localvectordb.visualization.plots module

Static matplotlib plots for embedding visualisation.

localvectordb.visualization._plots.plot_embedding_map(projection: EmbeddingProjection, color_by: List[str] | None = None, title: str = 'Document Embedding Map', save_path: str | Path | None = None, queries: List[QueryOverlay] | None = None, figsize: tuple = (10, 8), **kwargs) → Figure

Scatter plot of projected document embeddings.

Parameters:

  • projection (EmbeddingProjection) – Dimensionality-reduced coordinates.

  • color_by (list of str, optional) – Category labels for colouring each point.

  • title (str) – Plot title.

  • save_path (str or Path, optional) – If provided, save figure to this path.

  • queries (list of QueryOverlay, optional) – Query overlays to display on the map.

  • figsize (tuple) – Figure size.

Return type:

matplotlib.figure.Figure

localvectordb.visualization._plots.plot_similarity_matrix(sim_matrix: DocumentSimilarityMatrix, title: str = 'Document Similarity Matrix', save_path: str | Path | None = None, figsize: tuple | None = None, **kwargs) → Figure

Heatmap of pairwise document similarities.

Parameters:

  • sim_matrix (DocumentSimilarityMatrix) – Similarity matrix to plot.

  • title (str) – Plot title.

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

  • figsize (tuple, optional) – Figure size. Auto-scaled if None.

Return type:

matplotlib.figure.Figure

localvectordb.visualization._plots.plot_clusters(projection: EmbeddingProjection, clusters: ClusterResult, title: str = 'Document Clusters', save_path: str | Path | None = None, figsize: tuple = (10, 8), **kwargs) → Figure

Scatter plot of projected embeddings coloured by cluster.

Parameters:

  • projection (EmbeddingProjection) – Dimensionality-reduced coordinates.

  • clusters (ClusterResult) – Cluster assignments.

  • title (str) – Plot title.

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

  • figsize (tuple) – Figure size.

Return type:

matplotlib.figure.Figure