sie-qdrant

SIE integration for Qdrant.

Installation

pip install sie-qdrant

Dense embeddings

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
from sie_qdrant import SIEVectorizer

vectorizer = SIEVectorizer(base_url="http://localhost:8080", model="BAAI/bge-m3")

qdrant = QdrantClient("http://localhost:6333")
qdrant.create_collection(
    collection_name="documents",
    vectors_config=VectorParams(size=1024, distance=Distance.COSINE),
)

texts = ["first doc", "second doc"]
vectors = vectorizer.embed_documents(texts)
qdrant.upsert(
    collection_name="documents",
    points=[
        PointStruct(id=i, vector=v, payload={"text": t})
        for i, (t, v) in enumerate(zip(texts, vectors))
    ],
)

query_vec = vectorizer.embed_query("search text")
results = qdrant.query_points(
    collection_name="documents", query=query_vec, limit=5
)

Named vectors (dense + sparse)

SIE's multi-output encode produces dense and sparse vectors in one call. Qdrant supports sparse vectors natively via SparseVector(indices, values), so no expansion to full vocabulary length is needed:

from qdrant_client import QdrantClient
from qdrant_client.models import (
    Distance, VectorParams, PointStruct,
    SparseVectorParams, SparseVector,
)
from sie_qdrant import SIENamedVectorizer

vectorizer = SIENamedVectorizer(
    base_url="http://localhost:8080",
    model="BAAI/bge-m3",
    output_types=["dense", "sparse"],
)

qdrant = QdrantClient("http://localhost:6333")
qdrant.create_collection(
    collection_name="documents",
    vectors_config={"dense": VectorParams(size=1024, distance=Distance.COSINE)},
    sparse_vectors_config={"sparse": SparseVectorParams()},
)

named = vectorizer.embed_documents(["hello world"])
qdrant.upsert(
    collection_name="documents",
    points=[
        PointStruct(
            id=0,
            vector={
                "dense": named[0]["dense"],
                "sparse": SparseVector(**named[0]["sparse"]),
            },
            payload={"text": "hello world"},
        )
    ],
)

Storage advantage: Unlike integrations that expand sparse vectors to full vocabulary length (~30K floats), Qdrant stores sparse vectors in their native indices+values form, making hybrid search storage-efficient.

Testing

# Unit tests (no server needed)
pytest

# Integration tests (requires SIE + Qdrant)
pytest -m integration

Metadata

Release files for sie-qdrant 0.9.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for sie-qdrant 0.9.0
File	Size	Uploaded
sie_qdrant-0.9.0.tar.gz	10.2 kB	Sep 30, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for sie-qdrant 0.9.0
File	Interpreter	ABI	Platform	Reset
sie_qdrant-0.9.0-py3-none-any.whl 5.1 kB Sep 30, 2026	Python 3	none	any	Details

Total release size: 15.2 kB

Release files / sie_qdrant-0.9.0.tar.gz

Download URL	sie_qdrant-0.9.0.tar.gz
Size	10.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`10f9a43ad76f1c1ff7a12a710d4660356b9862f37a58a925ddeb5597d7fc720a`
BLAKE2b-256 checksum How to use checksums	`e028120a6f5f6b86c7b431277b31566c980bee268d61ea878b391882834e19ff`
Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/superlinked/sie
Publishing commit: github.com/superlinked/sie/tree/2337fad783aeb20d8ae974c287c503820c7d07fa

Workflow

Publishing configuration: github.com/superlinked/sie/blob/2337fad783aeb20d8ae974c287c503820c7d07fa/.github/workflows/release.yml
Publishing logs: github.com/superlinked/sie/actions/runs/36720830955/attempts/1

Artifact

Subject: sie_qdrant-0.9.0.tar.gz
SHA-256 checksum: 10f9a43ad76f1c1ff7a12a710d4660356b9862f37a58a925ddeb5597d7fc720a Verifying attestations

Release files / sie_qdrant-0.9.0-py3-none-any.whl

Download URL	sie_qdrant-0.9.0-py3-none-any.whl
Size	5.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`8b7e9720cb335c80aae450a6487be6c3a61e28ddbee24d6ec939c46441c541aa`
BLAKE2b-256 checksum How to use checksums	`eb40b9f61418008f10acbc02c6f7c39e8baa478709d39062ebbd0c3b2b0413f5`
Upload date	Sep 30, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/superlinked/sie
Publishing commit: github.com/superlinked/sie/tree/2337fad783aeb20d8ae974c287c503820c7d07fa

Workflow

Publishing configuration: github.com/superlinked/sie/blob/2337fad783aeb20d8ae974c287c503820c7d07fa/.github/workflows/release.yml
Publishing logs: github.com/superlinked/sie/actions/runs/36720830955/attempts/1

Artifact

Subject: sie_qdrant-0.9.0-py3-none-any.whl
SHA-256 checksum: 8b7e9720cb335c80aae450a6487be6c3a61e28ddbee24d6ec939c46441c541aa Verifying attestations