How to Perform Hybrid Search

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How to Perform Hybrid Search

You can combine MongoDB Vector Search and MongoDB 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 a MongoDB Vector Search index on the plot_embedding_voyage_4_large field. This field contains vector embeddings (of 2048 dimensions created using Voyage AI's voyage-4-large embedding model) that represent the summary of a movie's plot.

  2. Create a MongoDB Search index on the sample_mflix.embedded_movies collection.

  3. Run a query to combine the results from a [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch query and a [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search query by using the following techniques:

  4. Semantic boosting

  5. Reciprocal rank fusion

  6. Score Fusion

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 Relative Score 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. Combines results from different search methods into a single ranked result set by calculating the score of documents in the results.
Ranking Method Run [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch to retrieves semantically similar results.; Run [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search to retrieve full-text results while boosting the documents that were returned by [$vectorSearch.](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch Retrieves [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch and [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search results.; Merges both sets of results using the rank of each document in the respective result sets. Retrieves [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch and [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search results.; Merges both sets of results using the score of each document in the respective result sets.
Scoring Method Boosts the full-text results score for documents that match the results from the vector search, by the weighted vector search score. Calculates the score by using the following formula: 1.0/{document position in the results + constant value} Then, for each method of search, applies different weights (w) to give more importance to that method of search. For each document, the weighted reciprocal rank is calculated by multiplying the weight by the reciprocal rank of the document. weighted_reciprocal_rank = w x reciprocal_rank Calculates the score by using the following formula: score = w1 x score1 + w2 x score2
Search Features Supports [faceting](https://www.mongodb.com/docs/search/query/operators-collectors/facet.md#std-label-fts-facet-ref to group data into categories.; Supports [paginating](https://www.mongodb.com/docs/search/query/paginate-results.md#std-label-fts-paginate-results results using searchAfter and searchBefore. Supports further processing of results in subsequent aggregation stages such as [$group.](https://www.mongodb.com/docs/manual/reference/operator/aggregation/group.md#mongodb-pipeline-pipe.-group Supports further processing of results in subsequent aggregation stages such as [$group.](https://www.mongodb.com/docs/manual/reference/operator/aggregation/group.md#mongodb-pipeline-pipe.-group
Output Single result set that contains documents from [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search Single result set that contains documents from both search methods. 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 v8.0 or higher.

Note:

Ensure that your Atlas cluster has enough memory to store both MongoDB Search and MongoDB Vector Search indexes and run performant queries.

  • The [sample_mflix](https://www.mongodb.com/docs/manual/sample-data/sample-mflix.md#std-label-sample-mflix data loaded into your Atlas cluster.

  • [mongosh](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh to run queries on your Atlas cluster:

  • [Project Data Access Admin](https://www.mongodb.com/docs/atlas/reference/user-roles.md#mongodb-authrole-Project-Data-Access-Admin access to the project to create MongoDB Vector Search and MongoDB Search indexes.

Create the MongoDB Vector Search and MongoDB Search Indexes

This section demonstrates how to create the following indexes on the fields in the sample_mflix.embedded_movies collection:

  • A MongoDB Vector Search index on the plot_embedding_voyage_4_large field for running vector queries against that field.

  • A MongoDB Search index that dynamically indexes all dynamically indexable fields for running full-text search against those fields.

  • Connect to the cluster using [mongosh](https://www.mongodb.com/docs/mongodb-shell.md#mongodb-binary-bin.mongosh.

To learn more, see [Connect to a Cluster via mongosh.](https://www.mongodb.com/docs/atlas/mongo-shell-connection.md#std-label-connect-mongo-shell

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

shell use sample_mflix

Output:

shell switched to db sample_mflix

  1. Define the MongoDB Vector Search index.

Run the following command. This index definition indexes the plot_embedding_voyage_4_large field as the MongoDB Vector Search field when querying the collection.

shell db.embedded_movies.createSearchIndex( "hybrid-vector-search", "vectorSearch", { "fields": [ { "type": "vector", "path": "plot_embedding_voyage_4_large", "numDimensions": 2048, "similarity": "dotProduct" } ] } );

  1. Define the MongoDB Search index.

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

shell db.embedded_movies.createSearchIndex( "hybrid-full-text-search", "search", { "mappings": { "dynamic": true } } );

Run the Hybrid Search Queries

This section demonstrates how to query the data in the sample_mflix.embedded_movies collection for charming animal in the plot_embedding_voyage_4_large and fullplot fields by using the [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch and [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search pipeline stages, respectively. The query enhances relevancy of [$search](https://www.mongodb.com/docs/search/query/aggregation-stages/search.md#mongodb-pipeline-pipe.-search queries by boosting the full-text scores using [$vectorSearch](https://www.mongodb.com/docs/vector-search/query/aggregation-stages/vector-search-stage.md#mongodb-pipeline-pipe.-vectorSearch scores for the same documents.

Semantic Boosting Query

  1. Create the file for the embeddings.

Create a file named query-embeddings.js.

shell touch query-embeddings.js

Copy and paste the following embeddings into the file.

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