How to Combine Multiple `$vectorSearch` Queries

Starting in v8.1, the MongoDB $rankFusion aggregation stage supports $vectorSearch inside the input pipeline. You can use $rankFusion to combine multiple $vectorSearch queries against the same collection in the same pipeline. $rankFusion first executes all input pipelines independently, and then de-duplicates and combines the input pipeline results into a final ranked results set. To run multiple $vectorSearch queries against multiple collections in a single pipeline, use $unionWith.

Use Cases and Benefits

You can run the following types of $vectorSearch queries by using the $rankFusion pipeline:

Run multiple $vectorSearch queries for similar terms against the same field.
This allows you to perform a comprehensive search of your dataset for similar terms in the same query.
Run multiple $vectorSearch queries for the same term against multiple fields.
This allows you to search multiple fields in your dataset to determine which fields return the best results for the query.
Run multiple $vectorSearch queries for the same term against embeddings from different embedding models.
This allows you to search embeddings from different embedding models to determine the semantic interpretation differences between the different models.

About the Tutorial

This tutorial demonstrates how to run different $vectorSearch queries against the embedded_movies collection in the sample_mflix database. This collection includes a field named plot_embedding, which contains embeddings created by using OpenAI's text-embedding-ada-002 embedding model. You use this field for all sample queries in this tutorial.

For the second and third query types listed in Use Cases and Benefits, you must generate embeddings for another field and from another embedding model, respectively. To run the sample queries for these use cases, complete the steps in Generate the Embeddings.

This tutorial walks you through the following steps:

Set up an Atlas Vector Search index on the sample_mflix.embedded_movies collection.
Run $vectorSearch query with $rankFusion to perform the following searches:
- Search the plot_embedding field for movie plots that contain movies with plots similar to light-hearted comedy with ghosts and slapstick humor with paranormal events.
- Search the plot_embedding and title_embedding fields for movies with plots similar to battle between good and evil using embeddings from OpenAI.
- Search the plot_embedding and plot_voyageai_embedding fields for movies with plots similar to journey across lands using embeddings from OpenAI and Voyage AI respectively.

Before you begin, ensure that your Atlas cluster meets the requirements described in the Prerequisites.

Note

To run a $vectorSearch query with $rankFusion, your cluster must run MongoDB v8.1 or higher.

Generate the Embeddings

To query for the same term against multiple fields and to query for the same term against embeddings from different embedding models, you must prepare the dataset to include embeddings for different fields and from different embedding models. This section demonstrates how to generate embeddings for the following:

Field Name	Embedding Model	Query Type
`title`	OpenAI's `text-embedding-3-small`	Run a comprehensive query for the same term against the `title` field and the `plot_embedding` field embeddings
`plot`	VoyageAI's `voyage-3-large`	Run a query for the same term against VoyageAI's `voyage-3-large` embedding model and OpenAI's `text-embedding-ada-002` embedding model

You can skip this section if you want to run $vectorSearch queries against only the plot_embedding field.

Prerequisites

An environment to run interactive Python notebooks such as VS Code or Colab.
API key for:
- OpenAI models. You must have an OpenAI API Key.
- VoyageAI models. You must have a VoyageAI API Key.

Procedure

In this section, you generate embeddings by using OpenAI and Voyage AI models so you can run the following example queries:

Same Query Term Against Different Fields: For this query, you generate embeddings for the title field in the embedded_movies collection by using OpenAI's text-embedding-3-large embedding model.
Same Query Term Against Different Embedding Models: For this query, you generate embeddings for the plot field in the embedded_movies collection by using Voyage AI's voyage-3-large embedding model.

Set up the environment.

Create an interactive Python notebook by saving a file with the .ipynb extension, and then run the following command in the notebook to install the dependencies:

pip install --quiet --upgrade openai pymongo

Define and use a function to generate vector embeddings.

Paste and run the following code in your notebook after replacing the following placeholder values:

`<api-key>`	Your OpenAI API key (line 6).
`<connection-string>`	Your MongoDB cluster connection string (line 18).

The following code defines a function that generates vector embeddings by using a proprietary embedding model from OpenAI. Specifically, this code does the following:

Specifies the text-embedding-3-large embedding model.
Creates a function named get_embedding that calls the model's API to generate an embedding for a given text input.
Connects to the Atlas cluster and fetches 3500 documents from the sample_mflix.embedded_movies namespace.
Generates embeddings from each document's title field by using the get_embedding function.
Updates each document with a new field named title_embedding that contains the embedding value by using the MongoDB PyMongo Driver.

1 import os
2 from openai import OpenAI
3 import pymongo
4 
5 # Specify your OpenAI API key and embedding model
6 os.environ["OPENAI_API_KEY"] = "<api-key>"
7 model = "text-embedding-3-large"
8 openai_client = OpenAI()
9 
10 # Define a function to generate embeddings
11 def get_embedding(text):
12     """Generates vector embeddings for the given text."""
13 
14     embedding = openai_client.embeddings.create(input = [text], model=model).data[0].embedding
15     return embedding
16 
17 # Connect to your Atlas cluster
18 mongo_client = pymongo.MongoClient("<connection-string>")
19 db = mongo_client["sample_mflix"]
20 collection = db["embedded_movies"]
21 
22 # Filter to exclude null or empty summary fields
23 filter = { "title": {"$nin": [ None, "" ]} }
24 
25 # Get a subset of documents in the collection
26 documents = collection.find(filter).limit(3500)
27 
28 # Update each document with a new embedding field
29 updated_doc_count = 0
30 for doc in documents:
31     embedding = get_embedding(doc["title"])
32     collection.update_one( { "_id": doc["_id"] }, { "$set": { "title_embedding": embedding } } )
33     updated_doc_count += 1
34 
35 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

Note

It might take up to 15 minutes for the operation to complete.

