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 the following fields:

plot_embedding, which contains embeddings created by using OpenAI's text-embedding-ada-002 embedding model.
plot_embedding_voyage_3_large, which contains embeddings created by using Voyage AI's voyage-3-large embedding model. You use this field for all sample queries in this tutorial.

For the second query listed in Use Cases and Benefits, you must generate embeddings for another field. To try the sample query for this use case, 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_voyage_3_large 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_voyage_3_large and title_embedding fields for movies with plots similar to battle between good and evil using embeddings from Voyage AI.
- Search the plot_embedding and plot_embedding_voyage_3_large 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, you must prepare the dataset to include embeddings for different fields. This section demonstrates how to generate embeddings for the title field in the collection by using Voyage AI's voyage-3-large embedding model.

You can skip this section if you want to only try the other sample use cases.

Prerequisites

An environment to run interactive Python notebooks such as VS Code or Colab.
API key for Voyage AI embedding models.

Procedure

In this section, you generate embeddings for the title 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 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 Voyage AI 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 Voyage AI. 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 2048 dimension 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_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 outputDimension = 2048
10 vo = voyageai.Client()
11 # Define a function to generate embeddings
12 def get_embedding(data, input_type = "document"):
13   embeddings = vo.embed(
14       data, model = model, output_dimension = outputDimension, input_type = input_type
15   ).embeddings
16   return embeddings[0]
17 
18 # Connect to your MongoDB Atlas cluster
19 mongo_client = pymongo.MongoClient("<connection-string>")
20 db = mongo_client["sample_mflix"]
21 collection = db["embedded_movies"]
22 
23 # Filter to exclude null or empty plot fields
24 filter = {"title": {"$nin": [None, ""]}}
25 
26 # Get a subset of documents in the collection
27 documents = collection.find(filter).limit(3500)
28 
29 # Update each document with a new embedding field
30 updated_doc_count = 0
31 for doc in documents:
32     embedding = get_embedding(doc["title"])
33     if embedding is not None:
34         collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35         updated_doc_count += 1
36 
37 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_embedding_voyage_3_large",
15         "numDimensions": 2048,
16         "similarity": "dotProduct"
17       },
18       {
19         "type": "vector",
20         "path": "title_voyageai_embedding",
21         "numDimensions": 2048,
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 the third use case.
plot_embedding_voyage_3_large field as the vector type. This field contains vector embeddings that represent the summary of a movie's plot. The index definition:
- Specifies 2048 vector dimensions.
- Measures similarity using dotProduct similarity.
This field mapping is required all the queries in this tutorial.
title_voyageai_embedding field as the vector type. This field contains vector embeddings that represent the title of the movie. The index definition:
- Specifies 2048 vector dimensions.
- Measures similarity using dotProduct similarity.
This field mapping is required for the second 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	Voyage AI's `voyage-3-large`	2048
slapstick humor with paranormal events	HUMOR_INVOLVING_PARANORMAL	Voyage AI's `voyage-3-large`	2048
battle between good and evil	BATTLE_GOOD_EVIL	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_VOYAGEAI	Voyage AI's `voyage-3-large`	2048
journey across lands	JOURNEY_ACROSS_LANDS_OPENAI	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 to a Cluster 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
2048

