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_embedding
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_embedding
in thesample_mflix.embedded_movies
collection.Run Atlas Vector Search queries against the
plot_embedding
field in thesample_mflix.embedded_movies
collection.
➤ Use the Select your language drop-down menu to select the client to use to create the Atlas Vector Search index and run queries.
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
You can 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 clients to run queries on your Atlas cluster:
Note
You can run Atlas Vector Search queries by using any MongoDB Driver through the
$vectorSearch
aggregation stage. This tutorial includes examples for only the drivers in the preceding list.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_embedding
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 it's not already displayed, click Clusters in the sidebar.
The Clusters 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_index
.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 usingdotProduct
.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": "dotProduct" 8 }, 9 { 10 "type": "filter", 11 "path": "genres" 12 }, 13 { 14 "type": "filter", 15 "path": "year" 16 } 17 ] 18 }
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 db.collection.createSearchIndex()
method.
The following index definition indexes the plot_embedding
field as the vector
type and the genres
and year
fields as the filter
type in an Atlas Vector Search index. The
plot_embedding
field contains embeddings created using
OpenAI's text-embedding-ada-002
embeddings model. The
index definition specifies 1536
vector dimensions and
measures similarity using dotProduct
function.
1 db.embedded_movies.createSearchIndex( 2 "vector_index", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 }, 12 { 13 "type": "filter", 14 "path": "genres" 15 }, 16 { 17 "type": "filter", 18 "path": "year" 19 } 20 ] 21 } 22 );
Define the index.
Create a file named IndexService.cs
. Copy and paste the following
code into the file, and replace the <connection-string>
placeholder value.
1 namespace query_quick_start; 2 3 using MongoDB.Bson; 4 using MongoDB.Driver; 5 using System; 6 using System.Threading; 7 8 public class IndexService 9 { 10 // Replace the placeholder with your Atlas connection string 11 private const string MongoConnectionString = "<connection-string>"; 12 public void CreateVectorIndex() 13 { 14 try 15 { 16 // Connect to your Atlas cluster 17 var client = new MongoClient(MongoConnectionString); 18 var database = client.GetDatabase("sample_mflix"); 19 var collection = database.GetCollection<BsonDocument>("embedded_movies"); 20 21 var searchIndexView = collection.SearchIndexes; 22 var name = "vector_index"; 23 var type = SearchIndexType.VectorSearch; 24 25 var definition = new BsonDocument 26 { 27 { "fields", new BsonArray 28 { 29 new BsonDocument 30 { 31 { "type", "vector" }, 32 { "path", "plot_embedding" }, 33 { "numDimensions", 1536 }, 34 { "similarity", "dotProduct" } 35 }, 36 new BsonDocument 37 { 38 {"type", "filter"}, 39 {"path", "genres"} 40 }, 41 new BsonDocument 42 { 43 {"type", "filter"}, 44 {"path", "year"} 45 } 46 } 47 } 48 }; 49 50 var model = new CreateSearchIndexModel(name, type, definition); 51 52 searchIndexView.CreateOne(model); 53 Console.WriteLine($"New search index named {name} is building."); 54 55 // Polling for index status 56 Console.WriteLine("Polling to check if the index is ready. This may take up to a minute."); 57 bool queryable = false; 58 while (!queryable) 59 { 60 var indexes = searchIndexView.List(); 61 foreach (var index in indexes.ToEnumerable()) 62 { 63 if (index["name"] == name) 64 { 65 queryable = index["queryable"].AsBoolean; 66 } 67 } 68 if (!queryable) 69 { 70 Thread.Sleep(5000); 71 } 72 } 73 Console.WriteLine($"{name} is ready for querying."); 74 } 75 catch (Exception e) 76 { 77 Console.WriteLine($"Exception: {e.Message}"); 78 } 79 } 80 }
This index definition indexes the plot_embedding
field
as the vector
type and the genres
and year
fields
as the filter
type in an Atlas Vector Search index. The plot_embedding
field contains embeddings created using OpenAI's
text-embedding-ada-002
embeddings model. The index definition
specifies 1536
vector dimensions and measures similarity using
dotProduct
function.