Set up the environment.

Create an interactive Python notebook by saving a file with the .ipynb extension, and then run the following command in the notebook to install the dependencies:

pip install --quiet voyageai pymongo

Define and use a function to generate vector embeddings.

Paste and run the following code in your notebook after replacing the following placeholder values:

`<api-key>`	Your VoyageAI API key (line 6).
`<connection-string>`	Your MongoDB cluster connection string (line 18).

The following code defines a function that generates vector embeddings by using a proprietary embedding model from VoyageAI. Specifically, this code does the following:

Specifies the voyage-3-large embedding model.
Creates a function named get_embedding that calls the model's API to generate an embedding for a given text input.
Connects to the Atlas cluster and fetches 3500 documents from the sample_mflix.embedded_movies namespace.
Generates embeddings from each document's plot field by using the get_embedding function.
Updates each document with a new field named plot_voyageai_embedding that contains the embedding value by using the MongoDB PyMongo Driver.

1 import os
2 import pymongo
3 import voyageai
4 
5 # Specify your Voyage API key and embedding model
6 os.environ["VOYAGE_API_KEY"] = "<api-key>"
7 
8 model = "voyage-3-large"
9 vo = voyageai.Client()
10 # Define a function to generate embeddings
11 def get_embedding(data, input_type = "document"):
12   embeddings = vo.embed(
13       data, model = model, input_type = input_type
14   ).embeddings
15   return embeddings[0]
16 
17 # Connect to your MongoDB Atlas cluster
18 mongo_client = pymongo.MongoClient("<connection-string>")
19 db = mongo_client["sample_mflix"]
20 collection = db["embedded_movies"]
21 
22 # Filter to exclude null or empty plot fields
23 filter = {"plot": {"$nin": [None, ""]}}
24 
25 # Get a subset of documents in the collection
26 documents = collection.find(filter).limit(3500)
27 
28 # Update each document with a new embedding field
29 updated_doc_count = 0
30 for doc in documents:
31     embedding = get_embedding(doc["plot"])
32     if embedding is not None:
33         collection.update_one({"_id": doc["_id"]}, {"$set": {"plot_voyageai_embedding": embedding}})
34         updated_doc_count += 1
35 
36 print(f"Updated {updated_doc_count} documents.")

Updated 3500 documents.

Note

It might take up to 15 minutes for the operation to complete.

Create the Atlas Vector Search Index

In this section, you create an Atlas Vector Search index on the sample_mflix.embedded_movies namespace. The section demonstrates an index definition that can be used for running various queries. Before creating the index, complete the procedure to generate the embeddings.

Connect to the Atlas cluster using `mongosh`.

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

Switch to the database that contains the collection for which you want to create the index.

Example

use sample_mflix

switched to db sample_mflix

Run the `db.collection.createSearchIndex()` method.

1 db.embedded_movies.createSearchIndex(
2   "multiple-vector-search", 
3   "vectorSearch", 
4   {
5     "fields": [
6       {
7         "type": "vector",
8         "path": "plot_embedding",
9         "numDimensions": 1536,
10         "similarity": "dotProduct"
11       },
12       {
13         "type": "vector",
14         "path": "plot_voyageai_embedding",
15         "numDimensions": 1024,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_embedding",
21         "numDimensions": 3072,
22         "similarity": "dotProduct"
23       }
24     ]
25   }
26 );

This index definition indexes the following fields:

plot_embedding field as the vector type. This field contains vector embeddings that represent the summary of a movie's plot. The index definition:
- Specifies 1536 vector dimensions.
- Measures similarity using dotProduct similarity.
This field mapping is required for all the queries in this tutorial.
plot_voyageai_embedding field as the vector type. This field contains vector embeddings that represent the summary of a movie's plot. The index definition:
- Specifies 1024 vector dimensions.
- Measures similarity using dotProduct similarity.
This field mapping is required for the second use case.
title_embedding field as the vector type. This field contains vector embeddings that represent the title of the movie. The index definition:
- Specifies 3072 vector dimensions.
- Measures similarity using dotProduct similarity.
This field mapping is required for the third use case.

Run the Atlas Vector Search Queries

In this section, you query the embedded_movies collection in the sample_mflix database by using the index named multiple-vector-search that you created on the collection.

Create a file for the embeddings to use in the query.

Create a file named embeddings.js.
```
touch embeddings.js
```

Copy and paste the following embeddings into the embeddings.js file.

This file contains embeddings for the query terms used in the queries. The following table shows the query term, the variable that contains the embeddings for the query term, the embedding model used to generate the embeddings, and the number of dimensions.

Query Phrase	Variable	Embedding Model	Dimensions
light-hearted comedy with ghosts	COMEDY_INVOLVING_GHOSTS	OpenAI's `text-embedding-ada-002`	1536
slapstick humor with paranormal events	HUMOR_INVOLVING_PARANORMAL	OpenAI's `text-embedding-ada-002`	1536
journey across lands	JOURNEY_ACROSS_LANDS_VOYAGEAI	VoyageAI's `voyage-3-large`	1024
journey across lands	JOURNEY_ACROSS_LANDS_OPENAI	OpenAI's `text-embedding-ada-002`	1536
battle between good and evil	BATTLE_GOOD_EVIL_TITLE_SEARCH	OpenAI's `text-embedding-3-large`	3072
battle between good and evil	BATTLE_GOOD_EVIL_PLOT_SEARCH	OpenAI's `text-embedding-ada-002`	1536

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Save and close the file.

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.

Use the `sample_mflix` database.

