ray-qdrant
Write Ray datasets to Qdrant in parallel.
Installation
pip install ray-qdrant
Requires Python 3.10+, Ray 2.40+, and Qdrant 1.16+.
Usage
import numpy as np
import ray
import ray_qdrant
ds = ray.data.range(1_000).map_batches(
lambda batch: {
"id": batch["id"],
"vector": np.random.rand(len(batch["id"]), 384),
"text": [f"document {i}" for i in batch["id"]],
}
)
ray_qdrant.write_qdrant(ds, url="http://localhost:6333", collection="docs")
Each row becomes a point. By default, the id column holds the point ID (an unsigned integer or UUID) and the vector column holds the vector. Use id_column and vector_column to read them from other columns. Rows with a null ID are skipped, and every other column goes into the payload. Writes are upserts, and a missing collection is created with the vector size of the data.
Named vectors
ray_qdrant.write_qdrant(
ds, url="http://localhost:6333", collection="docs", vector_name="dense"
)
Parameters
| Parameter | Default | Description |
|---|---|---|
url |
required | URL of the Qdrant server. |
collection |
None |
Collection to write to. Set this or collection_column. |
collection_column |
None |
Column with each row's collection name. Not written to the payload. |
api_key |
$QDRANT_API_KEY |
API key. |
id_column |
"id" |
Column of point IDs. |
vector_column |
"vector" |
Column of vectors. |
vector_name |
None |
Named vector to write to. Defaults to the unnamed vector. |
batch_size |
256 |
Maximum points per upsert request. |
distance |
"Cosine" |
Distance metric for new collections: "Cosine", "Euclid", "Dot", or "Manhattan". |
client_kwargs |
None |
Extra arguments for qdrant_client.QdrantClient, like {"prefer_grpc": True}. |
ray_remote_args |
None |
Arguments passed to ray.remote for the write tasks. |
concurrency |
None |
Maximum number of concurrent write tasks. |
License
Apache 2.0
1 maintainer
Metadata
1 maintainer
Release files for ray-qdrant 0.1.1
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