Perform Hybrid Search with Atlas Vector Search and Atlas Search
On this page
You can combine Atlas Vector Search and Atlas Search queries into a hybrid search for unified results.
This tutorial demonstrates how to run a hybrid search on the
sample_mflix.embedded_movies
collection, which contains details
about movies. Specifically, this tutorial takes you through the
following steps:
Create an Atlas Vector Search index on the
plot_embeddings
field. This field contains vector embeddings that represent the summary of a movie's plot.Create an Atlas Search index on the
title
field in thesample_mflix.embedded_movies
collection. This field contains the movie's name as a text string.Run a query that uses reciprocal rank fusion to combine the results from a
$vectorSearch
query against theplot_embeddings
field and a$search
query against thetitle
field.
Why Hybrid Search?
A hybrid search is an aggregation of different search methods, such as a full-text and semantic search, for the same query criteria. While full-text is effective in finding exact matches for query terms, semantic search provides the added benefit of identifying semantically similar documents even if the documents don't contain the exact query term. This ensures that synonymous and contextually similar matches are also included in the combined results of both methods of search.
Conversely, if you have tokens for proper nouns or specific keywords in your dataset that you don't expect to be considered in the training of an embedding model in the same context that they are used in your dataset, your vector search might benefit from being combined with a full-text search.
You can also set weights for each method of search per query. Based on whether full-text or semantic search results are most relevant and appropriate for a query, you can increase the weight for that search method per query.
What is Reciprocal Rank Fusion?
Reciprocal rank fusion is a technique to combine results from different search methods, such as a semantic and a full-text search, into a single result set by performing the following actions:
Calculate the reciprocal rank of the documents in the results.
For each ranked document in each search result, first add the rank (
r
) of the document with a constant number,60
, to smooth the score (rank_constant
), and then divide1
by the sum ofr
andrank_constant
for the reciprocal rank of the document in the results.reciprocal_rank = 1 / ( r + rank_constant ) For each method of search, apply different weights (
w
) to give more importance to that method of search. For each document, the weighted reciprocal rank is calculated by multiplying the weight by the reciprocal rank of the document.weighted_reciprocal_rank = w x reciprocal_rank Combine the rank-derived and weighted scores of the documents in the results.
For each document across all search results, add the calculated reciprocal ranks for a single score for the document.
Sort the results by the combined score of the documents in the results.
Sort the documents in the results based on the combined score across the results for a single, combined ranked list of documents in the results.
Prerequisites
Before you begin, you must have the following:
An Atlas cluster with MongoDB version v6.0.11, or v7.0.2 or later.
Note
Ensure that your Atlas cluster has enough memory to store both Atlas Search and Atlas Vector Search indexes and run performant queries.
The sample data loaded into your Atlas cluster.
One of the following applications to run queries on your Atlas cluster:
Project Data Access Admin
access to the project to create Atlas Vector Search and Atlas Search indexes.
Create the Atlas Vector Search and Atlas Search Indexes
This section demonstrates how to create the following indexes on the
fields in the sample_mflix.embedded_movies
collection:
An Atlas Vector Search index on the
plot_embeddings
field for running vector queries against that field.An Atlas Search index on the
title
field for running full-text search against that field.
Procedure
Define the Atlas Vector Search index.
The following index definition indexes the plot_embeddings
field as
the Atlas Vector Search field when querying the collection.
To create an Atlas Vector Search index for a collection using mongosh
v2.1.2 or later, perform the following steps:
Connect to the Atlas cluster using
mongosh
.To learn more, see Connect via
mongosh
.Switch to the database that contains the collection for which you want to create the index.
use sample_mflix switched to db sample_mflix Run the
db.collection.createSearchIndex()
method.1 db.embedded_movies.createSearchIndex( 2 "rrf-vector-search", 3 "vectorSearch", 4 { 5 "fields": [ 6 { 7 "type": "vector", 8 "path": "plot_embedding", 9 "numDimensions": 1536, 10 "similarity": "dotProduct" 11 } 12 ] 13 } 14 );
Define the Atlas Search index.
The following index definition indexes the title
field as the
string type for querying the
field.
To create a Atlas Search index for a collection using mongosh
v2.1.2 or later, perform the following steps:
Connect to the Atlas cluster using
mongosh
.To learn more, see Connect via
mongosh
.Switch to the database that contains the collection for which you want to create the index.
use sample_mflix switched to db sample_mflix Run the
db.collection.createSearchIndex()
method.1 db.embedded_movies.createSearchIndex( 2 "rrf-full-text-search", 3 "search", 4 { 5 "mappings": { 6 "dynamic": false, 7 "fields": { 8 "title": [{ 9 "type": "string" 10 }] 11 } 12 } 13 } 14 );
Set up the Environment
Initialize your Node.js project.