Run the following command in the mongosh prompt:

use sample_mflix

Load the embeddings to use in the query.

Run the following command to load the embeddings in the embeddings.js file after replacing <path-to-file> with the absolute path to your embeddings.js file.
load('<path-to-file>/embeddings.js';
true
Verify that the embeddings loaded successfully.
You can verify by running a command similar to the following:
COMEDY_INVOLVING_GHOSTS.length
1536

Run the Atlas Vector Search queries against the `embedded_movies` collection.

Multiple Query Vectors Against a Single Document Vector

Perform a comprehensive search of the dataset for semantically similar terms to determine which query term returns the best results.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcef0a4'),
    plot: 'A paranormal expert and his daughter bunk in an abandoned house populated by 3 mischievous ghosts and one friendly one.',
    title: 'Casper',
    scoreDetails: {
      value: 0.01639344262295082,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 1,
          weight: 0.5,
          value: 0.9327191114425659,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.9237540364265442,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d3f29313caabd96a55'),
    plot: 'A teacher with paranormal abilities helps a group of ghosts graduate high school.',
    title: 'Ghost Graduation',
    scoreDetails: {
      value: 0.015512265512265512,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 3,
          weight: 0.5,
          value: 0.9280960559844971,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 6,
          weight: 0.5,
          value: 0.9170590043067932,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13abf29313caabd24011'),
    plot: 'A real-time dark comedy about a science geek who tangles with a clutch of Russian gangsters after he delivers them the wrong computer disk.',
    title: 'Nicotina',
    scoreDetails: {
      value: 0.014306006493506494,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 17,
          weight: 0.5,
          value: 0.9196028709411621,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.9184012413024902,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c6f29313caabd72c06'),
    plot: "Two exorcists literally remove the skeletons from the cupboards from people's homes. Some fairly embarrassing secrets are revealed along the way. A case where the skeletons have hidden themselves turns the lives of all those involved.",
    title: 'Skeletons',
    scoreDetails: {
      value: 0.013986697965571206,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.9218283891677856,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 12,
          weight: 0.5,
          value: 0.9145868420600891,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabcebf6f'),
    plot: 'After an accident leaves a young man dead, his spirit stays behind to warn his lover of impending danger, with the help of a reluctant psychic.',
    title: 'Ghost',
    scoreDetails: {
      value: 0.013748597081930415,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.9261202812194824,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 21,
          weight: 0.5,
          value: 0.9131492376327515,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabced85b'),
    plot: 'A comical Gothic horror-movie-type family tries to rescue their beloved uncle from his gold-digging new love.',
    title: 'Addams Family Values',
    scoreDetails: {
      value: 0.013574660633484163,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 5,
          weight: 0.5,
          value: 0.9271853566169739,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 25,
          weight: 0.5,
          value: 0.912155270576477,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceace9'),
    plot: 'A couple of recently deceased ghosts contract the services of a "bio-exorcist" in order to remove the obnoxious new owners of their house.',
    title: 'Beetlejuice',
    scoreDetails: {
      value: 0.013486782952706347,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 7,
          weight: 0.5,
          value: 0.9257347583770752,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 23,
          weight: 0.5,
          value: 0.9131345152854919,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13acf29313caabd281b1'),
    plot: 'Will and Jake Grimm are traveling con-artists who encounter a genuine fairy-tale curse which requires true courage instead of their usual bogus exorcisms.',
    title: 'The Brothers Grimm',
    scoreDetails: {
      value: 0.01324561403508772,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 16,
          weight: 0.5,
          value: 0.9197224974632263,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 15,
          weight: 0.5,
          value: 0.9143059849739075,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0859'),
    plot: 'A man struggles with memories of his past, including a wife he cannot remember, in a nightmarish world with no sun and run by beings with telekinetic powers who seek the souls of humans.',
    title: 'Dark City',
    scoreDetails: {
      value: 0.013167013167013167,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 18,
          weight: 0.5,
          value: 0.9192354083061218,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 14,
          weight: 0.5,
          value: 0.9144110083580017,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bcf29313caabd566d2'),
    plot: "A hotel handyman's life changes when the lavish bedtime stories he tells his niece and nephew start to magically come true.",
    title: 'Bedtime Stories',
    scoreDetails: {
      value: 0.01250758035172832,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 37,
          weight: 0.5,
          value: 0.9158861041069031,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 8,
          weight: 0.5,
          value: 0.9161232709884644,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b5f29313caabd41f29'),
    plot: '14-year-old Lulu moves to a small provincial town with her mother and younger brother. One night, her brother is struck by a beam of white light - actually the spirit of Herman Hartmann ...',
    title: 'Island of Lost Souls',
    scoreDetails: {
      value: 0.012404870624048707,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.9203757643699646,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 30,
          weight: 0.5,
          value: 0.9115088582038879,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9fd5'),
    plot: 'A troubled writer moves into a haunted house after inheriting it from his aunt.',
    title: 'House',
    scoreDetails: {
      value: 0.012207602339181285,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 12,
          weight: 0.5,
          value: 0.9210504293441772,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 35,
          weight: 0.5,
          value: 0.9111618995666504,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a8f29313caabd1db7e'),
    plot: 'Kung-Fu Action / Comedy / Horror / Musical about the second coming.',
    title: 'Jesus Christ Vampire Hunter',
    scoreDetails: {
      value: 0.012193362193362194,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 39,
          weight: 0.5,
          value: 0.9157752990722656,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 10,
          weight: 0.5,
          value: 0.9146226644515991,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce98c8'),
    plot: 'The planned reburial of a village elder goes awry as the corpse resurrects into a hopping, bloodthirsty vampire, threatening mankind. Therefore, a Taoist Priest and his two disciples attempt to stop the terror.',
    title: 'Mr. Vampire',
    scoreDetails: {
      value: 0.0115210355987055,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 15,
          weight: 0.5,
          value: 0.9200190305709839,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 43,
          weight: 0.5,
          value: 0.9108449220657349,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce784b'),
    plot: "The bizarre and musical tale of a girl who travels to another dimension through the gateway found in her family's basement.",
    title: 'Forbidden Zone',
    scoreDetails: {
      value: 0.011160714285714284,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 36,
          weight: 0.5,
          value: 0.916013240814209,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 24,
          weight: 0.5,
          value: 0.913086473941803,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.010583778966131907,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 42,
          weight: 0.5,
          value: 0.9153138995170593,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 28,
          weight: 0.5,
          value: 0.9117591381072998,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabcea63f'),
    plot: 'A group of people stop by a mansion during a storm and discover two magical toy makers, and their haunted collection of dolls.',
    title: 'Dolls',
    scoreDetails: {
      value: 0.010081053698074976,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 45,
          weight: 0.5,
          value: 0.9147992730140686,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 34,
          weight: 0.5,
          value: 0.9112522006034851,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13f3f29313caabddfde0'),
    plot: 'Two siblings discover a supernatural escape from a troubled home, but find their bond tested when reality threatens to tear their family apart.',
    title: 'One and Two',
    scoreDetails: {
      value: 0.010064412238325281,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 32,
          weight: 0.5,
          value: 0.9163687229156494,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 48,
          weight: 0.5,
          value: 0.9105724096298218,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b5f29313caabd44e20'),
    plot: 'While sorting the affairs of his late Uncle, a man accidentally stumbles across a series of dark secrets connected to an ancient horror waiting to be freed.',
    title: 'The Call of Cthulhu',
    scoreDetails: {
      value: 0.009441524895341587,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 43,
          weight: 0.5,
          value: 0.9152694344520569,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 49,
          weight: 0.5,
          value: 0.9104976654052734,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b3f29313caabd3e8a1'),
    plot: 'A folk tale - supernatural love story about a ghost who falls in love with a newlywed woman.',
    title: 'Paheli',
    scoreDetails: {
      value: 0.008064516129032258,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.9308393001556396,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 0, weight: 0.5 }
      ]
    }
  }
]

