deglib: Python Bindings for the Dynamic Exploration Graph

Python bindings for the high-performance C++ Dynamic Exploration Graph (DEG) library, enabling approximate nearest neighbor search (ANNS) and graph exploration with state-of-the-art recall vs. QPS trade-offs.

Installation
Quickstart & Examples
Concepts & Parameters
Example Projects
Publishing a New Version

Installation

From PyPI

pip install deglib

Development & Compiling from Source

We recommend using uv for fast virtual environment and dependency management.

1. Create and Activate Virtual Environment

cd python/
uv venv

2. Install Build Dependencies & Copy Core C++ Files

uv pip install setuptools==83.0.0 pybind11==3.0.4 build==1.5.0 wheel==0.48.0
uv run python setup.py copy_build_files

3. Install in Editable Mode

uv pip install -e . --no-build-isolation --verbose

Running Tests

Run the test suite using pytest:

uv run pytest

Code Formatting & Linting

Format Python files using ruff:

uv run ruff format .

Building Packages

Build source distributions (sdist) and binary wheels:

uv run python -m build

To build manylinux/musllinux wheels for Linux distribution via PyPI (ensure copy_build_files has been executed first):

uv run python setup.py copy_build_files
cibuildwheel --archs auto64 --output-dir dist

Quickstart & Examples

Basic Usage

import numpy as np
import deglib

num_samples, dims = 10_000, 128

# 1. Create random feature dataset and query vector
data = np.random.random((num_samples, dims)).astype(np.float32)
query = np.random.random(dims).astype(np.float32)

# 2. Build index directly from data
graph = deglib.builder.build_from_data(data, edges_per_vertex=32, callback="progress")

# 3. Query top-k nearest neighbors
indices, distances = graph.search(query, k=10, eps=0.1)

print("Nearest neighbors:", indices)
print("Distances:", distances)

Saving and Loading Graphs

# Save mutable graph to disk
graph.save_graph("index.deg")

# Load as compact, optimized read-only graph for production search
readonly_graph = deglib.load_readonly_graph("index.deg")

# Load as dynamic graph supporting further insertions and deletions
dynamic_graph = deglib.load_dynamic_graph("index.deg")

Incremental / Streaming Graph Construction

For continuous additions and removals, use GraphBuilder:

import numpy as np
import deglib
from deglib.builder import GraphBuilder, OptimizationTarget
from deglib.distances import FloatSpace, Metric

dims = 128
max_capacity = 10_000
edges_per_vertex = 32

# 1. Create feature space and empty mutable graph
space = FloatSpace.create(dims, metric=Metric.FP32_L2)
graph = deglib.DynamicExplorationGraph.create_empty(max_capacity, space, edges_per_vertex)

# 2. Initialize builder
builder = GraphBuilder(graph, optimization_target=OptimizationTarget.StreamingData, seed=42)

# 3. Add entries (individually or in batches)
labels = np.arange(100, dtype=np.uint32)
features = np.random.random((100, dims)).astype(np.float32)
builder.add_entry(labels, features)

# 4. Remove entries by external label
builder.remove_entry(42)

# 5. Build / optimize
builder.build()

Exploratory Search & Graph Navigation

DEG supports exploratory search directly from existing vertex labels:

# Explore graph starting from entry vertex label 105
explored_labels, distances = graph.explore(entry_label=105, k=10, eps=0.1, include_entry=False)

Memory Safety & Referencing C++ Buffers

When fetching feature vectors from a graph:

# Feature vector references internal C++ memory owned by graph
feature_vector = graph.get_feature_vector(42)

# If the graph might be deleted or modified, create an explicit copy:
safe_vector = graph.get_feature_vector(42, copy=True)

Concepts & Parameters

Internal Index vs. External Label

internal_index: Dense index in range 0..size-1 used internally for high-performance memory indexing.
external_label: User-defined unique identifier (uint32) assigned during add_entry(label, feature). Search and exploration results map back to external labels.

`OptimizationTarget`

Controls the topology optimization strategy:

OptimizationTarget.StreamingData: Default for continuous dynamic additions and deletions.
OptimizationTarget.LowLID: Optimized for datasets with low local intrinsic dimensionality (supports multithreaded building).
OptimizationTarget.HighLID: Optimized for datasets with high local intrinsic dimensionality (supports multithreaded building).

Search Parameter `eps`

The epsilon parameter expands the search priority queue during graph exploration.
Small values (e.g. eps=0.001 or eps=0.01): Faster query execution.
Higher values (e.g. eps=0.1 to eps=0.3): Higher recall rate.

API Reference

For a complete overview of all Python modules, classes, and function signatures, see API.md.

Example Projects

Ready-to-run example scripts with visual progress and evaluation are located in the examples/ directory:

examples/knng/: k-NN graph construction and evaluation.
examples/dynamic_data/: Dynamic streaming additions and deletions.
examples/static_data/: Static dataset indexing and ANNS benchmark.
examples/mips/: Maximum Inner Product Search (MIPS).

Publishing a New Version

Ensure all updates are fetched:
```
git checkout main && git pull
```
Update version in python/src/deglib/__init__.py.

Tag and push:

git add -A
git commit -m "vX.Y.Z"
git tag -a vX.Y.Z -m "vX.Y.Z"
git push && git push origin --tags

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Release history Release notifications | RSS feed

0.2.5

Aug 30, 2026 25 files

0.2.4

Aug 30, 2026 25 files

0.2.3

Aug 29, 2026 25 files

0.2.2

Aug 29, 2026 25 files

0.2.1

Aug 28, 2026 25 files

This release

0.2.0 This release

Aug 22, 2026 25 files

0.1.6

Sep 8, 2025 21 files

0.1.5

Aug 7, 2025 21 files

0.1.4

Jul 31, 2025 21 files

0.1.3

Apr 4, 2025 19 files

0.1.2

Dec 20, 2024 11 files

0.1.1

Dec 19, 2024 11 files

0.1.0

Oct 31, 2024 11 files

deglib 0.2.0

deglib: Python Bindings for the Dynamic Exploration Graph

Table of Contents

Installation

From PyPI

Development & Compiling from Source

Running Tests

Code Formatting & Linting

Building Packages

Quickstart & Examples

Basic Usage

Saving and Loading Graphs

Incremental / Streaming Graph Construction

Exploratory Search & Graph Navigation

Memory Safety & Referencing C++ Buffers

Concepts & Parameters

Internal Index vs. External Label

OptimizationTarget

Search Parameter eps

API Reference

Example Projects

Publishing a New Version

Project links

Key dates

Owner

2 maintainers

Credits

GitHub Statistics

Requires

Provides Extra

Tags

Classifiers

Download files

Source Distribution

Built Distributions

File details

File metadata

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`OptimizationTarget`

Search Parameter `eps`