How to Perform Semantic Search Against Data in Your Atlas Cluster
On this page
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 thesample_mflix.embedded_movies
collection.Run Atlas Vector Search queries against the
plot_embeddings
field in thesample_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.
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 theembedded_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. Theplot_embedding
field contains embeddings created using OpenAI'stext-embedding-ada-002
embedding model. The index definition specifies1536
vector dimensions and measures similarity usingeuclidean
.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 }
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.
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 thetitle
,genres
,plot
, andyear
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 usesada-002-text
embedding, which is the same as the vector embedding in theplot_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
$and
aggregation pipeline operator to find movie documents that match both the following criteria:Filter by the
genres
field to find movies that aren't in thedrama
,western
, orcrime
genre, but in theaction
,adventure
, orfamily
genre.Filter by the
year
field to find movies that were released between the years1960
and2000
, both inclusive.
1 [ 2 { 3 "$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 usesada-002-text
embedding, which is the same as the vector embedding in theplot_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
$or
aggregation pipeline operator to find movie documents that match either one of the following criteria:Filter by the
genres
field to find movies that aren't in thecrime
genre.Filter by the
year
field to find movies that were released in or before the year2015
and by thegenres
field to find movies in theaction
genre.
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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$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.
Run the semantic search query against the indexed fields.
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 thetitle
,genres
,plot
, andyear
fields in the results.Add a field named
score
that shows the vector search score for each document in the results.
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 $and
aggregation pipeline operator to find
movie documents that match both the following criteria:
Filter by the
genres
field to find movies that aren't in thedrama
,western
, orcrime
genre, but in theaction
,adventure
, orfamily
genre.Filter by the
year
field to find movies that were released between the years1960
and2000
, both inclusive.
1 db.embedded_movies.aggregate([ 2 { 3 "$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 "$lte": 2000 16 } 17 }] 18 }, 19 "queryVector": 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20 "numCandidates": 200, 21 "limit": 10 22 } 23 }, 24 { 25 "$project": { 26 "_id": 0, 27 "title": 1, 28 "genres": 1, 29 "plot": 1, 30 "year": 1, 31 "score": { $meta: "vectorSearchScore" } 32 } 33 } 34 ])
1 [ 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 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 $or
aggregation pipeline operator to find
movie documents that match either one of the following criteria:
Filter by the
genres
field to find movies that aren't in thecrime
genre.Filter by the
year
field to find movies that were released in or before the year2015
and by thegenres
field to find movies in theaction
genre.
1 db.embedded_movies.aggregate([ 2 { 3 "$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": { "$eq": "Action" } 14 }] 15 }] 16 }, 17 "queryVector": 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18 "numCandidates": 200, 19 "limit": 10 20 } 21 }, 22 { 23 "$project": { 24 "_id": 0, 25 "title": 1, 26 "genres": 1, 27 "plot": 1, 28 "released": 1, 29 "score": { $meta: "vectorSearchScore" } 30 } 31 } 32 ])
1 [ 2 { 3 year: 2008, 4 plot: 'A young Thai boxer learns the skills and inner meaning of martial arts.', 5 genres: [ 'Action' ], 6 title: 'Ong-bak 2', 7 score: 0.7976737022399902 8 }, 9 { 10 plot: 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.', 11 genres: [ 'Action', 'Drama', 'Family' ], 12 title: 'The Karate Kid', 13 year: 1984, 14 score: 0.7929348349571228 15 }, 16 { 17 plot: 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.', 18 genres: [ 'Action', 'Adventure', 'Fantasy' ], 19 title: 'Circle of Iron', 20 year: 1978, 21 score: 0.7852014899253845 22 }, 23 { 24 plot: 'A lionized account of the life of the martial arts superstar.', 25 genres: [ 'Action', 'Biography', 'Drama' ], 26 title: 'Dragon: The Bruce Lee Story', 27 year: 1993, 28 score: 0.7763040661811829 29 }, 30 { 31 plot: 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.', 32 genres: [ 'Action', 'Thriller' ], 33 title: 'Kung Fu Killer', 34 year: 2014, 35 score: 0.7731232643127441 36 }, 37 { 38 plot: 'A young woman is trained by a martial arts specialist to become a professional assassin.', 39 genres: [ 'Action', 'Crime', 'Romance' ], 40 title: 'Naked Killer', 41 year: 1992, 42 score: 0.7693743109703064 43 }, 44 { 45 plot: 'The life of the greatest karate master of a generation.', 46 genres: [ 'Documentary', 'Action', 'History' ], 47 title: 'The Real Miyagi', 48 year: 2015, 49 score: 0.768799901008606 50 }, 51 { 52 plot: 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.', 53 genres: [ 'Action', 'Drama', 'Sport' ], 54 title: 'Never Back Down', 55 year: 2008, 56 score: 0.768179714679718 57 }, 58 { 59 plot: 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.', 60 genres: [ 'Action', 'Drama' ], 61 title: 'Dragon Tiger Gate', 62 year: 2006, 63 score: 0.7679706811904907 64 }, 65 { 66 year: 2001, 67 plot: 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.', 68 genres: [ 'Action', 'Comedy', 'Sport' ], 69 title: 'Shaolin Soccer', 70 score: 0.7660527229309082 71 } 72 ] 73
Copy and paste the code for the pre-filter operator you want to try into the AtlasVectorSearchTutorial.java
file.