This sample query uses the $rankFusion with the following input pipeline stages:

$vectorSearch

Searches the plot_embedding field for the phrase light-hearted comedy with ghosts, specified in the queryVector field of the query as vector embeddings by using the COMEDY_INVOLVING_GHOSTS variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results from this stage to 50 documents.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

$vectorSearch

Performs a sequential vector search on the plot_embedding field for the phrase slapstick humor with paranormal events, specified in the queryVector field as vector embeddings by using the HUMOR_INVOLVING_PARANORMAL variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results from this stage to 50 documents.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

The sample query also specifies the following pipeline stages.

`$project`	Includes only the `plot` and `title` fields in the results. Adds a field named `scoreDetails` in the results.
`$limit`	Limits the returned results to 20 documents.

Atlas Vector Search merges the results for both the queries into a single result set. In the results:

The scoreDetails.value shows the raw score from that pipeline before it is weighted and combined by using reciprocal rank fusion.
The score.details.rank shows the rank of the document in the results of the pipeline.
The scoreDetails.details.value contains the weighted reciprocal rank score.

You can do the following:

Adjust the weights assigned to each pipeline in the query to further refine the results.
Increase the number of documents in the results if you see disjoint results.

Same Term Query Vector Against Multiple Document Vectors

Search multiple fields in the dataset to determine which fields return the best results for the query.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": BATTLE_GOOD_EVIL_PLOT_SEARCH,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL_TITLE_SEARCH,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a13b8f29313caabd4c898'),
    plot: 'Perseus, mortal son of Zeus, battles the minions of the underworld to stop them from conquering heaven and earth.',
    title: 'Clash of the Titans',
    scoreDetails: {
      value: 0.013814698839565298,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 29,
          weight: 0.5,
          value: 0.9173959493637085,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.70682692527771,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcef692'),
    plot: 'The angel Gabriel comes to Earth to collect a soul which will end the stalemated war in Heaven, and only a former priest and a little girl can stop him.',
    title: 'The Prophecy',
    scoreDetails: {
      value: 0.013575490735644836,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 19,
          weight: 0.5,
          value: 0.9186791181564331,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.6911849975585938,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce8f83'),
    plot: 'A small group of human resistance fighters fight a desperate guerilla war against the genocidal extra-terrestrials who dominate Earth.',
    title: 'V: The Final Battle',
    scoreDetails: {
      value: 0.013167013167013167,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 14,
          weight: 0.5,
          value: 0.9201459884643555,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 18,
          weight: 0.5,
          value: 0.6841024160385132,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0e15'),
    plot: 'A young boy is whisked away to the mythical land of Tao where he becomes the center of a conflict between an evil lord and a group of animal warriors.',
    title: 'Warriors of Virtue',
    scoreDetails: {
      value: 0.011963357943669675,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 9,
          weight: 0.5,
          value: 0.9214991927146912,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 46,
          weight: 0.5,
          value: 0.6675300002098083,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c6f29313caabd72148'),
    plot: "Frankenstein's creature finds himself caught in an all-out, centuries old war between two immortal clans.",
    title: 'I, Frankenstein',
    scoreDetails: {
      value: 0.011489173663278833,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.9267290830612183,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 86,
          weight: 0.5,
          value: 0.6572250127792358,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b3f29313caabd3c719'),
    plot: "The warrior Beowulf must fight and defeat the monster Grendel who is terrorizing towns, and later, Grendel's mother, who begins killing out of revenge.",
    title: 'Beowulf',
    scoreDetails: {
      value: 0.011074800290486565,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 42,
          weight: 0.5,
          value: 0.9154369831085205,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 21,
          weight: 0.5,
          value: 0.6825653910636902,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b6f29313caabd467f5'),
    plot: "Story centers on a battle during China's Warring States Period, a series of civil wars, which spanned from the 5th to the 3rd century B.C. Based on a popular Japanese manga, which was in turn based a Japanese novel inspired by Warring States history in China.",
    title: 'Battle of the Warriors',
    scoreDetails: {
      value: 0.011017628205128204,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 96,
          weight: 0.5,
          value: 0.9106720685958862,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7059011459350586,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce7dba'),
    plot: 'A film adaption of the myth of Perseus and his quest to battle both Medusa and the Kraken monster to save the Princess Andromeda.',
    title: 'Clash of the Titans',
    scoreDetails: {
      value: 0.01092896174863388,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 123,
          weight: 0.5,
          value: 0.9083951115608215,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.70682692527771,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0e61'),
    plot: 'A sci-fi update of the famous 6th Century poem. In a beseiged land, Beowulf must battle against the hideous creature Grendel and his vengeance seeking mother.',
    title: 'Beowulf',
    scoreDetails: {
      value: 0.010718294051627384,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 50,
          weight: 0.5,
          value: 0.9144304394721985,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 21,
          weight: 0.5,
          value: 0.6825653910636902,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13acf29313caabd274f4'),
    plot: "It's a high-kicking battle on the dark side when an ace vampire slayer and his beautiful sidekicks wage the ultimate martial-arts showdown with one of the most dangerous of the undead.",
    title: 'Vampire Effect',
    scoreDetails: {
      value: 0.009985688264376789,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 3,
          weight: 0.5,
          value: 0.9257449507713318,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 184,
          weight: 0.5,
          value: 0.6469712853431702,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b2f29313caabd385df'),
    plot: "A young boy is chosen as the defender of good and must team up with Japan's ancient spirits and creatures of lore to destroy the forces of evil.",
    title: 'The Great Yokai War',
    scoreDetails: {
      value: 0.009706875383670963,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 12,
          weight: 0.5,
          value: 0.9209381341934204,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 121,
          weight: 0.5,
          value: 0.6517701148986816,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13cbf29313caabd7ec91'),
    plot: 'A vengeful fairy is driven to curse an infant princess, only to discover that the child may be the one person who can restore peace to their troubled land.',
    title: 'Maleficent',
    scoreDetails: {
      value: 0.009554985402105635,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 67,
          weight: 0.5,
          value: 0.9130404591560364,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 29,
          weight: 0.5,
          value: 0.676243782043457,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139ff29313caabd000f6'),
    plot: 'After enslavement & near extermination by an alien race in the year 3000, humanity begins to fight back.',
    title: 'Battlefield Earth',
    scoreDetails: {
      value: 0.009166125892277742,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 74,
          weight: 0.5,
          value: 0.9121060371398926,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 32,
          weight: 0.5,
          value: 0.6739504933357239,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c2f29313caabd67b22'),
    plot: '"An Epic Battle for World Domination: In the faraway land of Mirabillis, the warlord Dragon-a Eye has unleashed his terrifying forces to hunt down the source of all power, a legendary ...',
    title: 'Knights of Bloodsteel',
    scoreDetails: {
      value: 0.009083069847657374,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 11,
          weight: 0.5,
          value: 0.9210658073425293,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 185,
          weight: 0.5,
          value: 0.646918773651123,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a13daf29313caabdad051'),
    plot: 'Bilbo and Company are forced to engage in a war against an array of combatants and keep the Lonely Mountain from falling into the hands of a rising darkness.',
    title: 'The Hobbit: The Battle of the Five Armies',
    scoreDetails: {
      value: 0.008857968369829683,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 36,
          weight: 0.5,
          value: 0.916031539440155,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 77,
          weight: 0.5,
          value: 0.6581593751907349,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcef28d'),
    plot: 'A young prince fights to free his people with the help of his family mascot, a small dragon who grows upon eating metal.',
    title: 'The Legend of Galgameth',
    scoreDetails: {
      value: 0.008791371158392434,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 34,
          weight: 0.5,
          value: 0.9167085886001587,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 84,
          weight: 0.5,
          value: 0.6572535037994385,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf06fe'),
    plot: 'A young prince fights to free his people with the help of his family mascot, a small dragon who grows upon eating metal.',
    title: 'The Legend of Galgameth',
    scoreDetails: {
      value: 0.008791371158392434,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 34,
          weight: 0.5,
          value: 0.9167085886001587,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 84,
          weight: 0.5,
          value: 0.6572535037994385,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139ef29313caabcfbd25'),
    plot: 'A demon, raised from infancy after being conjured by and rescued from the Nazis, grows up to become a defender against the forces of darkness.',
    title: 'Hellboy',
    scoreDetails: {
      value: 0.008647627352663324,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 39,
          weight: 0.5,
          value: 0.9158757328987122,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 79,
          weight: 0.5,
          value: 0.6577332615852356,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13ecf29313caabdd205d'),
    plot: 'An orphan, whose father has been killed by dark power, attempts to bring justice back to the town.',
    title: 'Rise of the Legend',
    scoreDetails: {
      value: 0.008620689655172414,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 56,
          weight: 0.5,
          value: 0.9139308929443359,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 56,
          weight: 0.5,
          value: 0.6645089387893677,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b0f29313caabd3505e'),
    plot: 'The mythical world starts a rebellion against humanity in order to rule the Earth, so Hellboy and his team must save the world from the rebellious creatures.',
    title: 'Hellboy II: The Golden Army',
    scoreDetails: {
      value: 0.008435282945086867,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9181161522865295,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 161,
          weight: 0.5,
          value: 0.6486427783966064,
          details: []
        }
      ]
    }
  }
]

