Redis Document Store for Retrieval Augmented Generation (RAG)

When implementing Retrieval Augmented Generation (RAG), a capable document store is necessary. This guide will explain how to leverage a Redis database as the document store.

Leveraging the Redis Document Store

To utilize the Redis document store, you’ll need to include the following dependency:

<dependency>
    <groupId>io.quarkiverse.langchain4j</groupId>
    <artifactId>quarkus-langchain4j-redis</artifactId>
    <version>0.13.1</version>
</dependency>

This extension relies on the Quarkus Redis client. Ensure the default Redis datasource is configured appropriately. For detailed guidance, refer to the Quarkus Redis Quickstart and the Quarkus Redis Reference.

The Redis document store’s functionality is built on the Redis JSON and Redis Search modules. Ensure these modules are installed, or consider using the Redis Stack. When the quarkus-langchain4j-redis extension is present, the default image used for Redis is redis-stack:latest but this can be changed by setting quarkus.redis.devservices.image-name=someotherimage in your application.properties file.
The Redis document store requires the dimension of the vector to be set. Add the quarkus.langchain4j.redis.dimension property to your application.properties file and set it to the dimension of the vector. The dimension depends on the embedding model you use. For example, AllMiniLmL6V2QuantizedEmbeddingModel produces vectors of dimension 384. OpenAI’s text-embedding-ada-002 produces vectors of dimension 1536.

Upon installing the extension, you can utilize the Redis document store using the following code:

package io.quarkiverse.langchain4j.samples;

import static dev.langchain4j.data.document.splitter.DocumentSplitters.recursive;

import java.util.List;

import jakarta.enterprise.context.ApplicationScoped;
import jakarta.inject.Inject;

import dev.langchain4j.data.document.Document;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import io.quarkiverse.langchain4j.redis.RedisEmbeddingStore;

@ApplicationScoped
public class IngestorExampleWithRedis {

    /**
     * The embedding store (the database).
     * The bean is provided by the quarkus-langchain4j-redis extension.
     */
    @Inject
    RedisEmbeddingStore store;

    /**
     * The embedding model (how is computed the vector of a document).
     * The bean is provided by the LLM (like openai) extension.
     */
    @Inject
    EmbeddingModel embeddingModel;

    public void ingest(List<Document> documents) {
        EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
                .embeddingStore(store)
                .embeddingModel(embeddingModel)
                .documentSplitter(recursive(500, 0))
                .build();
        // Warning - this can take a long time...
        ingestor.ingest(documents);
    }
}

Configuration Settings

By default, the extension utilizes the default Redis datasource for storing and indexing the documents. Customize the behavior of the extension by exploring various configuration options:

Configuration property fixed at build time - All other configuration properties are overridable at runtime

Configuration property

Type

Default

quarkus.langchain4j.redis.client-name

The name of the Redis client to use. These clients are configured by means of the redis-client extension. If unspecified, it will use the default Redis client.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_CLIENT_NAME

string

quarkus.langchain4j.redis.dimension

The dimension of the embedding vectors. This has to be the same as the dimension of vectors produced by the embedding model that you use. For example, AllMiniLmL6V2QuantizedEmbeddingModel produces vectors of dimension 384. OpenAI’s text-embedding-ada-002 produces vectors of dimension 1536.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_DIMENSION

long

required

quarkus.langchain4j.redis.index-name

Name of the index that will be used in Redis when searching for related embeddings. If this index doesn’t exist, it will be created.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_INDEX_NAME

string

embedding-index

quarkus.langchain4j.redis.metadata-fields

Names of extra fields that will be stored in Redis along with the embedding vectors. This corresponds to keys in the dev.langchain4j.data.document.Metadata map. Storing embeddings with metadata fields unlisted here is possible, but these fields will then not be present in the returned EmbeddingMatch objects.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_METADATA_FIELDS

list of string

quarkus.langchain4j.redis.distance-metric

Metric used to compute the distance between two vectors.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_DISTANCE_METRIC

l2, ip, cosine

cosine

quarkus.langchain4j.redis.vector-field-name

Name of the key that will be used to store the embedding vector.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_VECTOR_FIELD_NAME

string

vector

quarkus.langchain4j.redis.scalar-field-name

Name of the key that will be used to store the embedded text.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_SCALAR_FIELD_NAME

string

scalar

quarkus.langchain4j.redis.prefix

Prefix to be applied to all keys by the embedding store. Embeddings are stored in Redis under a key that is the concatenation of this prefix and the embedding ID.

If the configured prefix does not ends with :, it will be added automatically to follow the Redis convention.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_PREFIX

string

embedding:

quarkus.langchain4j.redis.vector-algorithm

Algorithm used to index the embedding vectors.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_VECTOR_ALGORITHM

flat, hnsw

hnsw

Under the Hood

Each ingested document is saved as a JSON document in Redis, containing the embedding stored as a vector. The document store also generates an index for each ingested document. To retrieve relevant documents, the extension employs the Redis search command.