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 thetitle
,genres
,plot
, andyear
fields in the results.Add a field named
score
that shows the vector search score for each document in the results.
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 thedrama
,western
, orcrime
genre, but in theaction
,adventure
, orfamily
genre.Filter by the
year
field to find movies that were released between the years1960
and2000
, both inclusive.
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions; 5 import static java.util.Arrays.asList; 6 7 import com.mongodb.client.MongoClient; 8 import com.mongodb.client.MongoClients; 9 import com.mongodb.client.MongoCollection; 10 import com.mongodb.client.MongoDatabase; 11 import com.mongodb.client.model.*; 12 import com.mongodb.client.model.search.VectorSearchOptions; 13 import java.util.Arrays; 14 import java.util.List; 15 import com.mongodb.client.model.search.FieldSearchPath; 16 import org.bson.Document; 17 import org.bson.conversions.Bson; 18 19 public class AtlasVectorSearchTutorial { 20 public static void main( String[] args ) { 21 // specify connection 22 String uri = "<connection-string>"; 23 24 // establish connection and set namespace 25 try (MongoClient mongoClient = MongoClients.create(uri)) { 26 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 27 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 28 29 // define $vectorSearch query options 30 List<Double> queryVector = 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31 String indexName = "vector-search-tutorial"; 32 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 33 int numCandidates = 200; 34 int limit = 10; 35 List<String> genresNotInCriteria = Arrays.asList("Drama", "Western", "Crime"); 36 List<String> genresInCriteria = Arrays.asList("Action", "Adventure", "Family"); 37 Bson criteria = Filters.and(Filters.nin("genres", genresNotInCriteria), Filters.in("genres", genresInCriteria), Filters.gte("year", 1960), Filters.lte("year", 2000)); 38 VectorSearchOptions options = vectorSearchOptions().filter(criteria); 39 40 // define pipeline 41 List<Bson> pipeline = asList( 42 vectorSearch( 43 fieldSearchPath, 44 queryVector, 45 indexName, 46 numCandidates, 47 limit, 48 options), 49 project( 50 fields(exclude("_id"), include("title"), include("plot"), include("year"), 51 include("genres"), metaVectorSearchScore("score")))); 52 53 // run query and print results 54 collection.aggregate(pipeline) 55 .forEach(doc -> System.out.println(doc.toJson())); 56 } 57 } 58 }
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 thecrime
genre.Filter by the
year
field to find movies that were released in or before the year2015
and by thegenres
field to find movies in theaction
genre.
