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
1 maintainer
Metadata
1 maintainer
Release files for sie-qdrant 0.10.1
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| sie_qdrant-0.10.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 15.2 kB
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