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How to Perform Hybrid Search

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:

  1. Create an Atlas Vector Search index on the plot_embedding field. This field contains vector embeddings that represent the summary of a movie's plot.

  2. Create an Atlas Search index on the sample_mflix.embedded_movies collection.

  3. Run a query to combine the results from a $vectorSearch query and a $search query by using the following techniques:

    • Semantic boosting

    • Reciprocal rank fusion

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.

You can apply the following techniques to achieve a hybrid search:

Technique

Semantic Boosting

Reciprocal Rank Fusion

Purpose

Improves the accuracy of the full-text search results by boosting documents that match vector search. Useful when retrieving very large result sets.

Combines results from different search methods into a single ranked result set by calculating the reciprocal rank of documents in the results.

Ranking Method

  • Retrieves $vectorSearch and $search results.

  • Merges both sets of results using the rank of each document in the respective result sets.

Scoring Method

Boosts the full-text results score for documents that match the results from the vector search, by the weighted vector search score.

Calculates the score by using the following formula:

1.0/{document position in the results + constant value}

Then, for each method of search, applies 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

Search Features

  • Supports faceting to group data into categories.

  • Supports paginating results using searchAfter and searchBefore.

Supports further processing of results in subsequent aggregation stages such as $group.

Output

Single result set that contains documents from $search

Single result set that contains documents from both search methods.

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_mflix data loaded into your Atlas cluster.

  • mongosh 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_embedding 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

1

Define the Atlas Vector Search index.

The following index definition indexes the plot_embedding 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:

  1. Connect to the Atlas cluster using mongosh.

    To learn more, see Connect via mongosh.

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

    use sample_mflix
    switched to db sample_mflix

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

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-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);
2

Define the Atlas Search index.

The following index definition automatically indexes all the dynamically indexable fields in the collection.

To create a Atlas Search index for a collection using mongosh v2.1.2 or later, perform the following steps:

  1. Connect to the Atlas cluster using mongosh.

    To learn more, see Connect via mongosh.

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

    use sample_mflix
    switched to db sample_mflix

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

    1db.embedded_movies.createSearchIndex(
    2  "hybrid-full-text-search", 
    3  "search", 
    4  {
    5    "mappings": {
    6      "dynamic": true
    7    }
    8  }
    9);

Semantic Boosting

This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for star wars in the plot_embedding and plot fields by using the $vectorSearch and $search pipeline stages, respectively. The query enhances relevancy of $search queries by boosting the full-text scores using $vectorSearch scores for the same documents.

Procedure

1

Create the file for the embeddings.

  1. Create a file named query-embeddings.js.

    touch query-embeddings.js

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

    QUERY_EMBEDDING = 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  3. Save and close the file.

2

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.

3

Use the sample_mflix database.

Run the following command at mongosh prompt:

use sample_mflix
switched to db sample_mflix
4

Load the embeddings to use in the query.

Load the query-embeddings.js file into mongosh.

load('<path-to-query-embeddings.js-file>');
5

Run the following Atlas Search queries against the embedded_movies collection.