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_voyage_3_large",
11                 "queryVector": COMEDY_INVOLVING_GHOSTS,
12                 "numCandidates": 2000,
13                 "limit": 50
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": HUMOR_INVOLVING_PARANORMAL,
23                 "numCandidates": 2000,
24                 "limit": 50
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$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: 0.8094863891601562,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 1,
          weight: 0.5,
          value: 0.7794423699378967,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaf10'),
    plot: "When Peter Plunkett's Irish castle turned hotel is about to be repossesed, he decides to spice up the attraction a bit for the 'Yanks' by having his staff pretend to haunt the castle. The ...",
    title: 'High Spirits',
    scoreDetails: {
      value: 0.01575682382133995,
      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: 0.7949001789093018,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 5,
          weight: 0.5,
          value: 0.7545012831687927,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce912c'),
    plot: 'Three unemployed parapsychology professors set up shop as a unique ghost removal service.',
    title: 'Ghostbusters',
    scoreDetails: {
      value: 0.015038444142921756,
      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: 0.7781286239624023,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7519128918647766,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a0f29313caabd056df'),
    plot: "A girl calls on her brother's imaginary friend to banish a mischievous boogeyman who has framed her for his pranks.",
    title: "Don't Look Under the Bed",
    scoreDetails: {
      value: 0.014913831197525408,
      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: 0.7534659504890442,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7626002430915833,
          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: 0.014854753521126762,
      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: 0.7640036344528198,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 4,
          weight: 0.5,
          value: 0.7570045590400696,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58456'),
    plot: 'Bertram Pincus is a man whose people skills leave much to be desired. When Pincus dies unexpectedly, but is miraculously revived after seven minutes, he wakes up to discover that he now has the annoying ability to see ghosts.',
    title: 'Ghost Town',
    scoreDetails: {
      value: 0.014709063378758382,
      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: 0.7774010300636292,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 9,
          weight: 0.5,
          value: 0.7500401735305786,
          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: 0.014186507936507936,
      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: 0.785067617893219,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 20,
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          value: 0.7415448427200317,
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    }
  },
  {
    _id: ObjectId('573a1398f29313caabcebb96'),
    plot: "A mother/daughter pair of witches descend on a yuppie family's home and cause havoc, one at a time since they share one body & the other must live in a cat the rest of the time. Now it's up...",
    title: 'Wicked Stepmother',
    scoreDetails: {
      value: 0.013683634373289545,
      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: 27,
          weight: 0.5,
          value: 0.7426956295967102,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 3,
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          value: 0.7574828267097473,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a1394f29313caabcdf488'),
    plot: 'The Bowery Boys find themselves in London, in an old mansion complete with a dungeon, an ominous bell tower and the ghost of an old hangman.',
    title: 'Loose in London',
    scoreDetails: {
      value: 0.013235294117647059,
      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: 0.7734314203262329,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 25,
          weight: 0.5,
          value: 0.7381086349487305,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceaddd'),
    plot: 'Two cops are brought back to life to chase down supernatural criminals.',
    title: 'Dead Heat',
    scoreDetails: {
      value: 0.012903762903762904,
      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: 0.7512531280517578,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 18,
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          value: 0.7426376342773438,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabcec582'),
    plot: 'Con artists plan to fleece the eccentric family using an accomplice who claims to be their long lost Uncle Fester.',
    title: 'The Addams Family',
    scoreDetails: {
      value: 0.012698412698412698,
      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,
          value: 0.7411460280418396,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 10,
          weight: 0.5,
          value: 0.748729944229126,
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        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9a71'),
    plot: 'Peanut butter is the secret ingredient for magic potions made by two friendly ghosts. Eleven-year-old Michael loses all of his hair when he gets a fright and uses the potion to get his hair...',