Define the index.
Create a file named vector-index.go
. Copy and paste the following
code into the file, and replace the <connectionString>
placeholder value.
1 package main 2 3 import ( 4 "context" 5 "fmt" 6 "log" 7 "time" 8 9 "go.mongodb.org/mongo-driver/bson" 10 "go.mongodb.org/mongo-driver/mongo" 11 "go.mongodb.org/mongo-driver/mongo/options" 12 ) 13 14 func main() { 15 ctx := context.Background() 16 17 // Replace the placeholder with your Atlas connection string 18 const uri = "<connectionString>" 19 20 // Connect to your Atlas cluster 21 clientOptions := options.Client().ApplyURI(uri) 22 client, err := mongo.Connect(ctx, clientOptions) 23 if err != nil { 24 log.Fatalf("failed to connect to the server: %v", err) 25 } 26 defer func() { _ = client.Disconnect(ctx) }() 27 28 // Set the namespace 29 coll := client.Database("sample_mflix").Collection("embedded_movies") 30 indexName := "vector_index" 31 opts := options.SearchIndexes().SetName(indexName).SetType("vectorSearch") 32 33 type vectorDefinitionField struct { 34 Type string `bson:"type"` 35 Path string `bson:"path"` 36 NumDimensions int `bson:"numDimensions"` 37 Similarity string `bson:"similarity"` 38 } 39 40 type filterField struct { 41 Type string `bson:"type"` 42 Path string `bson:"path"` 43 } 44 45 type indexDefinition struct { 46 Fields []vectorDefinitionField `bson:"fields"` 47 } 48 49 vectorDefinition := vectorDefinitionField{ 50 Type: "vector", 51 Path: "plot_embedding", 52 NumDimensions: 1536, 53 Similarity: "dotProduct"} 54 genreFilterDefinition := filterField{"filter", "genres"} 55 yearFilterDefinition := filterField{"filter", "year"} 56 57 indexModel := mongo.SearchIndexModel{ 58 Definition: bson.D{{"fields", [3]interface{}{ 59 vectorDefinition, 60 genreFilterDefinition, 61 yearFilterDefinition}}}, 62 Options: opts, 63 } 64 65 // Create the index 66 searchIndexName, err := coll.SearchIndexes().CreateOne(ctx, indexModel) 67 if err != nil { 68 log.Fatalf("failed to create the search index: %v", err) 69 } 70 log.Println("New search index named " + searchIndexName + " is building.") 71 72 // Await the creation of the index. 73 log.Println("Polling to check if the index is ready. This may take up to a minute.") 74 searchIndexes := coll.SearchIndexes() 75 var doc bson.Raw 76 for doc == nil { 77 cursor, err := searchIndexes.List(ctx, options.SearchIndexes().SetName(searchIndexName)) 78 if err != nil { 79 fmt.Errorf("failed to list search indexes: %w", err) 80 } 81 82 if !cursor.Next(ctx) { 83 break 84 } 85 86 name := cursor.Current.Lookup("name").StringValue() 87 queryable := cursor.Current.Lookup("queryable").Boolean() 88 if name == searchIndexName && queryable { 89 doc = cursor.Current 90 } else { 91 time.Sleep(5 * time.Second) 92 } 93 } 94 95 log.Println(searchIndexName + " is ready for querying.") 96 }
This index definition indexes the plot_embedding
field
as the vector
type and the genres
and year
fields
as the filter
type in an Atlas Vector Search index. The plot_embedding
field contains embeddings created using OpenAI's
text-embedding-ada-002
embeddings model. The index definition
specifies 1536
vector dimensions and measures similarity using
dotProduct
function.
Add the MongoDB Java Sync Driver to your project.
Add the Java driver version 5.2 or higher as a dependency in your project, depending on your package manager:
If you are using Maven, add the following dependencies to the
dependencies
array in your project'spom.xml
file:pom.xml<dependencies> <!-- MongoDB Java Sync Driver v5.2.0 or later --> <dependency> <groupId>org.mongodb</groupId> <artifactId>mongodb-driver-sync</artifactId> <version>[5.2.0,)</version> </dependency> </dependencies> Run your package manager to install the dependencies to your project.
