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 0.7.3

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Source distribution (sdist)

Source distribution for sie-qdrant 0.7.3
File	Size	Uploaded
sie_qdrant-0.7.3.tar.gz	10.1 kB	Sep 3, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for sie-qdrant 0.7.3
File	Interpreter	ABI	Platform	Reset
sie_qdrant-0.7.3-py3-none-any.whl 5.1 kB Sep 3, 2026	Python 3	none	any	Details

Total release size: 15.2 kB

Release files / sie_qdrant-0.7.3.tar.gz

Download URL	sie_qdrant-0.7.3.tar.gz
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Size	5.1 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`35d559aa667212228328345620d4c2d4ef9f0266a890520877cd6a92a54d9c17`
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Upload date	Sep 3, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/superlinked/sie-internal
Publishing commit: github.com/superlinked/sie-internal/tree/403a93b8735a74a3a46856c8e22ac13ee957ba12

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Publishing configuration: github.com/superlinked/sie-internal/blob/403a93b8735a74a3a46856c8e22ac13ee957ba12/.github/workflows/release-python.yml
Publishing logs: github.com/superlinked/sie-internal/actions/runs/33777896440/attempts/1

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