    title: 'The Peanut Butter Solution',
    scoreDetails: {
      value: 0.012561274509803922,
      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: 0.7384192943572998,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 8,
          weight: 0.5,
          value: 0.7511122226715088,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a6f29313caabd16c49'),
    plot: 'Elvis and JFK, both alive and in nursing homes, fight for the souls of their fellow residents as they battle an ancient Egyptian Mummy.',
    title: 'Bubba Ho-Tep',
    scoreDetails: {
      value: 0.012092758571631812,
      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: 0.736645519733429,
          details: []
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        {
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          rank: 11,
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          value: 0.7481721043586731,
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      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf1044'),
    plot: 'When a shy groom practices his wedding vows in the inadvertent presence of a deceased young woman, she rises from the grave assuming he has married her.',
    title: 'Corpse Bride',
    scoreDetails: {
      value: 0.011513157894736843,
      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: 20,
          weight: 0.5,
          value: 0.7498250007629395,
          details: []
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        {
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          rank: 35,
          weight: 0.5,
          value: 0.7354456186294556,
          details: []
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  },
  {
    _id: ObjectId('573a1398f29313caabceb9b4'),
    plot: "Neil Gallagher found the secret to Toulon's puppets who come to life and then killed himself. Alex and his psychic friends come to investigate and are stalked by Toulon's puppets who have a...",
    title: 'Puppetmaster',
    scoreDetails: {
      value: 0.01003996003996004,
      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: 0.7304129004478455,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 31,
          weight: 0.5,
          value: 0.7357485294342041,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d3f29313caabd96a55'),
    plot: 'A teacher with paranormal abilities helps a group of ghosts graduate high school.',
    title: 'Ghost Graduation',
    scoreDetails: {
      value: 0.0078125,
      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: 0.7812073826789856,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 0, weight: 0.5 }
      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcefcce'),
    plot: 'Adaption of the famous Oscar Wilde tale about a young American girl that helps a British ghost find rest and forgiveness.',
    title: 'The Canterville Ghost',
    scoreDetails: {
      value: 0.007692307692307693,
      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: 0.7810665965080261,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 0, weight: 0.5 }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabcec258'),
    plot: 'The puppets return, this time they hunt some Paranormal Researchers to take their brain fluid for the dead/living puppet master, Andre Toulon.',
    title: 'Puppet Master II',
    scoreDetails: {
      value: 0.007575757575757576,
      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: 0, weight: 0.5 },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 6,
          weight: 0.5,
          value: 0.7542119026184082,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13edf29313caabdd4424'),
    plot: 'A ghost returns back from his world to prove something. But on earth, he has something more to do for his country.',
    title: 'Bhoothnath Returns',
    scoreDetails: {
      value: 0.007246376811594203,
      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: 0.7711119651794434,
          details: []
        },
        { inputPipelineName: 'vectorPipeline2', rank: 0, weight: 0.5 }
      ]
    }
  },
  {
    _id: ObjectId('573a13bdf29313caabd58274'),
    plot: 'Banku, his mother, Anjali Sharma and father move in to their new house -- the Nath villa, unaware of the fact that the house is inhabited by a ghost. It is learnt the ghost is not too happy...',
    title: 'Bhoothnath',
    scoreDetails: {
      value: 0.007142857142857143,
      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: 0.7709513902664185,
          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_voyage_3_large 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_voyage_3_large 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 same 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_voyage_3_large",
11                 "queryVector": BATTLE_GOOD_EVIL,
12                 "numCandidates": 2000,
13                 "limit": 200
14               }
15             }
16           ],
17           vectorPipeline2: [
18             {
19               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "title_voyageai_embedding",
22                 "queryVector": BATTLE_GOOD_EVIL,
23                 "numCandidates": 2000,
24                 "limit": 200
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$project": {
41       _id: 1,
42       title: 1,
43       plot: 1,
44       scoreDetails: {"$meta": "scoreDetails"}
45     }
46   },
47   {
48     "$limit": 20
49   }
50 ]);

