40 lines
1.3 KiB
Plaintext
40 lines
1.3 KiB
Plaintext
[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.
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### Usage
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "sk-xx"
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config = {
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"vector_store": {
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"provider": "qdrant",
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"config": {
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"collection_name": "test",
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"host": "localhost",
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"port": 6333,
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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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m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
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```
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### Config
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Let's see the available parameters for the `qdrant` config:
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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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| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
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| `client` | Custom client for qdrant | `None` |
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| `host` | The host where the qdrant server is running | `None` |
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| `port` | The port where the qdrant server is running | `None` |
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| `path` | Path for the qdrant database | `/tmp/qdrant` |
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| `url` | Full URL for the qdrant server | `None` |
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| `api_key` | API key for the qdrant server | `None` |
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| `on_disk` | For enabling persistent storage | `False` | |