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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.

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.

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:

  1. Set up an Atlas Vector Search index on the sample_mflix.embedded_movies collection.

  2. 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.

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.

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.

1

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
2

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.

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

Note

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

1

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
2

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.

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

Note

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

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.

1

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

2

Example

use sample_mflix
switched to db sample_mflix
3
1db.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:

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.

1
  1. Create a file named embeddings.js.

    touch embeddings.js
  2. 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

    COMEDY_INVOLVING_GHOSTS=[0.004941695835441351, -0.02559443935751915, 0.005922815762460232, -0.00457746721804142, 0.015435416251420975, 0.004721845965832472, -0.00922712404280901, -0.03711850196123123, -0.005847345106303692, -0.018559250980615616, 0.015540419146418571, 0.014175382442772388, -0.01201626192778349, -0.014949778094887733, 0.01496290322393179, -0.01627543941140175, 0.037801019847393036, -0.014608519151806831, 0.012226266786456108, -0.02979455329477787, -0.008072092197835445, 0.006520019378513098, -0.007862087339162827, -0.0045380908995866776, 0.013145041652023792, 0.0033420431427657604, 0.02821951173245907, -0.01892676018178463, -0.017417345196008682, 0.0041902693919837475, 0.012659403495490551, -0.018703630194067955, -0.016380442306399345, -0.01016558613628149, -0.012994100339710712, -0.012705342844128609, -0.012305019423365593, -0.002349438378587365, 0.009824327193200588, -0.0038588540628552437, -0.014674145728349686, 0.00844616536051035, -0.010749665088951588, -0.004905601032078266, -0.004029483534395695, 0.01388662401586771, 0.012298456393182278, -0.014464139938354492, -0.010355903767049313, 0.005634058266878128, 0.017456721514463425, 0.010342778638005257, -0.002628352027386427, -0.023770015686750412, 0.010500282980501652, 0.0073239472694695, 0.002121385419741273, 0.004876068793237209, 0.023087497800588608, -0.007875212468206882, -0.010375591926276684, -0.0357009619474411, -0.03772226721048355, 0.003940887283533812, 0.015619170852005482, -0.0007407621596939862, -0.005611088592559099, -0.007553641218692064, 0.002821951173245907, -0.015002279542386532, 0.0413186140358448, 0.015606045722961426, -0.0006152509595267475, 0.015789801254868507, 0.03646223247051239, -0.01849362440407276, -0.016997333616018295, 0.008662733249366283, -0.0011238583829253912, 0.010283715091645718, 0.017574848607182503, -0.018283618614077568, -0.01202282402664423, 0.013610991649329662, 0.0002618918369989842, 0.010920294560492039, -0.030450820922851562, 0.024255653843283653, -0.015081031247973442, -0.007291133981198072, 0.024557536467909813, 0.01844112202525139, 0.043812431395053864, 0.02342875674366951, 0.0002134920796379447, 0.007068003062158823, -0.016564195975661278, 0.022076845169067383, -0.0011181160807609558, -0.0181261133402586, -0.017417345196008682, 0.003123834263533354, -0.020580554381012917, -0.003812915412709117, 0.021026816219091415, -0.054233960807323456, 0.015750424936413765, 0.0288495272397995, 0.01657732203602791, 0.023848768323659897, -0.0060212560929358006, 0.0059129721485078335, 0.011622500605881214, -0.026329459622502327, -0.026605091989040375, -0.02298249490559101, 0.030477071180939674, 0.02417690120637417, -0.013013788498938084, 0.007232069969177246, -0.0028728118631988764, -0.01673482544720173, 0.014175382442772388, -0.026421338319778442, 0.023179374635219574, 0.016091683879494667, -0.010519971139729023, -0.02653946541249752, 0.0004511840525083244, -0.022247474640607834, 0.024111274629831314, -0.01613106019794941, 0.019963663071393967, -0.010539659298956394, -0.034677185118198395, 0.022391853854060173, -0.025174429640173912, 0.029217038303613663, -0.0409773550927639, -0.023336879909038544, -0.002090212656185031, 0.017902983352541924, -0.02279873937368393, -0.006250949576497078, 0.004839974455535412, 0.0013174574123695493, 0.0026956195943057537, -0.0015545340720564127, 0.011727503500878811, -0.011084361001849174, 0.012895660474896431, -0.023376254364848137, 0.007396136876195669, 0.004078703932464123, 0.006943312007933855, 0.004036046098917723, 0.001967162359505892, 0.016012931242585182, -0.034388426691293716, -0.01849362440407276, -0.010821854695677757, -0.0061590722762048244, 0.016957957297563553, -0.011327180080115795, 0.007205819245427847, 0.030634576454758644, 0.0013330437941476703, -0.02126307412981987, -0.0026808534748852253, -0.015238535590469837, 0.016682324931025505, 0.04410118982195854, -0.028744524344801903, 0.017771728336811066, -0.027090730145573616, 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  3. Save and close the file.

2

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

3

Run the following command in the mongosh prompt:

use sample_mflix
4
  1. 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
  2. Verify that the embeddings loaded successfully.

    You can verify by running a command similar to the following:

    COMEDY_INVOLVING_GHOSTS.length
    1536
5

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