---
title: "Valkey"
description: "Use Valkey as an open-source vector store in Deja Vu for high-performance key-value storage with vector search."
---
# Valkey Vector Store

[Valkey](https://valkey.io/) is an open source (BSD) high-performance key/value datastore that supports a variety of workloads and rich datastructures including vector search.

## Installation

```bash
pip install dejavu-memory[vector_stores]
```

## Usage

```python
config = {
    "vector_store": {
        "provider": "valkey",
        "config": {
            "collection_name": "test",
            "valkey_url": "valkey://localhost:6379",
            "embedding_model_dims": 1536,
            "index_type": "flat"
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

## Parameters

Here are the parameters available for configuring Valkey:

| Parameter | Description | Default Value |
| --- | --- | --- |
| `collection_name` | The name of the collection to store the vectors | `dejavu` |
| `valkey_url` | Connection URL for the Valkey server | `valkey://localhost:6379` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `index_type` | Vector index algorithm (`hnsw` or `flat`) | `hnsw` |
| `hnsw_m` | Number of bi-directional links for HNSW | `16` |
| `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
| `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
| `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
| `distance_metric` | Distance metric for vector similarity | `cosine` |

## Cluster Mode

To use Valkey with cluster mode enabled (CME), set `cluster_mode` to `true`:

```python
config = {
    "vector_store": {
        "provider": "valkey",
        "config": {
            "collection_name": "memories",
            "valkey_url": "valkey://cluster-endpoint:6379",
            "embedding_model_dims": 1536,
            "cluster_mode": True
        }
    }
}
```

When cluster mode is enabled, the connector uses `ValkeyCluster` instead of the standalone client, which handles `MOVED`/`ASK` redirections automatically. Search queries are coordinated across all shards by the valkey-search module's built-in coordinator. See the [valkey-search documentation](https://github.com/valkey-io/valkey-search) for details on cluster mode behavior.