1vectorWeight = 1;  
2vectorCutoff = 0.7; 
3numResults = 20;
4numCandidates = numResults * 10;  
5
6// Execute the vector search  
7vectorResults = db.embedded_movies.aggregate([  
8    {  
9        $vectorSearch: {  
10            index: "hybrid-vector-search", 
11            path: "plot_embedding",
12            queryVector: QUERY_EMBEDDING,   
13            numCandidates: numCandidates,   
14            limit: numResults  
15        }  
16    },  
17    {  
18        $project: {  
19            _id: 1,  
20            title: 1,  
21            plot: 1,
22            vectorScore: { $meta: "vectorSearchScore" }  
23        }  
24    },  
25    {  
26        $match: {  
27            vectorScore: { $gte: vectorCutoff }  
28        }  
29    }  
30]).toArray();  
31
32// Create a map for fast lookup  
33vectorScoresMap = {};  
34vectorResults.forEach(result => {  
35    vectorScoresMap[result._id] = result.vectorScore * vectorWeight;  
36});  
37
38// Execute the text search with boosting based just on ID  
39textResults = db.embedded_movies.aggregate([  
40    {  
41        $search: {  
42            index: "hybrid-full-text-search",  
43            compound: {  
44                should: [  
45                    {  
46                        text: {  
47                            query: "star wars",  
48                            path: "plot"  
49                        }  
50                    },  
51                    ...Object.keys(vectorScoresMap).map(id => ({  
52                        equals: {  
53                            path: "_id",  
54                            value: id,  
55                            score: { boost: { value: vectorScoresMap[id] } }  
56                        }  
57                    }))  
58                ]  
59            }  
60        }  
61    },  
62    {
63        $limit: 20
64    },
65    {  
66        $project: {  
67            _id: 1,  
68            title: 1,  
69            plot: 1,
70            score: { $meta: "searchScore" }  
71        }  
72    }  
73]).toArray();  
[
  {
    _id: ObjectId('573a139df29313caabcfa90b'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1397f29313caabce6f53'),
    plot: 'In this Star Wars take-off, the peaceful planet of Jillucia has been nearly wiped out by the Gavanas, whose leader takes orders from his mother (played a comic actor in drag) rather than ...',
    title: 'Message from Space',
    score: 4.401333808898926
  },
  {
    _id: ObjectId('573a1398f29313caabce9851'),
    plot: 'Johnathan Cabot is a champion gymnast. In the tiny, yet savage, country of Parmistan, there is a perfect spot for a "star wars" site. For the US to get this site, they must compete in the ...',
    title: 'Gymkata',
    score: 4.284864902496338
  },
  {
    _id: ObjectId('573a1394f29313caabcdf65b'),
    plot: 'A fantastic tale of war, love, family and ambition set in the midst of the Japanese Civil Wars of the sixteenth century.',
    title: 'Ugetsu',
    score: 2.858368396759033
  },
  {
    _id: ObjectId('573a13a4f29313caabd1137f'),
    plot: "A producer's film is endangered when his star walks off, so he decides to digitally create an actress to substitute for the star, becoming an overnight sensation that everyone thinks is a real person.",
    title: 'S1m0ne',
    score: 2.842216730117798
  },
  {
    _id: ObjectId('573a139af29313caabcefe79'),
    plot: 'An all star baseball player becomes the unhealthy focus of a down on his luck salesman.',
    title: 'The Fan',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a3f29313caabd0c992'),
    plot: 'The vampire Lestat becomes a rock star whose music wakes up the queen of all vampires.',
    title: 'Queen of the Damned',
    score: 2.6598150730133057
  },
  {
    _id: ObjectId('573a13a8f29313caabd1da86'),
    plot: 'During the Napoleonic Wars, a brash British captain pushes his ship and crew to their limits in pursuit of a formidable French war vessel around South America.',
    title: 'Master and Commander: The Far Side of the World',
    score: 2.643709897994995
  },
  {
    _id: ObjectId('573a139af29313caabcf0e7d'),
    plot: 'A puppeteer discovers a portal that leads literally into the head of the movie star, John Malkovich.',
    title: 'Being John Malkovich',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13b6f29313caabd4614f'),
    plot: 'A former pop star who now writes commercial jingles for a living experiences a mid-life crisis.',
    title: 'The Good Night',
    score: 2.612804889678955
  },
  {
    _id: ObjectId('573a13cbf29313caabd7fbc9'),
    plot: 'Bertrand Tavernier is in top form with this gripping, superbly mounted drama set against the savage Catholic/Protestant wars that ripped France apart in the 16th century. Based on a novella...',
    title: 'The Princess of Montpensier',
    score: 2.4938809871673584
  },
  {
    _id: ObjectId('573a139bf29313caabcf2d8e'),
    plot: 'Updated version of the classic Rodgers and Hammerstein musical of the classic fairy-tale, with an all-star, multi-racial cast.',
    title: 'Cinderella',
    score: 2.44028377532959
  },
  {
    _id: ObjectId('573a1399f29313caabcec853'),
    plot: 'An action movie star researching a role is allowed to tag along with a hardboiled New York policeman, who finds him superficial and irritating.',
    title: 'The Hard Way',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13b4f29313caabd3f550'),
    plot: 'A cop, a lawyer, and an assassin cross paths after the murder of a federal witness and the kidnapping of a famous pop star.',
    title: 'Divergence',
    score: 2.325138807296753
  },
  {
    _id: ObjectId('573a13aff29313caabd2fe20'),
    plot: 'An intimate story set during the 1860s in which a young Irish woman Sarah and her family find themselves on both sides of the turbulent wars between British and Maori during the British colonization of New Zealand.',
    title: 'River Queen',
    score: 2.298485040664673
  },
  {
    _id: ObjectId('573a13aef29313caabd2df95'),
    plot: 'Story of two sisters that grew up in a small Serbian village in the beginning of the 1930s. The village is torn up by wars and years long blood oath. There are no men left in the village. ...',
    title: 'Tears for Sale',
    score: 2.268857479095459
  },
  {
    _id: ObjectId('573a13b8f29313caabd4d8e1'),
    plot: "Thomas Michael, Paolo Mancini, Chris Klein and Joe Mantegna star in director Matthiew Klinck's holiday-themed comedy concerning two downsized Easter bunnies whose friendship starts to ...",
    title: 'Hank and Mike',
    score: 2.254227876663208
  },
  {
    _id: ObjectId('573a139af29313caabcf124d'),
    plot: 'As the Clone Wars near an end, the Sith Lord Darth Sidious steps out of the shadows, at which time Anakin succumbs to his emotions, becoming Darth Vader and putting his relationships with Obi-Wan and Padme at risk.',
    title: 'Star Wars: Episode III - Revenge of the Sith',
    score: 2.2399840354919434
  },
  {
    _id: ObjectId('573a13c1f29313caabd633ac'),
    plot: "Black Dynamite is the greatest African-American action star of the 1970s. When his only brother is killed by The Man it's up to him to find justice.",
    title: 'Black Dynamite',
    score: 2.1875147819519043
  },
  {
    _id: ObjectId('573a13dbf29313caabdaf598'),
    plot: "Just as Clint Eastwood's star-making spaghetti Western A Fistful of Dollars was inspired by Akira Kurosawa's Yojimbo, Japanese-Korean filmmaker Sang-il Lee (Villain) has decided to ...",
    title: 'Unforgiven',
    score: 2.1875147819519043
  }
]

