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-0.6.26.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-0.6.26-py3-none-any.whl (5.1 kB view details)

Uploaded Python 3

File details

Details for the file sie_qdrant-0.6.26.tar.gz.

File metadata

  • Download URL: sie_qdrant-0.6.26.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-0.6.26.tar.gz
Algorithm Hash digest
SHA256 cb1b5f7b27df7a6a720fa69968f84307db17eee4fa8b8953ff4095516585e35d
MD5 f07dfc9b55fe7bef372789476277ae00
BLAKE2b-256 6d22f836d19f177c95346bbe4f96fab290665424477e5225f548a28007678d81

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.6.26.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-0.6.26-py3-none-any.whl.

File metadata

  • Download URL: sie_qdrant-0.6.26-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-0.6.26-py3-none-any.whl
Algorithm Hash digest
SHA256 31fd502c3f910295d9db8d47ef58d1b9f59ae6515e662590bbfb10ab6e280506
MD5 7e68bd9e452142b211c87edd24ffe3af
BLAKE2b-256 6fa798d7183b78630f23c3cc4ba2b832ba23c21cff6f72420d64b7c08870ab95

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_qdrant-0.6.26-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

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

This release

0.6.26 This release

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