This sample query uses the $rankFusion with the following input pipeline stages:

$vectorSearch

Searches the plot_embedding field for the phrase battle between good and evil specified in the queryVector field of the query as vector embeddings by using the BATTLE_GOOD_EVIL_PLOT_SEARCH variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results to 200 documents only.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

$vectorSearch

Performs a sequential vector search on the title_embedding field for the string battle between good and evil, specified in the queryVector field as vector embeddings by using the BATTLE_GOOD_EVIL_TITLE_SEARCH variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results to 200 documents only.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

The sample query also specifies the following pipeline stages.

`$project`	Includes only the `plot` and `title` fields in the results. Adds a field named `scoreDetails` in the results.
`$limit`	Limits the returned results to 20 documents.

Atlas Vector Search merges the results for both the queries into a single result set. In the results:

The scoreDetails.value shows the raw score from that pipeline before it is weighted and combined by using reciprocal rank fusion.
The score.details.rank shows the rank of the document in the results of the pipeline.
The scoreDetails.details.value contains the weighted reciprocal rank score, which shows which fields return the most relevant result for the query term.

For example, the first and second documents in the results suggest a significant match for the term in the title field. You can do the following:

Adjust the weights assigned to each pipeline in the query to further refine the results.
Increase the number of documents in the results if you see disjoint results.

Same Term Query Using Multiple Models

Search embeddings from different embedding models to determine the semantic interpretation differences between the different models.