About the Query

The sample query boosts the $search results based on $vectorSearch result scores to return the most relevant documents. For semantic boosting, you perform the following steps:

  1. Run the $vectorSearch query.

    Use an over request value to specify the number of candidates more than the specified limit to consider. The over request allows you to accomplish the following:

    • Include a large number and broad pool of documents to consider in the initial search.

    • Refine scoring computation in subsequent stages.

    The query uses the following calculation to determine the number of candidates to consider during the vector search:

    numCandidates = limit * over request

    The results are filtered based on a minimum score threshold (vectorCutoff).

  2. Scale the vector search score to adjust the influence of the vector search results on the scoring and ranking of documents.

    Use the vectorWeight parameter to set the weight and multiply the vector search scores by the vectorWeight. This controls the importance of the vector search results and ensures that only the most semantically relevant documents are considered.

  3. Run the $search query and boost the score of the documents that match the results from the vector search.

Reciprocal Rank Fusion

This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for star wars in the plot_embedding 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

1

Set up the embeddings to use in the query.

  1. Create a file named query-embeddings.js.

  2. Copy and paste the following embeddings in the file.

  3. Save and close the file.

2

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.

3

Use the sample_mflix database.

Run the following command at mongosh prompt:

use sample_mflix
switched to db sample_mflix
4

Load the embeddings to use in the query.

Run the following command to load the embeddings:

load('<path-to-query-embeddings.js');

To verify that the embeddings loaded successfully, run the following command:

STAR_WARS_EMBEDDING.length
1536
5

Run the following Atlas Search and Atlas Vector Search query against the embedded_movies collection.