Run the following commands in your terminal to create a new directory named
hybrid-search-mongodb
and initialize your project:mkdir hybrid-search-mongodb cd hybrid-search-mongodb npm init -y Install and import dependencies.
Run the following command:
npm install mongodb Update your
package.json
file.In your project's
package.json
file, specify thetype
field as shown in the following example, and then save the file.{ "name": "hybrid-search-mongodb", "type": "module", ... Create an
.env
file.In your project, create a
.env
file to store your Atlas connection string.ATLAS_CONNECTION_STRING = "<connection-string>"; Replace the
<api-key>
and<connection-string>
placeholder values with your SRV connection string for your Atlas cluster. Your connection string should use the following format:mongodb+srv://<db_username>:<db_password>@<clusterName>.<hostname>.mongodb.net Note
Minimum Node.js Version Requirements
Node.js v20.x introduced the
--env-file
option. If you are using an older version of Node.js, add thedotenv
package to your project, or use a different method to manage your environment variables.
Define the Atlas Vector Search index.
The following index definition indexes the plot_embeddings
field as
the Atlas Vector Search field when querying the collection.
Create a file called
avs-index.js
, and paste the following code into it:import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Vector Search index const index = { name: "rrf-vector-search", type: "vectorSearch", definition: { "fields": [ { "type": "vector", "numDimensions": 1536, "path": "plot_embedding", "similarity": "dotProduct" } ] } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); Run the following command to execute the code:
node --env-file=.env avs-index.js Atlas might take few minutes to create the index. Atlas sends an email when the index is ready for use. In the Atlas UI, the newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.
Define the Atlas Search index.
The following index definition indexes the title
field as the
string type for querying the
field.
Create a file called
fts-index.js
, and paste the following code into it:import { MongoClient } from 'mongodb'; // Connect to your Atlas deployment const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); async function run() { try { const database = client.db("sample_mflix"); const collection = database.collection("embedded_movies"); // Define your Atlas Search index const index = { name: "rrf-full-text-search", type: "search", definition: { "mappings": { "dynamic": false, "fields": { "title": [{ "type": "string" }] } } } } // Call the method to create the index const result = await collection.createSearchIndex(index); console.log(result); } finally { await client.close(); } } run().catch(console.dir); Run the following command to execute the code:
node --env-file=.env fts-index.js Atlas might take few minutes to create the index. Atlas sends an email when the index is ready for use. In the Atlas UI, the newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.
Run a Combined Semantic Search and Full-Text Search Query
This section demonstrates how to query the data in the
sample_mflix.embedded_movies
collection for star wars in the
plot_embeddings
and title
fields by using the
$vectorSearch
and $search
pipeline stages and
combine each document's scores from both stages to re-sort the documents
in the results. This ensures that documents appearing in both searches
appear at the top of the combined results.
Procedure
Connect to your cluster using mongosh
.
Open mongosh
in a terminal window and connect to your
cluster. For detailed instructions on connecting, see
Connect via mongosh
.
Use the sample_mflix
database.
Run the following command at mongosh
prompt:
use sample_mflix
switched to db sample_mflix
Run the following Atlas Search queries against the embedded_movies
collection.
1 var vector_weight = 0.1; 2 var full_text_weight = 0.9; 3 db.embedded_movies.aggregate([ 4 { 5 "$vectorSearch": { 6 "index": "rrf-vector-search", 7 "path": "plot_embedding", 8 "queryVector": 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81 }, 82 { 83 "$project": { 84 "fts_score": 1, 85 "_id": "$docs._id", 86 "title": "$docs.title" 87 } 88 } 89 ] 90 } 91 }, 92 { 93 "$group": { 94 "_id": "$title", 95 "vs_score": {"$max": "$vs_score"}, 96 "fts_score": {"$max": "$fts_score"} 97 } 98 }, 99 { 100 "$project": { 101 "_id": 1, 102 "title": 1, 103 "vs_score": {"$ifNull": ["$vs_score", 0]}, 104 "fts_score": {"$ifNull": ["$fts_score", 0]} 105 } 106 }, 107 { 108 "$project": { 109 "score": {"$add": ["$fts_score", "$vs_score"]}, 110 "_id": 1, 111 "title": 1, 112 "vs_score": 1, 113 "fts_score": 1 114 } 115 }, 116 {"$sort": {"score": -1}}, 117 {"$limit": 10} 118 ])
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
If you sort the results in ascending order by replacing the value
of score
on line 116 with 1
, Atlas Vector Search returns the following
results:
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
Define the combined query pipeline.
