         }]
18       },
19       "queryVector": 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20       "numCandidates": 200,
21       "limit": 10
22     }
23   },
24   {
25     "$project": {
26       "_id": 0,
27       "title": 1,
28       "genres": 1,
29       "plot": 1,
30       "year": 1,
31       "score": { $meta: "vectorSearchScore" }
32     }
33   }
34 ]

1 year: 1993
2 plot: "A botched mid-air heist results in suitcases full of cash being search…"
3 genres: Array (3)
4 title: "Cliffhanger"
5 score: 0.7694521546363831
6 
7 genres: Array (3)
8 title: "It's a Mad, Mad, Mad, Mad World"
9 year: 1963
10 score: 0.7638954520225525
11 plot: "The dying words of a thief spark a madcap cross-country rush to find s…"
12 
13 score: 0.7538407444953918
14 year: 1991
15 plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…"
16 genres: Array (3)
17 title: "Hudson Hawk"
18 
19 plot: "In 1997, when the US President crashes into Manhattan, now a giant max…"
20 genres: Array (2)
21 title: "Escape from New York"
22 year: 1981
23 score: 0.7487208843231201
24 
25 title: "The Romanov Stones"
26 year: 1993
27 score: 0.7467736005783081
28 plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…"
29 genres: Array (2)
30 
31 plot: "An attempted robbery turns to be an unexpected recruitment when two un…"
32 genres: Array (3)
33 title: "Crime Busters"
34 year: 1977
35 score: 0.7437351942062378
36 
37 plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …"
38 genres: Array (3)
39 title: "Three Kings"
40 year: 1999
41 score: 0.7425670623779297
42 
43 plot: "A professional thief is hired by the FBI to steal a data tape from a c…"
44 genres: Array (3)
45 title: "Black Moon Rising"
46 year: 1986
47 score: 0.7397696375846863
48 
49 plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…"
50 genres: Array (3)
51 title: "Deep Rising"
52 year: 1998
53 score: 0.7392246127128601
54 
55 year: 1964
56 score: 0.7357995510101318
57 plot: "A young man comes to the rescue of his girlfriend abducted by thieves …"
58 genres: Array (3)
59 title: "That Man from Rio"

The following query searches the plot_embedding field using the vector embeddings for the string martial arts. The query uses ada-002-text embedding, which is the same as the vector embedding in the plot_embedding field. It specifies aggregation pipeline and comparison query operators to demonstrate a combined use of the operators to filter the data.

The filter uses the $or aggregation pipeline operator to find movie documents that match either one of the following criteria:

Filter by the genres field to find movies that aren't in the crime genre.
Filter by the year field to find movies that were released in or before the year 2015 and by the genres field to find movies in the action genre.

1 [
2   {
3     "$vectorSearch": {
4       "index": "vector-search-tutorial",
5       "path": "plot_embedding",
6       "filter": {
7         "$or": [{
8           "genres": { "$ne": "Crime" }
9         }, {
10           "$and": [{
11             "year": { "$lte": 2015 }
12           }, {
13             "genres": { "$eq": "Action" }
14           }]
15         }]
16       },
17       "queryVector": 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18       "numCandidates": 200,
19       "limit": 10
20     }
21   },
22   {
23     "$project": {
24       "_id": 0,
25       "title": 1,
26       "genres": 1,
27       "plot": 1,
28       "released": 1,
29       "score": { $meta: "vectorSearchScore" }
30     }
31   }
32 ]

1 title: "Ong-bak 2"
2 score: 0.7976737022399902
3 year: 2008
4 plot: "A young Thai boxer learns the skills and inner meaning of martial arts…"
5 genres: Array (1)
6 
7 score: 0.7929348349571228
8 plot: "A handyman/martial arts master agrees to teach a bullied boy karate an…"
9 genres: Array (3)
10 title: "The Karate Kid"
11 year: 1984
12 
13 plot: "A young martial artist embarks on an adventure, encountering other mar…"
14 genres: Array (3)
15 title: "Circle of Iron"
16 year: 1978
17 score: 0.7852014899253845
18 
19 plot: "A lionized account of the life of the martial arts superstar."
20 genres: Array (3)
21 title: "Dragon: The Bruce Lee Story"
22 year: 1993
23 score: 0.7763040661811829
24 
25 score: 0.7731232643127441
26 plot: "A martial arts instructor from the police force gets imprisoned after …"
27 genres: Array (2)
28 title: "Kung Fu Killer"
29 year: 2014
30 
31 plot: "A young woman is trained by a martial arts specialist to become a prof…"
32 genres: Array (3)
33 title: "Naked Killer"
34 year: 1992
35 score: 0.7693743109703064
36 
37 plot: "The life of the greatest karate master of a generation."
38 genres: Array (3)
39 title: "The Real Miyagi"
40 year: 2015
41 score: 0.768799901008606
42 
43 plot: "At his new high school, a rebellious teen is lured into an underground…"
44 genres: Array (3)
45 title: "Never Back Down"
46 year: 2008
47 score: 0.768179714679718
48 
49 score: 0.7679706811904907
50 plot: "Three young martial arts masters emerge from the back streets of Hong …"
51 genres: Array (2)
52 title: "Dragon Tiger Gate"
53 year: 2006
54 
55 title: "Shaolin Soccer"
56 score: 0.7660527229309082
57 year: 2001
58 plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
59 genres: Array (3)

