Redis

Import from @anvia/redis.

RedisVectorStoreConnectOptions

type RedisDistance = "COSINE" | "L2" | "IP";

type RedisVectorStoreConnectOptions = {
  client?: RedisClientLike;
  indexName: string;
  keyPrefix?: string;
  vectorSize: number;
  createIfMissing?: boolean;
  distance?: RedisDistance;
};

Purpose: connection options for a Redis vector index backed by RediSearch.

Return behavior: consumed by RedisVectorStore.connect(...).

Notable errors: missing indices reject when createIfMissing is false; index creation requires vectorSize. The keyPrefix defaults to anvia:{indexName}:.

Design note: connect(...) uses FT.CREATE to create a RediSearch index over HASH keys. Each document is stored as a HASH with __anvia_document_id, __anvia_document, and __anvia_vector (Float32Buffer) fields plus any metadata fields.

RedisVectorStore

class RedisVectorStore<T, Metadata extends VectorMetadata = VectorMetadata> {
  static connect<T, Metadata extends VectorMetadata = VectorMetadata>(
    options: RedisVectorStoreConnectOptions,
  ): Promise<RedisVectorStore<T, Metadata>>;
  upsertDocuments(documents: Array<EmbeddedDocument<T, Metadata>>): Promise<void>;
  index(model: EmbeddingModel): RedisVectorIndex<T, Metadata>;
}

Purpose: Redis-backed document storage using RediSearch vector fields.

Return behavior: connect(...) resolves a store; index(...) binds it to an embedding model.

Notable errors: connection and upsert calls reject on Redis errors; upsertDocuments(...) throws when a document has no embeddings or metadata uses reserved __anvia_* keys.

RedisVectorIndex

class RedisVectorIndex<T, Metadata extends VectorMetadata = VectorMetadata>
  implements VectorSearchIndex<T, Metadata> {
  search(request: VectorSearchRequest): Promise<Array<VectorSearchResult<T, Metadata>>>;
  searchIds(request: VectorSearchRequest): Promise<Array<{ score: number; id: string }>>;
  asTool(options: VectorSearchToolOptions): Tool<{ query: string; topK?: number }, unknown>;
}

Purpose: query-time Redis search adapter.

Return behavior: embeds the query, executes an FT.SEARCH KNN query, deduplicates multi-embedding document IDs, and returns normalized results.

Notable errors: embedding or Redis query failures reject.

filterToRedisQuery

function filterToRedisQuery(filter: VectorFilter | undefined): string;

Purpose: convert Anvia vector filters to RediSearch query string syntax.

Return behavior: returns "*" when no filter is supplied.

Notable errors: none directly.