Create a file called combined-query.js
, and paste the following
code into it:
1 import { MongoClient } from 'mongodb'; 2 3 async function run() { 4 // Connect to your Atlas cluster 5 const client = new MongoClient(process.env.ATLAS_CONNECTION_STRING); 6 7 try { 8 await client.connect(); 9 const database = client.db("sample_mflix"); 10 const collection = database.collection("embedded_movies"); 11 12 const vectorWeight = 0.1; 13 const fullTextWeight = 0.9; 14 const pipeline = [ 15 { 16 "$vectorSearch": { 17 "index": "rrf-vector-search", 18 "path": "plot_embedding", 19 "queryVector": 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20 "numCandidates": 100, 21 "limit": 20 22 } 23 }, { 24 "$group": { 25 "_id": null, 26 "docs": {"$push": "$$ROOT"} 27 } 28 }, { 29 "$unwind": { 30 "path": "$docs", 31 "includeArrayIndex": "rank" 32 } 33 }, { 34 "$addFields": { 35 "vs_score": { 36 "$multiply": [ 37 vectorWeight, { 38 "$divide": [ 39 1.0, { 40 "$add": ["$rank", 60] 41 } 42 ] 43 } 44 ] 45 } 46 } 47 }, { 48 "$project": { 49 "vs_score": 1, 50 "_id": "$docs._id", 51 "title": "$docs.title" 52 } 53 }, { 54 "$unionWith": { 55 "coll": "movies", 56 "pipeline": [ 57 { 58 "$search": { 59 "index": "rrf-full-text-search", 60 "phrase": { 61 "query": "star wars", 62 "path": "title" 63 } 64 } 65 }, { 66 "$limit": 20 67 }, { 68 "$group": { 69 "_id": null, 70 "docs": {"$push": "$$ROOT"} 71 } 72 }, { 73 "$unwind": { 74 "path": "$docs", 75 "includeArrayIndex": "rank" 76 } 77 }, { 78 "$addFields": { 79 "fts_score": { 80 "$multiply": [ 81 fullTextWeight, { 82 "$divide": [ 83 1.0, { 84 "$add": ["$rank", 60] 85 } 86 ] 87 } 88 ] 89 } 90 } 91 }, 92 { 93 "$project": { 94 "fts_score": 1, 95 "_id": "$docs._id", 96 "title": "$docs.title" 97 } 98 } 99 ] 100 } 101 }, { 102 "$group": { 103 "_id": "$title", 104 "vs_score": {"$max": "$vs_score"}, 105 "fts_score": {"$max": "$fts_score"} 106 } 107 }, { 108 "$project": { 109 "_id": 1, 110 "title": 1, 111 "vs_score": {"$ifNull": ["$vs_score", 0]}, 112 "fts_score": {"$ifNull": ["$fts_score", 0]} 113 } 114 }, { 115 "$project": { 116 "score": {"$add": ["$fts_score", "$vs_score"]}, 117 "_id": 1, 118 "title": 1, 119 "vs_score": 1, 120 "fts_score": 1 121 } 122 }, 123 {"$sort": {"score": -1}}, 124 {"$limit": 10} 125 ]; 126 127 // run pipeline 128 const result = collection.aggregate(pipeline); 129 130 for await (const doc of result) { 131 console.log(doc); 132 } 133 } catch (err) { 134 console.log(err.stack); 135 } 136 finally { 137 await client.close(); 138 } 139 } 140 run().catch(console.dir);
Execute the code to run the queries against the embedded_movies
collection.
