How to Perform Hybrid Search
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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_embeddingsfield. This field contains vector embeddings that represent the summary of a movie's plot.Create an Atlas Search index on the
titlefield in thesample_mflix.embedded_moviescollection. 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
$vectorSearchquery against theplot_embeddingsfield and a$searchquery against thetitlefield.
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 divide1by the sum ofrandrank_constantfor 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 Adminaccess 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_embeddingsfield for running vector queries against that field.An Atlas Search index on the
titlefield for running full-text search against that field.
Procedure
In Atlas, go to the Clusters page for your project.
If it's not already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.
If it's not already displayed, select your desired project from the Projects menu in the navigation bar.
If the Clusters page is not already displayed, click Database in the sidebar.
The Clusters page displays.
Go to the Atlas Search page for your cluster.
You can go the Atlas Search page from the sidebar, the Data Explorer, or your cluster details page.
In the sidebar, click Atlas Search under the Services heading.
From the Select data source dropdown, select your cluster and click Go to Atlas Search.
The Atlas Search page displays.
Click the Browse Collections button for your cluster.
Expand the database and select the collection.
Click the Search Indexes tab for the collection.
The Atlas Search page displays.
Click the cluster's name.
Click the Atlas Search tab.
The Atlas Search page displays.
Define the Atlas Vector Search index.
Click Create Search Index.
Under Atlas Vector Search, select JSON Editor and then click Next.
In the Database and Collection section, find the
sample_mflixdatabase, and select theembedded_moviescollection.In the Index Name field, enter
rrf-vector-search.Replace the default definition with the following index definition and then click Next.
Define the Atlas Vector Search index.
Replace the default definition with the following index definition.
This index definition indexes the
plot_embeddingfield as the vector type. Theplot_embeddingfield contains embeddings created using OpenAI'stext-embedding-ada-002embedding model. The index definition specifies1536vector dimensions and measures similarity usingdotProduct.1 { 2 "fields": [ 3 { 4 "type": "vector", 5 "path": "plot_embedding", 6 "numDimensions": 1536, 7 "similarity": "dotProduct" 8 } 9 ] 10 }
Enter the Index Name, and set the Database and Collection.
In the Index Name field, enter
rrf-full-text-search.Note
If you name your index
default, you don't need to specify anindexparameter when using the $search pipeline stage. Otherwise, you must specify the index name using theindexparameter.In the Database and Collection section, find the
sample_mflixdatabase, and select theembedded_moviescollection.
Define the Atlas Search index.
The following index definition indexes the title field as the
string type for querying the
field.
You can use the Atlas Search Visual Editor or the Atlas Search JSON Editor in the Atlas user interface to create the index.
Click Next.
Click Refine Your Index.
In the Index Configurations section, toggle to disable Dynamic Mapping.
In the Field Mappings section, click Add Field to display the Add Field Mapping window.
Click Customized Configuration.
Select
titlefrom the Field Name dropdown.Select String from the Data Type dropdown.
Click Add.
Replace the default index definition with the following definition.
1 { 2 "mappings": { 3 "dynamic": false, 4 "fields": { 5 "title": [{ 6 "type": "string" 7 }] 8 } 9 } 10 } Click Next.
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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9 "numCandidates": 100, 10 "limit": 20 11 } 12 }, { 13 "$group": { 14 "_id": null, 15 "docs": {"$push": "$$ROOT"} 16 } 17 }, { 18 "$unwind": { 19 "path": "$docs", 20 "includeArrayIndex": "rank" 21 } 22 }, { 23 "$addFields": { 24 "vs_score": { 25 "$multiply": [ 26 vector_weight, { 27 "$divide": [ 28 1.0, { 29 "$add": ["$rank", 60] 30 } 31 ] 32 } 33 ] 34 } 35 } 36 }, { 37 "$project": { 38 "vs_score": 1, 39 "_id": "$docs._id", 40 "title": "$docs.title" 41 } 42 }, 43 { 44 "$unionWith": { 45 "coll": "movies", 46 "pipeline": [ 47 { 48 "$search": { 49 "index": "rrf-full-text-search", 50 "phrase": { 51 "query": "star wars", 52 "path": "title" 53 } 54 } 55 }, { 56 "$limit": 20 57 }, { 58 "$group": { 59 "_id": null, 60 "docs": {"$push": "$$ROOT"} 61 } 62 }, { 63 "$unwind": { 64 "path": "$docs", 65 "includeArrayIndex": "rank" 66 } 67 }, { 68 "$addFields": { 69 "fts_score": { 70 "$multiply": [ 71 full_text_weight, { 72 "$divide": [ 73 1.0, { 74 "$add": ["$rank", 60] 75 } 76 ] 77 } 78 ] 79 } 80 } 81 }, 82 { 83 "$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 103 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_weightfull_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