1 import static com.mongodb.client.model.Aggregates.*; 2 import static com.mongodb.client.model.Projections.*; 3 import static com.mongodb.client.model.search.SearchPath.fieldPath; 4 import static com.mongodb.client.model.search.VectorSearchOptions.vectorSearchOptions; 5 import static java.util.Arrays.asList; 6 7 import com.mongodb.client.MongoClient; 8 import com.mongodb.client.MongoClients; 9 import com.mongodb.client.MongoCollection; 10 import com.mongodb.client.MongoDatabase; 11 import com.mongodb.client.model.*; 12 import com.mongodb.client.model.search.VectorSearchOptions; 13 import java.util.List; 14 import com.mongodb.client.model.search.FieldSearchPath; 15 import org.bson.Document; 16 import org.bson.conversions.Bson; 17 18 public class AtlasVectorSearchTutorial { 19 public static void main( String[] args ) { 20 // specify connection 21 String uri = "<connection-string>"; 22 23 // establish connection and set namespace 24 try (MongoClient mongoClient = MongoClients.create(uri)) { 25 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 26 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 27 28 // define $vectorSearch query options 29 List<Double> queryVector = 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30 String indexName = "vector-search-tutorial"; 31 FieldSearchPath fieldSearchPath = fieldPath("plot_embedding"); 32 int numCandidates = 200; 33 int limit = 10; 34 Bson criteria = Filters.or(Filters.lte("year", 2015), Filters.and(Filters.lte("year", 2015), Filters.eq("genres", "Action"))); 35 VectorSearchOptions options = vectorSearchOptions().filter(criteria); 36 37 // define pipeline 38 List<Bson> pipeline = asList( 39 vectorSearch( 40 fieldSearchPath, 41 queryVector, 42 indexName, 43 numCandidates, 44 limit, 45 options), 46 project( 47 fields(exclude("_id"), include("title"), include("plot"), include("year"), 48 include("genres"), metaVectorSearchScore("score")))); 49 50 // run query and print results 51 collection.aggregate(pipeline) 52 .forEach(doc -> System.out.println(doc.toJson())); 53 } 54 } 55 }
Note
To run the sample code in your Maven environment, add the following code above the import statements in your file.
package com.mongodb.drivers;
Replace the <connection-string>
with your Atlas connection string and then save the file.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Compile and run AtlasVectorSearchTutorial.java
file.
javac AtlasVectorSearchTutorial.java java AtlasVectorSearchTutorial
1 { 2 "year": 1993, 3 "plot": "A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", 4 "genres": ["Action", "Adventure", "Thriller"], 5 "title": "Cliffhanger", 6 "score": 0.7694521546363831 7 } 8 { 9 "plot": "The dying words of a thief spark a madcap cross-country rush to find some treasure.", 10 "genres": ["Action", "Adventure", "Comedy"], 11 "title": "It's a Mad, Mad, Mad, Mad World", 12 "year": 1963, 13 "score": 0.7638954520225525 14 } 15 { 16 "year": 1991, 17 "plot": "A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", 18 "genres": ["Action", "Adventure", "Comedy"], 19 "title": "Hudson Hawk", 20 "score": 0.7538407444953918 21 } 22 { 23 "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.", 24 "genres": ["Action", "Sci-Fi"], 25 "title": "Escape from New York", 26 "year": 1981, 27 "score": 0.7487208843231201 28 } 29 { 30 "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.", 31 "genres": ["Action", "Thriller"], 32 "title": "The Romanov Stones", 33 "year": 1993, 34 "score": 0.7467736005783081 35 } 36 { 37 "plot": "An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", 38 "genres": ["Action", "Comedy", "Adventure"], 39 "title": "Crime Busters", 40 "year": 1977, 41 "score": 0.7437351942062378 42 } 43 { 44 "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.", 45 "genres": ["Action", "Adventure", "Comedy"], 46 "title": "Three Kings", 47 "year": 1999, 48 "score": 0.7425670623779297 49 } 50 { 51 "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 ...", 52 "genres": ["Action", "Sci-Fi", "Thriller"], 53 "title": "Black Moon Rising", 54 "year": 1986, 55 "score": 0.7397696375846863 56 } 57 { 58 "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.", 59 "genres": ["Action", "Adventure", "Horror"], 60 "title": "Deep Rising", 61 "year": 1998, 62 "score": 0.7392246127128601 63 } 64 { 65 "plot": "A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", 66 "genres": ["Action", "Adventure", "Comedy"], 67 "title": "That Man from Rio", 68 "year": 1964, 69 "score": 0.7357995510101318 70 }