1 db.embedded_movies.aggregate([
2   {
3     $rankFusion: {
4       input: {
5         pipelines: {
6           vectorPipeline1: [
7             {
8               "$vectorSearch": {
9                 "index": "multiple-vector-search",
10                 "path": "plot_embedding",
11                 "queryVector": JOURNEY_ACROSS_LANDS_OPENAI,
12                 "numCandidates": 2000,
13                 "limit": 100
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_voyageai_embedding",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('573a139af29313caabcefa6c'),
    plot: 'A Englishman returns after a long time abroad and tells his strange stories about the lands he visited which are allegories about the real world.',
    title: "Gulliver's Travels",
    scoreDetails: {
      value: 0.015549662487945998,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 8,
          weight: 0.5,
          value: 0.9156127572059631,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7671917676925659,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d0f29313caabd8bfb8'),
    plot: 'A young man crosses over North and South Korea to deliver the pain and longings of separated families.',
    title: 'Poongsan',
    scoreDetails: {
      value: 0.015512265512265512,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 6,
          weight: 0.5,
          value: 0.9168118238449097,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 3,
          weight: 0.5,
          value: 0.7560752630233765,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b6f29313caabd48c52'),
    plot: 'In a mythical land, a man and a young boy investigate a series of unusual occurrences.',
    title: 'Tales from Earthsea',
    scoreDetails: {
      value: 0.014785823005001086,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 3,
          weight: 0.5,
          value: 0.9192964434623718,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 13,
          weight: 0.5,
          value: 0.7426372170448303,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcef85f'),
    plot: "After the death of his mother, a young Brazilian decides to leave his country and travel to his mother's native land. In a foreign land, he finds love and danger.",
    title: 'Foreign Land',
    scoreDetails: {
      value: 0.014407131011608624,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 12,
          weight: 0.5,
          value: 0.9135410785675049,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.746292233467102,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d4f29313caabd99ec6'),
    plot: "A ten-year-old cartographer secretly leaves his family's ranch in Montana where he lives with his cowboy father and scientist mother and travels across the country aboard a freight train to receive an award at the Smithsonian Institute.",
    title: 'The Young and Prodigious T.S. Spivet',
    scoreDetails: {
      value: 0.014162077104642014,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 22,
          weight: 0.5,
          value: 0.9102069735527039,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
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          value: 0.760703444480896,
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      ]
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  },
  {
    _id: ObjectId('573a13e8f29313caabdc9e14'),
    plot: 'A young Scottish man travels across America in pursuit of the woman he loves, attracting the attention of an outlaw who is willing to serve as a guide.',
    title: 'Slow West',
    scoreDetails: {
      value: 0.013811642565813867,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 2,
          weight: 0.5,
          value: 0.9210423827171326,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 27,
          weight: 0.5,
          value: 0.7315881848335266,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13f7f29313caabde65b4'),
    plot: 'Our figurine sized supermen hero embarks on an epic surreal journey that will take him across the Ethiopian post apocalyptic landscape in search of a way to get on the hovering spacecraft that for years has become a landmark in the skies.',
    title: 'Crumbs',
    scoreDetails: {
      value: 0.013606071825249907,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 13,
          weight: 0.5,
          value: 0.9113836288452148,
          details: []
        },
        {
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          rank: 14,
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          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1395f29313caabce2911'),
    plot: "Upon finding a book that relates his grandfather's story, an officer ventures through Spain meeting a wide array of characters, most of whom have a story of their own to tell.",
    title: 'The Saragossa Manuscript',
    scoreDetails: {
      value: 0.013494318181818182,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 28,
          weight: 0.5,
          value: 0.9088537693023682,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7538963556289673,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a1392f29313caabcdaa70'),
    plot: 'Leo Vincey, told by his dying uncle of a lost land visited 500 years ago by his ancestor, heads out with family friend Horace Holly to try to discover the land and its secret of immortality...',
    title: 'She',
    scoreDetails: {
      value: 0.01324561403508772,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 15,
          weight: 0.5,
          value: 0.910618782043457,
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        {
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      ]
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  },
  {
    _id: ObjectId('573a1391f29313caabcd91dc'),
    plot: 'Breck Coleman leads hundreds of settlers in covered wagons from the Mississippi River to their destiny out West.',
    title: 'The Big Trail',
    scoreDetails: {
      value: 0.012714460784313725,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 4,
          weight: 0.5,
          value: 0.9187002182006836,
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        {
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          rank: 42,
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          value: 0.72636878490448,
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      ]
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  },
  {
    _id: ObjectId('573a139af29313caabcf0e95'),
    plot: 'A man, having fallen in love with the wrong woman, is sent by the sultan himself on a diplomatic mission to a distant land as an ambassador. Stopping at a Viking village port to restock on supplies, he finds himself unwittingly embroiled on a quest to banish a mysterious threat in a distant Viking land.',
