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Perform Hybrid Search with Atlas Vector Search and Atlas Search

On this page

  • Why Hybrid Search?
  • What is Reciprocal Rank Fusion?
  • Prerequisites
  • Create the Atlas Vector Search and Atlas Search Indexes
  • Procedure
  • Run a Combined Semantic Search and Full-Text Search Query
  • Procedure
  • About the Query
  • Learn by Watching

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 title field in the sample_mflix.embedded_movies collection. This field contains the movie's name as a text string.

  3. Run a query that uses reciprocal rank fusion to combine the results from a $vectorSearch query against the plot_embedding field and a $search query against the title 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:

  1. 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 divide 1 by the sum of r and rank_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

  2. 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.

  3. 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 clients to run queries on your Atlas cluster:

    • Search Tester

    • mongosh

    • Compass

    • C#

    • Java

    • MongoDB Node Driver

    • PyMongo

  • 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  "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);
2

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:

  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  "rrf-full-text-search", 
    3  "search", 
    4  {
    5    "mappings": {
    6      "dynamic": false,
    7      "fields": {
    8        "title": [{
    9          "type": "string"
    10        }]
    11      }
    12    }
    13  }
    14);
1

Set up the Environment

  1. 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

  2. Install and import dependencies.

    Run the following command:

    npm install mongodb

  3. Update your package.json file.

    In your project's package.json file, specify the type field as shown in the following example, and then save the file.

    {
       "name": "hybrid-search-mongodb",
       "type": "module",
       ...

  4. Create an .env file.

    1. In your project, create a .env file to store your Atlas connection string.

      ATLAS_CONNECTION_STRING = "<connection-string>";

    2. 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 the dotenv package to your project, or use a different method to manage your environment variables.

2

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.

  1. 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);

  2. 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.

3

Define the Atlas Search index.

The following index definition indexes the title field as the string type for querying the field.

  1. 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);

  2. 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_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

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.

2

Use the sample_mflix database.

Run the following command at mongosh prompt:

use sample_mflix
switched to db sample_mflix
3

Run the following Atlas Search queries against the embedded_movies collection.

1var vector_weight = 0.1;
2var full_text_weight = 0.9;
3db.embedded_movies.aggregate([
4  {
5    "$vectorSearch": {
6      "index": "rrf-vector-search",
7      "path": "plot_embedding",
8      "queryVector": 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67        }, {
68          "$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": "$title",
95      "vs_score": {"$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
  }
]
1

Define the combined query pipeline.

Create a file called combined-query.js, and paste the following code into it:

1import { MongoClient } from 'mongodb';
2
3async 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                "$vectorSearch": {
17                  "index": "rrf-vector-search",
18                  "path": "plot_embedding",
19                  "queryVector": 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77                    }, {
78                      "$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}
140run().catch(console.dir);
2

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_embedding 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:

  • vs_score

  • _id

  • title

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:

  • fts_score

  • _id

  • title

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:

  • vs_score

  • fts_score

  • _id

  • title

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

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