Redis Storage

@flowrag/storage-redis provides KV and vector storage on Redis Stack, combining fast key-value operations with vector similarity search via RediSearch.

Installation

npm install @flowrag/storage-redis redis

Configuration

import { createClient } from 'redis';
import { RedisKVStorage, RedisVectorStorage } from '@flowrag/storage-redis';

const client = createClient({ url: 'redis://localhost:6379' });
await client.connect();

const kv = new RedisKVStorage({ client });
const vector = new RedisVectorStorage({ client, dimensions: 384 });

KV Storage

Key-value storage for documents, chunks, and LLM cache.

const kv = new RedisKVStorage({
  client,
  prefix: 'flowrag:kv:', // default
});

Option	Type	Default	Description
client	RedisClient	—	Connected redis client (required)
prefix	string	flowrag:kv:	Key prefix for namespacing

Vector Storage

Vector similarity search using RediSearch (HNSW index, cosine distance).

const vector = new RedisVectorStorage({
  client,
  dimensions: 384,
  index: 'flowrag-vectors', // default
  prefix: 'flowrag:vec:',   // default
});

Option	Type	Default	Description
client	RedisClient	—	Connected redis client (required)
dimensions	number	—	Vector dimensions (required)
index	string	flowrag-vectors	RediSearch index name
prefix	string	flowrag:vec:	Hash key prefix

The index is created automatically on first use with HNSW algorithm and COSINE distance metric.

WARNING

Requires Redis Stack or a Redis instance with the RediSearch module for vector storage.

Full Example

import { defineSchema } from '@flowrag/core';
import { createFlowRAG } from '@flowrag/pipeline';
import { createClient } from 'redis';
import { RedisKVStorage, RedisVectorStorage } from '@flowrag/storage-redis';
import { SQLiteGraphStorage } from '@flowrag/storage-sqlite';
import { LocalEmbedder } from '@flowrag/provider-local';
import { GeminiExtractor } from '@flowrag/provider-gemini';

const client = createClient({ url: 'redis://localhost:6379' });
await client.connect();

const rag = createFlowRAG({
  schema: defineSchema({
    entityTypes: ['SERVICE', 'DATABASE'],
    relationTypes: ['USES', 'PRODUCES'],
  }),
  storage: {
    kv: new RedisKVStorage({ client }),
    vector: new RedisVectorStorage({ client, dimensions: 384 }),
    graph: new SQLiteGraphStorage({ path: './data/graph.db' }),
  },
  embedder: new LocalEmbedder(),
  extractor: new GeminiExtractor(),
});