javac AtlasVectorSearchTutorial.java java AtlasVectorSearchTutorial
1 { 2 "year": 2008, 3 "plot": "A young Thai boxer learns the skills and inner meaning of martial arts.", 4 "genres": ["Action"], 5 "title": "Ong-bak 2", 6 "score": 0.7976737022399902 7 } 8 { 9 "plot": "A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", 10 "genres": ["Action", "Drama", "Family"], 11 "title": "The Karate Kid", 12 "year": 1984, 13 "score": 0.7929348349571228 14 } 15 { 16 "plot": "A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.", 17 "genres": ["Action", "Adventure", "Fantasy"], 18 "title": "Circle of Iron", 19 "year": 1978, 20 "score": 0.7852014899253845 21 } 22 { 23 "plot": "A lionized account of the life of the martial arts superstar.", 24 "genres": ["Action", "Biography", "Drama"], 25 "title": "Dragon: The Bruce Lee Story", 26 "year": 1993, 27 "score": 0.7763040661811829 28 } 29 { 30 "plot": "A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", 31 "genres": ["Action", "Thriller"], 32 "title": "Kung Fu Killer", 33 "year": 2014, 34 "score": 0.7731232643127441 35 } 36 { 37 "plot": "A young woman is trained by a martial arts specialist to become a professional assassin.", 38 "genres": ["Action", "Crime", "Romance"], 39 "title": "Naked Killer", 40 "year": 1992, 41 "score": 0.7693743109703064 42 } 43 { 44 "plot": "The life of the greatest karate master of a generation.", 45 "genres": ["Documentary", "Action", "History"], 46 "title": "The Real Miyagi", 47 "year": 2015, 48 "score": 0.768799901008606 49 } 50 { 51 "plot": "At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", 52 "genres": ["Action", "Drama", "Sport"], 53 "title": "Never Back Down", 54 "year": 2008, 55 "score": 0.768179714679718 56 } 57 { 58 "plot": "Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", 59 "genres": ["Action", "Drama"], 60 "title": "Dragon Tiger Gate", 61 "year": 2006, 62 "score": 0.7679706811904907 63 } 64 { 65 "year": 2001, 66 "plot": "A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", 67 "genres": ["Action", "Comedy", "Sport"], 68 "title": "Shaolin Soccer", 69 "score": 0.7660527229309082 70 }
Copy and paste the sample query for the pre-filter operator you want to try into the atlas-vector-search-tutorial.js
file.
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 thetitle
,genres
,plot
, andyear
fields in the results.Add a field named
score
that shows the vector search score for each document in the results.
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 $and
aggregation pipeline operator to find
movie documents that match both the following criteria:
Filter by the
genres
field to find movies that aren't in thedrama
,western
, orcrime
genre, but in theaction
,adventure
, orfamily
genre.Filter by the
year
field to find movies that were released between the years1960
and2000
, both inclusive.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector-search-tutorial', 21 'path': 'plot_embedding', 22 'filter': { 23 '$and': [ 24 { 25 'genres': { 26 '$nin': [ 27 'Drama', 'Western', 'Crime' 28 ], 29 '$in': [ 30 'Action', 'Adventure', 'Family' 31 ] 32 } 33 }, { 34 'year': { 35 '$gte': 1960, 36 '$lte': 2000 37 } 38 } 39 ] 40 }, 41 'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, 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'vectorSearchScore' 54 } 55 } 56 } 57 ]; 58 59 // run pipeline 60 const result = coll.aggregate(agg); 61 62 // print results 63 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 64 } finally { 65 await client.close(); 66 } 67 } 68 run().catch(console.dir);