[
  {
    _id: ObjectId('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: 0.01278772378516624,
      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: 0.7586066126823425,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 32,
          weight: 0.5,
          value: 0.7333235144615173,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.012362637362637362,
      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: 0.752047598361969,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 24,
          weight: 0.5,
          value: 0.7401053309440613,
          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: 0.012301804264625477,
      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: 58,
          weight: 0.5,
          value: 0.7400728464126587,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 2,
          weight: 0.5,
          value: 0.7624473571777344,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce699b'),
    plot: "On a post-apocalyptic Earth, a wizard and his faire folk comrades fight an evil wizard who's using technology in his bid for conquest.",
    title: 'Wizards',
    scoreDetails: {
      value: 0.012173796072101156,
      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: 0.767616868019104,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 58,
          weight: 0.5,
          value: 0.725697934627533,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13bbf29313caabd54a66'),
    plot: 'It is AD 924, at the end of the United Shilla Dynasty. Continuous riots sweep the land ruled by a corrupted government. Evil forces are rampant and malicious demons roam the land. YI Kwak, ...',
    title: 'Demon Empire',
    scoreDetails: {
      value: 0.011527727217570683,
      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: 63,
          weight: 0.5,
          value: 0.7390550374984741,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7534297704696655,
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      ]
    }
  },
  {
    _id: ObjectId('573a13adf29313caabd29ca7'),
    plot: 'A raging god of battle and a master samurai duke it out in a series of sword fights in a remote temple.',
    title: 'Aragami',
    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: 0.7687976360321045,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 86,
          weight: 0.5,
          value: 0.7192319631576538,
          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: 0.01138143468727159,
      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: 97,
          weight: 0.5,
          value: 0.7345550060272217,
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        {
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          rank: 1,
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          value: 0.7787338495254517,
          details: []
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      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabceb2ff'),
    plot: 'Dragon, a bloody dictator, is challenged by Lancelot, who comes to save the girl, and to liberate the people.',
    title: 'Ubit drakona',
    scoreDetails: {
      value: 0.011166103331723805,
      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: 0.752510666847229,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 49,
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          value: 0.7284656167030334,
          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: 0.010186322686322687,
      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: 0.747940719127655,
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        {
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      ]
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  },
  {
    _id: ObjectId('573a1398f29313caabcebfc6'),
    plot: 'Warlords Kagetora and Takeda each wish to prevent the other from gaining hegemony in feudal Japan. The two samurai leaders pursue one another across the countryside, engaging in massive ...',
    title: 'Heaven and Earth',
    scoreDetails: {
      value: 0.009934548854604956,
      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: 0.7576944231987,
          details: []
        },
        {
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          rank: 126,
          weight: 0.5,
          value: 0.7146676778793335,
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      ]
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  },
  {
    _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: 0.009608277900960829,
      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: 0.7629513740539551,
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        {
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          rank: 186,
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  },
  {
    _id: ObjectId('573a139af29313caabcefeb7'),
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    scoreDetails: {
      value: 0.009457712458324828,
      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: 147,
          weight: 0.5,
          value: 0.7299161553382874,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 11,
          weight: 0.5,
          value: 0.7455743551254272,
          details: []
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      ]
    }
  },
  {
    _id: ObjectId('573a139af29313caabcf00f7'),
    plot: 'A shape-shifting mountain man and a group of children team up to protect an enchanted forest from evil lumberjacks.',
    title: 'Forest Warrior',
    scoreDetails: {
      value: 0.009457712458324828,
      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: 147,
          weight: 0.5,
          value: 0.7299161553382874,
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        {
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          rank: 11,
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          details: []
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      ]
    }
  },
  {
    _id: ObjectId('573a13c0f29313caabd62ff5'),
    plot: 'Wind and Cloud find themselves up against a ruthless Japanese warlord intent on invading China.',
    title: 'Storm Warriors',
    scoreDetails: {
      value: 0.009268707482993197,
      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: 38,
          weight: 0.5,
          value: 0.7453598976135254,
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        {
          inputPipelineName: 'vectorPipeline2',
          rank: 60,
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          value: 0.7255600094795227,
          details: []
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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: 0.009126425003202255,
      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: 151,
          weight: 0.5,
          value: 0.7297327518463135,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 14,
          weight: 0.5,
          value: 0.7440595626831055,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d0f29313caabd8d3f1'),
    plot: 'A short tale about the demons that slumber deep in the human soul and have the power to push people into the abyss of blind hate, fury and rage.',
    title: 'Paths of Hate',
    scoreDetails: {
      value: 0.009109311740890687,
      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: 70,
          weight: 0.5,
          value: 0.7381929159164429,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 35,
          weight: 0.5,
          value: 0.732455313205719,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1399f29313caabcee104'),
    plot: 'The Son of Satan attempts to bring his father to Earth with the aid of some mystic stones.',
    title: 'Warlock: The Armageddon',
    scoreDetails: {
      value: 0.0087205094803879,
      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: 0.7456430196762085,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 87,
          weight: 0.5,
          value: 0.7191024422645569,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13cef29313caabd880e3'),
    plot: 'Eight classic monsters fight to the death in an explosive wrestling tournament set inside an abandoned and cursed graveyard.',
    title: 'Monster Brawl',
    scoreDetails: {
      value: 0.008630407214315908,
      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: 91,