1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "hybrid-vector-search",
7      "path": "plot_embedding",
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67        }, {
68          "$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": "$_id",
95      "title": {"$first": "$title"},
96      "vs_score": {"$max": "$vs_score"},
97      "fts_score": {"$max": "$fts_score"}
98    }
99  },
100  {
101    "$project": {
102      "_id": 1,
103      "title": 1,
104      "vs_score": {"$ifNull": ["$vs_score", 0]},
105      "fts_score": {"$ifNull": ["$fts_score", 0]}
106    }
107  },
108  {
109    "$project": {
110      "score": {"$add": ["$fts_score", "$vs_score"]},
111      "_id": 1,
112      "title": 1,
113      "vs_score": 1,
114      "fts_score": 1
115    }
116  },
117  {"$sort": {"score": -1}},
118  {"$limit": 10}
119])
{
  _id: new ObjectId('573a13c0f29313caabd62f62'),
  title: 'Star Wars: The Clone Wars',
  vs_score: 0.0015625,
  fts_score: 0.015,
  score: 0.0165625
}
{
  _id: new ObjectId('573a1397f29313caabce68f6'),
  title: 'Star Wars: Episode IV - A New Hope',
  vs_score: 0.0016666666666666668,
  fts_score: 0.014754098360655738,
  score: 0.016420765027322405
}
{
  _id: new ObjectId('573a139af29313caabcf0f5f'),
  title: 'Star Wars: Episode I - The Phantom Menace',
  vs_score: 0.0016393442622950822,
  fts_score: 0.014516129032258065,
  score: 0.016155473294553146
}
{
  _id: new ObjectId('573a1397f29313caabce77d9'),
  title: 'Star Wars: Episode V - The Empire Strikes Back',
  vs_score: 0.0016129032258064516,
  fts_score: 0.014285714285714285,
  score: 0.015898617511520736
}
{
  _id: new ObjectId('573a1397f29313caabce8cdb'),
  title: 'Star Wars: Episode VI - Return of the Jedi',
  vs_score: 0.0015873015873015873,
  fts_score: 0.0140625,
  score: 0.015649801587301587
}
{
  _id: new ObjectId('573a139af29313caabcf124d'),
  title: 'Star Wars: Episode III - Revenge of the Sith',
  vs_score: 0.0013698630136986301,
  fts_score: 0.013846153846153847,
  score: 0.015216016859852476
}
{
  _id: new ObjectId('573a139af29313caabcf1258'),
  title: 'Star Wars: Episode II - Attack of the Clones',
  vs_score: 0.0014925373134328358,
  fts_score: 0.013636363636363637,
  score: 0.015128900949796473
}
{
  _id: new ObjectId('573a1397f29313caabce6f53'),
  title: 'Message from Space',
  vs_score: 0.0015384615384615387,
  fts_score: 0,
  score: 0.0015384615384615387
}
{
  _id: new ObjectId('573a139df29313caabcfa90b'),
  title: 'Message from Space',
  vs_score: 0.0015151515151515154,
  fts_score: 0,
  score: 0.0015151515151515154
}
{
  _id: new ObjectId('573a13d9f29313caabda92ff'),
  title: 'Guardians of the Galaxy',
  vs_score: 0.0014705882352941176,
  fts_score: 0,
  score: 0.0014705882352941176
}

If you sort the results in ascending order by replacing the value of score on line 117 with 1, Atlas Vector Search returns the following results:

{
  _id: new ObjectId('573a13b0f29313caabd34a3e'),
  title: 'Cowboys & Aliens',
  vs_score: 0.0012658227848101266,
  fts_score: 0,
  score: 0.0012658227848101266
}
{
  _id: new ObjectId('573a13a3f29313caabd0ec59'),
  title: 'Planet of the Apes',
  vs_score: 0.001282051282051282,
  fts_score: 0,
  score: 0.001282051282051282
}
{
  _id: new ObjectId('573a139bf29313caabcf3d4b'),
  title: 'Planet of the Apes',
  vs_score: 0.001298701298701299,
  fts_score: 0,
  score: 0.001298701298701299
}
{
  _id: new ObjectId('573a1397f29313caabce7509'),
  title: 'Starcrash',
  vs_score: 0.0013157894736842105,
  fts_score: 0,
  score: 0.0013157894736842105
}
{
  _id: new ObjectId('573a13b0f29313caabd33d15'),
  title: 'Zathura: A Space Adventure',
  vs_score: 0.0013333333333333335,
  fts_score: 0,
  score: 0.0013333333333333335
}
{
  _id: new ObjectId('573a1398f29313caabce8d67'),
  title: 'Space Raiders',
  vs_score: 0.0013513513513513514,
  fts_score: 0,
  score: 0.0013513513513513514
}
{
  _id: new ObjectId('573a1398f29313caabce90bd'),
  title: 'The Ewok Adventure',
  vs_score: 0.001388888888888889,
  fts_score: 0,
  score: 0.001388888888888889
}
{
  _id: new ObjectId('573a1398f29313caabce9091'),
  title: 'Dune',
  vs_score: 0.0014084507042253522,
  fts_score: 0,
  score: 0.0014084507042253522
}
{
  _id: new ObjectId('573a13d4f29313caabd9887f'),
  title: 'Abiogenesis',
  vs_score: 0.0014285714285714286,
  fts_score: 0,
  score: 0.0014285714285714286
}
{
  _id: new ObjectId('573a13d5f29313caabd9c312'),
  title: 'Guardians of the Galaxy',
  vs_score: 0.0014492753623188406,
  fts_score: 0,
  score: 0.0014492753623188406
}

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.

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

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