node --env-file=.env combined-query.js
[ { _id: 'Star Wars: Episode IV - A New Hope', vs_score: 0.0016666666666666668, fts_score: 0, score: 0.0016666666666666668 }, { _id: 'Star Wars: Episode I - The Phantom Menace', vs_score: 0.0016393442622950822, fts_score: 0, score: 0.0016393442622950822 }, { _id: 'Star Wars: Episode V - The Empire Strikes Back', vs_score: 0.0016129032258064516, fts_score: 0, score: 0.0016129032258064516 }, { _id: 'Star Wars: Episode VI - Return of the Jedi', vs_score: 0.0015873015873015873, fts_score: 0, score: 0.0015873015873015873 }, { _id: 'Star Wars: The Clone Wars', vs_score: 0.0015625, fts_score: 0, score: 0.0015625 }, { _id: 'Message from Space', vs_score: 0.0015384615384615387, fts_score: 0, score: 0.0015384615384615387 }, { _id: 'Star Wars: Episode II - Attack of the Clones', vs_score: 0.0014925373134328358, fts_score: 0, score: 0.0014925373134328358 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 } ]
If you sort the results in ascending order by replacing the value
of score
on line 123 with 1
, Atlas Vector Search returns the following
results:
[ { _id: 'Cowboys & Aliens', vs_score: 0.0012658227848101266, fts_score: 0, score: 0.0012658227848101266 }, { _id: 'Planet of the Apes', vs_score: 0.001298701298701299, fts_score: 0, score: 0.001298701298701299 }, { _id: 'Starcrash', vs_score: 0.0013157894736842105, fts_score: 0, score: 0.0013157894736842105 }, { _id: 'Zathura: A Space Adventure', vs_score: 0.0013333333333333335, fts_score: 0, score: 0.0013333333333333335 }, { _id: 'Space Raiders', vs_score: 0.0013513513513513514, fts_score: 0, score: 0.0013513513513513514 }, { _id: 'Star Wars: Episode III - Revenge of the Sith', vs_score: 0.0013698630136986301, fts_score: 0, score: 0.0013698630136986301 }, { _id: 'The Ewok Adventure', vs_score: 0.001388888888888889, fts_score: 0, score: 0.001388888888888889 }, { _id: 'Dune', vs_score: 0.0014084507042253522, fts_score: 0, score: 0.0014084507042253522 }, { _id: 'Abiogenesis', vs_score: 0.0014285714285714286, fts_score: 0, score: 0.0014285714285714286 }, { _id: 'Guardians of the Galaxy', vs_score: 0.0014705882352941176, fts_score: 0, score: 0.0014705882352941176 } ]
About the Query
The sample query retrieves the sorted search results from the semantic search and the full-text search, and assigns a reciprocal rank score to the documents in the results based on their position in the results array. The reciprocal rank score is calculated by using the following formula:
1.0/{document position in the results + constant value}
The query then adds the scores from both the searches for each document, ranks the documents based on the combined score, and sorts the documents to return a single result.
Query Variables
The sample query defines the following variables to add weight to the score, with a lower number providing higher weight:
vector_weight
full_text_weight
The weighted reciprocal rank score is calculated by using the following formula:
weight x reciprocal rank
Query Stages
The sample query uses the following pipeline stages to perform a semantic search on the collection and retrieve the reciprocal rank of the documents in the results:
Searches the plot_embeddings field for the string star
wars specified as vector embeddings in the queryVector
field of the query. The query uses ada-002-text embedding,
which is the same as the vector embedding in the plot_embedding
field. The query also specifies a search for up to 100
nearest neighbors and limit the results to 20 documents
only. This stage returns the sorted documents from the semantic
search in the results. | |
Groups all the documents in the results from the semantic search
in a field named docs . | |
Unwinds the array of documents in the docs field and store
the position of the document in the results array in a field
named rank . | |
Adds a new field named vs_score that contains the reciprocal
rank score for each document in the results. Here, reciprocal
rank score is calculated by dividing 1.0 by the sum of
rank and a rank constant value of 60 . Then, the weighted
reciprocal rank is calculated by multiplying vector_weight
weight by the reciprocal rank score. | |
Includes only the following fields in the results:
|
The sample query uses the $unionWith
stage to perform a text
search on the collection and retrieve the reciprocal rank of the
documents in the results:
Searches for movies that contain the term star wars in the
title field. This stage returns the sorted documents from
the full-text search in the results. | |
Limits the output to 20 results only. | |
Groups all the documents from the full-text search in a field
named docs . | |
Unwinds the array of documents in the docs field and store
the position of the document in the results array in a field
named rank . | |
Adds a new field named fts_score that contains the reciprocal
rank score for each document in the results. Here, reciprocal
rank score is calculated by dividing 1.0 by the sum of
rank and a rank constant value of 60 . Then, the weighted
reciprocal rank is calculated by multiplying full_text_weight
weight by the reciprocal rank score. | |
Includes only the following fields in the results:
|
The sample query uses the following stages to combine the results of the semantic and text search and return a single ranked list of documents in the results:
Groups the documents in the results from the preceding stages by
title , vs_score , and fts_score . | |
Includes only the following fields in the results:
| |
Adds a field named score that contains the sum of
vs_score and fts_score to the results. | |
Sorts the results by score in descending order. | |
Limits the output to 10 results only. |
Learn by Watching
Watch a demonstration of an application that showcases hybrid search queries combining Atlas Search full-text and vector search to return a single merged result set. The application implements Relative Score Fusion (RSF) and Reciprocal Rank Fusion (RRF) to return a merged set created by using a rank fusion algorithm.
Duration: 2.43 Minutes