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
- From PyPI
- Build from Source
Quickstart & Examples
Concepts & Parameters
Example Projects
API Reference
Development Setup

Installation

From PyPI

pip install deglib

Build from Source

To build and install the package directly from the repository:

cd python/
python setup.py copy_build_files
pip install .

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 (multithreaded by default)
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)

Graph Types & Lifecycles

All search graphs are represented by DynamicExplorationGraph, backed by one of three internal graph engines:

Fixed-Capacity Mutable (SizeBoundedGraph): Memory is preallocated for a fixed maximum capacity. Fast and memory-efficient for static/batch datasets.

space = deglib.FloatSpace.create(dims=128, metric=deglib.Metric.FP32_L2)
graph = deglib.create_empty(capacity=10_000, feature_space=space, edges_per_vertex=32)

Chunk-Allocated Mutable (DynamicGraph): Dynamically allocates memory in chunks (e.g. 1024 vertices per chunk). Ideal for streaming datasets where total capacity is unknown.
```
graph = deglib.create_dynamic_empty(feature_space=space, edges_per_vertex=32, chunk_size=1024)
```
Read-Only Deployment (ReadOnlyGraph): Stripped of mutation structures for minimal memory footprint and maximum query throughput.
```
readonly_graph = graph.to_readonly()
```

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")

# Load as fixed-capacity mutable graph
mutable_graph = deglib.load_mutable_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.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()

Filtered Search & Candidate Reranking

from deglib.search import Filter, rerank

# Search only within allowed external labels
allowed_ids = np.array([1, 5, 10, 42, 99], dtype=np.int32)
search_filter = Filter(allowed_ids)

indices, distances = graph.search(query, k=5, eps=0.1, filter_labels=search_filter)

# Exact distance reranking across candidates
queries = np.random.random((10, dims)).astype(np.float32)
candidates = np.random.randint(0, 1000, size=(10, 50), dtype=np.uint32)
base_vectors = np.random.random((1000, dims)).astype(np.float32)

top_indices, top_distances = rerank(
    space=graph.get_feature_space(),
    queries=queries,
    candidate_indices=candidates,
    base_vectors=base_vectors,
    k_top=10,
    return_distances=True,
)

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_external_label=105, k=10, eps=0.1, include_entry=False)

Graph Optimization, Quantization & Reranking Pipeline

A complete end-to-end pipeline demonstrating FLAS pre-sorting, multithreaded graph building, RNG edge pruning, Int8 quantization, ReadOnlyGraph conversion, and exact FP32 candidate reranking:

import numpy as np
import deglib
from deglib.optimization import presort, prune_non_rng_edges, ScalarQuantizerInt8
from deglib.search import rerank

num_vectors, dims = 10_000, 128
data = np.random.randn(num_vectors, dims).astype(np.float32)
query = np.random.randn(1, dims).astype(np.float32)

# 1. Pre-sort vectors using FLAS for improved memory locality and index construction speed
perm = presort(data, metric=deglib.Metric.FP32_InnerProduct, callback="progress")
sorted_data = data[perm]

# 2. Build exploration graph on unquantized/original data
graph = deglib.builder.build_from_data(
    sorted_data,
    metric=deglib.Metric.FP32_InnerProduct,
    edges_per_vertex=32,
    callback="progress",
)

# 3. Prune redundant non-RNG edges to optimize graph topology
pruned_count = prune_non_rng_edges(graph)
print(f"Pruned {pruned_count} redundant edges.")

# 4. Fit Int8 quantizer on base dataset and quantize features for memory reduction and ultra-fast search
quantizer = ScalarQuantizerInt8()
quantizer.fit(sorted_data)
quant_int8_data = quantizer.quantize(sorted_data)
int8_space = deglib.FloatSpace.create(dims, deglib.Metric.Int8_InnerProduct)

# 5. Convert mutable graph to a compact ReadOnlyGraph equipped with the quantized features
readonly_graph = graph.to_readonly(feature_space=int8_space, custom_features=quant_int8_data)

# 6. Quantize query using the EXACT SAME calibrated scale, then search
quant_query = quantizer.quantize(query)
candidate_indices = readonly_graph.search(quant_query, k=20, eps=0.1, return_distances=False)

# 7. Exact distance reranking of top candidates on original float32 data
fp32_space = deglib.FloatSpace.create(dims, deglib.Metric.FP32_InnerProduct)
final_top_indices, distances = rerank(
    space=fp32_space,
    queries=query,
    candidate_indices=candidate_indices,
    base_vectors=sorted_data,
    k_top=10,
    return_distances=True,
)

print("Top-10 nearest neighbor indices:", final_top_indices[0])
print("Top-10 exact distances:", distances[0])

Concepts & Parameters

`OptimizationTarget`

Controls the topology optimization strategy:

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

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.