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 $or
aggregation pipeline operator to find
movie documents that match either one of the following criteria:
Filter by the
genres
field to find movies that aren't in thecrime
genre.Filter by the
year
field to find movies that were released in or before the year2015
and by thegenres
field to find movies in theaction
genre.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas cluster 4 const uri = "<connection-string>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 await client.connect(); 11 12 // set namespace 13 const database = client.db("sample_mflix"); 14 const coll = database.collection("embedded_movies"); 15 16 // define pipeline 17 const agg = [ 18 { 19 '$vectorSearch': { 20 'index': 'vector-search-tutorial', 21 'path': 'plot_embedding', 22 'filter': { 23 '$or': [ 24 { 25 'genres': { 26 '$ne': 'Crime' 27 } 28 }, { 29 '$and': [ 30 { 31 'year': { 32 '$lte': 2015 33 } 34 }, { 35 'genres': { 36 '$eq': 'Action' 37 } 38 } 39 ] 40 } 41 ] 42 }, 43 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 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-0.027261203, 0.024100672, 0.000711282, 0.001071394, -0.0037263047, -0.007829792, -0.0049456456, -0.021603463, 0.03274986, 0.01866404, -0.032567773, -0.0044318964, 0.017935688, 0.018638028, -0.015282402, -0.009377542, 0.03012259, 0.015178352, -0.004792821, -0.048305396, 0.007075427, 0.020784067, 0.010678172, 0.0019509449, 0.0045684627, 0.02242286, -0.011374009, 0.024477856, -0.0021736778, -0.021291312, -0.009188951, 0.02692304, -0.0038628709, -0.02442583, -0.029394237, 0.019054228, 0.01606278, -0.0035051976, -0.00012284856, 0.01911926, 0.0088768, 0.007036408, 0.0015461239, -0.0030711126, 0.0034629272, -0.015152339, 0.02097916, -0.031163093, 0.0061909985, 0.012694148, -0.021902608, -0.030044552, 0.031553283, 0.0002003783, -0.037119977, 0.0003367412, 0.015347433, 0.003105254, 0.034102518, 0.001911926, -0.008102925, 0.0003306445, 0.011647142, -0.004288827, 0.019665524, -0.011907267, 0.01391674, 0.0014428863, -0.01655702, -0.0020387375, 0.009975832, -0.0009982334, -0.018586002, -0.0137606645, -0.031787395, 0.02481602, 0.0059308726, -0.0011941409, -0.009598649, 0.0126811415, 0.015828667, -0.0058918535, -0.0040807263, -0.00406772, -0.0015233628, -0.01402079, -0.0052577965, 0.0018290109, -0.018182807, -0.014736137, 0.014423986, -0.001820882, -0.013799684, 0.0029085337, 0.009949819, 0.02407466, 0.01992565, -0.01986062, 0.02892601, 0.0029768168, 0.00044018193, -0.012882739, 0.022487892, 0.0061682374, 0.014528036, 0.0021785551, 0.006304804, 0.0010722068, -0.0075696665, -0.0040417076, 0.0097937435, -0.01216089, -0.0044026324, -0.018494958, 0.053898104, -0.0037100469, -0.013929747, 0.0210572, 0.014293923, 0.028613858, -0.005791055, -0.008863793, -0.021668496, 0.0011803217, 0.0053781047, -0.020588972, -0.030304678, -0.015893698, -0.0041295, 0.04705679, 0.020198783, -0.007010395, 0.0039344057, 0.00019905735, 0.010587128, 0.0026549108, -0.015165345, -0.023996623, 0.01476215, -0.0021200269, 0.0458342, 0.02221476, -0.021174256, 0.009000359, 0.027573355, -0.007634698, -0.0053520924, -0.0032190592, 0.010196939, 0.014033797, 0.01203733, -0.032307647, -0.00873373, -0.016609045, -0.014593068, -0.014658099, 0.015828667, -0.024087666, 0.03043474, -0.0020598727, 0.006327565, 0.0019850864, -0.004236802, 0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 44 'numCandidates': 200, 45 'limit': 10 46 } 47 }, { 48 '$project': { 49 '_id': 0, 50 'title': 1, 51 'genres': 1, 52 'plot': 1, 53 'released': 1, 54 'score': { 55 '$meta': 'vectorSearchScore' 56 } 57 } 58 } 59 ]; 60 61 // run pipeline 62 const result = coll.aggregate(agg); 63 64 // print results 65 await result.forEach((doc) => console.dir(JSON.stringify(doc))); 66 } finally { 67 await client.close(); 68 } 69 } 70 run().catch(console.dir);
Replace the <connection-string>
in the query and then save the file.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Query your collection.
