---
categories:
- docs
- develop
- stack
- oss
- rs
- rc
- oss
- kubernetes
- clients
description: Learn how to index and query vector embeddings with Redis
linkTitle: Index and query vectors
title: Index and query vectors
scope: example
relatedPages:
- /develop/clients/jedis/queryjson
- /develop/clients/jedis/vecsets
- /develop/ai/search-and-query
topics:
- Redis Query Engine
- JSON
- hash
- vectors
weight: 3
---

[Redis Query Engine]({{< relref "/develop/ai/search-and-query" >}})
lets you index vector fields in [hash]({{< relref "/develop/data-types/hashes" >}})
or [JSON]({{< relref "/develop/data-types/json" >}}) objects (see the
[Vectors]({{< relref "/develop/ai/search-and-query/vectors" >}}) 
reference page for more information).
Among other things, vector fields can store *text embeddings*, which are AI-generated vector
representations of the semantic information in pieces of text. The
[vector distance]({{< relref "/develop/ai/search-and-query/vectors#distance-metrics" >}})
between two embeddings indicates how similar they are semantically. By comparing the
similarity of an embedding generated from some query text with embeddings stored in hash
or JSON fields, Redis can retrieve documents that closely match the query in terms
of their meaning.

The example below uses the [HuggingFace](https://huggingface.co/) model
[`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
to generate the vector embeddings to store and index with Redis Query Engine.
The code is first demonstrated for hash documents with a
separate section to explain the
[differences with JSON documents](#differences-with-json-documents).

{{< note >}}From [v6.0.0](https://github.com/redis/jedis/releases/tag/v6.0.0) onwards,
`Jedis` uses query dialect 2 by default.
Redis query engine methods such as [`ftSearch()`]({{< relref "/commands/ft.search" >}})
will explicitly request this dialect, overriding the default set for the server.
See
[Query dialects]({{< relref "/develop/ai/search-and-query/advanced-concepts/dialects" >}})
for more information.
{{< /note >}}

## Initialize

If you are using [Maven](https://maven.apache.org/), add the following
dependencies to your `pom.xml` file:

```xml
<dependency>
    <groupId>redis.clients</groupId>
    <artifactId>jedis</artifactId>
    <version>7.2.0</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](https://gradle.org/), add the following
dependencies to your `build.gradle` file:

```bash
implementation 'redis.clients:jedis:7.2.0'
implementation 'ai.djl.huggingface:tokenizers:0.33.0'
implementation 'ai.djl.pytorch:pytorch-model-zoo:0.33.0'
implementation 'ai.djl:api:0.33.0'
```

## Import dependencies

Import the following classes in your source file:

{{< clients-example set="HomeQueryVec" step="import" lang_filter="Java-Sync" description="Foundational: Import required libraries for vector embeddings, Redis operations, and search functionality" difficulty="beginner" >}}
{{< /clients-example >}}

## Define a helper method

The embedding model represents the vectors as an array of `float` values,
which is the format required by Redis Query Engine.
Also, when you store vectors in a hash object, you must encode the vector
array as a `byte` string. To simplify this situation, you can declare a helper
method `floatsToByteString()` that takes the `float` array that the
embedding model returns and encodes it as a `byte` string:

{{< clients-example set="HomeQueryVec" step="helper_method" lang_filter="Java-Sync" description="Foundational: Create a helper method to convert float arrays to binary strings for vector storage in hash objects" difficulty="beginner" >}}
{{< /clients-example >}}

## Create a tokenizer instance

The next step is to generate the embeddings using the
[`all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
model. The vectors that represent the
embeddings have 384 components, regardless of the length of the input
text, but note that the input is truncated to 256
tokens (see
[Word piece tokenization](https://huggingface.co/learn/nlp-course/en/chapter6/6)

{{< clients-example set="HomeQueryVec" step="tokenizer" lang_filter="Java-Sync" description="Practical pattern: Initialize a tokenizer and embedding model for generating vector representations from text" difficulty="beginner" >}}
{{< /clients-example >}}

## Create the index

Connect to Redis and delete any index previously created with the
name `vector_idx`. (The `ftDropIndex()` call throws an exception if
the index doesn't already exist, which is why you need the
`try...catch` block.)

{{< clients-example set="HomeQueryVec" step="connect" lang_filter="Java-Sync" description="Foundational: Connect to Redis and clean up existing vector indexes" difficulty="beginner" >}}
{{< /clients-example >}}

Next, create the index.
The schema in the example below includes three fields: the text content to index, a
[tag]({{< relref "/develop/ai/search-and-query/advanced-concepts/tags" >}})
field to represent the "genre" of the text, and the embedding vector generated from
the original text content. The `embedding` field specifies
[HNSW]({{< relref "/develop/ai/search-and-query/vectors#hnsw-index" >}})
indexing, the
[L2]({{< relref "/develop/ai/search-and-query/vectors#distance-metrics" >}})
vector distance metric, `Float32` values to represent the vector's components,
and 384 dimensions, as required by the `all-MiniLM-L6-v2` embedding model.

The `FTCreateParams` object specifies hash objects for storage and a
prefix `doc:` that identifies the hash objects to index.

{{< clients-example set="HomeQueryVec" step="create_index" lang_filter="Java-Sync" description="Foundational: Create a vector search index for hash documents with HNSW algorithm and L2 distance metric" difficulty="intermediate" >}}
{{< /clients-example >}}

## Add data

You can now supply the data objects, which will be indexed automatically
when you add them with [`hset()`]({{< relref "/commands/hset" >}}), as long as
you use the `doc:` prefix specified in the index definition.

Use the `predict()` method of the `Predictor` object
as shown below to create the embedding that represents the `content` field.
The `predict()` method returns a `float[]` array which is then converted to a `byte`
string using the helper method. Use the `byte` string representation when you are
indexing hash objects (as in this example), but use the array of `float` directly for
JSON objects (see [Differences with JSON objects](#differences-with-json-documents)
below). Note that when you set the `embedding` field, you must use an overload
of `hset()` that requires `byte` arrays for each of the key, the field name, and
the value, which is why you must include the `getBytes()` calls on the strings.

{{< clients-example set="HomeQueryVec" step="add_data" lang_filter="Java-Sync" description="Foundational: Store hash documents with vector embeddings generated from text content" difficulty="beginner" >}}
{{< /clients-example >}}

## Run a query

After you have created the index and added the data, you are ready to run a query.
To do this, you must create another embedding vector from your chosen query
text. Redis calculates the vector distance between the query vector and each
embedding vector in the index as it runs the query. You can request the results to be
sorted to rank them in order of ascending distance.

The code below creates the query embedding using the `predict()` method, as with
the indexing, and passes it as a parameter when the query executes (see
[Vector search]({{< relref "/develop/ai/search-and-query/query/vector-search" >}})
for more information about using query parameters with embeddings).
The query is a
[K nearest neighbors (KNN)]({{< relref "/develop/ai/search-and-query/vectors#knn-vector-search" >}})
search that sorts the results in order of vector distance from the query vector.

{{< clients-example set="HomeQueryVec" step="query" lang_filter="Java-Sync" description="Vector similarity search: Find semantically similar documents by comparing query embeddings with indexed vectors using L2 distance" difficulty="intermediate" >}}
{{< /clients-example >}}

Assuming you have added the code from the steps above to your source file,
it is now ready to run, but note that it may take a while to complete when
you run it for the first time (which happens because the tokenizer must download the
`all-MiniLM-L6-v2` model data before it can
generate the embeddings). When you run the code, it outputs the following result text:

```
Results:
ID: doc:1, Distance: 0.114169836044, Content: That is a very happy person
ID: doc:2, Distance: 0.610845506191, Content: That is a happy dog
ID: doc:3, Distance: 1.48624765873, Content: Today is a sunny day
```

Note that the results are ordered according to the value of the `distance`
field, with the lowest distance indicating the greatest similarity to the query.
As expected, the text *"That is a very happy person"*
is the result judged to be most similar in meaning to the query text
*"That is a happy person"*.

## Differences with JSON documents

Indexing JSON documents is similar to hash indexing, but there are some
important differences. JSON allows much richer data modeling with nested fields, so
you must supply a [path]({{< relref "/develop/data-types/json/path" >}}) in the schema
to identify each field you want to index. However, you can declare a short alias for each
of these paths (using the `as()` option) to avoid typing it in full for
every query. Also, you must specify `IndexDataType.JSON` when you create the index.

The code below shows these differences, but the index is otherwise very similar to
the one created previously for hashes:

{{< clients-example set="HomeQueryVec" step="json_schema" lang_filter="Java-Sync" description="Foundational: Create a vector search index for JSON documents with JSON paths and field aliases" difficulty="intermediate" >}}
{{< /clients-example >}}

An important difference with JSON indexing is that the vectors are
specified using arrays of `float` instead of binary strings, so you don't need
a helper method to convert the array to a binary string.

Use [`jsonSet()`]({{< relref "/commands/json.set" >}}) to add the data
instead of [`hset()`]({{< relref "/commands/hset" >}}). Use instances
of `JSONObject` to supply the data instead of `Map`, as you would for
hash objects.

{{< clients-example set="HomeQueryVec" step="json_data" lang_filter="Java-Sync" description="Foundational: Store JSON documents with vector embeddings as arrays (different from hash binary format)" difficulty="beginner" >}}
{{< /clients-example >}}

The query is almost identical to the one for the hash documents. This
demonstrates how the right choice of aliases for the JSON paths can
save you having to write complex queries. An important thing to notice
is that the vector parameter for the query is still specified as a
binary string (created using the `floatsToByteString()` method), even though
the data for the `embedding` field of the JSON was specified as an array.

{{< clients-example set="HomeQueryVec" step="json_query" lang_filter="Java-Sync" description="Vector similarity search: Query JSON documents using vector embeddings with field aliases for simplified syntax" difficulty="intermediate" >}}
{{< /clients-example >}}

Apart from the `jdoc:` prefixes for the keys, the result from the JSON
query is the same as for hash:

```
Results:
ID: jdoc:1, Distance: 0.114169836044, Content: That is a very happy person
ID: jdoc:2, Distance: 0.610845506191, Content: That is a happy dog
ID: jdoc:3, Distance: 1.48624765873, Content: Today is a sunny day
```

## Learn more

See
[Vector search]({{< relref "/develop/ai/search-and-query/query/vector-search" >}})
for more information about the indexing options, distance metrics, and query format
for vectors.