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

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

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

Names of fields that will store textual metadata associated with embeddings. NOTE: Filtering based on textual metadata fields is not supported at the moment.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_TEXTUAL_METADATA_FIELDS

Names of fields that will store numeric metadata associated with embeddings.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_NUMERIC_METADATA_FIELDS

Metric used to compute the distance between two vectors.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_DISTANCE_METRIC

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

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_VECTOR_FIELD_NAME

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

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_SCALAR_FIELD_NAME

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

Algorithm used to index the embedding vectors.

Environment variable: QUARKUS_LANGCHAIN4J_REDIS_VECTOR_ALGORITHM