Run the following command to query your collection:
node atlas-vector-search-tutorial.js
'{ "year":1993, "plot":"A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.", "genres":["Action","Adventure","Thriller"], "title":"Cliffhanger", "score":0.7694521546363831 }' `{ "plot":"The dying words of a thief spark a madcap cross-country rush to find some treasure.", "genres":["Action","Adventure","Comedy"], "title":"It's a Mad, Mad, Mad, Mad World", "year":1963, "score":0.7638954520225525 }` '{ "year":1991, "plot":"A cat burglar is forced to steal Da Vinci works of art for a world domination plot.", "genres":["Action","Adventure","Comedy"], "title":"Hudson Hawk", "score":0.7538407444953918 }' '{ "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.", "genres":["Action","Sci-Fi"], "title":"Escape from New York", "year":1981, "score":0.7487208843231201 }' '{ "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.", "genres":["Action","Thriller"], "title":"The Romanov Stones", "year":1993, "score":0.7467736005783081 }' '{ "plot":"An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.", "genres":["Action","Comedy","Adventure"], "title":"Crime Busters", "year":1977, "score":0.7437351942062378 }' '{ "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.", "genres":["Action","Adventure","Comedy"], "title":"Three Kings", "year":1999, "score":0.7425670623779297 }' '{ "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 ...", "genres":["Action","Sci-Fi","Thriller"], "title":"Black Moon Rising", "year":1986, "score":0.7397696375846863 }' '{ "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.","genres":["Action","Adventure","Horror"],"title":"Deep Rising","year":1998,"score":0.7392246127128601 }' '{ "plot":"A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.", "genres":["Action","Adventure","Comedy"], "title":"That Man from Rio", "year":1964, "score":0.7357995510101318 }'
node atlas-search-tutorial.js
'{ "year":2008, "plot":"A young Thai boxer learns the skills and inner meaning of martial arts.", "genres":["Action"], "title":"Ong-bak 2", "score":0.7976737022399902 }' '{ "plot":"A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.", "genres":["Action","Drama","Family"], "title":"The Karate Kid", "year":1984, "score":0.7929348349571228 }' '{ "plot":"A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life."," genres":["Action","Adventure","Fantasy"], "title":"Circle of Iron", "year":1978, "score":0.7852014899253845 }' '{ "plot":"A lionized account of the life of the martial arts superstar.", "genres":["Action","Biography","Drama"], "title":"Dragon: The Bruce Lee Story", "year":1993, "score":0.7763040661811829 }' '{ "plot":"A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.", "genres":["Action","Thriller"], "title":"Kung Fu Killer", "year":2014, "score":0.7731232643127441 }' '{ "plot":"A young woman is trained by a martial arts specialist to become a professional assassin.", "genres":["Action","Crime","Romance"], "title":"Naked Killer", "year":1992, "score":0.7693743109703064 }' '{ "plot":"The life of the greatest karate master of a generation.", "genres":["Documentary","Action","History"], "title":"The Real Miyagi", "year":2015, "score":0.768799901008606 }' '{ "plot":"At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.", "genres":["Action","Drama","Sport"], "title":"Never Back Down", "year":2008, "score":0.768179714679718 }' '{ "plot":"Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.", "genres":["Action","Drama"], "title":"Dragon Tiger Gate", "year":2006, "score":0.7679706811904907 }' '{ "year":2001, "plot":"A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.", "genres":["Action","Comedy","Sport"], "title":"Shaolin Soccer", "score":0.7660527229309082 }'
Copy and paste the query for the pre-filter operator that you want to try into the atlas-vector-search-tutorial.py
file.
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 thetitle
,genres
,plot
, andyear
fields in the results.Add a field named
score
that shows the vector search score for each document in the results.
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 $and
aggregation pipeline operator to find
movie documents that match both the following criteria:
Filter by the
genres
field to find movies that aren't in thedrama
,western
, orcrime
genre, but in theaction
,adventure
, orfamily
genre.Filter by the
year
field to find movies that were released between the years1960
and2000
, both inclusive.
1 import pymongo 2 import datetime 3 4 # connect to your Atlas cluster 5 client = pymongo.MongoClient("<connection-string>") 6 7 # define pipeline 8 pipeline = [ 9 { 10 '$vectorSearch': { 11 'index': 'vector-search-tutorial', 12 'path': 'plot_embedding', 13 'filter': { 14 '$and': [ 15 { 16 'genres': { 17 '$nin': [ 18 'Drama', 'Western', 'Crime' 19 ], 20 '$in': [ 21 'Action', 'Adventure', 'Family' 22 ] 23 } 24 }, { 25 'year': { 26 '$gte': 1960, 27 '$lte': 2000 28 } 29 } 30 ] 31 }, 32 'queryVector': [-0.020156775, -0.024996493, 0.010778184, -0.030058576, -0.03309321, 0.0031229265, -0.022772837, 0.0028351594, 0.00036870153, -0.02820117, 0.016245758, 0.0036232488, 0.0020519753, -0.0076454473, 0.0073380596, -0.007377301, 0.039267123, -0.013433489, 0.01428371, -0.017279103, -0.028358135, 0.0020160044, 0.00856761, 0.009653277, 0.0107912645, -0.026683854, 0.009594415, -0.020182934, 0.018077003, -0.015709465, 0.003310956, 0.0014878864, -0.015971072, -0.002411684, -0.029561523, -0.030450987, 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0.0036003583, 0.01061468, -0.019816685, -0.004659864, 0.023387613, -0.005461034, 0.004326316, 0.0037278912, -0.007540805, 0.00860031, 0.0015524705, 0.020039052, -0.0028367946, 0.0049509015, 0.009162764, 0.009705598, 0.013982862, 0.004852799, 0.0061869915, -0.0083910255, 0.012975678, -0.034558207, -0.029064473, -0.03058179, -0.019450437, 0.01062122, -0.014179068, -0.010012985, 0.007874353, -0.014126746, -0.009731758, -0.03398267, -0.000115883464, -0.0029725027, -0.024290156, 0.012864495, -0.00937859, -0.035264544, 0.0027959184, 0.012982218, -0.012609429, 0.0065270797, 0.010712783], 33 'numCandidates': 200, 34 'limit': 10 35 } 36 }, { 37 '$project': { 38 '_id': 0, 39 'title': 1, 40 'genres': 1, 41 'plot': 1, 42 'year': 1, 43 'score': { 44 '$meta': 'vectorSearchScore' 45 } 46 } 47 } 48 ] 49 50 # run pipeline 51 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 52 53 # print results 54 for i in result: 55 print(i) 56
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 $or
aggregation pipeline operator to find
movie documents that match either one of the following criteria:
Filter by the
genres
field to find movies that aren't in thecrime
genre.Filter by the
year
field to find movies that were released in or before the year2015
and by thegenres
field to find movies in theaction
genre.