    title: 'The 13th Warrior',
    scoreDetails: {
      value: 0.012639109697933228,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 14,
          weight: 0.5,
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  },
  {
    _id: ObjectId('573a13aaf29313caabd218b4'),
    plot: 'An adventurous girl finds another world that is a strangely idealized version of her frustrating home, but it has sinister secrets.',
    title: 'Coraline',
    scoreDetails: {
      value: 0.012270400481782595,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 21,
          weight: 0.5,
          value: 0.9102445840835571,
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        {
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          rank: 22,
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      ]
    }
  },
  {
    _id: ObjectId('573a13c9f29313caabd792a8'),
    plot: 'Settlers traveling through the Oregon desert in 1845 find themselves stranded in harsh conditions.',
    title: "Meek's Cutoff",
    scoreDetails: {
      value: 0.011859838274932614,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 10,
          weight: 0.5,
          value: 0.9155632257461548,
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        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 46,
          weight: 0.5,
          value: 0.7261408567428589,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf02db'),
    plot: 'Chris embarks on an odyssey of self-discovery that spans the globe. Kidnapped and enslaved by gun smugglers, sold by pirates and thrust into the murky underworld of gambling and kickboxing,...',
    title: 'The Quest',
    scoreDetails: {
      value: 0.011845039018952062,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 18,
          weight: 0.5,
          value: 0.910321056842804,
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        {
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          rank: 32,
          weight: 0.5,
          value: 0.7292750477790833,
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      ]
    }
  },
  {
    _id: ObjectId('573a1394f29313caabcdee62'),
    plot: 'The success of the journey focuses on keeping the Indian girl alive as well as themselves to complete trade with the Blackfeet.',
    title: 'The Big Sky',
    scoreDetails: {
      value: 0.01136950904392765,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 30,
          weight: 0.5,
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  },
  {
    _id: ObjectId('573a13d6f29313caabd9eb15'),
    plot: 'A tribe of Norse warriors traipse across a barren land after battle. Bloodied and wounded, their chief is near death. He is about to hand over power to his son when an army of a completely different kind descends upon them.',
    title: 'Tumult',
    scoreDetails: {
      value: 0.010817307692307692,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 5,
          weight: 0.5,
          value: 0.9174321293830872,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 100,
          weight: 0.5,
          value: 0.7155807614326477,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a13aef29313caabd2c923'),
    plot: 'A Chinese emissary is sent to the Gobi desert to execute a renegade soldier. When a caravan transporting a Buddhist monk and a valuable treasure is threatened by thieves, however, the two warriors might unite to protect the travelers.',
    title: 'Warriors of Heaven and Earth',
    scoreDetails: {
      value: 0.010793399638336348,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 19,
          weight: 0.5,
          value: 0.9103184342384338,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 52,
          weight: 0.5,
          value: 0.7232900261878967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13f3f29313caabddf32e'),
    plot: 'In a dry and dusty post-apocalyptic world, two wayfarers wander aimlessly until Leif finds a copy of The Wonderful Wizard of Oz. Using the world around him to interpret what he reads, Leif ...',
    title: 'OzLand',
    scoreDetails: {
      value: 0.010695187165775402,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 59,
          weight: 0.5,
          value: 0.9061647653579712,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 17,
          weight: 0.5,
          value: 0.7394661903381348,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf0e15'),
    plot: 'A young boy is whisked away to the mythical land of Tao where he becomes the center of a conflict between an evil lord and a group of animal warriors.',
    title: 'Warriors of Virtue',
    scoreDetails: {
      value: 0.010690045248868778,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 25,
          weight: 0.5,
          value: 0.9092057943344116,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 44,
          weight: 0.5,
          value: 0.7262298464775085,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceb2b3'),
    plot: 'When two girls move to the country to be near their ailing mother, they have adventures with the wonderous forest spirits who live nearby.',
    title: 'My Neighbor Totoro',
    scoreDetails: {
      value: 0.010009008107296567,
      description: 'value output by reciprocal rank fusion algorithm, computed as sum of (weight * (1 / (60 + rank))) across input pipelines from which this document is output, from:',
      details: [
        {
          inputPipelineName: 'vectorPipeline1',
          rank: 43,
          weight: 0.5,
          value: 0.9080972671508789,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 37,
          weight: 0.5,
          value: 0.7281997203826904,
          details: []
        }
      ]
    }
  }
]

This sample query uses the $rankFusion with the following input pipeline stages:

$vectorSearch

Searches the plot_embedding field for the string journey across lands specified in the queryVector field of the query as vector embeddings by using the JOURNEY_ACROSS_LANDS_OPENAI variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results to 100 documents.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

$vectorSearch

Performs a sequential vector search on the plot_voyageai_embedding field for the string journey across lands, specified in the queryVector field as vector embeddings by using the JOURNEY_ACROSS_LANDS_VOYAGEAI variable.
Specifies a search for up to 2000 nearest neighbors.
Limits the results to 100 documents.
Specifies a weight of 0.5 to influence that pipeline's rank contribution to the final score.

