PyGraphistry AI

Doc routing (local + canonical)

• First route with ../pygraphistry/references/pygraphistry-readthedocs-toc.md .

• Use ../pygraphistry/references/pygraphistry-readthedocs-top-level.tsv for section-level shortcuts.

• Only scan ../pygraphistry/references/pygraphistry-readthedocs-sitemap.xml when a needed page is missing.

• Use one batched discovery read before deep-page reads; avoid cat * and serial micro-reads.

• In user-facing answers, prefer canonical https://pygraphistry.readthedocs.io/en/latest/... links.

Typical workflow

• Build graph from nodes/edges.

• Run feature/embedding method (umap , embed , optional dbscan ).

• Inspect derived columns/features and visualize.

• Iterate on feature columns and sampling strategy.

Baseline examples

Similarity embedding / projection

g2 = graphistry.nodes(df, 'id').umap(X=['f1', 'f2', 'f3']) g2.plot()

Fit/transform flow for consistent projection on new batches

g_train = graphistry.nodes(df_train, 'id').umap(X=['f1', 'f2']) g_batch = g_train.transform_umap(df_batch, return_graph=True) g_batch.plot()

Semantic search over embedded features

g2 = graphistry.nodes(df, 'id').umap(X=['text_col']) results_df, query_vector = g2.search('suspicious login pattern')

Text-first workflow: featurize then search/cluster

g2 = graphistry.nodes(df, 'id').featurize(kind='nodes', X=['title', 'body']).umap(kind='nodes').dbscan() hits, qv = g2.search('credential stuffing campaign')

Precomputed embedding columns

embedding_cols = [c for c in df.columns if c.startswith('emb_')] g2 = graphistry.nodes(df, 'id').umap(X=embedding_cols) g_new = g2.transform_umap(df_new, return_graph=True)

Practical guardrails

• Start with small/representative samples before full runs.

• Keep explicit feature lists (X=... ) for reproducibility.

• Track engine/dataframe type for CPU vs GPU behavior.

• For anomaly workflows, document thresholds and false-positive assumptions.

• For graph ML tasks, route deeper model workflows to RGCN/link-prediction references.

• For text workflows, prefer featurize(...).umap(...).search(...) when queries are natural language.

• If users already have embeddings, reuse them via explicit embedding column lists (X=[...] ) before recomputing.

• When user asks for a concise workflow snippet, prefer one short code block and avoid long narrative wrappers.

Canonical docs

• GFQL + AI combos: https://pygraphistry.readthedocs.io/en/latest/gfql/combo.html

• API AI reference: https://pygraphistry.readthedocs.io/en/latest/api/ai.html

• AI notebook index: https://pygraphistry.readthedocs.io/en/latest/notebooks/ai.html

• Example RGCN notebook: https://pygraphistry.readthedocs.io/en/latest/demos/more_examples/graphistry_features/embed/simple-ssh-logs-rgcn-anomaly-detector.html