1 import pymongo 2 import datetime 3 4 # connect to your Atlas cluster 5 client = pymongo.MongoClient("<connection-string>") 6 7 # define pipeline 8 pipeline = [ 9 { 10 '$vectorSearch': { 11 'index': 'vector-search-tutorial', 12 'path': 'plot_embedding', 13 'filter': { 14 '$or': [ 15 { 16 'genres': { 17 '$ne': 'Crime' 18 } 19 }, { 20 '$and': [ 21 { 22 'year': { 23 '$lte': 2015 24 } 25 }, { 26 'genres': { 27 '$eq': 'Action' 28 } 29 } 30 ] 31 } 32 ] 33 }, 34 'queryVector': [-0.016465975, -0.0036450154, 0.001644484, -0.028249683, -0.02077106, 0.00031438665, -0.02351539, -0.017519485, -0.0025362284, -0.038888834, 0.023489377, 0.022695992, 0.009481592, 0.009995341, -0.0043506073, -0.0021297815, 0.033972453, 0.00070193375, 0.007335553, -0.017909674, 0.015191358, 0.015360439, -0.00020505243, -0.023801528, -0.013539557, -0.0015290531, 0.009631164, -0.026493832, -0.0245689, -0.02481602, 0.02832772, -0.012473041, -0.006538917, -0.021707514, -0.01676512, -0.005865841, 0.010359517, -0.013246916, 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0.015932716, 0.00049139425, -0.0023232503, -0.018351888, -0.025466334, -0.010457065, -0.00039587924, 0.0017330894, -0.024334786, -0.018599007, -0.0031442728, -0.030564804, 0.036807828, 0.0051895133, -0.015620566, 0.020680016, 0.021239286, 0.019171285, -0.00803139, -0.027963543, -0.0049261358, 0.018429926, 0.011217933, -0.015984742, -0.020575965, -0.007472119, -0.0071924836, -0.035975423, -0.028405758, 0.027573355, 0.0015770138, -0.009279994, -0.007634698, 0.007810283, 0.039929338, -0.029186135, 0.025245227, -0.02446485, -0.011770701, 0.009611655, -0.0033718832, -0.015451483, 0.007829792, -0.018403914], 35 'numCandidates': 200, 36 'limit': 10 37 } 38 }, { 39 '$project': { 40 '_id': 0, 41 'title': 1, 42 'genres': 1, 43 'plot': 1, 44 'year': 1, 45 'score': { 46 '$meta': 'vectorSearchScore' 47 } 48 } 49 } 50 ] 51 52 # run pipeline 53 result = client["sample_mflix"]["embedded_movies"].aggregate(pipeline) 54 55 # print results 56 for i in result: 57 print(i) 58
Replace the <connection-string>
in the query and then save the file.
Ensure that your connection string includes your database user's credentials. To learn more, see Connect via Drivers.