For more detailed installation instructions and version compatibility, see the MongoDB Java Driver documentation.
Define the index.
Create a file named VectorIndex.java
. Copy and paste the following
code into the file, and replace the <connectionString>
placeholder
value.
1 import com.mongodb.client.ListSearchIndexesIterable; 2 import com.mongodb.client.MongoClient; 3 import com.mongodb.client.MongoClients; 4 import com.mongodb.client.MongoCollection; 5 import com.mongodb.client.MongoCursor; 6 import com.mongodb.client.MongoDatabase; 7 import com.mongodb.client.model.SearchIndexModel; 8 import com.mongodb.client.model.SearchIndexType; 9 import org.bson.Document; 10 import org.bson.conversions.Bson; 11 12 import java.util.Arrays; 13 import java.util.Collections; 14 import java.util.List; 15 16 public class VectorIndex { 17 18 public static void main(String[] args) { 19 20 // Replace the placeholder with your Atlas connection string 21 String uri = "<connectionString>"; 22 23 // Connect to your Atlas cluster 24 try (MongoClient mongoClient = MongoClients.create(uri)) { 25 26 // Set the namespace 27 MongoDatabase database = mongoClient.getDatabase("sample_mflix"); 28 MongoCollection<Document> collection = database.getCollection("embedded_movies"); 29 30 // Define the index details with the filter fields 31 String indexName = "vector_index"; 32 Bson definition = new Document( 33 "fields", 34 Arrays.asList( 35 new Document("type", "vector") 36 .append("path", "plot_embedding") 37 .append("numDimensions", 1536) 38 .append("similarity", "dotProduct"), 39 new Document("type", "filter") 40 .append("path", "genres"), 41 new Document("type", "filter") 42 .append("path", "year"))); 43 44 // Define the index model 45 SearchIndexModel indexModel = new SearchIndexModel( 46 indexName, 47 definition, 48 SearchIndexType.vectorSearch()); 49 50 // Create the filtered index 51 try { 52 List<String> result = collection.createSearchIndexes(Collections.singletonList(indexModel)); 53 System.out.println("New search index named " + result.get(0) + " is building."); 54 } catch (Exception e) { 55 throw new RuntimeException("Error creating index: " + e); 56 } 57 58 // Wait for Atlas to build the index 59 System.out.println("Polling to check if the index is ready. This may take up to a minute."); 60 61 ListSearchIndexesIterable<Document> searchIndexes = collection.listSearchIndexes(); 62 Document doc = null; 63 while (doc == null) { 64 try (MongoCursor<Document> cursor = searchIndexes.iterator()) { 65 if (!cursor.hasNext()) { 66 break; 67 } 68 Document current = cursor.next(); 69 String name = current.getString("name"); 70 // When the index completes building, it becomes `queryable` 71 boolean queryable = current.getBoolean("queryable"); 72 if (name.equals(indexName) && queryable) { 73 doc = current; 74 } else { 75 Thread.sleep(500); 76 } 77 } catch (Exception e) { 78 throw new RuntimeException("Failed to list search indexes: " + e); 79 mongoClient.close(); 80 } 81 } 82 System.out.println(indexName + " is ready for querying."); 83 84 } catch (Exception e) { 85 throw new RuntimeException("Error connecting to MongoDB: " + e); 86 } 87 } 88 }
This index definition indexes the plot_embedding
field
as the vector
type and the genres
and year
fields
as the filter
type in an Atlas Vector Search index. The plot_embedding
field contains embeddings created using OpenAI's
text-embedding-ada-002
embeddings model. The index definition
specifies 1536
vector dimensions and measures similarity using
dotProduct
function.
Run the file to create the index.
Run the file in your IDE, or execute a command from the command line to run the code.
javac VectorIndex.java java VectorIndex
Successfully created a vector index named: [vector_index] It may take up to a minute for the index to build before you can query using it. Polling to confirm the index has completed building. vector_index index is ready to query
Define the index.
