Browse Recipes

Introduction

Redis Vector Library (RedisVL) is the production-ready TypeScript/Node.js client for AI applications built on Redis. Lightning-fast vector search meets enterprise-grade reliability.

| Core Capabilities | AI Extensions | Dev Utilities | | :-----------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------: | :----------------------------------------------------------------------: | | Index ManagementSchema design, data loading, CRUD ops | Semantic CachingReduce LLM costs & boost throughput | Vectorizers8+ embedding provider integrations | | Vector SearchSimilarity search with metadata filters | LLM MemoryAgentic AI context management | RerankersImprove search result relevancy | | Hybrid QueriesVector + text + metadata combined | Semantic RoutingIntelligent query classification | | | Multi-Query TypesVector, Range, Filter, Count queries | Embedding CachingCache embeddings for efficiency | |

Built for Modern AI Workloads

RedisVL helps you build production-ready AI applications:

• RAG Pipelines - Combine vector similarity search with metadata filtering to retrieve the most relevant context for your LLMs
• Semantic Caching - Cache LLM responses based on semantic similarity to improve response times and reduce costs
• AI Agents - Give your agents memory that persists across conversations and sessions, with semantic routing for quick intelligent decision-making
• Recommendation Systems - Find similar items quickly and rerank results based on user preferences or business logic

Getting Started

Install @redis/redisvl into your Node.js (>=22.0.0) environment using npm:

npm install @redis/redisvl

Or using yarn:

yarn add @redis/redisvl

Or using pnpm:

pnpm add @redis/redisvl

Redis

Choose from multiple Redis deployment options:

1. Redis Cloud: Managed cloud database (free tier available)

2. Redis Stack: Docker image for development

   docker run -d --name redis-stack -p 6379:6379 -p 8001:8001 redis/redis-stack:latest

3. Redis Enterprise: Commercial, self-hosted database

4. Redis Sentinel: High availability with automatic failover

   // Connect via Sentinel
   const redisUrl = 'redis+sentinel://sentinel1:26379,sentinel2:26379/mymaster';

5. Azure Managed Redis: Fully managed Redis Enterprise on Azure

Enhance your experience and observability with the free Redis Insight GUI.

Overview

Index Management

Schema Definition

Design a schema for your use case that models your dataset with built-in Redis and indexable fields (e.g. text, tags, numerics, geo, and vectors).

Load schema from a YAML file:

index:
    name: user-idx
    prefix: user
    storage_type: json

fields:
    - name: user
      type: tag
    - name: credit_score
      type: tag
    - name: job_title
      type: text
      attrs:
          sortable: true
    - name: embedding
      type: vector
      attrs:
          algorithm: flat
          dims: 4
          distance_metric: cosine
          datatype: float32

import { IndexSchema } from '@redis/redisvl';

// Load from YAML file
const schema = await IndexSchema.fromYAML('schemas/schema.yaml');

Or create from a plain object:

const schema = IndexSchema.fromObject({
    index: {
        name: 'user-idx',
        prefix: 'user',
        storage_type: 'json',
    },
    fields: [
        { name: 'user', type: 'tag' },
        { name: 'credit_score', type: 'tag' },
        {
            name: 'job_title',
            type: 'text',
            attrs: { sortable: true },
        },
        {
            name: 'embedding',
            type: 'vector',
            attrs: {
                algorithm: 'flat',
                datatype: 'float32',
                dims: 4,
                distance_metric: 'cosine',
            },
        },
    ],
});

Index Creation

Create a SearchIndex to manage your index in Redis:

import { createClient } from 'redis';
import { SearchIndex } from '@redis/redisvl';

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

// Create SearchIndex with schema and client
const index = new SearchIndex(schema, client);

// Create the index in Redis
await index.create();

// Check if index exists
const exists = await index.exists(); // true

// Get index information
const info = await index.info();

// Delete index (keeps data)
await index.delete();

// Delete index and drop all associated data
await index.delete({ drop: true });

Data Loading & Retrieval

Coming Soon

Data loading and retrieval methods (load(), fetch(), etc.) are currently under development.

Retrieval

Coming Soon - Query classes and search functionality are currently under development.

Define queries and perform advanced searches over your indices, including the combination of vectors, metadata filters, and more.

• VectorQuery - Flexible vector queries with customizable filters enabling semantic search (coming soon)
• RangeQuery - Vector search within a defined range paired with customizable filters (coming soon)
• FilterQuery - Standard search using filters and the full-text search (coming soon)
• CountQuery - Count the number of indexed records given attributes (coming soon)
• TextQuery - Full-text search with support for field weighting and BM25 scoring (coming soon)

Dev Utilities

Vectorizers

Coming Soon - Vectorizer integrations are currently under development.

Integrate with popular embedding providers to greatly simplify the process of vectorizing unstructured data for your index and queries:

• AzureOpenAI (coming soon)
• Cohere (coming soon)
• Custom (coming soon)
• GCP VertexAI (coming soon)
• HuggingFace (coming soon)
• Mistral (coming soon)
• OpenAI (coming soon)
• VoyageAI (coming soon)

Rerankers

Coming Soon - Reranker integrations are currently under development.

Integrate with popular reranking providers to improve the relevancy of the initial search results from Redis.

Extensions

Coming Soon - RedisVL Extensions are currently under development.

Semantic Caching

Coming Soon

Increase application throughput and reduce the cost of using LLM models in production by leveraging previously generated knowledge.

Semantic Routing

Coming Soon

Build fast decision models that run directly in Redis and route user queries to the nearest "route" or "topic".

Embedding Caching

Coming Soon

Reduce computational costs and improve performance by caching embedding vectors with their associated text and metadata.

LLM Memory

Coming Soon

Improve personalization and accuracy of LLM responses by providing user conversation context.

Why RedisVL?

If you're building AI applications, you need a database that can keep up. Redis has been handling real-time workloads at scale for years, and now with vector search capabilities, it's a natural fit for AI use cases.

RedisVL makes it easy to work with Redis for AI applications. Instead of wrestling with low-level commands, you get a clean TypeScript API for the things you actually need: vector search, semantic caching, and conversation memory. It's built on top of the official node-redis client, so you get all the reliability and performance you'd expect, with an interface designed specifically for AI workloads.

Helpful Links

For additional help, check out the following resources:

• Redis AI Recipes

Contributing

Please help us by contributing PRs, opening GitHub issues for bugs or new feature ideas, improving documentation, or increasing test coverage. Read more about how to contribute!