Run the command to query your collection.
python atlas-vector-search-tutorial.py
{ 'year': 1993, 'plot': 'A botched mid-air heist results in suitcases full of cash being searched for by various groups throughout the Rocky Mountains.', 'genres': ['Action', 'Adventure', 'Thriller'], 'title': 'Cliffhanger', 'score': 0.7694521546363831 } { 'plot': 'The dying words of a thief spark a madcap cross-country rush to find some treasure.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': "It's a Mad, Mad, Mad, Mad World", 'year': 1963, 'score': 0.7638954520225525 } { 'year': 1991, 'plot': 'A cat burglar is forced to steal Da Vinci works of art for a world domination plot.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'Hudson Hawk', 'score': 0.7538407444953918 } { '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.', 'genres': ['Action', 'Sci-Fi'], 'title': 'Escape from New York', 'year': 1981, 'score': 0.7487208843231201 } { '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.', 'genres': ['Action', 'Thriller'], 'title': 'The Romanov Stones', 'year': 1993, 'score': 0.7467736005783081 } { 'plot': 'An attempted robbery turns to be an unexpected recruitment when two unemployed men mistakenly break into a police office instead of a store.', 'genres': ['Action', 'Comedy', 'Adventure'], 'title': 'Crime Busters', 'year': 1977, 'score': 0.7437351942062378 } { '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.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'Three Kings', 'year': 1999, 'score': 0.7425670623779297 } { '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 ...', 'genres': ['Action', 'Sci-Fi', 'Thriller'], 'title': 'Black Moon Rising', 'year': 1986, 'score': 0.7397696375846863 } { '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.', 'genres': ['Action', 'Adventure', 'Horror'], 'title': 'Deep Rising', 'year': 1998, 'score': 0.7392246127128601 } { 'plot': 'A young man comes to the rescue of his girlfriend abducted by thieves and brought to Rio. An extravagant adventure ensues.', 'genres': ['Action', 'Adventure', 'Comedy'], 'title': 'That Man from Rio', 'year': 1964, 'score': 0.7357995510101318 }
python atlas-vector-search-tutorial.py
{ 'year': 2008, 'plot': 'A young Thai boxer learns the skills and inner meaning of martial arts.', 'genres': ['Action'], 'title': 'Ong-bak 2', 'score': 0.7976737022399902 } { 'plot': 'A handyman/martial arts master agrees to teach a bullied boy karate and shows him that there is more to the martial art than fighting.', 'genres': ['Action', 'Drama', 'Family'], 'title': 'The Karate Kid', 'year': 1984, 'score': 0.7929348349571228 } { 'plot': 'A young martial artist embarks on an adventure, encountering other martial artists in battle until one day he meets an aging blind man who will show him the true meaning of martial arts and life.', 'genres': ['Action', 'Adventure', 'Fantasy'], 'title': 'Circle of Iron', 'year': 1978, 'score': 0.7852014899253845 } { 'plot': 'A lionized account of the life of the martial arts superstar.', 'genres': ['Action', 'Biography', 'Drama'], 'title': 'Dragon: The Bruce Lee Story', 'year': 1993, 'score': 0.7763040661811829 } { 'plot': 'A martial arts instructor from the police force gets imprisoned after killing a man by accident. But when a vicious killer starts targeting martial arts masters, the instructor offers to help the police in return for his freedom.', 'genres': ['Action', 'Thriller'], 'title': 'Kung Fu Killer', 'year': 2014, 'score': 0.7731232643127441 } { 'plot': 'A young woman is trained by a martial arts specialist to become a professional assassin.', 'genres': ['Action', 'Crime', 'Romance'], ' title': 'Naked Killer', 'year': 1992, 'score': 0.7693743109703064 } { 'plot': 'The life of the greatest karate master of a generation.', 'genres': ['Documentary', 'Action', 'History'], 'title': 'The Real Miyagi', 'year': 2015, 'score': 0.768799901008606 } { 'plot': 'At his new high school, a rebellious teen is lured into an underground fight club, where he finds a mentor in a mixed martial arts veteran.', 'genres': ['Action', 'Drama', 'Sport'], 'title': 'Never Back Down', 'year': 2008, 'score': 0.768179714679718 } { 'plot': 'Three young martial arts masters emerge from the back streets of Hong Kong to help the powerless fight injustice.', 'genres': ['Action', 'Drama'], 'title': 'Dragon Tiger Gate', 'year': 2006, 'score': 0.7679706811904907 } { 'year': 2001, 'plot': 'A young Shaolin follower reunites with his discouraged brothers to form a soccer team using their martial art skills to their advantage.', 'genres': ['Action', 'Comedy', 'Sport'], 'title': 'Shaolin Soccer', ' score': 0.7660527229309082 }