          weight: 0.5,
          value: 0.7353591918945312,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 34,
          weight: 0.5,
          value: 0.7329039573669434,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1398f29313caabce9932'),
    plot: 'A young man must stop the Lord of Darkness from both destroying daylight and marrying the woman he loves.',
    title: 'Legend',
    scoreDetails: {
      value: 0.008511658574596896,
      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: 23,
          weight: 0.5,
          value: 0.7502411603927612,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 141,
          weight: 0.5,
          value: 0.7133878469467163,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13a5f29313caabd15bbd'),
    plot: "A desert warrior rises up against the evil army that is destroying his homeland. He captures the enemy's key sorcerer, takes her deep into the desert and prepares for a final showdown.",
    title: 'The Scorpion King',
    scoreDetails: {
      value: 0.00819672131147541,
      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: 0.7749131917953491,
          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_voyage_3_large 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 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_voyageai_embedding field for the string battle between good and evil, specified in the queryVector field as vector embeddings by using the BATTLE_GOOD_EVIL 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 fourth documents in the results suggest a significant match for the term in the plot field while the second and fifth documents suggest a significant match 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               "$vectorSearch": {
20                 "index": "multiple-vector-search",
21                 "path": "plot_embedding_voyage_3_large",
22                 "queryVector": JOURNEY_ACROSS_LANDS_VOYAGEAI,
23                 "numCandidates": 2000,
24                 "limit": 100
25               }
26             }
27           ]
28         }
29       },
30       combination: {
31         weights: {
32           vectorPipeline1: 0.5,
33           vectorPipeline2: 0.5
34         }
35       },
36       "scoreDetails": true
37     }
38   },
39   {
40     "$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: 0.7371870279312134,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.01527518656716418,
      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,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 7,
          weight: 0.5,
          value: 0.7268927097320557,
          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: 0.015151515151515152,
      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: 6,
          weight: 0.5,
          value: 0.7283568978309631,
          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: 0.01488095238095238,
      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: 12,
          weight: 0.5,
          value: 0.7198330163955688,
          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: 0.014785823005001086,
      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: 13,
          weight: 0.5,
          value: 0.9113836288452148,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 3,
          weight: 0.5,
          value: 0.7334519028663635,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.01464346349745331,
      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: 16,
          weight: 0.5,
          value: 0.7178072333335876,
          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: 0.014636752136752137,
      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: 5,
          weight: 0.5,
          value: 0.7327286005020142,
          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,
          weight: 0.5,
          value: 0.7353839874267578,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.013708920187793428,
      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,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 11,
          weight: 0.5,
          value: 0.7211478352546692,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.013606071825249907,
      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: 14,
          weight: 0.5,
          value: 0.911109983921051,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 13,
          weight: 0.5,
          value: 0.7196126580238342,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13c9f29313caabd792a8'),
    plot: 'Settlers traveling through the Oregon desert in 1845 find themselves stranded in harsh conditions.',
    title: "Meek's Cutoff",
    scoreDetails: {
      value: 0.013095238095238094,
      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,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 24,
          weight: 0.5,
          value: 0.712228000164032,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13d8f29313caabda5a4e'),
    plot: 'A savage warrior escapes slavery and hunted by his former masters, begins a perilous journey back to his homeland and his wife.',
    title: 'The Dragonphoenix Chronicles: Indomitable',
    scoreDetails: {
      value: 0.013080664095254067,
      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: 0.9163008332252502,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 29,
          weight: 0.5,
          value: 0.7103214859962463,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.012908496732026143,
      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,
          value: 0.9088267087936401,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 8,
          weight: 0.5,
          value: 0.7257336974143982,
          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: 0.012824675324675325,
      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: 10,
          weight: 0.5,
          value: 0.7230479121208191,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.012658227848101266,
      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: 19,
          weight: 0.5,
          value: 0.7152039408683777,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.012507817385866166,
      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,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 22,
          weight: 0.5,
          value: 0.7129499912261963,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.012237762237762238,
      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: 50,
          weight: 0.5,
          value: 0.7051275968551636,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a13b6f29313caabd46ae0'),
    plot: "In a countryside town bordering on a magical land, a young man makes a promise to his beloved that he'll retrieve a fallen star by venturing into the magical realm.",
    title: 'Stardust',
    scoreDetails: {
      value: 0.011742820602255552,
      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: 0.9228402376174927,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 81,
          weight: 0.5,
          value: 0.6994539499282837,
          details: []
        }
      ]
    }
  },
  {
    _id: ObjectId('573a1397f29313caabce8828'),
    plot: 'A warrior seeks his true origins in a seemingly prehistoric wasteland.',
    title: 'Yor, the Hunter from the Future',
    scoreDetails: {
      value: 0.01117448492608544,
      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: 0.9153545498847961,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 61,
          weight: 0.5,
          value: 0.7023824453353882,
          details: []
        }
      ]
    }
  },
  {
    _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: 0.010934744268077601,
      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,
          details: []
        },
        {
          inputPipelineName: 'vectorPipeline2',
          rank: 45,
          weight: 0.5,
          value: 0.7056041955947876,
          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_embedding_voyage_3_large 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 fifth documents in the results suggest more similar semantic representation by the model used in the vectorPipeline2 while the second and fourth document in the results suggest closer semantic interpretation by the model used in the vectorPipeline1.

