Vector set embeddings

Index and query embeddings with Redis vector sets

A Redis vector set lets you store a set of unique keys, each with its own associated vector. You can then retrieve keys from the set according to the similarity between their stored vectors and a query vector that you specify.

You can use vector sets to store any type of numeric vector but they are particularly optimized to work with text embedding vectors (see Redis for AI to learn more about text embeddings). The example below shows how to generate vector embeddings and then store and retrieve them using a vector set with Lettuce.

Initialize

If you are using Maven, add the following dependencies to your pom.xml file (note that you need Lettuce v6.8.0 or later to use vector sets):

<dependency>
    <groupId>io.lettuce</groupId>
    <artifactId>lettuce-core</artifactId>
    <version>6.8.0.RELEASE</version>
</dependency>

<dependency>
    <groupId>ai.djl.huggingface</groupId>
    <artifactId>tokenizers</artifactId>
    <version>0.33.0</version>
</dependency>

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-model-zoo</artifactId>
    <version>0.33.0</version>
</dependency>

<dependency>
    <groupId>ai.djl</groupId>
    <artifactId>api</artifactId>
    <version>0.33.0</version>
</dependency>

If you are using Gradle, add the following dependencies to your build.gradle file:

compileOnly 'io.lettuce:lettuce-core:6.8.0.RELEASE'
compileOnly 'ai.djl.huggingface:tokenizers:0.33.0'
compileOnly 'ai.djl.pytorch:pytorch-model-zoo:0.33.0'
compileOnly 'ai.djl:api:0.33.0'

In a new Java file, import the required classes:

The imports include the classes required to generate embeddings from text. This example uses an instance of the Predictor class with the all-MiniLM-L6-v2 model for the embeddings. This model generates vectors with 384 dimensions, regardless of the length of the input text, but note that the input is truncated to 256 tokens (see Word piece tokenization at the Hugging Face docs to learn more about the way tokens are related to the original text).

Create the data

The example data is contained in a List<Person> object with some brief descriptions of famous people.

Add the data to a vector set

The next step is to connect to Redis and add the data to a new vector set.

The predictor.predict() method that generates the embeddings returns a float[] array. The vadd() method that adds the embeddings to the vector set accepts a Double[] array, so it is useful to define a helper method to perform the conversion:

The code below connects to Redis, then iterates through all the items in the people list, generates embeddings for each person's description, and then adds the appropriate elements to a vector set called famousPeople. Note that the predict() call is in a try/catch block because it can throw exceptions if it can't download the embedding model (you should add code to handle the exceptions for production).

The call to vadd() also adds the born and died values from the original people list as attribute data. You can access this during a query or by using the vgetattr() method.

Query the vector set

You can now query the data in the set. The basic approach is to use the predict() method to generate another embedding vector for the query text. (This is the same method used to add the elements to the set.) Then, pass the query vector to vsim() to return elements of the set, ranked in order of similarity to the query.

Start with a simple query for "actors":

This returns the following list of elements (formatted slightly for clarity):

['Masako Natsume', 'Chaim Topol', 'Linus Pauling',
'Marie Fredriksson', 'Maryam Mirzakhani', 'Marie Curie',
'Freddie Mercury', 'Paul Erdos']

The first two people in the list are the two actors, as expected, but none of the people from Linus Pauling onward was especially well-known for acting (and there certainly isn't any information about that in the short description text). As it stands, the search attempts to rank all the elements in the set, based on the information contained in the embedding model. You can use the count parameter of vsim() to limit the list of elements to just the most relevant few items:

The reason for using text embeddings rather than simple text search is that the embeddings represent semantic information. This allows a query to find elements with a similar meaning even if the text is different. For example, the word "entertainer" doesn't appear in any of the descriptions, but if you use it as a query, the actors and musicians are ranked highest in the results list:

Similarly, if you use "science" as a query, you get the following results:

['Marie Curie', 'Linus Pauling', 'Maryam Mirzakhani',
'Paul Erdos', 'Marie Fredriksson', 'Freddie Mercury', 'Masako Natsume',
'Chaim Topol']

The scientists are ranked highest, followed by the mathematicians. This ranking seems reasonable given the connection between mathematics and science.

You can also use filter expressions with vsim() to restrict the search further. For example, repeat the "science" query, but this time limit the results to people who died before the year 2000:

Note that the boolean filter expression is applied to items in the list before the vector distance calculation is performed. Items that don't pass the filter test are removed from the results completely, rather than just reduced in rank. This can help to improve the performance of the search because there is no need to calculate the vector distance for elements that have already been filtered out of the search.

More information

See the vector sets docs for more information and code examples. See the Redis for AI section for more details about text embeddings and other AI techniques you can use with Redis.

You may also be interested in vector search. This is a feature of the Redis query engine that lets you retrieve JSON and hash documents based on vector data stored in their fields.

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