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:
Newer release available (0.7.2)

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-0.7.0.tar.gz (10.8 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-0.7.0-py3-none-any.whl (5.1 kB view details)

Uploaded Python 3

File details

Details for the file sie_qdrant-0.7.0.tar.gz.

File metadata

  • Download URL: sie_qdrant-0.7.0.tar.gz
  • Upload date:
  • Size: 10.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sie_qdrant-0.7.0.tar.gz
Algorithm Hash digest
SHA256 c9f5cc27e017a42951914d138497fa96584fee0160f01e712bc14d2f213e6796
MD5 2a883382d9de26e79828e54cdd0609b0
BLAKE2b-256 5c25469e7a4d9bb9923fe4d329f208290e56f391f557e86a17b3653fedc7d877

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.7.0.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-0.7.0-py3-none-any.whl.

File metadata

  • Download URL: sie_qdrant-0.7.0-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-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 7fc8ea285126c0e8478bf386a4435d15623124baf9e08a2f9faecc9427716169
MD5 c10909262f54ed861ef181f185d90447
BLAKE2b-256 0c73525edb65971fbb8f25c3df11438fa438fde10551f3956ec7b544992bca75

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.7.0-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

0.7.2

2 files

0.7.1

2 files

This release

0.7.0 This release

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