Kartik
75 lines
2.7 KiB
Plaintext
75 lines
2.7 KiB
Plaintext
---
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title: "Valkey"
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description: "Use Valkey as an open-source vector store in Mem0 for high-performance key-value storage with vector search."
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---
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# Valkey Vector Store
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[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.
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## Installation
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```bash
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pip install mem0ai[vector-stores]
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```
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## Usage
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```python
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config = {
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"vector_store": {
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"provider": "valkey",
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"config": {
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"collection_name": "test",
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"valkey_url": "valkey://localhost:6379",
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"embedding_model_dims": 1536,
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"index_type": "flat"
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alice", metadata={"category": "movies"})
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```
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## Parameters
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Here are the parameters available for configuring Valkey:
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| Parameter | Description | Default Value |
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| --- | --- | --- |
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| `collection_name` | The name of the collection to store the vectors | `mem0` |
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| `valkey_url` | Connection URL for the Valkey server | `valkey://localhost:6379` |
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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `index_type` | Vector index algorithm (`hnsw` or `flat`) | `hnsw` |
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| `hnsw_m` | Number of bi-directional links for HNSW | `16` |
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| `hnsw_ef_construction` | Size of dynamic candidate list for HNSW | `200` |
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| `hnsw_ef_runtime` | Size of dynamic candidate list for search | `10` |
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| `cluster_mode` | Enable cluster mode for Valkey cluster (CME) deployments | `false` |
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| `timezone` | Timezone for timestamp handling | `UTC` |
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## Cluster Mode
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To use Valkey with cluster mode enabled (CME), set `cluster_mode` to `true`:
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```python
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config = {
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"vector_store": {
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"provider": "valkey",
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"config": {
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"collection_name": "memories",
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"valkey_url": "valkey://cluster-endpoint:6379",
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"embedding_model_dims": 1536,
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"cluster_mode": True
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}
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}
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}
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```
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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.
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