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

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for superlinked-release from gravatar.com superlinked-release

Requires

Python >=3.10

Provides Extra

dev

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sie_qdrant-0.7.2.tar.gz (10.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

sie_qdrant-0.7.2-py3-none-any.whl (5.1 kB view details)

Uploaded Python 3

File details

Details for the file sie_qdrant-0.7.2.tar.gz.

File metadata

  • Download URL: sie_qdrant-0.7.2.tar.gz
  • Upload date:
  • Size: 10.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sie_qdrant-0.7.2.tar.gz
Algorithm Hash digest
SHA256 0ac277c3d2395465d196543cb9fa84904ea7a6d9cb8bcf9cbd7bc1c13cb19d75
MD5 7f04c825ad6cd9d48dd653f88b4feeb7
BLAKE2b-256 77714e2312b6d6ce92d4f019303e891592e996a6c83c4d71925d5b958901d910

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.7.2.tar.gz:

Publisher: release-python.yml on superlinked/sie-internal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file sie_qdrant-0.7.2-py3-none-any.whl.

File metadata

  • Download URL: sie_qdrant-0.7.2-py3-none-any.whl
  • Upload date:
  • Size: 5.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sie_qdrant-0.7.2-py3-none-any.whl
Algorithm Hash digest
SHA256 a74d1af8a3d0ac68e87a391eaf1160718ac9867b48a3a0938a8c0e027ed7bcd2
MD5 6b5ca89ac02527b0ca7a570cf3dc4a02
BLAKE2b-256 65941c41ca8ee6abd79b2cd48b80b6a0d8be4ec82ae0e3f645b2a0b4bdb85def

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.7.2-py3-none-any.whl:

Publisher: release-python.yml on superlinked/sie-internal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

0.7.2 This release

2 files

0.7.1

2 files

0.7.0

2 files

0.6.30

2 files

0.6.29

2 files

0.6.28

2 files

0.6.27

2 files

0.6.26

2 files

0.6.25

2 files

0.6.24

2 files

0.6.23

2 files

0.6.22

2 files

0.6.21

2 files

0.6.20

2 files

0.6.19

2 files

0.6.18

2 files

0.6.17

2 files

0.6.16

2 files

0.6.15

2 files

0.6.14

2 files

0.6.13

2 files

0.6.12

2 files

0.6.11

2 files

0.6.10

2 files

0.6.9

2 files

0.6.8

2 files

0.6.7

2 files

0.6.6

2 files

0.6.5

2 files

0.6.4

2 files

0.6.3

2 files

0.6.2

2 files

0.6.1

2 files

0.6.0

2 files

0.5.0

2 files

0.4.2

2 files

0.4.1

2 files

0.4.0

2 files

0.3.4

2 files

0.3.3

2 files

0.3.2

2 files

0.3.1

2 files

0.3.0

2 files

0.2.0

2 files

0.1.10

2 files

0.1.9

2 files

0.1.8

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page