docs/integrations/vector-databases/redis.mdx

---
title: "Redis / Valkey"
description: "Redis and Valkey vector store integration for semantic caching in Bifrost."
icon: "database"
---

## Redis

Redis provides high-performance in-memory vector storage using RediSearch-compatible APIs, ideal for applications requiring sub-millisecond response times and fast semantic search capabilities. Valkey deployments that expose compatible `FT.*` commands are supported through the same configuration.

### Key Features

- **High Performance**: Sub-millisecond cache retrieval with Redis's in-memory storage
- **Cost Effective**: Open-source solution with no licensing costs
- **HNSW Algorithm**: Fast vector similarity search with excellent recall rates
- **Connection Pooling**: Advanced connection management for high-throughput applications
- **TTL Support**: Automatic expiration of cached entries
- **Streaming Support**: Full streaming response caching with proper chunk ordering
- **Flexible Filtering**: Advanced metadata filtering with exact string matching

### Setup & Installation

**Redis Cloud:**
- Sign up at [cloud.redis.io](https://cloud.redis.io)
- Create a new database with RediSearch module enabled
- Get your connection details

**Local Redis with RediSearch:**
```bash
# Using Docker with Redis Stack (includes RediSearch)
docker run -d --name redis-stack -p 6379:6379 redis/redis-stack:latest
```

**Local Valkey Bundle:**
```bash
# Example Valkey bundle with search/vector support
docker run -d --name valkey-bundle -p 6379:6379 valkey/valkey-bundle:9.0.0
```

### Configuration Options

<Tabs group="redis-config">

<Tab title="Go SDK">

```go
// Configure Redis-compatible vector store (Redis or Valkey endpoint)
vectorConfig := &vectorstore.Config{
    Enabled: true,
    Type:    vectorstore.VectorStoreTypeRedis, // Keep type as "redis" for Valkey too
    Config: vectorstore.RedisConfig{
        Addr:     "localhost:6379",        // Redis/Valkey server address - REQUIRED
        Username: "",                      // Optional: Redis username
        Password: "",                      // Optional: Redis password
        DB:       0,                       // Optional: Redis database number (default: 0)

        // Optional: Connection pool settings
        PoolSize:        10,               // Maximum socket connections
        MaxActiveConns:  10,               // Maximum active connections
        MinIdleConns:    5,                // Minimum idle connections
        MaxIdleConns:    10,               // Maximum idle connections

        // Optional: Timeout settings
        DialTimeout:     5 * time.Second,  // Connection timeout
        ReadTimeout:     3 * time.Second,  // Read timeout
        WriteTimeout:    3 * time.Second,  // Write timeout
        ContextTimeout:  10 * time.Second, // Operation timeout
    },
}

// Create vector store
store, err := vectorstore.NewVectorStore(context.Background(), vectorConfig, logger)
if err != nil {
    log.Fatal("Failed to create vector store:", err)
}
```

</Tab>

<Tab title="config.json">

```json
{
  "vector_store": {
    "enabled": true,
    "type": "redis",
    "config": {
      "addr": "localhost:6379",
      "username": "",
      "password": "",
      "db": 0,
      "pool_size": 10,
      "max_active_conns": 10,
      "min_idle_conns": 5,
      "max_idle_conns": 10,
      "dial_timeout": "5s",
      "read_timeout": "3s",
      "write_timeout": "3s",
      "context_timeout": "10s"
    }
  }
}
```

**For Redis Cloud or Valkey service endpoints:**
```json
{
  "vector_store": {
    "enabled": true,
    "type": "redis",
    "config": {
      "addr": "your-redis-host:port",
      "username": "your-username",
      "password": "your-password",
      "db": 0,
      "context_timeout": "10s"
    }
  }
}
```

</Tab>

</Tabs>

### Redis-Specific Features

**Vector Search Algorithm:**
Redis uses the **HNSW (Hierarchical Navigable Small World)** algorithm for vector similarity search, which provides:

- **Fast Search**: O(log N) search complexity
- **High Accuracy**: Excellent recall rates for similarity search
- **Memory Efficient**: Optimized for in-memory operations
- **Cosine Similarity**: Uses cosine distance metric for semantic similarity

**Connection Pool Management:**
Redis provides extensive connection pool configuration:

```go
config := vectorstore.RedisConfig{
    Addr:            "localhost:6379",
    PoolSize:        20,                    // Max socket connections
    MaxActiveConns:  20,                    // Max active connections
    MinIdleConns:    5,                     // Min idle connections
    MaxIdleConns:    10,                    // Max idle connections
    ConnMaxLifetime: 30 * time.Minute,      // Connection lifetime
    ConnMaxIdleTime: 5 * time.Minute,       // Idle connection timeout
    DialTimeout:     5 * time.Second,       // Connection timeout
    ReadTimeout:     3 * time.Second,       // Read timeout
    WriteTimeout:    3 * time.Second,       // Write timeout
    ContextTimeout:  10 * time.Second,      // Operation timeout
}
```

### Performance Optimization

**Connection Pool Tuning:**
For high-throughput applications, tune the connection pool settings:

```json
{
  "vector_store": {
    "config": {
      "pool_size": 50,           // Increase for high concurrency
      "max_active_conns": 50,    // Match pool_size
      "min_idle_conns": 10,      // Keep connections warm
      "max_idle_conns": 20,      // Allow some idle connections
      "conn_max_lifetime": "1h", // Refresh connections periodically
      "conn_max_idle_time": "10m" // Close idle connections
    }
  }
}
```

**Memory Optimization:**
- **TTL**: Use appropriate TTL values to prevent memory bloat
- **Namespace Cleanup**: Regularly clean up unused namespaces

**Batch Operations:**
Redis supports efficient batch operations:

```go
// Batch retrieval
results, err := store.GetChunks(ctx, namespace, []string{"id1", "id2", "id3"})

// Batch deletion
deleteResults, err := store.DeleteAll(ctx, namespace, queries)
```

### Production Considerations

<Info>
**Search Module Required**: Redis/Valkey integration requires a search module/API that supports `FT.*` commands (index creation and vector search). If `FT.INFO` or `FT.SEARCH` is unavailable, semantic caching will not work.
</Info>

<Warning>
**Production Considerations**:
- Use Redis AUTH for production deployments
- Configure appropriate connection timeouts
- Monitor memory usage and set appropriate TTL values
</Warning>

For the VectorStore interface API and usage examples, see [Vector Store Architecture](/architecture/framework/vector-store). For semantic caching setup, see [Semantic Caching](/features/semantic-caching).