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Introduction

RedisVL is your TypeScript toolkit for AI on Redis. High-performance vector search engineered for scale.

Use RedisVL for:

  • Schema-driven index management - Define your data model once, use it everywhere
  • Vector search abstractions - Simplified APIs for similarity search and hybrid queries
  • Advanced tuning - Algorithm-specific parameters, distance normalization, hybrid policies
  • AI-native utilities - Semantic caching, LLM memory, vectorizer integrations
  • Type safety - Full TypeScript support with auto-complete and type checking
  • Production features - Batch operations, validation, error handling, TTL management

Installation​

npm install redisvl redis

Prerequisites​

You need Redis Stack (includes RediSearch and RedisJSON modules):

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

Or use Redis Cloud for a managed solution.

Quick Start​

import { SearchIndex, IndexSchema } from 'redisvl';
import { createClient } from 'redis';

const client = createClient();
await client.connect();

// Define schema with vector field
const schema = IndexSchema.fromObject({
    index: { name: 'products', prefix: 'product:', storage_type: 'hash' },
    fields: [
        { name: 'title', type: 'text' },
        { name: 'category', type: 'tag' },
        { name: 'price', type: 'numeric' },
        {
            name: 'embedding',
            type: 'vector',
            attrs: {
                dims: 384,
                algorithm: 'hnsw',
                distanceMetric: 'cosine'
            }
        },
    ],
});

const index = new SearchIndex(schema, client);
await index.create();

// Load data with embeddings
await index.load([
    { title: 'Laptop', category: 'electronics', price: 999, embedding: [...] },
    { title: 'Phone', category: 'electronics', price: 599, embedding: [...] },
]);

// Vector search with filters
import { VectorQuery } from 'redisvl';

const query = new VectorQuery({
    vector: queryEmbedding,
    vectorField: 'embedding',
    filter: '@category:{electronics} @price:[0 1000]',
    numResults: 10,
});

const results = await index.search(query);
console.log(`Found ${results.total} products`);

Core Capabilities​

🎯 Index Management​

  • Schema design with YAML or TypeScript objects
  • Automatic index creation and updates
  • CRUD operations with validation
  • Batch loading with configurable pipeline sizes

🔍 Vector Search​

  • Similarity search with metadata filters
  • Hybrid queries (vector + text + numeric)
  • Multiple distance metrics (cosine, L2, IP)
  • Distance normalization for user-friendly 0-1 similarity scores
  • Algorithm-specific tuning (HNSW, FLAT, SVS-VAMANA)
  • Hybrid search policies for optimal performance
  • Range queries and filtering

🤖 AI Extensions​

  • Semantic Caching - Cache LLM responses by semantic similarity
  • LLM Memory - Manage conversation history for AI agents
  • Vectorizers - HuggingFace, OpenAI, Cohere integrations
  • Semantic Routing - Intent-based query classification

Next Steps​

Learn the Basics​

  • Schema - Define your data structure with fields and storage types
  • Search Index - Create indexes, load data, and perform CRUD operations
  • Vectorizers - Generate embeddings for semantic search

Reference​