Note

The Pipeline Output pane displays the results of your query.

Expand your query results.

The Data Explorer might not display all the values in the documents it returns. To view all the values for the fields that you searched in the query path, expand the fields in the documents.

Connect to your cluster in `mongosh`.

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

Switch to the `sample_mflix` database.

use sample_mflix

switched to db sample_mflix

Run the semantic search query against the indexed fields.

Exclude the _id field and include only the title, genres, plot, and year fields in the results.
Add a field named score that shows the vector search score for each document in the results.

The filter uses the $and aggregation pipeline operator to find movie documents that match both the following criteria:

Filter by the genres field to find movies that aren't in the drama, western, or crime genre, but in the action, adventure, or family genre.
Filter by the year field to find movies that were released between the years 1960 and 2000, both inclusive.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector-search-tutorial",
5       "path": "plot_embedding",
6       "filter": {
7         "$and": [{
8           "genres": { 
9             "$nin": ["Drama", "Western", "Crime"] ,
10             "$in": ["Action", "Adventure", "Family"]
11           }, 
12         }, {
13           "year": { 
14             "$gte": 1960,
15             "$lte": 2000 
16           }
17         }]
18       },
19       "queryVector": 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24   {
25     "$project": {
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31       "score": { $meta: "vectorSearchScore" }
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1 [
2   {
3     year: 1993,
4     plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
5     genres: [ 'Action', 'Adventure', 'Thriller' ],
6     title: 'Cliffhanger',
7     score: 0.7694521546363831
8   },
9   {
10     plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
11     genres: [ 'Action', 'Adventure', 'Comedy' ],
12     title: "It's a Mad, Mad, Mad, Mad World",
13     year: 1963,
14     score: 0.7638954520225525
15   },
16   {
17     year: 1991,
18     plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
19     genres: [ 'Action', 'Adventure', 'Comedy' ],
20     title: 'Hudson Hawk',
21     score: 0.7538407444953918
22   },
23   {
24     plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
25     genres: [ 'Action', 'Sci-Fi' ],
26     title: 'Escape from New York',
27     year: 1981,
28     score: 0.7487208843231201
29   },
30   {
31     plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
32     genres: [ 'Action', 'Thriller' ],
33     title: 'The Romanov Stones',
34     year: 1993,
35     score: 0.7467736005783081
36   },
37   {
38     plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
39     genres: [ 'Action', 'Comedy', 'Adventure' ],
40     title: 'Crime Busters',
41     year: 1977,
42     score: 0.7437351942062378
43   },
44   {
45     plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
46     genres: [ 'Action', 'Adventure', 'Comedy' ],
47     title: 'Three Kings',
48     year: 1999,
49     score: 0.7425670623779297
50   },
51   {
52     plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...',
53     genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
54     title: 'Black Moon Rising',
55     year: 1986,
56     score: 0.7397696375846863
57   },
58   {
59     plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
60     genres: [ 'Action', 'Adventure', 'Horror' ],
61     title: 'Deep Rising',
62     year: 1998,
63     score: 0.7392246127128601
64   },
65   {
66     plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
67     genres: [ 'Action', 'Adventure', 'Comedy' ],
68     title: 'That Man from Rio',
69     year: 1964,
70     score: 0.7357995510101318
71   }
72 ]

The filter uses the $or aggregation pipeline operator to find movie documents that match either one of the following criteria:

Filter by the genres field to find movies that aren't in the crime genre.
Filter by the year field to find movies that were released in or before the year 2015 and by the genres field to find movies in the action genre.