Back

Combined Vector Search and Full-Text Search

Use Compatible Views

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	outputDimension = 2048
10	vo = voyageai.Client()
11	# Define a function to generate embeddings
12	def get_embedding(data, input_type = "document"):
13	embeddings = vo.embed(
14	data, model = model, output_dimension = outputDimension, input_type = input_type
15	).embeddings
16	return embeddings[0]
17
18	# Connect to your MongoDB Atlas cluster
19	mongo_client = pymongo.MongoClient("<connection-string>")
20	db = mongo_client["sample_mflix"]
21	collection = db["embedded_movies"]
22
23	# Filter to exclude null or empty plot fields
24	filter = {"title": {"$nin": [None, ""]}}
25
26	# Get a subset of documents in the collection
27	documents = collection.find(filter).limit(3500)
28
29	# Update each document with a new embedding field
30	updated_doc_count = 0
31	for doc in documents:
32	embedding = get_embedding(doc["title"])
33	if embedding is not None:
34	collection.update_one({"_id": doc["_id"]}, {"$set": {"title_voyageai_embedding": embedding}})
35	updated_doc_count += 1
36
37	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_embedding_voyage_3_large",
15	"numDimensions": 2048,
16	"similarity": "dotProduct"
17	},
18	{
19	"type": "vector",
20	"path": "title_voyageai_embedding",
21	"numDimensions": 2048,
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_voyage_3_large",
11	"queryVector": COMEDY_INVOLVING_GHOSTS,
12	"numCandidates": 2000,
13	"limit": 50
14	}
15	}
16	],
17	vectorPipeline2: [
18	{
19	"$vectorSearch": {
20	"index": "multiple-vector-search",
21	"path": "plot_embedding_voyage_3_large",
22	"queryVector": HUMOR_INVOLVING_PARANORMAL,
23	"numCandidates": 2000,
24	"limit": 50
25	}
26	}
27	]
28	}
29	},
30	combination: {
31	weights: {
32	vectorPipeline1: 0.5,
33	vectorPipeline2: 0.5
34	}
35	},
36	"scoreDetails": true
37	}
38	},
39	{
40	"$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

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.