Index and query vectors

[Redis Search]({{< 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.

In the example below, we use the Hugot library to generate vector embeddings to store and index with Redis Search. The code is first demonstrated for hash documents with a separate section to explain the differences with JSON documents.

{{< note >}}From v9.8.0 onwards, go-redis uses query dialect 2 by default. Redis Search 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

First, install [go-redis]({{< relref "/develop/clients/go" >}}) if you haven't already done so. Then, install Hugot using the following command:

go get github.com/knights-analytics/hugot

Add the following imports to your module's main program file:

{{< clients-example set="home_query_vec" step="import" lang_filter="Go" description="Foundational: Import required packages for vector search and embedding operations" difficulty="beginner" >}} {{< /clients-example >}}

Add a helper function

The Hugot model outputs the embeddings as a []float32 array. If you are storing your documents as [hash]({{< relref "/develop/data-types/hashes" >}}) objects, then you must convert this array to a byte string before adding it as a hash field. The function shown below uses Go's binary package to produce the byte string:

{{< clients-example set="home_query_vec" step="helper" lang_filter="Go" description="Foundational: Create a helper function to convert float32 embeddings to binary format for hash storage" difficulty="beginner" >}} {{< /clients-example >}}

Note that if you are using [JSON]({{< relref "/develop/data-types/json" >}}) objects to store your documents instead of hashes, then you should store the []float32 array directly without first converting it to a byte string (see Differences with JSON documents below).

Create the index

In the main() function, connect to Redis and delete any index previously created with the name vector_idx:

{{< clients-example set="home_query_vec" step="connect" lang_filter="Go" 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 specifies hash objects for storage and 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.

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

Create an embedder instance

You need an instance of the FeatureExtractionPipeline class to generate the embeddings. Use the code below to create an instance that uses the sentence-transformers/all-MiniLM-L6-v2 model:

{{< clients-example set="home_query_vec" step="embedder" lang_filter="Go" description="Practical pattern: Initialize a feature extraction pipeline for generating text embeddings" difficulty="beginner" >}} {{< /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 RunPipeline() method of FeatureExtractionPipeline as shown below to create the embeddings that represent the content fields. This method takes an array of strings and outputs a corresponding array of FeatureExtractionOutput objects. The Embeddings field of FeatureExtractionOutput contains the array of float values that you need for the index. Use the floatsToBytes() function defined above to convert this array to a byte string.

{{< clients-example set="home_query_vec" step="add_data" lang_filter="Go" description="Foundational: Generate embeddings and store hash documents with vector fields using HSet" difficulty="intermediate" >}} {{< /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 similarity between the query vector and each embedding vector in the index as it runs the query. It then ranks the results in order of this numeric similarity value.

The code below creates the query embedding using RunPipeline(), 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).

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

The code 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 Hugot must download the all-MiniLM-L6-v2 model data before it can generate the embeddings). When you run the code, it outputs the following text:

ID: doc:0, Distance:2.96992516518, Content:'That is a very happy person'
ID: doc:1, Distance:17.3678302765, Content:'That is a happy dog'
ID: doc:2, Distance:43.7771987915, Content:'Today is a sunny day'

The results are ordered according to the value of the vector_distance field, with the lowest distance indicating the greatest similarity to the query. As you would expect, the result for doc:0 with the content text "That is a very happy person" is the result that is 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 modelling 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 set OnJSON to true 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="home_query_vec" step="json_index" lang_filter="Go" description="Foundational: Create a vector search index for JSON documents with OnJSON option and JSON path specifications" difficulty="intermediate" >}} {{< /clients-example >}}

Use [JSONSet()]({{< relref "/commands/json.set" >}}) to add the data instead of [HSet()]({{< relref "/commands/hset" >}}). The maps that specify the fields have the same structure as the ones used for HSet().

An important difference with JSON indexing is that the vectors are specified using lists instead of binary strings. The loop below is similar to the one used previously to add the hash data, but it doesn't use the floatsToBytes() function to encode the float32 array.

{{< clients-example set="home_query_vec" step="json_data" lang_filter="Go" description="Foundational: Store JSON documents with vector embeddings as arrays using JSONSet" difficulty="intermediate" >}} {{< /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 (using the floatsToBytes() method), even though the data for the embedding field of the JSON was specified as an array.

{{< clients-example set="home_query_vec" step="json_query" lang_filter="Go" description="Vector query: Execute vector similarity search on JSON documents using path aliases for cleaner queries" difficulty="intermediate" >}} {{< /clients-example >}}

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

ID: jdoc:0, Distance:2.96992516518, Content:'That is a very happy person'
ID: jdoc:1, Distance:17.3678302765, Content:'That is a happy dog'
ID: jdoc:2, Distance:43.7771987915, 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.