1 db.embedded_movies.aggregate([
2   {
3     "$vectorSearch": {
4       "index": "vector-search-tutorial",
5       "path": "plot_embedding",
6       "filter": {
7         "$or": [{
8           "genres": { "$ne": "Crime" }
9         }, {
10           "$and": [{
11             "year": { "$lte": 2015 }
12           }, {
13             "genres": { "$eq": "Action" }
14           }]
15         }]
16       },
17       "queryVector": 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1	{
2	"fields": [
3	{
4	"type": "vector",
5	"path": "plot_embedding",
6	"numDimensions": 1536,
7	"similarity": "euclidean"
8	},
9	{
10	"type": "filter",
11	"path": "genres"
12	},
13	{
14	"type": "filter",
15	"path": "year"
16	}
17	]
18	}

1	[
2	{
3	"$vectorSearch": {
4	"index": "vector-search-tutorial",
5	"path": "plot_embedding",
6	"filter": {
7	"$and": [{
8	"genres": {
9	"$nin": ["Drama", "Western", "Crime"] ,
10	"$in": ["Action", "Adventure", "Family"]
11	},
12	}, {
13	"year": {
14	"$gte": 1960,
15	"$lte": 2000
16	}
17	}]
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20	"numCandidates": 200,
21	"limit": 10
22	}
23	},
24	{
25	"$project": {
26	"_id": 0,
27	"title": 1,
28	"genres": 1,
29	"plot": 1,
30	"year": 1,
31	"score": { $meta: "vectorSearchScore" }
32	}
33	}
34	]

1	year: 1993
2	plot: "A botched mid-air heist results in suitcases full of cash being search…"
3	genres: Array (3)
4	title: "Cliffhanger"
5	score: 0.7694521546363831
6
7	genres: Array (3)
8	title: "It's a Mad, Mad, Mad, Mad World"
9	year: 1963
10	score: 0.7638954520225525
11	plot: "The dying words of a thief spark a madcap cross-country rush to find s…"
12
13	score: 0.7538407444953918
14	year: 1991
15	plot: "A cat burglar is forced to steal Da Vinci works of art for a world dom…"
16	genres: Array (3)
17	title: "Hudson Hawk"
18
19	plot: "In 1997, when the US President crashes into Manhattan, now a giant max…"
20	genres: Array (2)
21	title: "Escape from New York"
22	year: 1981
23	score: 0.7487208843231201
24
25	title: "The Romanov Stones"
26	year: 1993
27	score: 0.7467736005783081
28	plot: "Patrick and Tony are hired by the wealthy gambler to steal the pricele…"
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31	plot: "An attempted robbery turns to be an unexpected recruitment when two un…"
32	genres: Array (3)
33	title: "Crime Busters"
34	year: 1977
35	score: 0.7437351942062378
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37	plot: "In the aftermath of the Persian Gulf War, 4 soldiers set out to steal …"
38	genres: Array (3)
39	title: "Three Kings"
40	year: 1999
41	score: 0.7425670623779297
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43	plot: "A professional thief is hired by the FBI to steal a data tape from a c…"
44	genres: Array (3)
45	title: "Black Moon Rising"
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47	score: 0.7397696375846863
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49	plot: "A group of heavily armed hijackers board a luxury ocean liner in the S…"
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53	score: 0.7392246127128601
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55	year: 1964
56	score: 0.7357995510101318
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59	title: "That Man from Rio"

1	title: "Ong-bak 2"
2	score: 0.7976737022399902
3	year: 2008
4	plot: "A young Thai boxer learns the skills and inner meaning of martial arts…"
5	genres: Array (1)
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7	score: 0.7929348349571228
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9	genres: Array (3)
10	title: "The Karate Kid"
11	year: 1984
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13	plot: "A young martial artist embarks on an adventure, encountering other mar…"
14	genres: Array (3)
15	title: "Circle of Iron"
16	year: 1978
17	score: 0.7852014899253845
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19	plot: "A lionized account of the life of the martial arts superstar."
20	genres: Array (3)
21	title: "Dragon: The Bruce Lee Story"
22	year: 1993
23	score: 0.7763040661811829
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25	score: 0.7731232643127441
26	plot: "A martial arts instructor from the police force gets imprisoned after …"
27	genres: Array (2)
28	title: "Kung Fu Killer"
29	year: 2014
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31	plot: "A young woman is trained by a martial arts specialist to become a prof…"
32	genres: Array (3)
33	title: "Naked Killer"
34	year: 1992
35	score: 0.7693743109703064
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37	plot: "The life of the greatest karate master of a generation."
38	genres: Array (3)
39	title: "The Real Miyagi"
40	year: 2015
41	score: 0.768799901008606
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43	plot: "At his new high school, a rebellious teen is lured into an underground…"
44	genres: Array (3)
45	title: "Never Back Down"
46	year: 2008
47	score: 0.768179714679718
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49	score: 0.7679706811904907
50	plot: "Three young martial arts masters emerge from the back streets of Hong …"
51	genres: Array (2)
52	title: "Dragon Tiger Gate"
53	year: 2006
54
55	title: "Shaolin Soccer"
56	score: 0.7660527229309082
57	year: 2001
58	plot: "A young Shaolin follower reunites with his discouraged brothers to for…"
59	genres: Array (3)

