sphereQL
Project high-dimensional embeddings onto a 3D sphere for fast semantic search, interactive visualization, and knowledge structure analysis. Built in Rust, exposed to Python via PyO3. These are the official Python bindings for the sphereQL workspace.
Install
pip install sphereql
The default wheel ships the core pipeline, projections, metalearning, visualization, and the in-memory vector-store bridge. The Qdrant and Pinecone bridges are compile-time features — build from source to get them:
cd sphereql-python
python -m maturin develop --release --features qdrant # adds QdrantBridge
Quick Start: Semantic Search
import sphereql
categories = ["science", "science", "cooking", "cooking"]
embeddings = [
[0.1, 0.9, 0.3, 0.0],
[0.2, 0.8, 0.4, 0.1],
[0.9, 0.1, 0.0, 0.5],
[0.8, 0.2, 0.1, 0.4],
]
pipeline = sphereql.Pipeline(categories, embeddings)
query = [0.15, 0.85, 0.35, 0.05]
results = pipeline.nearest(query, k=3)
for hit in results:
print(f"{hit.id} {hit.category} distance={hit.distance:.4f}")
Quick Start: Visualization
import sphereql
sphereql.visualize(categories, embeddings, title="My Embeddings", open_browser=True)
# Opens an interactive 3D sphere in your browser; set open_browser=False to only write the file
Quick Start: Vector DB Bridge
import sphereql
store = sphereql.InMemoryStore("my-collection", dimension=384)
store.upsert([
{"id": "doc-1", "vector": embedding_1, "metadata": {"category": "science"}},
{"id": "doc-2", "vector": embedding_2, "metadata": {"category": "cooking"}},
# ...
])
bridge = sphereql.VectorStoreBridge(store)
bridge.build_pipeline(category_key="category")
results = bridge.hybrid_search(query_vec, final_k=5, recall_k=20)
How It Works
sphereQL fits a projection to reduce embeddings to 3 dimensions, then maps them onto spherical coordinates (r, theta, phi). The radial component encodes magnitude/confidence, while angular position preserves semantic similarity. This enables angular-distance queries, cluster detection, concept paths, and interactive 3D visualization — all in projected space.
The pipeline supports four projection families, selected via the config
dict: "Pca" (default), "KernelPca", "LaplacianEigenmap"
(connectivity-preserving spectral projection over a k-NN similarity
graph), and "UmapSphere" (UMAP optimized directly on S², new in this
release — no standalone class; configure it through
config={"projection_kind": "UmapSphere", "umap": {...}}). Standalone
projection classes are also exposed for direct use: PcaProjection,
KernelPcaProjection, LaplacianEigenmap, and RandomProjection.
The auto-tuning and metalearning surface is bound too — corpus_features,
auto_tune, NearestNeighborMetaModel, DistanceWeightedMetaModel,
FeedbackEvent, and FeedbackAggregator.
# Non-default projection via config dict
pipeline = sphereql.Pipeline(
categories, embeddings,
config={"projection_kind": "LaplacianEigenmap"},
)
# Auto-tune over the search space
tuned, report = sphereql.auto_tune(categories, embeddings, budget=16)
Most of the Rust query surface has a 1:1 Python binding; the gaps are
tracked explicitly in the workspace's
.bindings-ignore.toml
allowlist and enforced by a drift checker in CI. run_self_tune and
bridge relation-type annotations are now bound; the remaining gaps are
config/report sub-types surfaced as dicts and Rust-only traits.
API Reference
Type stubs (python/sphereql/__init__.pyi) are auto-generated via
pyo3-stub-gen and ship with the wheel — IDEs, mypy, and pyright
pick them up automatically. They are generated at the vectordb feature
level, so the in-memory vector-store classes (InMemoryStore,
VectorStoreBridge) are covered; QdrantBridge / PineconeBridge
(behind their own features, not in the default wheel) are not.
Regenerate after binding changes with:
cd sphereql-python && cargo run --bin gen-stubs --features vectordb
Status
Pre-1.0 (0.3.0). Expect
breaking changes between minor versions. Source, issues, and full
documentation live in the
sphereQL repository.
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for sphereql 0.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sphereql-0.3.0.tar.gz | 1.5 MB | Details |
Built distributions (wheels)
Total release size: 28.5 MB
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| Tags | CPython 3.10 macOS 10.12+ x86-64 |
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