/docs/components/vectordbs/dbs/qdrant.mdx

---
title: "Qdrant"
description: "Use Qdrant as an open-source vector search engine in Mem0 for high-performance similarity search at scale."
---
[Qdrant](https://qdrant.tech/) is an open-source vector search engine. It is designed to work with large-scale datasets and provides a high-performance search engine for vector data.

### Usage

<CodeGroup>
```python Python
import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "sk-xx"

config = {
    "vector_store": {
        "provider": "qdrant",
        "config": {
            "collection_name": "test",
            "host": "localhost",
            "port": 6333,
        }
    }
}

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"})
```

```typescript TypeScript
import { Memory } from 'mem0ai/oss';

const config = {
  vectorStore: {
    provider: 'qdrant',
    config: {
      collectionName: 'memories',
      dimension: 1536,
      host: 'localhost',
      port: 6333,
    },
  },
};

const memory = new Memory(config);
const 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."}
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```
</CodeGroup>

### Config

Let's see the available parameters for the `qdrant` config:

<Tabs>
<Tab title="Python">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collection_name` | The name of the collection to store the vectors | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `client` | Custom client for qdrant | `None` |
| `host` | The host where the qdrant server is running | `None` |
| `port` | The port where the qdrant server is running | `None` |
| `path` | Path for the qdrant database | `/tmp/qdrant` |
| `url` | Full URL for the qdrant server | `None` |
| `api_key` | API key for the qdrant server | `None` |
| `https` | Whether to force HTTPS on or off. `None` lets the client decide; set `False` for plain HTTP Qdrant with API key authentication. | `None` |
| `on_disk` | For enabling persistent storage | `False` |
</Tab>
<Tab title="TypeScript">
| Parameter | Description | Default Value |
| --- | --- | --- |
| `collectionName` | The name of the collection to store the vectors | `mem0` |
| `dimension` | Dimensions of the embedding model | `1536` |
| `host` | The host where the Qdrant server is running | `None` |
| `port` | The port where the Qdrant server is running | `None` |
| `path` | Path for the Qdrant database | `/tmp/qdrant` |
| `url` | Full URL for the Qdrant server | `None` |
| `apiKey` | API key for the Qdrant server | `None` |
| `onDisk` | For enabling persistent storage | `False` |
</Tab>
</Tabs>