localvectordb.visualization.graph module

Similarity graph construction and visualisation.

localvectordb.visualization._graph.build_similarity_graph(sim_matrix: DocumentSimilarityMatrix, threshold: float = 0.3) → Dict[str, List[Dict[str, Any]]]

Build a graph structure from a similarity matrix.

Parameters:

  • sim_matrix (DocumentSimilarityMatrix) – Pairwise document similarity matrix.

  • threshold (float) – Minimum similarity for an edge to be included.

Returns:

{"nodes": [...], "edges": [...]} where each node is {"id": str, "index": int} and each edge is {"source": str, "target": str, "weight": float}.

Return type:

dict

localvectordb.visualization._graph.plot_similarity_graph(sim_matrix: DocumentSimilarityMatrix, threshold: float = 0.3, layout: str = 'spring', title: str = 'Document Similarity Graph', save_path: str | Path | None = None, figsize: tuple = (10, 8), **kwargs) → Figure

Visualise documents as a similarity graph.

Nodes represent documents; edges connect documents with similarity above threshold. Edge width and opacity are proportional to similarity.

Layout uses scikit-learn MDS to avoid a networkx dependency.

Parameters:

  • sim_matrix (DocumentSimilarityMatrix) – Pairwise similarity matrix.

  • threshold (float) – Edge threshold.

  • layout (str) – Layout algorithm ("spring" uses MDS on dissimilarity).

  • title (str) – Plot title.

  • save_path (str or Path, optional) – Save path.

  • figsize (tuple) – Figure size.

Return type:

matplotlib.figure.Figure