Supported Vector Databases
Qdrant
Qdrant 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
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)
m.add("Likes to play cricket on weekends", user_id="alice", metadata={"category": "hobbies"})
Config
Let’s see the available parameters for the qdrant config:
| 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 |
on_disk | For enabling persistent storage | False |