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1	[
2	{
3	year: 1993,
4	plot: 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.',
5	genres: [ 'Action', 'Adventure', 'Thriller' ],
6	title: 'Cliffhanger',
7	score: 0.7694521546363831
8	},
9	{
10	plot: 'The dying words of a thief spark a madcap cross-country rush to find some treasure.',
11	genres: [ 'Action', 'Adventure', 'Comedy' ],
12	title: "It's a Mad, Mad, Mad, Mad World",
13	year: 1963,
14	score: 0.7638954520225525
15	},
16	{
17	year: 1991,
18	plot: 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.',
19	genres: [ 'Action', 'Adventure', 'Comedy' ],
20	title: 'Hudson Hawk',
21	score: 0.7538407444953918
22	},
23	{
24	plot: 'In 1997, when the US President crashes into Manhattan, now a giant maximum security prison, a convicted bank robber is sent in for a rescue.',
25	genres: [ 'Action', 'Sci-Fi' ],
26	title: 'Escape from New York',
27	year: 1981,
28	score: 0.7487208843231201
29	},
30	{
31	plot: 'Patrick and Tony are hired by the wealthy gambler to steal the priceless Romanov stones, Russian jewels. They do it, but almost lose their lives when he double-crosses them. They turn around and get revenge.',
32	genres: [ 'Action', 'Thriller' ],
33	title: 'The Romanov Stones',
34	year: 1993,
35	score: 0.7467736005783081
36	},
37	{
38	plot: 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.',
39	genres: [ 'Action', 'Comedy', 'Adventure' ],
40	title: 'Crime Busters',
41	year: 1977,
42	score: 0.7437351942062378
43	},
44	{
45	plot: 'In the aftermath of the Persian Gulf War, 4 soldiers set out to steal gold that was stolen from Kuwait, but they discover people who desperately need their help.',
46	genres: [ 'Action', 'Adventure', 'Comedy' ],
47	title: 'Three Kings',
48	year: 1999,
49	score: 0.7425670623779297
50	},
51	{
52	plot: 'A professional thief is hired by the FBI to steal a data tape from a company under investigation. The analysis of this tape, will prove the criminal activities of this company. As this ...',
53	genres: [ 'Action', 'Sci-Fi', 'Thriller' ],
54	title: 'Black Moon Rising',
55	year: 1986,
56	score: 0.7397696375846863
57	},
58	{
59	plot: 'A group of heavily armed hijackers board a luxury ocean liner in the South Pacific Ocean to loot it, only to do battle with a series of large-sized, tentacled, man-eating sea creatures who have taken over the ship first.',
60	genres: [ 'Action', 'Adventure', 'Horror' ],
61	title: 'Deep Rising',
62	year: 1998,
63	score: 0.7392246127128601
64	},
65	{
66	plot: 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.',
67	genres: [ 'Action', 'Adventure', 'Comedy' ],
68	title: 'That Man from Rio',
69	year: 1964,
70	score: 0.7357995510101318
71	}
72	]

Prerequisites

Note

Create the Atlas Vector Search Index

Required Access

Procedure

In Atlas, go to the .css-h15tq0{font-style:normal;font-weight:700;}Clusters page for your project.

Go to the Atlas Search page for your cluster.

Define the Atlas Vector Search index.

Define the Atlas Vector Search index.

Review the index definition and then click Create Search Index.

Click Close to close the You're All Set! modal window and wait for the index to finish building.

Run Queries Using the $vectorSearch Aggregation Pipeline Stage

Overview

Procedure

In Atlas, go to the Clusters page for your project.

Go to the Collections page.

Go to the Aggregation tab for the collection.

Run the Atlas Vector Search queries against the indexed field.

Note

Expand your query results.

Connect to your cluster in mongosh.

Switch to the sample_mflix database.

Run the semantic search query against the indexed fields.

In Atlas, go to the Clusters page for your project.

Run Queries Using the `$vectorSearch` Aggregation Pipeline Stage

Connect to your cluster in `mongosh`.

Switch to the `sample_mflix` database.