Create a file named vector-index.js
. Copy and paste the following
code into the file, and replace the <connectionString>
placeholder value.
1 const { MongoClient } = require("mongodb"); 2 3 // connect to your Atlas deployment 4 const uri = "<connectionString>"; 5 6 const client = new MongoClient(uri); 7 8 async function run() { 9 try { 10 const database = client.db("sample_mflix"); 11 const collection = database.collection("embedded_movies"); 12 13 // define your Atlas Vector Search index 14 const index = { 15 name: "vector_index", 16 type: "vectorSearch", 17 definition: { 18 "fields": [ 19 { 20 "type": "vector", 21 "numDimensions": 1536, 22 "path": "plot_embedding", 23 "similarity": "dotProduct" 24 }, 25 { 26 "type": "filter", 27 "path": "genres" 28 }, 29 { 30 "type": "filter", 31 "path": "year" 32 } 33 ] 34 } 35 } 36 37 // run the helper method 38 const result = await collection.createSearchIndex(index); 39 console.log(`New search index named ${result} is building.`); 40 41 // wait for the index to be ready to query 42 console.log("Polling to check if the index is ready. This may take up to a minute.") 43 let isQueryable = false; 44 while (!isQueryable) { 45 const cursor = collection.listSearchIndexes(); 46 for await (const index of cursor) { 47 if (index.name === result) { 48 if (index.queryable) { 49 console.log(`${result} is ready for querying.`); 50 isQueryable = true; 51 } else { 52 await new Promise(resolve => setTimeout(resolve, 5000)); 53 } 54 } 55 } 56 } 57 } finally { 58 await client.close(); 59 } 60 } 61 run().catch(console.dir);
This index definition indexes the plot_embedding
field
as the vector
type and the genres
and year
fields
as the filter
type in an Atlas Vector Search index. The plot_embedding
field contains embeddings created using OpenAI's
text-embedding-ada-002
embeddings model. The index definition
specifies 1536
vector dimensions and measures similarity using
dotProduct
function.
Define the index.
Create a file named vector-index.py
. Copy and paste the following
code into the file, and replace the <connectionString>
placeholder value.
1 from pymongo.mongo_client import MongoClient 2 from pymongo.operations import SearchIndexModel 3 import time 4 5 # Connect to your Atlas deployment 6 uri = "<connectionString>" 7 client = MongoClient(uri) 8 9 # Access your database and collection 10 database = client["sample_mflix"] 11 collection = database["embedded_movies"] 12 13 # Create your index model, then create the search index 14 search_index_model = SearchIndexModel( 15 definition={ 16 "fields": [ 17 { 18 "type": "vector", 19 "path": "plot_embedding", 20 "numDimensions": 1536, 21 "similarity": "dotProduct" 22 }, 23 { 24 "type": "filter", 25 "path": "genres" 26 }, 27 { 28 "type": "filter", 29 "path": "year" 30 } 31 ] 32 }, 33 name="vector_index", 34 type="vectorSearch", 35 ) 36 37 result = collection.create_search_index(model=search_index_model) 38 print("New search index named " + result + " is building.") 39 40 # Wait for initial sync to complete 41 print("Polling to check if the index is ready. This may take up to a minute.") 42 predicate=None 43 if predicate is None: 44 predicate = lambda index: index.get("queryable") is True 45 46 while True: 47 indices = list(collection.list_search_indexes(result)) 48 if len(indices) and predicate(indices[0]): 49 break 50 time.sleep(5) 51 print(result + " is ready for querying.") 52 53 client.close()
This index definition indexes the plot_embedding
field
as the vector
type and the genres
and year
fields
as the filter
type in an Atlas Vector Search index. The plot_embedding
field contains embeddings created using OpenAI's
text-embedding-ada-002
embeddings model. The index definition
specifies 1536
vector dimensions and measures similarity using
dotProduct
function.
Run Queries Using the $vectorSearch
Aggregation Pipeline Stage
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 it's not already displayed, click Clusters 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_index", 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 Atlas UI 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.