The sample query also specifies the following pipeline stages.

`$project`	Includes only the `plot` and `title` fields in the results. Adds a field named `scoreDetails` in the results.
`$limit`	Limits the returned results to 20 documents.

Atlas Vector Search merges the results for both the queries into a single result set. In the results:

The scoreDetails.value shows the raw score from that pipeline before it is weighted and combined by using reciprocal rank fusion.
The score.details.rank shows the rank of the document in the results of the pipeline.
The scoreDetails.details.value contains the weighted reciprocal rank score, which shows the strengths and differences in the semantic interpretation of the query term by the different embedding models.

For example, the first and seconds documents in the results suggest closer semantic interpretation by the model used in the vectorPipeline2.

Back

Combined Vector Search and Full-Text Search

Transform Documents & Filter Collections

1	import os
2	from openai import OpenAI
3	import pymongo
4
5	# Specify your OpenAI API key and embedding model
6	os.environ["OPENAI_API_KEY"] = "<api-key>"
7	model = "text-embedding-3-large"
8	openai_client = OpenAI()
9
10	# Define a function to generate embeddings
11	def get_embedding(text):
12	"""Generates vector embeddings for the given text."""
13
14	embedding = openai_client.embeddings.create(input = [text], model=model).data[0].embedding
15	return embedding
16
17	# Connect to your Atlas cluster
18	mongo_client = pymongo.MongoClient("<connection-string>")
19	db = mongo_client["sample_mflix"]
20	collection = db["embedded_movies"]
21
22	# Filter to exclude null or empty summary fields
23	filter = { "title": {"$nin": [ None, "" ]} }
24
25	# Get a subset of documents in the collection
26	documents = collection.find(filter).limit(3500)
27
28	# Update each document with a new embedding field
29	updated_doc_count = 0
30	for doc in documents:
31	embedding = get_embedding(doc["title"])
32	collection.update_one( { "_id": doc["_id"] }, { "$set": { "title_embedding": embedding } } )
33	updated_doc_count += 1
34
35	print(f"Updated {updated_doc_count} documents.")

1	import os
2	import pymongo
3	import voyageai
4
5	# Specify your Voyage API key and embedding model
6	os.environ["VOYAGE_API_KEY"] = "<api-key>"
7
8	model = "voyage-3-large"
9	vo = voyageai.Client()
10	# Define a function to generate embeddings
11	def get_embedding(data, input_type = "document"):
12	embeddings = vo.embed(
13	data, model = model, input_type = input_type
14	).embeddings
15	return embeddings[0]
16
17	# Connect to your MongoDB Atlas cluster
18	mongo_client = pymongo.MongoClient("<connection-string>")
19	db = mongo_client["sample_mflix"]
20	collection = db["embedded_movies"]
21
22	# Filter to exclude null or empty plot fields
23	filter = {"plot": {"$nin": [None, ""]}}
24
25	# Get a subset of documents in the collection
26	documents = collection.find(filter).limit(3500)
27
28	# Update each document with a new embedding field
29	updated_doc_count = 0
30	for doc in documents:
31	embedding = get_embedding(doc["plot"])
32	if embedding is not None:
33	collection.update_one({"_id": doc["_id"]}, {"$set": {"plot_voyageai_embedding": embedding}})
34	updated_doc_count += 1
35
36	print(f"Updated {updated_doc_count} documents.")

1	db.embedded_movies.createSearchIndex(
2	"multiple-vector-search",
3	"vectorSearch",
4	{
5	"fields": [
6	{
7	"type": "vector",
8	"path": "plot_embedding",
9	"numDimensions": 1536,
10	"similarity": "dotProduct"
11	},
12	{
13	"type": "vector",
14	"path": "plot_voyageai_embedding",
15	"numDimensions": 1024,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_embedding",
21	"numDimensions": 3072,
22	"similarity": "dotProduct"
23	}
24	]
25	}
26	);

1	db.embedded_movies.aggregate([
2	{
3	$rankFusion: {
4	input: {
5	pipelines: {
6	vectorPipeline1: [
7	{
8	"$vectorSearch": {
9	"index": "multiple-vector-search",
10	"path": "plot_embedding",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$project": {
41	_id: 1,
42	title: 1,
43	plot: 1,
44	scoreDetails: {"$meta": "scoreDetails"}
45	}
46	},
47	{
48	"$limit": 20
49	}
50	]);

Use Cases and Benefits

About the Tutorial

Note

Generate the Embeddings

Prerequisites

Procedure

Set up the environment.

Define and use a function to generate vector embeddings.

Note

Set up the environment.

Define and use a function to generate vector embeddings.

Note

Create the Atlas Vector Search Index

Connect to the Atlas cluster using mongosh.

Switch to the database that contains the collection for which you want to create the index.

Example

Run the db.collection.createSearchIndex() method.

Run the Atlas Vector Search Queries

Create a file for the embeddings to use in the query.

Connect to your cluster in mongosh.

Use the sample_mflix database.

Load the embeddings to use in the query.

Run the Atlas Vector Search queries against the embedded_movies collection.

Multiple Query Vectors Against a Single Document Vector

Same Term Query Vector Against Multiple Document Vectors

Same Term Query Using Multiple Models

Connect to the Atlas cluster using `mongosh`.

Run the `db.collection.createSearchIndex()` method.

Connect to your cluster in `mongosh`.

Use the `sample_mflix` database.

Run the Atlas Vector Search queries against the `embedded_movies` collection.