vO.1.4 Release Notes¶

This release includes several bugfixes in the core package, new features and API changes described below.

Backend support¶

Neo4j¶

BlueGraph now supporsts Neo4j’s GDS library v1.6 (gds.beta.shortestPath.yens.stream becomes gds.shortestPath.yens.stream).

Gensim¶

In the new BlueGraph release, GensimNodeEmbedder interface is added. This interface allows wrapping gensim embedding models (in particular, added the implementation of poincare embedding).

from bluegraph.backends.gensim import GensimNodeEmbedder

embedder = GensimNodeEmbedder(
    "poincare", directed=True, size=6, epochs=100, negative=3)
embedding = embedder.fit_model(test_graph)

Downstream tasks with BlueGraph¶

Similarity API¶

This release includes a refactored SimilarityProcessor that now supports multiple backends (through dependency injection), in particular:

SimilarityIndex interface for backend-specific similarity indices;
FaissSimilarityIndex interface for faiss similarity index;
ScikitLearnSimilarityIndex interface for sklearn similarity index based on sklearn.neighbors.KDTree and sklearn.neighbors.BallTree;
A new similarity metric to ScikitLearnSimilarityIndex based on sklearn.neighbors.BallTree corresponding to Poincare distance;
SimilarityProcessor now takes on input a SimilarityIndex.

The two following snippets illustrate examples of usage of new interfaces.

from bluegraph.downstream.similarity import (FaissSimilarityIndex,
                                             ScikitLearnSimilarityIndex,
                                             SimilarityProcessor)

d = 64
initial_vectors = np.random.rand(100, 64)
new_vectors = np.random.rand(20, 64)

# Create a faiss-based similarity index
faiss_index = FaissSimilarityIndex(
    d, similarity=similarity, n_segments=n_segments)

point_names = [f"point{el}" for el in range(100)]

# Create a a similarity processor interface and supply point ids
faiss_processor = SimilarityProcessor(
    faiss_index, point_ids=point_names)

# Add vectors to the index
faiss_processor.add(initial_vectors, point_ids=point_names)

# Get vectors by point ids and query similar vectors
vectors = faiss_processor.get_vectors(point_names[:10])
result = faiss_processor.query_existing(point_names[:10], 20)

# Create a scikit-learn-based similarity index
sklearn_index = ScikitLearnSimilarityIndex(
    d, similarity="poincare",
    initial_vectors=initial_vectors

# Create a a similarity processor interface and supply point ids
sklearn_processor = SimilarityProcessor(
    sklearn_index, point_ids=point_names)

# Get vectors by point ids and query similar vectors
vectors = sklearn_processor.get_vectors(point_names[:10])
result = sklearn_processor.query_existing(point_names[:10], 20)

Building embedding pipelines¶

EmbeddingPipeline was moved into blugraph.downstream.pipelines
A tutorial notebook with embedding pipelines was added.