Supported Metrics & Data Types

Metric.FP32_L2: Euclidean distance (np.float32)
Metric.FP32_InnerProduct: Inner product / cosine distance (np.float32)
Metric.Uint8_L2: 8-bit unsigned integer Euclidean distance (np.uint8)
Metric.Uint8_InnerProduct: 8-bit unsigned integer inner product (np.uint8)
Metric.FP16_L2: 16-bit half-precision Euclidean distance (np.uint16)
Metric.FP16_InnerProduct: 16-bit half-precision inner product (np.uint16)
Metric.EVP_InnerProduct: Quantized Extreme Value Property bit-packed vectors (np.uint8)
Metric.Int8_InnerProduct: 8-bit signed integer inner product (np.int8)
Metric.Int8_L2: 8-bit signed integer Euclidean distance (np.int8)

API Reference

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

Example Projects

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

examples/knng/: k-NN graph construction benchmark using EVP quantization and FP16 reranking (SISAP 2026 Challenge Task 1).
examples/mips/: Maximum Inner Product Search (MIPS) benchmark using $(d+1)$-dimensional $L_2$ transformation, FLAS pre-sorting, and SIMD FP16 inner products (SISAP 2026 Challenge Task 2).
examples/static_data/: Static dataset indexing and ANNS benchmark.
examples/vibe/: VIBE benchmark for modern embedding datasets with scalar quantization and interactive Plotly visualizations.
examples/dynamic_data/: Dynamic streaming additions and deletions.
examples/sliding_window/: Sliding window continuous update benchmark against CleANN.

Development Setup

If you want to contribute, modify C++ bindings, run tests, or build release packages:

# 1. Setup virtual environment and dependencies (using uv)
cd python/
uv venv
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

# 2. Install in editable mode
uv pip install -e . --no-build-isolation --verbose

# 3. Run test suite
uv run pytest

# 4. Format code
uv run ruff format .

# 5. Build distribution packages (sdist and binary wheels)
uv run python -m build

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deglib 0.2.2

deglib: Python Bindings for the Dynamic Exploration Graph

Table of Contents

Installation

From PyPI

Build from Source

Quickstart & Examples

Basic Usage

Graph Types & Lifecycles

Saving and Loading Graphs

Incremental / Streaming Graph Construction

Filtered Search & Candidate Reranking

Exploratory Search & Graph Navigation

Graph Optimization, Quantization & Reranking Pipeline

Concepts & Parameters

OptimizationTarget

Search Parameter eps

Supported Metrics & Data Types

API Reference

Example Projects

Development Setup

Project links

Key dates

Owner

2 maintainers

Credits

GitHub Statistics

Requires

Provides Extra

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`OptimizationTarget`

Search Parameter `eps`

Algorithm	Hash digest
SHA256	`95f43a6c5941c45bd27d62974fe12e4dfe9ab47e4bac0d16cfaf318e0d04811b`
MD5	`edaa53653422a17227f92be33dd80204`
BLAKE2b-256	`4a7c3d3f041b180d3c36cbdde4d34cbd497aee17c61f0a35915b86fc29b26f76`

Algorithm	Hash digest
SHA256	`4565dc5c4e42c53f229d26e3c77ddd379f3003430330948cf6b1f445545ba89f`
MD5	`27ad3bce49f3eb5d64b5a46dd6c3c314`
BLAKE2b-256	`f3d07131b2e3356bfc73d42e3a4e913d0d96523848cc5e4205836d4ab56ca4cf`

Algorithm	Hash digest
SHA256	`4caabd6e93c6f850a9852bc7319ca460db92e29e3fcdc3a3075e895adff5164d`
MD5	`ceee4d4e4d46933b3897b25d3cf20b66`
BLAKE2b-256	`0fca02d8b3f216e9d7508d7ffac65be7bdfa7f2e5b69cb429998e5c913ff8af2`

Algorithm	Hash digest
SHA256	`c7975445f4aa1a1ce0cd523ca8824020b921f6db9e2bc29402cd4c54f763659d`
MD5	`178c395b119c1a1e192257de9c1b1ce1`
BLAKE2b-256	`2661018d59f11c0addcbd983626ed80623511c37bae51e7dbb98486ead0de00c`